ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 45,Issue 3,2019 Table of Contents
2019, 45(3):297-304.
DOI: 10.7519/j.issn.1000-0526.2019.03.001
Abstract:
In this paper, the main research achievements about the influence of solar activity on the climate and weather of the earth are introduced. Four key issues are discussed, which are: (1) solar activity and the energy budget of the earth system; (2) impact of solar activity on the earth’s climate, including climate change, greenhouse effect and volcanoes; (3) effect of solar activity on monsoon and weather; and (4) whether changes of solar activity will cause longterm cold climate of the earth. The elaboration to these issues is helpful for deeply understanding the fact and mechanism of the influence of solar activity on the climate and weather of the earth, and also the prospect of the possible changes of the earth’s climate in the future. Thus, I hope this paper could present a more comprehensive and profound understanding of the causes and driving forces of climate and weather change on the earth.
In this paper, the main research achievements about the influence of solar activity on the climate and weather of the earth are introduced. Four key issues are discussed, which are: (1) solar activity and the energy budget of the earth system; (2) impact of solar activity on the earth’s climate, including climate change, greenhouse effect and volcanoes; (3) effect of solar activity on monsoon and weather; and (4) whether changes of solar activity will cause longterm cold climate of the earth. The elaboration to these issues is helpful for deeply understanding the fact and mechanism of the influence of solar activity on the climate and weather of the earth, and also the prospect of the possible changes of the earth’s climate in the future. Thus, I hope this paper could present a more comprehensive and profound understanding of the causes and driving forces of climate and weather change on the earth.
2019, 45(3):305-317.
DOI: 10.7519/j.issn.1000-0526.2019.03.002
Abstract:
The formation and development of shortterm heavy precipitation caused by small and mesoscale convective systems are very rapid, and the prediction and warning of the location and period of precipitation are always difficult in forecasting operation. In recent years, the accuracy and resolution of mesoscale model have been improved, and it plays more and more important role in forecasting and warning of severe convective weather. In this paper, based on hourly precipitation forecast from Rapid Analysis and Forecasting System GRAPESRAFS (0.1°×0.1°) during August-September 2017, ensemble members are formed by the timelagged ensemble forecast method. The average TS score is used to calculate weight coefficients of corresponding ensemble members, and then frequency matching method is adopted to correct precipitation forecast bias. The conclusions drawn from this study are as follows. (1) For GRAPESRAFS, the most accurate precipitation forecast does not always come from the most recent ensemble member. Timelagged ensemble method can significantly improve the prediction ability of the model by automatically identifying the optimal forecast members. (2) The GRAPESRAFS hourly precipitation forecast presents systematic weak biases. After corrected by the frequency matching method, the hourly precipitation forecast gets more close to the actual situation in magnitude. (3) The timelagged ensemble method works better for central and eastern parts of China. (4) The frequency matching method works better for south of the Yangtze River, South China and Southwest China, where precipitation occurs more frequently with greater intensities. (5) The method can significantly improve the prediction capacity of the model for the location, amount and patterns of rainfalls in severe precipitation process of small and medium scales.
The formation and development of shortterm heavy precipitation caused by small and mesoscale convective systems are very rapid, and the prediction and warning of the location and period of precipitation are always difficult in forecasting operation. In recent years, the accuracy and resolution of mesoscale model have been improved, and it plays more and more important role in forecasting and warning of severe convective weather. In this paper, based on hourly precipitation forecast from Rapid Analysis and Forecasting System GRAPESRAFS (0.1°×0.1°) during August-September 2017, ensemble members are formed by the timelagged ensemble forecast method. The average TS score is used to calculate weight coefficients of corresponding ensemble members, and then frequency matching method is adopted to correct precipitation forecast bias. The conclusions drawn from this study are as follows. (1) For GRAPESRAFS, the most accurate precipitation forecast does not always come from the most recent ensemble member. Timelagged ensemble method can significantly improve the prediction ability of the model by automatically identifying the optimal forecast members. (2) The GRAPESRAFS hourly precipitation forecast presents systematic weak biases. After corrected by the frequency matching method, the hourly precipitation forecast gets more close to the actual situation in magnitude. (3) The timelagged ensemble method works better for central and eastern parts of China. (4) The frequency matching method works better for south of the Yangtze River, South China and Southwest China, where precipitation occurs more frequently with greater intensities. (5) The method can significantly improve the prediction capacity of the model for the location, amount and patterns of rainfalls in severe precipitation process of small and medium scales.
