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28 December 2023, Volume 39 Issue 6 Previous Issue    Next Issue

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Characteristics of wet potential vortex in two rainstorms at the eastern foothills of Helan Mountains Yong WANG,Yuying CHEN,Ting LI,Yang SU 2023, 39 (6):  1-9.  doi: 10.3969/j.issn.1673-503X.2023.06.001 Abstract ( 162 )   HTML ( 22 )   PDF (5326KB) ( 266 )   Save Based on hourly precipitation data from 512 automatic meteorological observation stations at the eastern foothills of Helan Mountains in Ningxia province, the ERA5 reanalysis data, and conventional meteorological detection data, the moist potential vortex characteristics of the two extreme rainstorm events occurred on July 22, 2018, and August 11, 2020, in this region were compared and analyzed.The occurrence and development mechanism of the two rainstorm events were explored.The results show that the two extreme rainstorm processes occur under the circulation situation of "high in the east and low in the west".The southerly flow on the west side of the western Pacific Subtropical High, the configuration of high and low air jets, and the typhoon provide favorable water vapor and dynamic conditions for the heavy rainfall processes.In the lower troposphere of 700~850 hPa, 3 ~ 6 h before the rainstorm, there is a dense isocontourt zone with the transition between the negative center of the positive pressure term of the moist potential vortex (MPV1) and the positive and negative center of the moist potential vortex baroclinic term (MPV2), and its movement development and duration correspond well to the occurrence and development of the rainstorm, which can indicate the rainstorm forecast.In the lower troposphere, the position where MPV1 negative region and MPV2 positive region are superposed is an area prone to heavy rainfall, and the stronger and longer the intensity and duration of MPV1 and MPV2 are, the stronger the precipitation will be.When MPV1 and MPV2 approach zero, the atmospheric stratification will be stable and the rainfall process will gradually end. Figures and Tables | References | Related Articles | Metrics

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Diagnosis and analysis of the extreme blizzard in Liaoning province from 6 to 9, November, 2021 Tingting ZHAO,Xin MENG,Lingfeng GAO,Zewen ZHOU,Hailong LIU,Jing GAO 2023, 39 (6):  10-17.  doi: 10.3969/j.issn.1673-503X.2023.06.002 Abstract ( 163 )   HTML ( 16 )   PDF (4023KB) ( 315 )   Save Using the conventional meteorological observational data and 1°×1° reanalysis data from the NCEP (National Centers for Environmental Prediction), the weather situation and physical quantity diagnosis and analysis of the extreme blizzard process in Liaoning province from 6 to 9, November, 2021 were carried out.The results show that the extreme blizzard process is mainly affected by the Northeast cold vortex and strong cyclone, the maximum snowfall is 80.8 mm, and the maximum snow depth measured at 20 stations exceeds 30 cm.The main cause of extreme blizzard is the long duration of strong water vapor transport and water vapor convergence, with the maximum values of 18 g·hPa-1·s-1·cm-1 and -7×10-5 g·hPa-1·cm-2·s-1, respectively.The strong ascending motion produced by abnormally strong convergence at the low level and divergence at the high level, with a central value of -3.0 Pa·s-1, is indicative of an extreme blizzard forecast.The baroclinic forcing and strong horizontal frontogenesis at 850 hPa are important mechanisms for the occurrence and development of this extreme snowfall event.The configuration of the moist potential vortex, with MPV1>0 and MPV2 < 0 indicates that the increase of cyclonic vorticity and symmetric instability provides favorable conditions for the occurrence of the blizzard process. Figures and Tables | References | Related Articles | Metrics

