ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 47,Issue 8,2021 Table of Contents
CUI Chunguang
,
DU Muyun
,
XIAO Yanjiao
,
LAI Anwei
,
LI Hongli
,
WANG Zhibin
,
WANG Jue
,
SUN Yuting
,
WANG Junchao
,
LENG Liang
,
WANG Bin
,
ZHANG Wen
,
PENG Juxiang
,
MA Hedi
,
KANG Zhaoping
2021, 47(8):901-918.
DOI: 10.7519/j.issn.1000-0526.2021.08.001
Abstract:
It is still extremely difficult and challenging for accurate prediction of convective weather systems. In order to improve the service ability in severe weather monitoring and prediction, the following studies have been carried out recently. The feature recognition algorithms for new mesocyclone and tornado vortex are developed and proved to be successful in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, more than twenty parameters are used in the study on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time intense precipitation). Rapidly updating cycle forecast system can effectively improve the quality of model initial values, which is very suitable for short-time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. The blending technology, which is expected to overcome the deficiency of the short-time quantitative precipitation forecast (QPF) by a mesoscale NWP model at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours, is under development, and it would have potential in enhancing the ability of rainfall forecast within the nowcasting period.
It is still extremely difficult and challenging for accurate prediction of convective weather systems. In order to improve the service ability in severe weather monitoring and prediction, the following studies have been carried out recently. The feature recognition algorithms for new mesocyclone and tornado vortex are developed and proved to be successful in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, more than twenty parameters are used in the study on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time intense precipitation). Rapidly updating cycle forecast system can effectively improve the quality of model initial values, which is very suitable for short-time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. The blending technology, which is expected to overcome the deficiency of the short-time quantitative precipitation forecast (QPF) by a mesoscale NWP model at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours, is under development, and it would have potential in enhancing the ability of rainfall forecast within the nowcasting period.
2021, 47(8):919-931.
DOI: 10.7519/j.issn.1000-0526.2021.08.002
Abstract:
Downburst is the most common weather phenomenon of convective storm, and forecasting its initial outbreak is one of the most challenging contents in severe convective storm forecasting. In this paper, a downburst nowcasting algorithm based on radar and radiosonde observation data was proposed. On the basis of ground clutter suppression and radial velocity dealiasing of radar base data and processing of sounding data to obtain 0℃, -20℃ and minimum equivalent potential temperature heights, the algorithm first identifies and tracks storm cells and calculates the hail indexes, then identifies the mid-level radial convergence characteristics and mesocyclone, making them associated with the identified storm cell. After that, many radar characteristics of storm cells are extracted. After statistical analysis of downburst and non-downburst cases, nine radar precursor factors of downburst were selected as the input of fuzzy logic method, and the nowcasting equation of downburst was established. The algorithm was tested with a downburst case which occurred in Jianli of Hubei Province on 1 June 2015, responsible for the “Oriental Star” cruise ship capsizing. The results show that the algorithm has predicted 8 times in 20:41-21:21 BT that the storm cell having caused the shipwreck will produce downburst. The first prediction time is 47 minutes earlier than that of the cruise ship capsizing at 21:28 BT. In addition, the effectiveness of the downburst nowcasting algorithm was evaluated by using all thunderstorm gales in Hubei Province from June to August 2019. The results show that the hit rate of downburst is 86.4%, and the average forecast time is 39 min. According to the echo pattern, the hit rates of downburst nowcasting for squall line, linear convection and non-linear convection are 93.2%, 90.5% and 75.6%, respectively. Actually, the algorithm module has been integrated into the automatic identification and warning system of classified severe convective weather developed by Wuhan Institute of Heavy Rain of CMA, and has been put into operation since 2019. The algorithm will be continuously optimized in the forecasting operation application in the future.