2019, 45(3):318-329.
DOI: 10.7519/j.issn.1000-0526.2019.03.003
Abstract:
In order to explore applications of satellite data with high resolution to the prediction of typhoon precipitation, a landfall typhoon case (Super Typhoon Meranti) was studied by using Himawari8 (H8) satellite data. The precipitation intensity was estimated first by using an improved QPE (quantitative precipitation estimation) method and then evaluated by observation dataset from CIMISS (China Integrated Meteorological Information Sharing System). The results are as follows. (1) The precipitation estimated from H8 satellite has a good correspondence with the ground observations in terms of rainfall area, but the satellite overestimates weak precipitation and underestimates heavy rainfall, especially at high altitudes, which is due to lack of consideration of terrains’ effect on promotion of precipitation in the QPE algorithm. (2) The time series of precipitation intensity from estimation and observation do not vary in same phase, and the observed precipitation lags behind the estimation. (3) Generally, the precipitation intensity estimated by the H8 satellite is positively correlated with the total precipitation later observed over a period (about 2-2.5 h). Besides, the correlation is also large between the total precipitation over a period (about 2-2.5 h) estimated by the satellite and the precipitation intensity measured on the ground later. This indicates that precipitation estimated by the H8 satellite could play an indicative role in the early warning of typhoon precipitation.
In order to explore applications of satellite data with high resolution to the prediction of typhoon precipitation, a landfall typhoon case (Super Typhoon Meranti) was studied by using Himawari8 (H8) satellite data. The precipitation intensity was estimated first by using an improved QPE (quantitative precipitation estimation) method and then evaluated by observation dataset from CIMISS (China Integrated Meteorological Information Sharing System). The results are as follows. (1) The precipitation estimated from H8 satellite has a good correspondence with the ground observations in terms of rainfall area, but the satellite overestimates weak precipitation and underestimates heavy rainfall, especially at high altitudes, which is due to lack of consideration of terrains’ effect on promotion of precipitation in the QPE algorithm. (2) The time series of precipitation intensity from estimation and observation do not vary in same phase, and the observed precipitation lags behind the estimation. (3) Generally, the precipitation intensity estimated by the H8 satellite is positively correlated with the total precipitation later observed over a period (about 2-2.5 h). Besides, the correlation is also large between the total precipitation over a period (about 2-2.5 h) estimated by the satellite and the precipitation intensity measured on the ground later. This indicates that precipitation estimated by the H8 satellite could play an indicative role in the early warning of typhoon precipitation.
2019, 45(3):330-344.
DOI: 10.7519/j.issn.1000-0526.2019.03.004
Abstract:
Based on the Xband phasedarray Doppler radar, Sband dualpolarization radar (SPOL) and conventional surface and upperair data, we analyzed the characteristics of a severe convective process in South China that happened on 3 June 2016. The results are summarized as follows. (1) The intensity, velocity and phase distribution of supercells can be observed by Doppler parameters and dualpolarization parameters of SPOL. The formation of hook echo is affected by the influx and outflow of supercells. (2) XPAR can obtain spatiotemporal resolution much higher than observation data of SPOL, which can make up for the serious shortage of elevation layer. The complete vertical structure of super cells can be observed and the shortterm evolution of supercells can be more accurately described. (3) Hypercubes near the hook echo brought cooling temperature, strong winds and heavy precipitation to the surface, and the areas with the supercells passing through had significant drop in temperature and strong winds. The results show that phasedarray radar and dualpolarization radar are more helpful to understand the evolution of supercells.
Based on the Xband phasedarray Doppler radar, Sband dualpolarization radar (SPOL) and conventional surface and upperair data, we analyzed the characteristics of a severe convective process in South China that happened on 3 June 2016. The results are summarized as follows. (1) The intensity, velocity and phase distribution of supercells can be observed by Doppler parameters and dualpolarization parameters of SPOL. The formation of hook echo is affected by the influx and outflow of supercells. (2) XPAR can obtain spatiotemporal resolution much higher than observation data of SPOL, which can make up for the serious shortage of elevation layer. The complete vertical structure of super cells can be observed and the shortterm evolution of supercells can be more accurately described. (3) Hypercubes near the hook echo brought cooling temperature, strong winds and heavy precipitation to the surface, and the areas with the supercells passing through had significant drop in temperature and strong winds. The results show that phasedarray radar and dualpolarization radar are more helpful to understand the evolution of supercells.
