Application of Rain and Snow Phase Criterion Based on Ice-phase Particle Content Forecast by CMA-MESO
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摘要: 利用中国气象局中尺度模式(CMA-MESO)云降水物理直接输出的水凝物混合比, 确定基于冰相水凝物占比的雨雪相态判据, 并应用于2023年1月14—15日我国大范围降水过程的雨雪相态判别。结果表明:该判据明显改善了基于温度和高度场的厚度判据对我国东部地区雨夹雪范围判别偏大、对分散性雨夹雪漏报的问题, 6~18 h时效雨夹雪预报TS评分较厚度判据提升75%~100%, 24 h时效降雪预报TS评分较厚度判据提升67%;对全国雨雪范围判别合理, 对小范围雨夹雪具有指示作用;对全国3~36 h时效降雨、降雪和雨夹雪预报TS评分为0.76~0.62, 0.69~0.63和0.11~0.08;对降雨和降雪存在一定空报和漏报, 对24 h时效雨夹雪空报明显;对相态转换过程有较好指示效果, 判别代表站相态转换开始时间误差为1~2 h, 对我国东部地区代表站的相态转换和雨夹雪持续时间判别优于厚度判据, 基于厚度判据雨夹雪预报持续时间偏长。研究结果可为雨雪相态业务预报提供客观预报产品参考。Abstract: The forecast of rain and snow phase is one of the difficulties in precipitation forecast, which is of great significance for disaster prevention and reduction. Rain or snow phase is mainly discriminated according to the traditional temperature-thickness criterion, or the combination of numerical model results with the judgment of forecasters on environmental conditions in present operatorial forecast. However, the determination of temperature-thickness criterions is subjective, complicated and various in different regions. The precipitation phase product of numerical model is based on temperature, humidity and liquid water content forecasts, resulting in errors of other variables besides microphysics introduced. Therefore, many uncertainties exist in the forecast of the transition of rain and snow, especially in the mixed phase. China Meteorological Administration mesoscale model (CMA-MESO) is a regional numerical model and has been applied to national operatorial weather forecast. Its precipitation phase is diagnosed using temperature, humidity and other basic atmospheric variables, including only rain, snow, freezing rain and hail, excluding mixed phase. Therefore, it is urgent to study on a more effective method for rain and snow, especially for the sleet forecast.A criterion for discriminating rain and snow phase is determined using the ice-phase particle content directly output from microphysics scheme of CMA-MESO, and applied to discriminate the range and transition of rain and snow in a widespread precipitation process in China during 14-15 January 2023. The proportion threshold of ice particles is firstly determined by the statistical threat scores. Results show that problems of larger range of sleet and underreporting scattered sleet in eastern China discriminated by traditional thickness criterion are obviously improved by ice-phase criterion. Threat scores for 6-18 h forecast of sleet increase by 75%-100%, and those for 24 h forecast of snow increase by 67% using ice-phase criterion compared with those using thickness criterion, respectively. Threat scores of 3-36 h forecast for rain, snow and sleet are 0.76 to 0.62, 0.69 to 0.63 and 0.11 to 0.08. There are false alarm and missing for rain and snow, respectively, and obvious false alarm for sleet within 24 h. The ice-phase criterion performs well on discriminating the transition process of rain and snow. The forecast error of phase transition start time at representative stations is about 1-2 h using ice-phase criterion, better than thickness criterion. Besides, the ice-phase criterion performs better in discriminating the duration of sleet for the representative station in eastern China too, while the thickness criterion will make forecast results longer than observations. These results could provide a more reliable and objective forecast product for the rain and snow phase forecast in operation.
