ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 47,Issue 3,2021 Table of Contents
2021, 47(3):261-273.
DOI: 10.7519/j.issn.1000-0526.2021.03.001
Abstract:
Extratropical cyclones are the most important “actors” in the daily weather stage in mid-latitude region. In autumn-winter season, a great number of rapidly intensifying extratropical cyclones occur over the mid- and high-latitude oceans, termed as “explosive cyclones”, which have received less public attention. Regarding to the theme “explosive cyclone”, this paper firstly reviews the historical background of researching extratropical cyclone, and then introduces the origin of the term “explosive cyclone”. The definitions of explosive cyclones given by several scholars are systematically analyzed. A modified definition of explosive cyclone considering the influence of strong winds is emphatically introduced. In addition, the current research status of explosive cyclones is summarized systematically. Finally, the future outlook of explosive cyclone study in next few decades is prospected.
Extratropical cyclones are the most important “actors” in the daily weather stage in mid-latitude region. In autumn-winter season, a great number of rapidly intensifying extratropical cyclones occur over the mid- and high-latitude oceans, termed as “explosive cyclones”, which have received less public attention. Regarding to the theme “explosive cyclone”, this paper firstly reviews the historical background of researching extratropical cyclone, and then introduces the origin of the term “explosive cyclone”. The definitions of explosive cyclones given by several scholars are systematically analyzed. A modified definition of explosive cyclone considering the influence of strong winds is emphatically introduced. In addition, the current research status of explosive cyclones is summarized systematically. Finally, the future outlook of explosive cyclone study in next few decades is prospected.
2 Advances in Application of Machine Learning to Severe Convective Weather Monitoring and Forecasting
2021, 47(3):274-289.
DOI: 10.7519/j.issn.1000-0526.2021.03.002
Abstract:
In recent years, the theory of machine learning and its applications to severe convective weather has been developed at an unprecedented speed. Various machine learning algorithms, such as random forest, decision tree, support vector machine, neural network and deep learning have played important roles in severe convective weather monitoring, nowcasting, short-term forecasting and short-range forecasting. These algorithms often have better performances than traditional methods. With the help of machine learning, it is easier to extract the mesoscale features of convective systems in high spatio-temporal resolution observation data, resulting in better performances of automatic convective weather identification and tracking and warning. Machine learning is also a good tool to effectively use the multi-source observation data, analyze the observation and numerical weather prediction (NWP) data. In addition, machine learning can also be an effective postprocessing method for NWP. It has been showed that machine learning can extract the features of severe weather occurrence from global or regional NWP data and give a reliable severe weather forecasts. Finally, the issues and outlooks of machine learning application are presented.
In recent years, the theory of machine learning and its applications to severe convective weather has been developed at an unprecedented speed. Various machine learning algorithms, such as random forest, decision tree, support vector machine, neural network and deep learning have played important roles in severe convective weather monitoring, nowcasting, short-term forecasting and short-range forecasting. These algorithms often have better performances than traditional methods. With the help of machine learning, it is easier to extract the mesoscale features of convective systems in high spatio-temporal resolution observation data, resulting in better performances of automatic convective weather identification and tracking and warning. Machine learning is also a good tool to effectively use the multi-source observation data, analyze the observation and numerical weather prediction (NWP) data. In addition, machine learning can also be an effective postprocessing method for NWP. It has been showed that machine learning can extract the features of severe weather occurrence from global or regional NWP data and give a reliable severe weather forecasts. Finally, the issues and outlooks of machine learning application are presented.
2021, 47(3):290-302.
DOI: 10.7519/j.issn.1000-0526.2021.03.003
Abstract:
Based on meteorological monitoring data in Guangdong Province, the Shanwei radar data and global reanalysis data CFSR, this study analyzed the processes of two record-breaking extremely severe precipitation events in August 2013 and August 2018 over the eastern part of Guangdong, aiming to expound the effect of boundary layer jet. The results are as follows. (1) The main influence systems of the two processes respectively were the long time slowly-moving residual circulation of Typhoon Utor and the outer circulation of monsoon depression. When the eastern region of Guangdong was located on the southeast side of typhoon circulation or the east side of monsoon depression, the convergence and uplift of boundary layer jet formed clear mesoscale energy fronts, which were the favorable conditions for severe precipita-tion. (2) Boundary layer jet provided abundant and continuous water vapor conditions for the severe rainfall, cooperated with the effect of special topographic friction and countercheck, thus remarkable convergence of water vapor flux was formed in the east of Guangdong. (3) During the development of persistent heavy rainfall, the atmospheric stratification was unstable for a long time, which was closely related to the continuous transport of warm and wet advection in the lower troposphere. The major differences between the two processes mainly lies in the differences of intensity and wind direction of boundary lay jet, thus the way of airflow convergence and scope of severe rainfall were significantly different.The role of boundary layer jet is revealed more significantly during the influence process of monsoon depression.