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Analysis of the characteristics and causes of explosive intensification of a persistent heavy fog event over Guanzhong Plain, Shaanxi in January 2019 Mian LIANG,Liujie PAN,Bei JIA,Wenlian YAN,Tianshu WANG,Xingxing GAO,Peirong LI 2023, 39 (6):  18-27.  doi: 10.3969/j.issn.1673-503X.2023.06.003 Abstract ( 119 )   HTML ( 10 )   PDF (4923KB) ( 135 )   Save Using 5 min dense observations from automatic meteorological stations, second-level sounding data, wind profile radar data, and ERA5 hourly reanalysis data from the European Centre, the circulation pattern, evolution characteristics, and causes of explosive intensification of a persistent heavy fog event in Guanzhong Plain, Shaanxi from January 10 to 12, 2019 were analyzed.The Results show that this heavy fog event has the characteristics of high intensity, long duration, and explosive enhancement in multiple locations.The highly humid environment after the rain provides favorable water vapor conditions for this heavy fog event.The stable and persisting strong inversion layer causes large amounts of moisture to accumulate near the surface and is difficult to diffuse, which provides favorable stratification conditions for the explosive intensification and persistence of this heavy fog.The convergence of the wind field formed by the special terrain of Guanzhong Plain makes the water vapor fully condense, which is conducive to the development of heavy fog.Triggering factors such as the sudden change of wind direction and the return of cold air near the surface, may have caused the short-term explosive intensification of heavy fog in multiple locations.The preemptive jump of the static stability index before the fog outbreak and the near-surface easterly backflow below 925 hPa under the static and stable weather background can be used as a reference index for the fog outbreak enhancement in this region. Figures and Tables | References | Related Articles | Metrics

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Analysis of the causes of a typical O3 and PM2.5 pollution weather process in Taiyuan city in August 2019 Wenya WANG,Lingyun ZHU,Wei GUO,Yan WANG,Ling CHEN 2023, 39 (6):  28-36.  doi: 10.3969/j.issn.1673-503X.2023.06.004 Abstract ( 85 )   HTML ( 6 )   PDF (2831KB) ( 204 )   Save Using the near-surface O3, PM2.5, meteorological elements, weather patterns, NCEP reanalysis data, and particulate laser radar data in Taiyuan city, combined with a backward trajectory model, the characteristics and causes of a typical O3 and PM2.5 pollution weather process occurred from August 19 to 20, 2019, in Taiyuan city were analyzed.The results indicate that during this pollution event, the concentration of O3 peaks earlier than that of PM2.5.On the 19th, the concentration of O3 is high and lasts for a long time, whereas on the 20th, the concentration of PM2.5 increases, but that of O3 significantly decreases.The stable layer of low wind and high temperature before the Hetao trough provides favorable conditions for the accumulation of O3 and PM2.5 on the 19th.The decrease of boundary layer height and easterly air transport led to the increase of O3 and PM2.5 concentrations after sunset on the 19th.Subsequently, the upward movement of the ground and the development of the boundary layer during the transit of the trough reduce the O3 and PM2.5 pollution near the ground to a certain extent.After the transit of the cold front on the 20th, the reduced height of the boundary layer and the high humidity environment provide favorable meteorological conditions for the rapid accumulation of PM2.5 near the ground.During the period when particulate pollution was dominant, the main reason for the low O3 concentration on the 20th is the decrease of near-ground solar radiation caused by low temperature and sudden increase of PM2.5 concentration.On the 19th, the high concentration of O3 pollution is mainly affected by the airflow from the southeast direction, while on the 20th, the airflow from central and southern Henan and the sand mass from Inner Mongolia are the main reasons for the increase of PM2.5 concentration. Figures and Tables | References | Related Articles | Metrics

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Verification technology of multi-model precipitation forecast in Liaoning province in summer 2020 Yue WANG,Chenghan LIU,Yunxia DUAN,Hongyu SUN,Jinglin CUI,Yumeng SU,Peiyu CHEN,Weilong BAN 2023, 39 (6):  37-43.  doi: 10.3969/j.issn.1673-503X.2023.06.005 Abstract ( 67 )   HTML ( 7 )   PDF (2359KB) ( 160 )   Save Based on precipitation data observed at regional meteorological stations from June to September 2020, we verified the precipitation forecasts from 7 numerical models.We divided the summer precipitation in Liaoning province into 5 types according to the precipitation intensity and selected 36-hour and 48-hour forecast times to test the distribution errors and correlations of the precipitation field, as well as the location of the rain-bands.The results show that the 36-hour forecast is better than the 48-hour forecast, and the amount of 48-hour forecast precipitation is generally larger than that of the real observations.In early summer, late summer, and early autumn, the forecast deviation of each model is relatively small, while in midsummer, the prediction deviation of the model is large, especially in those stations where the heavy precipitation occurs.According to the 36-hour correlation coefficient between the precipitation predictions and observations and scatter distribution map, the ECMWF (European Centre for Medium-Range Weather Forecasts) model has the best forecast performance in those stations where the precipitation is classified as weak and weaker.The NCEP (National Centers for Environmental Prediction) model and WRF_3KM (Weather Research and Forecasting) model followed; For the moderate, heavier, and heavy precipitation, the NCEP model has the best forecast performance, ECMWF, and WRF_3KM followed.Different models have different levels of deviation in the forecast of rain-band location, among which ECMWF has the best performance. Figures and Tables | References | Related Articles | Metrics

