ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 45,Issue 9,2019 Table of Contents
2019, 45(9):1189-1198.
DOI: 10.7519/j.issn.1000-0526.2019.09.001
Abstract:
Based on field experiments at Nanyue Mountain Meteorological Station and Huaihua National Reference Climatological Station in Hunan Province, the camera images of snow cover weather phenomena were collected minutely from January to March in 2018. The convolution neural network technology is employed for modelling and training using the experimental pictures at Nanyue Station, and the results are examined by the testing pictures of Nanyue Station and Huaihua Station. Furthermore, based on deep learning, the environmental layout requirements of snow cover image identification are discussed. The main conclusions are as follows. Recognition accuracy at Nanyue Station is 99.23%, omission rate is 0.49%, false identification rate is 0.28%, and the recognition result at daytime is better than at night. The accuracy increases to approximately 99.99% with high stability during the formation stage of snow cover, and it decreases with snow melting. There are a few false cases when the snow cover on the ground is in the early stage of snow cover formation and about to melt. There are occasional misjudgment because of background pollution of fog and rime. The test results of Huaihua Station are similar to those for Nanyue Station, with an accuracy of 97.78%, a false identification rate of 1.92% and 0.3% missed, but less stable. This is because of two reasons. First, the data of Huaihua were not used for modelling, and then the cameras were not well fixed leading to bad images. The test results show that the artificial intelligence identification model established in this paper can extract the key features of snow cover in different stages, and the identification result is well. In addition, the false accept and omission can be further eliminated by including the meteorological element conditions and judging the consistency of the snow cover. This method can provide important technical support for the automatic observation of snowcover weather phenomena.
Based on field experiments at Nanyue Mountain Meteorological Station and Huaihua National Reference Climatological Station in Hunan Province, the camera images of snow cover weather phenomena were collected minutely from January to March in 2018. The convolution neural network technology is employed for modelling and training using the experimental pictures at Nanyue Station, and the results are examined by the testing pictures of Nanyue Station and Huaihua Station. Furthermore, based on deep learning, the environmental layout requirements of snow cover image identification are discussed. The main conclusions are as follows. Recognition accuracy at Nanyue Station is 99.23%, omission rate is 0.49%, false identification rate is 0.28%, and the recognition result at daytime is better than at night. The accuracy increases to approximately 99.99% with high stability during the formation stage of snow cover, and it decreases with snow melting. There are a few false cases when the snow cover on the ground is in the early stage of snow cover formation and about to melt. There are occasional misjudgment because of background pollution of fog and rime. The test results of Huaihua Station are similar to those for Nanyue Station, with an accuracy of 97.78%, a false identification rate of 1.92% and 0.3% missed, but less stable. This is because of two reasons. First, the data of Huaihua were not used for modelling, and then the cameras were not well fixed leading to bad images. The test results show that the artificial intelligence identification model established in this paper can extract the key features of snow cover in different stages, and the identification result is well. In addition, the false accept and omission can be further eliminated by including the meteorological element conditions and judging the consistency of the snow cover. This method can provide important technical support for the automatic observation of snowcover weather phenomena.
2019, 45(9):1199-1212.
DOI: 10.7519/j.issn.1000-0526.2019.09.002
Abstract:
Severe mesoscale rainstorm struck Guangzhou heavily from deep night of 6 May to early morning of 7 May 2017, and convection initiation was closely related to southerly wind enhancing in boundary layer near Pearl River Delta at night. Comparison between two subgroups in ECWMF ensemble forecasts shows members with strong rainfall have significant lowlevel convergence, updraft, moist air and convective instability in initial conditions. GRAPES mesoscale model performed well in forecasting the dynamic process of southerly wind strengthening in lower level, producing good forecasts for the rainfall process. Ensemble sensitivity analysis reveals that the high sensitivity depends on the relative position, strength of key synoptic systems for high pressure in Yangtze River Basin, high pressure on South China Sea and low pressure trough residing on South China. Precipitation forecast sensitivity to temperature is closely relative to convective instability in boundary layer, the boundary structure with warmer air in nearsurface and colder air near the top of boundary layer would increase convective instability obviously. Three sets of convectivescale simulations are applied to analyze sensitivity of convective precipitation forecast to initial thermal disturbance, lowlevel wind disturbance, and cloud microphysical scheme differences. Convective precipitation forecast shows more sensitivities to initial thermal disturbance compared with lowlevel wind disturbance, and forecast uncertainty could be estimated comprehensively through different cloud microphysical schemes. By carefully analyzing forecast sensitivity due to perturbations in initial conditions and physical process in the convectivescale ensemble forecasts, the predictability of mesoscale heavy rainfall events in warm seasons could be improved.
