Ocean Brightness Temperature Research Articles

Correcting the FRA Systematic Error in VTEC Maps From SMOS Radiometric Data

The Faraday Rotation (FR) is a non-negligible effect at the L-band, which is the operation frequency of the Soil Moisture and Ocean Salinity (SMOS) mission. This effect introduces a rotation in the electromagnetic field polarization when propagating through the ionosphere that must be compensated. Recently, a methodology was developed in order to retrieve the Vertical Total Electron Content (VTEC) from SMOS radiometric data with the aim to better correct the Faraday rotation effect [1]. In that work, systematic patterns in the retrieved Faraday Rotation Angle (FRA) were detected. In this paper, these systematic patterns are characterized and corrected to improve the quality of the retrieved VTEC maps. These maps can be then re-used in the SMOS level 2 processor for the correction of the FRA in the mission. The impact of using the SMOS-derived VTEC maps instead of the VTEC data from GPS measurements on the ocean brightness temperature measurement has also been analyzed. Results of this analysis show that the usage of those maps allows a significant enhancement in the quality of the brightness temperatures, which will lead to an improvement on salinity retrievals.

Computation of Brightness Temperature of Sea-foam Modelled as Sequences of Thin Phase Screens using Matlab

Sea surface temperature of the ocean is a significant climate parameter. Satellites provide data for analysing and monitoring the sea surface temperature (SST). Satellite remote sensing provide thermal data in a short duration over large area. Temperature measurement by remote sensing is dependent on the principle that most objects emit electromagnetic (EM) radiation corresponding to temperature, wavelength and emissivity of the objects. Brightness temperatures are detected by thermal sensors, however, brightness temperature coincides with the real temperature of objects if they are black bodies. In this paper, we estimated the effective dielectric constant of sea foam layer which is a very important parameter in investigating ocean brightness temperature. This was done at WindSat frequencies and using a discretization method to evaluate the dielectric constant of a random distribution of air-bubbles discretized into slices of sea foam layer. For efficient evaluation of scattering by foam covered sea surface and measurement of brightness temperature in milli-Kelvin, we develop a discrete based physical model of sea foam which provides accurate estimate of the complex effective dielectric constant of sea foam. The foam covered sea foam layer is modelled as sequences of thin phase screens ( slices ofsea foam layer) with equal depth . Each layer comprised of random distribution of bubbles that follows a log-normal distribution pattern with geometrical and optical properties such as foam layer thickness, foam void fraction, foam volume fraction, sea surface temperature and sea surface salinity. Results of sea surface emissivity and brightness temperature as a function of polarization, angle of incidence, WindSat frequencies and thickness of sea foam are presented.

Direct Comparison Between Active C-Band Radar and Passive L-Band Radiometer Measurements: Extreme Event Cases

Co-located over extreme events, C-band co-polarized and cross-polarized normalized radar cross sections (NRCS) and L-band ocean surface roughness brightness temperature ( $T_{B,\text {rough}}$ ) are directly compared to analyze the similarities and differences between these two parameters at medium resolution (about 25 km). NRCS in VH-polarization and VV-polarization ( $\sigma _{0,\mathrm {VH}}$ , $\sigma _{0,\mathrm {VV}}$ ) were acquired by Sentinel-1 C-band synthetic aperture radar. $T_{B,\mathrm {rough}}$ is estimated from brightness temperatures ( $T_{B}$ ) measured by the L-band radiometer on-board the Soil Moisture Active Passive mission. When the rain rate is less than 20 mm/h, a striking linear relationship is found between active C-Band cross-polarized NRCS and passive L-Band $T_{B\mathrm {,rough}}$ : $\sigma _{0,\mathrm {VH}}(\theta _{\mathrm {SAR}}) \propto \tan (\theta _{\mathrm {SAR}}) \times T_{B,\mathrm {rough}}(\theta _{\mathrm {SMAP}}= 40^{\circ }$ ), without any apparent saturation for $T_{B,\mathrm {rough}}$ ranging from 3.5 to 17 K. Compared to both high $T_{B,\mathrm {rough}}$ and $\sigma _{0,\mathrm {VH}}$ , co-polarized $\sigma _{0,\mathrm {VV}}$ measurements saturate. As interpreted, this can correspond to a regime change of the air–sea interactions during extreme events. In heavy rain conditions, C-band co-polarized NRCS decreases for extreme situations. In these cases, the covariation between C-band cross-polarized NRCS and L-band $T_{B,\mathrm {rough}}$ is less evident. An accurate and unambiguous assessment of the impact of rain will deserve further investigations.

Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands

Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth’s shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs.

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

A Roughness Correction for Aquarius Sea Surface Salinity Using the CONAE MicroWave Radiometer

Aquarius (AQ)/SAC-D is a joint National Aeronautics and Space Administration (NASA)/Comision Nacional de Actividades Espaciales (CONAE; Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using a L-band radiometer/scatterometer that measures ocean brightness temperature (Tb) and radar backscatter (sigma-0). The application of L-band radiometry to retrieve SSS is a difficult task; therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that “warms” the ocean Tb. The baseline approach, to provide this ocean roughness correction, uses the AQ radar scatterometer measurement of ocean sigma-0 to infer the radiometric excess ocean emissivity. In contrast, this paper develops an alternate approach for the AQ ocean roughness correction using the MicroWave Radiometer (MWR) Tb measurements at Ka-band. The theoretical basis of this MWR ocean roughness correction algorithm is described, which translates these Ka-band measurements to L-band to remove the AQ Tb errors that are caused by ocean wind speed and direction. MWR ocean roughness correction results are compared with corresponding results from the AQ scatterometer method. Also, AQ SSS retrievals are presented using both sets of roughness corrections that demonstrate the relative effectiveness of the MWR and AQ scatterometer approaches.

Assessing the Measurement Consistency Between the Jason-2/AMR and SARAL/AltiKa/DFMR Microwave Radiometers Using Simultaneous Nadir Observations

SARAL/AltiKa has a Dual Frequency Microwave Radiometer (DFMR), and Jason-2 has an Advanced Microwave Radiometer (AMR). Both microwave radiometer sensors include a 23.8 GHz primary water sensing channel. The measurement consistencies between DFMR and AMR are important for establishing a consistent altimetry data set between SARAL/AltiKa and Jason-2 in order to accurately assess sea level rise in a long-term time series. This study investigates the measurement consistency in the 23.8 GHz channel between DFMR and AMR at the Simultaneous Nadir Overpasses (SNO's) between the two satellites and also at coldest ocean brightness temperature locations. Preliminary results show that while both instruments show no significant trends over the one year since the launch of SARAL, a consistent relative bias of 2.88 K (DFMR higher than AMR) with a standard deviation of 0.98 K is observed. The relative bias at the lowest brightness temperature from the SNO method (-3.82 K) is consistent with that calculated from coldest ocean method (-3.74 K). The relative bias exhibits strong latitude (and scene temperature) dependency, changing from -3.82 K at high latitudes to -0.92 K near the equator. There also exists an asymmetry between the northern and southern hemisphere. The relative bias increases toward the lower end of brightness temperature.

Brightness temperature model of sea foam layer at L-band

Permittivity of a sea foam layer is very important in investigating ocean brightness temperature model. At microwave frequency, the Rayleigh method is developed to estimate the effective permittivity of the sea foam layer. To simplify the tedious calculation of sea foam effective permittivity at L band (1.4 GHz), Pade’ approximation is adopted to fit the sea foam effective permittivity computed by the Rayleigh method. With this fitting formula, a new brightness temperature model of sea foam layer defined by certain geophysical parameters, such as air volume fraction (AVF), sea surface temperature (SST), sea surface salinity (SSS) and thickness of foam layer d, is given. Furthermore, the sensitivities of the brightness temperature model to SST, SSS, d and AVF of a sea foam layer at L band are discussed. The sensitivities are ranked from most to least in the order: (1) d; (2) AVF; (3) SSS; (4) SST. This result indicates that the measurement errors of d and AVF have significant impacts on the retrievals of SSS and SST. With the experimental brightness temperature data, the SSS and AFV are retrieved by cost function.