Downburst is the most common weather phenomenon of convective storm, and forecasting its initial outbreak is one of the most challenging contents in severe convective storm forecasting. In this paper, a downburst nowcasting algorithm based on radar and radiosonde observation data was proposed. On the basis of ground clutter suppression and radial velocity dealiasing of radar base data and processing of sounding data to obtain 0℃, -20℃ and minimum equivalent potential temperature heights, the algorithm first identifies and tracks storm cells and calculates the hail indexes, then identifies the mid-level radial convergence characteristics and mesocyclone, making them associated with the identified storm cell. After that, many radar characteristics of storm cells are extracted. After statistical analysis of downburst and non-downburst cases, nine radar precursor factors of downburst were selected as the input of fuzzy logic method, and the nowcasting equation of downburst was established. The algorithm was tested with a downburst case which occurred in Jianli of Hubei Province on 1 June 2015, responsible for the “Oriental Star” cruise ship capsizing. The results show that the algorithm has predicted 8 times in 20:41-21:21 BT that the storm cell having caused the shipwreck will produce downburst. The first prediction time is 47 minutes earlier than that of the cruise ship capsizing at 21:28 BT. In addition, the effectiveness of the downburst nowcasting algorithm was evaluated by using all thunderstorm gales in Hubei Province from June to August 2019. The results show that the hit rate of downburst is 86.4%, and the average forecast time is 39 min. According to the echo pattern, the hit rates of downburst nowcasting for squall line, linear convection and non-linear convection are 93.2%, 90.5% and 75.6%, respectively. Actually, the algorithm module has been integrated into the automatic identification and warning system of classified severe convective weather developed by Wuhan Institute of Heavy Rain of CMA, and has been put into operation since 2019. The algorithm will be continuously optimized in the forecasting operation application in the future.
LAI Anwei
,
MA Hedi
,
CUI Chunguang
,
KANG Zhaoping
,
WANG Zhibin
,
DU Muyun
,
XIAO Yanjiao
,
WANG Jue
2021, 47(8):932-952.
DOI: 10.7519/j.issn.1000-0526.2021.08.003
Abstract:
This paper investigates the influence of assimilating radar radial wind and reflectivity, and pseudo observation on the precipitation, gale and hail and other disastrous weather. At the same time, this paper also discusses the effect of different cold start time on precipitation forecast for a squall line event that occurred in the south of the Yangtze River on 4 May 2020. The results indicate that after assimilating the pseudo- water vapor and pseudo- in-cloud potential temperature at the cold start time, the water vapor and potential temperature are increased in the observed strong echo area. As the number of cycle assimilation increases, the negative water vapor increment mainly appears in the spurious convection of background, and the convection is inhibited. The increase in potential temperature mainly concentrates in the area where the observed echo is larger than the background simulated echo. Compared with only assimilating radar data, the assimilation of pseudo-water vapor and pseudo-in-cloud potential temperature can obviously improve the 0-3 h radar composite reflectivity and precipitation forecast. The simulated 2-5 km updraft helicity path is more consistent with the location of damage wind and hail disaster. The FSS scores at 1, 5, 10 mm threshold of 1 h accumulated precipitation in 0-6 h is significantly improved. With the increase of cycle assimilation times, the FSS score of pseudo observation experiments compared to radar experiments rises significantly at first, reaching the peak after 6-8 h cycle, and then the FSS score declines. In addition, different cold start time experiments show that after the background field is updated at 12:00 UTC, assimilating pseudo observation data has a positive contribution to improving precipitation prediction.
