ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 46,Issue 9,2020 Table of Contents
2020, 46(9):1129-1142.
DOI: 10.7519/j.issn.1000-0526.2020.09.001
Abstract:
Severe rainstorms struck South China on 7 May 2018, which were related to front-driven mesoscale convective system (MCS) and quasi-stationary MCS in warm zone. South-moving frontal MCS led to wide range of un-uniform precipitation, and warm-sector MCSs with short life time induced multiple mesoscale rainbands within the range of 30-200 km south to surface front. Quasi-stationary line-type MCS along the coast of South China grew up to 300 km in length, sustained more than 12 h and induced extremely severe rainfall beyond 300 mm. All types of MCS showed the low-echo-centroid structure on the vertical section of reflectivity, and the averaged 35 dBz echo-top was at the height of 5.5 km for frontal MCS cells, compared with 4.7 km for convective cells in warm sector. Analysis of raindrop size distribution revealed that MCS in coastal zone was with larger raindrop diameter and higher nuclei concentration compared with frontal MCS. Averaged large-scale precipitation efficiency of frontal convective system was at about 10%-15% according to ERA5 reanalysis, while the precipitation efficiency of MCS in warm zone could increase instantaneously more than 90%. Operational numerical models showed limited predictivity for convective precipitation in warm zone, while EC-Reforecasts showed improved performance on ensemble spread for the convective precipitation near South China coast. Mesoscale model’s forecast showed basic patterns of frontal MCS and warm-sector MCS, however obvious bias existed in the organization and intensity of MCS along coastal zone. Ensemble sensitivity analysis indicated that frontal MCS showed high sensitivity to synoptic forcing related to low pressure trough and low-level jet intensity, while convection in warm sector showed high sensitivity to CAPE in upstream environment.
Severe rainstorms struck South China on 7 May 2018, which were related to front-driven mesoscale convective system (MCS) and quasi-stationary MCS in warm zone. South-moving frontal MCS led to wide range of un-uniform precipitation, and warm-sector MCSs with short life time induced multiple mesoscale rainbands within the range of 30-200 km south to surface front. Quasi-stationary line-type MCS along the coast of South China grew up to 300 km in length, sustained more than 12 h and induced extremely severe rainfall beyond 300 mm. All types of MCS showed the low-echo-centroid structure on the vertical section of reflectivity, and the averaged 35 dBz echo-top was at the height of 5.5 km for frontal MCS cells, compared with 4.7 km for convective cells in warm sector. Analysis of raindrop size distribution revealed that MCS in coastal zone was with larger raindrop diameter and higher nuclei concentration compared with frontal MCS. Averaged large-scale precipitation efficiency of frontal convective system was at about 10%-15% according to ERA5 reanalysis, while the precipitation efficiency of MCS in warm zone could increase instantaneously more than 90%. Operational numerical models showed limited predictivity for convective precipitation in warm zone, while EC-Reforecasts showed improved performance on ensemble spread for the convective precipitation near South China coast. Mesoscale model’s forecast showed basic patterns of frontal MCS and warm-sector MCS, however obvious bias existed in the organization and intensity of MCS along coastal zone. Ensemble sensitivity analysis indicated that frontal MCS showed high sensitivity to synoptic forcing related to low pressure trough and low-level jet intensity, while convection in warm sector showed high sensitivity to CAPE in upstream environment.
2020, 46(9):1143-1152.
