-smos level 4 thematic sss research products -product ......modification status issue rev status *...

CATDS-CECOS-L4-PUDOC

Issue 2 – Rev 0

Centre Aval de Traitement des Données SMOS (CATDS)- Expertise Center -Ocean Salinity (CEC-OS)

Laboratoire d‟Océanographie Physique et Spatiale– Z.I. Pointe du Diable-B. P. 70, Plouzané – France

Tél. : +33 (0)2 98 22 44 10 – Fax : +33 (0)2. 98. 22. 45. 33 – E-mail : [email protected]

-SMOS Level 4 Thematic SSS Research products -Product User Manual-

Function Name Signature Date

Project Manager

Nicolas REUL (IFREMER C-ECOS)

CATDS -CECOS Team

11 May

2015

SMOS Level 4 Thematic SSS Research Products Product User Document

Ref Level4_SSS_PUD

Issue 1 Date 11/05/2015

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© IFREMER © CNES 2011

This document is the property of IFREMER and CNES, no part of it shall be reproduced or transmitted without the express prior

written authorisation of IFREMER and CNES

Document status

Title

SMOS Level 4 Thematic SSS Research Products -Products User Manual

Issue Revision Date Reason for the revision

1 0 11/05/2015 Initial version

Modification status

Issue Rev Status * Modified pages Reason for the modification

* I = Inserted D = deleted M = Modified

SMOS Level 4 Thematic SSS Research Products User Manual

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Ref Level4_SSS_PUD_V2

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Table of contents

1. INTRODUCTION ......................................................................................................... 5

2. PRODUCTS CHARACTERISTICS, FORMATS, FILE NAMING CONVENTIONS & DATA CITATION ...................................................................................................... 10

2.1 Level 4a product content ....................................................................................... 10 2.1.1. Convention for the L4a Netcdf files ................................................................. 14

2.1.2. SSS, SST and Wind speed from SMOS data ................................................. 15 2.1.2.1. SMOS SSS ............................................................................................................ 15 2.1.2.2. SST ECMWF ........................................................................................................ 15 2.1.2.3. Wind Speed ECMWF ............................................................................................ 16

2.1.3. Cummulated Evaporation OAFLUX ................................................................ 16 2.1.4. Cummulated Precipitation TRMM-3B42 ......................................................... 17 2.1.5. Cummulated Precipitation CMORPH .............................................................. 18

2.1.6. OSCAR zonal and meridional currents ........................................................... 20 2.1.7. Wind Stress zonal and meridional components from ASCAT ......................... 21

2.1.8. Mixed-Layer Depth ......................................................................................... 22 2.1.9. Salinity at the base of the Mixed Layer ........................................................... 23 2.1.10. Time Interpolated ISAS SSS fields ................................................................. 24

2.2. In Situ/SMOSL4a Match-up DataBase (MDB) files........................................... 25

2.2.1. ARGO float observations ................................................................................ 25 2.2.2. Ship ThermoSalinograph Data ....................................................................... 30 2.2.3. Surface drifter Data ......................................................................................... 36

2.2.4. Global tropical Moored Buoy Array data ......................................................... 41 2.2.5. Southern Ocean SSS from Seals ................................................................... 47

2.2.6. Match-Up Database netcdf Files Naming conventions ................................... 52 2.3. Level 4b Product content ................................................................................... 54

2.3.1. Convention for the L4b Netcdf files ................................................................. 56 2.3.2. Sea Surface Density ....................................................................................... 56 2.3.3. Mean Sea Level Anomaly ............................................................................... 57

2.3.4. Other variables in L4b products ...................................................................... 58 2.4. Level 4c Product content ................................................................................... 59

2.4.1. Methodology to evaluate anomalies ............................................................... 59 2.4.2. Level 4c product content ................................................................................. 60

2.4.3. Convention for the L4c Netcdf files ................................................................. 63

3. DATA CITATION ...................................................................................................... 65

4. PRODUCT ALGORITHM, VALIDITY, COVERAGE AND KNOWN FLAWS ............ 67

4.1. Product Algorithm and Validity ......................................................................... 67 4.2. Input Data and coverage .................................................................................... 67 4.3. Several known flaws ........................................................................................... 67

4.3.1. Remaining Major Issues in the L2OS data ..................................................... 67 4.3.2. Solar contaminations ...................................................................................... 70 4.3.3. RFI .................................................................................................................. 70

4.3.4. Land Contamination ........................................................................................ 74


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4.3.5. Major Geophysical correction issues: ............................................................. 75 3.4.1 Sky noise ....................................................................................................................... 75


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1. Introduction

This document is the product user manual for the CATDS/CEC-OS SMOS Level 4 Thematic sea

surface salinity (SSS) research products.

§ 2.1 describes the composite L4a products major characteristics,

§ 2.2 describes the composite L4 Match-Up databse products major characteristics,

§ 2.3 describes the composite L4b products major characteristics,

§ 2.4 describes the composite L4c products major characteristics,

§ 3 explains how to aknowledge the use of these data,

§ 4 gives an overview of the products coverage (e.g. input data features, missing output data,..) and

describe several known flaws in these datasets.

The scientific relevance for measuring Sea Surface Salinity (SSS) is more and more recognized in the

ocean community. SSS plays an important role in the dynamics of the thermohaline overturning

circulation, ENSO, and is the key tracer for the marine branch of the global hydrologic cycle, which

comprises about ¾ of the global precipitation and evaporation. Ocean surface salinity is also of key

importance for land-sea (river plumes), air-sea (ocean stratification, barrier layers, CO2 fluxes) and

ice-sea interactions, marine biology, marine chemistry (carbonate cycle) and marine bio-optic. SSS is

also essential to understanding the ocean‟s interior water masses, knowing that they derive their

underlying temperature and salinity properties during their most recent surface interval. In the IPCC

WGI Fifth Assesment Report published in October 2013, chapter 3.3 is dedicated to changes in

salinity and freshwater content. Multi-decadal SSS trends have been documented in tropical and

northern latitudes that are likely signatures of evaporation or precipitation trends, as predicted under

global warming scenarios. As reported:

Chapter 3: Observation: Ocean

… “It has not been possible to detect robust trends in regional precipitation and evaporation over the

ocean because observations over the ocean are sparse and uncertain … Ocean salinity, on the other

hand, naturally integrates the small difference between these two terms and has the potential to act as

a rain gauge for precipitation minus evaporation over the ocean … Diagnosis and understanding of

ocean salinity trends is also important because salinity changes, like temperature changes, affect

circulation and stratification, and therefore the ocean‟s capacity to store heat and carbon as well as to

change biological productivity.” …

SPM-5

“It is very likely that regions of high salinity where evaporation dominates have become more saline,

while regions of low salinity where precipitation dominates have become fresher since the 1950s.

These regional trends in ocean salinity provide indirect evidence that evaporation and precipitation

over the oceans have changed (medium confidence). “

In addition to the in situ observing network, two satellite missions (SMOS and Aquarius) are

currently in orbit and operating with the aim of measuring salinity from space for the first time, with


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differing technology approaches but operating in the same spectral region (L-Band). Strong efforts

have been carried out by the expert of the satellite salinity missions but also by the "first external

users" to demonstrate the scientific interest of the new SSS from space products. In particular,

scientific demonstrations and associated peer-reviewed publications have already revealed the strong

potential and new information of these new data for the following major ocean-atmosphere

processes:

Ocean surface response to precipitation and evaporation fluxes

New characterization of large-scale upwelling processes (fisheries)

Land-ocean freshwater flux monitoring (large tropical river plumes)

Ocean-atmosphere interactions (Barrier layers, Tropical cyclones, upper ocean stratification)

Surface salt distrtibution links with Interanual Climatic variability (La Nina, Indian Ocean

Dipole)

Near-surface transport and large scale inter-gyre exchanges of salt through major oceanic

currents (e.g., Gulf stream)

Thermo-haline circulation (allowing first spaceborne sea water density estimates),

new inputs for the Tropical instability waves monitoring

Thin sea ice monitoring

Surface wind speed estimation under Tropical cyclones and storms

These first demonstrations of scientific applications with the new salinity satellite missions is

supported by a constantly growing user community since the launch of the missions.

Thus, developing scientific analyses and tools that will help to better monitor sea surface salinity

changes will stimulate a growing user community and will be key to developing already identified

applications but also to stimulate new ones for this ECV. In fine, such effort shall certainly contribute

to our better understanding of the Earth‟s climate change and therefore to the IPCC initiative.

Given the novelty of the Satellite SSS data, the current user and science communities being interested

in SSS satellite data over the ocean are mostly connected to the mission expert team directly. The

main reason of the currently limited growth of the user community for this important ECV first

estimated from space is linked to the fact that this complex data are rather new and that data quality

still needs to be improved or mostly better understood for applications. At the same time well

informed users and experts start publishing very interesting scientific results. A non aware user

starting from Level 2 or 3 data might then be surprised by the level of expertise, processing and

analysis required to extract interesting scientific results from SMOS (and Aquarius) data.

Hence, one of the first community and impact driven objective of the products we propose here shall

therefore be to foster on a scientific level (pre-operational) the synergistic use of SSS in several parts

of the ocean community. Potential science applications include: freshwater budget for climate

change studies, El Nino/La Nina prediction, ocean surface circulation monitoring (coupling with

altimetry and SST), operational oceanography (model assimilation, climate indicators), land-sea

interactions, weather forecasts (oceanic rain gauge, extreme events), marine biology (fisheries), CO2

sequestration and ocean acidification.


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To reach these aims we developed 4 types of level 4 products. For this first version of the products,

we chose only to produce them at fixed spatial and temporal resolution which are 1/2° and weekly

composite.

-Level 4 a synergistic products:

These products include key geophysical variables to analyse the salinity budget in the upper ocean

mixed layer. These include:

-SSS from SMOS & in situ OI (ISAS),

-SST from ECMWF,

-wind speed modulus (ECMWF)

-wind stress components (ASCAT),

-ocean surface current components (OSCAR),

-Evaporation (OAFLUX),

-Precipitation (TRMM3B42 & CMORPH),

-Mixed Layer Depth (In situ OI),

-Salinity at the base of the mixed-layer

These fields are averaged or cummulated in time -or interpolated- over the week of the SMOS L4a

SSS and gridded at the same 1/2° spatial resolution.

L4aSSS In situ Match-up products

Quality controlled surface salinity and temperature measurements from Argo floats, ship TSG,

surface drifters, Tropical Moorings and sea seals data provided by the Coriolis, GOSUD, SAMOS

and LOCEAN data centers are collected weekly over each of the L4 composite product period and

co-locatized with SMOS L4a SSS products.

Supplementary data are provided with the in situ match-up database such as vertical profiles of S and

T if available, co-localized ASCAT and TRMM3B42 data, etc..

A match-up database file per in situ sensor type is provided for each week of the L4a products.

