HR: 0830h
AN: A21E-1018    [PDF]
TI: A Study of the Spatial and Vertical Structure of Modeled Hydrometeor Profiles: Insights for weather prediction modeling and precipitation retrieval from remote sensors
AU: * Smedsmo, J L
EM: smed0002@umn.edu
AF: Saint Anthony Falls Laboratory, Department of Civil Engineering, University of Minnesota, 2 3rd Avenue SE, Minneapolis, MN 55414 United States
AU: Venugopal, V
EM: venu@macedonia.safl.umn.edu
AF: Saint Anthony Falls Laboratory, Department of Civil Engineering, University of Minnesota, 2 3rd Avenue SE, Minneapolis, MN 55414 United States
AU: Kong, F
EM: fkong@ou.edu
AF: Center for Analysis and Prediction of Storms, University of Oklahoma, Sarkey's Energy Center, Suite 1110, 100 East Boyd Street, Norman, OK 73019 United States
AU: Foufoula-Georgiou, E
EM: efi@tc.umn.edu
AF: Saint Anthony Falls Laboratory, Department of Civil Engineering, University of Minnesota, 2 3rd Avenue SE, Minneapolis, MN 55414 United States
AU: Droegemeier, K K
EM: kkd@ou.edu
AF: Center for Analysis and Prediction of Storms, University of Oklahoma, Sarkey's Energy Center, Suite 1110, 100 East Boyd Street, Norman, OK 73019 United States
AU: Droegemeier, K K
EM: kkd@ou.edu
AF: School of Meteorology, University of Oklahoma, Sarkey's Energy Center, Suite 1110, 100 East Boyd Street, Norman, OK 73019 United States
AB: Weather models predict precipitation reaching the ground as the vertical flux of hydrometeors from the cloud (evaporation effects are also considered). Looking at the entire profile of hydrometeors throughout the cloud, rather than precipitation on the ground, may provide important insight into the strengths and weaknesses of the microphysical models used in weather prediction. Also, certain algorithms for precipitation retrieval from passive microwave sensors, e.g., as part of the Tropical Rainfall Measuring Mission (TRMM), heavily rely on the ability of Cloud Resolving Models (CRMs) to produce realistic profiles of hydrometeor size, shape, and concentration throughout the cloud. In this study, the Advanced Regional Predictions System (ARPS) was used to simulate a severe thunderstorm in Ft. Worth, Texas on March 28, 2000. This case study was run with other research objectives in mind, including assessing the effect of a data assimilation cycle using sophisticated WSR-88D radar data analysis on the ability of the ARPS model to predict a real life weather event. A previous study concluded that the model did a good job of producing the major features of the storm; this research aims at evaluating the ability of the model to reproduce realistic hydrometeor profiles for the storm. Since observations of 3D hydrometeor fields are not available for this storm, predicted radar reflectivity from the model is compared to WSR-88D Level II reflectivity. Although additional uncertainties are introduced in the reflectivity calculation, this gives an indirect method for assessing hydrometeor profiles. Mean profiles and probability distributions of reflectivity at all altitudes have been created to compare modeled versus observed fields. Initial comparisons reveal that, at a given precipitation rate, the spatial statistics of modeled reflectivity (estimated from the modeled 3D hydrometeors fields in the atmosphere) are significantly different than the statistics of observed radar echoes. This discrepancy may be the result of limitations in CRMs to produce the correct composition of hydrometeors in the cloud even if they predict the correct precipitation on the ground. However, part of the error is probably due to limitations in the estimate of reflectivity from hydrometeor fields, pointing out that caution must be exercised when model-estimated reflectivity fields are compared to observed fields. Investigations are underway to narrow in on possible causes of the lack of agreement. Future research using detailed hydrometeor observations, e.g., from polarimetric radar, is needed to further quantify the degree to which CRMs can produce realistic 3D hydrometeor fields. The results of such a study may be utilized to arrive at a calibration for model reflectivity based on observations - a calibration that may be used in precipitation retrieval algorithms.
DE: 0300 ATMOSPHERIC COMPOSITION AND STRUCTURE
DE: 0320 Cloud physics and chemistry
DE: 1640 Remote sensing
DE: 1655 Water cycles (1836)
DE: 1854 Precipitation (3354)
SC: Atmospheric Sciences [A]
MN: 2003 Fall Meeting