We demonstrate that spatial maps of significant wave height (SWH) with kilometric resolutions can be derived from the data acquired by the Ka-band radar interferometer (KaRIn) instrument onboard the surface water ocean topography (SWOT) mission by exploiting the measured interferometric decorrelation. We discuss the sensitivity to errors in the volumetric decorrelation estimates and show that a successful inversion of SWH, particularly in the outer part of KaRIn’s swath and for low values of SWH, requires factoring out all sources of decorrelation of instrumental origin to an exquisite precision. We then validate KaRIn’s SWH measurement against independent data, namely, GPS buoys, airborne LiDAR, Sentinel3, SWOT’s nadir altimeter, and the ECMWF global wave model. We show that biases between KaRIn and the other sensors are centimetric and that KaRIn is able to capture features in the 2-D SWH field of only a few kilometers. While KaRIn’s SWH measurement error is difficult to fully characterize due to the absence of 2-D ground-truth data valid at such fine spatial scales and spanning a wide range of sea states, we argue that the retrieved fields are dominated by signal rather than noise, except possibly in the last few kilometers of the swath at low SWH. We briefly discuss the implications in terms of advancing our understanding of the phenomena that shape the wave fields at small scales. The algorithm and calibration described in this article will be the basis for version D of the operational SWOT products.

The Surface Water and Ocean Topography (SWOT) mission is a collaboration between NASA and CNES that measures water extent, surface heights, and river slopes for inland water bodies and sea surface height (SSH), wind speed, and significant wave height (SWH) over open ocean. SWOT was launched on Dec. 15, 2022 and is currently operational. In this paper we discuss the algorithm for retrieving ocean surface wind speed from backscatter measurements obtained from the SWOT Ka-band Radar Interferometer (KaRIn). We validate that algorithm by comparing the retrieved wind speed to collocated measurements from the ASCAT ocean wind scatterometer onboard ESA’s MetOP-B and -C satellites.

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.

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.

The Soil Moisture Active Passive (SMAP) mission launched on Jan 31, 2015. The mission employs L-band radar and radiometer measurements to estimate soil moisture with 4\% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km [1]. Immediately following launch, there was a three month instrument checkout period, followed by six months of level 1 (L1) calibration and validation. A beta release of L1 radar data was available on July 1, 2015 and a validated release was made available starting on November 1, 2015. Work continued on L1 radar calibration and validation for several more months to improve the quality of the final product due to be released with the L2 validated release. In this presentation, we will discuss the radar processing algorithms and the calibration and validation activities for the L1 radar data.

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.

Uninhabited aerial vehicle synthetic aperture radar (UAVSAR) is a reconfigurable polarimetric L-band SAR that operates in quad-polarization mode and is specifically designed to acquire airborne repeat-track SAR data for interferometric measurements. In this paper, we present details of the UAVSAR radar performance, the radiometric calibration, and the polarimetric calibration. For the radiometric calibration, we employ an array of trihedral corner reflectors, as well as distributed targets. We show that UAVSAR is a well-calibrated SAR system for polarimetric applications, with absolute radiometric calibration bias better than 1 dB, residual root-mean-square (RMS) errors of ~0.7 dB, and RMS phase errors ~5.3°. For the polarimetric calibration, we have evaluated the methods of Quegan and Ainsworth et al. for crosstalk calibration and find that the method of Quegan gives crosstalk estimates that depend on target type, whereas the method of Ainsworth et al. gives more stable crosstalk estimates. We find that both methods estimate leakage of the copolarizations into the cross-polarizations to be on the order of -30 dB.

In this paper, we introduce a reprocessing of the entire SeaWinds on QuikSCAT mission. The goal of the reprocessing is to create a climate data record suitable for climate studies and to incorporate recent algorithm improvements. Three different levels of QuikSCAT data are produced at the Jet Propulsion Laboratory: L1B, geolocated, calibrated, backscatter measurements in chronological order by acquisition time; L2A, backscatter measurements binned into a geographical grid; and L2B, gridded ocean surface wind vectors. This reprocessing only changes the L2A and L2B data; we have not changed the L1B processing at all. We introduce new algorithms used in the L1B to L2A processing and in the L2A to L2B processing. After introducing our new algorithms, we show the validation studies performed to date, which include comparisons to numerical weather products, comparisons to buoy data sets, comparisons to other remote sensing instruments, and spectral considerations.

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.

