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| Titel |
Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 3 – Identification of optimal meteorological tags |
| VerfasserIn |
E. A. Smith, H. W.-Y. Leung, J. B. Elsner, A. V. Mehta, G. J. Tripoli, D. Casella, S. Dietrich, A. Mugnai, G. Panegrossi, P. Sanò |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 5 ; Nr. 13, no. 5 (2013-05-16), S.1185-1208 |
| Datensatznummer |
250018442
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| Publikation (Nr.) |
 copernicus.org/nhess-13-1185-2013.pdf |
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| Zusammenfassung |
| 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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