HR: 11:50h
AN: H32A-07    [Abstracts]
TI: Incorporating remotely sensed cloud and atmospheric thermodynamic data into a microphysically based precipitation model
AU: McPhee, J
EM: jmcphee@ing.uchile.cl
AF: Department of Civil and Environmental Engineering, UCLA, 5732 Boelter Hall, Los Angeles, CA 90095, United States
AU: McPhee, J
EM: jmcphee@ing.uchile.cl
AF: Facultad de Ciencias Fisicas y Matematicas, Universidad de Chile, Santiago, Chile
AU: * Margulis, S A
EM: margulis@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, UCLA, 5732 Boelter Hall, Los Angeles, CA 90095, United States
AB: In this work we formulate a precipitation model driven by remotely sensed cloud microphysical parameters and atmospheric thermodynamic structure. The primary objective in developing the model is to retain a simple structure capable of easily generating ensemble fields of precipitation at relatively high spatial and temporal resolution. The motivation for doing so is to ultimately use the model in a data assimilation framework. The precipitation model is based on a one-dimensional conceptualization of an atmospheric column that derives the liquid mass balance of a cloud layer and surface rainfall rate from thermodynamic principles and state-of-the-art and readily available satellite information. Specifically, cloud microphysical parameters obtained from the VISST/SIST algorithm include cloud top and base pressure, liquid and ice water content, and characteristic hydrometeor size, and are used to estimate precipitation leaving the cloud base. Profiles of atmospheric temperature and humidity obtained from the AIRS sensor aboard the AQUA satellite are used in estimating rainfall at ground level after accounting for evaporation and updraft in the subcloud layer. Combination of the aforementioned data within the proposed model yields high-resolution (4 x 4 km, half hourly) precipitation fields. Uncertainty in the precipitation fields are simulated using postulated a priori probability density functions for the relatively few model input parameters. The computed ensemble of precipitation fields can subsequently serve as a prior estimate for a data assimilation procedure that incorporates information from the variety of products used while reflecting the appropriate uncertainty in the estimates. This approach can be coupled with a recent application with insolation fields, yielding an ensemble of physically consistent forcing fields for hydrologic models conditioned on the wealth of available multi-scale data products.
DE: 1640 Remote sensing (1855)
DE: 1840 Hydrometeorology
DE: 3315 Data assimilation
DE: 3354 Precipitation (1854)
SC: Hydrology [H]
MN: 2007 Joint Assembly