ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 48,Issue 4,2022 Table of Contents
2022, 48(4):393-405.
DOI: 10.7519/j.issn.1000-0526.2022.011901
Abstract:
Based on EC, GFS, CMA-MESO and CMA-GFS, the 0-72 h ensemble forecasts of daily surface and high-altitude zonal wind and meridional wind from January to April 2020 from the four models for East China and surrounding areas (20°-40°N，110°-130°E) are evaluated with the augmented complex extended Kalman filter (ACEKF) method. The results show that the ACEKF method outperforms the bias-removed ensemble mean, super-ensemble forecast and single-mode forecasts, and can further reduce the wind speed forecast errors. ACEKF can improve the upper-air wind speed forecasts better than those at ground level. In complex terrain areas the improved wind speed forecast is much better. These results are also reflected in the root-mean-square error and anomaly correlation coefficient for all forecast times.
Based on EC, GFS, CMA-MESO and CMA-GFS, the 0-72 h ensemble forecasts of daily surface and high-altitude zonal wind and meridional wind from January to April 2020 from the four models for East China and surrounding areas (20°-40°N，110°-130°E) are evaluated with the augmented complex extended Kalman filter (ACEKF) method. The results show that the ACEKF method outperforms the bias-removed ensemble mean, super-ensemble forecast and single-mode forecasts, and can further reduce the wind speed forecast errors. ACEKF can improve the upper-air wind speed forecasts better than those at ground level. In complex terrain areas the improved wind speed forecast is much better. These results are also reflected in the root-mean-square error and anomaly correlation coefficient for all forecast times.
2022, 48(4):406-417.
DOI: 10.7519/j.issn.1000-0526.2021.102301
Abstract:
At present, a prototype of Convection Permitting Ensemble Prediction System of North China is developed by Beijing Institute of Urban Meteorology, China Meteorological Administration. In order to construct initial condition perturbation for this ensemble prediction system, an ensemble data assimilation (EDA) initial condition perturbation method is developed based on observation perturbation and background state from global ensemble using 3-dimension variation data assimilation (3D-Var). The batch experiments of EDA method in the Convection Permitting Ensemble Prediction System of North China are carried out and compared with another initial condition perturbation method of dynamical downscaling. The results show that the construction of observation perturbation is scientific, and can produce observation perturbation of normal distribution with the magnitude in accordance with observation error. For the initial field, the members of dynamic downscaling are sufficiently dispersive, and the EDA with observation will constrain the dispersion of members, while the EDA with perturbed observation can maintain the ensemble dispersion, and the generated perturbation can represent the uncertainty of both the background field and the observation. From the batch test results, the EDA can significantly reduce the forecast error of the short range compared with the dynamic downscaling, with the ensemble spread slightly decreased. The probability score also shows some improvements from EDA. The results of precipitation forecast show that the EDA method can significantly improve the precipitation forecast that it can provide more accurate magnitude and time period of local precipitation. The statistical score of precipitation forecast also show that EDA method has better probability forecast skill for light rain, moderate rain and heavy rain. unperturbed
At present, a prototype of Convection Permitting Ensemble Prediction System of North China is developed by Beijing Institute of Urban Meteorology, China Meteorological Administration. In order to construct initial condition perturbation for this ensemble prediction system, an ensemble data assimilation (EDA) initial condition perturbation method is developed based on observation perturbation and background state from global ensemble using 3-dimension variation data assimilation (3D-Var). The batch experiments of EDA method in the Convection Permitting Ensemble Prediction System of North China are carried out and compared with another initial condition perturbation method of dynamical downscaling. The results show that the construction of observation perturbation is scientific, and can produce observation perturbation of normal distribution with the magnitude in accordance with observation error. For the initial field, the members of dynamic downscaling are sufficiently dispersive, and the EDA with observation will constrain the dispersion of members, while the EDA with perturbed observation can maintain the ensemble dispersion, and the generated perturbation can represent the uncertainty of both the background field and the observation. From the batch test results, the EDA can significantly reduce the forecast error of the short range compared with the dynamic downscaling, with the ensemble spread slightly decreased. The probability score also shows some improvements from EDA. The results of precipitation forecast show that the EDA method can significantly improve the precipitation forecast that it can provide more accurate magnitude and time period of local precipitation. The statistical score of precipitation forecast also show that EDA method has better probability forecast skill for light rain, moderate rain and heavy rain. unperturbed
2022, 48(4):418-427.