L4b density products

L4b density products are satellite surface density fields. These surface density fields are so-called

"CATDS/CECOS Ifremer SMOS Level 4c research products" and are weekly composite products of

surface density at a spatial resolution of 0.50° x 0.50° deduced from the L4aSSS data and ECMWF

SST data. In addition, the products include:

-mean sea level anomalies from AVISO

-OSCAR currents components

-Wind stress components from ASCAT


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L4c anomaly products

L4c products are Anomaly fields. These surface salinity anomaly fields are so-called "CECI-OS

Ifremer SMOS Level 4c SSSA research products" and are weekly composite products at a spatial

resolution of 0.50° x 0.50° deduced from the L4aSSS data. The anomalies are evaluated by removing

an annual-averaged reference weekly composite field evaluated by averaging the SMOS L4aSSS data

over 5 years (2010 to 2014).

weekly anomalies for various thematic fields are also included such as:

-ECMWF sst anomalies

-Cummulated Evaporation anomalies from OAFlux

-Cummulated Precipitaions anomalies from TRMM3B42

-Cummulated Precipitation anomalies from CMORPH

-Mixed layer depth anomalies from APDRC

-Wind stress components anomalies from ASCAT

-Anomalies of the Salinity at the base of the Mixed Layer

-OSCAR currents components anoamlies

-ISAS temporally interpolated monthly SSS & SST fields anomalies

-surface density anaomalies

-MSLA


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2. Products characteristics, formats, file naming conventions & data citation

2.1 Level 4a product content

The CATDS/CEC-OS SMOS Level 4a Version 1 SSS research products are weekly (7 days)

composite at 50 km resolution. The products coverage is May 2010-December 2014.

In addition, these products also include an ensemble of geophysical parameters derived from well-

acknowledged products in the scientific communities that are useful for synergistic science

applications using SMOS data. These include Sea Surface Temperature (ECMWF), surface currents

(OSCAR), rain (TRMM), evaporation (OAFLUX), surface wind stresses (ASCAT), mixed-layer

depth from In situ OI(APDRC), Surface salinity from in situ OI (ISAS) and salinity at the base of the

mixed-layer depth estimated also from In Situ OI.

Table 1: Variable Name Dimension and Description for the CECOS L4a V01 research products

Variable Name Dimension Description

time

1 Central date of the time period over

which the SMOS data were combined

to generate the composite product.

Number of days since 1990-01-01

00:00:00

latitude nlat Vector of the latitude of the grid nodes

over which the composite product is

derived. Expressed in degrees North

from -90. to +90.

longitude nlon Vector of the longitude of the grid nodes


derived. Expressed in degrees East from

-180. to +180.

sss nlat × nlon Gridded Sea Surface Salinity from SMOS

[Practical Salinity Scale]. Missing Values=

-9999

sst nlat × nlon Gridded Sea Surface Temperature

colocated at SMOS pixels from ECMWF

forecasts [Kelvins]. Missing Values= -


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9999

Wind_Speed

nlat × nlon Gridded 10-m height wind speed

module colocated at SMOS pixels from

ECMWF forecasts [meter/seconds].

Missing Values= -9999

RFI_stat nlat × nlon Gridded percentage for Radio-

Frequency Interferences occurence

within the brighthness temperature data

set used for SSS product generation at a

given pixel [%].


Zonal_component_surface_currents nlat × nlon Gridded OSCAR surface current

(Ekman+Geostrophic) zonal component

interpolated in space and time at SMOS

pixels [m/s]. The 1/3° resolution 5 day

OSCAR data were re-gridded on a 1/2°

resolution grid and the 5-day currents

fields were linearly interpolated in time

on a daily basis. The mean current

components provided into the L4a

products are then the result of a time

averaging over the 7-day period of the

SMOS L4a product.

Missing Values= -9999.

Meridional_component_surface_currents nlat × nlon Gridded OSCAR surface current

(Ekman+Geostrophic) meridional

component interpolated in space and

time at SMOS pixels [m/s]. Missing

Values= -9999

Mean_bias_In_situ_minus_SMOS nlat × nlon Mean Bias between SMOS weekly L4a

data and In situ observations from the

Match-Up database files estimated over

the period May 2010-December 2014

[pss]



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Standard_Deviation_In_situ_minus_SMOS nlat × nlon Standard deviation of the differences

between SMOS weekly L4a data and In

situ observations from the Match-Up

database files estimated over the period

May 2010-December 2014 [pss]


TRMM3B42_accumulated_rain nlat × nlon Cummulated rain falls [mm] from

TRMM3B42 products over the period of

time of each weekly L4SSS product and

avegared on the LaSSS 50 km grid. The

1/4° resolution 3-hourly TRMM-3B42

data were re-gridded on the SMOS 1/2°

resolution grid and the cummulated rain

[mm] was evaluated over the 7-day

period corresponding to the SMOS L4a

product. Data are only available for the

50°S-50°N belt and for the period until 8

august 2014.


OAFlux_accumulated_Evaporation nlat × nlon Cummulated evaporation [mm] from

OAFLUX products over the period of


avegared on the L4aSSS 50 km grid.


MLD nlat × nlon Mixed-Layer depth estimated from

IPC/APDRC. The 1°X1° monthly MLD

orignial fields were interpolated in space

and time on a 1/2° grid and daily.The

MLD provided in the product is the

temporal mean of the daily interpolated

MLD over the 7-day period of the SMOS

L4a product.


surface_downward_northward_stress nlat × nlon Surface wind stress meridional

component included into our products

are based on the Advanced


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SCATterometer (ASCAT) daily data

produced and made available at

Ifremer/cersat on a 0.25° 0.25°

resolution (Bentamy and Croize-

Fillon 2012) since November 2008.


surface_downward_eastward_stress nlat × nlon Surface wind stress zonal component

included into our products are based on

the Advanced SCATterometer (ASCAT)

daily data produced and made available

at Ifremer/cersat on a 0.25° 0.25°




Salinity_MLD_base nlat × nlon The salinity values at the base of the

Mixed Layer Depth (MLD) [pss]

correspond at each grid point to the

temporally interpolated monthly ISAS

value at depth p. p is chosen as the

nearest standard depth levels lower

than the MLD value.


CMORPH_accumulated_rain nlat × nlon Cummulated rain falls [mm] from

CMORPH products over the period of


avegared on the LaSSS 50 km grid.

CMORPH estimates cover a global belt

(−180°W to 180° E) extending from 60°S

to 60°N latitude and are available for the

complete period of the SMOS L4.V01

data


Time_interpolated_ISAS_sss nlat × nlon Optimal interpolation fields generated

using delayed time quality checked in

situ measurements (Argo and ship) by

IFREMER/LPO, using the In Situ Analysis

http://www.tandfonline.com/doi/full/10.1080/2150704X.2014.935522#CIT0002



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System (ISAS). The original files are

monthly fields at 0.5° resolution. A

linear interpolation in time was used to

estimate the fields at the center time of

the 7-day period of the SMOS L4a

product.


date_start 1 Start date of the time period over which

the SMOS data were considered to

generate the composite product

date_stop 1 End date of the time period over which



2.1.1. Convention for the L4a Netcdf files

The generic filename convention for the weekly composite L4a products is given as defined below:

CECOS_SMOS_L4aSSS_0.5deg_YYYY.DD_YYYY.DD_V01.nc

Colored symbols indicate digital variables defined as follows:

The first YYYY (4 digits) and second DD (3 digits) variables indicate the year and the ordinal number

of the day in year corresponding to the start date of the time period over which the SMOS weekly

data were considered to generate the composite product.

The third YYYY (4 digits) and fourth DD (3 digits) variables indicate the year and the ordinal number

of the day in year corresponding to the end date of the time period over which the SMOS weekly


The last variable 01 (2 digits) indicate the product processing version.

Example: for the weekly composite product at 0.5 degree resolution generated from 27 aug 2010 (day of

year=239) to 2 of Sep 2010 (day of year=245) using the processing version 1, the file name is:

CECOS_SMOS_L4aSSS_0.5deg_2010.239_2010.245_V01.nc

In the following we describe in more detail the content of the products


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2.1.2. SSS, SST and Wind speed from SMOS data

2.1.2.1. SMOS SSS

Each L4 netcdf file correspond to 7-day period composites between May 2010 and December 2014 and

include the temporally & spatially averaged L4 SSS at 50 km resolution.

2.1.2.2. SST ECMWF

Example of 7-days mean gridded Sea Surface Temperature colocated at SMOS pixels from ECMWF forecasts.


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2.1.2.3. Wind Speed ECMWF

Example of a 7-days averaged gridded 10-m height wind speed modulus colocated at SMOS pixels from

ECMWF forecasts.

2.1.3. Cummulated Evaporation OAFLUX

For ocean evaporation, we used the data from the Objectively Analyzed Air-sea Fluxes (OAFlux) project (Yu.,

2007), available from the Woods Hole Oceanographic Institution (WHOI) at

http://oaflux.whoi.edu/data.html.


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The daily 1-degree gridded OAFlux evaporation products were spatially interpolated on the SMOs product

1/2 degree grid and accumulated in time [mm] over the 7-day period corresponding to the SMOS L4a

product.

Note: at the time the version V01 of L4 products are generated, no OAFLUX Evaporation data are available

after end september 2014.

Yu, L. 2007. “Global Variations in Oceanic Evaporation (1958–2005): The Role of the Changing Wind Speed. ”

Journal of Climate 20: 5376–5390. doi: http://dx.doi.org/10.1175/ 2007JCLI1714.1.

2.1.4. Cummulated Precipitation TRMM-3B42

satellite rain rate estimates that we used in the present products are the so-called ‘TRMM and Other

Satellites’ (3B42) products, obtained through the NASA/Giovanni server

(http://reason.gsfc.nasa.gov/OPS/Giovanni). The 3B42 estimates are 3-hourly rain rate at a spatial resolution

of 0.25° with spatial extent covering a global belt (−180°W to 180° E) extending from 50°S to 50°N latitude.

The major inputs into the 3B42 algorithm are IR data from geostationary satellites and Passive Microwave

data from the TRMM microwave imager (TMI), special sensor microwave imager (SSM/I), Advanced

Microwave Sounding Unit (AMSU) and Advanced Microwave Sounding Radiometer-Earth Observing System

(AMSR-E).

The 1/4° resolution 3-hourly TRMM-3B42 data were re-gridded on a 1/2° resolution grid and the

cummulated rain [mm} was evaluated over the 7-day period corresponding to the SMOS L4a product.

Important Notice:

At the time our products were generated, the 3B42 data were not available after 8 august 2014.


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2.1.5. Cummulated Precipitation CMORPH

As a complement to the TRMM3B42, we also include the cummulated rain evaluation based on the CMORPH

v1.0 products derived by NOAA.

CMORPH (CPC MORPHing technique) produces global precipitation analyses at very high spatial and

temporal resolution. This technique uses precipitation estimates that have been derived from low orbiter

satellite microwave observations exclusively, and whose features are transported via spatial propagation

information that is obtained entirely from geostationary satellite IR data. At present NOAA incorporate

precipitation estimates derived from the passive microwaves aboard the DMSP 13, 14 & 15 (SSM/I), the

NOAA-15, 16, 17 & 18 (AMSU-B), and AMSR-E and TMI aboard NASA's Aqua and TRMM spacecraft,

respectively. These estimates are generated by algorithms of Ferraro (1997) for SSM/I, Ferraro et al. (2000)

for AMSU-B and Kummerow et al. (2001) for TMI. Note that this technique is not a precipitation estimation

algorithm but a means by which estimates from existing microwave rainfall algorithms can be combined.

