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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Sea Surface Salinity Retrievals from Aquarius Using Neural Networks

Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement within-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30 ̊. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.

Soldo, Yan↗
NASA NTRS2020

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the NASA/GMAO Seasonal Forecast System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO assimilating the different satellite SSS. In general, for all phases of ENSO, SSS assimilation improves forecasts. The far right column compares ensemble means for assimilation of individual and combined SMOS, Aquarius, and SMAP SSS forecasts. Finally, the latest forecasts are presented comparing assimilation versus no- assimilation of satellite SSS for single forecasts over the last year.

Hackert, E.↗
NASA NTRS2016

Applications of SMAP Data to Retrieval of Ocean Surface Wind and Salinity

We have examined the L-band radiometer and radar data from NASA’s Soil Moisture Active Passive (SMAP) mission for ocean research and applications. We find that the SMAP data are in excellent agreement with the geophysical model function (GMF) derived from the Aquarius data up to a wind speed of 20 ms-1. For severe wind conditions, the higher resolution data from SMAP allowed us to assess the sensitivity of L-band radiometer signals to hurricane force winds. We applied the L-band GMF to the retrieval of ocean surface wind and SSS from the SMAP data. Comparison with the European Center for Medium-Range Weather Forecasting, WindSat and RapidSCAT wind speeds suggests that SMAP’s radiometer wind speed reaches an excellent accuracy of about 1.1-1.7 ms-1 below a wind speed of 20 ms-1. We have also found that the maximum wind speed derived from the SMAP radiometer data can reach 140 knots for severe storms and are generally in good agreement with the hurricane track analysis and operational aircraft Stepped Frequency Microwave Radiometer wind speeds. The spatial patterns of the SMAP SSS agree well with climatological distributions, but exhibit several unique spatial and temporal features.

ocean surface wind↗