Application of Rain and Snow Phase Criterion Based on Ice-phase Particle Content Forecast by CMA-MESO

Wang Lei; Chen Qiying; Hu Jianglin; Xu Guoqiang

doi:10.11898/1001-7313.20230602

Vol.34, NO.6, 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2023 > 34(6): 655-667

Wang Lei, Chen Qiying, Hu Jianglin, et al. Application of rain and snow phase criterion based on ice-phase particle content forecast by CMA-MESO. J Appl Meteor Sci, 2023, 34(6): 655-667. DOI: 10.11898/1001-7313.20230602.

Citation:

Wang Lei, Chen Qiying, Hu Jianglin, et al. Application of rain and snow phase criterion based on ice-phase particle content forecast by CMA-MESO. J Appl Meteor Sci, 2023, 34(6): 655-667. DOI: 10.11898/1001-7313.20230602.

Citation:

PDF( 8470 KB)

Application of Rain and Snow Phase Criterion Based on Ice-phase Particle Content Forecast by CMA-MESO

DOI: 10.11898/1001-7313.20230602

Wang Lei^{1, 2},
Chen Qiying^{1, 2},
Hu Jianglin^{1, 2},
Xu Guoqiang^{1, 2
,
,}

1.
CMA Earth System Modeling and Prediction Center, Beijing 100081
2.
Key Laboratory of Earth System Modeling and Prediction, CMA, Beijing 100081

Received Date: 2023-05-10
Rev Recd Date: 2023-08-17
Publish Date: 2023-11-27

Abstract

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.
- rain and snow phase;
- phase transition criterion;
- CMA-MESO;
- numerical weather prediction model

FullText(HTML)

References(45)

Figures and Tables

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)

DownLoad: Full-Size Img PowerPoint

Fig. 2 Precipitation phase in observations and CMA-MESO forecast discriminated by thickness criterion and ice-phase criterion initialized at 0800 BT 14 Jan 2023 and observations during 14-15 Jan 2023

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

Fig. 4 Threat score and bias for rain, sleet and snow discriminated by ice-phase criterion based on CMA-MESO forecast initialized at 0800 BT 14 Jan 2023

DownLoad: Full-Size Img PowerPoint

Fig. 5 Mean threat score and bias for rain, sleet and snow discriminated by ice-phase criterion based on CMA-MESO forecast of every 3 hours from 0200 BT 14 Jan to 1400 BT 15 Jan in 2023

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

Fig. 7 Profiles of hydrometeor content at Qingyang Station during 14-15 Jan 2023

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

Fig. 9 Profiles of hydrometeor content at Suiyang Station during 14-15 Jan 2023

DownLoad: Full-Size Img PowerPoint

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 2022-11-12T12:00	2022-11-11T11:00 2022-11-12T02:00 2022-11-12T11:00
2	2022-02-16—17	2022-02-16T21:00 2022-02-17T12:00 2022-02-17T22:00	2022-02-16T20:00 2022-02-17T11:00 2022-02-17T20:00
3	2022-02-11—13	2022-02-11T03:00 2022-02-12T23:00 2022-02-13T09:00	2022-02-11T02:00 2022-02-12T20:00 2022-02-13T08:00
4	2022-01-25—27	2022-01-25T06:00 2022-01-26T04:00 2022-01-27T04:00 2022-01-27T22:00	2022-01-25T05:00 2022-01-26T02:00 2022-01-27T02:00 2022-01-27T20:00
5	2022-01-20—24	2022-01-20T21:00 2022-01-21T09:00 2022-01-22T06:00 2022-01-22T15:00 2022-01-23T09:00 2022-01-24T10:00	2022-01-20T20:00 2022-01-21T08:00 2022-01-22T05:00 2022-01-22T14:00 2022-01-23T08:00 2022-01-24T08:00
6	2022-01-04—07	2022-01-04T18:00 2022-01-05T10:00 2022-01-06T16:00 2022-01-07T17:00	2022-01-04T17:00 2022-01-05T08:00 2022-01-06T12:00 2022-01-07T14:00
7	2021-12-25—27	2021-12-25T09:00 2021-12-26T13:00 2021-12-27T14:00	2021-12-25T08:00 2021-12-26T11:00 2021-12-27T11:00
8	2021-11-29—30	2021-11-29T13:00	2021-11-29T11:00

