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  • Author or Editor: D. W. Wang x
  • Journal of Applied Meteorology and Climatology x
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J. Wang
,
M. R. Hjelmfelt
,
W. J. Capehart
, and
R. D. Farley

Abstract

Numerical simulations of two snowfall events over the Black Hills of South Dakota are made to demonstrate the use and potential of a coupled atmospheric and land surface model. The Coupled Atmospheric–Hydrologic Model System was used to simulate a moderate topographic snowfall event of 10–11 April 1999 and a blizzard event of 18–23 April 2000. These two cases were chosen to provide a contrast of snowfall amounts, locations, and storm dynamics. The model configuration utilized a nested grid with an outer grid of 16-km spacing driven by numerical forecast model data and an inner grid of 4 km centered over the Black Hills region. Simulations for the first case were made with the atmospheric model, the Advanced Regional Prediction System (ARPS) alone, and with ARPS coupled with the National Center for Atmospheric Research Land Surface Model (LSM). Results indicated that the main features of the precipitation pattern were captured by ARPS alone. However, precipitation amounts were greatly overpredicted. ARPS coupled with LSM produced a very similar precipitation pattern, but with precipitation amounts much closer to those observed. The coupled model also permits simulation of the resulting snow cover and snowmelt. Simulated percentage snow melting occurred somewhat more rapidly than that of the observed. Snow–rain discrimination may be taken from the precipitation type falling out of the atmospheric model based on the microphysical parameterization, or by the use of a surface temperature criteria, as used in most large-scale models. The resulting snow accumulation patterns and amounts were nearly identical. The coupled model configuration was used to simulate the second case. In this case the simulated precipitation and snow depth maximum over the eastern Black Hills were biased to the east and north by about 24 km. The resulting spatial correlation of the simulated snowfall and observations was only 0.37. If this bias is removed, the shifted pattern over the Black Hills region has a correlation of 0.68. Snow-melting patterns for 21 and 22 April appeared reasonable, given the spatial bias in the snowfall simulation.

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J. R. Wang
,
J. D. Spinhirne
,
P. Racette
,
L. A. Chang
, and
W. Hart

Abstract

Simultaneous measurements with the millimeter-wave imaging radiometer (MIR), cloud lidar system (CLS), and the MODIS airborne simulator (MAS) were made aboard the NASA ER-2 aircraft over the western Pacific Ocean on 17–18 January 1993. These measurements were used to study the effects of clouds on water vapor profile retrievals based on millimeter-wave radiometer measurements. The CLS backscatter measurements (at 0.532 and 1.064 μm) provided information on the heights and a detailed structure of cloud layers; the types of clouds could be positively identified. All 12 MAS channels (0.6–13 μm) essentially respond to all types of clouds, while the six MIR channels (89–220 GHz) show little sensitivity to cirrus clouds. The radiances from the 12-μm and 0.875-μm channels of the MAS and the 89-GHz channel of the MIR were used to gauge the performance of the retrieval of water vapor profiles from the MIR observations under cloudy conditions. It was found that, for cirrus and absorptive (liquid) clouds, better than 80% of the retrieval was convergent when one of the three criteria was satisfied; that is, the radiance at 0.875 μm is less than 100 W cm−3 sr−1, or the brightness at 12 μm is greater than 260 K, or brightness at 89 GHz is less than 270 K (equivalent to cloud liquid water of less than 0.04 g cm−2). The range of these radiances for convergent retrieval increases markedly when the condition for convergent retrieval was somewhat relaxed. The algorithm of water vapor profiling from the MIR measurements could not perform adequately over the areas of storm-related clouds that scatter radiation at millimeter wavelengths.

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William S. Olson
,
Christian D. Kummerow
,
Song Yang
,
Grant W. Petty
,
Wei-Kuo Tao
,
Thomas L. Bell
,
Scott A. Braun
,
Yansen Wang
,
Stephen E. Lang
,
Daniel E. Johnson
, and
Christine Chiu

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

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