Applying Design of Experiments to Numerical Weather Prediction

• Activity Number: 190 - Contributed Poster Presentations: Section on Statistics and the Environment
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Jeffrey Smith* and Judah L. Cleveland and John W. Raby and Richard S. Penc
• Companies: U.S. Army Research Laboratory and US Army Research Laboratory and US Army Research Laboratory and US Army Research Laboratory
• Keywords: forecast; weather; model; experiment design; numerical weather prediction; WRF-ARW

Abstract

Numerical Weather Prediction (NWP) is the science of forecasting weather or climatic conditions based on past and present weather observations using computational methods applied to mathematical representations of the atmospheric physical processes. Temporally, weather forecasts range from hours to days, while climate forecasts range from weeks to years or decades. Spatially, forecasts can cover small scale, highly resolved “local” weather conditions over small domains to large scale global weather features and climatic patterns. A key feature in NWP is the range of possible parameterization schemes available to the user to configure the NWP for a specific domain and forecast regime. In this poster, we apply statistical design of experiments to understand and attribute error in the forecast to parameterization schemes. By applying statistical design of experiments we are able to judiciously sample the NWP to obtain data at well-chosen points from which we can make the requisite statistical inferences regarding the tendencies of the parameterization schemes.