ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 49,Issue 2,2023 Table of Contents
2023, 49(2):129-145.
DOI: 10.7519/j.issn.1000-0526.2022.100301
Abstract:
The downburst is the outburst of divergent flow on or near the ground induced by a strong convective downdraft. A single downburst affects a small area of several kilometers, and the downburst cluster can extend over several hundreds of kilometers resulting in many noncontinuous surface damaging gusts. Its enhancing mechanism may not be limited to the strong downdraft divergent outflow. This article reviews the definition of downburst, and dicusses downbursts under two different situations. One is the downburst induced by isolated storms and the other is the downbursts embedded in mesoscale convective systems, including the formation processes of downburst and warning technology of downburst based on Doppler weather radar. On the basis of the above review, the formation mechanism of downburst and the difficulties of warning are discussed, and the much-needed issues related to downburst are listed.
The downburst is the outburst of divergent flow on or near the ground induced by a strong convective downdraft. A single downburst affects a small area of several kilometers, and the downburst cluster can extend over several hundreds of kilometers resulting in many noncontinuous surface damaging gusts. Its enhancing mechanism may not be limited to the strong downdraft divergent outflow. This article reviews the definition of downburst, and dicusses downbursts under two different situations. One is the downburst induced by isolated storms and the other is the downbursts embedded in mesoscale convective systems, including the formation processes of downburst and warning technology of downburst based on Doppler weather radar. On the basis of the above review, the formation mechanism of downburst and the difficulties of warning are discussed, and the much-needed issues related to downburst are listed.
2023, 49(2):146-156.
DOI: 10.7519/j.issn.1000-0526.2022.022301
Abstract:
Based on the principle of the melting layer detection algorithm (MLDA) from National Severe Storms Laboratory, some detection tests are carried out using S-band dual-polarization radar volume data from Jinan and Qingdao radar stations in July and August 2020. According to the test results, some improvement measures such as radial continuity check, increasing the range of scan elevation and adjusting the threshold of total radar bin number for identifying the melting layer are integrated into the MLDA, and the recognition effect of MLDA on the melting layer before and after improvement are compared and the conclusions are as follows. The melting layer could be identified by the MLDA, but the mean absolute error of the top height of the melting layer is large and the melting layer bottom height is too low. After the radial continuity check is integrated into the MLDA (MLDA-R1), the mean absolute error of the top height of the melting layer gets smaller obviously, the temperature of the bottom height and the thickness of the melting layer are within a reasonable range, but the number of the test not having detected the melting layer increases markedly. On the basis of the MLDA-R1, 3.3° elevation is added to the scanning area (MLDA-R2) and the threshold of total number of radar bin for recognizing the melting layer is adjusted (MLDA-R3). Then, the number of the test not having detected the melting layer is significantly reduced, and the identification effect of the height information of the melting layer is improved. Some areas containing precipitation echo and non-precipitation echo could be falsely detected as the melting layer by MLDA after improvement so that the recognition effect of the melting layer is influenced. In general, MLDA after the improvement is more applicable to S-band dual-polarization radar in China and it can be utilized to support the radar hydrometer classification and quantitative precipitation estimation.
Based on the principle of the melting layer detection algorithm (MLDA) from National Severe Storms Laboratory, some detection tests are carried out using S-band dual-polarization radar volume data from Jinan and Qingdao radar stations in July and August 2020. According to the test results, some improvement measures such as radial continuity check, increasing the range of scan elevation and adjusting the threshold of total radar bin number for identifying the melting layer are integrated into the MLDA, and the recognition effect of MLDA on the melting layer before and after improvement are compared and the conclusions are as follows. The melting layer could be identified by the MLDA, but the mean absolute error of the top height of the melting layer is large and the melting layer bottom height is too low. After the radial continuity check is integrated into the MLDA (MLDA-R1), the mean absolute error of the top height of the melting layer gets smaller obviously, the temperature of the bottom height and the thickness of the melting layer are within a reasonable range, but the number of the test not having detected the melting layer increases markedly. On the basis of the MLDA-R1, 3.3° elevation is added to the scanning area (MLDA-R2) and the threshold of total number of radar bin for recognizing the melting layer is adjusted (MLDA-R3). Then, the number of the test not having detected the melting layer is significantly reduced, and the identification effect of the height information of the melting layer is improved. Some areas containing precipitation echo and non-precipitation echo could be falsely detected as the melting layer by MLDA after improvement so that the recognition effect of the melting layer is influenced. In general, MLDA after the improvement is more applicable to S-band dual-polarization radar in China and it can be utilized to support the radar hydrometer classification and quantitative precipitation estimation.
