SMOS L2 Ocean Salinity – PM#25 1/20 Level 2 Ocean Salinity May 2013 OTT post-processor

SMOS L2 Ocean Salinity – PM#25 2/20 Real-time salinity retrieval requires near real-time OTT to track short term drift: while processing L1c, L2OS v610 extracts deltaTBs for South Pacific region and writes them to AUX_DTBXY. The OSCOTT runs once per day (TBC), reads AUX_DTBXY and a set of current deltaTBs (from AUX_DTBCUR), writes a new set of OTTs, and updates AUX_DTBCUR. deltaTBs (DTBXY_) current deltaTBs (DTBCUR) OTT post- processor OTTs L2OS processor L1c L2OS UDP & DAP OTT post-processor (OSCOTT)

SMOS L2 Ocean Salinity – PM#25 3/20 Previously the ARGANS OTT tool chain (compareTBs, Python scripts) extracted deltaTBs for South Pacific (typically 3x8x12M = 288M per orbit) and constructed OTT by interpolating measurements onto a 129x129 xi/eta rectangular grid. To avoid very large intermediate files, L2OS v610 will write deltaTBs binned on a 129x129 grid (5M per orbit) using a non-square xi/eta interpolation grid ±0.7 in xi, -0.7 to in eta (previously ±0.7 in xi & eta: large areas in each LUT were unused). No changes to AUX_OTTxF/D schema. AUX_DTBXY_ contains deltaTB data for each of the 3 models, and for each of the 8 polarisations (HH, VV, HHV real, HHV imag, VVH real, VVH imag, HH short, VV short). OSCOTT will merge AUX_DTBXY data to make OTT1/2/3 (each OTT contains 8 LUTs for ascending, & 8 LUTs for descending orbits). OTT post-processor overview

SMOS L2 Ocean Salinity – PM#25 4/20 for each selected measurement map xi,eta onto x,y grid count[x,y] += 1 sumDeltaTB[x,y] += TBsmos – Tbmodel sumDeltaRA[x,y] += (TBsmos – Tbmodel) / ra sumDeltaRA2[x,y] += ((TBsmos – Tbmodel) / ra) 2 flags[x,y] |= selected L1c & L2OS flags for x = 0 to 128 for y = 0 to 128 n = count[x,y] write n mean_deltaTB[x,y] = sumDeltaTB[x,y] / n write mean_deltaTB[x,y] stdra[x,y] = sqrt(((n * sumDeltaRA2[x,y] – sumDeltaRA[x,y] 2 ) / (n * (n – 1))) write stdra[x,y] write flags[x,y] OTT post-processor: L2OS algorithm AUX_DTBXY count[x,y] deltaTB[x,y] stdra[x,y] flags[x,y] Statistics for fov, affov, eaffov, & filtered by L1 flags Flags allow for off-line analysis:

SMOS L2 Ocean Salinity – PM#25 5/20 OTT post-processor components: AUX_DTBXY n x regions: ID snapshot start/stop times snapshot start/stop ID for 3 models for 8 polarisations 4 x statistics count[x,y] deltaTB[x,y] stdra[x,y] flags[x,y] AUX_DTBXY_ datablock Statistics for FOV, AFFOV, EAFFOV, & filtered by flags[x,y}; min, max, mean, median, std Any number of regions can be defined in AUX_CNFOSF (but v610 OSCOTT will only use South Pacific to make OTTs). Could be useful for drift monitoring...

SMOS L2 Ocean Salinity – PM#25 6/20 Header contains quality information, used by OSCOTT to select ‘good’ sets of deltaTBs: OTT post-processor components: AUX_DTBXY Grid point quality information total grid points in region number of grid points used after filtering (configurable via ADF) number of grid points: too near to land/coast rain/ice contamination missing ECMWF data WS 7m/s Measurement quality information total measurements in region number of measurements used after filtering (configurable via ADF) number of measurements flagged: as sun point, sun tails with sun/moon/galactic glint contaminated by RFI (by L1 or L2) L1 processing error Quality information is from forward model 1 only. Criteria for ‘good’ deltaTBs TBD.

SMOS L2 Ocean Salinity – PM#25 7/20 Which statistics should be used to select/reject DTBXY deltaTB data? Grid point & measurement selection statistics? 2012 OTTs Ascending Descending

SMOS L2 Ocean Salinity – PM#25 8/20 Which statistics should be used to select/reject DTBXY deltaTB data? Grid point & measurement selection statistics? 2012 OTTs Ascending Descending

SMOS L2 Ocean Salinity – PM#25 9/20 Other statistics available: %RFI, %L1c degraded (eg missing VTEC) OTTs Ascending Descending

SMOS L2 Ocean Salinity – PM# /20 OTT post-processor: quality selection criteria Which statistics should be used to select/reject DTBXY deltaTB data? v600 OTT for T (v600 statistics are after binning measurements) deltaTBstdra