Inter-satellite calibration of the NOAA AMSU-A measurements over tropical oceans

Time series of monthly mean brightness temperatures over the tropical ocean (20°S to 20°N) measured under clear-sky condition by two Advance Microwave Sounding Unit-A (AMSU-A) radiometers onboard the polar-orbiting NOAA-18 and NOAA-19 satellites are investigated for the time period from 2009 to 2012. To demonstrate the characteristics of the data over this test site, time series of monthly mean ocean brightness temperatures are constructed separately for the ascending and descending passes. These time series provide a useful pattern of annual variation of the AMSU-A measurements for inter-calibration of microwave radiometers onboard multiple satellites to estimate the Intersatellite/intersensor Calibration Biases (ICBs) at individual channels. Differences (∆T=TN18−TN19) of monthly mean brightness temperatures (as denoted by TN18 and TN19, respectively) between the NOAA-18 and NOAA-19 near-nadir measurements are used to estimate the ICBs at individual channels. It is found that the ∆T values distribute around individual ICBs with near zero slopes, except channel 8 which shows a noticeable anomalous trend in the ∆T time series. This abnormal behavior coincides with the degradation of the NOAA-19 noise equivalent delta temperatures (NEΔTs) which have greatly exceeded the AMSU-A specification since early 2010. This instrument problem is clearly detected in this study. Diurnal variations of the time series of data were corrected with a diurnal cycle of the measurements, which are modeled with a second-order Fourier series, from four AMSU-A radiometers onboard NOAA-15, -16, -18 and METOP-A, respectively. These four satellites have different local equator crossing times across the diurnal period. The results presented in this study will be useful for data users to have a better understanding of the measurements before using these AMSU-A data for weather forecasting or to create data sets for climate applications.

A Novel Ku-Band Radiometer/Scatterometer Approach for Improved Oceanic Wind Vector Measurements

This paper presents a conceptual conical-scanning radiometer/scatterometer (RadScat) instrument design for the purpose of improving satellite ocean vector wind retrievals under rain-free conditions. This technique combines the wind vector signature in the passive linearly polarized ocean brightness temperatures with the anisotropic signature of multiazimuthal radar cross-sectional measurements to retrieve oceanic surface wind vectors. The performance of the RadScat is evaluated using a Monte Carlo simulation based on actual measurements from the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer onboard the Advanced Earth Observing Satellite II. The results demonstrate significant improvements in wind vector retrievals, particularly in the near-subtrack swath, where the performance of conical-scanning scatterometers degrades.

Identification of Ocean-Reflected Radio-Frequency Interference Using WindSat Retrieval Chi-Square Probability

Ocean retrievals using passive microwave radiometers are sensitive to small fluctuations in ocean brightness temperatures. As such, the signals emanating from geostationary satellites that reflect off the ocean surface can result in large errors in ocean retrievals. Since geostationary communication satellites maintain fixed positions above the Earth and constantly transmit to predetermined regions while most other error sources, e.g., precipitation, are transient, time-averaged retrieval error statistics can be used to identify regions of measurements contaminated with radio-frequency interference (RFI). This letter describes a new method of identifying regions of ocean where ocean retrievals are affected by geostationary communication (television) satellites by using geophysical retrieval chi-square probability (goodness-of-fit) estimates. A three-month time-averaged collection of retrieval chi-square estimates is used to identify regions of the ocean where RFI may be present. This information is combined with information on geostationary satellite bandwidths, locations, and antenna contours to identify the source of the RFI. A mask derived from the analysis is used, in conjunction with satellite geometry calculations, to flag individual channels for RFI. These channels can then be ignored in the geophysical retrieval processing in order to produce uncontaminated ocean retrievals.

Postlaunch Calibration of the METOP-A Advanced Microwave Sounding Unit-A

The Advanced Microwave Sounding Unit-A (AMSU-A) on the Meteorological Operations Platform A (METOP-A) Satellite was successfully launched in October 2006. The AMSU-A, which was provided by NOAA, is one of a new generation of total-power microwave radiometers which have been flown on the NOAA-15 to NOAA-18 Satellites since May 1998. A systematic postlaunch calibration and validation (cal/val) of the instrument performance was conducted with on-orbit data. A brief report of the postlaunch assessment of the instrument performance is presented in this paper. Scan-by-scan examination of the radiometric calibration counts is employed to confirm normal functioning of the instrument and to detect any anomalous events, such as lunar contamination (LC) in the space radiometric counts, which are detected, flagged, and corrected using an algorithm for detection and correction of LC in AMSU-A data. The correction algorithm provides a practical approach for scan-by-scan correction of the LC in AMSU-A data and improves the accuracy of operational calibration of the NOAA Level IB data. The long-term trends of the space and warm calibration counts, channel gains, and housekeeping temperature sensors are monitored. Temperature sensitivity (or NEDeltaT) values for individual channels are also examined. The monthly mean angular distributions of brightness temperatures from the METOP-A and NOAA-15, -16, and -18 over the tropical ocean region of 20deg S-20deg N are obtained and theoretically modeled to demonstrate that the ocean can be used as a cold reference calibration target. The establishment of a natural Earth calibration target is an important addition to the few tools available to date for cal/val of spaceborne microwave radiometers. The measured ocean brightness temperatures from the four satellites provide eight measurements across the diurnal time for a unique opportunity of studying the diurnal variation of ocean brightness temperatures which show a pattern of daytime cooling and nighttime warming.

Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5

Abstract Significant improvements have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask (MOD35 and MYD35) for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2-μm water vapor absorption band have been added as well as updates to the 3.9–12- and 11–12-μm cloud tests. More non-MODIS ancillary input data have been added. Land and sea surface temperature maps provide crucial information for mid- and low-level cloud detection and lessen dependence on ocean brightness temperature variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud versus clear-sky signals, where visible and near-infrared (NIR) reflectances are high, but infrared brightness temperatures are relatively warm. Day and night Arctic cloud frequency results are compared to those created by the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-Extended (APP-X) algorithm. Day versus night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions are shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions. Continuing validation activities are described in Part II of this paper.

A GPS and Cold Ocean Brightness Temperature Calibration of the ERS-2 and TOPEX/Poseidon Microwave Radiometers

Sea-level change studies from altimetric satellites are reliant on range stability of the sea surface heights computed from orbital positioning and geophysically corrected data. One such correction, namely the wet tropospheric delay induced by the highly variable atmospheric water vapor content, is provided by radiometers onboard ERS-2 and TOPEX/Poseidon (T/P). In this study the long-term stability of the ERS-2 microwave radiometer (E2MR) and the T/P microwave radiometer (TMR) are investigated with the observed drift in the brightness temperatures approximated by reference to the coldest temperatures over the oceans. The E2MR stability is characterized by a gain anomaly fall in 1996 and a drift in the 23.8 GHz channel. For the TMR, investigations show that the dominant drift is about 0.2 K/year in the 18 GHz channel over the first 7–8 years but stabilizing and even decreasing slightly thereafter. In contrast, the 21 GHz and 37 GHz channels are comparatively stable. Utilizing correction formulae a modified wet tropospheric range is inferred from “small-change” analysis of the radiometric correction given on the altimetric Geophysical Data Records. The accuracy of this formulism is validated by independent comparison against GPS derived wet tropospheric delays inferred at 14 coastal IGS stations with near continuous data from September 1992 through to the present day. Comparisons between GPS results for ERS-2 and T/P show that the E2MR path delay is 14 mm short. For T/P, the spatial distribution of the wet tropospheric enhancement is further investigated to show that the nonuniformity can equate to a deviation in sea-level height change of about 0.1 mm/year compared with global average sea-level change. Finally, the altimetric range stability of T/P is revisited by comparison against time series from the global network of tide gauges. Analysis shows that the validated TMR drift correction results in a residual trend of −0.27 ± 0.11 mm/yr which is not significant at the 3σ level.

On the Estimation of Land Surface Parameters by Using FY-3A Microwave Radiometer Imager(MWRI)

FY3 is new generation polar orbit meteorological satellite of China plan to launch in 2006. There is total of 11 different remote sensing sensor onboard it, design to get the geophysical parameters of atmosphere, land, and ocean surfaces at the same time all day and night and in all weather conditions. The MWRI is a 10-channel five-frequency linearly polarized, passive microwave radiometer imager system onboard the FY3, which measures atmospheric, ocean, and terrain microwave brightness temperatures at 10.65, 18.7, 22.3, 36.5, and 89 GHz. In this paper, in order to derive surface temperature and soil moisture from the MWRI data, a new developed microwave RT model, AIEM was used to simulate the microwave emission characteristic of bare soil, and an new surface soil moisture inversion algorithm was established, which is only need the 10.65GHz V and H channel data. Applying the algorithm to AMSR-E orbit data, which is very similar with the FY3/MWRI, the daily globe soil moisture and surface temperature distribution was derived.