This paper investigates the influence of assimilating radar radial wind and reflectivity, and pseudo observation on the precipitation, gale and hail and other disastrous weather. At the same time, this paper also discusses the effect of different cold start time on precipitation forecast for a squall line event that occurred in the south of the Yangtze River on 4 May 2020. The results indicate that after assimilating the pseudo- water vapor and pseudo- in-cloud potential temperature at the cold start time, the water vapor and potential temperature are increased in the observed strong echo area. As the number of cycle assimilation increases, the negative water vapor increment mainly appears in the spurious convection of background, and the convection is inhibited. The increase in potential temperature mainly concentrates in the area where the observed echo is larger than the background simulated echo. Compared with only assimilating radar data, the assimilation of pseudo-water vapor and pseudo-in-cloud potential temperature can obviously improve the 0-3 h radar composite reflectivity and precipitation forecast. The simulated 2-5 km updraft helicity path is more consistent with the location of damage wind and hail disaster. The FSS scores at 1, 5, 10 mm threshold of 1 h accumulated precipitation in 0-6 h is significantly improved. With the increase of cycle assimilation times, the FSS score of pseudo observation experiments compared to radar experiments rises significantly at first, reaching the peak after 6-8 h cycle, and then the FSS score declines. In addition, different cold start time experiments show that after the background field is updated at 12:00 UTC, assimilating pseudo observation data has a positive contribution to improving precipitation prediction.
2021, 47(8):953-965.
DOI: 10.7519/j.issn.1000-0526.2021.08.004
Abstract:
Based on WRF v3.9 model, the stochastically perturbed parameterization is used to perturb MYNN boundary layer and RUC land surface process scheme parameters to simulate a heavy rainstorm in southwestern mountains of China. The optimal settings for parameter perturbations of MYNN boundary layer and RUC land surface process in mountain rainstorm ensemble forecast are explored. The main conclusions are as follows. In the random disturbance MYNN boundary layer scheme (SPPM) and RUC land surface process scheme (SPPR) parameters, the disturbance is mainly for the variables at the surface and the lower level of the model. The disturbance energy gradually develops from lower levels to higher levels in the model, and the SPPM can get greater disturbance energy than SPPR. The SPPM scheme is more sensitive to the variation of temporal correlation parameters than the spatial correlation parameters. However, as the perturbation energy of SPPR scheme is generally small, the variations of spatiotemporal correlation parameters have relatively small influence on its ensemble prediction performance. In SPPM scheme, a better ensemble prediction skill can be obtained by the temporal correlation selection for 6 h and the spatial scale selection for 70 km, while in SPPR scheme, a better ensemble prediction skill can be obtained by the temporal correlation for 6 h and the spatial scale for 50 km.
Based on WRF v3.9 model, the stochastically perturbed parameterization is used to perturb MYNN boundary layer and RUC land surface process scheme parameters to simulate a heavy rainstorm in southwestern mountains of China. The optimal settings for parameter perturbations of MYNN boundary layer and RUC land surface process in mountain rainstorm ensemble forecast are explored. The main conclusions are as follows. In the random disturbance MYNN boundary layer scheme (SPPM) and RUC land surface process scheme (SPPR) parameters, the disturbance is mainly for the variables at the surface and the lower level of the model. The disturbance energy gradually develops from lower levels to higher levels in the model, and the SPPM can get greater disturbance energy than SPPR. The SPPM scheme is more sensitive to the variation of temporal correlation parameters than the spatial correlation parameters. However, as the perturbation energy of SPPR scheme is generally small, the variations of spatiotemporal correlation parameters have relatively small influence on its ensemble prediction performance. In SPPM scheme, a better ensemble prediction skill can be obtained by the temporal correlation selection for 6 h and the spatial scale selection for 70 km, while in SPPR scheme, a better ensemble prediction skill can be obtained by the temporal correlation for 6 h and the spatial scale for 50 km.
2021, 47(8):966-981.
DOI: 10.7519/j.issn.1000-0526.2021.08.005
Abstract:
To investigate the influence of stochastically perturbed parameterization tendencies (SPPT) on convective-scale ensemble prediction under complex topography conditions, sensitivity experiments were conducted on three parameters of SPPT random perturbed field, including temporal scale, spatial scale and grid standard deviation, to explore its prediction effect. The results showed that the SPPT built with the parameters of 90 km spatial scale, 3 h time scale and 0.525 grid standard deviation performed best in this case. The spreads of upper-air physical quantities (zonal wind field, temperature field and humidity field) and near-surface physical quantities (10 m wind and 2 m temperature) increase rapidly. The spread/RMSE that considers prediction errors is also better than other experiments. Although the ensemble mean of 3 h accumulated precipitation is not significantly improved at all categories compared with other experiments, the ETS scores of precipitation grades ≥10 mm, ≥25 mm and ≥50 mm are close to or higher than those of the control experiment, and the probability prediction skills are better. On the whole, the influence of spatial scale parameter on spread is more obvious than that of time scale. The increase of perturbation amplitude plays a positive role in increasing spread, and meanwhile can improve the probability prediction skills of precipitation different magnitudes.
