What Is the Added Value of a Convection-Permitting Model for Forecasting Extreme Rainfall over Tropical East Africa?
What Is the Added Value of a Convection-Permitting Model for Forecasting Extreme Rainfall over Tropical East Africa?
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DOI：
10.1175/mwr-d-17-0396.1
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发表时间：
2018-09-01
影响因子：
3.2
通讯作者：
Boyd, Douglas F. A.
中科院分区：
文献类型：
--
作者：
Woodhams, Beth J.;Birch, Cathryn E.;Boyd, Douglas F. A.
热带地区对流性降水的预报是数值天气预报的一个主要挑战。在热带地区使用允许对流（CP）的预测模式已经落后于中纬度地区，尽管这种模式在这一地区的巨大潜力。在科学文献中，很少有热带地区的CP模型的评估，特别是在一个较长的时间段。本文评价了英国气象局东非地区业务CP模式和全球业务预报模式对对流风暴的预报，预报时间为2年。引入了一种新的局部形式的分数技能分数，它显示了在整个空间域的模型技能的变化。总体而言，CP模型和全球模型都优于24小时持续性预测。CP模式显示出比全球模式更高的技能，特别是在每日以下的时间尺度和陆地上的风暴。CP模式对维多利亚湖上空的预报也得到了改善，命中率提高了20%。相反，在中纬度地区的研究，这两个模型的技能显示出很大的依赖于一天中的时间和相对较小的依赖于48小时内预测的预测提前期。虽然这些结果提供了更多的动力，预报员使用CP模式，以产生subdaily的预测与更多的细节，有一个明确的需要更多的现场观测数据同化到模型和验证。向集合预报的转变可能会带来进一步的好处。
Forecasting convective rainfall in the tropics is a major challenge for numerical weather prediction. The use of convection-permitting (CP) forecast models in the tropics has lagged behind the midlatitudes, despite the great potential of such models in this region. In the scientific literature, there is very little evaluation of CP models in the tropics, especially over an extended time period. This paper evaluates the prediction of convective storms for a period of 2 years in the Met Office operational CP model over East Africa and the global operational forecast model. A novel localized form of the fractions skill score is introduced, which shows variation in model skill across the spatial domain. Overall, the CP model and the global model both outperform a 24-h persistence forecast. The CP model shows greater skill than the global model, in particular on subdaily time scales and for storms over land. Forecasts over Lake Victoria are also improved in the CP model, with an increase in hit rate of up to 20%. Contrary to studies in the midlatitudes, the skill of both models shows a large dependence on the time of day and comparatively little dependence on the forecast lead time within a 48-h forecast. Although these results provide more motivation for forecasters to use the CP model to produce subdaily forecasts with increased detail, there is a clear need for more in situ observations for data assimilation into the models and for verification. A move toward ensemble forecasting could have further benefits.