Based on meteorological monitoring data in Guangdong Province, the Shanwei radar data and global reanalysis data CFSR, this study analyzed the processes of two record-breaking extremely severe precipitation events in August 2013 and August 2018 over the eastern part of Guangdong, aiming to expound the effect of boundary layer jet. The results are as follows. (1) The main influence systems of the two processes respectively were the long time slowly-moving residual circulation of Typhoon Utor and the outer circulation of monsoon depression. When the eastern region of Guangdong was located on the southeast side of typhoon circulation or the east side of monsoon depression, the convergence and uplift of boundary layer jet formed clear mesoscale energy fronts, which were the favorable conditions for severe precipita-tion. (2) Boundary layer jet provided abundant and continuous water vapor conditions for the severe rainfall, cooperated with the effect of special topographic friction and countercheck, thus remarkable convergence of water vapor flux was formed in the east of Guangdong. (3) During the development of persistent heavy rainfall, the atmospheric stratification was unstable for a long time, which was closely related to the continuous transport of warm and wet advection in the lower troposphere. The major differences between the two processes mainly lies in the differences of intensity and wind direction of boundary lay jet, thus the way of airflow convergence and scope of severe rainfall were significantly different.The role of boundary layer jet is revealed more significantly during the influence process of monsoon depression.
2021, 47(3):303-316.
DOI: 10.7519/j.issn.1000-0526.2021.03.004
Abstract:
The warm-sector rainstorms over the Sichuan Basin were defined and classified into four types based on the synoptic situation, including the southwest vortex (SWV), the edge of subtropical high (ESH), the southwest jet (SWJ), and the southeast wind (SEW) types. By using the conventional and the hourly precipitation data of automatic weather station from May to September during 2008-2018, we statistically analyzed the spatio-temporal distribution of warm-sector rainstorms and the nature of precipitation as well as the mesoscale characteristics and causes for their formation. The major characteristics obtained from the research are that the four types of warm-secfor rainstorms usually occur in windward slope of mountain, bellmouth topography and near the uneven surface which is transitional zones between plain, hills and uplands, etc. The SWV and SWJ types have a large area, and the former mainly occurs in the middle and south of the basin and the latter from the middle of the basin to the north of Longmen Mountains and Daba Mountains. The ESH and SEW types are decentralized precipitation, and the precipitations usually occur in the west of the basin. All the four types of precipitation have substantial diurnal variation, showing a single peak type, which is strengthened at night but weakened during the day. The warm-sector rainstorms consist of stable and convective precipitations. The heavier the daily rainfall is, the more obvious convective nature the precipitation has, of which, the convective precipitation of ESH and SEW types is obvious, 〖JP2〗and the stable precipitation of SWV and SWJ types is obvious. The warm-sector rainstorms are directly caused by the development of MβCSs, the lifetimes of SWV and SWJ types are ≥6 h,〖JP〗 and those of ESH and SEW types are ≤6 h. All the four types of convective precipitation (20-50 mm·h-1) last for no more than 3 h, and the flash heavy rain (≥50 mm·h-1) lasts for no more than 1 h, otherwise, it is very easy to cause extreme precipitation events. SWV and SWJ types are prone to extreme heavy rainfall. The four types of rainstorms occur in the unstable environment with high energy and high humidity. The average CAPE value is more than 1 〖KG-*5〗000 J·kg-1, K index is about 40 ℃, θse of 850 hPa is about 85 ℃, and the average specific humidity can reach 16 g·kg-1。
The warm-sector rainstorms over the Sichuan Basin were defined and classified into four types based on the synoptic situation, including the southwest vortex (SWV), the edge of subtropical high (ESH), the southwest jet (SWJ), and the southeast wind (SEW) types. By using the conventional and the hourly precipitation data of automatic weather station from May to September during 2008-2018, we statistically analyzed the spatio-temporal distribution of warm-sector rainstorms and the nature of precipitation as well as the mesoscale characteristics and causes for their formation. The major characteristics obtained from the research are that the four types of warm-secfor rainstorms usually occur in windward slope of mountain, bellmouth topography and near the uneven surface which is transitional zones between plain, hills and