SMOS L2 Ocean Salinity Level 2 Ocean Salinity February, 2011 ARGANS & L2OS ESL Tools for monitoring time drifts in SMOS L2OS parameters over selected ocean regions

SMOS L2 Ocean Salinity Aim – to create a tool (set of scripts) for monitoring SMOS data for pre-defined regions of interest, in particular – to observe time drifts, seasonal variations of the parameters; at first stages – maybe to help identifying L1 and instrumental problems causing drift in L2OS parameters. The results are supposed to be derived from SMOS UDP/DAP product, processed automatically, in real time (as the products appear) or post-processed; The results should be ultimately suitable for web access, similar to current ARGANS web interface to DPGS processed SMOS DAP products.

SMOS L2 Ocean Salinity Regions of Interest SouthernPacific; NorthAtlantic; TropicalPacificSW; SouthOfTasmania; EquatorialOcean; TarfayaModelregion; AmazonPlume; GulfOfBiscay; MediterranianSea; SouthernOcean; IntertropicalPacific; Spanish-IEO_Mooring;

SMOS L2 Ocean Salinity Model_1 retrieving WSn instead of UN10 and VN10 Contents

SMOS L2 Ocean Salinity List of geophysical parameters to monitor Roughness 1: SSS, Sigma_SSS, SST, Sigma_SST, UN10, Sigma_UN10, VN10, Sigma_VN10, TEC, Sigma_TEC; Roughness 2: SSS, Sigma_SSS, SST, Sigma_SST, TEC, Sigma_TEC, Uwav ( L20S_3.16 and lower ), Sigma_Uwav ( L20S_3.16 and lower ), WSn ( L20S_3.17 and higher ), Sigma_WSn ( L20S_3.17 and higher ), omega, Sigma_omega, phi_wsn, Sigma_phi_wsn; Roughness 3: SSS, Sigma_SSS, TEC, Sigma_TEC, WSn, Sigma_WSn, HS, Sigma_HS, phi_wsn (L20S_3.17 and higher), Sigma_phi_wsn (L20S_3.17 and higher); Cardioid: Acard, Sigma_Acard, SST, Sigma_SST;

SMOS L2 Ocean Salinity Statistical output Min; Max; Mean; Median; Stdev; Kurtosis; Skew;

SMOS L2 Ocean Salinity Selection algorithm For each day in specified range (say, from 15/07/2010 to 31/12/2010) -identify list of orbits within +/- n days, say n=3; -exclude from the list the orbits with wrong asc/desc and full/dual pol; -exclude orbits not having a point(s) in pre-defined Lat/Lon box For each half-orbit from the list reduced according to the algorithm above -read UDP and DAP products; -exclude -999 marked points; -exclude points at the X-swath edges (say beyond +/-300 km band); -exclude points not satisfying defined UDP flag filters; For all the points selected above, calculate statistics listed on the previous page, for each field on the page 6, plot histogram and store statistics in a file corresponding the central day of this 2*n sampling; Combine statistics for each day into time line curves;

SMOS L2 Ocean Salinity Output of the exercise N folders (corresponding to N days of observation) containing set of histograms (examples on the next page) suitable for animation to watch day-by-day changes in long-term trends for specified parameters; N folders (corresponding to N days of observation) containing set of text files with individual statistics for the time period centred around the day of observation Set of time curves with dates for X-axis and one of the statistic characteristics – for Y-axis, say, mean, median, kurtosis etc. Mean and median curves are supplied by errorbars corresponding to stdev values; separate curves are produced for (a) not filtered GP sets, (b) filtered by Xswath location and (c) filtered by UDP quality flags; Kurtosis and Skew curves are plotted without errorbars but combined into triplets non- filtered/Xswath-filtered/UDP-filtered;

SMOS L2 Ocean Salinity Examples

SMOS L2 Ocean Salinity Examples of histograms

SMOS L2 Ocean Salinity S/Pacif asc orbs 2010_10_21 3 days,148102,66213,-30.0,0.0,-150.0,-120.0,SSS,2 min, max, mean, median, std, skew, kurtosis , 39.22, , , , , # in-box, filtered, x-swath selected ######################## --STATISTICS EXTRA-- ################################################ , 39.22, , , , , # in-box, NO filt, x-swath select , , , , 1.02, , # in-box, NO filt, NO x-swath sel ### Total number of processed GP's in selected orbits: ### Number of GP's after removing '-999': ### Number of GP's in the box (no filters): ### Number of GP's in the box, selected over x-swath: ### Number of GP's in the box, filtered: # $BOX COORDINATES$ # ###### South: ###### North: ###### West: ###### East: # $OUTPUT$ # ###### Field: SSS ###### Roughness Model: 2 ###### Anomaly: NO # $X-swath FILTER$ km # # $UDP FILTERS$ # -Fg_ctrl_sigma = 0 -Fg_ctrl_chi2 = 0 -Fg_ctrl_chi2_P = 0 -Fg_ctrl_marq = 0 -Fg_ctrl_valid = 1 -Fg_ctrl_reach_maxiter = 0 # $ORBITS$ # /mnt/satabeast3/smos/data/2010/10/18/SM_OPER_MIR_OSDAP2_ T140029_ T145431_316_001_1, A /mnt/satabeast3/smos/data/2010/10/18/SM_OPER_MIR_OSDAP2_ T154034_ T163433_316_001_1, A /mnt/satabeast3/smos/data/2010/10/22/SM_OPER_MIR_OSDAP2_ T162504_ T171858_316_001_1, A /mnt/satabeast3/smos/data/2010/10/22/SM_OPER_MIR_OSDAP2_ T144455_ T153854_316_001_1, A /mnt/satabeast3/smos/data/2010/10/20/SM_OPER_MIR_OSDAP2_ T160246_ T165647_316_001_1, A /mnt/satabeast3/smos/data/2010/10/20/SM_OPER_MIR_OSDAP2_ T142241_ T151642_316_001_1, A /mnt/satabeast3/smos/data/2010/10/19/SM_OPER_MIR_OSDAP2_ T132133_ T141534_316_001_1, A /mnt/satabeast3/smos/data/2010/10/19/SM_OPER_MIR_OSDAP2_ T150137_ T155539_316_001_1, A /mnt/satabeast3/smos/data/2010/10/21/SM_OPER_MIR_OSDAP2_ T152349_ T161750_316_001_1, A /mnt/satabeast3/smos/data/2010/10/21/SM_OPER_MIR_OSDAP2_ T134346_ T143745_316_001_1, A /mnt/satabeast3/smos/data/2010/10/23/SM_OPER_MIR_OSDAP2_ T140558_ T145957_316_001_1, A /mnt/satabeast3/smos/data/2010/10/23/SM_OPER_MIR_OSDAP2_ T154603_ T164002_316_001_1, A Example of stat file

SMOS L2 Ocean Salinity Examples of timelines, different types of filtering

SMOS L2 Ocean Salinity Sub-areas of SouthernPacific 30S S – 10S 25S – 20S

SMOS L2 Ocean Salinity Examples of timelines

SMOS L2 Ocean Salinity Examples of timelines m/s

SMOS L2 Ocean Salinity Summary We have selected a way and prepare a set of tools/scripts for statistical monitoring of retrieved SMOS parameters for selected Ocean areas; We are trying to identify and test proper statistical characteristics (mean/median/kurtosis etc); We are working on optimal filtering criteria; one preliminary conclusion – from statistical point of view, Xswath filtering does most of the job, comparing to other types of filtering;