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Comparative analysis of heavy rain forecast in Sichuan province from two numerical models based on the object recognition method Jiajin WANG,Binyan WANG,Dixiang XIAO,Ke-ji LONG 2023, 39 (6):  44-50.  doi: 10.3969/j.issn.1673-503X.2023.06.006 Abstract ( 74 )   HTML ( 4 )   PDF (2235KB) ( 163 )   Save According to the deviation test of 31 heavy rain process forecasts in Sichuan province in 2021, 3 forecasting cases predicted by the ECMWF (European Centre for Medium-Range Weather Forecasts) model and the CMA-MESO (China Meteorological Administration Mesoscale Model) model were selected for comparisons.For these three cases, the rain bands predicted by the ECMWF model were westerly biased, and those produced by the CMA-MESO were better.Based on the object-oriented verification method, the characteristics and main reasons for the bias of the two models in the heavy precipitation falling area (≥25 mm) were compared from four aspects: location deviation, area deviation, rain band trend, and precipitation intensity.The results show that the falling region of the precipitation forecasted by the ECMWF model biases westward and northward, and the westward deviation distance (59.06~123.67 km) is significantly larger than the northerly deviation distance (8.23~53.59 km), while that forecasted by the CMA-MESO model is closer to the forecast.The precipitation areas forecasted by the ECMWF and CMA-MESO models both exceed the actual areas with the ECMWF model being over 7.0%~34.3% and CMA-MESO model being over 25.2%~45.9%, respectively.The deviation between the average precipitation forecast and the actual precipitation is -3.5%~20.0%, but the extreme precipitation forecast is larger than the actual precipitation deviation, with a deviation range of 50.1%~196.9%.The test analysis shows that the CMA-MESO model can provide a correction reference for the heavy rainfall process in Sichuan province, which occurs at the edge of subtropical high and is affected by the plateau vortex or the southwest vortex when the ECMWF model forecasts the heavy rainfall area (≥25 mm) to the west. Figures and Tables | References | Related Articles | Metrics

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Simulation and projection of climate change in Central China based on CMIP6 multi-model ensemble Miao WANG,Pengcheng QIN,Chenxu SHE,Xiaofang ZHAO,Mingwei YANG 2023, 39 (6):  51-60.  doi: 10.3969/j.issn.1673-503X.2023.06.007 Abstract ( 100 )   HTML ( 10 )   PDF (3857KB) ( 202 )   Save Utilizing daily meteorological observation data from Central China from 1961-2014, along with downscaled and bias-corrected data from 12 CMIP6 models from 1961-2100, this study assesses the spatial and temporal distributions of temperature and precipitation simulations of the CMIP6 models for the region, using the selected temperatures from six models and the precipitation data from four models. Based on the ensemble mean of the selected model results, the change trends in temperature and precipitation for Central China during different periods from 2021 to 2100 under three future scenarios i.e. SSP1-2.6, SSP2-4.5, and SSP5-8.5 were analyzed. The results indicate that the ensemble mean provides better interannual variability in temperature simulations than those of precipitation, while the spatial simulation of precipitation is better than that of temperature. Under all three scenarios, both regional temperature and precipitation show increasing trends, with temperature rise rates of 0.13 ℃/decade, 0.30 ℃/decade, and 0.62 ℃/decade, and precipitation rise rates of 16.2 mm/decade, 12.3 mm/decade, and 19.3 mm/decade, respectively. For the future period from 2021 to 2100 under the three scenarios, precipitation in Central China is projected to decrease in the south and to increase in the north. Near-term and mid-term temperatures are expected to decrease in the west and to increase in the central and eastern parts, while the long-term trend indicates that temperature increases across the region and only decreases in the western mountainous area of Hubei. Figures and Tables | References | Related Articles | Metrics

