WindSat Observations Research Articles

A Facet Statistical Model for Polarimetric Passive Microwave Signatures of Mountainous Terrain

The impact of topography on satellite passive microwave data hinders the soil moisture retrievals over mountainous regions. Current two-scale facet statistical microwave emission models for ocean-like rough surfaces and mountainous terrain use a 2-D slope distribution which is not explicitly linked to the small-scale scattering. We have developed a modified two-scale polarimetric model in sensor coordinates that decomposes the total emission into two statistically independent components, the incidence angle tilt and polarization orientation, which are directly and uniquely linked to the small-scale scattering and can thus provide insight into the physical process of scattering from topographic roughness. Based on tilt angle and polarization orientation angle (POA) distributions derived from digital elevation model (DEM) data, we examined the azimuthal modulation of the polarimetric signatures and their dependence on the soil moisture and vegetation conditions. We also compared the simulated third Stokes parameters against WindSat satellite observations over four regions in the American Southwest. Good agreements are found between the model simulation and WindSat observations.

Influence of tropical-extratropical interactions on the dynamics of extreme rainfall event: A case study from Indian region

A diagnostic investigation of an extreme rainfall episode that occurred over the central and north Indian region is carried out in this study using data from a suite of observations from space-borne instruments and the reanalysis datasets. This event is unique in the sense that the organized tropical and extratropical forcing stimulated the intense rainfall on 01 Jan 2012. The WindSat (multi-frequency polarimetric microwave radiometer) observations indicate the source of the moisture flux coming from the adjoining tropical Ocean. The dynamical and thermo-dynamical contributions are evaluated based on the atmospheric instability analysis using the Atmospheric Infrared Sounder (AIRS) and reanalysis datasets by computing various stability indices such as total totals (TT) index, Potential Vorticity (PV), static stability and the Convective Available Potential Energy (CAPE). High TT index values (>40 K) are observed both in satellite and reanalysis data indicating thermodynamic instability. PV intrusion to low latitudes is also observed with extreme rainfall occurrence ahead of the PV tongue. The vertical structure of PV intrusion shows remarkable features with enhanced upward motions ahead of the intrusion representing the dynamical instability. The reduced static stability, increased CAPE and upper-level cyclonic anomalies together with enhanced moisture in the lower troposphere coming from the adjoining tropical Indian Ocean regulate the amplitude and region of occurrence of the extreme rainfall. Therefore, this study identifies the significant implications of tropical and extra-tropical influences that generate the thermodynamical and dynamical instabilities for the occurrence of the extreme rainfall event over the Central and Northern parts of India.

Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s−1 and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.

Refinement of SMOS Multiangular Brightness Temperature Toward Soil Moisture Retrieval and Its Analysis Over Reference Targets

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer (RM) was launched on August 16, 2011, on board HY-2 satellite. The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations, respectively, from January to June 2012. The wind speed root-mean-square (RMS) difference of the comparisons with in-situ data is 1.89 m/s for themeasurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform, respectively. On a global scale, the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat, the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above. With analyzing the global map of a mean difference between HY-2 RM and WindSat, it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions. In the open sea, there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations, while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

A Probability Distribution Method for Detecting Radio-Frequency Interference in WindSat Observations

The detection of radio-frequency interference (RFI) continues to be an important problem for satellite-based microwave radiometers. This paper introduces two new probability-distribution-based techniques for computing the likelihood that a given brightness temperature observation contains an RFI signal. These methods extend the spectral difference method already being employed for the detection of RFI signals, and they allow for simultaneous observation-by-observation RFI detection of both land- and sea-based brightness temperature observations. This paper starts by laying out the theoretical groundwork for both techniques. It will then expand upon that groundwork to detail its practical application in analyzing WindSat brightness temperature observations. Finally, this paper compares the resulting probability indices from the detailed algorithms with spectral difference and principle component analysis indices computed from WindSat observations for various geographic regions. These comparisons will show effective RFI signal detection by these new techniques for RFI signal strengths as low as 4–5 K.

Soil Moisture Retrievals From the WindSat Spaceborne Polarimetric Microwave Radiometer

An existing methodology to derive surface soil moisture from passive microwave satellite observations is applied to the WindSat multifrequency polarimetric microwave radiometer. The methodology is a radiative-transfer-based model that has successfully been applied to a series of (historical) satellite sensors, including the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Brightness temperature observations from the WindSat and AMSR-E radiometers were compared, and the WindSat observations were adjusted to overcome small sensor differences (e.g., frequency, bandwidth, incidence angle, and original sensor calibration procedure). The method to relate Ka-band brightness temperature observations to land surface temperature was adapted to the overpass times of WindSat. Statistical analysis with both satellite-observed and in situ soil moistures indicates that the quality of the newly derived WindSat soil moisture product is similar to that obtained with AMSR-E after the adjustment of the WindSat brightness temperature observations. The average correlation coefficients ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ) between satellite soil moisture and in situ observations are similar for the two satellites with average values of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.60 for WindSat and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.62 for AMSR-E as calculated from 33 sites. On a global scale, the average correlation coefficient between the two satellite soil moisture products is high with a value of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.83. The results of this study demonstrate that soil moisture from WindSat is consistent with existing soil moisture products derived from AMSR-E using the land parameter retrieval model. Therefore, the soil moisture retrievals from these two satellites could easily be combined to increase the temporal resolution of satellite-derived soil moisture observations.

