A broadband microwave radiometer technique at X-band for rain and drop size distribution estimation

Radiometric brightness temperatures below about 12 GHz provide accurate estimates of path attenuation through precipitation and cloud water. Multiple brightness temperature measurements at X-band frequencies can be used to estimate rainfall rate and parameters of the drop size distribution once correction for cloud water attenuation is made. Employing a stratiform storm model, calculations of the brightness temperatures at 9.5, 10, and 12 GHz are used to simulate estimates of path-averaged median mass diameter, number concentration, and rainfall rate. The results indicate that reasonably accurate estimates of rainfall rate and information on the drop size distribution can be derived over ocean under low to moderate wind speed conditions.     

Backpropagation neural-network-based retrieval of atmospheric water vapor and cloud liquid water from IRS-P4 MSMR

A new multiparameter retrieval algorithm based on a backpropagation neural network (BPNN) has been developed for deriving integrated water vapor (WV) and cloud liquid water (CLW) contents over oceans from brightness temperatures (BTs) measured by the Multi-frequency Scanning Microwave Radiometer (MSMR) launched onboard Indian Remote Sensing satellite IRS-P4. The MSMR measures brightness temperatures in vertical and horizontal polarizations at 6.0-, 10.65-, 18.0-, and 21.0-GHz frequencies. The data are available at three spatial grid resolutions of 150, 75, and 50 km. In this paper, a BPNN has been trained using brightness temperatures simulated through radiative transfer model and simulated surface and atmospheric parameters. The present algorithm has been compared with the operational MSMR retrieval algorithm based on statistical regression using the same dataset. The validation of WV with in situ data (Vaisala radiosonde) is presented. Moreover, comparison of WV and CLW derived from MSMR using BPNN with the finished products from the Special Sensor Microwave/Imager and the Tropical Rainfall Measuring Mission Microwave Imager has also been carried out. The complexity of the BPNN in retrieval of geophysical products, individually and simultaneously, has also been discussed. Simultaneous retrieval of WV and CLW improves the results.     

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.     

A Practical Method for Retrieving Land Surface Temperature From AMSR-E Over the Amazon Forest

Remote sensing of land surface temperature (LST) using infrared (IR) sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is only capable of retrieval under clear-sky conditions. Such LST observations over tropical forests are very limited due to clouds and rainfall, particularly during the wet season and high atmospheric water-vapor content. In comparison, low-frequency microwave radiances are minimally influenced by meteorological conditions. Exploring this advantage, we have developed an algorithm to retrieve LST over the Amazonian forest. The algorithm uses multifrequency polarized microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System (AMSR-E). Relationships between polarization ratio and surface emissivity are established for forested and nonforested areas, such that LST can solely be calculated from microwave radiance. Results are presented over three time scales: at each orbit, daily, and monthly. Results are evaluated by comparing with available air-temperature records on daily and monthly intervals. Our findings indicate that the AMSR-E-derived LST agrees well with in situ measurements. Results during the wet season over the tropical forest suggest that the AMSR-E LST is robust under all-weather conditions and shows higher correlation to meteorological data (r = 0.70) than the IR-based LST approaches (r = 0.42).     

Polarimetric microwave wind radiometer model function and retrieval testing for WindSat

A geophysical model function (GMF), relating the directional response of polarimetric brightness temperatures to ocean surface winds, is developed for the WindSat multifrequency polarimetric microwave radiometer. This GMF is derived from the WindSat data and tuned with the aircraft radiometer measurements for very high winds from the Hurricane Ocean Wind Experiment in 1997. The directional signals in the aircraft polarimetric radiometer data are corroborated by coincident Ku-band scatterometer measurements for wind speeds in the range of 20-35 m/s. We applied an iterative retrieval algorithm using the polarimetric brightness temperatures from 18-, 23-, and 37-GHz channels. We find that the root-mean-square direction difference between the Global Data Assimilation System winds and the closest WindSat wind ambiguity is less than 20/spl deg/ for above 7-m/s wind speed. The retrieval analysis supports the consistency of the Windrad05 GMF with the WindSat data.     

