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.     

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.     

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     

PIn. II. Comparative evaluation of SSM/I and TMI precipitable waterestimate for the Mediterranean Sea

For pt.I see ibid., vol.39, no.12, p.2566-74 (2001). To estimate integrated precipitable water vapor along with liquid water path and water vapor effective profile (i.e. standard atmospheric profile approximation), utilizing the Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometers, an operative procedure was developed and assessed. This procedure is based on a fast nonlinear physical inversion algorithm (PIn) developed by the authors. A large data set of near-coincident TMI and SSM/I data acquisitions were collected and used to supply the procedure. Retrieved parameters were compared against retrievals achieved with well-accepted statistical algorithms, and consistency between TMI and SSM/I retrievals was confirmed. As far as TMI and SSM/I precipitable water retrieving consistency is concerned, this research revealed a linear relationship up to 20 kg/m² and a general overestimate of TMI retrieving, for higher values. A new algorithm for obtaining integrated precipitable water from TMI brightness temperatures was introduced and the goodness of its accuracy was reported. The procedure proved to be reliable and portable and its integrated precipitable water vapor retrieving was assessed to be as accurate as the best radiometric retrieving algorithms, reported in literature. For SSM/I data, developed-procedure liquid water path estimates seemed to be in good agreement with statistical retrievals. Eventually the procedure provided effective water vapor vertical profiles which belong to a deterministic distribution area characterized by an upper and lower limit; it was confirmed that SSM/I and TMI vertical profile distribution areas mainly overlap even if they are characterized by different sensitivities to profile parameters     

Precipitation retrieval from spaceborne microwave radiometers basedon maximum a a posteriori probability estimation

A retrieval technique for estimating rainfall rate and precipitating cloud parameters from spaceborne multifrequency microwave radiometers is described. The algorithm is based on the maximum a posteriori probability criterion (MAP) applied to a simulated data base of cloud structures and related upward brightness temperatures. The cloud data base is randomly generated by imposing the mean values, the variances, and the correlations among the hydrometeor contents at each layer of the cloud vertical structure, derived from the outputs of a time-dependent microphysical cloud model. The simulated upward brightness temperatures are computed by applying a plane-parallel radiative transfer scheme. Given a multifrequency brightness temperature measurement, the MAP criterion is used to select the most probable cloud structure within the cloud-radiation data base. The algorithm is computationally efficient and has been numerically tested and compared against other methods. Its potential to retrieve rainfall over land has been explored by means of Special Sensor Microwave/Imager measurements for a rainfall event over Central Italy. The comparison of estimated rain rates with available raingauge measurements is also shown     

Land surface temperature derived from the SSM/I passive microwavebrightness temperatures

Passive microwave brightness temperatures from the Defense Meteorological Space Program Special Sensor Microwave/Imager (SSM/I) were used to determine surface temperature over land areas in the central plains of the United States. A regression analysis comparing all of the SSM/I channels and minimum screen air temperatures (representing the surface temperature) showed good correlations, with root-mean-square errors of 2-3 degC. Pixels containing large amounts of water, snow, and falling rain, as classified with SSM/I brightness temperatures, were excluded from the analysis. The use of independent ground truth data such as soil moisture or land surface type was not required to obtain the correlations between brightness temperatures and surface temperatures     

A Time-Varying Radiometric Bias Correction for the TRMM Microwave Imager

Recent intersatellite radiometric comparisons of the Tropical Rainfall Measurement Mission Microwave Imager (TMI) with polar orbiting satellite radiometer data and modeled clear-sky radiances have uncovered a time-variable radiometric bias in the TMI brightness temperatures. The bias is consistent with a source that generally cools during orbit night and warms during sunlight exposure. The likely primary source has been identified as a slightly emissive parabolic antenna reflector. This paper presents an empirical brightness temperature correction to TMI based on the position around each orbit and the Sun elevation above the orbit plane. The results of radiometric intercomparisons with WindSat and special sensor microwave imager are presented, which demonstrate the effectiveness of the recommended correction approach based on four years of data.     