2023, 49(2):157-169.
DOI: 10.7519/j.issn.1000-0526.2022.061801
Abstract:
To reduce the deviation between radar rainfall field and surface rainfall observations, this paper proposes calibrating the radar rainfall field with surface microwave links including the variational calibration method, the Kalman filter calibration method, the mean calibration method and the Kriging calibration method. The rainfall rates retrieved by two microwave links are used to calibrate the Sband radar rainfall field in two precipitation cases of different types. The calibration results are then compared with the measurements of rain gauges. The conclusions are as follows. Firstly, all the four calibration methods are proved effective to reduce the bias between the radarbased rainfall estimates and the gauge measured rainfall. The problems of the underestimation of heavy precipitation in precipitation Case Ⅰ and the overestimation of weak precipitation in Case Ⅱ are both partly solved. The statistical errors including mean absolute error (MAE)， mean error (ME) and root mean square error (RMSE) are all significantly lowered after calibration. The improvement degrees of the statistical errors from high to low are ME, RMSE and MAE. Secondly, the effectiveness of the Kriging calibration method is the best among the four methods. The performances of the variational calibration method and the mean calibration method are better than that of Kalman filter calibration method. The Kriging calibration method is the most effective to reduce ME and RMSE and the variation method is most effective for MAE. Thirdly, the Kriging calibration method and the variational calibration method can derive calibration factor fields which vary with time and spatial position, while the mean calibration method and Kalman filter method can only obtain a mean calibration factor for each time. These results suggest that microwave link can be an effective alternative to calibrate the radar rainfall field.
To reduce the deviation between radar rainfall field and surface rainfall observations, this paper proposes calibrating the radar rainfall field with surface microwave links including the variational calibration method, the Kalman filter calibration method, the mean calibration method and the Kriging calibration method. The rainfall rates retrieved by two microwave links are used to calibrate the Sband radar rainfall field in two precipitation cases of different types. The calibration results are then compared with the measurements of rain gauges. The conclusions are as follows. Firstly, all the four calibration methods are proved effective to reduce the bias between the radarbased rainfall estimates and the gauge measured rainfall. The problems of the underestimation of heavy precipitation in precipitation Case Ⅰ and the overestimation of weak precipitation in Case Ⅱ are both partly solved. The statistical errors including mean absolute error (MAE)， mean error (ME) and root mean square error (RMSE) are all significantly lowered after calibration. The improvement degrees of the statistical errors from high to low are ME, RMSE and MAE. Secondly, the effectiveness of the Kriging calibration method is the best among the four methods. The performances of the variational calibration method and the mean calibration method are better than that of Kalman filter calibration method. The Kriging calibration method is the most effective to reduce ME and RMSE and the variation method is most effective for MAE. Thirdly, the Kriging calibration method and the variational calibration method can derive calibration factor fields which vary with time and spatial position, while the mean calibration method and Kalman filter method can only obtain a mean calibration factor for each time. These results suggest that microwave link can be an effective alternative to calibrate the radar rainfall field.
2023, 49(2):170-177.
DOI: 10.7519/j.issn.1000-0526.2022.080301
Abstract:
In order to study the cloud characteristics of the southeast coast of China, a laser ceilometer CL5 by Vaisala was used to detect cloud characteristics in Xiamen from 1 January 2016 to 31 December 2020. By using the time proportion algorithm to calculate the cloud fraction, this paper analyzes the distributions of cloud layers, cloud-base height and cloud fraction via data analysis method. The results show that the cloud structure in the southeast coast of China is dominated by single-layer clouds (43.59%), supplemented by double-layer clouds (16.42%), and the probability of occurrence of clouds with more than three layers is rare (5.25%). During the observation period, there were mainly low- and mid-level clouds. Compared with that in other seasons, the concentration of cloud distribution density in summer is smaller, and the cloud-base height difference between the lowest layer cloud and the highest layer cloud is larger. High clouds are more likely to occur between 18:00 BT and 06:00 BT, especially in summer, showing the characteristics of obvious diurnal variation.