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Analysis of the applicability of CLDAS wind field data in Liaoning province in 2020 Weihua LIU,Wei JIN,Huipin WANG,Chunxiang SHI,Shulin QU,Guojing HAN,Miao YU 2023, 39 (6):  61-68.  doi: 10.3969/j.issn.1673-503X.2023.06.008 Abstract ( 63 )   HTML ( 6 )   PDF (1788KB) ( 88 )   Save Using the 10 m wind field data from 286 meteorological stations in Liaoning province in 2020 and the 10 m wind field data from the Land Data Assimilation System (CLDAS) of the China Meteorological Administration, the correlation coefficient (COR), mean bias (ME), root mean square error (RMSE) and mean absolute error (MAE) between the hourly wind speed data from CLDAS interpolated to the stations and the observed wind speed data at the stations were calculated to analyze and evaluate the applicability of the CLDAS wind field data in Liaoning province. The results show that the CLDAS gridded data with a 1 km resolution is closer to the observed data than those with a 5 km resolution, and the biases of the nearest neighbor interpolation method are smaller than those of the bilinear interpolation method. For the 286 stations in Liaoning province, the hourly CLDAS wind field data has a correlation coefficient below 0.95 with the observed data for only 1.7% of the total stations. The biases between the CLDAS and the observed wind speeds are larger in the coastal low-lying areas and northern Liaoning than in other inland areas. The mean biases between the CLDAS and the observed wind speeds are negative. Among the seasons, the mean bias is smallest in autumn, followed by summer and winter, and largest in spring. For the diurnal variation, the biases are smallest at night in summer and autumn, followed by winter, and largest in spring. During the daytime, the bias is smallest in winter, followed by summer and autumn, and largest in spring. Case studies of 3 strong wind events in Liaoning province all indicate that the CLDAS wind field data has good applicability. Figures and Tables | References | Related Articles | Metrics

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Projected changes of extreme precipitation in Liaohe River Basin at global warming levels of 1.5 ℃ and 2.0 ℃ Xue AO,Qingfei ZHAI,Chunyu ZHAO,Yan CUI,Shujiang GENG,Yiqiu YU,Xiaoyu ZHOU,Jingwei LI 2023, 39 (6):  69-79.  doi: 10.3969/j.issn.1673-503X.2023.06.009 Abstract ( 54 )   HTML ( 1 )   PDF (4246KB) ( 102 )   Save Based on the estimated data of the Sixth Coupled Model Intercomparison Project (CMIP6) conducted by the median resolution model BCC-CSM2-MR from the National Climate Center, methods including bilinear interpolation, trend analysis, anomaly analysis, etc. are used to analyze the changes of extreme precipitation over the Liaohe River Basin under the global warming of 1.5 ℃ and 2.0 ℃. The results show that the anomalous percentage increase of the annual average precipitation over the Liaohe River Basin at the global warming of 1.5 ℃ rises with the increase of emission scenarios, reaching 5.82% under the SSP5-8.5 emission scenario. Under the global warming of 2.0 ℃, the annual and seasonal precipitations over the Liaohe River Basin all show increasing trends, especially for summer precipitation; under the SSP2-4.5 and SSP5-8.5 scenarios, the precipitation decreases from southwest to northeast, with a remarkable increase of over 15% in western Liaoning. Under different emission scenarios, the extreme precipitation indices over the Liaohe River Basin all show increasing trends, with significant growths in daily precipitation intensity, the number of heavy precipitation days, and the proportion of heavy precipitation. With the increase of emission scenarios, the growth rate of extreme precipitation indices rises and is two times more than under the SSP5-8.5 scenario compared to that under the SSP2-4.5 scenario. Under the SSP5-8.5 scenario, the precipitation intensity, the number of heavy precipitation days, the proportion of heavy precipitation, the threshold of heavy precipitation, the maximum number of consecutive wet days, and the maximum 10-day precipitation amount will reach 11.66 mm/d, 15.15 d, 59.08%, 32.94 mm, 9.69 d, 201.29 mm by the end of 21st century, increased by 5.58 mm/d, 5.15 d, 37.08%, 10.15 mm, 5.55 d, 102.86 mm compared to those in the baseline period. Under the global warming of 2.0 ℃, the increases of six extreme precipitation indices are more significant than those under 1.5 ℃ warming, and the extreme precipitation over the Liaohe River Basin shows a consistent increasing trend under the SSP3-7.0 and SSP5-8.5 scenarios. Figures and Tables | References | Related Articles | Metrics