2019, 45(3):345-361.
DOI: 10.7519/j.issn.1000-0526.2019.03.005
Abstract:
Based on the reanalysis data of ERAinterim and sea ice concentration data of the British Atmospheric Data Centre (BADC), the main spatiotemporal variation characteristics of the prevailing Eurasian weather pattern at 500 hPa in the middle troposphere over 3330 days during 1979-2016 and its possible relationship with the anomalies in the midlower troposphere over the Arctic and the decrease of Arctic sea ice in the recent years are analyzed by using the complex vector empirical orthogonal function (CVEOF). The results show that CVEOF1 accounts for 15.82% of the total anomalous kinetic energy and its two subpatterns are represented by tripole patterns (0° phase and 180° phase) and dipolar patterns (90° phase and 270° phase). The weather patterns of 180° phase and 270° phase were observed in winter when the midlow troposphere over Actic in winter was warmer than normal and the warm Arcticcold Eurasian atmosphere circulation prevailed in the Northern Hemisphere. In addition, sea ice concentration of the Barents Sea east to the Beaufort Sea was less than normal in autumn, which might be one of the impact factors. Furthermore, the prevailing weather patterns (180° phase and 270° phase) have been increasing in the recent years, and are also closely linked to the extreme weather events in winter. Take the winter of 2005/2006 and the winter of 2011/2012 examples. During the cold events of the two years, the frequency of 180° phase and 270° phase was obviously more than normal, accounting for the majority days of winter. Therefore, sea ice of the Barents Sea east to the Beaufort Sea less than normal in autumn, Arctic middle and lower troposphere in winter warmer than normal, 180° phase and 270° phase weather patterns prevailing in winter might be the impact factors for frequent winter extreme weather events.
Based on the reanalysis data of ERAinterim and sea ice concentration data of the British Atmospheric Data Centre (BADC), the main spatiotemporal variation characteristics of the prevailing Eurasian weather pattern at 500 hPa in the middle troposphere over 3330 days during 1979-2016 and its possible relationship with the anomalies in the midlower troposphere over the Arctic and the decrease of Arctic sea ice in the recent years are analyzed by using the complex vector empirical orthogonal function (CVEOF). The results show that CVEOF1 accounts for 15.82% of the total anomalous kinetic energy and its two subpatterns are represented by tripole patterns (0° phase and 180° phase) and dipolar patterns (90° phase and 270° phase). The weather patterns of 180° phase and 270° phase were observed in winter when the midlow troposphere over Actic in winter was warmer than normal and the warm Arcticcold Eurasian atmosphere circulation prevailed in the Northern Hemisphere. In addition, sea ice concentration of the Barents Sea east to the Beaufort Sea was less than normal in autumn, which might be one of the impact factors. Furthermore, the prevailing weather patterns (180° phase and 270° phase) have been increasing in the recent years, and are also closely linked to the extreme weather events in winter. Take the winter of 2005/2006 and the winter of 2011/2012 examples. During the cold events of the two years, the frequency of 180° phase and 270° phase was obviously more than normal, accounting for the majority days of winter. Therefore, sea ice of the Barents Sea east to the Beaufort Sea less than normal in autumn, Arctic middle and lower troposphere in winter warmer than normal, 180° phase and 270° phase weather patterns prevailing in winter might be the impact factors for frequent winter extreme weather events.
2019, 45(3):362-370.
DOI: 10.7519/j.issn.1000-0526.2019.03.006
Abstract:
Ozone is a significant trace gas in the atmosphere. It has both chemically and radiatively active characteristics, directly affecting global climate change and the Earth’s environment. The OMI on board the AURA, uses both OMITOMS and OMIDOAS algorithms to retrieve total ozone column (TOC). The purpose of this study is to produce a comparable result about the similarities and differences of the two algorithms in China. Firstly, this paper analyzes the statistical characteristics of the two algorithm products, and the results show that the two have good consistency. Secondly, the relationship between the two ozone products and the position of the pixel are analyzed separately. In the pixels that are not affected by the rowanomaly, both algorithms are not affected by the position of the pixel. In addition, the paper also studies the effects of cloud and solar zenith angle on the two algorithms. The difference between the two products is greater in cloudy pixels. When the cloud fraction is 70%, the difference reaches the largest amount. Moreover, the deviation increases with the increase of the solar zenith angle in cloudy conditions. Stratospheric SO2 and absorbent aerosols have no significant effect on both algorithms.
