Quantifying uncertainties in radar forward models through a comparison between CloudSat and SPartICus reflectivity factors

Autor: Gerald G. Mace, Jeana Mascio
Rok vydání: 2017
Předmět:
Zdroj: Journal of Geophysical Research: Atmospheres. 122:1665-1684
ISSN: 2169-8996
2169-897X
DOI: 10.1002/2016jd025183
Popis: Interpretations of remote sensing measurements collected in sample volumes containing ice-phase hydrometeors are very sensitive to assumptions regarding the distributions of mass with ice crystal dimension otherwise known as mass-dimensional or m-D relationships. How these microphysical characteristics vary in nature is highly uncertain resulting in significant uncertainty in algorithms that attempt to derive bulk microphysical properties from remote sensing measurements. This uncertainty extends to radar reflectivity factors forward calculated from model output because the statistics of the actual m-D in nature is not known. To investigate the variability in m-D relationships in cirrus clouds, reflectivity factors measured by CloudSat are combined with particle size distributions (PSDs) collected by coincident in situ aircraft using an optimal estimation-based (OE) retrieval of the m-D power law. The PSDs were collected by twelve flights of the SPEC Learjet during the Small Particles in Cirrus (SPartICus) campaign. We find that no specific habit emerges as preferred and instead we find that the microphysical characteristics of ice crystal populations tend to be distributed over a continuum defying simple categorization. With the uncertainties derived from the OE algorithm, the uncertainties in forward modeled backscatter cross-section and, in turn, radar reflectivity is calculated using a bootstrapping technique allowing us to infer the uncertainties in forward modeled radar reflectivity that would be appropriately applied to remote sensing simulator algorithms.
Databáze: OpenAIRE
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