Therefore, this method is extremely flexible such that any precipitation estimates from any microwave

satellite source can be incorporated.

With regard to spatial resolution, although the preciptation estimates are available on a grid with a spacing

of 8 km (at the equator), the resolution of the individual satellite-derived estimates is coarser than that -

more on the order of 12 x 15 km or so. The finer "resolution" is obtained via interpolation.

In effect, IR data are used as a means to transport the microwave-derived precipitation features during

periods when microwave data are not available at a location. Propagation vector matrices are produced by

computing spatial lag correlations on successive images of geostationary satellite IR which are then used to

propagate the microwave derived precipitation estimates. This process governs the movement of the


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precipitation features only. At a given location, the shape and intensity of the precipitation features in the

intervening half hour periods between microwave scans are determined by performing a time-weighting

interpolation between microwave-derived features that have been propagated forward in time from the

previous microwave observation and those that have been propagated backward in time from the following

microwave scan. NOAA refer to this latter step as "morphing" of the features.

For the present CATDS products, we only considered the 3-hourly products at 1/4 degree resolution.The

entire CMORPH record (December 2002 - present) for 3-hourly, 1/4 degree lat/lon resolution can be found

at 3-hourly, 1/4 x 1/4 degree

ftp://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/RAW/

CMORPH estimates cover a global belt (−180°W to 180° E) extending from 60°S to 60°N latitude and are

available for the complete period of the SMOS L4.V01 data (May 2010-Dec 2014).

Reference: Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global

precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.. J.

Hydromet., 5, 487-503.

ftp://ftp.cpc.ncep.noaa.gov/precip/global_CMORPH/3-hourly_025deg


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2.1.6. OSCAR zonal and meridional currents

Here we used the 1/3° resolution global surface current products from Ocean Surface Current Analyses

Realtime (OSCAR) (Bonjean and Lagerloef, 2002; http://www.oscar.noaa.gov), directly calculated from

satellite altimetry and ocean vector winds.

The OSCAR data processing system calculates sea surface velocities from satellite altimetry (AVISO), vector

wind fields (scatterometer winds), as well as from sea surface temperature (Reynolds-Smith) using quasi-

steady geostrophic, local wind-driven, and thermal wind dynamics. Near real time velocities are calculated

on both a 1°x1° and 1/3°x1/3° grid on a ~5 day time base over the global ocean. Surface currents are

provided on the OSCAR website (http://www.oscar.noaa.gov) starting from 1992 along with validations with

drifters and moorings. The 1/3° resolution is available for ftp download through

ftp://ftp.esr.org/pub/datasets/SfcCurrents/ThirdDegree.

The 1/3° resolution 5 day OSCAR data were re-gridded on a 1/2° resolution grid and the 5-day currents fields

were linearly interpolated in time on a daily basis. The mean current components provided into the L4a

products are then the result of a time averaging over the 7-day period of the SMOS L4a product.


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2.1.7. Wind Stress zonal and meridional components from ASCAT

Surface wind stress component included into our products are based on the Advanced SCATterometer

(ASCAT) daily data produced and made available at Ifremer/cersat on a 0.25° 0.25° resolution (Bentamy

and Croize-Fillon 2012) since November 2008.

The daily field 1/4° fields were averaged at the SMOS L4 products 1/2° resolution and the mean wind stress

components over the 7-day period of the SMOS L4a product was evaluated .

Bentamy, A., and D. Croizé-Fillon. 2012. “Gridded Surface Wind Fields From Metop/ASCAT Measurements.”

International Journal Remote Sensing 33: 1729–1754. doi:10.1080/ 01431161.2011.600348



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2.1.8. Mixed-Layer Depth

For the Mixed Layer Depth (MLD) estimate, we used the monthly 1°X1° MLD available at the International

Pacific Research Center/Asia-Pacific Data-Research Center (IPRC/APDRC):

see

http://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean/Mixed_Layer_Gridded_mon

thly_mean.info.

Mixed Layer Depth (MLD) is defined here as the depth, on which density increases from

10m to the value equivalent to the temperature drop of 0.2°C.

The 1°X1° monthly MLD ields were interpolated in space and time on a 1/2° grid and daily.

The MLD provided in the product is the temporal mean of the dailky interpolated MLD over the 7-day period

of the SMOS L4a product.

http://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean/Mixed_Layer_Gridded_monthly_mean.info



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2.1.9. Salinity at the base of the Mixed Layer

The salinity values at the base of the Mixed Layer Depth (MLD) correspond at each grid point to the monthly

ISAS value at depth p. p is chosen as the nearest standard depth levels lower than the MLD value. For

example, if at a specific longitude/latitude, the MLD is 52 m, we use the ISAS salinity value at depth = 55 m.

Recalling that ISAS product are given at 151 standard depth levels between 0 and 2000 m (0 3 5 10 15 20 25

30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 110:10:800 820:20:2000). The obtained monthly Sd maps are

linearly interpolated on a daily basis and then averaged in time to match the SMOS weekly time resolution.

Original MLD data are obtained from :

http://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean/Mixed_Layer_Gridded_mon

thly_mean.info




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2.1.10. Time Interpolated ISAS SSS fields

The global reference SSS maps we used to correct the large scale biases in our L4 products are the optimal

interpolation fields generated using delayed time quality checked in situ measurements (Argo and ship) by

IFREMER/LPO, Laboratoire de physique des oceans using the In Situ Analysis System (ISAS) (D7CA2S0 re-

analysis product) (see a method description on http://wwz.ifremer.fr/lpo/SO-Argo-France/Products/Global-

Ocean- 252 T-S/Monthly-fields-2004-2010 and in (Gaillard et al., 2009)).

For the L4aSSS.V01 products, we used the monthly evolving 1/2°x1/2° fields derived from may 2010 to end

2014.


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2.2. In Situ/SMOSL4a Match-up DataBase (MDB) files

2.2.1. ARGO float observations

Ensemble of ARGO float upper level measurements collected during the L4SSS product period (May 2010-

Dec 2014).

Salinity and Temperature measurements from Argo floats are provided by the Coriolis data centre

(http://www.coriolis.eu.org/). We considered only delayed mode ARGO salinity and temperature float data

with Quality index quality=1 and 2 observations. We collected the float data weekly over each of the L4

composite product period and performed a co-location with SMOS L4 products

The upper ocean salinity and temperature values recorded between 0m and 10m depth are considered as

ARGO sea surface salinities and will be referred to as Argo SSS and SST.

The following variables and auxilliary data re included into each in situ match-up netcdf file:

-latitude of the location where co-localized ARGO floats surfaced

-longitude of the location where ARGO floats surfaced

-date at which ARGO floats surfaced


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-sss ARGO (the upper in the upper 10 m)

-sst ARGO (the upper in the upper 10 m)

-depth of the SSS measurement (m)

- platform number

-Rain from TRMM-3B42 at ARGO float location and date

- 7days -long time series of the 3-hourly rain data from TRMM-3B42 colocated atl ARGO float location and

prior to and incuding ARGO data,

-Wind speed components from ASCAT daily fields interpolated at ARGO float location and date,

-7days -long time series of the Daily wind speed components from ASCAT daily fields colocated at ARGO float

location and prior to and incuding ARGO data date

-SMOS L4 SSS closest in space (within 0.25° radius) from ARGO observation and generated during the week

including the float SSS observation

Exemple of comparisons of weekly L4 product with ARGO observations.

Table 2: Variable Name Dimension and Description for the CECOS L4a MDB ARGO V01 research products


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time

1 Central date of the time period

over which the SMOS data were

combined to generate the

composite product. Number of

days since 1990-01-01 00:00:00

latitude N_prof Vector of the latitudes of the

N_prof ARGO floats that surfaced

during the period over which the

L4a SSS composite product is

derived. Expressed in degrees

North from -90. to +90.

longitude N_prof Vector of the longitude of the

N_prof ARGO floats that surfaced


composite L4a SSS product is

derived. Expressed in degrees East

from -180. to +180.

SSS_PRES

N_prof Sea Pressure of SSS sampling at

ARGO between -10m and surface

[Decibar]. Missing Values= -9999

sss_at_argo_float N_prof Sea Surface salinity measured by

ARGO float [practical salinity scale].


sst_at_argo_float N_prof Sea Surfacetemperature measured

by ARGO float [degree C].


SMOS_sss N_prof SMOS L4a Sea Surface salinity co-

localized at ARGO float location

[practical salinity scale]. Missing

Values= -9999

ECMWF_sst N_prof ECMWF L4a Sea Surface

temperature co-localized at ARGO

float location [degree C]. Missing

Values= -9999


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latitude_of_SMOS_obs

N_prof Vector of the latitudes of the

closest L4a produtc 1/2° grid node

from the ARGO float locations that

surfaced during the period over

which the L4a SSS composite

product is derived. Expressed in

degrees North from -90. to +90.

longitude_of_SMOS_obs

N_prof Vector of the longitudes of the


from the ARGO float locations that



product is derived.. Expressed in

degrees East from -180. to +180.

Date_at_argo_float N_prof Date of the time at which each

argo float surfaced. Number of

days since 0000-01-01 00:00:00


Distance_to_coasts_at_argo_float N_prof Distance to coasts evaluated from

a USGS land mask [kms]


PLATEFORM_NUMBER STRING8x N_prof WMO float identifier

PSAL

N_LEVELSxN_prof Vertical profiles of Salinity at each

ARGO float [practical salinity unit].


TEMP

N_LEVELSxN_prof Vertical profiles of Temperature at

each ARGO float [degree C].


PRES

N_LEVELSxN_prof Sea Pressure at each level of each

profile [Decibars]



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Ascat_daily_wind_at_ARGO N_prof Co-localized daily 1/4°x1/4° Ascat

wind speed at each ARGO float

[meter per seconds]


TRMM3B42_3hourly_RR_at_ARGO N_prof 3-hourly rain rate from TRMM3B42

co-localized at ARGO [mm/h]


Ascat_7_prior_days_wind_at_ARGO N_days_windxN_prof Preceeding 7 days time series of

Ascat wind speed Co-localized at

each ARGO float location and date

from daily 1/4°x1/4° Ascat wind

speed [meter per seconds]


TRMM3B42_7_prior_days_RR_at_ARGO N_3H_RAINxN_prof Preceeding 7 days 3-hourly time

series of TRMM3B42 co-localized

rain rate at each ARGO float

location and date from TRMM3B42

[millimeter per hour]


date_start 1 Start date of the time period over

which the SMOS L4a data were

considered to generate the

composite product

date_stop 1 End date of the time period over



composite product


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2.2.2. Ship ThermoSalinograph Data

Spatial Distribution of the GOSUD V3 TSG Delayed-Mode database for May 2010-Dec 2014

Thermo-salinograph data are provided by the GOSUD V3 datasets (see http://www.gosud.org/). We

considered only Delayed Mode data, used only adjusted values and only collected TSG data that exhibit

quality flags=1 & 2. We complemented the data set by adding the last processing or reprocessing of the

thermosalinometer data from the French research vessels. The later are processed by IFREMER/LPO

(http://wwz.ifremer.fr/lpo/La-recherche/Projets-en-cours/SSS-InSitu/SSS-Research-vessels). Note that no

data from this delayed-mode database is yet available for year 2014.

