The MWI digitally correlates the v-pol and h-pol channels at 10.85, 18.85 and 36.75 GHz to generate the modified Stokes vector (v-pol, h-pol, 3rdStokes and 4thStokes). In addition to using the hot and cold views to obtain gain and offset, the coupling from each of the other polarizations needs to be removed to calibrate 3rdand 4thStokes. The 1stStokes coupling is easily computed using the hot and cold views, but the 2ndStokes coupling cannot be computed with the onboard calibration scheme and must be determined pre-launch. Decoupling the 3rdand 4thStokes channels requires knowledge of the receiver phase, which is obtained via onboard correlated noise sources. Additional coupling from the 2ndStokes into the 3rdStokes caused by misalignment of the antenna to the Earth is calibrated geometrically using the measured attitude. Faraday Rotation of the polarization is estimated and corrected from the total electron content in the atmosphere. Detailed antenna pattern measurements allow for the calibration of the beam squint via a modified Backus Gilbert approach.

The MWSDPS for WSF-M processes raw WSFM sensor and spacecraft auxiliary data into geolocated brightness temperatures and environmental data products. The software will be used in an operational environment at two government weather centrals and sites. The environmental data products include ocean surface vector winds (OSVW), sea ice age, sea ice concentration, snow water equivalent and soil moisture. The sensor data is also used for tropical cyclone intensity (TCI) processing. Each data product is produced on a grid with required horizontal spatial resolution and sampling interval. In addition to data products, the software flags various exclusion, degradation, and validation conditions. The data products are validated pre-launch through ground truth and proxy sensor data. Post-launch, the data is validated against other sensors and community accepted ground truth.

The on-orbit calibration of the WSF-M MWI microwave radiometer will include orbital maneuvers to estimate parameters that are difficult to accurately determine on the ground before launch. The planned orbital maneuvers are similar to those previously used for the GMI sensor and consist of a deep-space pitch maneuver, an inertial hold maneuver, and a nadir pitch maneuver.To prepare for the upcoming launch and calibration period, we simulate all calibration maneuvers, estimate the calibration parameters of interest from the simulated data, and evaluate the accuracy of the extracted parameters.

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 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.

In this work, we will present results from a sea ice concentration and age retrieval algorithm using microwave radiances. The algorithm has four notable features: (1) the use of a 11 GHz channel, (2) satellite brightness temperatures that have been calibrated in an absolute sense, (3) algorithm parameterization in terms of surface emissivity rather than brightness temperature, and (4) detection of sea ice <; 30 cm thick. The resulting sea ice concentrations and sea ice types are compared with those retrieved by the National Snow and Ice Data Center and Canadian Ice Service. One novel feature of this approach is that it provides estimates of sea ice emissivities that are correct in an absolute sense. We believe that this is the first time that absolute measurements of sea ice emissivity have been derived from satellite observations.

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.

Retrieval of the brightness temperature (TB) at the L-band is studied in the context of the remote sensing of ocean surface salinity. The measurement of antenna temperature and the retrieval of TBare simulated with a radiative transfer model and an observing system model of an orbiting spacecraft. Two sets of antenna gain patterns are used: 1) theoretical analysis and 2) measurements of a 1/10th-size scale model. The latter set notably shows the large cross-polarization coupling from the first Stokes transmit into the third Stokes receive. The large cross-polarization coupling causes an error of up to 4° in the estimate of the Faraday rotation angle (the size of the angle itself is mostly less than 15° in the severe ionospheric condition). By this amount of the error, an additional rotation is introduced to the retrieval of the second and third Stokes TBs in front of the feed horn before the Faraday rotation correction. The additional rotation also degrades the performance of the antenna pattern correction (APC). However, when the Faraday rotation correction is performed by the square sum of the two retrieved Stokes, the retrieved TBbelow the ionosphere and at the top of the atmosphere after the Faraday correction becomes insensitive to the additional rotation (i.e., being insensitive to the error in the Faraday angle estimate and the rotational error in the APC). The formal proof of the insensitivity is presented. The first and second Stokes TBs at the top of the atmosphere observed at 5.6-s intervals from space may be retrieved with an error smaller than 0.1-K rms without the accurate ancillary information of the gain pattern and the Faraday rotation angle, assuming correct calibration, 0.08 K NEΔT for the radiometer noise, and accurate correction or flagging of the solar and galaxy radiation.

