Our presentation describes the creations of a match-up set between the SMAP (Soil Moisture Active Passive) and WindSat sensors. The combined set is used for developing and testing ocean retrieval algorithms for sea surface temperature (ST), sea surface salinity (SSS) and ocean surface vector winds (OSVW) that will be measured by the ESA Copernicus Imaging Microwave Radiometer (CIMR).

Our presentation describes the steps and performance estimates of the WSF-M algorithms for retrieving ocean surface vector winds, sea-ice concentration, and sea-ice age.

We model the sea foam emissivity at frequencies from 1 to 89 GHz. This model is part of the work done by an international science team to develop a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. A study of the sensitivity to different foam properties (foam layer thickness and upper limit of the foam void fraction) guided the effort to tune the foam emissivity model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using the tuned foam model.

The model expressing the dielectric constant of sea water at microwave frequencies as a function of salinity and temperature is an important element in remote sensing of sea surface salinity. It is also important independently as a description of the physical properties of salt water. A major milestone was the development in the late 1970’s by Klein and Swift of a model based on laboratory measurements at L- and S-band and a functional form supported by theory for polar molecules and previous work on freshwater. Much of the subsequent work has focused on measurements at higher frequency and determining model parameters tuned to apply for applications such as remote sensing of sea surface temperature. Interest in the dielectric constant at 1.4 GHz (L-band) increased again with the development of SMOS and Aquarius to measure salinity from space. But there have been few new measurements at L-band and often confusion regarding the applicability of new models at 1.4 GHz. The objective of this manuscript is to compare available models in the context of how well they represent the dielectric constant of sea water at 1.4 GHz. Among the criteria applied will be the recent measurements at the George Washington University of the dielectric constant at 1.4 GHz.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [2], [3]. Pre-launch calibration activities had been performed since 2012 on the engineering model of the radiometer. Post-launch calibration activities have been performed to fine-tune and validate the results from the pre-launch calibration. The major calibration activities and lessons learned in the past 8 years will be described in the following sessions.

We present and discuss algorithms for measuring wind speeds with WindSat, ASMR-E and AMSR2 in tropical cyclones. The algorithms are trained from SMAP wind speeds, which have been proven to provide reliable wind speeds in strong storms and which are not affected by rain. They perform well under heavy precipitation where most other passive wind speed retrievals fail.

We present and discuss the details of the SMAP radiometer calibration that is used in the NASA/RSS ocean surface salinity algorithm. The values for antenna spillover, noise diode and reflector emissivity are fine-tuned to give accurate antenna and brightness temperature values for the global ocean and the Amazon rain forest. The calibration is dynamically adjusted to account for small calibration drifts over time. An adjustment of the thermal model for the reflector temperature is necessary in order to mitigate biases that occur when the SMAP spacecraft goes in and out of solar eclipse. An important test of the reflector emission is an analysis of SMAP brightness temperature differences between ascending and descending swaths. The radiometric calibration accuracy of the NASA/RSS SMAP salinity data set is better than 0.1 Kelvin, which is necessary for accurate ocean surface salinity retrievals.

The SMAP L-band microwave radiometer is in its extended mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cycles. Instrument behavior has been stable over the past 4 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) were released to the public in 2018 for various science activities. Now the radiometer data are under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are investigated to obtain the optimal solution. In addition, the correction to the radiometer measurement when the SMAP radar transmitter was operational will also be revisited for the next data release. The performances of the calibration revisit and Radio-Frequency Interference (RFI) trends will be presented as well.

We present comparisons between satellite sea surface salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. We reprocess Aquarius retrievals using the dielectric constant model and ancillary sea surface temperature product used for SMOS. We also quantify the impact of the recently revised atmospheric model for Aquarius end of mission product. One recurrent feature of the SSS difference between satellite retrieval and in situ observation has been its dependence on sea surface temperature. We discuss the performances of the latest algorithms in mitigating this bias and possible improvements in theoretical models.

The SMAP L-band microwave radiometer has completed its 3-year primary mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cyclces. Instrument behavior is stable over the past 3 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) was released to the public in 2018 for various science activities. Now the radiometer is under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are being used to obtain the optimal solution. In addition, the correction to the impact on the radiometer measurement when the SMAP radar transmitter was on will also be revisited for next data release.