In addition, we considered the "research" quality data from the US Shipboard Automated Meteorological and

Oceanographic System (SAMOS) initiative. Data are available at http://samos.coaps.fsu.edu/html/.

Spatial Distribution of the SAMOS TSG "research" quality database for May 2010-Dec 2014


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All Match-Up database files contain the following information:

-latitude of the location where the co-localized SMOS/TSG data were acquired,

-longitude of the location the co-localized SMOS/TSG data were acquired,

-date at which TSG data were acquired,

-sss measured by TSG (the upper in the upper 10 m),

-sst measured by TSG (the upper in the upper 10 m),

-spatially filtered TSG SSS data, using a running median filter of 25km half-width,

-spatially TSG SST data, using a running median filter of 25km half-width,

-depth of the TSG intake at which SSS & SST measurements are conducted,

- platform number,

- ship call sign,

- Rain from TRMM-3B42 at float location and date,

- 7days -long time series of the 3-hourly rain data from TRMM-3B42 colocated at TSG location and prior to

and incuding TSG data date,

-Wind speed components from ASCAT daily fields interpolated at TSG sample location and date,

-7days -long time series of the Daily wind speed components from ASCAT daily fields colocated at TSG

sample location and prior to and incuding TSG sample date

-SMOS L4 SSS closest in space (within 0.25° radius) from each high resolution TSG observation and generated

during the week including the TSG SSS observation

Note: the spatially filtered SSS & SST TSG data are obtained for each HR sample along the ship track by

searching for all points belonging to the track around that particular sample within a radius of 25 km. The

filtered SSS is then obtained by averaging the SSS over these points.

Exemple netcdf file:

CECOS_MDB_TSG_L4aSSS_0.5deg_2010.316_2010.322_V01.nc

Table 3: Variable Name Dimension and Description for the CECOS L4a MDB TSG V01 research products


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time





days since 1990-01-01 00:00:00

Latitude_at_TSG DAYD, N_ship Matrix of the latitudes of the

N_ship (number of distinct ships)

as function of time along track

(DAYD) that measured SSS during

the period over which the L4a SSS

composite product is derived.

Expressed in degrees North from -

90. to +90.

longitude_at_TSG DAYD, N_ship Matrix of the longitudes of the

N_ship (number of distinct ships

during the week) as function of

time along track (DAYD) that

measured SSS during the period

over which the L4a SSS composite



PRES

DAYD, N_ship Matrix of the Sea Pressure of SSS

intake sampling [Decibar] of the



time along track (DAYD) that

measured SSS during the period

over which the L4a SSS composite

product is derived.


sss_at_TSG DAYD, N_ship Matrix of the along-track high

resolution Sea Surface salinity

measured by the N_ship (number

of distinct ships during the week)

as function of time along track

(DAYD) [practical salinity scale].


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sst_at_TSG DAYD, N_ship Matrix of the along-track high

resolution Sea Surface

temperature measured by the



time along track (DAYD) [degree C].


Filtered_sss_at_TSG DAYD, N_ship Matrix of the along-track spatially

filtered at 50km resolution Sea

Surface salinity measured by the



time along track (DAYD) [practical

salinity scale]. Missing Values= -

9999

Filtered_sst_at_TSG DAYD, N_ship Matrix of the along-track spatially


Surface temperature measured by

the N_ship (number of distinct

ships during the week) as function

of time along track (DAYD) [degree

C].


SMOS_sss_at_TSG DAYD, N_ship Matrix of the SMOS L4a Sea

Surface salinity co-localized at each

TSG location along track for N_ship

(number of distinct ships during

the week) as function of time along

track (DAYD). [practical salinity

scale]. Missing Values= -9999

ECMWF_sst_at_TSG DAYD, N_ship Matrix of the ECMWF L4a Sea

Surface temperature co-localized

at each TSG location along track

for N_ship (number of distinct

ships during the week) as function


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of time along track (DAYD).

[degree C]. Missing Values= -9999

latitude_of_closest_SMOS_obs

DAYD, N_ship Matrix of the latitudes of the

closest L4a SSS produtc 1/2° grid

node from the TSG locations for



time along track (DAYD). Expressed

in degrees North from -90. to +90.

longitude_of_closest_SMOS_obs

DAYD, N_ship Matrix of the longitudes of the


node from the TSG locations for



time along track (DAYD). Expressed

in degrees East from -180. to +180.

Date_at_TSG DAYD, N_ship Matrix of the time at which each

TSG sampled was measured. This

time is provided for N_ship

(number of distinct ships during

the week) as function of the

number of time samples along

track (DAYD). Number of days since

1990-01-01 00:00:


Distance_to_coasts_at_TSG DAYD, N_ship Distance to coasts evaluated for

each TSG sample from a USGS land

mask [kms].


PLATEFORM_NAME N_ship x STRING19 Ship name


SHIP_CALL_SIGN

N_ship x STRING6 ship call sign


Ascat_daily_wind_at_TSG DAYD, N_ship Co-localized daily 1/4°x1/4° Ascat

wind speed at each TSG track


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location & date. [meter per

seconds]


TRMM3B42_3hourly_RR_at_TSG DAYD, N_ship 3-hourly rain rate from TRMM3B42

co-localized at each TSG track

location & date [mm/h]


Ascat_7_prior_days_wind_at_TSG N_ship x

N_DAYS_WINDx

DAYD

Preceeding 7 days time series of


each TSG track location and date




TRMM3B42_7_prior_days_RR_at_TSG N_ship x N_3H_RAIN

xDAYD

Preceeding 7 days 3-hourly time


rain rate at each TSG track location

& date [millimeter per hour]





composite product




composite product


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2.2.3. Surface drifter Data

Spatial Distribution of the "research" quality database for May 2010-Dec 2014

Surface Drifter data are provided by the LOCEAN datasets (see https://www.locean-

ipsl.upmc.fr/smos/drifters/). We considered only validated data. Mathc-Up databse files between SMOS and

drifter observations include the following informations:

-latitude of the location where the co-localized SMOS/drifter data were acquired,

-longitude of the location the co-localizedSMOS/drifter data were acquired,

-date at which drifter data were aquired,

-sss measured at drifter (the upper in the upper 10 m),

-sst measured at drifter (the upper in the upper 10 m),

-spatially filtered drifter SSS data, using a running median filter of 25km half-wdith,

-spatially TSGdrifter data, using a running median filter of 25km half-wdith,

- platform number,


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- instrument name,

- Rain from TRMM-3B42 at float location and date,

- 7days -long time series of the 3-hourly rain data from TRMM-3B42 colocated at drifter location and prior to

and incuding TSG data date,

-Wind speed components from ASCAT daily fields interpolated at drifter sample location and date,

-7days -long time series of the Daily wind speed components from ASCAT daily fields colocated at drifter

sample location and prior to and incuding drifter sample date

-SMOS L4 SSS closest in space (within 0.25° radius) from drifter observation and generated during the week

including the drifter SSS observation

Table 4: Variable Name Dimension and Description for the CECOS L4a MDB drifter V01 research products


time





days since 1990-01-01 00:00:00

Latitude_at_drift DAYD, N_drifters Matrix of the latitudes of the

N_drifters (number of distinct

drifters) as function of time along

track (DAYD) that measured SSS





longitude_at_DRIFT DAYD, N_drifters Matrix of the longitudes of the


drifters during the week) as

function of time along track (DAYD)

that measured SSS during the

period over which the L4a SSS


Expressed in degrees East from -

180. to +180.

PRES DAYD, N_drifters Matrix of the Sea Pressure of SSS


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intake sampling [Decibar] of the




that measured SSS during the

period over which the L4a SSS



sss_at_DRIFT DAYD, N_drifters Matrix of the along-track high

resolution Sea Surface salinity

measured by the N_drifters

(number of distinct drifters during

the week) as function of time along

track (DAYD) [practical salinity

scale]. Missing Values= -9999

sst_at_DRIFT DAYD, N_drifters Matrix of the along-track high

resolution Sea Surface

temperature measured by the




[degree C].


Filtered_sss_at_DRIFT DAYD, N_drifters Matrix of the along-track spatially


Surface salinity measured by the





Values= -9999

Filtered_sst_at_DRIFT DAYD, N_drifters Matrix of the along-track spatially


Surface temperature measured by

the N_drifters (number of distinct




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[degree C].


SMOS_sss_at_DRIFT DAYD, N_drifters Matrix of the SMOS L4a Sea

Surface salinity co-localized at each

DRIFT location along track for



function of time along track

(DAYD). [practical salinity scale].


ECMWF_sst_at_DRIFT DAYD, N_drifters Matrix of the ECMWF L4a Sea

Surface temperature co-localized

at each DRIFT location along track

for N_drifters (number of distinct



(DAYD). [degree C]. Missing

Values= -9999

latitude_of_closest_SMOS_obs

DAYD, N_drifters Matrix of the latitudes of the


node from the DRIFT locations for




(DAYD). Expressed in degrees


longitude_of_closest_SMOS_obs

DAYD, N_drifters Matrix of the longitudes of the


node from the DRIFT locations for




(DAYD). Expressed in degrees East

from -180. to +180.

Date_at_DRIFT DAYD, N_drifters Matrix of the time at which each

DRIFT sampled was measured. This

time is provided for N_drifters


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(number of distinct drifters during

the week) as function of the

number of time samples along

track (DAYD). Number of days since

1990-01-01 00:00:


Distance_to_coasts_at_DRIFT DAYD, N_drifters Distance to coasts evaluated for

each DRIFT sample from a USGS

land mask [kms].


PLATEFORM_NAME N_drifters x

STRING19

Ship name


SHIP_CALL_SIGN

N_drifters x STRING6 ship call sign


Ascat_daily_wind_at_DRIFT DAYD, N_drifters Co-localized daily 1/4°x1/4° Ascat

wind speed at each DRIFT track

location & date. [meter per

seconds]


TRMM3B42_3hourly_RR_at_DRIFT DAYD, N_drifters 3-hourly rain rate from TRMM3B42

co-localized at each DRIFT track

location & date [mm/h]


Ascat_7_prior_days_wind_at_DRIFT N_drifters x

N_DAYS_WINDx

DAYD

Preceeding 7 days time series of


each DRIFT track location and date




TRMM3B42_7_prior_days_RR_at_DRIFT N_drifters x

N_3H_RAIN xDAYD

Preceeding 7 days 3-hourly time


rain rate at each DRIFT track

location & date [millimeter per


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hour]





composite product




composite product

2.2.4. Global tropical Moored Buoy Array data

Surface time series of salinity from the Global Tropical Moored Buoy Array were also collected and co-

localized with SMOS L4 data. The Global Tropical Moored Buoy Array is a multi-national effort to provide

data in real-time for climate research and forecasting. Major components include the TAO/TRITON array in

the Pacific, PIRATA in the Atlantic, and RAMA in the Indian Ocean.

