Diagnosing Model Errors from Time-Averaged Tendencies in the Weather Research and Forecasting (WRF) Model
Diagnosing Model Errors from Time-Averaged Tendencies in the Weather Research and Forecasting (WRF) Model
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根据天气研究和预报 (WRF) 模型中的时间平均趋势诊断模型误差
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Snyder
中科院分区：
文献类型：
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作者：
S. Cavallo;J. Berner;C. Snyder
摘要数值天气模式的准确预报依赖于在大范围尺度上准确表示物理过程的能力。本文评估了模型时间趋势的效用，在给定的提前时间在许多预测平均，诊断系统的预报偏差在高级研究版本的天气研究和预报（WRF）模型在2010年北大西洋飓风季节使用连续循环集合数据同化（DA）。错误的强烈低层加热起源于行星边界层参数化，作为使用固定的海面温度的结果，影响向上的表面感热通量。在对流层700 hPa中心的深层观测到0.5 K的暖温偏差，主要来源于Kain-Fritsch对流参数化。不像global mo.
AbstractAccurate predictions in numerical weather models depend on the ability to accurately represent physical processes across a wide range of scales. This paper evaluates the utility of model time tendencies, averaged over many forecasts at a given lead time, to diagnose systematic forecast biases in the Advanced Research version of the Weather Research and Forecasting (WRF) Model during the 2010 North Atlantic hurricane season using continuously cycled ensemble data assimilation (DA). Erroneously strong low-level heating originates from the planetary boundary layer parameterization as a consequence of using fixed sea surface temperatures, impacting the upward surface sensible heat fluxes. Warm temperature bias is observed with a magnitude 0.5 K in a deep tropospheric layer centered 700 hPa, originating primarily from the Kain–Fritsch convective parameterization.This study is the first to diagnose systematic forecast bias in a limited-area mesoscale model using its forecast tendencies. Unlike global mo...