SMOS L2 Ocean Salinity – PM# /20 Which statistics should be used to select/reject DTBXY deltaTB data? v610 OTT for T Put threshold on sigma(std)? OTT post-processor: quality selection criteria AF: mean = min = -4.0 max = 6.6 σ = 1.5 EAF: mean = 0.48 min = max = 46.7 σ = 4.4 A+EAF: mean = 0.22 min = max = 46.7 σ = 3.4 AF: mean = 1.33 min = 0.0 max = 4.0 σ = 0.50 EAF: mean = 1.14 min = 0.0 max = 4.9 σ = 0.41 A+EAF: mean = 1.22 min = 0.0 max = 4.9 σ = 0.46 SM_TEST_AUX_DTBXY__ T014022_ T023334_606_001_8

SMOS L2 Ocean Salinity – PM# /20 OTT post-processor: comparing AUX_DTBXY v600 Transect at xi=0 (single bin) v610

SMOS L2 Ocean Salinity – PM# /20 OTT post-processor components: AUX_DTBCUR n x regions: ID snapshot start/stop times snapshot start/stop ID for 3 models for 8 polarisations 4 x statistics count[x,y] deltaTB[x,y] stdra[x,y] flags[x,y] AUX_DTBXY_ datablock m x AUX_DTBXY + extra sets: set type (L1c, mean...) region ID orbit direction (A/D) snapshot start/stop times snapshot start/stop ID filename for 3 models for 8 polarisations 4 x statistics count[x,y] deltaTB[x,y] stdra[x,y] flags[x,y] AUX_DTBCUR datablock OSCOTT v610 OSCOTT will only process South Pacific orbits into OTT m x AUX_DTBXY

SMOS L2 Ocean Salinity – PM# /20 orbits = read input AUX_DTBCUR orbit = read each AUX_DTBXY if region ID = South Pacific & good quality orbits += orbit for dir = A, D // orbit direction discard orbits input validity start = input AUX_DTBCUR output validity start = mean snapshot time(orbits) if (input validity start < output validity start) for model = 1 to 3 compute OTT(model) from orbits write OTT(model) write orbits to new AUX_DTBCUR OSCOTT preliminary algorithm Alternative orbit discarding algorithms: // fixed number of orbits, time varying while count(orbits(dir)) > max orbits discard oldest orbit // fixed window of n days for orbit in orbits if date(orbit) < max days discard orbit Alternative OTT computation algorithms: mean or weighted mean? separate spatial biases & drift multi-region

SMOS L2 Ocean Salinity – PM# /20 for orbit in orbits for x = 0 to 128 for y = 0 to 128 n = orbit.count[x,y] count [x,y ] += n sum[x,y] += orbit.deltaTB[x,y] * n for x = 0 to 128 for y = 0 to 128 ott.deltaTB [x,y] = sum[x,y] / count [x,y] Could use stdra[x,y] too? OTT post-processor weighted merging

SMOS L2 Ocean Salinity – PM# / T T T T T T T T T T OTT post-processor sample run Job run timeInputsOutputs T010000AUX_DTBCUR_ T AUX_DTBCUR_ T AUX_DTBXY__ T AUX_OTT1F__ T AUX_DTBXY__ T AUX_OTT2F__ T AUX_DTBXY__ T AUX_OTT3F__ T AUX_DTBXY__ T T010000AUX_DTBCUR_ T AUX_DTBCUR_ T AUX_DTBXY__ T AUX_OTT1F__ T AUX_DTBXY__ T AUX_OTT2F__ T AUX_OTT3F__ T T T T T T T T T T T Output product validity start is mean time of all snapshots used to make OTT (& written to DTBCUR) OSCOTT first run can be without an empty AUX_DTBCUR

SMOS L2 Ocean Salinity – PM# /20 OTT post-processor orchestration DPGS could run OSCOTT once per day with all new AUX_DTBXY from previous day For reprocessing the required speed increase is x18, and a run of L2OS takes 4 hours max, so ESA propose to run OSCOTT each time 3 days of DTBXY are available. Do we want DPGS to run OSCOTT once per day (ie different from foreseen reprocessing), or once every 3 days?

SMOS L2 Ocean Salinity – PM# /20 OTT interpolation Prior to v610, L2OS performed nearest neighbour interpolation when applying OTT LUTs to correct L1c TBs. But we see ±1K jumps between xi/eta cells in OTT: Maybe we should perform 2D interpolation in v610?

SMOS L2 Ocean Salinity – PM# /20 DTBXY grid resolution What resolution is best for AUX_DTBXY? Grid point density varies within snapshot – highest at leading edge of AFFOV Typical total 5600 grid points, >100 along leading edge (±0.25 xi) x2 oversampling suggests >600 for xi ±0.75

SMOS L2 Ocean Salinity – PM# /20 Drift statistics AUX_DTBCUR header could/should contain summary deltaTBs for OTT generated from the set of deltaTBs in the AUX_DTBCUR datablock. Which are useful for monitoring? For deltaTB[x,y] and stdra[x,y] min, max, mean, median, std for all of FOV (excluding border) AFFOV (excluding border) EAFFOV (excluding border) filtered by AFFOV, border, sun point/tails Other?