uplands, etc. The SWV and SWJ types have a large area, and the former mainly occurs in the middle and south of the basin and the latter from the middle of the basin to the north of Longmen Mountains and Daba Mountains. The ESH and SEW types are decentralized precipitation, and the precipitations usually occur in the west of the basin. All the four types of precipitation have substantial diurnal variation, showing a single peak type, which is strengthened at night but weakened during the day. The warm-sector rainstorms consist of stable and convective precipitations. The heavier the daily rainfall is, the more obvious convective nature the precipitation has, of which, the convective precipitation of ESH and SEW types is obvious, 〖JP2〗and the stable precipitation of SWV and SWJ types is obvious. The warm-sector rainstorms are directly caused by the development of MβCSs, the lifetimes of SWV and SWJ types are ≥6 h,〖JP〗 and those of ESH and SEW types are ≤6 h. All the four types of convective precipitation (20-50 mm·h-1) last for no more than 3 h, and the flash heavy rain (≥50 mm·h-1) lasts for no more than 1 h, otherwise, it is very easy to cause extreme precipitation events. SWV and SWJ types are prone to extreme heavy rainfall. The four types of rainstorms occur in the unstable environment with high energy and high humidity. The average CAPE value is more than 1 〖KG-*5〗000 J·kg-1, K index is about 40 ℃, θse of 850 hPa is about 85 ℃, and the average specific humidity can reach 16 g·kg-1。
2021, 47(3):317-326.
DOI: 10.7519/j.issn.1000-0526.2021.03.005
Abstract:
This paper applies deep learning method on establishing a model to discriminate the precipitation type. Hundreds of thousands of precipitation samples obtained from sounding and observation data of China from 1996 to 2015 were divided into rain and snow events. The 2016-2017 data were tested, and a case test was conducted on a rain and snow weather process over China in late January 2018. Furthermore, the application of deep learning method to discrimination and forecasting of precipitation type was discussed. The main conclusions are as follows. Discrimination accuracy of the model is 98.2%, which is more improved than the traditional index threshold method. TS scores of rain and snow are 97.4% and 94.4%, false discriminate rates are 1.7% and 2.0%, and omission rates are 1.0% and 3.7%, respectively. The case test denotes that the model discrimination results based on the observation data are basically consistent with the observation data. The ECMWF precipitation type products and the model results also have a good forecast performance for precipitation type over China. Compared with ECMWF, the model forecast for rain and snow separating line is more consistent with observation. The test results show that the discrimination model established in this paper can extract the key features of precipitation type of rain and snow. The application of deep learning method to discrimination and forecasting of precipitation type is feasible and advantageous. Thus, this method could provide important technical support for the objective identification and prediction of precipitation type.
This paper applies deep learning method on establishing a model to discriminate the precipitation type. Hundreds of thousands of precipitation samples obtained from sounding and observation data of China from 1996 to 2015 were divided into rain and snow events. The 2016-2017 data were tested, and a case test was conducted on a rain and snow weather process over China in late January 2018. Furthermore, the application of deep learning method to discrimination and forecasting of precipitation type was discussed. The main conclusions are as follows. Discrimination accuracy of the model is 98.2%, which is more improved than the traditional index threshold method. TS scores of rain and snow are 97.4% and 94.4%, false discriminate rates are 1.7% and 2.0%, and omission rates are 1.0% and 3.7%, respectively. The case test denotes that the model discrimination results based on the observation data are basically consistent with the observation data. The ECMWF precipitation type products and the model results also have a good forecast performance for precipitation type over China. Compared with ECMWF, the model forecast for rain and snow separating line is more consistent with observation. The test results show that the discrimination model established in this paper can extract the key features of precipitation type of rain and snow. The application of deep learning method to discrimination and forecasting of precipitation type is feasible and advantageous. Thus, this method could provide important technical support for the objective identification and prediction of precipitation type.
6 Characteristics and Circulation Analysis of Intraseasonal Variability of Winter Temperature in China
2021, 47(3):327-336.