The Global Tropical Moored Buoy Array is a contribution to the Global Ocean Observing System (GOOS),

Global Climate Observing System (GCOS), and the Global Earth Observing System of Systems (GEOSS). Data

can be accessed here: http://www.pmel.noaa.gov/tao/global/global.html

Data collected within TAO/TRITON, PIRATA and RAMA comes primarily from ATLAS and TRITON moorings.

These two mooring systems are functionally equivalent in terms of sensors, sample rates, and data quality.

http://www.pmel.noaa.gov/tao/proj_over/mooring.shtml

http://www.jamstec.go.jp/jamstec/TRITON/real_time/overview.php/po.php


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We selected data measured at 1 meter depth and standard quality (pre-deployement calibration applied) &

highest quality (pre/post calibration agree)

We generated one Match-up DataBase (MDB) files per tropical basin (TAO/TRITON, PIRATA and RAMA) for

the whole period May 2010-Dec 2014. Each MDB file includes for each mooring of the basin:

-Mooring latitude

-Mooring longitude

-Date of SSS measurement

-Depth of mooring salinity measurement

-Depth of mooring temperature measurement

-Daily mooring SSS (Quality Flag=1 (Highest quality),2 (standard))

-Daily mooring SST Quality Flag=1 (Highest quality),2 (standard))

-Daily mooring wind speed Quality Flag=1 (Highest quality),2 (standard))

-Daily mooring rain rate (Quality Flag=1 (Highest quality),2 (standard))

-Daily mooring salinity profile (Quality Flag=1 (Highest quality),2 (standard))

-Daily mooring temperature profile Quality Flag=1 (Highest quality),2 (standard))

-Weekly time averaged mooring SSS

-Weekly time averaged mooring SSS quality flag"

-Weekly time averaged mooring SST"

-Weekly time averaged mooring wind speed

-Weekly accummulated precipitation"

-Weekly time averaged mooring salinity (profiles)

-Weekly time averaged mooring temperature (profiles)

-SMOS L4 SSS at mooring

-ECMWF L4 SST at mooring

-ECMWF L4 wind speed at mooring

-TRMM 3B42 (v7) weekly accummulated precipitation at mooring


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Note that SMOS L4 SSS, ECMWF SST wind speed and TRMM rain were obtained at the moorings by bilinearly

interpolating in space the 0.5° data at the mooring location

Table 5 Variable Name Dimension and Description for the CECOS L4a MDB mooringsV01 research

products


time





days since 1990-01-01 00:00:00

daily_date DAYD Vector of the Number of days since

1990-01-01 00:00:00 at which

daily SSS was measured at the

moorings. Valid for all moorings.

latitude N_moorings Vector of the latitudes of the

N_mooring for a given basin

(number of distinct TAO, or Pirata,

or RAMA moorings)

Expressed in degrees North from -

90. to +90.

longitude N_moorings Vector of the longitudes of the

N_mooring for a given basin

(number of distinct moorings for

TAO, or Pirata, or RAMA)

Expressed in degrees East from -

180. to +180.

depth_s

DEPTH_S Vector of Depths at which salinity

measurements were performed.

Valid for all moorings. [meter]


depth_t

DEPTH_T Vector of Depths at which

temperature measurements were

performed. Valid for all moorings.


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[meter]


Daily_moooring_sss N_moorings, DAYD Matrix of the daily-averaged times

series (DAYD) of in situ SSS as

measured by the N_mooring

(number of distinct moorings for a

given basin [practical salinity scale].


Daily_moooring_sst N_moorings, DAYD Matrix of the daily-averaged times

series (DAYD) of in situ SST as



given basin [degrees C]. Missing

Values= -9999

Daily_moooring_wind_speed N_moorings, DAYD Matrix of the daily-averaged times

series (DAYD) of in situ wind

speed as measured by the

N_mooring (number of distinct

moorings for a given basin [m/s].


Daily_moooring_rain_rate N_moorings, DAYD Matrix of the daily-averaged times

series (DAYD) of in situ rain rate

as measured by the N_mooring


given basin [mm/h]. Missing

Values= -9999

Daily_moooring_s N_moorings, DAYD,

DEPTH_S

Matrix of the daily-averaged times

series (DAYD) of salinity profiles

as measured at the N_mooring


given basin) and at the depths

DEPTH_S [practical salinity scale].



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Daily_moooring_t N_moorings, DAYD,

DEPTH_T

Matrix of the daily-averaged times

series (DAYD) of tempearture

profiles as measured by the


moorings for a given basin) and at

the depth DEPTH_T [degrees C].


Weekly_moooring_sss N_moorings, time Matrix of the weekly-averaged

times series (time) of in situ SSS as



given basin [practical salinity scale].


Weekly_moooring_sst N_moorings, time Matrix of the weekly-averaged

times series (time) of in situ SST as



given basin [degrees C]. Missing

Values= -9999

Weekly_moooring_wind_speed N_moorings, time Matrix of the weekly-averaged

times series (time) of in situ wind

speed as measured by the


moorings for a given basin [m/s].


weekly_moooring_accumulated_rain N_moorings, time Matrix of the weekly-cummulated

times series (time) of rain as

measured in situ by the


moorings for a given basin [mm].


Weekly_moooring_s N_moorings, time,

DEPTH_S

Matrix of the weeky-averaged

times series (time) of salinity

profiles as measured at the



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the depths DEPTH_S [practical

salinity scale]. Missing Values= -

9999

Weekly_moooring_t N_moorings, time,

DEPTH_T

Matrix of the weekly-averaged

times series (time) of tempearture

profiles as measured by the



the depth DEPTH_T [degrees C].


Weekly_SMOS_SSS N_moorings, time Maxtrix of the weekly L4a SMOS

SSS time series interpolated at

each of the N_moorings locations.

[practical salinity unit].


Weekly_ECMWF_SST N_moorings, time Maxtrix of the weekly L4a ECMWF

SST time series interpolated at

each of the N_moorings locations.

[degree celcius].


Weekly_ECMWF_wind_speed N_moorings, time Maxtrix of the weekly L4a ECMWF

wind speed time series

interpolated at each of the

N_moorings locations. [m/s].


Weekly_TRMM3B42_accumulated_rain N_moorings, time Matrix of the weekly-cummulated

times series (time) from

TRMM3B42 co-localized at the


moorings for a given basin [mm].






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composite product




composite product

Example of a SSS time series at TAO/TRITTON Mooring in the east equatorial pacific (2°N, 156°E): the plot

shows the mooring 1m depth SSS daily (blue), weekly (black) and the SMOS L4 SSS (red) time series.

2.2.5. Southern Ocean SSS from Seals


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The instrumentation of southern elephant seals with satellite-linked CTD tags has offered unique temporal

and spatial coverage of the Southern Oceans since 2004. This includes extensive data from the Antarctic

continental slope and shelf regions during the winter months, which is outside the conventional areas of

Argo autonomous floats and ship-based studies. This landmark dataset of around 75,000 temperature and

salinity profiles from 20–140 °E, concentrated on the sector between the Kerguelen Islands and Prydz Bay,

continues to grow through the coordinated efforts of French and Australian marine research teams. The seal

data (MEOP-CTD in-situ data collection) are quality controlled and calibrated using delayed-mode

techniques involving comparisons with other existing profiles as well as cross-comparisons similar to

established protocols within the Argo community, with a resulting accuracy of ±0.03 °C in temperature and

±0.05 in salinity or better.

The seal SSS dataset is acessible at the Coriolis data center (http://www.coriolis.eu.org/Observing-the-

Ocean/Marine-Mammals).

The upper ocean salinity and temperature values recorded between 0m and 10m depth by the seals are

considered asSEAL sea surface salinities and will be referred to as SEAL SSS and SST in the CEC Match-ups.


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The following variables and auxilliary data re included into each in situ match-up netcdf file:

-latitude of the location where co-localized SEAL floats surfaced

-longitude of the location where SEAL floats surfaced

-date at which SEAL floats surfaced

-sss SEAL (the upper in the upper 10 m)

-sst SEAL (the upper in the upper 10 m)

-depth of the SSS measurement (m)

- platform number

-Rain from CMORPH at SEAL float location and date

- 7days -long time series of the 3-hourly rain data from CMORPH colocated atl SEAL float location and prior to

and incuding SEAL data,

-Wind speed components from ASCAT daily fields interpolated at SEAL float location and date,

-7days -long time series of the Daily wind speed components from ASCAT daily fields colocated at SEAL float

location and prior to and incuding SEAL data date

-SMOS L4 SSS closest in space (within 0.25° radius) from SEAL observation and generated during the week

including the float SSS observation

Roquet, F. et al. A Southern Indian Ocean database of hydrographic profiles

obtained with instrumented elephant seals. Sci. Data 1:140028 doi: 10.1038/sdata.2014.28 (2014).

Table 6: Variable Name Dimension and Description for the CECOS L4a MDB SEAL V01 research products


time





days since 1990-01-01 00:00:00

latitude N_prof Vector of the latitudes of the

N_prof SEAL floats that surfaced





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longitude N_prof Vector of the longitude of the

N_prof SEAL floats that surfaced


composite L4a SSS product is

derived. Expressed in degrees East

from -180. to +180.

SSS_PRES

N_prof Sea Pressure of SSS sampling at

SEAL between -10m and surface

[Decibar]. Missing Values= -9999

sss_at_seal_float N_prof Sea Surface salinity measured by

SEAL float [practical salinity scale].


sst_at_seal_float N_prof Sea Surfacetemperature measured

by SEAL float [degree C].


SMOS_sss N_prof SMOS L4a Sea Surface salinity co-

localized at SEAL float location


Values= -9999

ECMWF_sst N_prof ECMWF L4a Sea Surface

temperature co-localized at SEAL

float location [degree C]. Missing

Values= -9999

latitude_of_SMOS_obs

N_prof Vector of the latitudes of the


from the SEAL float locations that





longitude_of_SMOS_obs

N_prof Vector of the longitudes of the


from the SEAL float locations that




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product is derived.. Expressed in


Date_at_seal_float N_prof Date of the time at which each seal

float surfaced. Number of days

since 0000-01-01 00:00:00


Distance_to_coasts_at_seal_float N_prof Distance to coasts evaluated from

a USGS land mask [kms]


PLATEFORM_NUMBER STRING8x N_prof WMO float identifier

PSAL

N_LEVELSxN_prof Vertical profiles of Salinity at each

SEAL float [practical salinity unit].


TEMP

N_LEVELSxN_prof Vertical profiles of Temperature at

each SEAL float [degree C].


