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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. [ABSTRACT FROM AUTHOR]

Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 σo measurements include 1) direct Ku-band σo intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the Aquarius scatterometer calibration, starting with the instrument calibration. We examine the stability of Aquarius as quantified using the loop-back power and estimated receiver gain to shown Aquarius has been extremely stable to order 0.1 dB since mission start. We show the temperatures of scatterometer components not contained in the loop-back path have been controlled precisely to 0.5°C to minimize any temperature-dependent losses. Combined, these results show Aquarius produces accurate $\sigma_0$ over the mission lifetime. In the next section, we discuss the stability as quantified using external models and again show stability to order 0.1 dB in very good agreement with instrument-only methods. Then, we discuss the methods used to absolutely calibrate Aquarius $\sigma_0$ with respect to previous L-band radar systems. We show that Aquarius is relatively calibrated to order 0.1 dB for copolarization channels and better than 0.2 dB for cross-polarization channels. Finally, we discuss the calibration of the Aquarius wind speed product. We compare the Aquarius wind speed with radiometer wind speed products, other radar scatterometers, and numerical weather products. We show that the Aquarius wind speed product is on par with previous scatterometers in data quality. [ABSTRACT FROM PUBLISHER]

The impact of ocean waves on L-band brightness temperatures and backscatter from the ocean surface was analyzed using Aquarius data. Matchups of Aquarius data with significant wave height (SWH) from National Oceanic and Atmospheric Administration (NOAA) WaveWatch3 reanalysis and the ocean surface wind are generated. We perform the analysis using two different wind speed products: special sensor microwave imager/sounder (SSMI/S) and National Center for Environmental Prediction (NCEP) operational data. Interestingly, the influence of SWH manifests itself differently for these two wind speed products. Conditionally, averaged normalized radar cross-section (\sigma _0) and brightness temperatures (\textT\text{B}) by NCEP wind speed show strong influence by SWH over the entire range of NCEP wind speeds with a larger impact at lower wind speeds. However, performing the same analysis conditioned on the SSMI/S wind speed, the conclusion becomes entirely different: the SWH effects appear small at low wind speeds (< 5\,\textm\texts^ - 1) and increase with increasing wind speed. The apparent contradiction is a result of the differing characteristics of NCEP and SSMI/S wind speeds. NCEP wind is an atmospheric model estimate of surface wind velocity, whereas the SSMI/S wind is a microwave remote sensing product, representing the characteristics of sea surface roughness. The impact of SWH needs to be considered when the SSMI/S wind speed is assimilated into the numerical weather prediction models, such as the NCEP. [ABSTRACT FROM PUBLISHER]

The effect of rain on surface salinity stratification is analyzed to develop a rain roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1–2 cm within the surface and the calibration reference SSS from HYCOM (\textSSS_\textHYCOM) a few meters below the surface. We used the rain impact model (RIM) to adjust \textSSS_\textHYCOM to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements (\textSSS_\textRIM). When calibrated with \textSSS_\textRIM, the residuals, i.e., the difference between measured and model predicted brightness temperature ${{\text T}_{\text B}}, are considered as rain-induced roughness. It was found that rain-induced roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with (\textSSS_\textCAP\RC) or without (\textSSS_\text{CAP}) rain roughness correction. We find that the simultaneously retrieved wind speed with rain roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-induced roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of - 0.184\;\textpsu \left(\textmm\;\texth^ - \mathbf1 \right)^ - \mathbf1, representing the salinity stratification between the two depths (1–2 cm versus 45 cm). [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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-1 root-mean-square error. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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\cdot\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\cdot\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\cdot\s^-1 wind 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\cdot\s^-1. The wind direction retrievals also appear reasonable and accurate above 10 \m\cdot\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\cdot\s^-1. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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 overpredicts A0 and α1 coefficients. 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 for TV and a 7-K change for TH for a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K in TV and TH at 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). [ABSTRACT FROM AUTHOR]