Ozone is a significant trace gas in the atmosphere. It has both chemically and radiatively active characteristics, directly affecting global climate change and the Earth’s environment. The OMI on board the AURA, uses both OMITOMS and OMIDOAS algorithms to retrieve total ozone column (TOC). The purpose of this study is to produce a comparable result about the similarities and differences of the two algorithms in China. Firstly, this paper analyzes the statistical characteristics of the two algorithm products, and the results show that the two have good consistency. Secondly, the relationship between the two ozone products and the position of the pixel are analyzed separately. In the pixels that are not affected by the rowanomaly, both algorithms are not affected by the position of the pixel. In addition, the paper also studies the effects of cloud and solar zenith angle on the two algorithms. The difference between the two products is greater in cloudy pixels. When the cloud fraction is 70%, the difference reaches the largest amount. Moreover, the deviation increases with the increase of the solar zenith angle in cloudy conditions. Stratospheric SO2 and absorbent aerosols have no significant effect on both algorithms.
2019, 45(3):371-380.
DOI: 10.7519/j.issn.1000-0526.2019.03.007
Abstract:
In this paper, the reasons of the active jamming echo formation from the Doppler weather radar, and the morphological characteristics of PPI images from different types of active jamming echoes are analyzed. The concepts of radial energy of reflectivity factor and the firstorder difference of radial energy are put forward, and the azimuth of active disturbance echo is identified by using the firstorder difference of radial energy. Algorithm for the radar data from several cities such as Beijing, Changsha, Shantou, Yueyang is tested. The results show that the algorithm for various forms of active jamming echoes can accurately identify their locations. Especially when multiple locations interference echoes appear at the same time and interference echo coexists with precipitation echo, the algorithm is still able to identify and filter active jamming echo, retaining the whole precipitation echo.
In this paper, the reasons of the active jamming echo formation from the Doppler weather radar, and the morphological characteristics of PPI images from different types of active jamming echoes are analyzed. The concepts of radial energy of reflectivity factor and the firstorder difference of radial energy are put forward, and the azimuth of active disturbance echo is identified by using the firstorder difference of radial energy. Algorithm for the radar data from several cities such as Beijing, Changsha, Shantou, Yueyang is tested. The results show that the algorithm for various forms of active jamming echoes can accurately identify their locations. Especially when multiple locations interference echoes appear at the same time and interference echo coexists with precipitation echo, the algorithm is still able to identify and filter active jamming echo, retaining the whole precipitation echo.
2019, 45(3):381-394.
DOI: 10.7519/j.issn.1000-0526.2019.03.008
Abstract:
Based on mesoscale numerical simulation data and 15 surface meteorological observation data from 2007 to 2016, characterizing wind speed of local wind field is defined and the climatic characteristics of the diurnal variation of the local circulation in the BeijingTianjinHebei Region are analyzed. Also, the influence of air pollution and transportation in this area are analyzed. In addition, the atmospheric heavy pollution process in Beijing Area from 30 December 2016 to 7 January 2017 is studied. The conclusions are as follows. The change of the lower wind field in the BeijingTianjinHebei Region is the consequence of the interaction between the atmospheric circulation and the diurnal variation of the local wind fields. The mountainvalley breeze causes the prevailing wind in the atmospheric boundary layer along the plains of Taihang Mountain and Yanshan Mountain to blow by north and south. The daily variation of the mountainvalley breeze of the BeijingTianjinHebei Region has the characteristics of clockwise changes, which means that valley wind turns to mountain wind over night to morning and mountain wind turns to valley wind from afternoon to night. During the turning in the afternoon from valley wind to mountain wind, the “bow” airflow passage from southwest to northeast along east side of the Taihang Mountain and southern side of the Yanshan Mountain is easy to form. This airflow channel is formed at about 21:00 BT January, lasting about 3 h, and in July, it froms at about 18:00 BT lasting about 9 h. During the winter afternoon to the evening, valley wind is very strong, making pollutants accumulate easily in front of mountains by the barrier of the mountain and causing the pollution concentration to increase. The same situation occurs in summer after midnight.