Determination of cloud liquid water content using the SSM/I

As part of a calibration/validation effort for the Special Sensor Microwave/Imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85 V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean     

Bayesian algorithm for microwave-based precipitation retrieval: description and application to TMI measurements over ocean

A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.     

A physical-statistical approach to match passive microwave retrieval of rainfall to Mediterranean climatology

A physical-statistical approach to simulate cloud structures and their upward radiation over the Mediterranean is described. It aims to construct a synthetic database of microwave passive observations matching the climatological conditions of this geographical region. The synthetic database is conceived to train a Bayesian maximum a posteriori probability inversion scheme to retrieve precipitating cloud parameters from spaceborne microwave radiometric data. The initial microphysical a priori information on vertical profiles of cloud parameters is derived from a mesoscale cloud-resolving model. In order to complement information from cloud models and to match simulations to the conditions of the area of interest, a new approach is proposed. Climatological constraints over the Mediterranean are derived on a monthly basis from available radiosounding profiles, rain-gauge network measurements, and colocated METEOSAT infrared measurements. In order to introduce the actual surface background in the radiative-transfer simulations, a further constraint is represented by the monthly average and variance maps of surface emissivity derived from Special Sensor Microwave Imager (SSM/I) clear-air observations. A validation of the forward model is carried out by comparing a large set of brightness temperatures measured by the SSM/I with the synthetic cloud radiative database to asses its representativeness and range of variability.     

An empirical algorithm for cloud liquid water from MSMR and its utilization in rain identification

In this paper, an empirical method to estimate cloud liquid water from Indian Remote Sensing P4 (IRS-P4) Multi-frequency Scanning Microwave Radiometer (MSMR) measurements is presented. MSMR brightness temperatures are collocated with concurrent observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)-derived cloud liquid water. The multiple-correlation coefficient between TMI-derived cloud liquid water and logarithmic of MSMR-derived brightness temperatures, and their differences at 18- and 21-GHz channels, is found to be about 82.4%. The relationship thus obtained has an rms error of 8.75 mgcm/sup -2/ in the measurements of cloud liquid water from MSMR with respect to TMI measurements. Verification of the algorithm is carried out with another set of concurrent measurements from MSMR and TMI. Further, the MSMR-derived cloud liquid water over the global oceans and for extreme weather conditions (cyclone) are compared with that from TMI and the Special Sensor Microwave/Imager (SSM/I) for independent verification. The cloud liquid water from MSMR is further used to successfully delineate rain events for quantitative estimation of rain rate from MSMR.     

Microwave radiometric technique to retrieve vapor, liquid and ice.I. Development of a neural network-based inversion method

With the advent of the microwave radiometer, passive remote sensing of clouds and precipitation has become an indispensable tool in a variety of meteorological and oceanographical applications. There is wide interest in the quantitative retrieval of water vapor, cloud liquid, and ice using brightness temperature observations in scientific studies such as Earth's radiation budget and microphysical processes of winter and summer clouds. Emission and scattering characteristics of hydrometeors depend on the frequency of observation. Thus, a multifrequency radiometer has the capability of profiling cloud microphysics. Sensitivities of vapor, liquid, and ice with respect to 20.6, 31.65 and 90 GHz brightness temperatures are studied. For the model studies, the atmosphere is characterized by vapor density and temperature profiles and layers of liquid and ice components. A parameterized radiative transfer model is used to quantify radiation emanating from the atmosphere. It is shown that downwelling scattering of radiation by an ice layer results in enhancement at 90 GHz brightness temperature. Once absorptive components such as vapor and liquid are estimated accurately, then it is shown that the ice water path can be retrieved using ground-based three-channel radiometer observations. In this paper the authors developed two- and three-channel neural network-based inversion models. Success of a neural network-based approach is demonstrated using a simulated time series of vapor, liquid, and ice. Performance of the standard explicit inversion model is compared with an iterative inversion model. In part II of this paper, actual radiometer, and radar field measurements are utilized to show practical applicability of the inverse models     

Effects of precipitation and cloud ice on brightness temperaturesin AMSU moisture channels