An Artificial-Neural-Network-Based Integrated Regional Model for Rain Retrieval Over Land and Ocean

An integrated regional model is proposed for rain-rate retrievals over land/ocean from the brightness temperature (Tb) values of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The polarization-corrected temperature calculated from the 85.5-GHz channels is also considered as one of the inputs along with the nine channel Tb values. This model is applicable over the region between and . For this purpose, an artificial neural network is utilized. The collocated precipitation radar (PR) near-surface rain rates as given by a 2A25 data product is considered as a target value. The methodology consists of the separation of land and ocean pixels, the separation of stratiform and convective pixels over land/ocean, and the selection of important features (inputs) for the multilayer perceptron network by the feature selection technique for each group. For the separation of land/ocean pixels, the Tb values of the 10.65-GHz vertical channel are utilized. The values are utilized to separate the stratiform and convective pixels both over land and ocean. The rain retrieval from the developed model is validated with TRMM PR. Overall result shows the better agreement of the model-retrieved rain rate with the PR observation compared to the TMI (2A12) rain rate particularly over land. The rain retrieved from the developed model is further validated with Doppler weather radar. A reasonably good agreement is observed between these two estimations.     

A soft computing approach for rainfall retrieval from the TRMM microwave imager

A neural network model for rainfall retrieval over ocean from remotely sensed microwave (MW) brightness temperature (BT) is proposed. BT data are obtained from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The BT values from different channels of TMI over the Pacific Ocean (163/spl deg/ to 177/spl deg/W and 18/spl deg/ to 34/spl deg/S) are the input features. The near-surface rainfall rate from the Precipitation Radar (PR) are considered as a target. The proposed model consists of a neural network with online feature selection (FS) and clustering techniques. A K-means clustering algorithm is applied to cluster the selected features. Different networks have been trained to give an instantaneous rainfall rate with all input features as well as with selected features obtained by applying the FS algorithm. It is found that the hybrid network utilizing FS and clustering techniques performs better. The developed network is also validated with two independent datasets on March 14, 2000 over the Atlantic Ocean having stratiform rain and on March 21, 2000 over the Pacific Ocean having both stratiform and convective rain. In both cases, the hybrid network performs well with correlation coefficient improving to 0.78 and 0.81, respectively, in contrast to 0.70 and 0.75 for the network with all features. The rainfall rate retrieved from the hybrid network is also compared with the TMI surface rain rate, and a correlation of 0.84 and 0.75 is found for the two events. The proposed hybrid model is validated with a Doppler Weather Radar, and correlation of 0.52 is observed.     

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.     

Spatial scales of tropical precipitation inferred from TRMM microwave imager data

The local spatial scales of tropical precipitating systems were studied using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rate imagery from the TRMM satellite. Rain rates were determined from TMI data using the Goddard Profiling (GPROF) Version 5 algorithm. Following the analysis of Ricciardulli and Sardeshmukh (RS), who studied local spatial scales of tropical deep convection using global cloud imagery (GCI) data, active precipitating months were defined alternatively as those having greater than either 0.1 mm/h or 1 mm/h of rain for more than 5% of the time. Spatial autocorrelation values of rain rate were subsequently computed on a 55/spl times/55 km grid for convectively active months from 1998 to 2002. The results were fitted to an exponential correlation model using a nonlinear least squares routine to estimate a spatial correlation length at each grid cell. The mean spatial scale over land was 90.5 km and over oceans was 122.3 km for a threshold of 0.1 mm/h of rain with slightly higher values for a threshold of 1 mm/h of rain. An error analysis was performed which showed that the error in these determinations was of order 2% to 10%. The results of this study should be useful in the design of convective schemes for general circulation models and for precipitation error covariance models for use in numerical weather prediction and associated data assimilation schemes. The results of the TMI study also largely concur with those of RS, although the more direct relationship between the TMI data and rain rate relative to the GCI imagery provide more accurate correlation length estimates. The results also confirm the strong impact of land in producing short spatial scale convective rain.     