To investigate the influence of stochastically perturbed parameterization tendencies (SPPT) on convective-scale ensemble prediction under complex topography conditions, sensitivity experiments were conducted on three parameters of SPPT random perturbed field, including temporal scale, spatial scale and grid standard deviation, to explore its prediction effect. The results showed that the SPPT built with the parameters of 90 km spatial scale, 3 h time scale and 0.525 grid standard deviation performed best in this case. The spreads of upper-air physical quantities (zonal wind field, temperature field and humidity field) and near-surface physical quantities (10 m wind and 2 m temperature) increase rapidly. The spread/RMSE that considers prediction errors is also better than other experiments. Although the ensemble mean of 3 h accumulated precipitation is not significantly improved at all categories compared with other experiments, the ETS scores of precipitation grades ≥10 mm, ≥25 mm and ≥50 mm are close to or higher than those of the control experiment, and the probability prediction skills are better. On the whole, the influence of spatial scale parameter on spread is more obvious than that of time scale. The increase of perturbation amplitude plays a positive role in increasing spread, and meanwhile can improve the probability prediction skills of precipitation different magnitudes.
2021, 47(8):982-994.
DOI: 10.7519/j.issn.1000-0526.2021.08.006
Abstract:
Based on the precipitation data from China Meteorological Administration, ERA5 (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) reanalysis data and the TBB (black-body temperature) data from FY-4A, the factors for the severe torrential rain causing landslide on 23 July 2019 were investigated. The results are as follows. The heaviest precipitation at 22:00 BT was caused by the convective cloud belt with TBB below -82℃. The synoptic systems were 750-700 hPa low trough and the cold front in the north of Sichuan Basin. Before the torrential rain, the cold front in the north of Sichuan Basin drove the air with large energy to flow from the basin to northwestern Guizhou. With the development of southern airflow in the south of Jichang Town, Shuicheng County of Guizhou Province, sufficient water vapor conditions were provided for the occurrence of the severe rainstorm. Meanwhile, the enhancement of warm and humid airflow made the convective instability in the lower layer of Jichang Town increase. At the beginning of the precipitation period (20:00 BT 22 July), the upward movement was mainly below 700 hPa, which was related to the influence of the easterly airflow that encountered the topographic obstruction and climbed along the slope of the terrain at Jichang Town. As the synoptic-scale cyclonic circulation extended into Guizhou Province, widespread precipitation occurred only in the narrow zones of the cyclonic circulation. After Barnes band-pass filter analysis, it is found that there were a number of small-scale cyclones and anticyclones in the synoptic-scale cyclonic flow field, and two small cyclone circulation on the west side of Jichang Town. The strong convergence zone on the north side of them was just the severe precipitation zone. At the same time, Jichang Town was also located in a small-scale saddle-shaped field region, which was obviously favorable for the convergence at low and middle levels, superimposed to the topographic upward motion. Therefore, the sudden severe torrential rain, resulting in the formation of landslides.