DOI: 10.7519/j.issn.1000-0526.2021.03.006
Abstract:
The mean variance of daily air temperature over China in winter was used as the index of intra-seasonal variation of air temperature. Its characteristics was analyzed and its circulation background induced by intra seasoual variability of winter temperature in China was explored. The results show that the internal variability of winter temperature in China is generally weakening, and it has obvious response characteristics to the trend of climate warming. The interdecadal change coincides with the interdecadal transition time of the East Asian winter monsoon. When the temperature seasonal variability is abnormally strong, the average circulation field in winter shows a quasi-positive pressure structure, the polar vortex in the stratosphere is weak, the high latitudes in the troposphere distribute similarly to Scandinavian teleconnection type, the middle and low latitudes have a large area of negative anomaly, the near strata of Siberia is strong, and North Atlemtic Oscillation (NAO) is a negative phase. Further analysis shows that NAO can affect the intraseasonal variation of temperature by affecting the high frequency variation of Siberian high pressure. In the end, through extracting the internal variability of atmospheric circulation on four time scales of weather season, the seasonal variability of air temperature is regulated by Siberian high and East Asian cold vortex. In addition, on the synoptic scale, frequent fluctuations of the Aleutian low and stable deactivation of the upper European ridge are closely related to the temperature variability, and the seasonal scales, including Eurasian blocking high and Okhosk Sea blocking high anomalies, have significant effects on the temperature variability.
The mean variance of daily air temperature over China in winter was used as the index of intra-seasonal variation of air temperature. Its characteristics was analyzed and its circulation background induced by intra seasoual variability of winter temperature in China was explored. The results show that the internal variability of winter temperature in China is generally weakening, and it has obvious response characteristics to the trend of climate warming. The interdecadal change coincides with the interdecadal transition time of the East Asian winter monsoon. When the temperature seasonal variability is abnormally strong, the average circulation field in winter shows a quasi-positive pressure structure, the polar vortex in the stratosphere is weak, the high latitudes in the troposphere distribute similarly to Scandinavian teleconnection type, the middle and low latitudes have a large area of negative anomaly, the near strata of Siberia is strong, and North Atlemtic Oscillation (NAO) is a negative phase. Further analysis shows that NAO can affect the intraseasonal variation of temperature by affecting the high frequency variation of Siberian high pressure. In the end, through extracting the internal variability of atmospheric circulation on four time scales of weather season, the seasonal variability of air temperature is regulated by Siberian high and East Asian cold vortex. In addition, on the synoptic scale, frequent fluctuations of the Aleutian low and stable deactivation of the upper European ridge are closely related to the temperature variability, and the seasonal scales, including Eurasian blocking high and Okhosk Sea blocking high anomalies, have significant effects on the temperature variability.
2021, 47(3):337-347.
DOI: 10.7519/j.issn.1000-0526.2021.03.007
Abstract:
In August 2019, the GLC-12A-type mobile X-band dual-polarization weather radar, developed by Fourteen Research Institute of China Electronics Technology Group, was used to conduct field tests in Dafeng, Jiangsu Province. In this paper, the data quality of the mobile X-band dual-polarization radar was analyzed by using the field observation data of this radar and the CINRAD/SA radar in Yancheng, and the data of six rainfall stations in Jiangsu Province. The results show that the attenuation of the radar after passing through the rain area is related to the distance. For the same precipitation process, the farther the distance, the greater the attenuation. In order to reduce the observation error caused by attenuation, the difference reflectivity Zdr and correlation coefficient ρhv(0) are corrected by signal-to-noise ratio (SNR) to improve the data quality for ground or biological clutter removing. Then the differential propagation phase shift rate Kdp or differential propagation phase dp after quality control was used to express the total bidirectional reflectivity factor attenuation correction at the distance of R in rain. At last, data analysis shows that the data quality of the revised mobile X-band dual-polarization radar has been improved.
In August 2019, the GLC-12A-type mobile X-band dual-polarization weather radar, developed by Fourteen Research Institute of China Electronics Technology Group, was used to conduct field tests in Dafeng, Jiangsu Province. In this paper, the data quality of the mobile X-band dual-polarization radar was analyzed by using the field observation data of this radar and the CINRAD/SA radar in Yancheng, and the data of six rainfall stations in Jiangsu Province. The results show that the attenuation of the radar after passing through the rain area is related to the distance. For the same precipitation process, the farther the distance, the greater the attenuation. In order to reduce the observation error caused by attenuation, the difference reflectivity Zdr and correlation coefficient ρhv(0) are corrected by signal-to-noise ratio (SNR) to improve the data quality for ground or biological clutter removing. Then the differential propagation phase shift rate Kdp or differential propagation phase dp after quality control was used to express the total bidirectional reflectivity factor attenuation correction at the distance of R in rain. At last, data analysis shows that the data quality of the revised mobile X-band dual-polarization radar has been improved.
2021, 47(3):348-358.