Severe mesoscale rainstorm struck Guangzhou heavily from deep night of 6 May to early morning of 7 May 2017, and convection initiation was closely related to southerly wind enhancing in boundary layer near Pearl River Delta at night. Comparison between two subgroups in ECWMF ensemble forecasts shows members with strong rainfall have significant lowlevel convergence, updraft, moist air and convective instability in initial conditions. GRAPES mesoscale model performed well in forecasting the dynamic process of southerly wind strengthening in lower level, producing good forecasts for the rainfall process. Ensemble sensitivity analysis reveals that the high sensitivity depends on the relative position, strength of key synoptic systems for high pressure in Yangtze River Basin, high pressure on South China Sea and low pressure trough residing on South China. Precipitation forecast sensitivity to temperature is closely relative to convective instability in boundary layer, the boundary structure with warmer air in nearsurface and colder air near the top of boundary layer would increase convective instability obviously. Three sets of convectivescale simulations are applied to analyze sensitivity of convective precipitation forecast to initial thermal disturbance, lowlevel wind disturbance, and cloud microphysical scheme differences. Convective precipitation forecast shows more sensitivities to initial thermal disturbance compared with lowlevel wind disturbance, and forecast uncertainty could be estimated comprehensively through different cloud microphysical schemes. By carefully analyzing forecast sensitivity due to perturbations in initial conditions and physical process in the convectivescale ensemble forecasts, the predictability of mesoscale heavy rainfall events in warm seasons could be improved.
2019, 45(9):1213-1226.
DOI: 10.7519/j.issn.1000-0526.2019.09.003
Abstract:
Using the NCEP/NCAR reanalysis data, the circulation background and the largescale water vapor transport characteristics of a rarelyseen persistent heavy rainfall that occurred in Hunan Province in late June to early July 2017 was analyzed first and then the trajectory model was used to simulate the trajectory of the air mass. The characteristics of water vapor transport and the regional water vapor budget were quantitatively analyzed according to the three stages of rainfall process. The results showed that the effective disposition and stable maintenance of the weather system were the main causes for the persistence of heavy rainfall. Persistent heavy rainfall was linked with global water vapor transport and convergence, and the evolution of lowlevel jet directly affected the rainfall area and intensity of heavy rain. There were mainly three water vapor passages corridors to the heavy rain process. The first was the Somali jet stream through the Bay of Bengal and Southwest China into the heavy rain area, the second was the crossequatorial flow from the Southern Hemisphere of central and eastern Indian Ocean through the Bay of Bengal and northern South China Sea into the heavy rain area, and the third was the crossequatorial flow through South China Sea into the heavy rain area. In the third stage, there was another passage from the equatorial Pacific across the Philippines into the South China Sea and then into heavy rain area. During the first two stages, the water vapor transportation was mainly from the Bay of Bengal, and then from the South China Sea, and in the third stage, the water vapor from the Bay of Bengal and the South China Sea (including the Western Pacific) were about equal. Affected by the terrain, the water vapor from the Bay of Bengal was mainly transported to the storm zone at 700 hPa, and the water vapor from other passages was mainly transported to the 850 hPa and the lower levels. The water vapor transportation came mainly from the low level of southern and western boundaries which converged in the form of horizontal water vapor flux convergence over the low level of the rainstorm area, and was transported to the middle and upper troposphere through strong vertical ascending movement,condensing and resulting in precipitation. The intensity of precipitation was well related to the strength of the water vapor inflow on the boundaries and the regional water vapor convergence.
Using the NCEP/NCAR reanalysis data, the circulation background and the largescale water vapor transport characteristics of a rarelyseen persistent heavy rainfall that occurred in Hunan Province in late June to early July 2017 was analyzed first and then the trajectory model was used to simulate the trajectory of the air mass. The characteristics of water vapor transport and the regional water vapor budget were quantitatively analyzed according to the three stages of rainfall process. The results showed that the effective disposition and stable maintenance of the weather system were the main causes for the persistence of heavy rainfall. Persistent heavy rainfall was linked with global water vapor transport and convergence, and the evolution of lowlevel jet directly affected the rainfall area and intensity of heavy rain. There were mainly three water vapor passages corridors to the heavy rain process. The first was the Somali jet stream through the Bay of Bengal and Southwest China into the heavy rain area, the second was the crossequatorial flow from the Southern Hemisphere of central and eastern Indian Ocean through the Bay of Bengal and northern South China Sea into the heavy rain area, and the third was the crossequatorial flow through South China Sea into the heavy rain area. In the third stage, there was another passage from the equatorial Pacific across the Philippines into the South China Sea and then into heavy rain area. During the first two stages, the water vapor transportation was mainly from the Bay of Bengal, and then from the South China Sea, and in the third stage, the water vapor from the Bay of Bengal and the South China Sea (including the Western Pacific) were about equal. Affected by the terrain, the water vapor from the Bay of Bengal was mainly transported to the storm zone at 700 hPa, and the water vapor from other passages was mainly transported to the 850 hPa and the lower levels. The water vapor transportation came mainly from the low level of southern and western boundaries which converged in the form of horizontal water vapor flux convergence over the low level of the rainstorm area, and was transported to the middle and upper troposphere through strong vertical ascending movement,condensing and resulting in precipitation. The intensity of precipitation was well related to the strength of the water vapor inflow on the boundaries and the regional water vapor convergence.
2019, 45(9):1227-1237.