Based on mesoscale numerical simulation data and 15 surface meteorological observation data from 2007 to 2016, characterizing wind speed of local wind field is defined and the climatic characteristics of the diurnal variation of the local circulation in the BeijingTianjinHebei Region are analyzed. Also, the influence of air pollution and transportation in this area are analyzed. In addition, the atmospheric heavy pollution process in Beijing Area from 30 December 2016 to 7 January 2017 is studied. The conclusions are as follows. The change of the lower wind field in the BeijingTianjinHebei Region is the consequence of the interaction between the atmospheric circulation and the diurnal variation of the local wind fields. The mountainvalley breeze causes the prevailing wind in the atmospheric boundary layer along the plains of Taihang Mountain and Yanshan Mountain to blow by north and south. The daily variation of the mountainvalley breeze of the BeijingTianjinHebei Region has the characteristics of clockwise changes, which means that valley wind turns to mountain wind over night to morning and mountain wind turns to valley wind from afternoon to night. During the turning in the afternoon from valley wind to mountain wind, the “bow” airflow passage from southwest to northeast along east side of the Taihang Mountain and southern side of the Yanshan Mountain is easy to form. This airflow channel is formed at about 21:00 BT January, lasting about 3 h, and in July, it froms at about 18:00 BT lasting about 9 h. During the winter afternoon to the evening, valley wind is very strong, making pollutants accumulate easily in front of mountains by the barrier of the mountain and causing the pollution concentration to increase. The same situation occurs in summer after midnight.
2019, 45(3):395-406.
DOI: 10.7519/j.issn.1000-0526.2019.03.009
Abstract:
The event of an advection fog occurred in Jiangsu Province during 18-19 March 2013. It firstly formed in the north bank of Yangtze River and then spread to northern Jiangsu Province. In this study, the formation mechanism and characteristics of this fog event are analyzed, and the transmission mechanism of the fog is particularly emphasized. The results indicate that this advection fog event was formed by the influence of warm and humid advection after being controlled by cold air. The cold front transit was the basic condition for the formation of advection fog. The north lift of the denatured cold high pressure from East China Sea was helpful for the formation of this advection fog event. The fog area moved northward as the result of the promoting role of southeast wind from the ground and watervapor transfer toward southeast at low altitude. The waters of Yangtze River, Hongze Lake and Gaoyou Lake played significant roles in enhancing the development of the advection fog. The heavy fog developed gradually with insignificant explosive increase and coexistence of cloud and fog as the main characteristics. These findings have practical values to the forecasting of advection fog.
The event of an advection fog occurred in Jiangsu Province during 18-19 March 2013. It firstly formed in the north bank of Yangtze River and then spread to northern Jiangsu Province. In this study, the formation mechanism and characteristics of this fog event are analyzed, and the transmission mechanism of the fog is particularly emphasized. The results indicate that this advection fog event was formed by the influence of warm and humid advection after being controlled by cold air. The cold front transit was the basic condition for the formation of advection fog. The north lift of the denatured cold high pressure from East China Sea was helpful for the formation of this advection fog event. The fog area moved northward as the result of the promoting role of southeast wind from the ground and watervapor transfer toward southeast at low altitude. The waters of Yangtze River, Hongze Lake and Gaoyou Lake played significant roles in enhancing the development of the advection fog. The heavy fog developed gradually with insignificant explosive increase and coexistence of cloud and fog as the main characteristics. These findings have practical values to the forecasting of advection fog.
2019, 45(3):407-414.