DOI: 10.7519/j.issn.1000-0526.2020.09.002
Abstract:
Based on a flight case of the national MA60 seeding aircraft which took place on 21 October 2018, several flight parameters and meteorological elements observed from onboard various instruments are compared and analyzed to investigate their difference and verify the detection data quality. The results show that the deviations of the longitude, latitude and altitude location are relatively small showed on the GPS of aircraft platform, BeiDou Navigation Satellite System (BDS) and aircraft integrated meteorological measurement system (AIMMS-20). The observation of altitude on aircraft’s barometric altimeter is sharply lower than it showed on BDS and AIMMS-20. The changing trend of ambient pressure, true air speed （TAS）, temperature, relative humidity and other elements are almost the same as shown on aircraft platform air data system (ADS), AIMMS-20 and cloud image probe (CIP). The observed values are also close to each other on ADS and AIMMS-20. The instantaneous changes of wind on AIMMS-20 are more accurate than on the ADS. The TAS on CIP is significantly slower than on AIMMS-20, 10 m·s-1 on average. The changing trend of environment pressure is the same on AIMMS-20 and CIP. There is 1.4℃ environment temperature lower on the average on ADS than on AIMMS-20, and there is obvious inversion when the lowest value happens. The ambient temperature on CIP probe is 0.6℃ lower than on AIMMS-20 and relative humidity is 8.6% lower than on AIMMS-20. The differences of aircraft location and those environment elements between different airborne observation sources are the result of different installation locations of those detection instruments or sensors, as well as the influence of inhomogeneous cloud structures. This comparison and analysis could not only provide some scientific bases for reasonable application of the airborne detection data, but also help to guide the design of airborne integrated mission systems for future national and local high-performance seeding and detecting aircraft constructions.
Based on a flight case of the national MA60 seeding aircraft which took place on 21 October 2018, several flight parameters and meteorological elements observed from onboard various instruments are compared and analyzed to investigate their difference and verify the detection data quality. The results show that the deviations of the longitude, latitude and altitude location are relatively small showed on the GPS of aircraft platform, BeiDou Navigation Satellite System (BDS) and aircraft integrated meteorological measurement system (AIMMS-20). The observation of altitude on aircraft’s barometric altimeter is sharply lower than it showed on BDS and AIMMS-20. The changing trend of ambient pressure, true air speed （TAS）, temperature, relative humidity and other elements are almost the same as shown on aircraft platform air data system (ADS), AIMMS-20 and cloud image probe (CIP). The observed values are also close to each other on ADS and AIMMS-20. The instantaneous changes of wind on AIMMS-20 are more accurate than on the ADS. The TAS on CIP is significantly slower than on AIMMS-20, 10 m·s-1 on average. The changing trend of environment pressure is the same on AIMMS-20 and CIP. There is 1.4℃ environment temperature lower on the average on ADS than on AIMMS-20, and there is obvious inversion when the lowest value happens. The ambient temperature on CIP probe is 0.6℃ lower than on AIMMS-20 and relative humidity is 8.6% lower than on AIMMS-20. The differences of aircraft location and those environment elements between different airborne observation sources are the result of different installation locations of those detection instruments or sensors, as well as the influence of inhomogeneous cloud structures. This comparison and analysis could not only provide some scientific bases for reasonable application of the airborne detection data, but also help to guide the design of airborne integrated mission systems for future national and local high-performance seeding and detecting aircraft constructions.
2020, 46(9):1153-1164.
DOI: 10.7519/j.issn.1000-0526.2020.09.003
Abstract:
Using monthly wind speed data from 1979 to 2018 in Liaoning Province and reanalysis data, combined the UMR (urban minus rural) method and OMR (observation minus reanalysis) method combined with satellite remote sensing classification methods, this paper quantitatively analyzed the impact of urbanization of Liaoning Province on near-surface wind speed. The results show that in the past 40 years, the annual and four season wind speeds in Liaoning Province have shown a decreasing trend. The decreasing rate at urban stations is significantly faster than that at rural stations. The trend of UMR value （urban-rural anomaly） is -0.11 m·s-1(10 a)-1， and the contribution rate of urbanization impact is 73.3%. In spatial distribution, the decreasing trend of urban agglomerations in central and northern Liaoning is more obvious, and the wind speed decreases relatively slowly in the south and southeast. The urbanization impact calculated by UMR method shows increasing zonal distribution from west to east. The decreasing trend at the reanalysis data is closer to that of rural stations. The decreasing rate of wind speed in spring is the most obvious. The changing trend of OMR value is -0.10 m·s-1·(10 a)-1, and the corresponding urbanization impact contribution rate is 66.7 %. The urbanization impact and contribution rate calculated by the two methods are relatively consistent, and both can reflect the impact of urbanization on wind speed to a certain extent. Reanalysis data show that the wind speed in the Bohai Strait has a slight increasing trend, and the high-value areas with reduced wind speed are located in the northern Bohai Sea and the northern Yellow Sea. In a word, the spatial distribution of the urbanization impact calculated by the two methods is that the western and southern regions are less affected by urbanization while the central and eastern regions are more affected and their consistency is better.