In order to study the cloud characteristics of the southeast coast of China, a laser ceilometer CL5 by Vaisala was used to detect cloud characteristics in Xiamen from 1 January 2016 to 31 December 2020. By using the time proportion algorithm to calculate the cloud fraction, this paper analyzes the distributions of cloud layers, cloud-base height and cloud fraction via data analysis method. The results show that the cloud structure in the southeast coast of China is dominated by single-layer clouds (43.59%), supplemented by double-layer clouds (16.42%), and the probability of occurrence of clouds with more than three layers is rare (5.25%). During the observation period, there were mainly low- and mid-level clouds. Compared with that in other seasons, the concentration of cloud distribution density in summer is smaller, and the cloud-base height difference between the lowest layer cloud and the highest layer cloud is larger. High clouds are more likely to occur between 18:00 BT and 06:00 BT, especially in summer, showing the characteristics of obvious diurnal variation.
2023, 49(2):178-187.
DOI: 10.7519/j.issn.1000-0526.2022.050601
Abstract:
By using the ground-based vertical remote sensing equipment constructed by the Beijing National Comprehensive Meteorological Observation and Test Base for the Super-Large City Observation Test, and the observation data of radiosonde, laser aerosol radar, microwave radiometer and wind profiler radar from May to August 2021, and according to different detection advantages of the equipment and the diurnal variation of the boundary layer, this paper performs the joint inversion to the observation data of laser aerosol radar, microwave radiometer, and wind profiler radar to obtain the all-weather atmospheric boundary layer height. The comparison of the height of the boundary layer obtained from the joint inversion with the height of the all-weather atmospheric boundary layer provided by the sounding data calculation and the ERA5 reanalysis data suggests that the joint inversion boundary layer height is in good agreement with the atmospheric boundary layer height provided by the ERA5 data. The laser aerosol radar is suitable for the observation of the convective boundary layer during the daytime, and the microwave radiometer is suitable for the observation of the stable boundary layer at night. The use of microwave radiometer and wind profiler radar to jointly retrieve the height of the atmospheric boundary layer can improve the performance of a single device during rainfall. The joint inversion of the atmospheric boundary layer height result and the single device inversion of the atmospheric boundary layer height are in line with the diurnal variation of the atmospheric boundary layer. The joint inversion of boundary layer height obtained in this article is compared with the height difference of the atmospheric boundary layer from the sounding data, and their standard deviation is 62 m. Compared with the average value of the atmospheric boundary layer height in a certain range provided by the ERA5 data, the joint inversion of the boundary layer height could more accurately reflect the atmospheric boundary layer height in a smaller range.
By using the ground-based vertical remote sensing equipment constructed by the Beijing National Comprehensive Meteorological Observation and Test Base for the Super-Large City Observation Test, and the observation data of radiosonde, laser aerosol radar, microwave radiometer and wind profiler radar from May to August 2021, and according to different detection advantages of the equipment and the diurnal variation of the boundary layer, this paper performs the joint inversion to the observation data of laser aerosol radar, microwave radiometer, and wind profiler radar to obtain the all-weather atmospheric boundary layer height. The comparison of the height of the boundary layer obtained from the joint inversion with the height of the all-weather atmospheric boundary layer provided by the sounding data calculation and the ERA5 reanalysis data suggests that the joint inversion boundary layer height is in good agreement with the atmospheric boundary layer height provided by the ERA5 data. The laser aerosol radar is suitable for the observation of the convective boundary layer during the daytime, and the microwave radiometer is suitable for the observation of the stable boundary layer at night. The use of microwave radiometer and wind profiler radar to jointly retrieve the height of the atmospheric boundary layer can improve the performance of a single device during rainfall. The joint inversion of the atmospheric boundary layer height result and the single device inversion of the atmospheric boundary layer height are in line with the diurnal variation of the atmospheric boundary layer. The joint inversion of boundary layer height obtained in this article is compared with the height difference of the atmospheric boundary layer from the sounding data, and their standard deviation is 62 m. Compared with the average value of the atmospheric boundary layer height in a certain range provided by the ERA5 data, the joint inversion of the boundary layer height could more accurately reflect the atmospheric boundary layer height in a smaller range.