Surface roughness and foam are two main components of ocean surface microwave thermal emission. Surface roughness provides scattering element and modifies local incidence angle. Air in foam alters the dielectric property of the surface layer. Bubbles in foam also alter the curvature of foam- water interface and modify emission and scattering properties of the water surface itself. Whitecap coverage Wc is the most accessible oceanographic information to represent surface foam. For emission analysis, it is necessary to establish a function relating to Wc and the effective air fraction Fa interacting with electromagnetic (EM) waves. An empirical relation is established through analyzing several microwave radiometer data sets in high winds covering a wide range of frequency, incidence angle, and vertical and horizontal polarizations. A physical interpretation of the proposed Fa (Wc) relationship is discussed. The relationship is used to quantify several important characteristics of surface foam relevant to microwave emission, including effective air fraction and skin depth as functions of wind speed, microwave frequency, and incidence angle.

The SMAP microwave radiometer will see its third anniversary of operations on March 31, 2018. Instrument behavior is stable over 33 months of operation to date. The physical temperature of the internal calibration sources varies 0.5°C. The bias current of the noise source drifted by less than 0.1%. The avalanche breakdown voltage of the noise diode shows 0.01% seasonal variation. The average NEDT of the radiometer has maintained a stable 1-K value over the period. This stable behavior of the hardware is critical for the consistent calibration. The reflector emissivity was re-estimated using on-orbit data. Use of the new value nearly eliminates bias caused by solar eclipse during the southern hemisphere winter. The radiometer data were recalibrated using, as earlier, global ocean and cold sky views with additional ocean and land views at nadir incidence. The Version 4 recalibrated data exhibit 0.1-K RMS stability over average global ocean and monthly cold-sky views.

We present comparisons between satellite sea surface salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. To do so, we reprocess Aquarius retrievals using the dielectric constant model and ancillary sea surface temperature product used for SMOS. We quantify their impact on the differences between SMOS and Aquarius, and validate the various Aquarius algorithms using in situ salinity measurements. Among the significant difference in retrieved sea surface salinity are the dependence to sea surface temperature and coastal biases. New approaches to for land contamination correction will be presented.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [3]. The SMAP digital backend back-end enables implementation of advanced Radio Frequency Interference (RFI) detection and mitigation algorithms for corrupted L-band measurements [6]. The radiometer uncalibrated raw counts are converted to Level 1B antenna temperatures and brightness temperature (TB) values [2]. These TB values are used with other ancillary data to retrieve soil-moisture products on a 40km global grid. The error requirement for the SMAP radiometer is 1.3K and calibration drift is less than 0.4 K/month to measure soil-moisture with volumetric fraction uncertainty of less than 0.04 m3/m3.

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 a.m./6 p.m. sun-synchronous orbit at 685-km altitude. Since April 2015, the radiometer has been under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods, including the SMAP L1B TA2TB [from antenna temperature (TA) to the Earth's surface brightness temperature (TB)] algorithm and TA forward models, are outlined, and validation approaches for calibration stability/quality are described in this paper, including future work. Results show that the current radiometer L1B data product (version 3) satisfies its requirements (uncertainty <;1.3 K and calibration drift <;0.4 K/months, and geolocation uncertainty <;4 km) although there are biases in TA over cold sky and in TB comparing with the Soil Moisture and Ocean Salinity TB v620 data products.

WindSat has been providing accurate ocean vector winds (OVWs) for over a decade. WindSat makes polarimetric brightness temperature measurements of Earth looking forwards and backwards. However, because the overlap of these two swaths is relatively narrow, the benefit of two-look polarimetric (2LP) retrieval accuracy has not been utilized. This paper derives OVW from WindSat 2LP measurements using a radiative transfer model and maximum-likelihood estimation. The purpose of this paper is a comparison of WindSat 2LP wind direction accuracy with WindSat one-look, QuikSCAT, and Advanced Scatterometer (ASCAT) wind directions. Retrievals are compared with anemometer measurements on collocated moored buoys. Statistics are examined for both the first-ranked wind direction ambiguity and the selected ambiguity after median filtering initialized with a numerical weather prediction wind field. For winds above 8 m/s, WindSat 2LP retrievals have the most accurate first-ranked direction compared with all other sensors. For winds of 6-9 m/s, the standard deviation relative to buoys is 17°, and for 9-20 m/s, it is less than 10°. One-look standard deviations are nearly twice as large. At low winds, QuikSCAT provides the most accurate wind directions, for first-ranked and selected ambiguity. Thus, scatterometer and radiometer OVW measurements provide complementary capabilities. The accuracy of 2LP OVW is particularly relevant now that new internal calibration technology allows for a 360° conical scan of earth observations. Moreover, new low-cost designs would make it possible to affordably deploy a constellation of OVW sensors capable of providing accurate winds under a wide range of conditions, described herein.