Directional Signals in Windsat Observations of Hurricane Ocean Winds

In this paper, wind-direction signals in passive microwave polarimetry for ocean surfaces under hurricane force winds are presented. We performed analysis of Windsat data for several Atlantic hurricanes from 2003 to 2005. The polarimetric third Stokes parameter observations from the Windsat 10-, 18-, and 37-GHz channels were collocated with the ocean-surface winds from the National Oceanic and Atmospheric Administration Hwind analysis. The collocated data were binned as a function of wind speed and wind direction. The 10-GHz data show clear 4-K peak-to-peak directional signals at 50-60-m/s wind speed after correction for atmospheric attenuation. The signals in the 18- and 37-GHz channels were unclear at above 40-m/s wind speeds, probably caused by the impact of clouds and rain. The data were expanded by sinusoidal series of the relative azimuth angles between the Hwind analysis and observation directions. The coefficients of the sinusoidal series suggest decreasing response to wind direction for increasing wind speed, but the 10-GHz data appear to be fairly constant for up to 50-m/s wind speeds.

The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions

The physics of remote sensing sea surface measurements is still poorly understood under severe weather conditions. Wind vector algorithms are usually developed for non‐precipitating atmospheres and for wind speeds less than 20 m/s. In this study, we analyze observations from the QuikSCAT Ku‐band scatterometer collocated with the WindSat full polarimetric microwave radiometer to estimate the potential of these two instruments for sea surface wind retrieval under severe weather conditions. The Jason altimeter provides independent measurements of wind speed and rain rate for comparison purposes. The sensitivity of the radar cross‐sections and brightness temperatures to the wind speed and direction is directly studied from the observations and compared with semi empirical models. This study clearly demonstrates that wind vector retrieval under extreme condition is feasible. Comparisons between QuikSCAT and WindSat coincident observations evidence a better sensitivity of the active mode to low and moderate winds and more sensitivity to high wind speeds in the passive mode. Although the WindSat observations are affected by water vapor, cloud, and rain, especially at and above 18 GHz, the measurements are sensitive to wind speed even at high wind speeds. Contrarily to the active instrument, there is no saturation at high winds. The sensitivity clearly tends to increase for winds above 15 m/s. For the wind direction, the amplitude of the azimuthal modulation in the active mode decreases with increasing wind speed, while it increases for the passive measurements. The development of specific wind retrievals under severe weather conditions is encouraged and a simple illustration is provided.

Observations of Land Surface Passive Polarimetry With the WindSat Instrument

WindSat provides an opportunity to explore the passive microwave polarimetric signatures of land surfaces. In order to accommodate the large sensor footprint, large homogeneous regions with unique features were used. These included forest, rangeland, desert, and agricultural conditions. WindSat observations at horizontal and vertical polarizations over land surfaces were found to be well calibrated and consistent with other passive microwave sensors. Isotropic regions (e.g., Amazon rainforest) had no polarimetric response at all azimuth angles. Results showed that land surfaces with aligned features (topography or row structured vegetation) produced systematic variations in the third and fourth Stokes parameters. These responses were found to be in good agreement with previous sea surface studies. Analysis of the temporal trends of the variation in polarimetric measurements for a specific azimuth angle could be attributed to the crop growth cycle in the agricultural region. Further analyses will seek to isolate specific features that could be used in applications such as soil moisture retrieval.

Comparison of WindSat brightness temperatures with two-scale model predictions

Predictions of the polarized microwave brightness temperatures over the ocean are made using a two-scale surface bidirectional reflectance model combined with an atmospheric radiative transfer model. The reflected atmospheric radiation is found to contribute significantly to the magnitude and directional dependence of the brightness temperatures. The predicted brightness temperatures are also sensitive to the form of the shortwave spectrum. Calculations are made using a new physically based model for the wave spectrum, and preliminary comparisons are made with WindSat observations at 10.7, 18.7, and 37 GHz, for wind speeds ranging from 0-20 m/s and for vertically integrated atmospheric water vapor concentrations from 0-70 mm. Predictions of the mean (azimuthally averaged) brightness temperatures for vertical and horizontal polarization agree quite well with WindSat observations over this range of wind speeds and water vapor concentrations. The predicted azimuthal variations of the third and fourth Stokes parameters also agree fairly well with the observations, except for the fourth Stokes parameter at 37 GHz. Further adjustments of the wave spectrum are expected to improve the agreement.

Calibration of WindSat polarimetric channels with a vicarious cold reference

Absolute calibration of WindSat's third and fourth Stokes brightness temperatures (T/sub 3/ and T/sub 4/) is needed at the tenth of Kelvin level in order to adequately resolve their dependence on wind direction. Previous aircraft based fully polarimetric microwave radiometers have generally relied on circle flights, during which a single area of the ocean is observed at all azimuth angles, to estimate residual biases in the calibration of its polarimetric channels. WindSat, the first spaceborne fully polarimetric microwave radiometer, operates in low Earth orbit and thus cannot execute this traditional calibration technique. A new method is presented to estimate the residual biases that are present in WindSat's T/sub 3/ and T/sub 4/ estimates. The method uses a vicarious cold reference brightness temperature applied to measurements made by WindSat at /spl plusmn/45/spl deg/ slant linear (T/sub P/ and T/sub M/) and left- and right-hand circular (T/sub L/ and T/sub R/) polarization. WindSat derives the third and fourth Stokes brightness temperatures by the differences T/sub P/-T/sub M/ and T/sub L/-T/sub R/, respectively. The method is demonstrated by applying it to the 10.7-GHz WindSat observations. Calibration biases of 0.2-0.6 K are determined with a precision of 0.04 K.