Using monthly wind speed data from 1979 to 2018 in Liaoning Province and reanalysis data, combined the UMR (urban minus rural) method and OMR (observation minus reanalysis) method combined with satellite remote sensing classification methods, this paper quantitatively analyzed the impact of urbanization of Liaoning Province on near-surface wind speed. The results show that in the past 40 years, the annual and four season wind speeds in Liaoning Province have shown a decreasing trend. The decreasing rate at urban stations is significantly faster than that at rural stations. The trend of UMR value （urban-rural anomaly） is -0.11 m·s-1(10 a)-1， and the contribution rate of urbanization impact is 73.3%. In spatial distribution, the decreasing trend of urban agglomerations in central and northern Liaoning is more obvious, and the wind speed decreases relatively slowly in the south and southeast. The urbanization impact calculated by UMR method shows increasing zonal distribution from west to east. The decreasing trend at the reanalysis data is closer to that of rural stations. The decreasing rate of wind speed in spring is the most obvious. The changing trend of OMR value is -0.10 m·s-1·(10 a)-1, and the corresponding urbanization impact contribution rate is 66.7 %. The urbanization impact and contribution rate calculated by the two methods are relatively consistent, and both can reflect the impact of urbanization on wind speed to a certain extent. Reanalysis data show that the wind speed in the Bohai Strait has a slight increasing trend, and the high-value areas with reduced wind speed are located in the northern Bohai Sea and the northern Yellow Sea. In a word, the spatial distribution of the urbanization impact calculated by the two methods is that the western and southern regions are less affected by urbanization while the central and eastern regions are more affected and their consistency is better.
2020, 46(9):1165-1177.
DOI: 10.7519/j.issn.1000-0526.2020.09.004
Abstract:
A heavy rainfall process which occurred over the Taihang Mountains in central and northern Shanxi Province during 4-5 August 2019 was analyzed by using the GRAPES cloud analysis system, with which numerical experiments were also designed. The effects of the introduction of lightning mapping imager event (LMIE) data of FY-4A on the radar reflectivity, cloud microphysical variables, and rainfall prediction of the model calculation were mainly analyzed. The results indicate that the radar echo calculated by adding the LMIE data was closer to the measured radar echo, and the cloud microphysical elements such as cloud water, cloud ice and snow got adjusted, significantly increasing the content of cloud microphysical elements in the area where lightning occurs. The extreme centers of microphysical elements were generally consistent with the distribution of active areas of lightning. Thus, cloud information initialization could effectively improve the accuracy of precipitation forecast within 24 hours and reduce the spin-up phenomenon of numerical model. With the addition of LMIE data, the accuracy of 1-12 h precipitation forecast could be further improved.
A heavy rainfall process which occurred over the Taihang Mountains in central and northern Shanxi Province during 4-5 August 2019 was analyzed by using the GRAPES cloud analysis system, with which numerical experiments were also designed. The effects of the introduction of lightning mapping imager event (LMIE) data of FY-4A on the radar reflectivity, cloud microphysical variables, and rainfall prediction of the model calculation were mainly analyzed. The results indicate that the radar echo calculated by adding the LMIE data was closer to the measured radar echo, and the cloud microphysical elements such as cloud water, cloud ice and snow got adjusted, significantly increasing the content of cloud microphysical elements in the area where lightning occurs. The extreme centers of microphysical elements were generally consistent with the distribution of active areas of lightning. Thus, cloud information initialization could effectively improve the accuracy of precipitation forecast within 24 hours and reduce the spin-up phenomenon of numerical model. With the addition of LMIE data, the accuracy of 1-12 h precipitation forecast could be further improved.