Sea surface temperature (SST) serves as a crucial ancillary input field to the retrieval algorithm for sea surface salinity (SSS) from L-band satellite radiometers, such as Soil Moisture and Ocean Salinity mission, Aquarius, and Soil Moisture Active Passive mission. It is needed for inverting the radiative transfer model equation of the ocean surface emissivity, which depends both on ocean surface salinity and ocean surface temperature. Our analysis studies the sensitivity of the performance of the Aquarius salinity retrieval algorithm to the ancillary SST that is used in the algorithm. We have retrieved Aquarius salinities using four different SST fields as ancillary input, namely, the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)-only Optimum Interpolation SST (OISST), the SST from WindSat, the SST from the Canadian Meteorological Center (CMC), and the Multi-scale Ultra-high Resolution (MUR) SST from the National Aeronautics and Space Administration's Jet Propulsion Laboratory. The retrieved Aquarius SSS is compared with ground truth data; thus, the performance of the salinity retrieval algorithm in all four cases can be evaluated. The WindSat, CMC, and MUR SST products, which are all based on or are assimilating SST measurements from the passive microwave (MW) sensors, give better performance than the NOAA AVHRR-only OISST, which does not use any MW SST data, but which is solely based on the in situ data and observations from the infrared AVHRR sensor. The CMC SST gives the best overall performance for the retrieved SSS. The sensitivity of the SSS retrievals and therefore the performance differences between the various ancillary input fields increases in cold water.

We present a method for formally assessing random and systematic uncertainties in the Aquarius salinity retrievals. The method is based on performing multiple retrievals by perturbing the various inputs to the retrieval algorithm. This results in calculating the sensitivity of the Aquarius salinity to these inputs. Together with an error model for the uncertainties in the input parameters it is possible to calculate the uncertainty in the retrieved SSS. It is important to distinguish between random uncertainties, which get suppressed when computing weekly or monthly averages and systematic uncertainties, which do not get suppressed by taking averages. We compare the results of the formal uncertainty estimates with uncertainty estimates based on comparing the Aquarius salinities with those from external validation sources finding excellent agreement.

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) was built and tested by Ball Aerospace and Technologies Corporation (Ball) under a contract with the GPM program at the NASA Goddard Space Flight Center. Ball has supported the initial on-orbit operations to verify calibration performance and provide a final set of operational calibration algorithms. The GMI instrument was launched onboard the GPM spacecraft on February 28th, 2014. GMI was turned on and completed all deployments March 1st, 2014. Spin up and the start of science operations proceeded on March 4th, 2014. GMI has operated nearly continuously since then and has completed a year of successful operation on-orbit. This paper presents the on-orbit performance of the instrument and the results from the calibration activities.

We present validation results for the Aquarius Version 3.0 ocean surface salinity product that has been released in June 2014. We focus on comparing the Aquarius salinity in the tropics with measurements from the ARGO drifter network, moored PMEL buoys and HYCOM. The results are stratified as function of surface rain rate. As the measurements occur at different depth below the ocean surface, this allows an assessment of salinity stratification within the very upper ocean layer and rain induced freshening effects. We separate the rain freshening effects from local biases in the Aquarius salinity that correlate with sea surface temperature and are likely related to uncertainties in the geophysical model that is used in the salinity retrieval algorithm. We derive an adjustment for this SST-dependent biases and evaluate the performance of this bias-adjusted salinity product. A comparison between the standard Aquarius salinity product and the CAP salinity is also presented.

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) instrument was launched onboard the GPM core spacecraft in February 2014. The instrument has exhibited highly stable operations through the duration of the calibration/validation period. This paper provides an overview of the GMI instrument and a report of early on-orbit commissioning activities. It discusses the on-orbit radiometric sensitivity and stability for each channel, hot load performance, noise diode stability and early indicators of absolute calibration performance.

This paper gives an overview of the algorithm for retrieving sea surface salinity from the AQUARIUS L-band radiometer and its physical background.

