Tuning of length‐scale and observation‐error for radar data assimilation using four dimensional variational (4D‐Var) method

摘要

The effects of tuning of length‐scale and observation‐error on heavy rainfall forecasts are investigated. Length scale and observation error are tuned based on observation minus background (O ? B) covariances and theoretically expected cost function values, respectively. Tuned length scale and observation error are applied to radar data assimilation using the Four Dimensional Variational (4D‐Var) method. Length‐scale tuning leads to improved Quantitative Precipitation Forecast (QPF) skill for heavy precipitation, better analyses, and reduced errors of wind, temperature, humidity, and hydrometeor forecasts. The effects of observation‐error tuning are not as significant as those of length‐scale tuning, and they are limited to improvements in QPF skill. This is because tuned observation errors are close to pre‐assumed values. Proper tuning of length‐scale and observation‐error is essential for radar data assimilation using the 4D‐Var method. Tuning of length‐scale of background error correlation and observation‐error is applied to radar data assimilation using Four Dimensional Variational (4D‐Var) method. Tuning of length‐scale and observation‐error improves Quantitative Precipitation Forecast (QPF) skill of heavy rainfall cases and it reduces errors of meteorological‐variable forecasts.