2020, 46(9):1178-1188.
DOI: 10.7519/j.issn.1000-0526.2020.09.005
Abstract:
From January to March 2017, we carried out an integrated field experiment in complex terrain of Winter Olympic Venue in Xiaohaituo Mountain of Beijing. The observation network consisted of 7 auto-matic weather stations (AWSs), 17 HOBO temperature and humidity data-loggers and 2 comprehensive super stations. During the 7 d intensive observing period (IOP), GPS radiosondes were launched every three hours. In this paper, the temperature profiles based on AWSs and HOBOs are compared with radio sounding temperature profiles. Based on the data of IOP, the characteristics of mid-mountain clouds (MMC) that have critical negative effects on alpine skiing are preliminarily analyzed. The results show that when MMC occurs, there are diurnal variations in surface wind direction. MMC tends to occur between 1 〖KG-*5〗100 m and 1 〖KG-*5〗700 m above ASL, shown up as a saturated moisture layer by radio soundings. In general, the top of MMC is at same height of the bottom of inversion layer, below which MMC exists. During nighttime, affected by downslope winds and down-valley winds, wind directions are more northwesterly or westerly below MMC. Inside MMC, winds blow more easterly or southeasterly at lower speeds through the day. Above MMC and inversions, westerly or northwesterly winds are obvious. Leeward subsidence-induced inversions and easterly moisture transportation in large scope could be the main causes for the MMC in Xiaohaituo Mountain.
From January to March 2017, we carried out an integrated field experiment in complex terrain of Winter Olympic Venue in Xiaohaituo Mountain of Beijing. The observation network consisted of 7 auto-matic weather stations (AWSs), 17 HOBO temperature and humidity data-loggers and 2 comprehensive super stations. During the 7 d intensive observing period (IOP), GPS radiosondes were launched every three hours. In this paper, the temperature profiles based on AWSs and HOBOs are compared with radio sounding temperature profiles. Based on the data of IOP, the characteristics of mid-mountain clouds (MMC) that have critical negative effects on alpine skiing are preliminarily analyzed. The results show that when MMC occurs, there are diurnal variations in surface wind direction. MMC tends to occur between 1 〖KG-*5〗100 m and 1 〖KG-*5〗700 m above ASL, shown up as a saturated moisture layer by radio soundings. In general, the top of MMC is at same height of the bottom of inversion layer, below which MMC exists. During nighttime, affected by downslope winds and down-valley winds, wind directions are more northwesterly or westerly below MMC. Inside MMC, winds blow more easterly or southeasterly at lower speeds through the day. Above MMC and inversions, westerly or northwesterly winds are obvious. Leeward subsidence-induced inversions and easterly moisture transportation in large scope could be the main causes for the MMC in Xiaohaituo Mountain.
2020, 46(9):1189-1198.
DOI: 10.7519/j.issn.1000-0526.2020.09.006
Abstract:
In this paper, the dual-polarization weather radar data are used to improve the quality of the automatic rain gauge data. The quality of the rain gauge data is improved by using the inverse distance weighting method (IDW), and the rain guage data after quality control are used to correct the rainfall data derived from dual-polarization radar. The quality control method is determined by using the revised radar-gauge data, and the quality of gauge data is comprehensively controlled from the perspective of spatial consistency and observation consistency. The comprehensive quality control method is verified by using a typhoon precipitation process. The results show that there are some misjudgments in the spatial consistency of rain gauge observations by only using IDW method, mainly due to the abruptness of local precipitation, the inhomogeneity of rainfall structure and the sparse distribution of rain gauges. The misjudgments caused by the simple use of spatial consistent property control method can be effectively reduced by combining with polarization radar. The rainfall estimations of radar have been greatly improved after the rain gauge calibration and correction so that the effectiveness of the polarization radar used in the quality control of the rain gauge is improved. During typhoon rainfall, about 5% of the rain gauges per hour are considered as the error sites with wrong results. This comprehensive quality control method has some misjudgments when the distribution of the rain gauge is sparse or when the rain gauge lies in the edge of precipitation.
