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This paper proposes Bayesian networks (BNs) that combine polarization corrected temperature (PCT) and scattering index (SI) methods to identify rainfall intensity. To learn BN network structures, meta-heuristic techniques includin...
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This paper proposes Bayesian networks (BNs) that combine polarization corrected temperature (PCT) and scattering index (SI) methods to identify rainfall intensity. To learn BN network structures, meta-heuristic techniques including tabu search (TS), simulated annealing (SA) and genetic algorithm (GA) were empirically evaluated and compared for efficiency. The proposed models were applied to the Tanshui river basin in Taiwan. The meteorological data from the Special Sensor Microwave/lmager (SSM/I) of the National Oceanic and Atmospheric Administration (NOAA) comprises seven passive microwave brightness temperatures, and was used to detect rain rates. The data consisted of 71 typhoons affecting the watershed during 2000-2012. A preliminary analysis using simple meta-heuristic BNs identified the main attributes, namely the brightness temperatures of 19,22, 37 and 85 GHz for rainfall retrieval. Based on the preliminary analysis of a simple BN run, the advanced BNs combined with SI and PCT successfully demonstrated improved rain rate retrieval accuracy. To compare the proposed meta-heuristic BNs, the traditional SI method, the SI-based support vector regression model (SI-SVR), and artificial neural network (ANN) were used as benchmarks. The results showed that (1) meta-heuristic BN techniques can be used to identify the vital attributes of the rainfall retrieval problem and their causal relationships and (2) according to a comparison of BNs combined with PCT and SI and artificial intelligence (Al)-based models (SI-SVR and ANN), in heavy, torrential, and pouring rainfall, models of BNs combined with PCT and SI provide a superior retrieval performance than that of AI-based models. Therefore, this study confirms that meta-heuristic BNs combined with PCT and SI is an efficient tool for addressing rainfall retrieval problems.
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An error model for Bayesian Monte Carlo retrieval algorithms which explicitly accounts for uncertainty introduced by the use of a finite database of realizations, as well as uncertainties associated with the modelling and measurem...
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An error model for Bayesian Monte Carlo retrieval algorithms which explicitly accounts for uncertainty introduced by the use of a finite database of realizations, as well as uncertainties associated with the modelling and measurement components of the retrieval is described. The model provides a rigorous estimate of the uncertainty in all retrieved parameters as well as a breakdown of this uncertainty into two components attributable to an imperfect database, and modelling and measurement uncertainties, respectively. This error information is critical for algorithm development, model validation and, in particular, in variational data assimilation where the relative accuracy of the observations and the background forecast determines how much the latter is modified in the assimilation process. Using the error model, uncertainties in the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) instantaneous surface rainfall product (2A12) are found to range from 40 to 60 percent in rainfallup to 20 mm h~(-1). In heavier rain, uncertainties rapidly increase due to heavy attenuation in all TMI channels which requires surface rainfall to be inferred solely from the non-unique relationship between surface rain rate and the ice-scattering signature in the strongly coupled 37 and 85 GHz brightness temperatures. In light rain, the fact that the database attempts to approximate nature's infinite cloud probability density function by a finite set of realizations, and the inherent inability of theTMI brightness temperatures to completely distinguish between all cloud profiles in the database, dominate retrieval uncertainties. Between 4 and 10 mm h~(-1) both error components are comparable while measurement and model uncertainties dominate in heavier rainfall. Preliminary attempts to incorporate radar reflectivity data to reduce profile database uncertainties show promise, but can lead to a compensating increase in the modelling and measurement-error component. Results highlight the need for studying sources of systematic error in the cloud database such as errors in cloud microphysical assumptions, beam-filling errors, or biases in the radiative-transfer calculations used to simulate brightness temperatures for each profile. In addition, the utility of the error model for estimating uncertainties in any Bayesian Monte Carlo retrieval algorithm is demonstrated.
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A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the con...
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A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm–mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a pro- f that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.
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In this Part 1 paper concerning a new Cloud Dynamics and Radiation Database (CDRD) algorithm, improvements in obtaining satellite retrievals of rainfall from multispectral passive microwave (PMW) radiometer measurements are obtain...
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In this Part 1 paper concerning a new Cloud Dynamics and Radiation Database (CDRD) algorithm, improvements in obtaining satellite retrievals of rainfall from multispectral passive microwave (PMW) radiometer measurements are obtained by transforming a conventional Cloud Radiation Database (CRD) algorithm. The improvements arise by combining parameter constraints derived from model-based dynamical–thermodynamical–hydrological (DTH) meteorological profile variables and additional geographical–seasonal (GS) factors, together with multispectral PMW brightness temperatures (TBs), into a specialized knowledge database underpinning a Bayesian retrieval algorithm. The so-called knowledge variables are produced by a high-resolution nonhydrostatic cloud-resolving model (CRM). The associated knowledge TBs are produced by a calibrated PMW radiative-transfer-equation model system (RMS) that relates CRM environments to expected satellite-view top-of-atmosphere TBs. By first applying the RMS to thousands of meteorological–microphysical situations simulated by the CRM and then by marshaling into the specialized database all the concomitant modeled microphysical profiles, TBs, and linked DTH/GS profiles/factors (from which optimal constraint tags can be derived), it becomes possible to use the database for the Bayesian interpretation of analogous measured TBs and tags. The main purpose of the new algorithm is to reduce ambiguity (nonuniqueness) effects that plague predecessor CRD algorithms. Such schemes restrict the interpretation of observed TBs by ignoring observable DTH/GS parameters that help constrain the influence microphysical profile sets (i.e., the associated hydrometeors, their size distributions, and their concomitant vertical distributions) that feed into the retrieval solutions. A Version 1 CDRD algorithm is tested against its CRD predecessor on two case studies of precipitation over Italy's Lazio region which were observed with various- satellite PMW radiometers. The measured TBs and corresponding tags obtained from gridded operational global model analyses are used in juxtaposition to produce the final rainfall retrievals. The retrievals are verified against coincident precision polarimetric C-band radar measurements. Skillful improvement is found for a case of intense convective rainfall where even CRD-type algorithm accuracy should be expected, as well as for a case of mixed convective-stratiform rainfall where either algorithms might otherwise be expected to be somewhat inaccurate.
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