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Study on the relationship between the number of strong cold air days and the interannual variation of sea surface temperature anomalies from 1961 to 2019 in Beijing-Tianjin-Hebei region of China Guohong ZHANG,Xiaoqiong WANG,Yalin ZHANG 2023, 39 (6):  80-86.  doi: 10.3969/j.issn.1673-503X.2023.06.010 Abstract ( 74 )   HTML ( 5 )   PDF (3482KB) ( 152 )   Save Utilizing meteorological observatory data of daily minimum temperature from 1961 to 2019 in Beijing-Tianjin-Hebei region of China, along with NOAA′s monthly sea surface temperature and NCEP/NCAR′s monthly 500 hPa geopotential height field, this study employs physical statistical methods to analyze the variation characteristics of the number of strong cold air days during winter in the region and its relation to sea surface temperatures. This study also explains the mechanism behind the Pacific North American (PNA) teleconnection pattern influenced by sea temperatures in the Niño 3.4 region. The results indicate that the average annual number of strong cold air days in Beijing-Tianjin-Hebei region varies between 0.0 to 8.7 days, with a spatial distribution decreasing from northwest to southeast. Interannually, the number of strong cold air days during winter in Beijing-Tianjin-Hebei significantly correlating with the preceding summer, autumn, and concurrent winter sea temperatures are mainly in the central and eastern equatorial Pacific. Significant negative correlation areas in the westerly jet region and northeastern North Atlantic appear in the preceding autumn and concurrent winter. A pronounced positive correlation with the equatorial Indian Ocean is maintained concurrently. Anomalies in the winter strong cold air days in Beijing-Tianjin-Hebei region are influenced by variations of 500 hPa geopotential height field features such as the Ural Mountain high-pressure ridge, Baikal Lake low-pressure, the anomalous high-pressure over mid-low latitudes of East Asia, and the East Asian trough. Concurrently, sea temperature anomalies in the Niño 3.4 region affect the variation of height field through the PNA teleconnection pattern, indirectly influencing the number of strong cold air days in the region. Figures and Tables | References | Related Articles | Metrics

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Characteristics and source analysis of PM2.5 chemical components at the Shangdianzi regional background station in North China in 2015 Xiaofang JIA,Yang LI,Xiaoqing SUN,Fan DONG 2023, 39 (6):  87-95.  doi: 10.3969/j.issn.1673-503X.2023.06.011 Abstract ( 41 )   HTML ( 4 )   PDF (2158KB) ( 143 )   Save Based on the PM2.5 sample data collected from Shangdianzi regional atmospheric background station in 2015, the variation characteristics of PM2.5 mass concentration and its chemical components were analyzed, and its sources were traced by mass closure, backward trajectory, and potential source contribution methods. The results show that the annual average PM2.5 mass concentration at Shangdianzi station in 2015 was 44.9 μg·m-3, with average concentrations of 58.1 μg·m-3 in spring, 30.9 μg·m-3 in summer, 39.7 μg·m-3 in autumn, and 51.3 μg·m-3 in winter. Compared with 2009 to 2010, they decreased by 33%, 56%, 46%, and 9% respectively. The annual average mass concentrations of SO42-, NO3- and NH4+ are 8.5±9.2, 6.4±8.3, and 3.9±4.7 μg·m-3, respectively. The average annual concentrations of organic carbon and elemental carbon are 8.9±6.3 μg·m-3 and 1.6±1.2 μg·m-3, respectively. The daily average concentration ratio of NO3- to SO42- is 0.82, which is higher than the observed results in 2004, 2012 and 2013. The main components of PM2.5 are soil, organic matter, sulfates, and nitrates, accounting for 34%, 23%, 22%, and 15%, respectively. Compared with 2004, the proportion of sulfate in PM2.5 at Shangdianzi station has decreased, while the proportion of soil components has increased. The PM2.5 at Shangdianzi station primarily originates from the southern, northwestern, and northern of the station, and regional transmission from Hebei and Shandong provinces has a great impact on the high concentration of PM2.5 in Shangdianzi Station. Figures and Tables | References | Related Articles | Metrics