DOI: 10.7519/j.issn.1000-0526.2019.09.004
Abstract:
Based on the surface observation data from the representative station over eastern Hexi Corridor, the NCEP 2.5°×2.5° monthly average upperair sounding data from surface to 300 hPa during 1971-2016, and the daily upperair densely observed data every 10 m at 07:00 BT and 19:00 BT from 2006 to 2016 in Minqin, the interannual variation characteristics of winter sandstorm over eastern Hexi Corridor in recent 45 years are analyzed. At the same time, the synoptic causes, physical quantity field and nearsurface boundary layer characteristics of the two sandstorm processes in November 2016 are also analyzed. The results show that the number of winter sandstorms over eastern Hexi Corridor has decreased in recent 45 years. The main causes of strong wind and dust weather are not only related to the intensity of largescale cold and warm airs and circulation situation, but also closely related to cold front transit time, diurnal variation, wind speed and humidity near the surface. Near surface, the temperature inversion layer is thick and strong from night to morning, atmospheric stratification is stable, which weakens the strength of sandstorms, but surface inversion from afternoon to evening is thin and weak, strengthening the atmospheric instability, momentum down propagation and wind speed, thus further enhancing the strenght of sandstorms. The drier the surface layer, the faster the wind speed, and the stronger the sandstorm.
Based on the surface observation data from the representative station over eastern Hexi Corridor, the NCEP 2.5°×2.5° monthly average upperair sounding data from surface to 300 hPa during 1971-2016, and the daily upperair densely observed data every 10 m at 07:00 BT and 19:00 BT from 2006 to 2016 in Minqin, the interannual variation characteristics of winter sandstorm over eastern Hexi Corridor in recent 45 years are analyzed. At the same time, the synoptic causes, physical quantity field and nearsurface boundary layer characteristics of the two sandstorm processes in November 2016 are also analyzed. The results show that the number of winter sandstorms over eastern Hexi Corridor has decreased in recent 45 years. The main causes of strong wind and dust weather are not only related to the intensity of largescale cold and warm airs and circulation situation, but also closely related to cold front transit time, diurnal variation, wind speed and humidity near the surface. Near surface, the temperature inversion layer is thick and strong from night to morning, atmospheric stratification is stable, which weakens the strength of sandstorms, but surface inversion from afternoon to evening is thin and weak, strengthening the atmospheric instability, momentum down propagation and wind speed, thus further enhancing the strenght of sandstorms. The drier the surface layer, the faster the wind speed, and the stronger the sandstorm.
2019, 45(9):1238-1247.
DOI: 10.7519/j.issn.1000-0526.2019.09.005
Abstract:
Statistical analysis of flash heavy rain in Jiangxi warm season from 2010 to 2016 is conducted based on hourly precipitation data from 1597 automatic weather stations. The precipitation field is divided into five regions by REOF: south part of northern Jiangxi (Region Ⅰ), Fuzhou and central Ganzhou (Region Ⅱ), northern Jiangxi (Region Ⅲ), southern and northern Ganzhou (Region Ⅳ), and western of central Jiangxi (Region Ⅴ). The high frequency areas of flash heavy rain are mainly distributed near mountains and river valleys. They are the eastern side of the Luoxiao Mountain, the western foot of Wuyi Mountain, the Xinjiang, the Le’an and the Changjiang River valleys. The frequency of flash heavy rain near the river valley is the highest in the Changjiang River valley (16.9 times one year), the highest near the mountains is the east side of the Luoxiao Mountain (12.6 times one year). The extreme flash heavy rain areas are located in the mountain areas of Northeast Shangrao (3.7 times one year) and the Jinjiang River valley (3.3 times one year). Flash heavy rain mainly occurred in the 3rd pentad of May, middle of June and July, and from 2nd to 3rd pentads of August. The diurnal variation characteristics of Regions Ⅳ and Ⅴ are single peak types, diurnal variation characteristics of Regions Ⅰ, Ⅱ and Ⅲ are bimodal patterns. The main peaks are concentrated at 17:00 BT in the afternoon; the second peak is during 08:00-10:00 BT in the morning. The contribution rate of flash heavy rain to rainstorm is basically above 40%, nearly half of the rainstorm weather processes in Regions Ⅰ and Ⅱ are contributed by flash heavy rain. The Xinjiang River valley is the center of rainstorm rainfall, but not the center of flash heavy rain rainfall. However, the Changjiang River valley and the western foot of Wuyi Mountain are both the centers of rainstorm rainfall and the flash heavy rain rainfall.