PRES

N_LEVELSxN_prof Sea Pressure at each level of each

profile [Decibars]


Ascat_daily_wind_at_SEAL N_prof Co-localized daily 1/4°x1/4° Ascat

wind speed at each SEAL float

[meter per seconds]


TRMM3B42_3hourly_RR_at_SEAL N_prof 3-hourly rain rate from TRMM3B42

co-localized at SEAL [mm/h]


Ascat_7_prior_days_wind_at_SEAL N_days_windxN_prof Preceeding 7 days time series of


each SEAL float location and date



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TRMM3B42_7_prior_days_RR_at_SEAL N_3H_RAINxN_prof Preceeding 7 days 3-hourly time


rain rate at each SEAL float location

and date from TRMM3B42

[millimeter per hour]





composite product




composite product

2.2.6. Match-Up Database netcdf Files Naming conventions

The generic filename convention for the weekly MDB L4a products is given as defined below:

CECOS_MDB_sensor_0.5deg_YYYY.DD_YYYY.DD_V01.nc


The first variable sensor indicate the in situ sensor types:

Sensor="ARGO" for Coriolis DM ARGO floats Match-Ups

Sensor="TSG" for GOSUDV3 TSG DM Match-Ups

Sensor="TSG_SAMOS" for SAMOS TSG Match-Ups

Sensor="TAO" for NOAA/AOML TAO moorings Match-Ups

Sensor="PIRATA" for NOAA/AOML PIRATA moorings Match-Ups

Sensor="RAMA" for NOAA/AOML RAMA moorings Match-Ups

Sensor="drifter" for LOCEAN surface drifter Match-Ups

Sensor="seal" for Coriolis DM sea seals CTD Match-Ups


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The second YYYY (4 digits) and third DD (3 digits) variables indicate the year and the ordinal number



The fourth YYYY (4 digits) and fifth DD (3 digits) variables indicate the year and the ordinal number




Example: for the ARGO MDB product at 0.5 degree resolution generated from 27 aug 2010 (day of


CECOS_MDB_ARGO_0.5deg_2010.239_2010.245_V01.nc


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2.3. Level 4b Product content

The CATDS/CEC-OS SMOS Level 4b Version 1 Sea Surface Density (SSD) research products are weekly (7 days)

composite of satellite sea surface denisty at 50 km resolution. The products coverage is May 2010-December

2014. They include some useful other variables to scientifically exploit satellite Sea surface density fields:

SSS, SST, AVISO mean sea level anomaly, ekman+geostrophic OSCAR currents and wind stress components.

Table 7: Variable Name Dimension and Description for the CECOS L4b V01 research products


time


which the SMOS data were combined

to generate the composite product.

Number of days since 1990-01-01

00:00:00

latitude Nlat Vector of the latitude of the grid nodes


derived. Expressed in degrees North

from -90. to +90.

longitude nlon Vector of the longitude of the grid nodes


derived. Expressed in degrees East from

-180. to +180.

sss nlat × nlon Gridded Sea Surface Salinity from SMOS

[Practical Salinity Scale]. Missing Values=

-9999

sst nlat × nlon Gridded Sea Surface Temperature

colocated at SMOS pixels from ECMWF

forecasts [Kelvins]. Missing Values= -

9999

SLA

nlat × nlon AVISO daily 1/4°x1/4° MSLA averaged in

space and time to match the L4a SSS

1/2° Grid and weeks [meter]. Missing

Values= -9999



within the brighthness temperature data

set used for SSS product generation at a


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given pixel [%].


Zonal_component_surface_currents nlat × nlon Gridded OSCAR surface current

(Ekman+Geostrophic) zonal component

interpolated in space and time at SMOS

pixels [m/s]. The 1/3° resolution 5 day

OSCAR data were re-gridded on a 1/2°

resolution grid and the 5-day currents

fields were linearly interpolated in time

on a daily basis. The mean current



averaging over the 7-day period of the

SMOS L4a product.


Meridional_component_surface_currents nlat × nlon Gridded OSCAR surface current


component interpolated in space and

time at SMOS pixels [m/s]. Missing

Values= -9999

Sea_Surface_Density nlat × nlon Weekly composite of Sea surface density

deduced from the SMOS L4aSSS and

ECMWF L4a SST[kg/m3]


surface_downward_northward_stress nlat × nlon Surface wind stress meridional

component included into our products








surface_downward_eastward_stress nlat × nlon Surface wind stress zonal component

included into our products are based on

the Advanced SCATterometer (ASCAT)



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daily data produced and made available

at Ifremer/cersat on a 0.25° 0.25°




date_start 1 Start date of the time period over which



date_stop 1 End date of the time period over which



2.3.1. Convention for the L4b Netcdf files

The generic filename convention for the weekly composite L4b products is given as defined below:

CECOS_SMOS_L4bdens_0.5deg_YYYY.DD_YYYY.DD_V01.nc









Example: for the weekly composite product at 0.5 degree resolution generated from 27 aug 2010 (day of


CECOS_SMOS_L4bdens_0.5deg_2010.239_2010.245_V01.nc

In the following we describe in more detail the content of the products

2.3.2. Sea Surface Density

The L4b products include weekly maps of sea surface density evaluated from SMOS L4a SSS and

SMOS/ECMWF L4a SST. Satellite Sea Surface Density (SSD) is calculated using the appropiate thermal

expansion coefficient and the appropriate saline contraction coefficient of seawater from Absolute Salinity

and Conservative Temperature. We used the computationally-efficient 48-term expression for density in

terms of absolute salinity (SA), conservative temperature (CT) and sea presure p (IOC et al., 2010).



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IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and

use of thermodynamic properties.Intergovernmental Oceanographic Commission, Manuals and Guides No.

56, UNESCO (English), 196 pp. We used the matlab code available from the TEOS-10 web site. The software

is available from http://www.TEOS-10.org

Example of weekly L4b sea surface density SSD weekly composite.

2.3.3. Mean Sea Level Anomaly

Exemple of Mean Sea Level anomaly Composite corresponding to a SMOS L4a SSS product period.

http://www.teos-10.org/

http://www.teos-10.org/


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We include weekly averaged Sea Level Anomalies in the L4b products. These are derived from Delayed-Time

merged Global Ocean Gridded Sea Level Anomalies SSALTO/Duacs L4 products. These correspond to sea

surface height above Mean Sea Surface products from multi-satellite observations over Global Ocean. The

orginial products are daily at 1/4° resolution. The later were averaged in space and time to be gridded on the

same grid than the L4aSSS & SST products.

See http://www.aviso.altimetry.fr/

2.3.4. Other variables in L4b products

In Level 4b, we aslo reproduced some of the variables already included into the Level 4a products. These

include SMOS L4a SSS, ECMWF L4a SST, OSCAR current and Ascat daily wind stress components. The reader

is referred to §8.4.2, §8.4.6 & §8.4.7 for details.


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2.4. Level 4c Product content

The CATDS/CEC-OS SMOS Level 4c Version 1 anomalies research products are weekly (7 days) composite of

the anomalies of an ensemble of geophysical variables with respect their mean seasonnal cycle evaluated

during the complete SMOS L4a product period (May 2010-dec 2014) and at 50 km resolution. The products

coverage is May 2010-December 2014. They include some useful variables to scientifically exploit satellite

Sea surface salinity anomaly fields: SSSA, SSTA, AVISO mean sea level anomaly, precipitation and evaporation

anaomalies, ekman+geostrophic OSCAR currents anomalies, and wind stress components anomalies

2.4.1. Methodology to evaluate anomalies

Figure: Top: example of weekly L4a SSS time series (blue) at the mouth of the amazon river (4.75°N,

45.25°W) and its associated Mean Annual Cycle (black curve). Bottom: corresponding times series of the L4c

SSS anomaly at that particular location.

Anomalies of all variables (except for the mean sea level anomalies) included in L4c products are estimated

by removing from each variable fields, the locally estimated in space and time Mean Annual Cycle

contribution (MAC) :


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var_anomaly(lat,lon,t)=var(lat,lon,t)-MAC(lat,lon,t)

where (lat,lon) are geographic coordinates and t is central time of the weekly products. The Mean Annual

Cycle contribution for a given variable is obtained by averaging the ensemble of observations at a given grid

node (defined by lat & lon) from the same weeks of all year between 2010 and 2014. These anomalies are

therefore representative of interannual variability around the mean seasonal cycle of each variable.

For fields with original temporal resolution coarser than weekly (e.g. ISAS is originally provided monthly), a

linear interpolation in time of the monthly anomalies was performed.

2.4.2. Level 4c product content

Table 9: Variable Name Dimension and Description for the CECOS L4c V01 research products


time


which the SMOS data were

combined to generate the composite

product. Number of days since 1990-

01-01 00:00:00

latitude nlat Vector of the latitude of the grid

nodes over which the composite



longitude nlon Vector of the longitude of the grid

nodes over which the composite



SSSA_SMOS nlat × nlon Gridded Sea Surface Salinity anomaly

from SMOS [Practical Salinity Scale].


SSTA_ECMWF nlat × nlon Gridded Sea Surface Temperature

anomaly colocated at SMOS pixels

from ECMWF forecasts [degree

celsius]. Missing Values= -9999

SSSA_ISAS nlat × nlon Gridded Sea Surface Salinity anomaly

from ISAS [Practical Salinity Scale].



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SSTA_ISAS nlat × nlon Gridded Sea Surface Temperature

anomaly from ISAS [degree Celsius].


SLA

nlat × nlon AVISO daily 1/4°x1/4° MSLA averaged

in space and time to match the L4c

SSSA 1/2° Grid and weeks [meter].




within the brighthness temperature

data set used for SSS product

generation at a given pixel [%].


OAFLux_accumulated_Evaporation_anomaly nlat × nlon Gridded Weekly anomaly of

cummulated Evaporation as

estimated from OAFlux [mm]

Missing values=-9999

Zonal_component_surface_currents_anomalies nlat × nlon Gridded OSCAR surface current

(Ekman+Geostrophic) zonal

component anomalies interpolated in

space and time at SMOS pixels [m/s].

The 1/3° resolution 5 day OSCAR data

were re-gridded on a 1/2° resolution

grid and the 5-day currents fields

were linearly interpolated in time on

a daily basis. The mean current



averaging over the 7-day period of

the SMOS L4a product.


Meridional_component_surface_currents_anomaly nlat × nlon Gridded OSCAR surface current


component anomalies interpolated in

space and time at SMOS pixels [m/s].



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Sea_Surface_Density_anomalies nlat × nlon Weekly composite of Sea surface

density anomalies deduced from the

SMOS L4aSSS and ECMWF L4a

SST[kg/m3]


surface_downward_northward_stress_anomaly nlat × nlon Surface wind stress meridional

component anomalies included into

our products are based on the

Advanced SCATterometer (ASCAT)

daily data produced and made

available at Ifremer/cersat on a

0.25° 0.25° resolution (Bentamy

and Croize-Fillon 2012) since

November 2008.


surface_downward_eastward_stress_anomaly nlat × nlon Surface wind stress zonal component

anomaly included into our products








Salinity_anomaly_MLD_base nlat × nlon Anomalies of the salinity values at the

base of the Mixed Layer Depth (MLD)

[pss] correspond at each grid point to

the temporally interpolated monthly

ISAS value at depth p. p is chosen as

the nearest standard depth levels

lower than the MLD value.


