COMPARISONS FOR RAMS MODELS (V3A, V4.3 AND V6.0)

Chen, K

关键词: 97;    R CODES;    DEW POINT;    WEATHER;    FORECASTING;    SAVANNAH RIVER PLANT;    EMERGENCY PLANS;    COMPARATIVE EVALUATIONS;    WIND;    AMBIENT TEMPERATURE;    FLOW RATE;   

DOI  :  10.2172/917784
RP-ID  :  WSRC-STI-2007-00467
PID  :  OSTI ID: 917784
Others  :  TRN: US200817%%692

学科分类：环境科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The Regional Atmospheric Modeling System (RAMS) is an atmospheric numerical model developed by scientists at Colorado State University and the ASTER Division of Mission Research Corporation for simulating and forecasting meteorological phenomena. RAMS v3a and v4.3 are being used by the Savannah River National Laboratory (SRNL) as an operational tool for weather forecast and emergency response for the Savannah River Site (SRS). ATmospheric, Meteorological, and Environmental Technologies (ATMET) is now the proprietor of RAMS. The latest upgrade (v6.0) was officially released on January 11, 2006. ATG plans to eventually replace the RAMS v3a and v4.3 with the RAMS v6.0 for operational site forecasting if the newest version provides a significant improvement in the numerical forecast. A study to compare the three model (v3a, v4.3 and v6.0) results with respect to surface stations observations was conducted and is the subject of this report. Two cases were selected for simulation by these three RAMS models. One simulation started at 0 Z on April 3, 2007 and represents a warm weather case (high temperature of 26 C and low temperature of 16 C) at SRS, while the other simulation started at 0 Z on April 7, 2007 and represents a cold weather case (high temperature of 9 C and low temperature of -1 C) at SRS. The wind speeds, wind directions, temperatures and the dew point temperatures predicted by the three RAMS models were interpolated to 46 surface observation locations. The interpolated results were compared with the observation data. Statistically, the differences between the three model results were very small. For the present configurations, the predictions from RAMS v6.0 are no better than the older models with the exception of wind direction. The proposed path forward would be to fine tune the RAMS v6.0 model input parameters to improve the predictions. This should also provide insights into current weaknesses in all RAMS versions.