Statistical analysis of flash heavy rain in Jiangxi warm season from 2010 to 2016 is conducted based on hourly precipitation data from 1597 automatic weather stations. The precipitation field is divided into five regions by REOF: south part of northern Jiangxi (Region Ⅰ), Fuzhou and central Ganzhou (Region Ⅱ), northern Jiangxi (Region Ⅲ), southern and northern Ganzhou (Region Ⅳ), and western of central Jiangxi (Region Ⅴ). The high frequency areas of flash heavy rain are mainly distributed near mountains and river valleys. They are the eastern side of the Luoxiao Mountain, the western foot of Wuyi Mountain, the Xinjiang, the Le’an and the Changjiang River valleys. The frequency of flash heavy rain near the river valley is the highest in the Changjiang River valley (16.9 times one year), the highest near the mountains is the east side of the Luoxiao Mountain (12.6 times one year). The extreme flash heavy rain areas are located in the mountain areas of Northeast Shangrao (3.7 times one year) and the Jinjiang River valley (3.3 times one year). Flash heavy rain mainly occurred in the 3rd pentad of May, middle of June and July, and from 2nd to 3rd pentads of August. The diurnal variation characteristics of Regions Ⅳ and Ⅴ are single peak types, diurnal variation characteristics of Regions Ⅰ, Ⅱ and Ⅲ are bimodal patterns. The main peaks are concentrated at 17:00 BT in the afternoon; the second peak is during 08:00-10:00 BT in the morning. The contribution rate of flash heavy rain to rainstorm is basically above 40%, nearly half of the rainstorm weather processes in Regions Ⅰ and Ⅱ are contributed by flash heavy rain. The Xinjiang River valley is the center of rainstorm rainfall, but not the center of flash heavy rain rainfall. However, the Changjiang River valley and the western foot of Wuyi Mountain are both the centers of rainstorm rainfall and the flash heavy rain rainfall.
2019, 45(9):1248-1261.
DOI: 10.7519/j.issn.1000-0526.2019.09.006
Abstract:
Depending on the New Generation Doppler Weather Radar, many studies on winter weather processes have been carried out in China. However, as the radar network in China is gradually upgraded to the dual-polarization radar, how to apply the polarization parameters to winter forecasting operation has become an important issue to be solved at present. Using the identification method of hydrometeor and statistical method of snowfall accumulation time based on bright band identification and the observation data of a snowfall process detected by the C-band dual-polarization radar in Linan on 5 December 2015, this study analyzes the spatio-temporal evolution characteristics of radar parameters, bright band and the distribution of snowfall accumulation time associated, and compares the analysis results with the ground and radiosonde data, preliminarily exploring the advantages of the dual-polarization radar in winter snow forecast. The results indicate that: (1) The echo strength of winter rainfall is weaker than that of continuous precipitation in summer. The differential reflectivity factor and the correlation coefficient between rain and snow is not significant, making it difficult to identify the winter hydrometeor with the fuzzy logic method. (2) The bright band in this process is an irregular ring or linear shape that deviates from the radar station. It is not horizontal at some spots, and sometimes there is a vertical bright band. (3) The distribution of snowfall accumulation time, obtained by the proposed method of hydrometeor identification and the method of snowfall accumulation time statistics, is basically consistent with the observed snow depth distribution. These methods provide a possibility for estimating snow depth in some regions. (4) The evolution of bright band is corresponding with the spatio-temporal changes of the ground and radiosonde temperature. Compared with the single-polarization radar, it’s more reliable to identify bright band using the dualpolarization radar.
Depending on the New Generation Doppler Weather Radar, many studies on winter weather processes have been carried out in China. However, as the radar network in China is gradually upgraded to the dual-polarization radar, how to apply the polarization parameters to winter forecasting operation has become an important issue to be solved at present. Using the identification method of hydrometeor and statistical method of snowfall accumulation time based on bright band identification and the observation data of a snowfall process detected by the C-band dual-polarization radar in Linan on 5 December 2015, this study analyzes the spatio-temporal evolution characteristics of radar parameters, bright band and the distribution of snowfall accumulation time associated, and compares the analysis results with the ground and radiosonde data, preliminarily exploring the advantages of the dual-polarization radar in winter snow forecast. The results indicate that: (1) The echo strength of winter rainfall is weaker than that of continuous precipitation in summer. The differential reflectivity factor and the correlation coefficient between rain and snow is not significant, making it difficult to identify the winter hydrometeor with the fuzzy logic method. (2) The bright band in this process is an irregular ring or linear shape that deviates from the radar station. It is not horizontal at some spots, and sometimes there is a vertical bright band. (3) The distribution of snowfall accumulation time, obtained by the proposed method of hydrometeor identification and the method of snowfall accumulation time statistics, is basically consistent with the observed snow depth distribution. These methods provide a possibility for estimating snow depth in some regions. (4) The evolution of bright band is corresponding with the spatio-temporal changes of the ground and radiosonde temperature. Compared with the single-polarization radar, it’s more reliable to identify bright band using the dualpolarization radar.
2019, 45(9):1262-1277.