CMORPH_accumulated_rain_anomaly nlat × nlon Cummulated rain falls anomaly [mm]

from CMORPH products over the

period of time of each weekly L4SSS

product and avegared on the LaSSS 50




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km grid. CMORPH estimates cover a

global belt (−180°W to 180° E)

extending from 60°S to 60°N latitude

and are available for the complete

period of the SMOS L4.V01 data


TRMM3B42_accumulated_rain_anomaly nlat × nlon Cummulated rain falls anomaly [mm]

from TRMM3B42 products over the

period of time of each weekly L4SSS

product and avegared on the LaSSS 50

km grid.


Mixed_Layer_Depth_anomaly nlat × nlon Mixed-Layer depth anomaly

estimated from IPC/APDRC. The 1°X1°

monthly MLD orignial fields were

interpolated in space and time on a

1/2° grid and daily.The MLD provided

in the product is the temporal mean

of the daily interpolated MLD over the

7-day period of the SMOS L4a

product.




considered to generate the composite

product



considered to generate the composite

product

2.4.3. Convention for the L4c Netcdf files

The generic filename convention for the weekly composite L4b products is given as defined below:

CECOS_SMOS_L4cano_0.5deg_YYYY.DD_YYYY.DD_V01.nc



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Example: for the weekly composite anomaly products at 0.5 degree resolution generated from 27 aug 2010

(day of year=239) to 2 of Sep 2010 (day of year=245) using the processing version 1, the file name is:

CECOS_SMOS_L4cano_0.5deg_2010.239_2010.245_V01.nc


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3. Data citation

The following example show how to cite the use of these CATDS L4 reserach product data sets in a

publication. List the data set title, the producing center, the year of data set release, the version

number, and the dates of the data you used (for example, May to June 2014):

"The SMOS L4 data were obtained from the Ocean Salinity Expertise Center (CECOS) of the CNES-

IFREMER Centre Aval de Traitemenent des Donnees SMOS (CATDS), at IFREMER, Plouzane

(France). V01, [list the dates of the data used]."

In addition, in any publication using the other variables than SSS included into our products, all users

shall aknowledge the data they used as follows:

-Surface current components are based on the 1/3 resolution global surface current products from

Ocean Surface Current Analyses Real time (OSCAR) (Bonjean and Lagerloef 2002;

http://www.oscar. noaa.gov), as processed by CATDS/CECOS"

-The global ocean evaporation products were provided by the WHOI OAFlux project

(http://oaflux.whoi.edu) funded by the NOAA Climate Observations and Monitoring (COM)

program.

-Satellite TRMM rain rate estimates that we used in the present study are based on the so-called

„TRMM and Other Satellites‟ (3B42) products, obtained through the NASA/Giovanni server

(http://reason.gsfc.nasa.gov/OPS/Giovanni).

-Satellite CMORPH rain rate estimates that we used in the present study are based on National

Center for Atmospheric Research Staff (Eds) datasets. "The Climate Data Guide: CMORPH (CPC

MORPHing technique): High resolution precipitation (60S-60N)." Retrieved from

https://climatedataguide.ucar.edu/climate-data/cmorph-cpc-morphing-technique-high-resolution-

precipitation-60s-60n. -

-The 3-D monthly fields of in situ OI temperature and salinity in NetCdf format can be found at the

following DOI reference: Fabienne Gaillard (2015). ISAS-13 temperature and salinity gridded fields.

Pôle Océan. http://doi.org/z77

-Surface wind stress component included into our products are based on the Advanced

SCATterometer (ASCAT) daily data produced and made available at Ifremer/cersat on a

0.25° / 0.25° resolution (Bentamy and Croize-Fillon ) since November 2008. Bentamy, A., and D.

Croizé-Fillon. 2012. “Gridded Surface Wind Fields From Metop/ASCAT Measurements.”

International Journal Remote Sensing 33: 1729–1754. doi:10.1080/ 01431161.2011.600348

-For the Mixed Layer Depth (MLD) estimate, we used the monthly 1°X1° MLD available at the

International Pacific Research Center/Asia-Pacific Data-Research Center (IPRC/APDRC):

http://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean/Mixed_Layer_Gridded

_monthly_mean.info

Salinity measurements from Argo floats, GOSUD TSG and Seals data are provided by the Coriolis

data center (http:// www.coriolis.eu.org/). We acknowledge the use of freely available Argo data

collected by the International Argo Project and the national programs that contribute to it. We thank

the GOSUD Project (http://www.gosud.org) for providing free access to the TSG data.


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Salinity measurements from SAMOS TSG are provided by the US Shipboard Automated

Meteorological and Oceanographic System (SAMOS) initiative. Data are available at

http://samos.coaps.fsu.edu/html/.

The altimeter products (msla) were produced by Ssalto/Duacs and distributed by Aviso with support

from Cnes.


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4. Product Algorithm, Validity, coverage and known flaws

4.1. Product Algorithm and Validity

The Algorithms used to generate these datasets are described in a dedicated document: the Algorithm

Theoretical Breadboard Document. A preliminary assessment of the validity of these products is now

being conducted: a deicated Product Validation document will be provided soon on the web site

when finalized. Both documents shall be downloadable in the Document directory of the

CATDS/CECOS ftp server.

4.2. Input Data and coverage

The input data used to generate the present version of the CATDS CECOS-research Level 4 products

are the ESA v5.50 L1B data from may 2010 to december 2014. The operational L1B data are used as

input to our L3 & L4 processors for the period from january 2014 to dec 2014.

The data aquired during the first four months of the commissioning phase in 2010 (from January to

end of May) were not reprocessed because of reduced data quality during that period just post-

launch. Users interested in data from that period can still access the V01 products which were

generated covering that period of time. Reader interested in the detailed data availability is refered to

the ESA monitoring facility: https://earth.esa.int/web/guest/missions/esa-operational-eo-

missions/smos/available-data-processing

Note that the last week of 2011 is missing because of electrical tests of the instrument at this period.

4.3. Several known flaws

4.3.1. Remaining Major Issues in the L2OS data

Some open issues remain in the level 1 algorithm which strongly impinge on the Level 2, 3 and 4 Sea

Surface Salinity retrievals and their quality, but should be improved, though not fully resolved, with

the next Level v620 processor now under deployement.

In addition, it is clear that the L2OS algorithm, independently of the L1 data quality, has not yet

reached a full maturity with remaining uncertainties in the forward models, not-yet-accounted for

geophysical effects (waves, currents, diurnal effects,..), auxiliary data and non-optimal tuning of the

overall algorithm(data filtering, retrieval methods,..).


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Figure 1:see legend below

Figure 2: "First Order" remaining issues in the SMOS SSS data.


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The first map above is showing the difference between a SMOS Level 3 monthly-averaged product

based on L2 data uncorrected for large-scale biases and an objectively analysed field of in situ

observations (so-called ISAS data). The second map is showing a similar comparison but for another

period of the year and reveals very strong latitudinal biases north of 20°N.

The remaining issues at level 1 and 2 that need to be addressed by SMOS L1 & L2 teams in order to

significantly improve the L2OS data quality can be summarized as follows:

At Level 1:

Strong (from the oceanographer perspective<=>±1 psu) and systematic brightness

temperature data contamination over the oceans by land masses within an about 800 kms-

width band along the world coasts,

Seasonal and latitudinal unexpected variations (from the oceanographer perspective) in the

Brightness temperature data,

Inaccuracies in the Radio Frequency Interferences (RFI) contamination filtering,

Remaining noise in the Brightness temperature associated with solar radiation impacts on the

reconstructed images which impact retrieved SSS data quality,

Systematic spatial biases in the reconstructed TB images (partially mitigated at L2 by the

OTT),

Inaccuracies in the Total Electronic Content,

Uncertainties in the polarization purity of the L1C data

At Level 2:

Non Optimal Radio Frequency Interferences (RFI) contamination filtering,

Decreased sensitivity of the L-band signal to SSS in cold seas,

Remaining sea water dielectric constant modelling uncertainties,

Inaccuracies in the corrections for the sea surface roughness effects (wave & currents

impacts),

Inaccuracies in the corrections for extraterrestrial radiation glints (galactic and solar)

Non yet accounted for geophysical effects in the forward models (rain impact, diurnal cycle

of SST,..)

Inaccuracies in the geophysical auxiliary data sets used as priors in the retrieval scheme to

characterize the oceanic and meteorological conditions in the observed scenes,

Non yet fully optimal iterative inversion methodology and data filtering (quality control)

strategies


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The combination of the above listed issues in L1 & L2 products produces some well-known

inhomogeneities of the retrieved L2 SSS data quality over the globe, which may prevent the use of

SMOS products in certain oceanographic applications.

4.3.2. Solar contaminations

Because the direct sun aliases contamines the Extended Field of View of the antenna, in our

re-processing, the SSS retrievals were limited to the Alias-free Field of view domain (AF-FOV) of

the instrument. Nevertheless, the direct sun aliases are sometimes located at the border of the Alias

Free domain, particulary at the end of the years (November to December) in descending passes. To

minimize this spurious contribution, a mask was applied around the location of the direct sun &

aliases, eliminating reconstructed brightness temperatures within a radius of 0.05 in the cosine

director coordinates of the antenna plane.

As a very strong local source, the imaged sun disk and its aliases induce spurious brightness

temperature tails after interferometric image reconstruction. While a sun correction is applied in the

ESA level 1 processing, residual solar contributions that were not perfectly corrected by that

processing may have produced some spurious signal in the SSS data at the end of the years. In

particular, the sun disk alias propagating on the bottom left border of the AF-FOV may explain the

presence of stripe-like too fresh/too salty anomalies detected in the composite SSS data that are seen

to progressively amplify from october to december.

4.3.3. RFI

SMOS multi-angular brightness temperature measurements at 1.4 GHz are strongly affected

by radio frequency interferences (RFI) from radar networks, TV and radio links in what shall be a

protected band. These intereferences are numerous over land in Europe and Asia, but can be also

encountered in some other areas of Africa, America and Greenland and in some numerous islands

over the world. Over the oceans, the signals emanating from land sources can extend very far away

from the coasts and have dramatic consequences on the accuracy of sea surface salinity remote

sensing from SMOS in some key oceanic areas like the north atlantic, north pacific and north indian

oceans. The signature of RFI in SMOS data is highly variable in time and space and strongly depends

on the instrument probing polarization and observation angles. Because of the interferometric

principle, local strong RFI signals in the physical space can pollute a very extended area in the

Fourier domain of the synthetic antenna and contaminate large portion of the SMOS reconstructed

brightness temperature images. In particular, RFI sources located in the aliased regions of the image

can impact the data in the (extended) alias-free field of view. Ideally, detection and Mitigation

techniques of these spurious signals in SMOS data shall therefore be performed from the raw data at

the visibility level prior image reconstruction and shall consider instantaneous acquisitions (snap-

shot information) and deal with the whole field of view images. Nevertheless, because of the strong

amplitude of the RFI contamination with respect geophysical signal over the ocean, simple detection

algorithm can be applied to the reconstructed brightness temperature data, identifying samples that

deviate anomalously from the average of their neighbors in space, time and probing angles.