Extinction by ice and rain at the AMSU frequencies used in water vapor profile retrievals is investigated with DMSP observations and brightness temperature simulations of a convective storm system. The simulations are based on mesoscale forecast model output of atmospheric, cloud, and rain profiles from which the absorption and scattering due to both liquid and frozen hydrometeors are calculated. Comparison with satellite observations indicates discrepancies of more than 90% (up to 60 K), of which only about 20% results from ignoring scattering by model-prescribed ice. The major source of error is the inability of the forecast model to produce the spatially localized high ice concentrations which cause the low microwave brightness temperatures. A criterion based on the difference between measured brightness temperatures at 183.31±3 and 183.31±1 GHz is suggested to screen out convective events before water vapor retrieval. Application to the case study examined improved agreement between simulated and observed brightness temperatures by up to a factor of two     

A nonlinear optimization algorithm for WindSat wind vector retrievals

WindSat is a space-based polarimetric microwave radiometer designed to demonstrate the capability to measure the ocean surface wind vector using a radiometer. We describe a nonlinear iterative algorithm for simultaneous retrieval of sea surface temperature, columnar water vapor, columnar cloud liquid water, and the ocean surface wind vector from WindSat measurements. The algorithm uses a physically based forward model function for the WindSat brightness temperatures. Empirical corrections to the physically based model are discussed. We present evaluations of initial retrieval performance using a six-month dataset of WindSat measurements and collocated data from other satellites and a numerical weather model. We focus primarily on the application to wind vector retrievals.     

Diurnal Variation of the AMSU-A Brightness Temperatures Over the Amazon Rainforest

Brightness temperatures over the Amazon rainforest are obtained from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) instruments onboard three NOAA satellites (NOAA-15, -16, and -17, respectively) for the months of July, August, and October, 2002. The three AMSU-A instruments provided six daily measurements, separated by 2.5-5.5 h of the diurnal time intervals, over the Amazon rainforest region, and these measurements offer a unique opportunity for investigation of the diurnal variation of the brightness temperatures over the Amazon rainforests. The angular distributions of brightness temperatures over the Amazon rainforest are very stable and can be simulated with a radiative transfer model, which consists of an atmospheric radiative component and a rainforest-canopy model that treats the rainforest as a uniform layer with an effective canopy temperature. The simulated results agree well with the observations. The diurnal variation of brightness temperatures over the Amazon rainforest is simulated with a Fourier-series model. It shows that a second order of Fourier series can reproduce the observed pattern of diurnal variation of the brightness temperatures at zenith angles of 0deg, 28.7deg, and 58.1deg, respectively. In a practical application, the coefficients of Fourier-series expansion can be used to generate the brightness temperatures as a function of diurnal hours. These results can be applied to postlaunch calibration of satellite-borne microwave radiometer with different equator crossing time. In addition, the results presented in this paper indicate that the Amazon rainforest can be used as a hot calibration reference target. The availability of a land calibration target is important for calibration and validation of spaceborne microwave radiometers     

Subsurface object sensing with a multifrequency microwave radiometer

Experimental results are reported for sensing of subsurface objects with a multifrequency radiometer (MFRAD) system. Properties of the MFRAD system are reviewed, and the calibration and experimental procedures are discussed. Results with subsurface metallic, styrofoam, and plastic targets are then provided that demonstrate an oscillatory behavior in brightness temperatures versus frequency in the presence of a subsurface object. Measured data are also compared with a simple layered medium brightness temperature model and show reasonable agreement with predicted trends of brightness temperatures versus frequency. The oscillatory behaviors versus frequency obtained in the presence of both metallic and nonmetallic subsurface objects should prove advantageous for designing object detection procedures.     

Estimates of brightness temperatures from scanning radiometer data

In the analysis of antenna temperature maps of the earth obtained by satellite-borne microwave radiometers, estimates of brightness temperatures or averages of brightness temperatures over areas considerably smaller than the region sensed at a given position of the radiometer antenna are often needed. An application of the Backus-Gilbert methodology is made to obtain an objective criterion of the best resolution (in a least squares sense) obtainable from a given system and to investigate the trade-off between resolution and noise in the derived average brightness temperatures. The mathematically related problem of simultaneously analyzing antenna temperature measurements made at different frequencies by antennas with noncoincident antenna patterns is also considered.     