2023, 49(2):188-200.
DOI: 10.7519/j.issn.1000-0526.2022.081001
Abstract:
In this paper, radar extrapolation and high-resolution numerical weather prediction (NWP) are blended to get a 6 h quantitative precipitation forecast for the Yangtze River Delta Region of China. The method used here is based on a mainstream blending framework, and modifications and calibrations are conducted done to both the extrapolation and NWP to improve the result. The traditional COTREC extrapolation is extended by using a mosaic system of 11 radars within the region and NWP data. Intensity and position calibration for the NWP are conducted based on Weibull function fitting and object recognition. A weighted blending of extrapolation and NWP is then carried out based on scale and forecast time. Finally, real-time Z-R relation conversion is performed. The results show that the blending method can extend forecast time of the extrapolation forecast, and calibrate the intensity and position bias of NWP, thus making a better result as a whole.
In this paper, radar extrapolation and high-resolution numerical weather prediction (NWP) are blended to get a 6 h quantitative precipitation forecast for the Yangtze River Delta Region of China. The method used here is based on a mainstream blending framework, and modifications and calibrations are conducted done to both the extrapolation and NWP to improve the result. The traditional COTREC extrapolation is extended by using a mosaic system of 11 radars within the region and NWP data. Intensity and position calibration for the NWP are conducted based on Weibull function fitting and object recognition. A weighted blending of extrapolation and NWP is then carried out based on scale and forecast time. Finally, real-time Z-R relation conversion is performed. The results show that the blending method can extend forecast time of the extrapolation forecast, and calibrate the intensity and position bias of NWP, thus making a better result as a whole.
2023, 49(2):201-212.
DOI: 10.7519/j.issn.1000-0526.2022.090601
Abstract:
On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, during which Jinjiang Station observed a maximum cumulative rainfall of 562.5 mm in 24 hours which broke many local historical records. This paper analyzes the synoptic background, synoptic-scale triggering and maintenance mechanisms as well as evolution of the precipitation characteristics in this extreme rainfall event by using the NCEP/NCAR reanalysis data and multi-source observations. The results show that this extreme rainfall occurred under the condition of the low-level warm and humid southerly airflow. The updraft movement caused by the double low-level jets was the main synoptic-scale triggering mechanism. The upper-level jet also provided favorable unstable energy and water vapor. The outflow boundary generated by weak cold pool maintained for a long time at the same place and continuously lifted the warm and humid unstable air, resulting in extreme cumulative rainfall. During the whole rainfall process, the convective structure showed a low centroid. In the mature stage of convection, the average particle sizes of near-surface in the Jinjiang and Gangmei areas were relatively alike. The rainfall efficiency of Jinjiang was higher than that of Gangmei, which was mainly reflected in the larger number concentration of raindrops.
On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, during which Jinjiang Station observed a maximum cumulative rainfall of 562.5 mm in 24 hours which broke many local historical records. This paper analyzes the synoptic background, synoptic-scale triggering and maintenance mechanisms as well as evolution of the precipitation characteristics in this extreme rainfall event by using the NCEP/NCAR reanalysis data and multi-source observations. The results show that this extreme rainfall occurred under the condition of the low-level warm and humid southerly airflow. The updraft movement caused by the double low-level jets was the main synoptic-scale triggering mechanism. The upper-level jet also provided favorable unstable energy and water vapor. The outflow boundary generated by weak cold pool maintained for a long time at the same place and continuously lifted the warm and humid unstable air, resulting in extreme cumulative rainfall. During the whole rainfall process, the convective structure showed a low centroid. In the mature stage of convection, the average particle sizes of near-surface in the Jinjiang and Gangmei areas were relatively alike. The rainfall efficiency of Jinjiang was higher than that of Gangmei, which was mainly reflected in the larger number concentration of raindrops.
