G. d'Auria

Model-based prediction of amplitude scintillation variance due to clear-air tropospheric turbulence on Earth-satellite microwave links

A statistical method to predict tropospheric amplitude scintillation parameters along Earth-space microwave links from meteorological data is proposed. The evaluation of the mean value and the variance of the refractive-index structure constant and of the scintillation power (i.e. the variance of the log-amplitude fluctuations of the received electromagnetic field) is carried out from conventional radio-sounding measurements. A large radio-sounding data set, collected in Northern Italy over ten years is utilized to simulate clear-air amplitude scintillation variance at microwaves and millimeter-waves on slant paths. Scintillation statistics of interest for link-budget design are also derived from the radio-sounding data set for short and long-term applications. Scintillation prediction formulas, based on measurements of surface temperature and relative humidity, are also derived and regression coefficient tables are given on an hourly and a monthly basis. Comparisons of short-term and long-term prediction results with Olympus down-link measurements at 19.8 GHz are shown and discussed. A model investigation about the statistical correlation between scintillation power and brightness temperature is performed, deriving an extension of the estimation methods to include integrated water vapor measurements from ground-based microwave radiometers.

Case study of intense scintillation events on the OTS path

Selected events of enhanced field amplitude fluctuations recorded on the Orbital Test Satellite (OTS) path have been analyzed. The statistical properties of the beacon signals together with their correlation with the concurrent radiometric signal suggest that both scintillation and a variable attenuation mechanism act to produce the field fluctuations. Analysis of the coherence between the beacon and the radiometric signals gives an indication of the fluctuation frequency range over which each effect prevails. The comparison between scintillation data collected by a large (17 m) and a small (3 m) antenna indicates that the turbulence of the refractive index was particularly high during the course of the considered events. >

Evaluation of statistical models for clear-air scintillation prediction using olympus satellite measurements

The objective of this study is to evaluate and to compare some of the statistical models for the monthly prediction of clear-air scintillation variance and amplitude from ground meteorological measurements. Two new statistical methods, namely the direct and the modelled physical-statistical prediction models, are also introduced and discussed. They are both based on simulated data of received scintillation power derived from a large historical radiosounding set, acquired in a mid-latitudue site. The long-term predictions derived from each model are compared with measurements from the Olympus satellite beacons at the Louvain-la-Neuve site at 12·5 and 29·7 GHz and at the Milan site at 19·77 GHz during 1992. The model intercomparison is carried out by checking the assumed best-fitting probability density function for the variance and log-amplitude fluctuations and analysing the proposed relationships between scintillation parameters and ground meteorological measurements. Results are discussed in order to understand the potentials and the limits of each prediction model within this case study. The agreement with Olympus measurements is found to be mainly dependent on the proper parametrization of prediction models to the radiometeorological variables along the earth–satellite path. ©1997 by John Wiley & Sons, Ltd.

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.

Model for estimating the refractive-index structure constant in clear-air intermittent turbulence.

We explain discrepancies in comparing estimations of the refractive-index structure constant C(n)(2) in clear air by means of different techniques by taking into account atmospheric intermittency effects. We formulate a model of C(n)(2) in intermittent turbulence on the basis of the Tatarskii theory, and we calculate the mean value of C(n)(2) through a probabilistic approach. We deduce a factor, which gives a measure of the statistical reduction of turbulence that is due to intermittency, within the model framework. A procedure for estimating the mean value of C(n)(2) from data of a specific radiosonde observation is illustrated.

Assessment of polarimetric features to discriminate land cover from the MAESTRO 1 campaign

Abstract This paper is mainly devoted to the analysis of the discrimination capability of a radar polarimeter using a purely statistical approach. The statistical analysis is intended to find the set of variates that best summarizes the dilTerences between classes. We have analysed the advantages of a fully polarimetric sensor with respect to a conventional radar that does not retain the phase relation between dilTerent polarizations. In this work, we have used the radar images acquired by the DC-8/AIRSAR over the Flevoland test site “Holland” during the MASTRO I campaign. The canonical discriminant analysis gives the best results to summarize the information content of the data and to reduce the dimension of the variables to be considered in the classification. The phase information only shows significant discrimination power when several independent samples are averaged. A good speckle reduction technique improves the classification results, even when the phase information is not taken into account.

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.

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.

Mac 91 on Montespertolk an Experiment for Agro-Hydrology

Remote sensing techniques can be a very useful method for monitoring hydrological parameters in large watersheds at a relatively low cost. However the operational capability of remote sensing is not yet fully exploited and much research is in progress especially on the use of new sensors and on the algorithms for the extraction of geophysical parameters. A research activity, which aims at a better understanding of the information that can be obtained from multifrequency polarimetric Synthetic Aperture Radar to be used in hydrology, has been started in the framework of SIR-C/X-SAR Project. The site of Montespertoli (Italy) was imaged three times during the Multisensor Airborne Campaign (MAC 91), carried out in summer 1991 on several sites in Europe, based on NASA/JPL AIRSAR. This paper presents an overview of the experiments which have been carried out during the campaign and a preliminary analysis of the obtained results