Improving US Winter Storm Forecasts using Simulated Dropsondes in the GFS Model
Abstract
Observing System Simulation Experiments (OSSEs) can be a cost-effective approach to evaluate the potential impact of new observing systems. In this study, the NCEP Global Forecast System (GFS) is initialized with simulated dropsonde data of temperature, wind, and humidity fields over the Pacific Ocean, and the accuracy of predicting winter storms over the continental United States is compared with the T511 ECMWF Joint OSSE Nature Run. Interestingly, the addition of perfect simulated dropsonde data improves GFS forecasts for one winter storm but not the other. Details of the two winter storms are compared to better understand under which circumstances the addition of dropsonde data may improve forecasts for winter storms in the United States.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2015
- Bibcode:
- 2015AGUFM.A41E0111E
- Keywords:
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- 1616 Climate variability;
- GLOBAL CHANGE;
- 1626 Global climate models;
- GLOBAL CHANGE;
- 1640 Remote sensing;
- GLOBAL CHANGE;
- 1655 Water cycles;
- GLOBAL CHANGE