Patrizia Basili

Precipitation retrieval from spaceborne microwave radiometers based on 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.

Remotely sensing cloud properties from microwave radiometric observations by using a modeled cloud database

As a first step for remote sensing cloud properties, a database of cloud genera has been established. This is derived from a microphysical model, and it considers the statistical profiles of four hydrometeor species for each cloud genus. From this database the corresponding radiative database is obtained making use of a radiative transfer model, so for each cloud genus the simulated microwave response at the special sensor microwave imager channels is found. The cloud and radiative databases allow the retrieval of the genera of the cloud and other relevant properties from satellite observations. An automatic cloud genus classifier has also been implemented. Several tests have been carried out, and the results are presented.

Mapping the atmospheric water vapor by integrating microwave radiometer and GPS measurements

This paper deals with a procedure to generate maps of the integrated precipitable water vapor (IPWV) over the Mediterranean area by using estimates from a global positioning system (GPS) network over land and from the Special Sensor Microwave/Imager (SSM/I) over sea. In particular, we investigate the application of the kriging geostatistical technique to obtain regularly spaced IPWV values. The horizontal spatial structure of water vapor retrieved by SSM/I is explored by computing variograms that provide a measure of dissimilarity between pairs of IPWV values for the region of interest. Because the water vapor density decreases with height, the GPS station elevation is accounted for in the interpolation procedure. In this respect, the potential of the kriging with external drift relative to the ordinary kriging is evaluated by applying a test based on the cross-validation approach. Case studies are presented and qualitatively compared to the corresponding Meteosat infrared images. A quantitative comparison with an independent source of information, such as IPWV computed from radiosonde observations and from European Centre for Medium-Range Weather Forecasts analysis, is also performed.

A Low-Cost Microwave Radiometer for the Detection of Fire in Forest Environments

This paper deals with the development of a microwave noise-adding radiometer, which is purposely designed for the fire detection in forest environments. The sensor operates at 12.65 GHz and exploits commercial Satellite Television (SAT-TV) components such as a parabolic dish and a low-noise block. First, a simple system model is presented to estimate the radiometric contrast due to the presence of fire (increase in the antenna noise temperature with respect to the background) at a certain distance from the receiving antenna. Then, the design of the sensor is addressed, underlining the key technologies that allow the required performance to be attained at low industrial costs. An experimental characterization of the developed radiometer is reported focusing on both accuracy and sensitivity issues. Due to a continuous gain calibration based on the noise-adding procedure, the antenna noise temperature can be retrieved with an absolute error of 4 K without any thermal stabilization of the instrument electronics. Last, real fire detection experiments have been carried out both in laboratory and open-space environments. A radiometric contrast of 8.8 K has been recorded for a wooden fire of 0.2 placed at a distance of about 30 m from the antenna.

Fire Detection by Microwave Radiometric Sensors: Modeling a Scenario in the Presence of Obstacles

This paper deals with the problem of fire detection in the presence of obstacles that are nontransparent to visible or infrared wavelengths. Exploiting the obstacle penetration capability of microwaves, a solution based on passive microwave radiometry has been proposed. To investigate such a solution, a theoretical model of the scene sensed by a microwave radiometer is developed, accounting for the presence of both fire spot and wall-like obstacles. By reversing the models equations, it is possible to directly relate the obstacle emissivity, reflectivity, and transmissivity to the antenna noise temperatures measured in several conditions. These temperatures have been sensed with a portable low-cost instrument. The selected 12.65-GHz operation frequency features good wall penetration capability to be balanced with a reasonable antenna size. In order to verify the aforementioned model, several fire experiments have been carried out, resulting in an overall good agreement between measurements and developed theory. In particular, a 2-cm-thick plasterboard wall, typically used for indoor building construction, shows a transmissivity equal to 0.86 and can easily be penetrated by a microwave radiometer in the X-band.

Satellite-Based Retrieval of Precipitable Water Vapor Over Land by Using a Neural Network Approach

A method based on neural networks is proposed to retrieve integrated precipitable water vapor (IPWV) over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Water vapor values provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to train the network. The performance of the network was demonstrated by using a separate data set of AMSR-E observations and the corresponding IPWV values from ECMWF. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data during clear-sky conditions. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas. In addition, results were compared with the IPWV values obtained from in situ instruments, a ground-based radiometer, and a global positioning system (GPS) receiver located in Rome, and a local network of GPS receivers in Como. An rms agreement of 0.34 cm was found between the ground-based radiometer and the neural network retrievals, and of 0.35 cm and 0.40 cm with the GPS located in Rome and Como, respectively.