Water vapor profiling over ocean surface from airborne 90 and 183GHz radiometric measurements under clear and cloudy conditions

Radiometric measurements at 90 GHz and three sideband frequencies near the water vapor absorption line at 183.3 GHz were made with the Advanced Microwave Moisture Sounder (AMMS) aboard the NASA DC-8 aircraft over some regions of the Pacific Ocean during November 1989. These measurements were used to retrieve atmospheric water vapor profiles over ocean surface using the algorithm developed by T.T. Wilheit (1979). The algorithm incorporates a mechanism to estimate cloud liquid water when the estimated relative humidity is greater than 95%. The results are compared with the estimated values from the measurements of Special Sensor Microwave Imager (SSMI) and TIROS Operational Vertical Sounder (TOVS). The water vapor profiles estimated from AMMS are generally higher at low altitudes and lower at high altitudes compared to those from the TOVS measurements. Values of total precipitable water estimated from the AMMS and SSM/I are in general agreement. Cloud liquid water vapor profiles retrieved from the AMMS show more fluctuations than those from SSM/I     

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     

Retrieval of land and sea brightness temperatures from mixedcoastal pixels in passive microwave data

A technique is presented to separate uncontaminated land and sea brightness temperatures from mixed coastal pixels in 37-GHz vertically polarized passive microwave data from the Special Sensor Microwave Imager (SSM/I) instrument. Combining a mathematical model of the instrument response over several neighboring footprints with a GIS representation of the coastline yields a relationship between land and sea brightness temperatures and radiation measurements made at the satellite. Inverting this relationship allows separate land and sea brightness temperature values to be derived for each mixed coastal pixel in the original image. The technique has been successfully applied to 37-GHz vertically polarized SSM/I imagery for test areas covering the Gulf of Aden and the British Isles. Errors in the retrieved brightness temperatures were estimated to be of the order of 1-2 K     

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.     

The forward problem and corrections for the SSM/T satellitemicrowave temperature sounder

The relationship between atmospheric temperatures and the brightness temperatures measured by the Special Sensor Microwave/Temperature (SSM/T) radiometer is addressed. Physical algorithms for retrieving temperature profiles explicitly employ the physics of radiative transfer. Consequently, it is necessary to do the forward problem accurately before attempting the retrieval problem. The various problems that arise in doing forward calculations over oceans are discussed. These problems include determining when the measurements are cloud contaminated, the amount of liquid water in the fields of view, the surface emissivity under both clear and cloudy conditions, the corrections for liquid water contamination of the measurements, the adjustments for deficiencies in the radiative transfer model, and the compensation for measurement discrepancies by using shrinkage estimation. These procedures are developed for SSM/T data, and their validity is checked by comparing the forward calculations with the corresponding satellite measurements. Application of the procedures to an independent data set confirms their veracity     

The accuracy of preliminary WindSat vector wind measurements: comparisons with NDBC buoys and QuikSCAT

Two preliminary, six-month long global WindSat vector wind datasets are validated using buoys and QuikSCAT measurements. Buoy comparisons yield speed and direction root mean square accuracies of 1.4 m/s and 25/spl deg/ for the "NESDIS0" product and 1.3 m/s and 23/spl deg/ for the more recently produced "B1" product from the Naval Research Laboratory. WindSat along- and across-wind random component errors of 0.7-1.0 and 2.6-2.8 m/s (respectively) are larger than those calculated for QuikSCAT in the same period. Global WindSat-QuikSCAT comparisons generally confirmed the buoy analyses. While simple rain flags based directly on WindSat brightness temperature measurements alone are shown to overflag for rain systematically, the advanced "Environmental Data Record" rain flag in the B1 product matches well with Special Sensor Microwave/Imager rain detection frequency and preserves the accuracy of the unflagged vector wind measurements.     

The influence of clouds on microwave brightness temperatures viewing downward over open seas

Microwave brightness temperatures for the case of downward viewing from above the earth's atmosphere over water for the 1- to 2-cm wavelength range are calculated for comparison with observations. A model of the troposphere which contains homogeneous layer clouds of varied thickness and liquid water content is used to compute estimates of the influence which clouds would have on real observations. It is assumed that only pure absorption is important for the cloud droplet-size distributions and droplet densities used. Results of the computations indicate that most water clouds will contribute a measurable amount to the microwave emission of the atmosphere and, in some cases, can be the principal source of received radiation. Comparisons of the computed cases with measurements obtained with a high flying aircraft are shown to be in reasonable agreement. These results are significant because they demonstrate that water clouds cannot be neglected in the application of passive microwave techniques to remote probing of the earth's atmosphere and because they indicate that quantitative measures of cloud liquid water contents and cloud thickness might be acquired through multi-frequency measurements.     

Post-launch calibration of the TRMM microwave imager

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     

The complex dielectric constant of pure and sea water from microwave satellite observations

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.