The soil moisture active passive (SMAP) instrument has been used to infer sea surface wind speed from its brightness temperature measurements. To do so, the SMAP wind speed retrieval process requires the removal of sea surface temperature (sst) and sea surface salinity (sss) impact on brightness temperature. Estimating the so-called excess surface emissivity ($\Delta {e}$), that is the sst normalized difference between the measured brightness temperature of the sea surface and the corresponding brightness temperature of a flat surface, is one way of accomplishing such a task. In this article, we investigate whether SMAP$\Delta {e}$contains residual dependencies to sst and sss. To do so, v5.0 SMAP brightness temperature measurements, derived by the Jet Propulsion Laboratory, are used. For any fixed numerical weather prediction model wind speed above 15 m/s, down to a 20% decrease in SMAP$\Delta {e}$is observed as the sst increases from 274 to 304 K. For any fixed wind speed between 8 and 15 m/s, the sst residual dependence is weaker with SMAP$\Delta {e}$exhibiting a 1%–2% decrease as the sst increases. Below 8 m/s, this pattern becomes prevalent again, when the significant wave height (Hs) is greater than 3.5 m. SMAP$\Delta {e}$decreases as much as 50% with increasing sss, most notably below 8 and above 15 m/s when Hs is considered. This analysis has also shown that below 8 m/s and for swell dominant seas, a decrease in either sss or sst results in a decrease in SMAP$\Delta {e}$sensitivity to wind-induced sea surface roughness.

The Soil Moisture Active Passive instrument (SMAP) sea surface wind speed potential dependence to sea surface salinity (sss) and sea surface temperature (sst) is explored. SMAP JPL v5.0 and SMAP REMSS v0.10 are used for this analysis. The SMAP wind speed error (i.e. SMAP-NCEP) is bin averaged per sss and per sst ranges, respectively, and plotted against NCEP wind. This analysis shows that both SMAP wind speed products exhibit clear dependence to both sss and sst: for NCEP winds less than 15 m/s, sst bin averaged error curves for both products show spreads below 1 m/s; for NCEP winds greater than 15 m/s, the maximum spread between the error curves can be greater than 2 m/s. Spreads between sss bin averaged curves can be as high as 5 m/s. This maximum spread is reduced below 1 m/s if data with salinity less than 32 psu is excluded.

We consider the impacts of wind direction and model function choice on retrieved wind speed statistics using SMAP Version 5 L2 product retrieval algorithms. Attempts to retrieve ocean wind direction using SMAP measurements lead to arti-facts in wind speed statistics below 12 m/s which are absent if the wind direction is taken from ancillary data. Model functions derived from Aquarius matchups achieve biases within 0.5 psu and standard deviations within 2 m/s at speeds greater than 5 m/s, with the better performance from NCEP-based matchups. The SMAP-derived model function obtains bet-ter agreement with the global wind speed distribution of the NCEP ancillary product, but with greater scatter and poorer performance near coastal regions. Further effort is needed to improve SMAP wind retrievals for use in operational applications.

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from SMAP (JPL V5 and RSS V4) in coastal region using in situ data collected by saildrones during the North American West Coast Survey. Collocated satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) related with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 to 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where exists large freshwater variance. In regions closer to land, stdD for JPL V5 increases to 0.8 (1.4) psu in the zone 20-40km (<20km). RSS V4 delivers 42% less data in 20-40km, and almost no data within 20km. In attempt to reduce the uncertainty of SMAP SSS near land and understand the discrepancy between JPL and RSS products, we investigate saildrone collocations with SMAP Level 2 (direct output from retrieval), in terms of distance to land, and saildrone's simultaneous measurements of sea surface temperature and surface wind speed (both are important ancillary parameters for SSS retrieval). Our analysis identified quite different areas of future improvement for JPL and RSS algorithms.

Satellite observed sea surface salinity (SSS) reflects the spatial and temporal variability of surface freshwater, which is critical to monitoring the climate change in the Arctic Ocean. Our previous study found that SMAP SSS shows signatures consistent with the inter-annual anomalies of observed sea ice concentration and river discharge, but with large uncertainty (~1 psu) compared with limited in-situ data. One of error sources is the un-corrected sea ice contamination effect. The JPL SMAP algorithm retrieves SSS at each wind-salinity-cell if the matchup sea ice concentration (SIC) is less than 3% with no ice correction implemented yet. Since L-band brightness temperature (TB) of sea ice is much higher than that of seawater, SSS retrieved from TB from a field of view (FOV) mixed with water and ice will result in false fresh signature if the sea ice effect is not accurately accounted for. In this study, we develop an empirical observation-driven sea ice correction algorithm. We characterize the sea ice signature using SMAP TB and ancillary SIC data. The sea ice effect is corrected near ice edge where SIC is under a predetermined threshold for SSS retrieval. We consider the seasonal variation of TB over ice to be likely associated with seasonal change of physical temperature and summer melting pond. The sea ice fraction (ICEF) is calculated by integration of SIC over SMAP FOV weighted by antenna gain patterns. An empirical sea ice correction algorithm is proposed. The impact of sea ice correction is demonstrated by comparing TBs with or without sea ice correction.