DOI: 10.7519/j.issn.1000-0526.2019.03.010
Abstract:
Interannual variation in the intensity of the East Asian winter monsoon (EAWM) is closely related to that in winter haze days in the middle and eastern China, providing a possible physical factor for shortterm climate prediction of haze days. Using the NCEP/NCAR reanalysis data and the meteorological observation data of Anhui Province from 1980 to 2016, the statistical analysis method is used to study the relationship between the number of haze days in Anhui Province and six different EAWM indices (EAWMI) in January, and the main EAWMI for winter haze days in different regions of Anhui Province are extracted, and then the monthly predictive model of winter haze days in Anhui Province is established and verified. The results show that: (1) The number of climatic haze days in January is negatively related to the six EAWMI. For both the northern part of the Huaihe River and regions between the Yangtze River and the Huaihe River, the correlation coefficient between the East Asia large trough intensity index and the number of climatic haze days is the highest. For along and to the south of the Yangtze River, the correlation coefficient between the Siberia highintensity index and the number of climatic haze days is the highest. (2) The prediction models for the number of haze days are established in three different regions, and all of them have passed the significance test with α=0.01 level. The verification results show that the predicted haze days are very similar with actual situations, and no predictive errors appear in three regions, which indicates that all the prediction models present a good predictive performance. (3) In the predictive work on winter haze days in Anhui Province, it is better to use the output by the ECMWF SYSTEM4 model than that by NCEP CFS2.
Interannual variation in the intensity of the East Asian winter monsoon (EAWM) is closely related to that in winter haze days in the middle and eastern China, providing a possible physical factor for shortterm climate prediction of haze days. Using the NCEP/NCAR reanalysis data and the meteorological observation data of Anhui Province from 1980 to 2016, the statistical analysis method is used to study the relationship between the number of haze days in Anhui Province and six different EAWM indices (EAWMI) in January, and the main EAWMI for winter haze days in different regions of Anhui Province are extracted, and then the monthly predictive model of winter haze days in Anhui Province is established and verified. The results show that: (1) The number of climatic haze days in January is negatively related to the six EAWMI. For both the northern part of the Huaihe River and regions between the Yangtze River and the Huaihe River, the correlation coefficient between the East Asia large trough intensity index and the number of climatic haze days is the highest. For along and to the south of the Yangtze River, the correlation coefficient between the Siberia highintensity index and the number of climatic haze days is the highest. (2) The prediction models for the number of haze days are established in three different regions, and all of them have passed the significance test with α=0.01 level. The verification results show that the predicted haze days are very similar with actual situations, and no predictive errors appear in three regions, which indicates that all the prediction models present a good predictive performance. (3) In the predictive work on winter haze days in Anhui Province, it is better to use the output by the ECMWF SYSTEM4 model than that by NCEP CFS2.
2019, 45(3):415-425.
DOI: 10.7519/j.issn.1000-0526.2019.03.011
Abstract:
In this study, a hailstorm that occurred in southwest of Guizhou Province on 30 March 2014 is simulated using the mesoscale model WRF (Weather Research and Forecasting). Combined with NCEP/FNL reanalysis data, ground, sounding, Doppler radar data, satellite products and other observation data, this hailstorm process is comprehensively analyzed. In addition, the characteristics of the hailstorm and its environmental conditions, the evolution characteristics of the hailstorm and the characteristics of the cloud microphysical structure, the cloud physical mechanism of the hailstorm are analyzed. The results show that this hailstorm was a typical lowpressure convergence line hailstorm, and the hail position on surface was at (or southern to) shear line at 700 hPa, closing to the ground convergence line. The convective cloud of this process underwent the initial stage, the consolidation stage, the maturehail stage and the eastward shift stage. Thus, the microphysical structure of convective cloud over Guizhou Province has the characteristics of mixed phase cloud with ice crystal and snow in upper layer, cloudwater and graupel in midlayer, and rainwater and hail in lower layer. The most important sources for the rainwater and hail are the graupel and cloudwater. During the process of hail formation, the accretion between graupel and supercooled cloudwater, and the automatic conversion of graupel are the main microphysical mechanism.
In this study, a hailstorm that occurred in southwest of Guizhou Province on 30 March 2014 is simulated using the mesoscale model WRF (Weather Research and Forecasting). Combined with NCEP/FNL reanalysis data, ground, sounding, Doppler radar data, satellite products and other observation data, this hailstorm process is comprehensively analyzed. In addition, the characteristics of the hailstorm and its environmental conditions, the evolution characteristics of the hailstorm and the characteristics of the cloud microphysical structure, the cloud physical mechanism of the hailstorm are analyzed. The results show that this hailstorm was a typical lowpressure convergence line hailstorm, and the hail position on surface was at (or southern to) shear line at 700 hPa, closing to the ground convergence line. The convective cloud of this process underwent the initial stage, the consolidation stage, the maturehail stage and the eastward shift stage. Thus, the microphysical structure of convective cloud over Guizhou Province has the characteristics of mixed phase cloud with ice crystal and snow in upper layer, cloudwater and graupel in midlayer, and rainwater and hail in lower layer. The most important sources for the rainwater and hail are the graupel and cloudwater. During the process of hail formation, the accretion between graupel and supercooled cloudwater, and the automatic conversion of graupel are the main microphysical mechanism.