DownLoad: Download CSV

Table 2 Mean threat score for sleet and snow determined by various thresholds

阈值	TS评分
阈值	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

DownLoad: Download CSV

References

[1]	Changnon S A.Characteristics of ice storms in the United States.J Appl Meteor Climatol, 2003, 42(5):630-639. doi: 10.1175/1520-0450(2003)042<0630:COISIT>2.0.CO;2
[2]	Zhao L N, Ma Q Y, Yang G M, et al. Disasters and its impact of a severe snow storm and freezing rain over Southern China in January 2008. Climatic Environ Res, 2008, 13(4): 556-566. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH200804020.htm
[3]	Lin J L, Li Y, Liu L S. A heavy precipitation process over the Tibetan Plateau under the joint effects of a tropical cyclone and vortex. J Appl Meteor Sci, 2023, 34(2): 166-178. doi: 10.11898/1001-7313.20230204
[4]	Stewart R E, King P. Freezing precipitation in winter storms. Mon Wea Rev, 1987, 115(7): 1270-1280. doi: 10.1175/1520-0493(1987)115<1270:FPIWS>2.0.CO;2
[5]	Ryzhkov A V, Zrnić D S. Discrimination between rain and snow with a polarimetric radar. J Appl Meteor Climatol, 1998, 37(10): 1228-1240. doi: 10.1175/1520-0450(1998)037<1228:DBRASW>2.0.CO;2
[6]	Chang Y, Guo X L, Tang J, et al. Microphysical characteristics and precipitation formation mechanisms of convective clouds over the Tibetan Plateau. J Appl Meteor Sci, 2021, 32(6): 720-734. doi: 10.11898/1001-7313.20210607
[7]	Guo X L, Fu D H, Guo X, et al. Advances in aircraft measurements of clouds and precipitation in China. J Appl Meteor Sci, 2021, 32(6): 641-652. doi: 10.11898/1001-7313.20210601
[8]	Zhao L N, Mu X X, Ma C P, et al. A review on stable precipitation type forecast in winter. J Appl Meteor Sci, 2021, 32(1): 12-24. doi: 10.11898/1001-7313.20210102
[9]	Stewart R E, Thériault J M, Henson W. On the characteristics of and processes producing winter precipitation types near 0℃. Bull Amer Meteor Soc, 2015, 96(4): 623-639. doi: 10.1175/BAMS-D-14-00032.1
[10]	Xu A H, Qiao L, Zhan F X, et al. Diagnosis of a cold wave weather event in March 2005. Meteor Mon, 2006, 32(3): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200603008.htm
[11]	Zheng J, Xu A H, Xu B. Contrastive analysis of the freezing rain and heavy snow processes in 2008. Meteor Disaster Reduction Res, 2008, 31(2): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-HXQO200802008.htm
[12]	Li J B, Li G E, Pei Y J, et al. Analysis on the phase transformation of precipitation during a strong cold wave happened in spring. Meteor Mon, 2009, 35(7): 87-94. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200907014.htm
[13]	Liang H, Ma F Q, Li D W, et al. Diagnostic analysis of heavy snow in February 2009 and its forecast error in Shenyang. J Meteor Environ, 2010, 26(4): 22-27. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201004005.htm
[14]	Birk K, Lenning E, Donofrio K, et al. A revised bourgouin precipitation-type algorithm. Wea Forecasting, 2021, 36(2): 425-438. doi: 10.1175/WAF-D-20-0118.1
[15]	Bocchieri J R. A new operational system for forecasting precipitation type. Mon Wea Rev, 1979, 107(6): 637-649. doi: 10.1175/1520-0493(1979)107<0637:ANOSFF>2.0.CO;2
[16]	Bourgouin P. A method to determine precipitation types. Wea Forecasting, 2000, 15(5): 583-592. doi: 10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2
[17]	Hux J D, Knappenberger P C, Michaels P J, et al. Development of a discriminant analysis mixed precipitation(DAMP) forecast model for mid-Atlantic winter storms. Wea Forecasting, 2001, 16(2): 248-259. doi: 10.1175/1520-0434(2001)016<0248:DOADAM>2.0.CO;2
[18]	Heppner P. Snow versus rain: Looking beyond the "magic" numbers. Wea Forecasting, 1992, 7(4): 683-691. doi: 10.1175/1520-0434(1992)007<0683:SVRLBT>2.0.CO;2
[19]	Czys R R, Scott R W, Tang K C, et al. A physically based, nondimensional parameter for discriminating between locations of freezing rain and ice pellets. Wea Forecasting, 1996, 11(4): 591-598. doi: 10.1175/1520-0434(1996)011<0591:APBNPF>2.0.CO;2
[20]	Duan Y X, Li D Q, Li D W, et al. Analysis on precipitation phase characteristics and its forecast methods of Shenyang. Arid Meteor, 2016, 34(1): 51-57;74. https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201601007.htm