Third-order small-perturbation method for scattering from dielectric rough surfaces

The small-perturbation method (SPM) for rough surface scattering, originally derived by Rice [Commun. Pure Appl. Math.4, 361 (1951)], has been applied extensively to problems in optics, remote sensing, and propagation. Typical uses of the theory involve only the first- or second-order scattered fields in surface height, owing to increasing complexity of the SPM equations as order increases. The SPM equations are solved in a systematic manner that permits third order in surface-height terms to be determined apparently for the first time for scattering from a dielectric surface rough in two directions. Sample results for both periodic and nonperiodic surfaces show that third-order field terms can contribute to fourth-order scattered power and also to a third-order specular-reflection coefficient correction for surfaces with nonvanishing bispectra. The latter case is of particular interest in passive remote sensing of the ocean, since these third-order terms contribute to the first prediction of a first azimuthal harmonic of ocean brightness temperatures.

Atmospheric effect on microwave polarimetric passive remote sensing of ocean surfaces

A theoretical emission model of combined ocean surface and atmosphere is presented to predict the microwave emissivity of the ocean. The modeled ocean surface is one‐dimensional with a random rough profile. The electromagnetic scattering from the surface is calculated based on the extended boundary condition method. Realizations of rough surfaces are created using Monte Carlo simulations. The bistatic scattering coefficients are computed from the ensemble average. The millimeter‐wave propagation model is used to evaluate the absorption of microwave radiation at all height levels in the atmosphere. An expression for the observed brightness temperatures is derived by solving the radiative transfer equations. The radiative transfer model results show a good agreement with the measured data from the 1995 NASA Jet Propulsion Laboratory WIND radiometer (WINDRAD) campaign. An approximate model is provided to estimate the atmospheric effect on the ocean brightness temperatures based on the overall atmospheric attenuation. The approximate model also compares well with the WINDRAD data. Further comparisons are made between the approximate formula and the radiative transfer results on the ratio of the third Stokes parameter in the atmosphere to the one in free space by varying the atmospheric conditions, surface roughness, and radiation frequencies. The approximate formula shows its usefulness for the prediction of the ocean brightness temperatures.

Polarimetric microwave brightness signatures of ocean wind directions

The sensitivities of wind direction signals in passive microwave brightness temperatures of sea surfaces to wind speed, incidence angle, polarization, and frequency are presented in this paper. The experimental data were acquired from a series of aircraft flights from 1993 through 1996 by the Jet Propulsion Laboratory (JPL) using JPL 19 and 37 GHz polarimetric radiometers (WINDRAD). Fourier analysis of the data versus mind direction was carried out and the coefficients of Fourier series are illustrated against the wind speed at 45/spl deg/, 55/spl deg/, and 65/spl deg/ incidence angles. There is a good agreement between the JPL aircraft flight data and Wentz's Special Sensor Microwave/Imager (SSM/I) geophysical model function for the vertically polarized brightness temperatures, but Wentz's SSM/I wind direction model for horizontal polarization shows a significantly stronger upwind and downwind asymmetry than the aircraft flight data. Comparison of the dual-frequency WINDRAD data show's that the wind direction signals are similar at 19 and 37 GHz, although the 37 GHz data have slightly stronger signals than the 19 GHz data. In general, the azimuthal variations of brightness temperatures increase with increasing wind speed from low to moderate winds, then level off and decrease at high minds. The only exception is the U measurements at 65/spl deg/ incidence angle, which have a stronger than expected signal at low winds. An exponential function was proposed to model the sensitivities of wind direction signals to wind speeds. The coefficients of the empirical model are provided in this paper and are useful for the simulation of ocean brightness temperatures and for the development of geophysical retrieval algorithms.

Application of Fourier transforms for microwave radiometric inversions

Existing microwave radiometer technology now provides a suitable method for remote determination of the ocean surface's absolute brightness temperature. To extract the brightness temperature of the water from the antenna temperature, an unstable Fredholm integral equation of the first kind is solved. Fourier transform techniques are used to invert the integral after it is placed into a cross correlation form. Application and verification of the methods to a two-dimensional modeling of a laboratory wave tank system are included. The instability of the ill-posed Fredholm equation is examined and a restoration procedure is included which smooths the resulting oscillations. With the recent availability and advances of fast Fourier transform (FFT) techniques, the method presented becomes very attractive in the evaluation of large quantities of data. Actual radiometric measurements of sea water are inverted using the restoration method, incorporating the advantages of the FFT algorithm for computations.