DOI: 10.7519/j.issn.1000-0526.2019.09.007
Abstract:
Using the hourly air temperature and wind data of 77 regional meteorological stations from 2011 to 2014, the influence of surface wind on urban heat island (UHI) in Shanghai and the cause of seasonal spatial distribution of UHI were studied. The influence of onshore wind on urban heat island intensity (IUHI) was preliminarily revealed from sealand thermal difference. Besides, the interannual variation of surface wind speed and IUHI in different seasons in Shanghai was studied based on the monthly air temperature and wind data of seven national meteorological stations from 1961 to 2014. The main conclusions are as follows: (1) The location of UHI center is closely related to wind direction and wind speed. The UHI center at night has the feature of moving to the leeward side of urban area when the average wind speed threshold is 2 m·s-1. With the increase of wind speed, the UHI area extends to the leeward side of city, but the IUHI decreases. (2) UHI characteristics are obvious in Shanghai at night, especially in autumn and winter, followed by spring and summer in order. UHI center appears in the northwest of urban areas at night in spring and summer, while UHI center is stabilized in urban areas at night in autumn and winter, showing typical UHI. In the daytime in each season, there is a largescale warming phenomenon in the downwind area. Seasonal surface prevailing wind determines the seasonal spatial distribution characteristics of UHI. (3) Onshore wind inhibits warming in the daytime, especially obvious in spring and summer. Affected by this phenomenon, the high temperature tends to appear in inland area. The IUHI is larger in the western urban area than in the eastern urban area in spring and summer. Onshore wind inhibits cooling at night, which is the most obvious and caused obvious warming in the eastern coastal areas in autumn and winter. The IUHI is larger in the eastern urban area than that in the western urban area in autumn and winter. Both the landsea thermal difference in different seasons and the prevailing wind speed determine the magnitude and impact scope of the onshore wind. (4) The average annual surface wind speed in each season has a significant negative correlation with the corresponding IUHI. The seasonal wind speed shows a decreasing trend (the most obvious in spring and winter) from 1961 to 2014 in Shanghai, which is good for IUHI increasing. Since the 21st century, the increasing trend of IUHI in all seasons has slowed down (the most obvious in summer and autumn), but wind speed is not the main factor causing the increasing trend of IUHI to slow down.
Using the hourly air temperature and wind data of 77 regional meteorological stations from 2011 to 2014, the influence of surface wind on urban heat island (UHI) in Shanghai and the cause of seasonal spatial distribution of UHI were studied. The influence of onshore wind on urban heat island intensity (IUHI) was preliminarily revealed from sealand thermal difference. Besides, the interannual variation of surface wind speed and IUHI in different seasons in Shanghai was studied based on the monthly air temperature and wind data of seven national meteorological stations from 1961 to 2014. The main conclusions are as follows: (1) The location of UHI center is closely related to wind direction and wind speed. The UHI center at night has the feature of moving to the leeward side of urban area when the average wind speed threshold is 2 m·s-1. With the increase of wind speed, the UHI area extends to the leeward side of city, but the IUHI decreases. (2) UHI characteristics are obvious in Shanghai at night, especially in autumn and winter, followed by spring and summer in order. UHI center appears in the northwest of urban areas at night in spring and summer, while UHI center is stabilized in urban areas at night in autumn and winter, showing typical UHI. In the daytime in each season, there is a largescale warming phenomenon in the downwind area. Seasonal surface prevailing wind determines the seasonal spatial distribution characteristics of UHI. (3) Onshore wind inhibits warming in the daytime, especially obvious in spring and summer. Affected by this phenomenon, the high temperature tends to appear in inland area. The IUHI is larger in the western urban area than in the eastern urban area in spring and summer. Onshore wind inhibits cooling at night, which is the most obvious and caused obvious warming in the eastern coastal areas in autumn and winter. The IUHI is larger in the eastern urban area than that in the western urban area in autumn and winter. Both the landsea thermal difference in different seasons and the prevailing wind speed determine the magnitude and impact scope of the onshore wind. (4) The average annual surface wind speed in each season has a significant negative correlation with the corresponding IUHI. The seasonal wind speed shows a decreasing trend (the most obvious in spring and winter) from 1961 to 2014 in Shanghai, which is good for IUHI increasing. Since the 21st century, the increasing trend of IUHI in all seasons has slowed down (the most obvious in summer and autumn), but wind speed is not the main factor causing the increasing trend of IUHI to slow down.
2019, 45(9):1278-1287.
DOI: 10.7519/j.issn.1000-0526.2019.09.008
Abstract:
In this paper, the precipitation cloud system of southwest vortex weather on 3 May 2017 was analyzed based on the observation data of Ka-band cloud radar, microwave radiometer and micro rain radar at Huangsi National Observation Station in Xingtai, Hebei Province. The results showed that this precipitation process was a stable stratiform cloud process. And the falling speed of particles in the cloud gradually increased from high level to low level. The precipitation first appeared in the developing stage of cloud, and then in the mature stage of cloud. The curves of relative humidity, water vapor content, liquid water content and temperature in the cloud appeared to jump and peak at the same time before raining every time. All the indicators obviously declined after the end of precipitation, then recovered and rose to the second and third peaks with precipitation. The microwave radiometer data were continuously used to retrieve “cloud vapor pressure and ice-saturated vapor pressure difference (e-Ei)” in time and space. When the supercooled water and the large-value area of supercooled water vapor in the cloud coincides with the positive area of e-Ei, the Bergeron process is relatively strong in cold clouds, which is helpful to quantitatively determine the location of heavy precipitation and the potential area for artificial precipitation. Based on the data of cloud radar, microwave radiometer and micro rain-radar, we think that there were two chances for weather modification operation during the rainfall process of this time. The first chance was from 13:45 BT to the time when the cloud top dropped to 6 km, and the second was longer, and the cloud condition was more favorable for operation. That was the period from 17:40 BT to 21:15 BT when the cloud top height was maintained at the 8-10 km height. What’s more, the suitable working height was from 4 km to 8 km (-20-0℃).