Retrieving atmospheric temperature profiles by microwave radiometry using a priori information on atmospheric spatial-temporal evolution

A new approach is presented to determine atmospheric temperature profiles by combining measurements coming from different sources and taking into account evolution models derived by conventional meteorological observations. Using a historical database of atmospheric parameters and related microwave brightness temperatures, the authors have developed a data assimilation procedure based on the geostatistical Kriging method and the Kalman filtering suitable for processing satellite radiometric measurements available at each satellite pass, data of a ground-based radiometer, and temperature profiles from radiosondes released at specific times and locations. The Kalman filter technique and the geostatistical Kriging method as well as the principal component analysis have proved very powerful in exploiting climatological a priori information to build spatial and temporal evolution models of the atmospheric temperature field. The use of both historical radiosoundings (RAOBs) and a radiative transfer code allowed the estimation of the statistical parameters that appears in the models themselves (covariance and cross-covariance matrices, observation matrix, etc.). The authors have developed an algorithm, based on a Kalman filter supplemented with a Kriging geostatistical interpolator, that shows a significant improvement of accuracy in vertical profile estimations with respect to the results of a standard Kalman filter when applied to real satellite radiometric data.

Spectra of Atmospheric Variables as Deduced from Ground-Based Radiometry

Ground-based radiometric observations have proven to be effective means for remotely determining both the static and the dynamic thermal vertical structures of the lower troposphere. Since the meteorological parameters are random fields, the atmospheric radiance measured by a ground-based radiometer fluctuates randomly in time, and under suitable conditions, these fluctuations result essentially from atmospheric temperature fluctuations. A relationship between the spectral density of the output of the radiometer and the spectrum of the atmospheric temperature is obtained, and in particular the special case of frozen turbulence is investigated. In the experiment which is reported, the downgoing radiance has been measured in several bands of the infrared in which the atmosphere exhibits different absorptions. The low-frequency spectral density of the fluctuating radiance has been computed both by a suitably windowed fast Fourier transform and by the maximum-entropy methods. The latter technique is shown to yield either high spectral resolution or enhanced smoothing, according to the order of the prediction filter which controls the spectral-estimation procedure. Data are presented on two classes of spectra corresponding to different stability conditions of the atmospheric boundary layer.

Identification of rainy periods from ground based microwave radiometry

Abstract In this paper the authors present the results of a study aiming at detecting rainy data in measurements collected by a dual band ground-based radiometer. The proposed criterion is based on the ratio of the brightness temperatures observed in the 20–30 GHz band without need of any ancillary information. A major result obtained from the probability density of the ratio computed over one month of data is the identification of threshold values between clear sky, cloudy sky and rainy sky, respectively. A linear fit performed by using radiometric data and concurrent rain gauge measurements shows a correlation coefficient equal to 0.56 between the temperature ratio and the observed precipitation.

Intercomparison of inversion algorithms to retrieve rain rate from SSM/I by using an extended validation set over the Mediterranean area

The capability of some inversion algorithms to estimate surface rain rate at the midlatitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. For this purpose, an extended database has been derived from coincident SSM/I images and half-hourly rain rate data obtained from a rain gauge network, placed along the Tiber River basin in Central Italy, during nine years (from 1992 to 2000). The database has been divided in a training set, to calibrate the empirical algorithms, and in a validation one, to compare the results of the considered techniques. The proposed retrieval methods are based on both empirical and physical approaches. Among the empirical methods, a regression, an artificial feedforward neural network, and a Bayesian maximum a posteriori (MAP) inversion have been considered. Three algorithms available in the literature are also included as benchmarks. As physical algorithms, the MAP method and the minimum mean square estimator have been used. Moreover, in order to test the behavior of the algorithms with different kinds of precipitation, a classification of rainy events, based on some statistical parameters derived from rain gauge measurements, has been performed. From this classification, an attempt to identify the type of event from radiometric data has been carried out. The purposes of this paper are to determine whether the use of an extended training set, referred to a limited geographical area, can improve the SSM/I skill in rain detection and estimation and, mainly, to confirm the validity of the physical approach adopted in previous works. It will be shown that, among all the estimators, the neural network presents the best performances and that the physical techniques provide results only slightly worse than those given by empirical methods, but with the well-known advantage of an easy application to different geographical zones and different sensors.