In this paper, the dual-polarization weather radar data are used to improve the quality of the automatic rain gauge data. The quality of the rain gauge data is improved by using the inverse distance weighting method (IDW), and the rain guage data after quality control are used to correct the rainfall data derived from dual-polarization radar. The quality control method is determined by using the revised radar-gauge data, and the quality of gauge data is comprehensively controlled from the perspective of spatial consistency and observation consistency. The comprehensive quality control method is verified by using a typhoon precipitation process. The results show that there are some misjudgments in the spatial consistency of rain gauge observations by only using IDW method, mainly due to the abruptness of local precipitation, the inhomogeneity of rainfall structure and the sparse distribution of rain gauges. The misjudgments caused by the simple use of spatial consistent property control method can be effectively reduced by combining with polarization radar. The rainfall estimations of radar have been greatly improved after the rain gauge calibration and correction so that the effectiveness of the polarization radar used in the quality control of the rain gauge is improved. During typhoon rainfall, about 5% of the rain gauges per hour are considered as the error sites with wrong results. This comprehensive quality control method has some misjudgments when the distribution of the rain gauge is sparse or when the rain gauge lies in the edge of precipitation.
7 A Case Study of Aircraft Observation of Aerosol Vertical Distribution and Activation Characteristics
2020, 46(9):1199-1209.
DOI: 10.7519/j.issn.1000-0526.2020.09.007
Abstract:
In order to study the vertical distribution of aerosol and cloud condensation nuclei (CCN) spectra, we combind the aerosol data with CCN data from aircraft and surface observations conducted in Shanxi on 30 July 2014 to analyze the vertical distribution of the parameters C and k of the CCN spectrum (N=C·Sk) and the activation characteristics of aerosols. The results show that the vertical stratification of aerosols in this process is obvious, and the vertical distribution of aerosols in different regions is different. According to the characteristics of potential temperature change, it can be divided into four layers from bottom to top. The vertical distribution characteristics such as aerosol concentration, effective diameter and particle spectrum are closely related to the stratification. The k is affected by the aerosol’s chemical composition and the particle spectrum, and the k of each layer is different. The k value of the first layer increases with height and the maximum value is 1. The k value of the second layer decreases first then goes up. It is the lowest between 1 〖KG-*5〗700 m and 2 〖KG-*5〗000 m, being 0.3. The third layer has little change in k, about 0.8, and the fourth layer stabilizes to 0.6. The aerosol sources are different in each layer by the backward trajectory modeling, and the properties of the aerosol are consistent with the ground aerosol properties of the corresponding source. The air masses at heights of 1 〖KG-*5〗000 m and 2 〖KG-*5〗000 m from the North China Plain in the southeast of Shanxi and the Loess Plateau to the west, respectively. The air mass above 3 〖KG-*5〗000 m is from Mongolia in the northwest. So, differences in vertical aerosol sources are responsible for the apparent stratification of the aerosol distribution and CCN spectrum.
In order to study the vertical distribution of aerosol and cloud condensation nuclei (CCN) spectra, we combind the aerosol data with CCN data from aircraft and surface observations conducted in Shanxi on 30 July 2014 to analyze the vertical distribution of the parameters C and k of the CCN spectrum (N=C·Sk) and the activation characteristics of aerosols. The results show that the vertical stratification of aerosols in this process is obvious, and the vertical distribution of aerosols in different regions is different. According to the characteristics of potential temperature change, it can be divided into four layers from bottom to top. The vertical distribution characteristics such as aerosol concentration, effective diameter and particle spectrum are closely related to the stratification. The k is affected by the aerosol’s chemical composition and the particle spectrum, and the k of each layer is different. The k value of the first layer increases with height and the maximum value is 1. The k value of the second layer decreases first then goes up. It is the lowest between 1 〖KG-*5〗700 m and 2 〖KG-*5〗000 m, being 0.3. The third layer has little change in k, about 0.8, and the fourth layer stabilizes to 0.6. The aerosol sources are different in each layer by the backward trajectory modeling, and the properties of the aerosol are consistent with the ground aerosol properties of the corresponding source. The air masses at heights of 1 〖KG-*5〗000 m and 2 〖KG-*5〗000 m from the North China Plain in the southeast of Shanxi and the Loess Plateau to the west, respectively. The air mass above 3 〖KG-*5〗000 m is from Mongolia in the northwest. So, differences in vertical aerosol sources are responsible for the apparent stratification of the aerosol distribution and CCN spectrum.