8 Statistical Characteristics and Convection Indicators of Hailstorm over Tianjin in Recent 11 Years
2023, 49(2):213-223.
DOI: 10.7519/j.issn.1000-0526.2022.072201
Abstract:
Based on the NCEP FNL (1°×1°) global analysis data and surface meteorological observations, the spatio-temporal characteristics of the 70 hailstorms in Tianjin from 2009 to 2019 are statistically analyzed, and the fusion sounding data are constructed to calculate the environmental parameters for hailstorms. Additionally, the environmental conditions for hailstorms in different months, different circulation patterns and different sizes are compared, and the corresponding prediction indices are given. The results show that the hailstorm in Tianjin mainly occurs from April to September, especially the hail and big hail days in June account for 49.4% and 60.0%, and the probability of hail and big hail from 12:00 BT to 20:00 BT account for 74.8% and 100%, 〖JP2〗respectively. Besides, the environmental parameters for hailstorms, such as convective available potential energy (CAPE), lift index (LI) , vertical wind shear (SHR), wet bulb 0℃ layer height (HWBZ) and total precipitable water (TPW), have obvious monthly variations and they should be noted significantly as prediction indicators. In April and September, the hailstorms mostly occur in low CAPE and high SHR conditions, but in July and August, the hailstorms often occur in medium-high CAPE and low SHR conditions. Additionally, the average HWBZ is 0.4-0.9 km lower than dry bulb 0℃ layer height (HDBZ), except that a very small number of hail in July and August HWBZ is in the range of 4.0-4.2 km. The other hails always occur in HWBZ<3.9 km conditions. The environmental conditions of cold vortex pattern and northwest flow pattern are not significantly different, but they are obviously different from the western trough pattern. The hailstorms of〖JP2〗 cold vortex pattern and northwest flow pattern usually occur under SHR>12 m·s-1, HWBZ<3.6 km and TPW>27 kg·〖JP〗m-2, while hailstorms of the western trough pattern mostly occur under SHR>9 m·s-1, HWBZ<3.9 km and TPW>36 kg·m-2. As for the hail, it is closely related to HWBZ and SHR. Take June as example. Small hails generally occur under HWBZ<3.9 km and SHR>9 m·s-1, while big hails appear under HWBZ<3.5 km and SHR>11 m·s-1.
Based on the NCEP FNL (1°×1°) global analysis data and surface meteorological observations, the spatio-temporal characteristics of the 70 hailstorms in Tianjin from 2009 to 2019 are statistically analyzed, and the fusion sounding data are constructed to calculate the environmental parameters for hailstorms. Additionally, the environmental conditions for hailstorms in different months, different circulation patterns and different sizes are compared, and the corresponding prediction indices are given. The results show that the hailstorm in Tianjin mainly occurs from April to September, especially the hail and big hail days in June account for 49.4% and 60.0%, and the probability of hail and big hail from 12:00 BT to 20:00 BT account for 74.8% and 100%, 〖JP2〗respectively. Besides, the environmental parameters for hailstorms, such as convective available potential energy (CAPE), lift index (LI) , vertical wind shear (SHR), wet bulb 0℃ layer height (HWBZ) and total precipitable water (TPW), have obvious monthly variations and they should be noted significantly as prediction indicators. In April and September, the hailstorms mostly occur in low CAPE and high SHR conditions, but in July and August, the hailstorms often occur in medium-high CAPE and low SHR conditions. Additionally, the average HWBZ is 0.4-0.9 km lower than dry bulb 0℃ layer height (HDBZ), except that a very small number of hail in July and August HWBZ is in the range of 4.0-4.2 km. The other hails always occur in HWBZ<3.9 km conditions. The environmental conditions of cold vortex pattern and northwest flow pattern are not significantly different, but they are obviously different from the western trough pattern. The hailstorms of〖JP2〗 cold vortex pattern and northwest flow pattern usually occur under SHR>12 m·s-1, HWBZ<3.6 km and TPW>27 kg·〖JP〗m-2, while hailstorms of the western trough pattern mostly occur under SHR>9 m·s-1, HWBZ<3.9 km and TPW>36 kg·m-2. As for the hail, it is closely related to HWBZ and SHR. Take June as example. Small hails generally occur under HWBZ<3.9 km and SHR>9 m·s-1, while big hails appear under HWBZ<3.5 km and SHR>11 m·s-1.