DOI: 10.7519/j.issn.1000-0526.2021.03.008
Abstract:
A fire inventory, surface monitoring data and numerical models were used to analyze the biomass burning aerosol (BBA) emission in Indochina Peninsula and its effects on haze in Southwest China and pre-monsoon precipitation in South China. The BBA emissions in Indochina Peninsula mainly occur in March and April every year, and the peak emissions appear in late March to early April. Eastern Myanmar and Northern Laos are the major regions of the BBA emissions. The BBA in Indochina Peninsula mainly affects the haze in the southern cities of Yunnan, China at the surface. The emission from Myanmar is the most significant contributor to the pollution in these cities. The BBA in Indochina Peninsula could be transported over most parts of Southern China by the low-level southwest jet at the height from 800 hPa to 600 hPa. The transported BBA over Southern China could alter the spatial fraction of the pre-monsoon precipitation process by suppressing the convective precipitation and enhancing the non-convective precipitation, and making precipitation more concentrated near the shear line.
A fire inventory, surface monitoring data and numerical models were used to analyze the biomass burning aerosol (BBA) emission in Indochina Peninsula and its effects on haze in Southwest China and pre-monsoon precipitation in South China. The BBA emissions in Indochina Peninsula mainly occur in March and April every year, and the peak emissions appear in late March to early April. Eastern Myanmar and Northern Laos are the major regions of the BBA emissions. The BBA in Indochina Peninsula mainly affects the haze in the southern cities of Yunnan, China at the surface. The emission from Myanmar is the most significant contributor to the pollution in these cities. The BBA in Indochina Peninsula could be transported over most parts of Southern China by the low-level southwest jet at the height from 800 hPa to 600 hPa. The transported BBA over Southern China could alter the spatial fraction of the pre-monsoon precipitation process by suppressing the convective precipitation and enhancing the non-convective precipitation, and making precipitation more concentrated near the shear line.
2021, 47(3):359-372.
DOI: 10.7519/j.issn.1000-0526.2021.03.009
Abstract:
The characteristics of TCs and forecast difficulties over Northwestern Pacific in 2018 were analyzed by using the best-track data of CMA (1949-2018), CMA operational TC forecasting data of 2018, ECMWF forecast products and NCEP RTG_SST (real-time global sea surface temperature, 0.083°×0.083°) data. The results showed that the total TC genesis number was much more in 2018 than climate average and the TC generating location was more eastward, but the TCs in South China Sea were much more active. The TC genesis time concentrate in summer and the TC genesis number in summer was much more than climate average. The TC genesis clusters and coexisting multiple TCs were frequently seen. The annual lifetime of TCs was longer and the accumulative cyclone energy was higher. However, the overall TC intensity was much weaker, and the proportion of weaker TCs was abnormally higher. The number and frequency of land falling TC were much higher, and the landing TC locations were northward, but the landfalling TC intensity was abnormally weaker. The track forecast errors of CMA this year decreased compared with those of 2017 for all forecast leading time, with the value of 72, 124, 179, 262 and 388 km for 24, 48, 72, 96 and 120 h lead time. Especially, the track errors decreased obviously for long lead-time. The intensity errors respectively were 3.7, 5.1, 5.5, 6.6 and 7.1 m·s-1 for 24, 48, 72, 96 and 120 h lead time. The track forecast difficulties were mainly caused by complex interaction between binary typhoons or among multiple TCs as well as the large uncertainty caused by the saddle circulation. Meanwhile, the uncertainty of intensity forecast of TCs near off-shore and the complex moisture transport among multiple TCs are the main causes for the difficulties of TC intensity forecasting. The forecast problems would be solved effectively if there were more observations, in-depth mechanism studies and more effective forecasting techniques.
The characteristics of TCs and forecast difficulties over Northwestern Pacific in 2018 were analyzed by using the best-track data of CMA (1949-2018), CMA operational TC forecasting data of 2018, ECMWF forecast products and NCEP RTG_SST (real-time global sea surface temperature, 0.083°×0.083°) data. The results showed that the total TC genesis number was much more in 2018 than climate average and the TC generating location was more eastward, but the TCs in South China Sea were much more active. The TC genesis time concentrate in summer and the TC genesis number in summer was much more than climate average. The TC genesis clusters and coexisting multiple TCs were frequently seen. The annual lifetime of TCs was longer and the accumulative cyclone energy was higher. However, the overall TC intensity was much weaker, and the proportion of weaker TCs was abnormally higher. The number and frequency of land falling TC were much higher, and the landing TC locations were northward, but the landfalling TC intensity was abnormally weaker. The track forecast errors of CMA this year decreased compared with those of 2017 for all forecast leading time, with the value of 72, 124, 179, 262 and 388 km for 24, 48, 72, 96 and 120 h lead time. Especially, the track errors decreased obviously for long lead-time. The intensity errors respectively were 3.7, 5.1, 5.5, 6.6 and 7.1 m·s-1 for 24, 48, 72, 96 and 120 h lead time. The track forecast difficulties were mainly caused by complex interaction between binary typhoons or among multiple TCs as well as the large uncertainty caused by the saddle circulation. Meanwhile, the uncertainty of intensity forecast of TCs near off-shore and the complex moisture transport among multiple TCs are the main causes for the difficulties of TC intensity forecasting. The forecast problems would be solved effectively if there were more observations, in-depth mechanism studies and more effective forecasting techniques.
