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Volume 13, issue 5
Nat. Hazards Earth Syst. Sci., 13, 1185–1208, 2013
https://doi.org/10.5194/nhess-13-1185-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: 13th Plinius Conference on Mediterranean Storms: disasters...

Nat. Hazards Earth Syst. Sci., 13, 1185–1208, 2013
https://doi.org/10.5194/nhess-13-1185-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 May 2013

Research article | 16 May 2013

Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 3 – Identification of optimal meteorological tags

E. A. Smith1, H. W.-Y. Leung2, J. B. Elsner3, A. V. Mehta4, G. J. Tripoli5, D. Casella6, S. Dietrich6, A. Mugnai6, G. Panegrossi6, and P. Sanò6 E. A. Smith et al.
  • 1Center for Research on the Changing Earth System (CRCES) [HQ: Catonsville, MD 21228], Tallahassee, FL 32312, USA
  • 2Dept. of Meteorology (MISU), Stockholm University, 106 91 Stockholm, Sweden
  • 3Dept. of Geography, Florida State University, Tallahassee, FL 32306, USA
  • 4NASA/Goddard Space Flight Center with Univ. of Maryland Baltimore County/Joint Center for Earth Systems Technology, Greenbelt, MD 20771, USA
  • 5Dept. of Atmospheric and Oceanic Sciences (AOS), University of Wisconsin, Madison, WI 53706, USA
  • 6Institute of Atmospheric Sciences and Climate (ISAC), Italian National Research Council (CNR), 00133 Rome, Italy

Abstract. In the first two parts of this study we have presented a performance analysis of our new Cloud Dynamics and Radiation Database (CDRD) satellite precipitation retrieval algorithm on various convective and stratiform rainfall case studies verified with precision radar ground truth data, and an exposition of the algorithm's detailed design in conjunction with a proof-of-concept analysis vis-à-vis its theoretical underpinnings. In this third part of the study, we present the underlying analysis used to identify what we refer to as the optimal metrological and geophysical tags, which are the optimally effective atmospheric and geographic parameters that are used to refine the selection of candidate microphysical profiles used for the Bayesian retrieval. These tags enable extending beyond the conventional Cloud Radiation Database (CRD) algorithm by invoking meteorological-geophysical guidance, drawn from a simulated database, which affect and are in congruence with the observed precipitation states. This is guidance beyond the restrictive control provided by only simulated radiative transfer equation (RTE) model-derived database brightness temperature (TB) vector proximity information in seeking to relate physically consistent precipitation profile solutions to individual satellite-observed TB vectors. The first two parts of the study have rigorously demonstrated that the optimal tags effectively mitigate against solution ambiguity, where use of only a CRD framework (TB guidance only) leads to pervasive non-uniqueness problems in finding rainfall solutions. Alternatively, a CDRD framework (TB + tag guidance) mitigates against non-uniqueness problems through improved constraints. It remains to show how these optimal tags are identified. By use of three statistical analysis procedures applied to a database from 120 North American atmospheric simulations of precipitating storms (independent of the 60 simulations for the European-Mediterranean basin region used in the Parts 1 and 2 studies), we examine 25 separate dynamical-thermodynamical-hydrological (DST) and geophysical parameters for their relationships to rainfall variables – specifically, surface rain rate and columnar liquid/ice/total water paths of precipitating hydrometeors. The analysis identifies seven optimal parameter tags which exceed all others in the strengths of their correlations to the precipitation variables but also have observational counterparts in the operational global forecast model outputs. The seven optimal tags are (1 and 2) vertical velocities at 700 and 500 hPa; (3) equivalent potential temperature at surface; (4) convective available potential energy; (5) moisture flux 50 hPa above surface; (6) freezing level height; and (7) terrain height, i.e., surface height.

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