In this paper we discuss some observations of the Soil Moisture Active Passive (SMAP) mission's high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the copolarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of detecting extreme winds up to the category 5 wind speed regime providing unique capabilities as compared to traditional scatterometer with C and Ku-band radars.

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and have been directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. In this talk we present the SMAP Sea Surface Salinity (SSS) dataset and algorithm.

Arctic freshwater fluxes make this region key to regulate ocean currents and global climate. Hence, the Arctic Ocean sea surface salinity (SSS) knowledge is crucial to describe some of the processes that govern climate change.Recently, Barcelona Expert Center (BEC) deployed their version 2 of SSS Arctic data retrieved from Soil Moisture and Ocean Salinity mission (SMOS) mission. Nevertheless, in the context of the ESA Arctic+ initiative, BEC has planned to introduce improvements in all the processing levels. Some of the planned improvements include: (i) optimizing the projection grid of level 1, (ii) studying the performance of different dielectric models in the Artic region and (iii) producing an additional level 4 product. The SSS Arctic products from Soil Moisture Active Passive (SMAP) mission could be used to produce a level 4 by merging them with this new version of SSS level 3 produced from SMOS.Big data techniques are applied to produce debiased SSS maps. These techniques will be refined by introducing a new grouping method for the statistical study of the data.The aim of this work is to obtain a more accurate version of the Arctic salinity maps starting at 2011. The new SMOS SSS maps are expected to better capture the Arctic river plumes and thus they will help to better understand the freshwater inflow/outflow in the Arctic Ocean.

We investigate the variation of sea surface salinity (SSS) retrieved in the Hudson Bay from L-band microwave measurements of SMAP and SMOS for open water seasons 2015-2017. We analyzed the differences between three SSS products (SMAP from JPL and RSS, and SMOS from BEC), and assessed in the context of seasonal and inter-annual variation of the freshwater contents, considering river runoffs, sea ice changes, and surface forcing (P-E). We found SMAP JPL (V4.2) shows reasonable responses to the freshwater inputs which is dominated by the sea ice change particularly early in the melting season. This result demonstrates the potential of currently available L-band missions in monitoring cryospheric changes; also underscore urgent needs of in situ salinity measurements in the region to improve retrieval algorithms.

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and may be directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. We have found that the SMAP radiometer displays sensitivity all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical C and Ku-band ocean wind scatterometers. In previous work we have validated the SMAP high wind speeds against Rapid Scatterometer (RapidScat) and Stepped Frequency Microwave Radiometer (SFMR). In this work we consider the size of the SMAP cyclones and compare them to the Automated Tropical Cyclone Forecasting (ATCF) system B-deck files and we update the SFMR analysis with an additional year of data to strengthen the previous conclusions.

Sea surface salinity (SSS) plays a critical role in the water cycle. In the Arctic Ocean, SSS responses to river discharge, sea ice melting/freezing and drifting, surface freshwater forcing (precipitation and evaporation), and ocean transport through open straits from/to Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse. The L-band microwave radiometer on board of NASA SMAP mission provides salinity measurements since April 2015, at 40 km resolution with global ocean coverage in ~3 days. With improved land/ice correction and RFI detection, SMAP SSS are retrieved in ice-free regions near the river mouth and ice edge. The collocated SMAP SSS and in situ salinity data collected by AXCTD from Ocean Melting Greenland (OMG) 2016 field campaign along Greenland coast show reasonable agreement in revealing the freshening signature, which is not seen in ocean model output. During the open water season (August), SMAP SSS were retrieved in Chukchi Sea (2015), in Prudhoe Bay and East Siberian Sea (2016), and in Kara Sea in both years. The SSS contrast between the two years is consistent with sea ice concentration observations. Variability of SSS in Kara Sea is correlated positively with adjacent river discharges but negatively with the salinity of water transported from Atlantic Ocean.

The Soil Moisture Active Passive (SMAP) mission was launched on January 31, 2015. It is a combined L-band active/passive system envisioned for the measurement of soil moisture over land. In addition to the soil moisture measurement, the SMAP data readily permit retrievals of ocean surface winds and sea surface salinity. In the previous work, we have found that the SMAP radiometer displays sensitivity to ocean surface wind all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical$C$- and$Ku$-bands ocean wind scatterometers. In this letter, we use the Rapid Scatterometer and stepped frequency microwave radiometer to further validate these SMAP radiometer-only high-wind speed retrievals. In addition, we consider the size of the retrieved high wind speeds, validating them with the Automated Tropical Cyclone Forecasting system B-deck files.