DOI: 10.7519/j.issn.1000-0526.2021.122101
Abstract:
Based on daily temperature data in winter, the relationship between number of extreme low temperature (NELT) and Arctic Oscillation (AO) in Beijing-Tianjin-Hebei Region in winter is analyzed, the AO-related interdecadal atmospheric circulation anomalies in late winter (February) is explored. The results show that NELT in winter in Beijing-Tianjin-Hebei Region has been decreasing since 1961. The correlation is weakening between NELT and AO after the middle 1980s compared to that before the middle 1980s, but strengthening in early winter, and that remains weak in late winter after early 2000s. Furthermore, the interdecadal variation in the relationship between NELT and AO in late winter in Beijing-Tianjin-Hebei Region is largely attributed to the AO-related atmospheric circulation anomalies under different interdecadal backgrounds. When significant correlation exists between NELT and AO, through impacting the Siberian high, Aleutian low, Ural blocking high and East Asian trough, a positive AO phase is associated with less frequency of extreme low temperature, and vice versa. In contrast, AO displays a more zonal symmetry structure, and the climate circulation system in mid-high latitudes is weak, which confines the influence of winter AO on NELT and leads to an insignificant late winter AO-NELT relationship.
Based on daily temperature data in winter, the relationship between number of extreme low temperature (NELT) and Arctic Oscillation (AO) in Beijing-Tianjin-Hebei Region in winter is analyzed, the AO-related interdecadal atmospheric circulation anomalies in late winter (February) is explored. The results show that NELT in winter in Beijing-Tianjin-Hebei Region has been decreasing since 1961. The correlation is weakening between NELT and AO after the middle 1980s compared to that before the middle 1980s, but strengthening in early winter, and that remains weak in late winter after early 2000s. Furthermore, the interdecadal variation in the relationship between NELT and AO in late winter in Beijing-Tianjin-Hebei Region is largely attributed to the AO-related atmospheric circulation anomalies under different interdecadal backgrounds. When significant correlation exists between NELT and AO, through impacting the Siberian high, Aleutian low, Ural blocking high and East Asian trough, a positive AO phase is associated with less frequency of extreme low temperature, and vice versa. In contrast, AO displays a more zonal symmetry structure, and the climate circulation system in mid-high latitudes is weak, which confines the influence of winter AO on NELT and leads to an insignificant late winter AO-NELT relationship.
2022, 48(4):428-441.
DOI: 10.7519/j.issn.1000-0526.2021.102001
Abstract:
Based on four machine deep learning algorithms (PredRNN++, MIM, CrevNet and PhyDNet), radar data and precipitation data in Wuhan from 2012 to 2019, this study investigates the possibility of application of artificial intelligence (AI) technology in the nowcasting of Wuhan Region. The forecasting skills of radar echo nowcasting are examined in terms of mean square error (MSE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR) and critical success index (CSI), then compared with the semi-Lagrangian optical flow method. The results are summarized as follows. The MSE and FAR are the lowest and SSIM is the highest in the MIM algorithm. The POD and CSI of PredRNN++ are the highest. The POD, CSI and SSIM of machine learning are higher than semi-Lagrangian optical flow, while the FAR and MSE of machine learning are much lower, of which the SSIM, POD and CSI of machine algorithms are improved by 3.2%-24.7% than semi-Lagrangian optical flow, but the MSE and FAR are reduced by 13.1%-43.3%. Within 30 minutes, except the CrevNet algorithm, the skills of other algorithms are similar to that of semi-Lagrangian optical flow. 30 minutes later, the skills of both machine algorithm and semi-Lagrangian optical flow decline significantly with the increase of forecast lead time. However, the skill of machine algorithms declines much more slowly. Especially after 60 minutes, the skill of semi-Lagrangian optical flow descends more quickly indicating the advantage of machine learning algorithms for long-term prediction. In addition, the descending rates at different forecast lead times for different score indexes are different among the machine algorithms. The CIS of PredRNN++ is the highest in any intensity, MIM and PhyDNet performance is better than semi-Lagrangian optical flow for radar echo intensity exceeding 40 dBz, but CrevNet shows better skill for radar echo intensity exceeding 50 dBz. The POD and CSI of machine algorithms and semi-Lagrangian optical flow decline significantly with the increase of forecast intensity of radar echo, while the FAR increases quickly, but the increase of FAR rate of machine learning algorithm is much slower. To sum up, the analysis of four different echo patterns and different development trends shows that the machine learning algorithm has the ability not only to predict the change of radar echo intensity in a certain content, but also to predict the time node of the evolution tendency of intensity and acreage, which are basically consistent with the observation. These results suggest that the ability of machine deep learning to predict the movement of radar echo is better than that of semi-Lagrangian optical flow, indicating its possible wide prospect for operational application.