[21]	Xu H, Zong Z P. Analysis on characteristics of thermal vertical structure evolution during the transition of precipitation type in winter. Plateau Meteor, 2014, 33(5): 1272-1280. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201405011.htm
[22]	Qi L B, Zhang Y. Research on winter precipitation types' discrimination criterion in eastern China. Meteor Mon, 2012, 38(1): 96-102. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201201012.htm
[23]	Forbes R, Tsonevsky I, Hewson T, et al. Towards predicting high-impact freezing rain events. ECMWF Newsletter, 2014, 141: 15-21.
[24]	Dong Q, Hu N, Zong Z P. Application and verification of the ECMWF precipitation type forecast product(PTYPE). Meteor Mon, 2020, 46(9): 1210-1221. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202009008.htm
[25]	Gascón E, Hewson T, Haiden T. Improving predictions of precipitation type at the surface: Description and verification of two new products from the ECMWF ensemble. Wea Forecasting, 2018, 33(1): 89-108.
[26]	Dong Q, Zhang F, Zong Z P. Objective precipitation type forecast based on ECMWF ensemble prediction product. J Appl Meteor Sci, 2020, 31(5): 527-542. doi: 10.11898/1001-7313.20200502
[27]	Scheuerer M, Gregory S, Hamill T M, et al. Probabilistic precipitation-type forecasting based on GEFS ensemble forecasts of vertical temperature profiles. Mon Wea Rev, 2017, 145(4): 1401-1412.
[28]	Peng X Y, Qiu W, Li W J, et al. Apply the data digging technique on discerning the precipitation type. Bull Sci Technol, 2018, 34(1): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB201801010.htm
[29]	Chen S, Chen Y, He L F, et al. Discrimination analysis of snow and rain occurring under critical temperature conditions in central and eastern China. Meteor Mon, 2019, 45(8): 1037-1051. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201908001.htm
[30]	Lu H, Zhai P M, Qin W J, et al. A particle swarm optimization-neural network ensemble prediction model for persistent freezing rain and snow storm in Southern China. J Appl Meteor Sci, 2015, 26(5): 513-524. doi: 10.11898/1001-7313.20150501
[31]	Dong Q, Huang X Y, Zong Z P. Comparison of artificial neural network and linear regression methods in forecasting precipitation types. Meteor Mon, 2013, 39(3): 324-332. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201303007.htm
[32]	Huang J W, Cai R H, Yao R, et al. Application of deep learning method to discrimination and forecasting of precipitation type. Meteor Mon, 2021, 47(3): 317-326. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202103005.htm
[33]	Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101
[34]	Tong H, Zhang Y T. Diagnosis of precipitation types and its application in the GRAPES-MESO forecasts. Trans Atmos Sci, 2019, 42(4): 502-512. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201904003.htm
[35]	ECMWF. Part Ⅳ: Physical Processes//ECMWF. IFS Documentation Cy43R1. ECMWF, 2016: 118-120.
[36]	Xue J S, Chen D H. Scientific Design and Application of Numerical Forecasting System GRAPES. Beijing: Science Press, 2008.
[37]	Huang L P, Deng L T, Wang R C, et al. Key technologies of CMA-MESO and application to operational forecast. J Appl Meteor Sci, 2022, 33(6): 641-654. doi: 10.11898/1001-7313.20220601
[38]	Huang L P, Chen D H, Deng L T, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. doi: 10.11898/1001-7313.20170103
[39]	Rutledge S A, Hobbs P V. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Ⅷ: A model for the "seeder-feeder" process in warm-frontal rainbands. J Atmos Sci, 1983, 40(5): 1185-1206.
[40]	Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci, 1989, 46(20): 3077-3107.
[41]	Hong S Y, Dudhia J, Chen S H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Wea Rev, 2004, 132(1): 103-120.
[42]	Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos, 1997, 102(D14): 16663-16682.
[43]	Zhuang Z R, Chen J, Huang L P, et al. Impact experiments for regional forecast using blending method of global and regional analyses. Meteor Mon, 2018, 44(12): 1509-1517. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201812001.htm
[44]	Chen F, Dudhia J. Coupling an advanced land surface hydrology model with the Penn State-NCAR MM5 modeling system. Part Ⅰ: Model implementation and sensitivity. Mon Wea Rev, 2001, 129: 569-585.
[45]	Han J, Pan H L. Sensitivity of hurricane intensity forecast to convective momentum transport parameterization. Mon Wea Rev, 2006, 134(2): 664-674.