2020, 46(9):1210-1221.
DOI: 10.7519/j.issn.1000-0526.2020.09.008
Abstract:
The ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation type forecast products （PTYPE） were verified using the weather observations of more than 2 〖KG-*5〗000 stations in China over the winter months (October to the next March) during 2016-2018. The products include the deterministic forecasts from high-resolution model （HRD） and the probability forecasts from ensemble prediction system （EPS） and the verified precipitation types include rain, sleet, snow and freezing rain. The results show that the accuracy of deterministic forecasts of ECMWF HRD is mostly higher than 90% and the TSs of rain and snow are the highest, followed by the TS of freezing rain, and the TS of sleet is lower, which indicates that the forecast skill of sleet is limited. The rain and snow dividing line of deterministic forecasts shows the errors of a little southward in short range forecast and more and more significant northward following elongating lead times in medium range forecast. The area of sleet forecast is smaller than observations and the area of freezing rain forecast is bigger for the HRD forecast. The EPS offsets these errors partly by probability forecast. The probability forecast of rain from the EPS is smaller than the observation frequency and the probability forecast of snow is larger in short range and smaller in medium range forecasts than the observation frequency. However, there are some forecast skills for all of these probability forecasts. There are advantages of EPS compared to the HRD. For rain and snow, for some special cost/loss ratio events the EPS is better than the HRD. For sleet and freezing rain, the EPS is better than the HRD significantly, especially for the freezing rain.
The ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation type forecast products （PTYPE） were verified using the weather observations of more than 2 〖KG-*5〗000 stations in China over the winter months (October to the next March) during 2016-2018. The products include the deterministic forecasts from high-resolution model （HRD） and the probability forecasts from ensemble prediction system （EPS） and the verified precipitation types include rain, sleet, snow and freezing rain. The results show that the accuracy of deterministic forecasts of ECMWF HRD is mostly higher than 90% and the TSs of rain and snow are the highest, followed by the TS of freezing rain, and the TS of sleet is lower, which indicates that the forecast skill of sleet is limited. The rain and snow dividing line of deterministic forecasts shows the errors of a little southward in short range forecast and more and more significant northward following elongating lead times in medium range forecast. The area of sleet forecast is smaller than observations and the area of freezing rain forecast is bigger for the HRD forecast. The EPS offsets these errors partly by probability forecast. The probability forecast of rain from the EPS is smaller than the observation frequency and the probability forecast of snow is larger in short range and smaller in medium range forecasts than the observation frequency. However, there are some forecast skills for all of these probability forecasts. There are advantages of EPS compared to the HRD. For rain and snow, for some special cost/loss ratio events the EPS is better than the HRD. For sleet and freezing rain, the EPS is better than the HRD significantly, especially for the freezing rain.
2020, 46(9):1222-1234.