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and may be directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. We have found that the SMAP radiometer displays sensitivity all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical C and Ku-band ocean wind scatterometers. In this work we use the Rapid Scatterometer (RapidScat) and Stepped Frequency Microwave Radiometer (SFMR) to further validate these SMAP radiometer-only high-wind speed retrievals.

Sea surface salinity (SSS) retrieved from SMAP radiometer measurements is validated with in situ salinity measurements collected from Argo floats, tropical moored buoys and ship-based thermosalinograph (TSG) data. SMAP SSS achieved accuracy of 0.2 PSU on a monthly basis in comparison with Argo gridded data in the tropics and mid-latitudes. In tropical oceans, time series comparison of salinity measured at 1 m by moored buoys indicates that SMAP can track large salinity changes occurred within a month. Synergetic analysis of SMAP, SMOS and Argo data allows us to identify and exclude erroneous jumps or drift in some real-time buoy data from assessment of satellite retrieval. The resulting SMAP-buoy matchup analysis leads to an average standard deviation of 0.22 PSU and correlation coefficient of 0.73 on weekly scale; the average standard deviation reduced to 0.17 PSU and the correlation improved to 0.8 on monthly scale. SMAP L3 daily maps reveals salty water intrusion from the Arabian Sea into the Bay of Bengal during the Indian summer monsoon, consistent with the daily measurements collected from floats deployed during the Bay of Bengal Boundary Layer Experiment (BoBBLE) project field campaign. In the Mediterranean Sea, the spatial pattern of SSS from SMAP is confirmed by the ship-based TSG data.

The objective of this study is to investigate the effect of sea surface temperature (SST) on L-band microwave measurements and its implication on sea surface salinity (SSS) retrieval. Of particular interest is in the cold & fresh water where large SSS retrieval errors exist in comparison with Argo data. We found systematic SST dependence in Aquarius radar backscatter σoand radiometer excess emissivity Δe, with the emissivity of specular surface estimated using collocated HYCOM SSS and NCEP or SSMI/S wind. In the cold water under medium to high wind, σoand Δe show opposite trend on SST: σoreduces while Δe enhances by more than 10% relative to their corresponding values at the reference SST (15°C). The geographical distribution of matchups with SST-1show that data collected under these conditions are coincident with the area where dSSS (=SSS-SArgo) is largely positive. This is consistent with the SST trend observed in σo, which would result in overestimation in roughness if the SST effect not considered. However, the enhanced Δe in cold water is puzzling because it would cause even higher value for SSS retrieval if modeled as roughness. SSS retrieved with SST correction reduces bias but results on error improvement is mixed. We hypothesis there is an unknown defect in the dielectric constant model for cold water and propose an empirical correction for the Aquarius SSS retrieval.

In this talk we introduce the combined active / passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a combined active / passive vector wind and salinity product. We show the radiometer-only salinity product nears the Aquarius salinity accuracy requirements, that the radar-only vector wind product meets the QuikSCAT requirements, and that the combined active / passive salinity and vector wind product has performance better than both.

Ocean surface wind (U) is air in motion and stress (τ) is the turbulent transport of momentum between the ocean and the atmosphere. While the strong wind of a tropical cyclone (TC) causes destruction at landfall, it is the surface stress that drags down the TC. There was almost no stress measurement except in dedicated field campaigns and the stress we used was almost entirely derived from wind through a drag coefficient (CD), as defined by CD= τ /(ρ U2). In TC, there is difficulty in measuring strong wind and large uncertainty in the drag coefficient.

We investigated the use of L-band active and passive microwave data from the Soil Moisture Active Passive (SMAP) observatory for remote sensing of ocean surface winds during hurricanes. We analyzed the dependence of SMAP data on ocean surface wind speed and direction, and found excellent consistency with the geophysical model functions developed for the Aquarius L-band radar/radiometer although the spatial resolutions of SMAP and Aquarius are distinctly different. However the higher resolution data from SMAP allowed us to assess the sensitivity of L-band radiometer/radar signals to hurricane force winds. The matchup analysis with the data from typhoon Nangka confirms the feasibility of extrapolating the Aquarius model functions to very high winds. Therefore we applied the Aquarius model function to the retrieval of ocean winds for hurricanes for two options: 1) radiometer-only and 2) radar-only. Comparison of the SMAP winds with the RapidScat and National Center for Environmental Predictions (NCEP) wind was performed. We also compared the maximum wind speed in the SMAP products with the best track analysis and found a good agreement in general.