In this paper, the precipitation cloud system of southwest vortex weather on 3 May 2017 was analyzed based on the observation data of Ka-band cloud radar, microwave radiometer and micro rain radar at Huangsi National Observation Station in Xingtai, Hebei Province. The results showed that this precipitation process was a stable stratiform cloud process. And the falling speed of particles in the cloud gradually increased from high level to low level. The precipitation first appeared in the developing stage of cloud, and then in the mature stage of cloud. The curves of relative humidity, water vapor content, liquid water content and temperature in the cloud appeared to jump and peak at the same time before raining every time. All the indicators obviously declined after the end of precipitation, then recovered and rose to the second and third peaks with precipitation. The microwave radiometer data were continuously used to retrieve “cloud vapor pressure and ice-saturated vapor pressure difference (e-Ei)” in time and space. When the supercooled water and the large-value area of supercooled water vapor in the cloud coincides with the positive area of e-Ei, the Bergeron process is relatively strong in cold clouds, which is helpful to quantitatively determine the location of heavy precipitation and the potential area for artificial precipitation. Based on the data of cloud radar, microwave radiometer and micro rain-radar, we think that there were two chances for weather modification operation during the rainfall process of this time. The first chance was from 13:45 BT to the time when the cloud top dropped to 6 km, and the second was longer, and the cloud condition was more favorable for operation. That was the period from 17:40 BT to 21:15 BT when the cloud top height was maintained at the 8-10 km height. What’s more, the suitable working height was from 4 km to 8 km (-20-0℃).
9 Applicability Evaluation of the National Gridded Real-Time Observation Datasets in Jiangsu Province
2019, 45(9):1288-1298.
DOI: 10.7519/j.issn.1000-0526.2019.09.009
Abstract:
Based on the National Gridded Real-time Observation Datasets covering Jiangsu Province released by the National Meteorological Information Centre of CMA and observations of automatic stations, the consistency and accuracy of hourly 2 m temperature, 2 m relative humidity, 10 m wind and precipitation from July 2017 to June 2018 were evaluated in details by the error statistical analysis, skill score and other methods. The MODE method was applied to reveal the spatial deviation of precipitation between gridded products and observed rainfall records. The results indicate that the mean absolute error of 2 m temperature is between 0.5 and 0.8℃, the root mean square error is around 0.8℃ and the 2 m maximum temperature exhibits a better accuracy than the minimum temperature. The root mean square error range of 2 m relative humidity is 6%-7%, which means that the gridded 2 m temperature and 2 m relative humidity data are well consistent with observation. The accuracy of gridded 10 m wind direction is about 70% while that of wind speed is only 56%, showing a big difference from observation. The verification results of precipitation show that the gridded data with accuracy 90%-98% performs well in forecasting rain or no-rain. Nonetheless, it may still have a great impact on the precipitation frequency evaluation. TS score of light rain is higher than those in other classes, but it declines sharply when rainfall magnitude increases. Moderate rain or above has a relatively higher probability of detection, which means that the precipitation event is less detected than observed. Therefore, for the quantitative precipitation verification, it is not suitable to replace the observation data by the gridded data. Further study on 24 h accumulated rainfall bias between gridded data and observation indicates that the spatial structure of precipitation can be well described by gridded data. The spatial scores of precipitation designed in this article is above 0.9, which reflects the spatial distribution of actual precipitation. Generally, the gridded data in Jiangsu Plain Region can basically replace the automatic stations as the real-time meteorological field for forecast and model verification. However, there are still some problems as follows: (1) the 2 m temperature and 2 m relative humidity have large errors in the hilly areas of Jiangsu Province, and precipitation product from island stations has a lower accuracy; (2) the intensity of precipitation above heavy rain is weakened by the gridded data; (3) the wind speed value is lower than the observation, leaving a gap with the requirement of forecasting operation.
Based on the National Gridded Real-time Observation Datasets covering Jiangsu Province released by the National Meteorological Information Centre of CMA and observations of automatic stations, the consistency and accuracy of hourly 2 m temperature, 2 m relative humidity, 10 m wind and precipitation from July 2017 to June 2018 were evaluated in details by the error statistical analysis, skill score and other methods. The MODE method was applied to reveal the spatial deviation of precipitation between gridded products and observed rainfall records. The results indicate that the mean absolute error of 2 m temperature is between 0.5 and 0.8℃, the root mean square error is around 0.8℃ and the 2 m maximum temperature exhibits a better accuracy than the minimum temperature. The root mean square error range of 2 m relative humidity is 6%-7%, which means that the gridded 2 m temperature and 2 m relative humidity data are well consistent with observation. The accuracy of gridded 10 m wind direction is about 70% while that of wind speed is only 56%, showing a big difference from observation. The verification results of precipitation show that the gridded data with accuracy 90%-98% performs well in forecasting rain or no-rain. Nonetheless, it may still have a great impact on the precipitation frequency evaluation. TS score of light rain is higher than those in other classes, but it declines sharply when rainfall magnitude increases. Moderate rain or above has a relatively higher probability of detection, which means that the precipitation event is less detected than observed. Therefore, for the quantitative precipitation verification, it is not suitable to replace the observation data by the gridded data. Further study on 24 h accumulated rainfall bias between gridded data and observation indicates that the spatial structure of precipitation can be well described by gridded data. The spatial scores of precipitation designed in this article is above 0.9, which reflects the spatial distribution of actual precipitation. Generally, the gridded data in Jiangsu Plain Region can basically replace the automatic stations as the real-time meteorological field for forecast and model verification. However, there are still some problems as follows: (1) the 2 m temperature and 2 m relative humidity have large errors in the hilly areas of Jiangsu Province, and precipitation product from island stations has a lower accuracy; (2) the intensity of precipitation above heavy rain is weakened by the gridded data; (3) the wind speed value is lower than the observation, leaving a gap with the requirement of forecasting operation.
