Open AccessImplementation of a Digital Filter Initialization in the WRF Model and Its Application in the Rapid Refresh
Author(s) -
Steven E. Peckham,
Tatiana G. Smirnova,
Stanley G. Benjamin,
John M. Brown,
Jaymes S. Kenyon
Publication year - 2015
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-15-0219.1
Subject(s) - initialization , data assimilation , weather research and forecasting model , computer science , parameterized complexity , meteorology , noise (video) , filter (signal processing) , environmental science , algorithm , artificial intelligence , physics , image (mathematics) , computer vision , programming language
Because of limitations of variational and ensemble data assimilation schemes, resulting analysis fields exhibit some noise from imbalance in subsequent model forecasts. Controlling finescale noise is desirable in the NOAA’s Rapid Refresh (RAP) assimilation/forecast system, which uses an hourly data assimilation cycle. Hence, a digital filter initialization (DFI) capability has been introduced into the Weather Research and Forecasting Model and applied operationally in the RAP, for which hourly intermittent assimilation makes DFI essential. A brief overview of the DFI approach, its implementation, and some of its advantages are discussed. Results from a 1-week impact test with and without DFI demonstrate that DFI is effective at reducing high-frequency noise in short-term operational forecasts as well as providing evidence of reduced errors in the 1-h mass and momentum fields. However, DFI is also shown to reduce the strength of parameterized deep moist convection during the first hour of the forecast.