2023, 49(2):224-234.
DOI: 10.7519/j.issn.1000-0526.2022.110401
Abstract:
China Meteorological Administration (CMA) is one of the data exchange centers of global ensemble forecasting system. Global ensemble forecast systems (GEFS), including the European Center for Medium-Range Weather Forecasts (ECMWF), and the National Centers for Environmental Prediction (NCEP) have been used in real-time operation of the National Meteorological Centre (NMC) of CMA since 2010. The application of multi-model has promoted the progress of tropical cyclone forecasting operation of CMA. Based on the data of ECMWF and NCEP ensemble models from 2010 to 2019, we evaluate the performance and systematic deviation of their track forecast performance in following aspects: intensity, moving speed, month, ocean area, landing and turning. The ensemble mean forecast errors of the two models generally show a downward trend, and their systematic deviations are almost in the opposite directions. Separately, ECMWF ensemble forecast has a southwest bias, while NCEP ensemble tends to be northeastward, and is larger than the former in number. At present, the track prediction performance of ECMWF ensemble model is better than that of NCEP, especially for weak TCs and landing TCs. The results of the performance evaluation and deviation analysis could also provide a reference for actual TC forecasts when there are obvious differences between the products of the two ensemble models.
China Meteorological Administration (CMA) is one of the data exchange centers of global ensemble forecasting system. Global ensemble forecast systems (GEFS), including the European Center for Medium-Range Weather Forecasts (ECMWF), and the National Centers for Environmental Prediction (NCEP) have been used in real-time operation of the National Meteorological Centre (NMC) of CMA since 2010. The application of multi-model has promoted the progress of tropical cyclone forecasting operation of CMA. Based on the data of ECMWF and NCEP ensemble models from 2010 to 2019, we evaluate the performance and systematic deviation of their track forecast performance in following aspects: intensity, moving speed, month, ocean area, landing and turning. The ensemble mean forecast errors of the two models generally show a downward trend, and their systematic deviations are almost in the opposite directions. Separately, ECMWF ensemble forecast has a southwest bias, while NCEP ensemble tends to be northeastward, and is larger than the former in number. At present, the track prediction performance of ECMWF ensemble model is better than that of NCEP, especially for weak TCs and landing TCs. The results of the performance evaluation and deviation analysis could also provide a reference for actual TC forecasts when there are obvious differences between the products of the two ensemble models.
2023, 49(2):235-248.
DOI: 10.7519/j.issn.1000-0526.2022.110801
Abstract:
Using the spatial-temporal verification method of precipitation process, this paper evaluates the prediction effect of non-typhoon hourly precipitation in warm season (April to September of 2019-2020) of Hainan Island by Guangdong rapid update assimilation numerical prediction system (3 km resolution, CMA-GD), Shanghai numerical prediction system (CMA-SH9) and mesoscale weather numerical prediction system (CMA-MESO) of China Meteorological Administration. 〖JP2〗The results show that the three models 〖JP〗all can capture the spatial distribution of precipitation and the diurnal variation of precipitation under different flow field conditions. However, the frequency and intensity of precipitation in CMA-GD and CMA-SH9 are generally more and stronger, of which the frequency of precipitation in CMA-GD is more than 10%, the average hourly rainfall intensity of CMA-SH9 is nearly 4 mm·h-1 stronger. The precipitation intensity above 5 mm·h-1 of CMA-MESO is mostly distributed in the southwestern and central mountainous areas, which is quite different from the observed spatial distribution. The easiest precipitation start time and precipitation peak time of the three models are 1-3 h earlier than the observation, and the easiest precipita-tion end time is 1-3 h later than the observation; The predicted values of dew point temperature and unstable energy in the upper atmosphere of the model are too large, unstable energy appears earlier, the characteristic prediction of the near-surface inversion layer is distorted, the start time of precipitation forecast tends to be earlier, and the precipitation duration is too long. The sea-land wind convergence zone along the northern coast of Hainan Island is predicted to be strong in the daytime by the three models, especially CMA-SH9, which is consistent with obvious strong precipitation intensity of the model’s output. The forecast position of the sea-land wind convergence zone along the southern coast of CMA-GD at night is westward, which corresponds to the higher frequency of precipitation along the southwestern coast of the model. CMA-MESO has strong wind speed convergence in the southwestern and central mountainous areas of Hainan Island, and the corresponding precipitation forecast intensity is stronger than the observation.
