HR: 08:00h
AN: AE41A-01 INVITED     [PDF]
TI: Improving Convective Precipitation Forecasting Through Assimilation of Regional Lightning Measurements in a Mesoscale Model
AU: * Anagnostou, E N
EM: manos@engr.uconn.edu
AF: University of Connecticut, Civil and Environmental Eng. U-37, Storrs, CT 06269
AU: Papadopoulos, A
EM: tpapa@ncmr.gr
AF: National Marine Research Center, Institute of Oceanography, Ag. Kosmas, Helleniko,, Athens, 16452 Greece
AU: Chronis, T G
EM: tchronis@engr.uconn.edu
AF: University of Connecticut, Civil and Environmental Eng. U-37, Storrs, CT 06269
AB: A technique developed for assimilating regional lightning measurements to a mesoscale meteorological model is presented. The aim is to improve the short-term forecasting of convective rain rates by dynamically initializing the model's vertical air moisture distribution on the basis of real-time lightning information. Utilizing location, timing and flash rate data, retrieved from a long-range network of sferics receivers in Europe, a mesoscale meteorological model is informed as to when and where a deep moist convection is occurring. The model's convective parameterization scheme decides whether the air moisture that feeds the storm has to be enhanced. Moisture amounts, related to the observed flash rates, are added to the layers associated with a model estimated convective cloud and coincidental observation of lightning occurrence. Lower moisture amounts are added to model layers that are not within a convective cloud in order to trigger convection in the next time-steps. Our philosophy is to determine the most appropriate corrections to simulate more realistic moisture profiles for the model's convective parameterization scheme. The relationship between flash rate and the vertical distribution of air moisture is investigated for a number of experimental moisture adjustment profiles. The study is facilitated based on three flood inducing storm cases associated with deep convection in a warm-season environment over the Mediterranean region. We show that assimilating lightning data can improve the model's description of the mesoscale environment resulting in an improved forecast of convective precipitation at lead times ranging from one to twelve hours. The approach is general enough to apply to any mesoscale model, but with an expected varying degree of success.
DE: 3309 Climatology (1620)
DE: 3314 Convective processes
DE: 3324 Lightning
DE: 3329 Mesoscale meteorology
DE: 3374 Tropical meteorology
SC: Atmospheric and Space Electricity [AE]
MN: 2003 Fall Meeting