Based on four machine deep learning algorithms (PredRNN++, MIM, CrevNet and PhyDNet), radar data and precipitation data in Wuhan from 2012 to 2019, this study investigates the possibility of application of artificial intelligence (AI) technology in the nowcasting of Wuhan Region. The forecasting skills of radar echo nowcasting are examined in terms of mean square error (MSE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR) and critical success index (CSI), then compared with the semi-Lagrangian optical flow method. The results are summarized as follows. The MSE and FAR are the lowest and SSIM is the highest in the MIM algorithm. The POD and CSI of PredRNN++ are the highest. The POD, CSI and SSIM of machine learning are higher than semi-Lagrangian optical flow, while the FAR and MSE of machine learning are much lower, of which the SSIM, POD and CSI of machine algorithms are improved by 3.2%-24.7% than semi-Lagrangian optical flow, but the MSE and FAR are reduced by 13.1%-43.3%. Within 30 minutes, except the CrevNet algorithm, the skills of other algorithms are similar to that of semi-Lagrangian optical flow. 30 minutes later, the skills of both machine algorithm and semi-Lagrangian optical flow decline significantly with the increase of forecast lead time. However, the skill of machine algorithms declines much more slowly. Especially after 60 minutes, the skill of semi-Lagrangian optical flow descends more quickly indicating the advantage of machine learning algorithms for long-term prediction. In addition, the descending rates at different forecast lead times for different score indexes are different among the machine algorithms. The CIS of PredRNN++ is the highest in any intensity, MIM and PhyDNet performance is better than semi-Lagrangian optical flow for radar echo intensity exceeding 40 dBz, but CrevNet shows better skill for radar echo intensity exceeding 50 dBz. The POD and CSI of machine algorithms and semi-Lagrangian optical flow decline significantly with the increase of forecast intensity of radar echo, while the FAR increases quickly, but the increase of FAR rate of machine learning algorithm is much slower. To sum up, the analysis of four different echo patterns and different development trends shows that the machine learning algorithm has the ability not only to predict the change of radar echo intensity in a certain content, but also to predict the time node of the evolution tendency of intensity and acreage, which are basically consistent with the observation. These results suggest that the ability of machine deep learning to predict the movement of radar echo is better than that of semi-Lagrangian optical flow, indicating its possible wide prospect for operational application.
2022, 48(4):442-451.
DOI: 10.7519/j.issn.1000-0526.2022.011501
Abstract:
Based on VLF/LF three-dimensional lightning monitoring data, combined with S-band dual-polarization radar and ground observation data, the lightning characteristics of 31 hail cells in Fujian during 2017-2020 are analyzed by using statistical analysis and comparative analysis methods. The results show that before hailing, about two thirds of the lightning frequency peaks are more than 50 times per 6 min, and 80% hail cloud surface hail falls behind 3-25 min after the occurrence of the lightning peak. The total lightning frequency increases sharply before hail, and 70% of the hail storm cell average increase rate is more than 4 times per minute, and the time of lightning rapid jump ahead of hail is mostly 6-40 min. Cloud flash occurs the most in the mature stage of the three stages and the least in the developing stage. The results show that, before hail falling, above melting layer, the hail cloud is composed of hail and graupel, and under the melting layer, it is composed of dry hail, wet hail and rain particles, while the lower layer is mainly composed of wet hail. The lightning intensity is closely related to the echo intensity, the strongest echo height and the strong echo spreading height. These results could provide some references for hail recognition and lightning warning.
