Extended Abstract (1.4M)4.2
A Lightning Data Assimilation Technique for Mesoscale Forecast Models
Edward R. Mansell, CIMMS/Univ. of Oklahoma, Norman, OK; and C. L. Ziegler and D. R. MacGorman
Lightning observations have been assimilated into the COAMPS mesoscale model for improvement of forecast initial conditions. Data are used from the National Lightning Detection Network (NLDN, cloud-to-ground lightning detection) and a Lightning Mapping Array (LMA; total lightning detection) that was installed in western Kansas/eastern Colorado. The assimilation method uses lightning as a proxy for the presence or absence of deep convection. During assimilation, lightning data are used to control the Kain-Fritsch (KF) convection parameterization scheme (CPS). The KF scheme can be forced to try to produce convection where lightning indicated storms, and, conversely, can optionally be prevented from producing spurious convection where no lightning was observed. Up to 1 g/kg of water vapor may be added to the boundary layer when the KF convection is too weak. The method does not make any use lightning-rainfall relationships, rather allowing the KF scheme to generate heating and cooling rates from its modeled convection. The method could therefore be used easily for real-time assimilation of any source of lightning observations.
Results will be presented for a warm-season test case 20-21 July 2000, when storms initiated and developed in large systems in Kansas both days. The second round of convection began by 22:00 UTC (20 July), and storm system with strong outflow had developed by 00 UTC on 21 July. Lightning data were assimilated over a 24 hour period (starting at 00 UTC on 20 July), covering the first round of convection and the start of the second. A control run was spun up over the same period only with the usual 12-hourly update cycle. As expected, during the assimilation period the model produces substantially more accurate precipitation (rates and location) than the control forecast. Even when water vapor was added to enhance convection, the rainfall rates were generally less than those indicated by rain gauge data. A forecast was started from the resulting initial condition at 00 UTC on 21 July 2000.
The lightning assimilation was successful in generating the cold pool that was present in the surface observations at initialization of the forecast. The resulting forecast showed considerably more skill than the control forecast, especially in the first few hours as convection was triggered by the propagation of the cold pool boundary.
Supplementary URL: http://www.cimms.ou.edu/%7Emansell/mesoscale
Session 4, Assimilation of lightning data into forecast models
Tuesday, 31 January 2006, 8:30 AM-9:45 AM, A307
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