Using the spatial-temporal verification method of precipitation process, this paper evaluates the prediction effect of non-typhoon hourly precipitation in warm season (April to September of 2019-2020) of Hainan Island by Guangdong rapid update assimilation numerical prediction system (3 km resolution, CMA-GD), Shanghai numerical prediction system (CMA-SH9) and mesoscale weather numerical prediction system (CMA-MESO) of China Meteorological Administration. 〖JP2〗The results show that the three models 〖JP〗all can capture the spatial distribution of precipitation and the diurnal variation of precipitation under different flow field conditions. However, the frequency and intensity of precipitation in CMA-GD and CMA-SH9 are generally more and stronger, of which the frequency of precipitation in CMA-GD is more than 10%, the average hourly rainfall intensity of CMA-SH9 is nearly 4 mm·h-1 stronger. The precipitation intensity above 5 mm·h-1 of CMA-MESO is mostly distributed in the southwestern and central mountainous areas, which is quite different from the observed spatial distribution. The easiest precipitation start time and precipitation peak time of the three models are 1-3 h earlier than the observation, and the easiest precipita-tion end time is 1-3 h later than the observation; The predicted values of dew point temperature and unstable energy in the upper atmosphere of the model are too large, unstable energy appears earlier, the characteristic prediction of the near-surface inversion layer is distorted, the start time of precipitation forecast tends to be earlier, and the precipitation duration is too long. The sea-land wind convergence zone along the northern coast of Hainan Island is predicted to be strong in the daytime by the three models, especially CMA-SH9, which is consistent with obvious strong precipitation intensity of the model’s output. The forecast position of the sea-land wind convergence zone along the southern coast of CMA-GD at night is westward, which corresponds to the higher frequency of precipitation along the southwestern coast of the model. CMA-MESO has strong wind speed convergence in the southwestern and central mountainous areas of Hainan Island, and the corresponding precipitation forecast intensity is stronger than the observation.
2023, 49(2):249-256.
DOI: 10.7519/j.issn.1000-0526.2023.013001
Abstract:
The main characteristics of the general atmospheric circulation in November 2022 are as follows. There were two polar vortex centers in the Northern Hemisphere. The East Asian trough and the southern branch trough were weaker than in the same period of normal years. The western Pacific subtropical high was stronger during this month. The monthly mean precipitation across China was 26.9 mm, which is 33.2% more than normal (20.2 mm). The monthly average temperature was 4.8℃, 1.5℃ higher than the temperature (3.3℃) in the same periods of normal years. The cold and warm temperatures fluctuated greatly during the month. The cold air was not so active in the early and mid dekads. Accordingly, the temperature was higher and the precipitation was less in most of China. However, in the later period, affected by the strong cold air and the active southern branch trough, the temperature dropped sharply and precipitation increased significantly. In addition, there were one nationwide cold wave process and two widespread persistent fog-haze weather processes.
The main characteristics of the general atmospheric circulation in November 2022 are as follows. There were two polar vortex centers in the Northern Hemisphere. The East Asian trough and the southern branch trough were weaker than in the same period of normal years. The western Pacific subtropical high was stronger during this month. The monthly mean precipitation across China was 26.9 mm, which is 33.2% more than normal (20.2 mm). The monthly average temperature was 4.8℃, 1.5℃ higher than the temperature (3.3℃) in the same periods of normal years. The cold and warm temperatures fluctuated greatly during the month. The cold air was not so active in the early and mid dekads. Accordingly, the temperature was higher and the precipitation was less in most of China. However, in the later period, affected by the strong cold air and the active southern branch trough, the temperature dropped sharply and precipitation increased significantly. In addition, there were one nationwide cold wave process and two widespread persistent fog-haze weather processes.
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ISSN:1000-0526 CN:11-2282/P