Four-Dimensional Variational Data Assimilation for the Blizzard of 2000

Zupanski, Milija; Zupanski, Dusanka; Parrish, David F.; Rogers, Eric; DiMego, Geoffrey
August 2002
Monthly Weather Review;Aug2002, Vol. 130 Issue 8, p1967
Academic Journal
Four-dimensional variational (4DVAR) data assimilation experiments for the East Coast winter storm of 25 January 2000 (i.e., “blizzard of 2000”) were performed. This storm has received wide attention in the United States, because it was one of the major failures of the operational forecast system. All operational models of the U.S. National Weather Service (NWS) failed to produce heavy precipitation over the Carolina–New Jersey corridor, especially during the early stage of the storm development. The considered analysis cycle of this study is that of 0000 to 1200 UTC 24 January. This period was chosen because the forecast from 1200 UTC 24 January had the most damaging guidance for the forecasters at the National Weather Service offices and elsewhere. In the first set of experiments, the assimilation and forecast results between the 4DVAR and the operational three-dimensional variational (3DVAR) data assimilation method are compared. The most striking difference is in the accumulated precipitation amounts. The 4DVAR experiment produced almost perfect 24-h accumulated precipitation during the first 24 h of the forecast (after data assimilation), with accurate heavy precipitation over North and South Carolina. The operational 3DVAR-based forecast badly underforecast precipitation. The reason for the difference is traced back to the initial conditions. Apparently, the 4DVAR data assimilation was able to create strong surface convergence and an excess of precipitable water over Georgia. This initial convection was strengthened by a low-level jet in the next 6–12 h, finally resulting in a deep convection throughout the troposphere. In the second set of experiments, the impact of model error adjustment and precipitation assimilation is examined by comparing the forecasts initiated from various 4DVAR experiments. The results strongly indicate the need for the model error adjustment in the 4DVAR algorithm, as well as the clear benefit of...


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