A numerical simulator has been developed to study remote sensing from space in the spectral window at 1.413 GHz (L-band), and it has been used to optimize the cold-sky calibration (CSC) for the Aquarius radiometers. The celestial sky is a common cold reference in microwave radiometry. It is currently being used by the Soil Moisture and Ocean Salinity satellite, and it is planned that, after launch, the Aquarius/SAC-D observatory will periodically rotate to view “cold sky” as part of the calibration plan. Although radiation from the celestial sky is stable and relatively well known, it varies with location. In addition, radiation from the Earth below contributes to the measured signal through the antenna back lobes and also varies along the orbit. Both effects must be taken into account for a careful calibration. The numerical simulator has been used with the Aquarius configuration (antennas and orbit) to investigate these issues and determine optimum conditions for performing a CSC. This paper provides an overview of the simulator and the analysis leading to the selection of the optimum locations for a CSC.

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 Aquarius is a L-band passive/active instrument onboard a spacecraft due for launch in 2010, targeting sea surface salinity retrieval with an accuracy better than 0.2 psu. Measurement and retrieval of salinity by the Aquarius radiometer are simulated using a radiative transfer model. The cross-polarization coupling in the antenna pattern complicates the correction of the Faraday rotation effect in the ionosphere, causing errors in brightness temperature up to several Kelvin. The second-order correction for the Faraday effect is developed and reduces the retrieval error better than the required accuracy (~less 0.1 K).

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.

Aquarius is an L-band microwave instrument being developed to map the surface salinity field of the oceans from space. It is part of the Aquarius/SAC-D mission, a partnership between the USA (NASA) and Argentina (CONAE) with launch scheduled for early in 2009. 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 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.

The Sun is a sufficiently strong source of radiation at L-band to be an important source of interference for radiometers on future satellite missions such as SMOS, Aquarius, and Hydros designed to monitor soil moisture and sea surface salinity. Radiation from the Sun can impact passive remote sensing systems in several ways, including line-of-sight radiation that comes directly from the Sun and enters through antenna side lobes and radiation that is reflected from the surface to the radiometer. Examples are presented in the case of Aquarius, a pushbroom radiometer with three beams designed to monitor sea surface salinity. Near solar minimum, solar contamination is not a problem unless the Sun enters near the main beam. But near solar maximum, contamination from the Sun equivalent to a change of salinity on the order of 0.1 psu can occur even when the signal enters in sidelobes far from the main beam.

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.

One year of SeaWinds /spl sigma//sub 0/ data are used with SSM/I and TMI rain rates collocated within 5 minutes and tri-linearly interpolated ECMWF analyses to investigate the effects of rain on SeaWinds data. By parameterizing attenuation and volumetric backscattering in terms of rain rate, the modification of the surface radar cross-section by rain can be found. Uncertainty exists in the rain modification of the surface radar cross-section for different rain rate-wind speed regimes, but its magnitude and azimuthal behavior are sufficiently important to require attention for accurate rain contamination correction.

Channel 2 of the 9 Microwave Sounding Units (MSUs) flown on NOAA polar orbiting platforms provides a 23-year time series of middle-tropospheric temperature. These measurements may be of sufficient quality for climate studies if intersatellite calibration offsets and drifts can be accurately characterized and removed. One of the most important and difficult to characterize sources of long-term drift in the data is due to the evolution of the local observing time due to slow changes in the orbital parameters of each NOAA platform, which can alias diurnal temperature changes into the long-term time series. To account for this effect, we have constructed monthly diurnal climatologies of MSU Channel 2 brightness temperature using the hourly output of a general circulation model as input for a microwave radiative transfer model. We report the results of this calculation, and validate the result by comparing with MSU observations.