DOI: 10.7519/j.issn.1000-0526.2020.09.009
Abstract:
In order to understand the circulation characteristics of air pollution events in autumn and winter in Chongqing, this paper uses NCEP reanalysis data to classify the surface sea level pressure fields of air pollution events by using the self-organizing map (SOM) algorithm. Then by means of subjective comparative analysis, three types of typical surface pressure fields are summarized: uniform pressure pattern, low pressure pattern and the bottom pattern of high pressure. Among them, the uniform pressure pattern is divided into two types: one is the uniform pressure pattern between two cold fronts and the other is in the weak high pressure. The bottom pattern of high pressure can be divided into three types according to the center position of cold high pressure, including north high pressure type, northwest high pressure type and northeast high pressure type. By comparative analysis, it is found that the concentration of air pollutants on the bottom pattern of high pressure is the highest, thus the air pollution is the most serious. The analysis by using conventional observation data and L-band radiosonde data shows that all kinds of pollution weather patterns are characterized by high static wind frequency on the surface, low horizontal wind speed near the surface, high probability of inversion, stable atmospheric stratification and low height of atmospheric boundary layer. Based on the large-scale synoptic circulation, dynamic and thermal conditions and backward trajectory simulation, the causes for the three types of typical pollution weather processes are investigated, and the results would provide a reference for air pollution potential forecast and concentration prediction in Chongqing.
In order to understand the circulation characteristics of air pollution events in autumn and winter in Chongqing, this paper uses NCEP reanalysis data to classify the surface sea level pressure fields of air pollution events by using the self-organizing map (SOM) algorithm. Then by means of subjective comparative analysis, three types of typical surface pressure fields are summarized: uniform pressure pattern, low pressure pattern and the bottom pattern of high pressure. Among them, the uniform pressure pattern is divided into two types: one is the uniform pressure pattern between two cold fronts and the other is in the weak high pressure. The bottom pattern of high pressure can be divided into three types according to the center position of cold high pressure, including north high pressure type, northwest high pressure type and northeast high pressure type. By comparative analysis, it is found that the concentration of air pollutants on the bottom pattern of high pressure is the highest, thus the air pollution is the most serious. The analysis by using conventional observation data and L-band radiosonde data shows that all kinds of pollution weather patterns are characterized by high static wind frequency on the surface, low horizontal wind speed near the surface, high probability of inversion, stable atmospheric stratification and low height of atmospheric boundary layer. Based on the large-scale synoptic circulation, dynamic and thermal conditions and backward trajectory simulation, the causes for the three types of typical pollution weather processes are investigated, and the results would provide a reference for air pollution potential forecast and concentration prediction in Chongqing.
2020, 46(9):1235-1244.
DOI: 10.7519/j.issn.1000-0526.2020.09.010
Abstract:
This paper investigated the roughness, stability and temperature inversion (TI) properties of urban boundary layer based on the analysis of wind and temperature observations from Tianjin meteorological tower. The results indicated that there is significant roughness length and zeroplane displacement in all directions of the meteorological tower under the influence of surrounding buildings. The impact of urbanization on wind field is more evident below 80 m. Affected by the diurnal variation of turbulence intensity, the diurnal variation characteristics of wind speed in the upper and lower levels of meteorological towers are obviously different in each season. Based on the temperature differencewind speed method, atmospheric stratification is found more stable in autumn and winter. The occurrence of TI is more frequent under stable conditions and both the intensity and height of TI are much larger than those under unstable and neutral conditions. Furthermore, the occurrence of TI near ground is more frequent under stable conditions, which is very favorable to severe air pollution episode. The intensity of urban heat island is correlated with the temporal distribution of atmospheric stability.
This paper investigated the roughness, stability and temperature inversion (TI) properties of urban boundary layer based on the analysis of wind and temperature observations from Tianjin meteorological tower. The results indicated that there is significant roughness length and zeroplane displacement in all directions of the meteorological tower under the influence of surrounding buildings. The impact of urbanization on wind field is more evident below 80 m. Affected by the diurnal variation of turbulence intensity, the diurnal variation characteristics of wind speed in the upper and lower levels of meteorological towers are obviously different in each season. Based on the temperature differencewind speed method, atmospheric stratification is found more stable in autumn and winter. The occurrence of TI is more frequent under stable conditions and both the intensity and height of TI are much larger than those under unstable and neutral conditions. Furthermore, the occurrence of TI near ground is more frequent under stable conditions, which is very favorable to severe air pollution episode. The intensity of urban heat island is correlated with the temporal distribution of atmospheric stability.