10 Microphysical Structure and Evolution Characteristics of an Advection-Radiation Fog Event in Jinan
2019, 45(9):1299-1309.
DOI: 10.7519/j.issn.1000-0526.2019.09.010
Abstract:
A large-scale continuous heavy fog weather occurred in the central and eastern regions of China, including North China, Huanghuai Region, the middle and lower reaches of the Yangtze River, and South China during 3-6 January 2017. The low visibility below 70 m in Jinan lasted for more than 6 h and the minimum visibility was only 51 m. Based on the data from fog droplet collector and automatic weather station, the microphysical structure of the fog was analyzed, the main characteristics of fog in four “development-weakening” subprocesses were discussed, the evolution characters in different development and burst reinforcement phases of the fog were studied, and the causes of the dense fog and burst reinforcement were also discussed in this paper.
A large-scale continuous heavy fog weather occurred in the central and eastern regions of China, including North China, Huanghuai Region, the middle and lower reaches of the Yangtze River, and South China during 3-6 January 2017. The low visibility below 70 m in Jinan lasted for more than 6 h and the minimum visibility was only 51 m. Based on the data from fog droplet collector and automatic weather station, the microphysical structure of the fog was analyzed, the main characteristics of fog in four “development-weakening” subprocesses were discussed, the evolution characters in different development and burst reinforcement phases of the fog were studied, and the causes of the dense fog and burst reinforcement were also discussed in this paper.
2019, 45(9):1310-1321.
DOI: 10.7519/j.issn.1000-0526.2019.09.011
Abstract:
The decaying averaging and the simple linear regression methods were used to correct air temperature forecast in a fine-mesh grid point forecast system of Shaanxi Meteorological Service. Based on the dataset of daily 2 m maximum and minimum air temperature forecasts of 99 national meteorological stations in Shaanxi Province from ECMWF high resolution model in 2017, the abilities of the two methods to correct temperature prediction errors were analyzed and compared. The results showed that the prediction accuracies of daily 2 m maximum and minimum air temperatures are improved significantly by the two methods, whosecorrection abilities are gradually weakened with the extension of prediction time. There is a significant negative correlation between accuracy of the temperature forecast and skill-score of the two methods. The accuracies are all low for the daily 2 m maximum air temperature in Qinling Mountains and the area south to it and daily 2 m minimum air temperature in the north to Qinling Mountains, in which the skill-score is usually more than 40% and its maximum value is even larger than 70%. The systematic deficiencies of daily 2 m maximum and minimum air temperature forecasts are effectively reduced. As a result, the frequency with a smaller error range is increased, while the frequency with a larger error range is decreased. More advantages of the two methods are attained when the absolute errors are less than 2℃ for daily 2 m maximum air temperature forecast and more than 3℃ for daily 2 m minimum air temperature forecast. The ability of the simple linear regression method to correct daily 2 m maximum air temperature forecast is slightly better than that of the decaying averaging method, whose ability to correct daily 2 m minimum air temperature forecast is better than the simple linear regression method. The mixed correction of temperature forecast by the two methods is more effective.
The decaying averaging and the simple linear regression methods were used to correct air temperature forecast in a fine-mesh grid point forecast system of Shaanxi Meteorological Service. Based on the dataset of daily 2 m maximum and minimum air temperature forecasts of 99 national meteorological stations in Shaanxi Province from ECMWF high resolution model in 2017, the abilities of the two methods to correct temperature prediction errors were analyzed and compared. The results showed that the prediction accuracies of daily 2 m maximum and minimum air temperatures are improved significantly by the two methods, whosecorrection abilities are gradually weakened with the extension of prediction time. There is a significant negative correlation between accuracy of the temperature forecast and skill-score of the two methods. The accuracies are all low for the daily 2 m maximum air temperature in Qinling Mountains and the area south to it and daily 2 m minimum air temperature in the north to Qinling Mountains, in which the skill-score is usually more than 40% and its maximum value is even larger than 70%. The systematic deficiencies of daily 2 m maximum and minimum air temperature forecasts are effectively reduced. As a result, the frequency with a smaller error range is increased, while the frequency with a larger error range is decreased. More advantages of the two methods are attained when the absolute errors are less than 2℃ for daily 2 m maximum air temperature forecast and more than 3℃ for daily 2 m minimum air temperature forecast. The ability of the simple linear regression method to correct daily 2 m maximum air temperature forecast is slightly better than that of the decaying averaging method, whose ability to correct daily 2 m minimum air temperature forecast is better than the simple linear regression method. The mixed correction of temperature forecast by the two methods is more effective.