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.

Climatological time-series of tropospheric temperatures from the Microwave Sounding Unit (MSU) are potentially of great value in validating observations of climate change due to their excellent spatial and temporal coverage. However, the long-term trend signal is small relative to natural seasonal and inter-annual variability and inter-satellite biases, and is comparable to diurnal temperature drift arising from variation of the satellite local crossing time. Additional effects due to time-varying non-linear instrument response also contribute to instrument error. We discuss a consistent methodology for compensating for these factors and present an error model quantifying their significance in determination of long-term trends.

Analysis of SeaWinds Scatterometer data show coastal regions exhibiting a greater than 5 m/s wind speed difference between averaged morning (ascending) and evening (descending) observations. This diurnal wind variability is strongest at several Indian Ocean locations including Madagascar, northern Australia, and Sri Lanka, as well as the west coast of South America and on the Pacific Coast of Central America. Two of these regions are discussed here in greater detail.

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.

Three post-launch calibration methods are used to examine the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on-orbit performance. The first method is a statistical analysis of TMI ocean observations that reveals a systematic along-scan error. The second method is an intercomparison of TMI and SSM/I observations that shows a warm bias in TMI. The last method is an analysis of TMI observations taken during TRMMs deep-space maneuver. These deep-space observations confirm both the along-scan error found from method 1 and the warm bias found from method 2. The along-scan error exhibits distinctive features having amplitudes near 1 K. The warm bias, which is related to the scene temperature, can be as large are 5 K for ocean measurements. The physical explanation of a slightly emissive main reflector is proposed to explain the calibration errors.

Scatterometer instruments are active microwave sensors that transmit a series of microwave pulses and measure the returned echo power to determine the normalized radar backscattering cross section (sigma-0) of the ocean surface from which the speed and direction of near-surface ocean winds are derived. The NASA Scatterometer (NSCAT) was launched on board the ADEOS spacecraft in August 1996 and returned ten months of high-quality data before the failure of the ADEOS spacecraft terminated the data stream in June 1997. The purpose of this paper is to provide an overview of the NSCAT instrument and sigma-0 computation and to describe the process and the results of an intensive postlaunch verification, calibration, and validation effort. This process encompassed the functional and performance verification of the flight instrument, the sigma-0 computation algorithms, the science data processing system, and the analysis of the sigma-0 and wind products. The calibration process included the radiometric calibration of NSCAT using both engineering telemetry and science data and the radiometric beam balance of all eight antenna beams using both open ocean and uniform land targets. Finally, brief summaries of the construction of the NSCAT geophysical model function and the verification and validation of the wind products will be presented. The key results of this paper are as follows: The NSCAT instrument was shown to function properly and all functional parameters were within their predicted ranges. The instrument electronics subsystems were very stable and all of the key parameters, such as transmit power, receiver gain, and bandpass filter responses, were shown to be stable to within 0.1 dB. The science data processing system was thoroughly verified and the sigma-0 computation error was shown to be less than 0.1 dB. All eight antenna beams were radiometrically balanced, using natural targets, to an estimated accuracy of about 0.3 dB.

The possibility of retrieving both wind speed and direction from microwave radiometer measurements of the ocean is studied using Special Sensor Microwave/Imager (SSM/I) measurements collocated with buoy reports from the National Data Buoy Center (NDBC). A physically based algorithm is used to retrieve the wind speed. The RMS difference between the SSM/I and buoy wind speed is 1.6 m/s for 3321 comparisons. It is found that the SSM/I minus buoy wind speed difference is correlated with wind direction. When this wind direction signal is removed, the RMS difference between the SSM/I and buoy winds reduces to 1.3 m/s. The wind direction signal is used to make global, low-resolution maps of the monthly mean oceanic vector. The wind direction sensing capability of a prospective two-look satellite radiometer is also processed.>