2021, 47(3):373-380.
DOI: 10.7519/j.issn.1000-0526.2021.03.010
Abstract:
For the purpose of studying the lightning nowcasting early warning model of artificial intelligence, by relying on the convolutional neural network model and combining the radar data (MCR, VIL, ET) and lightning data of multiple time series, we conduct the application of the lightning nowcasting prediction method based on the structure of convolutional neural network. In addition, taking the radar and lightning data of Fujian Province in 2017 and 2018 as samples, we also finish the training and prediction research of the model. The training results show that the test set accuracy of 15-30 min model training samples is 0.798 〖KG-*5〗5. The verification analysis of the 20 lightning processes in Fujian Province in 2019 indicates that the TS score of the 15-30 min model for the nowcasting early warning of the dynamic-lift lightning process is 0.716, and the TS score of the localized thermal thunderstorm nowcasting warning in summer is 0.694. Compared with the conventional lightning warning algorithm which uses radar and lightning threshold control, these values have a certain improvement in accuracy, so having certain practical significance.
For the purpose of studying the lightning nowcasting early warning model of artificial intelligence, by relying on the convolutional neural network model and combining the radar data (MCR, VIL, ET) and lightning data of multiple time series, we conduct the application of the lightning nowcasting prediction method based on the structure of convolutional neural network. In addition, taking the radar and lightning data of Fujian Province in 2017 and 2018 as samples, we also finish the training and prediction research of the model. The training results show that the test set accuracy of 15-30 min model training samples is 0.798 〖KG-*5〗5. The verification analysis of the 20 lightning processes in Fujian Province in 2019 indicates that the TS score of the 15-30 min model for the nowcasting early warning of the dynamic-lift lightning process is 0.716, and the TS score of the localized thermal thunderstorm nowcasting warning in summer is 0.694. Compared with the conventional lightning warning algorithm which uses radar and lightning threshold control, these values have a certain improvement in accuracy, so having certain practical significance.
2021, 47(3):381-388.
DOI: 10.7519/j.issn.1000-0526.2021.03.011
Abstract:
The main characteristics of the general atmospheric circulation in December 2020 are as follows. Polar vortex in the Northern Hemisphere had a dipole distribution, the circulation presented a three-wave pattern in middle-high latitudes. The atmospheric circulation presented a great meridionality in mid-latitudes in Eurasia. The East Asian trough behaved strongly and the southern branch trough was weaker in this month. The monthly mean precipitation over China was 5.8 mm, 45.3 % less than normal (10.5 mm). The national monthly average temperature was -3.9℃, 0.7℃ lower than the normal. Totally, there were two strong cold air processes, two precipitation processes and two large-scale fog-haze events in this month. During 27-31 December, most of China suffered from the cold surge characterized by a sharply drop in temperatures and extremely wide areas being impacted. The lowest temperature in many places exceeded the historical extreme values.
The main characteristics of the general atmospheric circulation in December 2020 are as follows. Polar vortex in the Northern Hemisphere had a dipole distribution, the circulation presented a three-wave pattern in middle-high latitudes. The atmospheric circulation presented a great meridionality in mid-latitudes in Eurasia. The East Asian trough behaved strongly and the southern branch trough was weaker in this month. The monthly mean precipitation over China was 5.8 mm, 45.3 % less than normal (10.5 mm). The national monthly average temperature was -3.9℃, 0.7℃ lower than the normal. Totally, there were two strong cold air processes, two precipitation processes and two large-scale fog-haze events in this month. During 27-31 December, most of China suffered from the cold surge characterized by a sharply drop in temperatures and extremely wide areas being impacted. The lowest temperature in many places exceeded the historical extreme values.
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