2019, 45(9):1322-1334.
DOI: 10.7519/j.issn.1000-0526.2019.09.012
Abstract:
The characteristics of tropical cyclones (TCs) on Western North Pacific in 2017 were analyzed by using the best-track data, CMA operational forecast data, ECMWF products and NCEP SST_RTG data. Results can be summarized in the following three perspectives. The first is the tropical cyclogenesis. TCs have obvious characteristics of the cluster of tropical cyclogenesis. The generating locations are inclined to be westward. There are more TCs that generated on South China Sea than those in other areas. The second is TCs’ activities. It can be concluded that the annual activity level is low, TCs’ extreme intensity is rather weak, and the number of super typhoon is anomaly small. The third is TCs’ landing. More TCs made landfall this year, and the landing locations are inclined to be southward and the average intensity of landing TCs is weak in general. Estimations of track errors in this year show that there are small increases compared to those of 2016, with the value of 74, 137, 233, 318 and 428 km for 24, 48, 72, 96 and 120 h lead time, respectively. As for track forecast skill, CMA is ahead of JMA and JTWC except the 120 h lead time. The intensity errors are 3.6, 5.4, 6.6, 7.4 and 6.8 m·s-1 for 24, 48, 72, 96 and 120 h lead time, respectively, decreasing a little compared to the errors in 2016. It is noteworthy that the error of 24 h lead time is the lowest in records. The intensity forecast skill is between JMA and JTWC. The forecast difficulties are the complicated interaction between binary or among multiple TCs and the intensity forecast of RI TCs off-shore.
The characteristics of tropical cyclones (TCs) on Western North Pacific in 2017 were analyzed by using the best-track data, CMA operational forecast data, ECMWF products and NCEP SST_RTG data. Results can be summarized in the following three perspectives. The first is the tropical cyclogenesis. TCs have obvious characteristics of the cluster of tropical cyclogenesis. The generating locations are inclined to be westward. There are more TCs that generated on South China Sea than those in other areas. The second is TCs’ activities. It can be concluded that the annual activity level is low, TCs’ extreme intensity is rather weak, and the number of super typhoon is anomaly small. The third is TCs’ landing. More TCs made landfall this year, and the landing locations are inclined to be southward and the average intensity of landing TCs is weak in general. Estimations of track errors in this year show that there are small increases compared to those of 2016, with the value of 74, 137, 233, 318 and 428 km for 24, 48, 72, 96 and 120 h lead time, respectively. As for track forecast skill, CMA is ahead of JMA and JTWC except the 120 h lead time. The intensity errors are 3.6, 5.4, 6.6, 7.4 and 6.8 m·s-1 for 24, 48, 72, 96 and 120 h lead time, respectively, decreasing a little compared to the errors in 2016. It is noteworthy that the error of 24 h lead time is the lowest in records. The intensity forecast skill is between JMA and JTWC. The forecast difficulties are the complicated interaction between binary or among multiple TCs and the intensity forecast of RI TCs off-shore.
2019, 45(9):1335-1340.
DOI: 10.7519/j.issn.1000-0526.2019.09.013
Abstract:
The main characteristics of the general atmospheric circulation in June 2019 are as follows. There was one polar vortex center in the Northern Hemisphere, stronger than usual. The circulation in Eurasian middle-high latitudes showed a two-trough and one-ridge pattern. The strength of Western Paci-fic subtropical high was a little stronger than normal years. The monthly mean temperature in June was 20.6℃, 0.6℃ higher than the normal value, while the monthly mean precipitation amount was 99.8 mm, which is almost equal to the normal (99.3 mm). There were 4 heavy precipitation processes in central and eastern part of China, and in some places severe rainstorm and floods occurred in southern China. Meanwhile, North China, Huang-Huai Region and Yunnan Province, etc. rained less with high temperature during this month, causing drought to maintain. In addition, strong wind and hail disasters hit many provinces.
The main characteristics of the general atmospheric circulation in June 2019 are as follows. There was one polar vortex center in the Northern Hemisphere, stronger than usual. The circulation in Eurasian middle-high latitudes showed a two-trough and one-ridge pattern. The strength of Western Paci-fic subtropical high was a little stronger than normal years. The monthly mean temperature in June was 20.6℃, 0.6℃ higher than the normal value, while the monthly mean precipitation amount was 99.8 mm, which is almost equal to the normal (99.3 mm). There were 4 heavy precipitation processes in central and eastern part of China, and in some places severe rainstorm and floods occurred in southern China. Meanwhile, North China, Huang-Huai Region and Yunnan Province, etc. rained less with high temperature during this month, causing drought to maintain. In addition, strong wind and hail disasters hit many provinces.
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ISSN:1000-0526 CN:11-2282/P