Based on VLF/LF three-dimensional lightning monitoring data, combined with S-band dual-polarization radar and ground observation data, the lightning characteristics of 31 hail cells in Fujian during 2017-2020 are analyzed by using statistical analysis and comparative analysis methods. The results show that before hailing, about two thirds of the lightning frequency peaks are more than 50 times per 6 min, and 80% hail cloud surface hail falls behind 3-25 min after the occurrence of the lightning peak. The total lightning frequency increases sharply before hail, and 70% of the hail storm cell average increase rate is more than 4 times per minute, and the time of lightning rapid jump ahead of hail is mostly 6-40 min. Cloud flash occurs the most in the mature stage of the three stages and the least in the developing stage. The results show that, before hail falling, above melting layer, the hail cloud is composed of hail and graupel, and under the melting layer, it is composed of dry hail, wet hail and rain particles, while the lower layer is mainly composed of wet hail. The lightning intensity is closely related to the echo intensity, the strongest echo height and the strong echo spreading height. These results could provide some references for hail recognition and lightning warning.
2022, 48(4):452-458.
DOI: 10.7519/j.issn.1000-0526.2021.111701
Abstract:
The characteristics of water vapor, liquid water content, humidity and cloud base height in the north and south of Qinling Mountains are analyzed by using microwave radiometer. The results show that the annual average of vertically integrated water vapor in the north of Qinling Mountains is 18.52 kg·m-2, and that in the south of Qinling Mountains is 20.94 kg·m-2. The average height of more than 90% water vapor in the north of Qinling Mountains is 4.26 km, and that in the south of Qinling Mountains is 3.87 km. The average annual water content of vertical integrated liquid water content is 0.13 kg·m-2 in the south of Qinling Mountains and 0.12 kg·m-2 in the north of Qinling Mountains. The air humidity in the hinterland of Qinling Mountains is high. The annual average relative humidity in the south of Qinling Mountains is 75.3%, and that in the north of Qinling Mountains is 59.8%. The average relative humidity in the south of Qinling Mountains is 15.6% higher than that in the north of Qinling Mountains. The average height of cloud base is 3 〖KG-*5〗817.5 m in the south of Qinling Mountains and 4 〖KG-*5〗396 m in the north of Qinling Mountains. The annual average height of cloud base in the south of Qinling Mountains is 323.3 m, which is 42.2 m lower than that in the north of Qinling Mountains.
The characteristics of water vapor, liquid water content, humidity and cloud base height in the north and south of Qinling Mountains are analyzed by using microwave radiometer. The results show that the annual average of vertically integrated water vapor in the north of Qinling Mountains is 18.52 kg·m-2, and that in the south of Qinling Mountains is 20.94 kg·m-2. The average height of more than 90% water vapor in the north of Qinling Mountains is 4.26 km, and that in the south of Qinling Mountains is 3.87 km. The average annual water content of vertical integrated liquid water content is 0.13 kg·m-2 in the south of Qinling Mountains and 0.12 kg·m-2 in the north of Qinling Mountains. The air humidity in the hinterland of Qinling Mountains is high. The annual average relative humidity in the south of Qinling Mountains is 75.3%, and that in the north of Qinling Mountains is 59.8%. The average relative humidity in the south of Qinling Mountains is 15.6% higher than that in the north of Qinling Mountains. The average height of cloud base is 3 〖KG-*5〗817.5 m in the south of Qinling Mountains and 4 〖KG-*5〗396 m in the north of Qinling Mountains. The annual average height of cloud base in the south of Qinling Mountains is 323.3 m, which is 42.2 m lower than that in the north of Qinling Mountains.
2022, 48(4):459-469.
DOI: 10.7519/j.issn.1000-0526.2022.032201
Abstract:
The global surface temperature in 2021 was 1.11℃ (±0.13℃) higher than the pre-industrial level, being the seventh warmest year on record. Ocean heat content and global mean sea level have reached record highs, while the Antarctica and Arctic sea-ice extents were lower than normal. Many high-impact weather and climate events occurred across the world in 2021, including torrential rains and floods in Eurasia, severe droughts in North America and Asia, frequent tropical cyclones in the North Atlantic and North Indian Ocean, heatwaves and wildfires in Europe and North America, cold spells and snowstorms in Europe, America and Asia as well as severe convective weather events in many regions. Cause analyses suggest that in mid-July, central Europe was continuously controlled by the cut-off low pressure, and the water vapor from the Atlantic and Mediterranean was transported to central and western Europe, bringing extremely heavy precipitation and floods. In mid-February, the tropospheric polar vortex migrated southward from the Arctic to central North America, forming a stable low-pressure trough. Under the combined action of the low-pressure trough, favorable low-level water vapor transportation and the stratospheric sudden warming in the early stage, extreme low-temperatures and severe snowstorms attacked most regions of North America.
