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Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent DataRainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.
Document ID
Document Type
Reprint (Version printed in journal)
External Source(s)
Yang, Song
(George Mason Univ. Fairfax, VA, United States)
Olson, William S.
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Wang, Jian-Jian
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Bell, Thomas L.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Smith, Eric A.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Kummerow, Christian D.
(Colorado State Univ. Fort Collins, CO, United States)
Date Acquired
August 23, 2013
Publication Date
May 1, 2006
Publication Information
Publication: Journal of Applied Meteorology and Climatology
Volume: 45
Issue: 6
Subject Category
Meteorology And Climatology
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