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Applicability of CLDAS air temperature during road icing period on Hoji section of Beijing-Tibet expressway and its application in traffic risk early warning Xiaohong JIA,Qi LI 2023, 39 (6):  96-104.  doi: 10.3969/j.issn.1673-503X.2023.06.012 Abstract ( 54 )   HTML ( 1 )   PDF (2619KB) ( 96 )   Save Hourly air temperature data from 26 conventional and traffic meteorological stations along Hoji section (from Huhhot to Jining) of Beijing-Tibet Expressway during 2020-2022 were used to compare and analyze the temperature characteristics of high-resolution land data assimilation system (CLDAS) of China Meteorological Administration using mean error (ME), root mean square error (RMSE), mean absolute error (MAE) and accuracy rate (AR) during road icing period. The results show that CLDAS air temperature along the Hoji section of the G6 expressway is generally negatively biased during the icing period with ME varying from -1.2 to 1.4 ℃ daily. From Huhhot to Jining, CLDAS temperature changes from cold bias to warm bias during 20:00 to the next day 08:00, and the cold bias increases during 08:00-20:00. Elevation, slope, and aspect have impacts on the accuracy of CLDAS temperature with relatively small errors in low-altitude, plain, and sunny slope sections. After bias correction, the ME of CLDAS temperature approaches 0, and the hourly RMSE and MAE decrease by 0.0-0.5 ℃ each month, with AR increasing by 0-14%. The bias-corrected CLDAS temperature can be used for refined spatial-temporal characteristic analysis of air temperature and classification of early warning for icy road. Figures and Tables | References | Related Articles | Metrics

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Analysis of annual climate and assessment of integrated annual climate from 1961 to 2020 in Liaoning province Yitong LIN,Xiaoyu ZHOU,Chunyu ZHAO,Qian LI,Yihe FANG,Rong LIN,Dajun WANG 2023, 39 (6):  105-111.  doi: 10.3969/j.issn.1673-503X.2023.06.013 Abstract ( 94 )   HTML ( 3 )   PDF (1907KB) ( 197 )   Save The data from 61 national meteorological stations in Liaoning province from 1961 to 2020 was selected, covering 60 years of daily precipitation, temperature, and weather phenomena. Based on the climatic characteristics of Liaoning province, evaluation indices for flood, drought, cold, heat, and blizzard climate conditions were established, and an integrated annual climate assessment model for Liaoning province was constructed. This model enables quantitative assessments of individual climate factors such as flood, drought, low temperature, high temperature, and blizzard, as well as an integrated annual climate assessment. The results indicate that over the past 60 years, the appearance of high-temperature years in Liaoning province has significantly intensified, while cold-weather and blizzard years have significantly weakened. There are no significant trends for the appearance of drought and heavy rainfall years. The comprehensive climate outlook shows a decadal oscillation cycle of about 15 years, with a significant quasi-3-year interannual oscillation cycle since the 1980s. Verification of the assessment results through historically significant meteorological and climate events, disaster occurrence, and practical applications in operations indicates that the established annual climate assessment method is reasonable and can be used for monitoring and evaluating the climate outlook in Liaoning province. Figures and Tables | References | Related Articles | Metrics