The global surface temperature in 2021 was 1.11℃ (±0.13℃) higher than the pre-industrial level, being the seventh warmest year on record. Ocean heat content and global mean sea level have reached record highs, while the Antarctica and Arctic sea-ice extents were lower than normal. Many high-impact weather and climate events occurred across the world in 2021, including torrential rains and floods in Eurasia, severe droughts in North America and Asia, frequent tropical cyclones in the North Atlantic and North Indian Ocean, heatwaves and wildfires in Europe and North America, cold spells and snowstorms in Europe, America and Asia as well as severe convective weather events in many regions. Cause analyses suggest that in mid-July, central Europe was continuously controlled by the cut-off low pressure, and the water vapor from the Atlantic and Mediterranean was transported to central and western Europe, bringing extremely heavy precipitation and floods. In mid-February, the tropospheric polar vortex migrated southward from the Arctic to central North America, forming a stable low-pressure trough. Under the combined action of the low-pressure trough, favorable low-level water vapor transportation and the stratospheric sudden warming in the early stage, extreme low-temperatures and severe snowstorms attacked most regions of North America.
CHEN Yu
,
WANG Ling
,
ZHAO Junhu
,
ZHANG Yingxian
,
ZHAO Shanshan
,
LI Wei
,
ZOU Xukai
,
JIANG Yundi
,
SHI Shuai
,
HONG Jieli
,
HAN Rongqing
,
WANG Youmin
,
HOU Wei
,
ZHU Xiaojin
,
DAI Tanlong
,
CAI Wenyue
,
GUO Yanjun
,
Hailing
,
WANG Qiyi
2022, 48(4):470-478.
DOI: 10.7519/j.issn.1000-0526.2022.022501
Abstract:
In 2021, the annual climate anomaly was warmer and wetter in China. The annual mean temperature nationwide was 1.0℃ higher than normal, breaking the historical record since 1951, and the annual precipitation was 6.7% more than normal, ranking the 12th highest since 1951. The seasonal precipitation in spring, summer and autumn was above normal, but below normal in winter. The onsets of pre-flood rainy season in South China, rainy season in Southwest China, and Meiyu season were all later than normal, while the withdrawls of these rainy seasons were earlier than normal with deficient precipitation. However, the rainy season in North China and Northeast China as well as the autumn rain in West China all started earlier and ended later with abundant rainfall. The autumn rainfall in West China was the most since 1961. In 2021, floods were more severe than droughts in China. The torrential rain intensity in rainy season was very strong, featured with significant extremities. The extreme severe torrential rain in Henan Province caused serious life and property losses. Autumn rainfall in northern China was more than normal. The influences of drought, typhoon, hail and hale, frost and snow disaster losses were light.
In 2021, the annual climate anomaly was warmer and wetter in China. The annual mean temperature nationwide was 1.0℃ higher than normal, breaking the historical record since 1951, and the annual precipitation was 6.7% more than normal, ranking the 12th highest since 1951. The seasonal precipitation in spring, summer and autumn was above normal, but below normal in winter. The onsets of pre-flood rainy season in South China, rainy season in Southwest China, and Meiyu season were all later than normal, while the withdrawls of these rainy seasons were earlier than normal with deficient precipitation. However, the rainy season in North China and Northeast China as well as the autumn rain in West China all started earlier and ended later with abundant rainfall. The autumn rainfall in West China was the most since 1961. In 2021, floods were more severe than droughts in China. The torrential rain intensity in rainy season was very strong, featured with significant extremities. The extreme severe torrential rain in Henan Province caused serious life and property losses. Autumn rainfall in northern China was more than normal. The influences of drought, typhoon, hail and hale, frost and snow disaster losses were light.
2022, 48(4):479-493.