Based on the precipitation data from China Meteorological Administration, ERA5 (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) reanalysis data and the TBB (black-body temperature) data from FY-4A, the factors for the severe torrential rain causing landslide on 23 July 2019 were investigated. The results are as follows. The heaviest precipitation at 22:00 BT was caused by the convective cloud belt with TBB below -82℃. The synoptic systems were 750-700 hPa low trough and the cold front in the north of Sichuan Basin. Before the torrential rain, the cold front in the north of Sichuan Basin drove the air with large energy to flow from the basin to northwestern Guizhou. With the development of southern airflow in the south of Jichang Town, Shuicheng County of Guizhou Province, sufficient water vapor conditions were provided for the occurrence of the severe rainstorm. Meanwhile, the enhancement of warm and humid airflow made the convective instability in the lower layer of Jichang Town increase. At the beginning of the precipitation period (20:00 BT 22 July), the upward movement was mainly below 700 hPa, which was related to the influence of the easterly airflow that encountered the topographic obstruction and climbed along the slope of the terrain at Jichang Town. As the synoptic-scale cyclonic circulation extended into Guizhou Province, widespread precipitation occurred only in the narrow zones of the cyclonic circulation. After Barnes band-pass filter analysis, it is found that there were a number of small-scale cyclones and anticyclones in the synoptic-scale cyclonic flow field, and two small cyclone circulation on the west side of Jichang Town. The strong convergence zone on the north side of them was just the severe precipitation zone. At the same time, Jichang Town was also located in a small-scale saddle-shaped field region, which was obviously favorable for the convergence at low and middle levels, superimposed to the topographic upward motion. Therefore, the sudden severe torrential rain, resulting in the formation of landslides.
2021, 47(8):995-1008.
DOI: 10.7519/j.issn.1000-0526.2021.08.007
Abstract:
For a further study on the usage of active divectory topology diagrammer (ADTD) lightning data in rapid refresh assimilation system, this paper introduces a new relationship between lightning activity and radar echo characteristics in Central China to calculate lightning-proxy reflectivity and conducts three sets of numerical experiments to analyze a severe weather process that occurred in Jianli, Hubei Province on 1 June 2015. The influence of lightning data on radar reflectivity, cloud microphysical variables and precipitation forecast calculated by rapid refresh assimilation system is mainly analyzed and compared with the direct assimilation of radar reflectivity. Results are as follows. The ADTD lightning data can capture the heavy precipitation signal well. The assimilation of lightning-proxy reflectivity of new relationship can improve the precipitation forecast skill by adjusting the cloud microphysical variables. By comparing the results of precipitation, we find that after adding lightning-proxy reflectivity obtained by the new relationship, the missing report rate can be effectively reduced, and the model can respond to the precipitation forecast that is more closely to the observation in a short-time scale. Moreover, it can improve the accuracy of short-term prediction and achieve similar results to the direct assimilation of radar reflectivity.
For a further study on the usage of active divectory topology diagrammer (ADTD) lightning data in rapid refresh assimilation system, this paper introduces a new relationship between lightning activity and radar echo characteristics in Central China to calculate lightning-proxy reflectivity and conducts three sets of numerical experiments to analyze a severe weather process that occurred in Jianli, Hubei Province on 1 June 2015. The influence of lightning data on radar reflectivity, cloud microphysical variables and precipitation forecast calculated by rapid refresh assimilation system is mainly analyzed and compared with the direct assimilation of radar reflectivity. Results are as follows. The ADTD lightning data can capture the heavy precipitation signal well. The assimilation of lightning-proxy reflectivity of new relationship can improve the precipitation forecast skill by adjusting the cloud microphysical variables. By comparing the results of precipitation, we find that after adding lightning-proxy reflectivity obtained by the new relationship, the missing report rate can be effectively reduced, and the model can respond to the precipitation forecast that is more closely to the observation in a short-time scale. Moreover, it can improve the accuracy of short-term prediction and achieve similar results to the direct assimilation of radar reflectivity.
2021, 47(8):1009-1020.
