Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall From Satellite Passive Microwave Measurements: Part 2. Overcoming Database Profile Selection Ambiguity by Consideration of Meteorological Control on Microphysics

Casella, Daniele; Panegrossi, Giulia; Sanò, Paolo; Dietrich, Stefano; Mugnai, A.; Smith, Eric A.; Tripoli, Gregory J.; Formenton, Marco; Paola, Francesco Di; Leung, Wing-Yee Hester; Mehta, A. V.

doi:10.1109/tgrs.2013.2258161

Cited by 43 publications

(39 citation statements)

References 55 publications

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“…Besides corroborating the success of meteorological guidance in conditioning Bayesian retrieval solutions, Casella et al (2013) have demonstrated the high reliability of the coupled CRM-RMS modeling system's simulated TB manifold, quantified by the close agreement to its observed counterpart. See Panegrossi et al (1998), Casella (2010 and Casella et al (2013) for explanations of simulationobservation database TB manifolds.…”

Section: Rms Calibration Through Simulation-observation Database Manisupporting

confidence: 56%

“…The PR-OBS-1 algorithm represents a modified and improved methodology applied to the now conventional Cloud Radiation Database (CRD) methodology originally developed by Smith et al (1992Smith et al ( , 1994a and Mugnai et al (1993), and applied repeatedly since that time, e.g., see Pierdicca et al (1996), Bauer et al (2000Bauer et al ( , 2001Bauer et al ( , 2005, , Chen and Staelin (2003, Tassa et al (2003Tassa et al ( , 2006, Grecu et al (2004), Mugnai et al (2008) and Surussavadee and Staelin (2008a, b) as cases in point. In physical terms, the new methodology is referred to as a Cloud Dynamics and Radiation Database (CDRD) algorithm; see the series of papers by Sanò et al (2013), Casella et al (2013) and which explain Neural Network Operational the attributes of the new methodology and its performance when used in an applied retrieval setting. Figure 2 shows the algorithm flow diagram.…”

Section: Isac-rome's H-saf Pmw Precipitation Algorithms In Trmm Eramentioning

confidence: 99%

“…The hydrometeor profiles are represented as liquid/ice water content (LWC/IWC) elements at each model level, i.e., mixing density vectors. See Casella et al (2013) for an expanded discussion of the CDRD algorithm's NMS and RMS modeling system framework and for the analysis used to identify, on an a priori basis, an optimal set of six meteorological variables from which a subset of four are used as the current CDRD algorithm's constraint parameters.…”

Section: Inclusion Of Optimal Meteorological Parameters As Solution Cmentioning

confidence: 99%

“…Such schemes restrict interpretation of observed TBs by ignoring observable DTH information that helps constrain the influence microphysical profile subsets (i.e., the associated hydrometeors, their phases, habits, size distributions and related vertical distributions) that determine the Bayesian retrievals. In the underlying analysis required for developing the CDRD algorithm, we have found that optimal meteorological variables serve, in a generally quasi-orthogonal fashion with respect to the initial TB-based Bayesian microphysical profile solution subsets, to quarantine candidate profiles which are incongruent with ambient environmental conditions for given observational situations; see Casella et al (2013). This then serves to mitigate against the ambiguities that would otherwise arise in the solutions.…”

Section: Inclusion Of Optimal Meteorological Parameters As Solution Cmentioning

confidence: 99%

“…See Panegrossi et al (1998), Casella (2010 and Casella et al (2013) for explanations of simulationobservation database TB manifolds. This agreement is tantamount to the consistency required in data assimilation between modeled and observed influence variables.…”

Section: Rms Calibration Through Simulation-observation Database Manimentioning

confidence: 99%

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CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Mugnai

Smith

Tripoli

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed

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Section: Rms Calibration Through Simulation-observation Database Manisupporting

confidence: 56%

Section: Isac-rome's H-saf Pmw Precipitation Algorithms In Trmm Eramentioning

confidence: 99%

Section: Inclusion Of Optimal Meteorological Parameters As Solution Cmentioning

confidence: 99%

Section: Inclusion Of Optimal Meteorological Parameters As Solution Cmentioning

confidence: 99%

Section: Rms Calibration Through Simulation-observation Database Manimentioning

confidence: 99%

See 3 more Smart Citations

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Mugnai

Smith

Tripoli

et al. 2013

Nat. Hazards Earth Syst. Sci.

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An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework

Turk

Haddad

Kirstetter

et al. 2018

Quart J Royal Meteoro Soc

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Estimation of precipitation from space‐based passive microwave (PMW) radiometric brightness temperature (TB) observations that adapts to the wide variety of Earth surface background and environmental conditions is a long‐standing issue. Since these conditions are generally unknown from the TB observations, PMW‐based precipitation estimation techniques commonly utilize independent ancillary data sources, such as interpolated prognostic variables from numerical weather prediction forecast models, and discrete surface emissivity classifications. In some situations, the selection of these variables may restrain the algorithm performance under particular surface and atmospheric conditions. The objective of this article is to examine the emissivity principal component (EPC) analysis as a common stratification method for indexing, searching and weighting candidate precipitation profiles from a priori databases, adaptable for Bayesian‐based precipitation estimation algorithms applied to the Global Precipitation Measurement (GPM) Microwave Imager (GMI) or other PMW sensors, to minimize dependence upon ancillary data sources. The EPC has been previously shown to track the joint variability between the 10–89 GHz surface emissivity, total column precipitable water vapour (TPW) and surface temperature (Ts) conditions directly from the TB observations, and identify global locations of similar conditions. A parallel GMI precipitation retrieval was carried out where the identical a priori database was indexed by TPW, Ts and a surface emissivity class index. An independent validation of each precipitation retrieval scheme was carried out using GMI pixel‐matched Multi‐Radar Multi‐Source (MRMS) ground radar data over the continental USA and surrounding ocean waters. While the EPC‐based estimates demonstrated similar performance to the TPW‐based estimates over ocean backgrounds, a markedly improved detection, and reduction in bias, was found for moderate and higher (>5 mm/hr) rainfall rates over other backgrounds, especially vegetated surfaces and coastlines.

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Introduction to Passive Microwave Retrieval Methods

Kummerow

2020

Advances in Global Change Research

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Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall From Satellite Passive Microwave Measurements: Part 2. Overcoming Database Profile Selection Ambiguity by Consideration of Meteorological Control on Microphysics

Cited by 43 publications

References 55 publications

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework

Introduction to Passive Microwave Retrieval Methods

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