DOI: 10.7519/j.issn.1000-0526.2021.110501
Abstract:
The spatial-temporal characteristics were well predicted for flood season in 2021, including typical features such as “normal to poor climatic conditions nationwide, concurrent drought and flood disasters in different regions and stages, more frequently extreme weather and climate events, main rainfall belt located in northern China”. We further successfully predicted that heavy floods might occur in parts of Haihe River Basin, Songhuajiang River Basin, and the upper and lower reaches of Yangtze River. The issued operational forecasts well captured the main features of summer climate except two shortcomings. Firstly, the intensity of abnormal heavy rainfall in northern China was underestimated, partly due to the predictability limits of extreme events. Secondly, the inconsistency of drought tendency existed between the forecast and observation over the eastern part of Northwest China, the middle reaches of the Yangtze River and the eastern part of South China. The mid-summer issued forecast in late June predicted there would be heavy precipitation in the middle and lower reaches of the Yangtze River and most of northern China, which was more consistent with the observation. The possible impacts of several key predictors on the East Asian summer monsoon (EASM) in 2021 were analyzed comprehensively in March including the cold phase of the Pacific decadal oscillation (PDO), decaying La Ni〖AKn~D〗a event, less snow cover over the Tibetan Plateau in pre-winter, and the positive phase of the North Atlantic tripole mode. These predictors and dynamic models all forecasted the EASM in 2021 would be stronger than normal and might lead to more rainfall in northern China in summer. However, both the precursory signals and dynamic models cannot predict the intraseasonal variations of EASM three months in advance. Finally, the external forcing signals and circulation in 2021 are compared with that in 2020. It should be note that the factors affecting precipitation in flood season are complicated and need further studying.
The spatial-temporal characteristics were well predicted for flood season in 2021, including typical features such as “normal to poor climatic conditions nationwide, concurrent drought and flood disasters in different regions and stages, more frequently extreme weather and climate events, main rainfall belt located in northern China”. We further successfully predicted that heavy floods might occur in parts of Haihe River Basin, Songhuajiang River Basin, and the upper and lower reaches of Yangtze River. The issued operational forecasts well captured the main features of summer climate except two shortcomings. Firstly, the intensity of abnormal heavy rainfall in northern China was underestimated, partly due to the predictability limits of extreme events. Secondly, the inconsistency of drought tendency existed between the forecast and observation over the eastern part of Northwest China, the middle reaches of the Yangtze River and the eastern part of South China. The mid-summer issued forecast in late June predicted there would be heavy precipitation in the middle and lower reaches of the Yangtze River and most of northern China, which was more consistent with the observation. The possible impacts of several key predictors on the East Asian summer monsoon (EASM) in 2021 were analyzed comprehensively in March including the cold phase of the Pacific decadal oscillation (PDO), decaying La Ni〖AKn~D〗a event, less snow cover over the Tibetan Plateau in pre-winter, and the positive phase of the North Atlantic tripole mode. These predictors and dynamic models all forecasted the EASM in 2021 would be stronger than normal and might lead to more rainfall in northern China in summer. However, both the precursory signals and dynamic models cannot predict the intraseasonal variations of EASM three months in advance. Finally, the external forcing signals and circulation in 2021 are compared with that in 2020. It should be note that the factors affecting precipitation in flood season are complicated and need further studying.
10 Analysis of Characteristics and Causes of Precipitation Anomalies over Northern China in Autumn 2021
2022, 48(4):494-503.
DOI: 10.7519/j.issn.1000-0526.2022.032101
Abstract:
During the autumn of 2021 the climate of China featured higher temperature and more rainfall in general. The distribution of precipitation in China showed “more in the north and less in the south”. The autumn precipitation in West China is extremely heavy. The analysis of the causes of the unusually heavy precipitation in the northern region of China shows that from September to early October, the circulation in the middle and high latitudes of Europe and Asia was distributed in a pattern of “low in the west and high in the east”, the area between Baikal Lake and Balkhash Lake was under a significantly low trough, and the West Pacific subtropical high (WPSH) was stronger, stretching more westward and northward than normal, which was favorable for the intensive autumn rainfall in West China. Meanwhile, anticyclonic circulation anomaly over the western Sea of Japan was conducive to guiding the cold and wet airflow to the area between the Yellow River and the Yangtze River, and met with the warm and wet airflow from the Bay of Bengal and the South China Sea, forming an abnormal convergence area of water vapor flux and resulting in abnormal precipitation in the northern region. In addition, the abnormal more days with MJO is in the 3rd to 5th phases was also one of the favorable factors for precipitation in northern China. Further diagnosis shows that the SST in the tropical middle-east Pacific Ocean entered La Ni〖AKn~D〗a state again in autumn, and the SST condition of double-dip La Ni〖AKn~D〗a was favorable for the WPSH to be stronger and extend westward to the north. Therefore, the influence of the external forcing signal of SST combined with the circulation anomaly in the low and mid-high latitudes led to more precipitation in northern China during September to early October.
