Piero Ciotti

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.

Ground-based multifrequency microwave radiometry for rainfall remote sensing

Inversion algorithms for ground-based microwave radiometric retrieval of surface rain-rate, integrated cloud parameters, and slant-path attenuation are proposed and tested. The estimation methods are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures, representative of stratiform and convective rainy clouds. The discrete-ordinate method is used to solve the radiative transfer equation for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. Besides ordinary multiple regression, a variance-constrained regression algorithm is developed and applied to synthetic data in order to evaluate its robustness to noise and its potentiality. Selection of optimal frequency sets and polynomial retrieval algorithms for rainfall parameters is carried out and discussed. Ground-based radiometric measurements at 13.0, 23.8, and 31.7 GHz are used for experimentally testing the retrieval algorithms. Comparison with rain-gauge data and rain path-attenuation measurements, derived from the three ITALSAT satellite beacons at 18.7, 39.6, and 49.5 GHz acquired at Pomezia (Rome, Italy), are performed for two selected cases of moderate and intense rainfall during 1998.

Simulation of radiometric and attenuation measurements along Earth-satellite links in the 10- to 50-GHz band through horizontally finite convective rain cells

An iterative solution is illustrated of the three-dimensional radiative transfer equation for a horizontally finite and vertically inhomogeneous precipitating cloud. The method is applied to modeling a convective rain cell of cylindrical shape, characterized by spherical raindrops having a negative-exponential drop size distribution. The realistic model also takes into account the presence of a cloud and an ice layer above the rain cell itself. The simulated brightness temperature, the mean radiative temperature, and the path attenuation are evaluated in a three-dimensional geometry from a surface observation point in order to simulate a ground-based station with a beacon receiver and a multichannel radiometer. Numerical results are shown to illustrate the potential of the proposed model for different sets of frequency channels, observation geometries, cloud sizes and types, and precipitation intensities. After generating a large data set by varying the relevant rain cell parameters, regression analysis is applied to derive a statistical estimation of the total path attenuation from surface rain rate and ground-based radiometric measurements together with the frequency scaling factors for cumuliform clouds in the 10- to 50-GHz band.

Modeling and measurement of rainfall by ground-based multispectral microwave radiometry

The potential of ground-based multispectral microwave radiometers in retrieving rainfall parameters is investigated by coupling physically oriented models and retrieval methods with a large set of experimental data. Measured data come from rain events that occurred in the USA at Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Programs Southern Great Plains (SGP) site in Lamont, OK. Rain cloud models are specified to characterize both nonraining clouds, stratiform and convective rainfall. Brightness temperature numerical simulations are performed for a set of frequencies from 20 to 60 GHz at zenith angle, representing the channels currently deployed on a commercially available ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. A new statistical inversion algorithm, trained by synthetic data and based on principal component analysis is also developed to classify the meteorological background, to identify the rain regime, and to retrieve rain rate from passive radiometric observations. Rain rate estimate comparisons with simultaneous rain gauge data and rain effect mitigation methods are also discussed.

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.

A Sky Status Indicator to Detect Rain-Affected Atmospheric Thermal Emissions Observed at Ground

Radiometric measurements could provide continuous information about atmospheric conditions. In this paper, a sky status indicator (SSI) is proposed as a real-time recognition criterion for the detection, in particular, of the presence of rain events along the propagation path. The computation of the SSI is based on ground-based brightness temperature measurements, at 23.8 and 31.4 GHz, collected in Cabauw, Netherlands, in 2009 by the ESA Atmospheric Propagation and Profiling System (ATPROP) multichannel radiometer. A validation analysis is carried out between simulated data, which are computed by applying the radiative transfer equation to a database of radiosonde profiles collected in De Bilt, Netherlands, by the Royal Netherlands Meteorological Institute, and two data sets of radiometric observations at two elevation angles (θ equal to 90° and θ equal to 69.6°). The analysis based on SSI probability distribution functions has allowed for calculation of the boundary threshold values that are able to discriminate the status of the sky. Furthermore, performances of the SSI were validated against rainfall measurements collected at the ground by a rain gauge located near the ESA ATPROP multichannel radiometer.