A Climate Data Record of ocean surface winds is very valuable for climate research, as surface winds are key drivers of oceanic and atmospheric processes which regulate global and regional climate. However, producing a climate-quality global dataset of ocean surface winds by combining observations from different satellites is a very challenging task. Here we present a methodology ideal for merging different dataset to produce a Climate Data Record of ocean surface winds. Our first step has been the reprocessing of the wind vectors from the scatterometer QuikSCAT, which operated from 1999 to 2009. The QuikSCAT were reprocessed by using a new model function developed to improve retrievals at high wind speeds. QuikSCAT winds will serve as a backbone for our Climate Data Record. Our next step is to apply the same methodology and calibration method to the winds from the European scatterometer ASCAT, which started in 2007.

We present a model function for the emissivity of the wind roughened ocean surface for microwave frequencies between 6 and 90 GHz. It is an update, refinement, and extension of model functions we had developed previously. The basis of our analysis are brightness temperature (TB) measurements from the spaceborne microwave radiometer WindSat and the Special Sensor Microwave/Imager, which are collocated with independent measurements of surface wind speeds and directions. This allows the determination of the emissivity model function for Earth incidence angles (EIA) around 55°. We demonstrate that an essential part in the model development is the absolute calibration of the radiometer measurements over the ocean to the computed TB of the radiative transfer model, one of whose components the emissivity model function is. We combine our results with other established measurements for lower EIA and finally obtain a model function which can be used over the whole EIA range between 0° and 65°. Results for both the isotropic, wind direction independent part as well as the four Stokes parameters of the wind direction signal are presented. Special emphasis is made on the behavior at high wind speeds between 20 and 40 m/s by conducting a comparison with data from the step frequency microwave radiometer.

A summary of the strengths and weaknesses of passive (radiometers) versus active (scatterometer) wind retrievals is presented in Table 2. The radiometer provides a reliable rain detection and keeps better sensitivity at high wind speeds. Rain impacts scatterometer wind retrievals, especially at high winds. On the other hand, the scatterometer has significantly better sensitivity to wind direction than the radiometer especially in wind speeds below 6 m/s and in moderate or high rain. Daily gridded maps of the all-weather WindSat winds together with other WindSat products are available at www.remss.com from 2003 until current. The reprocessed QuikSCAT winds using the Ku2011 model function for the complete dataset (1999-2009) can be found on the same website.

Our study provides a detailed intercomparison between AMSR-E and WindSat top of the atmosphere brightness temperatures (TB) over land scenes.

Satellite microwave radiometers capable of accurately retrieving sea surface temperature (SST) have provided great advances in oceanographic research. A number of future satellite missions are planned to carry microwave radiometers of various designs and orbits. While it is well known that the 11 GHz SST retrievals are less accurate than the 7 GHz retrievals, particularly in colder waters, it has not been demonstrated using existing microwave data. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) provides the means to examine the accuracies of SST retrievals using these channels in a systematic manner. In this paper, the accuracies of SSTs at 7 and 11 GHz are determined using two approaches: modeled and empirical. The modeled accuracies are calculated using an emissivity model and climatology SSTs, while empirical accuracies are estimated through validation of AMSR-E 7 and 11 GHz SST retrievals using over six years of data. It was found that the 7 GHz SST retrievals have less errors due to radiometer noise and geophysical errors than the 11 GHz retrievals at all latitudes. Additionally, while averaging the 11 GHz retrievals will diminish error due to uncorrelated radiometer noise, the geophysical error is still higher than for the 7 GHz retrievals, particularly at the higher latitudes.

We have developed algorithms that retrieve ocean-surface wind speed and direction under rain using brightness-temperature (TB) measurements from passive satellite microwave radiometers. For accurate radiometer retrievals of wind speeds in the rain, it is essential to use TB signals at different frequencies, whose spectral signature makes it possible to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. The wind-speed retrieval accuracy of an algorithm that utilizes C-band frequencies and is trained for tropical cyclones ranges from 2.0 m/s in light rain to 4.0 m/s in heavy rain. We have also trained and tested global algorithms that are less accurate in tropical storms but can be applied under all conditions. The wind-direction retrieval accuracy degrades from about 10degin light rain to 30degat the onset of heavy rain. We compare the performance of wind-vector retrievals under rain from microwave radiometers with those from scatterometers and discuss advantages and shortcomings of both instruments. We have also analyzed the wind-induced sea-surface emissivity, including its wind-direction dependence for wind speeds up to 45 m/s.