2020, 46(9):1245-1253.
DOI: 10.7519/j.issn.1000-0526.2020.09.011
Abstract:
The summer maximum power load variation and its correlation with meteorological factors were analyzed by using the 15 min power load and the daily meteorological data in Wuhan from 2016 to 2018. Based on stepwise regression and double hidden layer BP neural network algorithm, the prediction model of summer maximum power load was established. The results show that there is a significant positive correlation between the mean temperature, average maximum temperature and average minimum temperature and the meteorological load. The correlation between the maximum load on the day and the load on the previous day is the best. The load on the day is most sensitive to the average temperature and comfort index in the previous two days. With historical load and meteorological data as joint forecasting factors, the stepwise regression and BP neural network algorithm have a good simulation effect on the maximum summer power load in Wuhan, especially on the maximum load of high-level operation caused by continuous high temperature in 2018. When the sensitivity is lower than 10%, the positive contribution of the meteoro-logical factor in the stepwise regression algorithm is less than the negative contribution, and the positive contribution in the BP neural network algorithm is higher than the negative contribution. But when the sensitivity is higher than 10%, the meteorological factors in both algorithms contribute positively.
The summer maximum power load variation and its correlation with meteorological factors were analyzed by using the 15 min power load and the daily meteorological data in Wuhan from 2016 to 2018. Based on stepwise regression and double hidden layer BP neural network algorithm, the prediction model of summer maximum power load was established. The results show that there is a significant positive correlation between the mean temperature, average maximum temperature and average minimum temperature and the meteorological load. The correlation between the maximum load on the day and the load on the previous day is the best. The load on the day is most sensitive to the average temperature and comfort index in the previous two days. With historical load and meteorological data as joint forecasting factors, the stepwise regression and BP neural network algorithm have a good simulation effect on the maximum summer power load in Wuhan, especially on the maximum load of high-level operation caused by continuous high temperature in 2018. When the sensitivity is lower than 10%, the positive contribution of the meteoro-logical factor in the stepwise regression algorithm is less than the negative contribution, and the positive contribution in the BP neural network algorithm is higher than the negative contribution. But when the sensitivity is higher than 10%, the meteorological factors in both algorithms contribute positively.
2020, 46(9):1254-1260.
DOI: 10.7519/j.issn.1000-0526.2020.09.012
Abstract:
The main characteristics of the general atmospheric circulation in June 2020 are as follows. There was one polar vortex center in the Eastern Hemisphere, stronger than usual. The circulation in Eurasian middle-high latitudes showed a multiwave pattern. The strength of Western Pacific subtropical high was stronger and the covered area was larger than in normal years. The monthly mean temperature was 20.7℃, 0.7℃ higher than normal. The monthly mean precipitation amount was 112.7 mm, which is 13.5% more than normal. Five regional heavy rainfall processes occurred in China this month and some places were hit by severe rainstorm and floods. Droughts were found in nothern part of North China, southern part of South China, northern Xinjiang and Yunnan etc. in June. Typhoon Nuri landed in Yangjiang, Guangdong Province, which is the second landing typhoon in China this year.
The main characteristics of the general atmospheric circulation in June 2020 are as follows. There was one polar vortex center in the Eastern Hemisphere, stronger than usual. The circulation in Eurasian middle-high latitudes showed a multiwave pattern. The strength of Western Pacific subtropical high was stronger and the covered area was larger than in normal years. The monthly mean temperature was 20.7℃, 0.7℃ higher than normal. The monthly mean precipitation amount was 112.7 mm, which is 13.5% more than normal. Five regional heavy rainfall processes occurred in China this month and some places were hit by severe rainstorm and floods. Droughts were found in nothern part of North China, southern part of South China, northern Xinjiang and Yunnan etc. in June. Typhoon Nuri landed in Yangjiang, Guangdong Province, which is the second landing typhoon in China this year.
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