Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates
Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates
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DOI：
10.1111/j.1600-0870.2010.00503.x
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发表时间：
2011-01
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影响因子：
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通讯作者：
S. Migliorini;M. Dixon;R. Bannister;S. Ballard
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文献类型：
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作者：
S. Migliorini;M. Dixon;R. Bannister;S. Ballard
提高降水以及严重雷暴和山洪等危险天气定量预测技能的关键策略是利用对流活动的观测（例如雷达）。本文提出了一种允许对流集合预报系统（EPS），旨在解决可预测时间尺度相对较短的局部天气事件的预报问题，并基于英国气象局统一模型的 1.5 公里网格长度版本。特别关注在 EPS 中使用的集合变换卡尔曼滤波器 (ETKF) 中使用雷达得出的降水强度预测观测值的影响。我们基于对两个夏季案例研究使用 24 名成员的预测集合的初步结果表明，对流规模 EPS 在 1 小时提前时间内对温度、水平风和相对湿度进行了相当可靠的预测，从排名直方图的检查中可以看出这一点。另一方面，排名直方图似乎还表明，EPS 对于 (i) 地表压力和 (ii) 地表降水强度的预测产生了太大的差异。这些可能表明，对于（i），用于生成表面压力等级直方图的表面压力观测误差标准偏差值太大，而对于（ii），可能是非高斯降水观测误差的结果。然而，需要进一步调查才能更好地理解这些发现。最后，将雷达降水预测观测纳入本文考虑的 24 成员 EPS 似乎并没有提高 1 小时提前时间预报技能。
A key strategy to improve the skill of quantitative predictions of precipitation, as well as hazardous weather such as severe thunderstorms and flash floods is to exploit the use of observations of convective activity (e.g. from radar). In this paper, a convection-permitting ensemble prediction system (EPS) aimed at addressing the problems of forecasting localized weather events with relatively short predictability time scale and based on a 1.5 km grid-length version of the Met Office Unified Model is presented. Particular attention is given to the impact of using predicted observations of radar-derived precipitation intensity in the ensemble transform Kalman filter (ETKF) used within the EPS. Our initial results based on the use of a 24-member ensemble of forecasts for two summer case studies show that the convectivescale EPS produces fairly reliable forecasts of temperature, horizontal winds and relative humidity at 1 h lead time, as evident from the inspection of rank histograms. On the other hand, the rank histograms seem also to show that the EPS generates too much spread for forecasts of (i) surface pressure and (ii) surface precipitation intensity. These may indicate that for (i) the value of surface pressure observation error standard deviation used to generate surface pressure rank histograms is too large and for (ii) may be the result of non-Gaussian precipitation observation errors. However, further investigations are needed to better understand these findings. Finally, the inclusion of predicted observations of precipitation from radar in the 24-member EPS considered in this paper does not seem to improve the 1-h lead time forecast skill.