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FSS-based Evaluation on Convective Weather Forecasts in North China from High Resolution Models
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摘要: 目前高分辨率数值预报模式已具有一定的对流系统结构和演变特征预报能力，但对其预报能力的客观评估仍存在较多不足。选取2017年7—9月华北地区在不同天气系统背景下、具有不同组织模态的7次对流天气个例，使用模糊检验方法中的分数技巧评分（fraction skill score，简称FSS）指标评估不同高分辨率模式（包括快速更新同化GRAPES_Meso，GRAPES_3 km及华东区域中尺度模式）对中小尺度对流过程的预报能力。结果表明：分数技巧评分能够实现当模式预报存在位移和强度偏差时仍然给出有价值的评分结果，其优势还在于可以给出表征模式空间位移偏差尺度的预报技巧尺度信息；所用3个模式的雷达回波强度预报均偏弱，当回波强度小于44 dBZ时，华东区域中尺度模式预报最接近实况，而对于44 dBZ以上的较强回波，GRAPES_3 km模式预报偏差最小；采用百分位阈值（通过升序排列求出预报和实况数列的相同百分位数作为其相应的阈值）进行检验发现，对于预报难度更大的高阈值、小尺度的对流事件，GRAPES_3 km模式预报能力更强。Abstract: High resolution numerical model has a certain ability to predict the structure and evolution characteristics of the convection system, however, it is not easy to exploit the advantage of high resolution numerical model forecast using traditional verification metrics. Fuzzy verification is a recently developed and popular spatial verification method used for high resolution numerical model. It compares characteristics of the adjacent area of the corresponding point in prediction and observation fields to assess the accuracy of the forecast. Fuzzy verification considers a certain space and time uncertainty instead of completely accurate matching between prediction and observation.The Method of Fraction Skill Score, which is a kind of fuzzy verification, is used to evaluate the convective weather forecast ability in North China from 3 different high-resolution models(including Rapid Analysis and Forecast System GRAPES_Meso, GRAPES_3 km and East China Regional Numerical Model). Seven convective cases in North China with different organization modes caused by different weather systems from July to September 2017 are selected to evaluate the prediction ability of the small and medium scale convection system of high resolution models. Results show that the fraction skill score (FSS) can achieve valuable assessment information when the model prediction has a certain displacement and intensity of deviation. Another dominant advantage of FSS is that it can examine the model forecast skill scale, referring to the smallest window scale over which the forecast output contains useful information. The forecast skill scale is different from the scale of weather system, it represents the scale of spatial displacement deviation. The prediction of radar echo intensity from three models is weaker than the observation, East China Regional Numerical Model is the closest to the observation with echoes below 44 dBZ, and the intensity deviation from GRAPES_3 km is the smallest with echoes above 44 dBZ. Those performances lead to difference of FSS curve between GRAPES_3 km and East China Regional Numerical Model in the case of lower threshold and higher threshold. In order to evaluate the spatial displacement deviation of the model, the frequency (percentile) threshold is adopted, which represents the size change of the verification object. With increasing percentile threshold, GRAPES_3 km forecast skill scale basically maintains at 150-200 km, but the forecast skill scale of East China Regional Numerical Model increases gradually from 100 km to 400 km against the percentile threshold, which suggests that GRAPES_3 km model is more capable of predicting the small scale convective events.
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图 1 用于理想试验构建的雷达回波带
(a)实况, (b)预报1, (c)预报2, (d)预报3
Fig. 1 Radar echo band for ideal experiment
(a)observation, (b)Forecast 1, (c)Forecast 2, (d)Forecast 3
图 2 不同阈值条件下理想试验FSS评分随窗区格点数的变化曲线
(a)阈值为35 dBZ, (b)阈值为50 dBZ, (c)阈值为第75百分位数
Fig. 2 Graphs of FSS against neighborhood length using thresholds of 35 dBZ(a), 50 dBZ(b) and the 75th percentile(c)
图 3 2017年9月21日20:00华北飑线过程雷达回波
(a)实况，(b)GRAPES_Meso 12 h时效预报，(c)GRAPES_3 km 12 h时效预报, (d)华东区域模式12 h时效预报
Fig. 3 Radar reflectivity of North China squall line at 2000 BT 21 Sep 2017
(a)observation, (b)12 h forecast from GRAPES_Meso, (c)12 h forecast from GRAPES_3 km, (d)12 h forecast from East China Regional Numerical Model
图 4 2017年8月5日14:00华北对流过程雷达回波
(a)实况，(b)GRAPES_Meso 6 h时效预报，(c)GRAPES_3 km 6 h时效预报，(d)华东区域模式6 h时效预报
Fig. 4 Radar reflectivity of North China convection case at 1400 BT 5 Aug 2017
(a)observation, (b)6 h forecast from GRAPES_Meso, (c)6 h forecast from GRAPES_3 km, (d)6 h forecast from East China Regional Numerical Model
图 5 2017年9月21日20:00华北飑线过程雷达回波实况与模式12 h时效预报随百分位数变化的回波强度变化曲线
Fig. 5 Radar echo intensity of observation and models against percentile values at 2000 BT 21 Sep 2017 during the North China squall line process
图 6 FSS评分随窗区尺度变化曲线(取第95百分位数阈值)
(a)2017年9月21日08:00起报12 h时效预报，(b)8月5日08:00起报6 h时效预报
Fig. 6 FSS against neighborhood length(threshold using the 95th percentile)
(a)12 h forecast initiating from 0800 BT 21 Sep 2017, (b)6 h forecast initiating from 0800 BT 5 Aug 2017
图 7 不同阈值条件下FSS评分随窗区尺度变化曲线
(a)30 dBZ，(b)40 dBZ，(c)50 dBZ，(d)55 dBZ，(e)第75百分位数，(f)第90百分位数，(g)第95百分位数，(h)第99百分位数
Fig. 7 FSS against neighborhood length using different thresholds
(a)30 dBZ, (b)40 dBZ, (c)50 dBZ, (d)55 dBZ, (e)the 75th percentile, (f)the 90th percentile, (g)the 95th percentile, (h)the 99th percentile
表 1 本文所用2017年强对流天气个例信息
Table 1 Information of severe convective weather cases in 2017
时间 模式起报时间 影响天气系统 过程特点 07-11T19:00 08:00起报11 h时效 东北冷涡，地面冷锋 团状回波，雷暴大风、冰雹 07-14T22:00 08:00起报14 h时效 500 hPa短波槽，低层切变线 分散强回波，短时强降水、雷暴大风 07-15T16:00 08:00起报8 h时效 地面倒槽 分散强回波，局地短时强降水 07-21T20:00 08:00起报12 h时效 副热带高压，低层切变线 分散强回波，局地短时强降水 07-23T08:00 20:00起报12 h时效 副热带高压，低层切变线 分散对流，局地短时强降水 08-05T14:00 08:00起报6 h时效 500 hPa槽前，低层切变线 线性对流，局地雷暴大风、短时强降水 09-21T20:00 08:00起报12 h时效 蒙古冷涡，地面锋面 飑线，雷暴大风为主 
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