This paper presents the procedure to perform cross-calibration of radar backscatter between the QuikSCAT and Oceansat-2 ocean wind scatterometers. Both QuikSCAT and Oceansat-2 are Ku-band dual pencil beam, rotating antenna scatterometers with similar design. There has been a joint effort by the Indian Space Research Organization, NASA, KNMI, and NOAA to perform calibration and validation of Oceansat-2 in order to extend the climate data record of ocean surface vector winds obtained by QuikSCAT. This has resulted in significant improvement in the quality of the normalized radar cross section (NRCS) data and the quality of the resultant winds produced using the Oceansat-2 NRCS measurements. An important aspect of this calibration is the reduction of the calibration bias between QuikSCAT and Oceansat-2. The nonspinning QuikSCAT scatterometer was repointed to achieve the same incidence angles for its two HH and VV polarized antenna beams as those utilized by Oceansat-2. The magnitudes of the NRCS (backscatter) measurements of the two scatterometers were then compared for two years in order to determine NRCS bias in decibels as a function of time. Biases for both antenna beams were computed. A wind speed/wind-relative azimuth angle histogram-matched method was applied to ocean data from the two scatterometers to determine the time series of the bias between the two. It has been determined that there was an ~0.5 dB drop in Oceansat-2 radar backscatter on August 20, 2010. As a result, we compute cross-calibration adjustments to apply to Oceansat-2 data before and after this distinct drop in backscatter.

We have developed a neural network technique for retrieving accurate 12.5-km resolution wind speeds from Ku-band scatterometer measurements in tropical cyclone conditions including typical rain events in such storms. The method was shown to retrieve accurate wind speeds up to 40 m/s when compared with aircraft reconnaissance data, including GPS dropwindsondes and Stepped-Frequency Microwave Radiometer surface wind speed measurements, and when compared to global best track maximum wind speeds. Wind directions were unchanged from the current (version 3) Jet Propulsion Laboratory (JPL) global wind vector product. The technique removes positive biases with respect to best track winds in the developing phase of tropical cyclones that occurred in the nominal (version 2) JPL QuikSCAT product. The new technique also reduces negative biases with respect to best track wind speeds that occurred in the nominal product (both versions 2 and 3) during the most extreme period of the lifetime of intense storms. The wind regime with the most notable improvement is 20-40 m/s (40-80 kn), with more modest improvement for higher winds and the improvement at lower winds comparable to that achieved previously by the version 3 JPL global rain-corrected product. The net effect of all the wind speed improvements is a much better measurement of storm intensity over time in the new product than what has been previously available. When compared with speed data from aircraft flights in Atlantic hurricanes, the new product exhibited a 1-2-m/s positive overall bias and a 3-m/s mean absolute error. The random error and systematic positive bias in the new scatterometer wind product is similar to that of the Hurricane Research Division H*WIND analyses when aircraft data are available for assimilation. This similarity may be explained by the fact that H*WIND data are used as ground truth to fit the coefficients used by the new technique to map radar measurements to wind speed. The fact that H*WIND was designed to match maximum winds while preserving radial symmetry may explain the overall positive biases that we observe in both H*WIND and the new scatterometer wind product which compared to aircraft reconnaissance data. The new scatterometer product could also be inheriting systematic biases in the presence of rain from H*WIND. Under the most extreme rain conditions, the radar signal from the surface can be lost. In such cases, the technique makes use of measurements in the 87.5-km region comprising the 7 $\times$ 7 neighboring cells around the target 12.5-km wind vector cell. In so doing, we sacrifice resolution in cases where the highest resolution region has no useful measurements. Even so, the most extreme rain conditions can result in reduced accuracy. The new technique has been used to retrieve wind fields for every tropical cyclone of tropical storm force or above that has been observed by QuikSCAT during the period of time from October 1999 to November 2009. The resulting data set has been made available online for use by the tropical cyclone research community.

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.

In February-March 2009, an airborne field campaign was conducted using the Passive Active L- and S-band (PALS) microwave sensor and the Ku-band Polarimetric Scatterometer to collect measurements of brightness temperature and near-surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for sea surface salinity (SSS) retrievals. Wind speeds encountered during the March 2, 2009, flight ranged from 5 to 25 m/s. The Global Positioning System (GPS) delay mapping receiver from the National Aeronautics and Space Administration (NASA) Langley Research Center was also flown to collect GPS signals reflected from the ocean surface and generate postcorrelation power-versus-delay measurements. These data were used to estimate ocean surface roughness. These estimates were found to be strongly correlated with PALS-measured brightness temperature. Initial results suggest that reflected GPS measurements made using small low-power instruments can be used to correct the roughness effects in radiometer brightness temperature measurements to retrieve accurate SSS.

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredictsA0anda1coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change forTVand a 7-K change forTHfor a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K inTVandTHat 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s).