SUN Quande
,
JIAO Ruili
,
XIA Jiangjiang
,
YAN Zhongwei
,
LI Haochen
,
SUN Jianhua
,
WANG Lizhi
,
LIANG Zhaoming
2019, 45(3):426-436.
DOI: 10.7519/j.issn.1000-0526.2019.03.012
Abstract:
Accurate prediction of wind speed is crucial for local weather forecasting services (e.g., dealing with wind power industry and the Olympic Winter Game). Based on three machine learning algorithms (LASSO regression, random forest and deep learning), this paper demonstrates three models for adjusting the 10 m wind speed in North China predicted by the numerical weather forecast model of ECMWF. Firstly, the LASSO regression algorithm is applied to identify the features which significantly affect the nearsurface wind speed, among all the available meteorological elements. The extracted feature set is used as input for each machine learning algorithm to establish a model to adjust the ECMWFpredicted wind speed. Feature extraction helps to reduce the amount of computation, storage overhead and the complexity of the model, hence to facilitate the generalization of the model. The results of the three machine learning algorithms are compared with that of the traditional MOS method. All the three machine learning methods show a better performance in adjusting the wind speed than that of MOS, indicating great potential of the machine learning methods in improving local weather forecast.
Accurate prediction of wind speed is crucial for local weather forecasting services (e.g., dealing with wind power industry and the Olympic Winter Game). Based on three machine learning algorithms (LASSO regression, random forest and deep learning), this paper demonstrates three models for adjusting the 10 m wind speed in North China predicted by the numerical weather forecast model of ECMWF. Firstly, the LASSO regression algorithm is applied to identify the features which significantly affect the nearsurface wind speed, among all the available meteorological elements. The extracted feature set is used as input for each machine learning algorithm to establish a model to adjust the ECMWFpredicted wind speed. Feature extraction helps to reduce the amount of computation, storage overhead and the complexity of the model, hence to facilitate the generalization of the model. The results of the three machine learning algorithms are compared with that of the traditional MOS method. All the three machine learning methods show a better performance in adjusting the wind speed than that of MOS, indicating great potential of the machine learning methods in improving local weather forecast.
2019, 45(3):437-444.
DOI: 10.7519/j.issn.1000-0526.2019.03.013
Abstract:
The main characteristics of the general atmospheric circulation in December 2018 are as follow. There was one polar vortex center in the Northern Hemisphere, located in the north of Greenland. The Eurasia midhigh latitude circulation presented a troughridgetrough pattern and a great meridionality. The south branch trough was strong, lying around 90°E averagely. The subtropical high stayed more westward than in normal years. The monthly mean precipitation over China was 18.7 mm, which is 73.1% higher than normal. The monthly mean temperature over China was -3.8℃, 0.6℃ lower than normal (-3.2℃). Two largescale rainfall, three cold air, and two foghaze processes occurred in December. From 5 to 11 December and from 27 December 2018 to 1 January 2019, due to the influence of cold wave, two largescale lowtemperature rain and snow processes hit most areas of central and eastern China with lowest temperature breaking historical extremes in many places.
The main characteristics of the general atmospheric circulation in December 2018 are as follow. There was one polar vortex center in the Northern Hemisphere, located in the north of Greenland. The Eurasia midhigh latitude circulation presented a troughridgetrough pattern and a great meridionality. The south branch trough was strong, lying around 90°E averagely. The subtropical high stayed more westward than in normal years. The monthly mean precipitation over China was 18.7 mm, which is 73.1% higher than normal. The monthly mean temperature over China was -3.8℃, 0.6℃ lower than normal (-3.2℃). Two largescale rainfall, three cold air, and two foghaze processes occurred in December. From 5 to 11 December and from 27 December 2018 to 1 January 2019, due to the influence of cold wave, two largescale lowtemperature rain and snow processes hit most areas of central and eastern China with lowest temperature breaking historical extremes in many places.
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ISSN:1000-0526 CN:11-2282/P