Experimental validation of a PO-based shape reconstruction algorithm

The effectiveness of a linear inverse scattering algorithm for the shape reconstruction of perfectly conducting objects is experimentally validated by processing measured data. The data are collected via an automatic system for free-space measurements under reflection mode geometry. The amplitude and phase of the scattered field is measured in a multistatic and multifrequency configuration for different locations (views) of the transmitting antenna. The reliability of the measurement setup is shown by a comparison with simulated data. No a priori knowledge of the shape of the scatterers is assumed. The results of inversions of experimental data collected under single-view illumination agree well with those of inversions of synthetic data, so showing the robustness of the algorithm.     

TOPEX/Poseidon microwave radiometer (TMR). III. Wet troposphererange correction algorithm and pre-launch error budget

For pt.II see ibid., vol.33, no.1, p.138-46 (1995). The sole mission function of the TOPEX/Poseidon microwave radiometer (TMR) is to provide corrections for the altimeter range errors induced by the highly variable atmospheric water vapor content. The three TMR frequencies are shown to be near-optimum for measuring the vapor-induced path delay within an environment of variable cloud cover and variable sea surface flux background. After a review of the underlying physics relevant to the prediction of 5-40 GHz nadir-viewing microwave brightness temperatures, the authors describe the development of the statistical, two-step algorithm used for the TMR retrieval of path delay. Test simulations are presented which demonstrate the uniformity of algorithm performance over a range of cloud liquid and sea surface wind speed conditions. The results indicate that the inherent algorithm error (assuming noise free measurements and an exact physical model) is less than 0.4 cm of retrieved path delay for a global representation of atmospheric conditions. An algorithm error budget is developed which predicts an overall algorithm accuracy of 0.9 cm when modeling uncertainties are included. When combined with expected TMR antenna and brightness temperature accuracies, an overall measurement accuracy of 1.2 cm for the wet troposphere range correction is predicted     

Remote sensing of atmospheric water content from satellites using microwave radiometry

Analysis is presented which substantiates the high correlation achieved in relating integrated water vapor and liquid water to brightness temperatures at frequencies near the 22.235 GHz water vapor line. The influence of atmospheric and surface variability is shown to be minimal over low emissivity sea surfaces. Determination of atmospheric water content using regression techniques is shown to follow directly from radiation transfer theory. Satellite data from the Nimbus-E Microwave Spectrometer (NEMS) aboard Nimbus-5 are compared with radiosonde water vapor measurements and cloud images recorded by the Temperature Humidity Infrared Radiometer aboard Nimbus 5.     

The beamfilling algorithm for retrieval of hydrometeor profileparameters from passive microwave measurements

The horizontal inhomogeneity of the atmosphere within a satellite microwave radiometer's field of view (FOV) has always been considered as a source of rainfall retrieval errors. The hydrometeor profile retrieval algorithm presented exploits it to obtain an approximation of a radiative transfer model, which allows relatively simple inversion. The atmosphere within the FOV is treated as a combination of horizontally homogeneous domains. Assuming that one of known “basic” hydrometeor profiles occurs in each domain, the inverse problem is reduced to a determination of “beamfilling coefficients.” The online procedure includes determination of beamfilling coefficients and a footprint-averaged hydrometeor profile as a linear combination of “basic” ones. Off-line procedures involve the selection of a minimum number of necessary “basic” brightness temperature vectors and correction of “basic” hydrometeor profiles to provide the best retrieval accuracy for a given cloud/radiative simulation. The performance of the algorithm is tested for both numerical simulations and TRMM/TMI data. Numerical simulation has allowed a comparison of the information content of radiometer measurements from SSM/I, TMI, and the future AMSR. The effectiveness of the algorithm is being tested for rain water integral and rain rate retrievals from TRMM TMI measurements