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图 1 2023年1月14日08:00 500 hPa高度场(等值线,单位:dagpm)、850 hPa风场(风羽) 和850 hPa水汽通量(填色)
(+为安徽青阳站和贵州绥阳站)
Fig. 1 500 hPa geopotential height (the contour, unit:dagpm), 850 hPa wind (the barb) and 850 hPa water vapor flux (the shaded)
(+ denote locations of Qingyang Station of Anhui and Suiyang Station of Guizhou)
图 3 CMA-MESO 2023年1月14日08:00起报的东部地区(25°~38°N,112°~122°E) 基于厚度判据和冰相水凝物判据的不同时刻降雨、降雪和雨夹雪预报TS评分和偏差评分
Fig. 3 Threat score and bias for rain, sleet and snow discriminated by thickness criterion and ice-phase criterion based on CMA-MESO forecast in eastern China (25°-38°N,112°-122°E) initialized at 0800 BT 14 Jan 2023
图 6 2023年1月14—15日青阳站观测和CMA-MESO 2023年1月14日08:00起报的2 m温度(黑色实线)、小时降水量(柱状图) 及基于冰相水凝物判据和厚度判据的降水相态
Fig. 6 2 m temperature (the black curve), hourly precipitation (the bar) and precipitation phase discriminated by ice-phase criterion and thickness criterion based on CMA-MESO forecast initialized at 0800 BT 14 Jan 2023 and observations at Qingyang Station during 14-15 Jan 2023
图 8 2023年1月14—15日绥阳站观测和CMA-MESO 2023年1月14日11:00起报的2 m温度(黑色实线)、降水量(柱状图) 及基于冰相水凝物判据得到的降水相态
Fig. 8 2 m temperature (the black curve), hourly precipitation (the bar) and precipitation phase discriminated by ice-phase criterion based on CMA-MESO forecast initialized at 1100 BT 14 Jan 2023 and observations at Suiyang Station during 14-15 Jan 2023
表 1 用于确定雨夹雪与雪判别阈值的个例
Table 1 Cases for determining threshold of sleet and snow
序号 雨雪过程时间 用于计算阈值的时刻 CMA-MESO起报时刻 1 2022-11-10—12 2022-11-11T14:00
2022-11-12T05:00
2022-11-12T12:002022-11-11T11:00
2022-11-12T02:00
2022-11-12T11:002 2022-02-16—17 2022-02-16T21:00
2022-02-17T12:00
2022-02-17T22:002022-02-16T20:00
2022-02-17T11:00
2022-02-17T20:003 2022-02-11—13 2022-02-11T03:00
2022-02-12T23:00
2022-02-13T09:002022-02-11T02:00
2022-02-12T20:00
2022-02-13T08:004 2022-01-25—27 2022-01-25T06:00
2022-01-26T04:00
2022-01-27T04:00
2022-01-27T22:002022-01-25T05:00
2022-01-26T02:00
2022-01-27T02:00
2022-01-27T20:005 2022-01-20—24 2022-01-20T21:00
2022-01-21T09:00
2022-01-22T06:00
2022-01-22T15:00
2022-01-23T09:00
2022-01-24T10:002022-01-20T20:00
2022-01-21T08:00
2022-01-22T05:00
2022-01-22T14:00
2022-01-23T08:00
2022-01-24T08:006 2022-01-04—07 2022-01-04T18:00
2022-01-05T10:00
2022-01-06T16:00
2022-01-07T17:002022-01-04T17:00
2022-01-05T08:00
2022-01-06T12:00
2022-01-07T14:007 2021-12-25—27 2021-12-25T09:00
2021-12-26T13:00
2021-12-27T14:002021-12-25T08:00
2021-12-26T11:00
2021-12-27T11:008 2021-11-29—30 2021-11-29T13:00 2021-11-29T11:00 表 2 不同阈值确定的雨夹雪和降雪预报平均TS评分
Table 2 Mean threat score for sleet and snow determined by various thresholds
阈值 TS评分 雨夹雪 降雪 1.00 0.118 0.805 0.95 0.118 0.808 0.90 0.118 0.815 0.85 0.119 0.820 0.80 0.115 0.826 0.75 0.114 0.835 0.70 0.115 0.835 0.65 0.113 0.848 0.60 0.114 0.854 0.55 0.113 0.853 0.50 0.112 0.859 -
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