During the autumn of 2021 the climate of China featured higher temperature and more rainfall in general. The distribution of precipitation in China showed “more in the north and less in the south”. The autumn precipitation in West China is extremely heavy. The analysis of the causes of the unusually heavy precipitation in the northern region of China shows that from September to early October, the circulation in the middle and high latitudes of Europe and Asia was distributed in a pattern of “low in the west and high in the east”, the area between Baikal Lake and Balkhash Lake was under a significantly low trough, and the West Pacific subtropical high (WPSH) was stronger, stretching more westward and northward than normal, which was favorable for the intensive autumn rainfall in West China. Meanwhile, anticyclonic circulation anomaly over the western Sea of Japan was conducive to guiding the cold and wet airflow to the area between the Yellow River and the Yangtze River, and met with the warm and wet airflow from the Bay of Bengal and the South China Sea, forming an abnormal convergence area of water vapor flux and resulting in abnormal precipitation in the northern region. In addition, the abnormal more days with MJO is in the 3rd to 5th phases was also one of the favorable factors for precipitation in northern China. Further diagnosis shows that the SST in the tropical middle-east Pacific Ocean entered La Ni〖AKn~D〗a state again in autumn, and the SST condition of double-dip La Ni〖AKn~D〗a was favorable for the WPSH to be stronger and extend westward to the north. Therefore, the influence of the external forcing signal of SST combined with the circulation anomaly in the low and mid-high latitudes led to more precipitation in northern China during September to early October.
11 The Analysis of Characteristics and Forecast Difficulties of TCs in Western North Pacific in 2020
2022, 48(4):504-515.
DOI: 10.7519/j.issn.10000526.2021.112401
Abstract:
The characteristics of TCs on Western North Pacific in 2020 are analyzed by using the besttrack data, CMA operational forecasting data, ECMWF ERAInterim reanalysis products and NCEP RTG_SST data. Results show that the number of typhoons and landing typhoons were less and the strength were weak in 2020. The generating locations were inclined more westward. The stage and group characteristics of typhoon activity were prominent, and the features of offshore rapid intensification (RI) were obvious. Estimations of track errors in 2020 show that there were some decreases compared to those of 2019, with the error values of 70, 117, 169, 222 and 276 km for the forecasts with 24, 48, 72, 96 and 120 h lead time, respectively. The strength errors were 3.9, 5.1, 5.5, 6.2 and 6.3 m·s-1 for the forecasts with 24, 48, 72, 96 and 120 h lead time, respectively and decreased a little compared to the errors of 2019. The 24 h errors were less than 4.0 m·s-1 for 4 consecutive years. In addition, the forecast difficulties in 2020 lie in that, first, the strength forecast of RI TCs offshore, and second, the strength forecast of typhoons going northward after landing.
The characteristics of TCs on Western North Pacific in 2020 are analyzed by using the besttrack data, CMA operational forecasting data, ECMWF ERAInterim reanalysis products and NCEP RTG_SST data. Results show that the number of typhoons and landing typhoons were less and the strength were weak in 2020. The generating locations were inclined more westward. The stage and group characteristics of typhoon activity were prominent, and the features of offshore rapid intensification (RI) were obvious. Estimations of track errors in 2020 show that there were some decreases compared to those of 2019, with the error values of 70, 117, 169, 222 and 276 km for the forecasts with 24, 48, 72, 96 and 120 h lead time, respectively. The strength errors were 3.9, 5.1, 5.5, 6.2 and 6.3 m·s-1 for the forecasts with 24, 48, 72, 96 and 120 h lead time, respectively and decreased a little compared to the errors of 2019. The 24 h errors were less than 4.0 m·s-1 for 4 consecutive years. In addition, the forecast difficulties in 2020 lie in that, first, the strength forecast of RI TCs offshore, and second, the strength forecast of typhoons going northward after landing.
2022, 48(4):516-525.
