Extended Abstract (1.1M)10B.3
Diagnosing the Intercept Parameter for Exponential Rain Drop Size Distribution Based on Video Disdrometer Observations
Guifu Zhang, University of Oklahoma, Norman, OK; and M. Xue, D. Daniel Dawson, and Q. Cao
A drop size distribution (DSD) model is usually assumed in quantitative precipitation estimation from remote sensing measurements, and in bulk microphysics parameterization schemes used in numerical weather prediction (NWP) models. An effective DSD model needs to have a minimum number of free parameters that are easy to determine while capturing the main physical properties associated with DSD. The exponential distribution N(D)=N0exp(-ΛD) with a fixed intercept parameter N0 is most commonly used in rainfall estimation and in single-moment microphysics parameterizations. Disdrometer observations and NWP model simulations show that the intercept parameter is not constant, but systematically depends on the rain type and rainfall rate.
In this study, a diagnostic relation of N0 as a function of rain water content is derived based on Two Dimensional Video Disdrometer (2DVD) measurements. The 2DVD data were collected in Florida during PRECIP'98 project and in Oklahoma for the spring seasons of 2005 and 2006. It is known that observation-derived N0 depends on sampling time and volume as well as the procedure of fitting an exponential distribution. To minimize the effects of error, two middle moments are used to estimate the two exponential DSD parameters: N0 and Λ (the slope parameter in the exponential DSD). Although there is a large scatter in the correlation plot, it is clear that N0 has a good correlation with the rain water content (W) and it increases as the water content increases so that a diagnostic N0-W relation can be derived. The diagnostic relation thus derived is used to parameterize DSD-related warm rain microphysical processes including rain evaporation and accretion. The diagnostic N0-based parameterization scheme is being applied in the Advanced Regional Prediction System (ARPS) model to show its impact on numerical forecasts and data assimilations. The ARPS simulation results suggest that this scheme has two main advantages: (i) it leads to less (more) evaporation for light (heavy) rain, preserving stratiform rain better, and (ii) it yields a larger (smaller) reflectivity factor for light (heavy) rain, leading to a generally better agreement between model forecast and radar observations.
Session 10B, Model Physics and Dynamics
Thursday, 28 June 2007, 2:00 PM-3:30 PM, Summit B
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