Collecting acquired data over the full SMOS mission period, we were in a position to re-analyze the

spatio-temporal characteristics of these signals and their varying signature as function of the

instrument probing configuration (incidence angle, ascending versus descending passes). A global


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RFI analysis over the world ocean was performed from that data ensemble. The large number of data

acquired at a given location on earth allowed us to clearly establish robust threshold detection criteria

for these contaminations to best filter out the major contaminations using a multiple criteria

mitigation approach. Nevertheless, it is clear that residual RFI-induced structures remain in our

products, particularly in the Northern Latitudes, North Indian Ocean, along the coast of Asia and

south of Madagascar. RFI density in the Mediteranean sea, Asia coastlines and Artic Sea induce a

low quality retrieval in these area. We advise not to use our data for oceanographic studies in these

zones.

Note: We strongly recommand to only use L4 data with RFI probability equal to zero.

Figure 1: these maps represent monthly averaged of the SMOS brightness temperature in terms of

first stokes parameter (Th+Th)/2 at an incidence angle of 47.5° and in ascending passes. Over Sea ice

the Tb is saturated because it is much higher than over the ocean. On the left: map for May 2011;

right: map for May 2012

One of the largest area of contamination is the Northern Hemisphere, in particular over the North

Pacific and Atlantic oceans. RFI are mostly induced here by the signals from the military radars of

the Distant Early Warning (DEW) line sites. As illustrated in Figure 1, until summer 2011, the

later RFI strongly contaminated SMOS data over all Canadian, Alaska waters and in the northern

Atlantic (>45deg N) on ascending passes. Descending passes were less affected because of the look-

angle of SMOS.

Over the years, investigations of exactly where the interferences come from have been made by ESA.

National authorities have collaborated with ESA to find out about the origin and how to switch these

unlawful emissions off, and so RFIs have waned. Over recent years, authorities from Canada and


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Greenland were informed, and requested to take actions. Canada started to refurbish their equipment

in autumn 2011, while Greenland switched off their transmitters in March 2011. At least 13 RFIs

have now been switched off in the northern latitudes.

As illustrated in Figure 2, the switch-offs have led to a significant improvement in SMOS

observations at these high latitudes, which were previously so contaminated that accurate salinity

measurements were not possible above 45 degrees latitude.

Figure 2: (Left) Objectively analyzed in situ observations, SSS from SMOS in ascending passes in

May 2011 (middle) and 2012 (right).

Despite these improvements and, RFI continues to plague both salinity and soil moisture retrievals,

and no solution proposed thus far can eliminate its impact in all cases. Most of the effort (including

our filtering methodologies) is directed towards filtering out contaminated brightness temperature in

the FOV where SSS is retrieved. However, much (but not all) of the RFI impact over the ocean is

related to sources over land, and the impact in the usable portion of the field of view can be difficult

to detect by simple thresholds on brightness temperatures. Therefore, inaccurate SSS retrievals are

still found along the most contaminated oceanic zones. Here is one example showing intermittent

contamination from radars in Alaska. The RFI induces large spatial ripples in the images far from the

sources, and the impact extends into the alias-free field of view.


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Figure 3: RFI contaminated SMOS snapshot, showing an extremely strong source located far away

from the usefull domain of the field of view but generating spurious signal where SSS retrieval is

performed (the black contoured domain is the AF-FOV). The green contoured domain is the

projection on earth of the fundamental hexagon delineating the Fourier component domain.

Our new approach applied for the CEC V02 products is therefore to search for RFI in the entire

fundamental hexagon using simple (empirically determined) thresholds on the brightness

temperatures (500 K for Txx and Tyy, and 200 K for the third Stokes parameter Uxy). If one use such

method to filter out RFI, a large amount of SMOS data along the world coastlines would be

eliminated, including bad and good retrievals. As the decison to keep or remove an SSS retrieval in

these contaminated area is not simple, we added a variable in the products (named "RFI_stat") giving

the probability of remote RFI detection in the ensemble of multi-angular measurements used for the

SSS retrieval. This RFI probability criterion was determined swath by swath and used to weight the

SSS swath data when generating Level 3 spatio-temporal composite products. Grid points where the

RFI probability flag was raised for more than 80 % of the input data were systematically eliminated.

Ilustrating maps of the probability of detecting such remote RFI events over the ocean for ascending

and descening passes over one month periods at 1/4 degree resolution are shown here below.

As can be seen on these exemple, in some oceanic area, more than 50 % of SMOS data are

contamined by RFI. As both good & bad quality data can be retrieved in such zones, our choice was

to process the SSS data even in these area and to let the user perform his own filtering. A user that

would like to work only with a priori free of RFI SSS SMOS data shall threrefore only select thoses

data where RFI_stat=0%.


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The mean RFI probability over the global ocean is stable with time and reach on average 3%.

It significantly decreased in the Northern Atlantic from about 10-12% in early 2010 to 4-5% end of

2012.

One of the worst contaminated oceanic area is the North Indian Ocean where RFI probability

systematically ranged between 10 and 35 %.

4.3.4. Land Contamination

The Land Sea contamination and associated scene-dependent biases in Synthetic Aperture Radiometry were first discussed by Anterrieu (2007) two-three years before launch. While the problem was evidenced and some potential correction methods proposed (Gibbs), these were not implemented into the L1 operational chain. The L1, L2 and L3 SSS data were then further shown to be systematically highly biased on a complete band along the world coast lines, as shown at the end of the SMOS mission commissioning phase in May 2009 (see Reul presentation at the commissioning review). While methods for correcting these biases have been investigated further by L1 team members since then (e.g., Gibbs-1 to 3), no practical method has been yet implemented in the L1 processors to correct for these flaws which still strongly impact the ocean data quality, 4 years after launch. This is principally because there were other priorities to be tackled at L1 until now, because the proposed L1 solutions (e.g. Gibbs-like ) are heavy in term of computing time and therefore difficult to implement into the L1 operational processor but most evidently, because the exact source for that very complex problem and the associated optimal solution have not been yet found nor proposed as an implementable correction at L1 yet. Efforts are currently undertaken by L1 teams to find a practical correction at L1 (e.g., floor error mitigation) and if a solution rapidly emerged from the L1 efforts this would be a great progress for L2OS. It is clear for L2/ESL teams that this is an image reconstruction issue and by construction a problem that shall be solved at L1 based on a sound understanding of the MIRAS interferometer principles. Nevertheless, in parallel, L2OS teams also anticipate that empirical corrections and/or adapted data filtering should also be done at L2 while waiting for more adapted L1 solutions.


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While the source of the problem is certainly complex to understand (such scene-dependent biases would be present in the data even if we had a perfect knowledge of the antenna patterns, see Anterrieu 2007), it is clear on L2 and L3 SSS data that the contamination present signatures with a rather systematic character (e;g., all monthly or 10-days averaged SSS fields are contaminated with very similar patterns). SMOS is a multi-dimensional probing system: a L2 SSS retrieved on a given Earth grid point at the L2 processor output is coming from an ensemble of multi-angular data, acquired at varying position within the field of view depending on the time of passage of the satellite over that point, with varying polarization. Being a scene-dependent bias, at a given location on Earth (lat,lon), the Land Sea Contamination (LSC) is then a function of the location within the FOV (xi,eta), of the polarisation mode (XX or YY or cross pol), of the type of pass (Asc or Desc), and more importantly of the fraction of land masses and their distribution within the unit circle of the FOV (F), itself a function of the type of pass and finally, of the brightness temperature differences between land masses and ocean scenes (ΔTBLAND-OCEAN) which is also a function of time t (natural variability). An empirical correction would therefore be a complex functional of the form: LSC(lati,loni,pass type=ASC or Desc)=func(xi,eta,p=polarisation, ΔTBLAND-OCEAN(t)) and could consist in correcting the L1C products before they are used as input to the L2 processor (so-called L1d product) . This functional do not need to be evaluated for all the points of the DGG grid: it can be restricted to a band along the coast (still TBD but which could be derived from a pre-deterrmined land-fraction over the FOV metric). It is anticipated that the LSC function dependencies on ΔTBLAND-

OCEAN will be a second order effect and as a first approximation might be neglected into the emprirical correction. The idea behind a potential empirical correction is based on the fact that the Level 3 observed biases are somehow apparently very stable in time over time scales ≥10 days. A mean bias correction could therefore be estimated from the 4 years of data and removed to the swath one.

4.3.5. Major Geophysical correction issues:

3.4.1 Sky noise

Modeling studies conducted by several teams prior to SMOS launch indicated that the downwelling

celestial radiations at L-band that are scattered back by the ocean surface toward the upper

hemisphere can be a source of brightness contamination affecting the quality of sea surface salinity

retrieval.

For sun-synchronous polar-orbiting satellite measurements of upwelling L-band radiation over the

ocean, like with SMOS, this so-called sky noise depends strongly on pass direction (ascending or

descending), time of year and surface roughness (wind speed).

Based upon the modeling studies for SMOS sensor, the impact is expected to be strongest for

descending passes in September-October and for ascending passes in March-April because for these

passes the reflections of the instrument viewing directions over the field of view tend to lie along the

galactic equator where L-band galactic emission is maximum.


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Left: model of the specularly reflected galactic signal in oct 2011 descending passes. Right: Biases between

SMOS SSS retrievals and World Ocean Atlas Climatology if one correct the sky-noise contribution to the

brightness temperature by assuming a perfecly flat ocean surface.

As illustrated in the above figure, assuming a perfectly flat ocean surface and correcting for the sky

noise using a simple specular reflection model result in significantly overpredicted SSS. To minimize

the impact of that spurious signal, the sky-noise correction therefore need to account for surface

roughness induced scattering impacts. Originally proposed Kirchhoff scattering model using the

Kudryavtsev wave spectrum has been shown to strongly underpredicts the scattered brightness near

the galactic plane and overpredicts the brightness away from the galactic plane under most surface

wind speeds. Kirchhoff scattering model evaluated at surface wind speed of 3 m/s better predicts the

scattered brightness under most wind conditions, but still underpredicts brightness near galactic plane

at low wind speeds, and overpredicts brightness at high wind speeds. Lack of wind speed dependence

is unrealistic. This was the solution used for generating the first version V01 of the CEC products.

Model for scattering of celestial sky brightness has been under continual refinement and more

accurate corrections for these geophysical effects are still under development in the frame of the ESA

level 2 processor improvment studies. For generating the Level 3 CEC products v02, we thus applied

a new semi-empirical correction algorithm for the sky noise based on the Geometric Optics (GO)

scattering solution. As found, semi-empirical models based upon GO produce improved predictions

relative to those based on Kirchhoff and retain wind speed dependence. Geometrical optics fits to the

data were found different for ascending and descending passes, possibly due to inaccurate

representation of scattering cross sections away from specular direction. Possible solution is to

introduce ascending and descending lookup tables for scattered celestial sky brightness but this was

not yet implemented for the V02 products. While clear improvements are expected with the new

GO-correction model, particularly when strong galactic sources are scattered toward the sensor,

residual erroneous correction of this effect in our alogorithm might have cause some non-geophysical

variability in the Level 3 composite SSS products, particularly in the low wind speed conditions.


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