Extended Abstract (2.7M)P1.21
New technique for cloud model controlled precipitation retrieval: Cloud Dynamics and Radiation Database (CDRD) data mining applications at global and regional scales
Joseph A. Hoch, University of Wisconsin, Madison, WI; and C. M. Medaglia, A. V. Mehta, A. Mugnai, E. A. Smith, and G. J. Tripoli
Precipitation measurements are obtained from space-based microwave radiometer observations - for example the Special Sensor Microwave Imager (SSM/I), the Advanced Microwave Scanning Radiometer (AMSR), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) through the application of retrieval algorithms. These algorithms relate a series of observations of upwelling microwave radiation at several frequencies measured in space to the microphysical structure of the clouds using a data base of model simulated clouds and their attendant radiative profiles. Thousands of microphysical profiles and their corresponding microwave brightness temperatures are produced from detailed cloud resolving model simulations and then stored in Cloud Radiation Databases (CRDs) for this purpose.
Recently, we have proposed that retrieval could be improved by including additional information in the CRD about the dynamical and thermodynamical state of the atmosphere at the time and location that the microphysical profile was produced. Microwave observations together with independent assessment of the atmospheric state from model predictions or other observation systems could then be used to more precisely select relevant profiles from the Cloud Dynamics and Radiation Database (CDRD).
In this paper, we discuss the concept of the CDRD design along with implementation of this system for precipitation retrieval. Also, the concept of data mining is introduced as a means to effectively retrieve subsets of useful information for a global database containing thousands of microphysical profiles. Data mining techniques are implemented on a global and regional scale. The variance of microphysical profiles is examined as the number of dynamical and thermodynamical tags increases for a particular retrieval.
Poster Session 1, Retrievals and Cloud Products
Monday, 30 January 2006, 2:30 PM-2:30 PM, Exhibit Hall A2
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