In this paper, we introduce the combined active/passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a CAP vector wind and salinity product. We show that the performance of the radiometer-only salinity product nears but is still inferior to the Aquarius salinity accuracy performance when aggregated on a monthly timescale. Then, we show that the radar-only vector wind product has reasonable accuracy away from the nadir track while suffering from inadequate measurement geometry in the middle of the swath. Finally, we demonstrate that the CAP salinity and vector wind performance is superior to individual algorithms and provides wind vectors nearly as good as RapidScat for low-to-moderate winds and possibly superior to traditional scatterometers for wind speeds larger than 12.5 m/s.

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%-18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20-40 m/s.

This paper introduces and validates the Aquarius scatterometer-only wind speed algorithm and the combined active passive (CAP) wind speed products. The scatterometer-only algorithm uses the co-polarized radar cross-section to determine the ocean surface wind speed with a maximum-likelihood estimator approach while the CAP algorithm uses both the scatterometer and radiometer channels to achieve a simultaneous ocean vector wind and sea surface salinity retrieval. We discuss complications in the speed retrieval due to the shape of the scatterometer model function at L-band and develop mitigation strategies. We find the performance of the Aquarius scatterometer-only wind speed is better than 1.00 ms-1, with best performance for low wind speeds and increasing noise levels as the wind speed increases. The CAP wind speed product is significantly better than the scatterometer-only due to the inclusion of passive measurements and achieves 0.70 ms-1root-mean-square error.

Aquarius is a combined passive/active L-band microwave instrument developed to map the ocean surface salinity field from space [1]. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis. The measurement principle is based on the response of the L-band (1.413 GHz) sea surface brightness temperatures to sea surface salinity.

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 m·s-1, the L-band ocean backscatter shows positive upwind-crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 m·s-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10-25- m·s-1wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active-passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 m·s-1. The wind direction retrievals also appear reasonable and accurate above 10 m·s-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 m·s-1.

The Earth Observing System Data and Information System (EOSDIS) is a comprehensive data and information system which archives, manages, and distributes Earth science data from the EOS spacecrafts. One non-existent capability in the EOSDIS is the retrieval of satellite sensor data based on weather events (such as tropical cyclones) similarity query output.

Spacebased scatterometer measures ocean surface roughness, which is in equilibrium with surface stress (momentum flux). Under general conditions, the variation of stress is reflected in the variation of winds. Eight years of QuikSCAT data are used to give a good representation of the probability distribution and power density of wind speed over global oceans and to provide useful applications. For hurricane-scale winds (> 35 m/s), present scatterometer measurements are not sensitive to increase in winds. Although strong efforts have been made to adjust the model functions for retrieving winds under moderate wind to the strong wind conditions and to improve the sensor design to retrieve strong winds, such effort is likely to be limited by the natural process of turbulent transport. Surface stress does not increase with wind in hurricane-scale winds due to flow separation.

The divergence of moisture transport integrated over the depth of the atmosphere over global oceans show similar geographic distribution as the surface fresh water flux (evaporationprecipitation). The temporal variations of the two terms also agree, from intraseasonal to interannual time scales, at selected locations. These two forcing terms were found to lead ocean surface salinity changes by 90 ° as expected. The interannual anomalies of the hydrologic parameters in the high latitude regions of North American and Eurasia are found the be opposite in phase, and their differences are found to have significant correlation with the moisture transport in the North Atlantic, suggesting N. Atlantic moisture transport is a bridge to the opposing hydrologic phases of the two continents. 1

The SeaWinds scatterometer onboard QuikSCAT covers approximately 90% of the global ocean under clear and cloudy condition in 24 h, and the standard data product has 25-km spatial resolution. Such spatial resolution is not sufficient to resolve small-scale processes, especially in coastal oceans. Based on range-compressed normalized backscatter and a modified wind retrieval algorithm, a coastal wind dataset at 12.5-km resolution was produced. Even with larger error, the high-resolution winds, in medium to high strength, would still be useful over coastal ocean. Using measurements from moored buoys from the National Buoy Data Center, the high-resolution QuikSCAT wind data are found to have similar accuracy as standard data in the open ocean. The accuracy of both high- and standard-resolution winds, particularly in wind directions, is found to degrade near shore. The increase in error is likely caused by the inadequacy of the geophysical model function/ambiguity removal scheme in addressing coastal conditions and light winds situations. The modified algorithm helps to bring the directional accuracy of the high-resolution winds to the accuracy of the standard-resolution winds in near-shore regions, particularly in the nadir and far zones across the satellite track.

By flying a number of ocean observing sensors together, Seasat demonstrated potential of not only sensor synergism, but also science synergism, which has illuminated the path of spacebased air-sea interaction studies in more than two decades since its demise. Two topics - El Nino and tropical cyclone, are discussed as examples of the science synergism inspire by Seasat.