DOI: 10.7519/j.issn.1000-0526.2021.08.008
Abstract:
A total of 29 typhoons were generated over western North Pacific and South China Sea in 2019, 2 more than the multiyear average of 27 in the same period, of which 6 typhoons landed in China, 1 less than the normal average of 7 in the same period. The overall strength of the landfall 〖JP2〗typhoon was weak rela〖JP〗tively, but the landfall intensity of “Lekima” was strong (52 m·s-1)， reaching the scale of super typhoon. Autumn typhoons were obviously more, especially, there were 6 typhoons in November, the most typhoons in November since 1949. Compared with the average error of the past 5 years (2014-2018), the typhoon track forecast error by the National Meteorological Centre in 2019, was increased in the 24-72 h forecast, while the forecast error of 96-120 h was greatly reduced. Compared with JMA (Japan Meteorological Agency) and the JTWC (Joint Typhoon Warning Center), CMA is in the leading position in the prediction level of 24 h and 96-120 h, the prediction error of 48-72 h is equivalent to the EC deterministic model, slightly higher than that of JMA, but lower than that of the JTWC.
A total of 29 typhoons were generated over western North Pacific and South China Sea in 2019, 2 more than the multiyear average of 27 in the same period, of which 6 typhoons landed in China, 1 less than the normal average of 7 in the same period. The overall strength of the landfall 〖JP2〗typhoon was weak rela〖JP〗tively, but the landfall intensity of “Lekima” was strong (52 m·s-1)， reaching the scale of super typhoon. Autumn typhoons were obviously more, especially, there were 6 typhoons in November, the most typhoons in November since 1949. Compared with the average error of the past 5 years (2014-2018), the typhoon track forecast error by the National Meteorological Centre in 2019, was increased in the 24-72 h forecast, while the forecast error of 96-120 h was greatly reduced. Compared with JMA (Japan Meteorological Agency) and the JTWC (Joint Typhoon Warning Center), CMA is in the leading position in the prediction level of 24 h and 96-120 h, the prediction error of 48-72 h is equivalent to the EC deterministic model, slightly higher than that of JMA, but lower than that of the JTWC.
2021, 47(8):1021-1028.
DOI: 10.7519/j.issn.1000-0526.2021.08.009
Abstract:
The main characteristics of the general atmospheric circulation in May 2021 is that the two polar vortex centers were in the Northern Hemisphere and stronger than usual. The circulation finished the transition from a threewave pattern to a fourwave pattern and the 500 hPa geopotential height presented the distribution of a fourwave pattern in middlehigh latitudes of Northern Hemisphere. The strength of Western Pacific subtropical high was stronger than that in normal years, and the South China Sea summer monsoon erupted in the sixth pentad. The monthly mean temperature was 16.9°C, 0.7°C higher than normal. The monthly mean precipitation amount was 74.8 mm, 8% more than normal. Six regional rainfall processes occurred in China this month. Meanwhile, several provinces were attacked by gales and hailstorm, including the tornadoes seen in Wuhan, Hubei Province and also that in Jiangsu Province, which led in economic losses and casualties to same degree. In addition, four dustsand events occurred in the northern part of China, making a wide range of impacts and being the most compared with such events in the same period since 2002. The drought maintained or developed in the provinces of Yunnan and Sichuan, but the drought condition in most parts of South China got relieved at the end of May.
The main characteristics of the general atmospheric circulation in May 2021 is that the two polar vortex centers were in the Northern Hemisphere and stronger than usual. The circulation finished the transition from a threewave pattern to a fourwave pattern and the 500 hPa geopotential height presented the distribution of a fourwave pattern in middlehigh latitudes of Northern Hemisphere. The strength of Western Pacific subtropical high was stronger than that in normal years, and the South China Sea summer monsoon erupted in the sixth pentad. The monthly mean temperature was 16.9°C, 0.7°C higher than normal. The monthly mean precipitation amount was 74.8 mm, 8% more than normal. Six regional rainfall processes occurred in China this month. Meanwhile, several provinces were attacked by gales and hailstorm, including the tornadoes seen in Wuhan, Hubei Province and also that in Jiangsu Province, which led in economic losses and casualties to same degree. In addition, four dustsand events occurred in the northern part of China, making a wide range of impacts and being the most compared with such events in the same period since 2002. The drought maintained or developed in the provinces of Yunnan and Sichuan, but the drought condition in most parts of South China got relieved at the end of May.
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ISSN:1000-0526 CN:11-2282/P