DOI: 10.7519/j.issn.1000-0526.2022.022101
Abstract:
Operational positioning and intensity estimation errors, track, intensity and landfall point forecast errors of 23 named typhoons over Western North Pacific and South China Sea in 2020 are evaluated in this paper. The verification results show that the total average positioning and intensity estimation errors of National Meteorological Centre (NMC), CMA, were 22.7 km and 1.3 m·s-1, respectively. Compared to the estimation in 2019, the value of positioning error was slightly larger than in 2019, but the intensity estimation error was a little bit smaller than in 2019. Since 2013, the overall track forecast performance within 72 h has not shown substantive improvement for both subjective and objective forecast methods. Moreover by 2020, the mean values of extreme large track error cases were still up to 2.3-3.0 times compared with their annual mean track errors, but the intensity forecast performance of NMC/CMA within 72 h 〖JP2〗was better than that of the other official typhoon〖JP〗 forecast agencies. In 2020, the landfall point of tropical Storm Nuri at Hailing Island, Yangjiang City, Guangdong Province was successfully predicted by all the official typhoon forecast agencies and some models also accurately forecasted in 24 h advance the Severe Typhoon Hagupit and Severe Tropical Storm Nangka’s landfall points in Yueqing City, Zhejiang Province and Qionghai City, Hainan Province, respectively.
Operational positioning and intensity estimation errors, track, intensity and landfall point forecast errors of 23 named typhoons over Western North Pacific and South China Sea in 2020 are evaluated in this paper. The verification results show that the total average positioning and intensity estimation errors of National Meteorological Centre (NMC), CMA, were 22.7 km and 1.3 m·s-1, respectively. Compared to the estimation in 2019, the value of positioning error was slightly larger than in 2019, but the intensity estimation error was a little bit smaller than in 2019. Since 2013, the overall track forecast performance within 72 h has not shown substantive improvement for both subjective and objective forecast methods. Moreover by 2020, the mean values of extreme large track error cases were still up to 2.3-3.0 times compared with their annual mean track errors, but the intensity forecast performance of NMC/CMA within 72 h 〖JP2〗was better than that of the other official typhoon〖JP〗 forecast agencies. In 2020, the landfall point of tropical Storm Nuri at Hailing Island, Yangjiang City, Guangdong Province was successfully predicted by all the official typhoon forecast agencies and some models also accurately forecasted in 24 h advance the Severe Typhoon Hagupit and Severe Tropical Storm Nangka’s landfall points in Yueqing City, Zhejiang Province and Qionghai City, Hainan Province, respectively.
2022, 48(4):525-532.
DOI: 10.7519/j.issn.1000-0526.2022.030701
Abstract:
The main characteristics of the general atmospheric circulation in January 2022 are as follow. There were two polar vortex centers in the Northern Hemisphere. The location of the East Asian trough was more eastward than in the same period of normal years. The southern branch trough was stronger during this month. The monthly mean precipitation over China was 18.2 mm, which is 25.5% more than normal (14.5 mm). However, during the month there were two widespread freezing rain and snow processes affecting the central and eastern parts of China, featured with a wide range of influence, heavy snowfall and complex phase patterns of rain and snow. Jiangxi, Hunan, Chongqing, Sichuan, Yunnan and Tibet etc. suffered at varying degrees from low temperature, freezing rain and snow disasters. Generally, cold air activities were weak during this month. The monthly mean temperature was -4.0℃, which is 0.8℃ higher than normal (-4.8℃). In addition, two fog-haze events occurred in this month.
The main characteristics of the general atmospheric circulation in January 2022 are as follow. There were two polar vortex centers in the Northern Hemisphere. The location of the East Asian trough was more eastward than in the same period of normal years. The southern branch trough was stronger during this month. The monthly mean precipitation over China was 18.2 mm, which is 25.5% more than normal (14.5 mm). However, during the month there were two widespread freezing rain and snow processes affecting the central and eastern parts of China, featured with a wide range of influence, heavy snowfall and complex phase patterns of rain and snow. Jiangxi, Hunan, Chongqing, Sichuan, Yunnan and Tibet etc. suffered at varying degrees from low temperature, freezing rain and snow disasters. Generally, cold air activities were weak during this month. The monthly mean temperature was -4.0℃, which is 0.8℃ higher than normal (-4.8℃). In addition, two fog-haze events occurred in this month.
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ISSN:1000-0526 CN:11-2282/P