The paper discusses the retrieval algorithm for sea surface temperature (SST) using the WindSat radiometer. We perform SST retrievals with and without the C-band channels. The results are validated against SST measurements from AMSR-E, the Reynolds optimum interpolated product from NOAA and infrared radiometer measurements of the skin SST that were taken on cruise ships.

The third Stokes parameter of ocean surface brightness temperatures measured by the WindSat instrument is sensitive to the rotation angle between the polarization vectors at the ocean surface and the instrument. This rotation angle depends on the spacecraft attitude (roll, pitch, yaw) as well as the Faraday rotation of the electromagnetic radiation passing through the Earth's ionosphere. Analyzing the WindSat antenna temperatures, we find biases in the third Stokes parameter as function of the along-scan position of up to 1.5 K in all feedhorns. This points to a misspecification of the reported spacecraft attitude. A single attitude correction of -0.16/spl deg/ roll and 0.18/spl deg/ pitch for the whole instrument eliminates all the biases. We also study the effect of Faraday rotation at 10.7 GHz on the accuracy of the third Stokes parameter and the sea surface wind direction retrieval and demonstrate how this error can be corrected using values from the International Reference Ionosphere for the total electron content when computing Faraday rotation.

Geolocation and pointing accuracy analyses of the WindSat flight data are presented. The two topics were intertwined in the flight data analysis and will be addressed together. WindSat has no unusual geolocation requirements relative to other sensors, but its beam pointing knowledge accuracy is especially critical to support accurate polarimetric radiometry. Pointing accuracy was improved and verified using geolocation analysis in conjunction with scan bias analysis. Two methods were needed to properly identify and differentiate between data time tagging and pointing knowledge errors. Matchups comparing coastlines indicated in imagery data with their known geographic locations were used to identify geolocation errors. These coastline matchups showed possible pointing errors with ambiguities as to the true source of the errors. Scan bias analysis of U, the third Stokes parameter, and of vertical and horizontal polarizations provided measurement of pointing offsets resolving ambiguities in the coastline matchup analysis. Several geolocation and pointing bias sources were incrementally eliminated resulting in pointing knowledge and geolocation accuracy that met all design requirements.

We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.

We analyze the wind direction signal for vertically (v) and horizontally (h) polarized microwave radiation at 37 GHz, 19 GHz and 11 GHz and an Earth incidence angle of 53 deg. We use brightness temperatures from SSM/I and TMI and wind vectors from buoys and the QUIKSCAT scatterometer. The wind vectors are space and time collocated with the radiometer measurements. Water vapor, cloud water and sea surface temperature are obtained from independent measurements and are uncorrelated with the wind direction. We find a signal that is noticeably smaller at low and moderate wind speeds than a previous analysis had indicated.

The CMIS Ocean Algorithm Suite retrieves sea surface temperature (SST) as well as sea surface wind speed and direction. We Monte-Carlo simulate ocean brightness temperatures, which are received by the CMIS sensor using a physical radiative transfer model (RTM), geophysical parameters from a numerical weather prediction (NWP) model and add sensor errors. The SST and wind speed algorithms are statistical regressions. The algorithm for retrieving wind direction is a combination of statistical regressions, maximum likelihood estimate and median filtering. One part of the simulated data is used for tuning the algorithm and one part is retained for evaluating the algorithm performance.

We analyze the wind direction signal for vertically (v) and horizontally (h) polarized microwave radiation at 37 GHz, 19 GHz, and 11 GHz; and an Earth incidence angle of 53/spl deg/. We use brightness temperatures from SSM/I and TMI and wind vectors from buoys and the QUIKSCAT scatterometer. The wind vectors are space and time collocated with the radiometer measurements. Water vapor, cloud water and sea surface temperature are obtained from independent measurements and are uncorrelated with the wind direction. We find a wind direction signal that is noticeably smaller at low and moderate wind speeds than a previous analysis had indicated. We attribute the discrepancy to errors in the atmospheric parameters that were present in the data set of the earlier study. We show that the polarization combination 2v-h is almost insensitive to atmospheric changes and agrees with the earlier results. The strength of our new signals agrees well with JPL aircraft radiometer measurements. It is significantly smaller than the prediction of the two-scale sea surface emission model for low and intermediate wind speeds.