Volume 51,Issue 6,2023 Table of Contents
LI Jun
,
MIN Min
,
LI Bo
,
WEI Xiaocheng
,
LIU Zijing
,
ZHENG Yongguang
,
ZHANG Xiaoling
,
QIN Danyu
,
SUN Fenglin
,
MA Zheng
,
WANG Lizhi
2023, 51(6):771-784.
DOI: 10.19517/j.1671-6345.20220476
Abstract:
Local severe convective storms significantly impact people’s lives and socio-economic development. Understanding the mechanism of severe convective storms and predicting the occurrence and development of local severe storms remains challenging. We investigate local severe convective storms’ environmental and thermodynamic characteristics in pre-convection environments by combining observations from China’s new-generation geostationary satellites (FY-4 series), which offer high spatial-temporal resolution, with numerical weather forecast products from the China Meteorological Administration (CMA) global forecast system (CMA-GFS). Furthermore, we explore how their changes impact the future development intensity of local convection. Results show a close association between cloud top cooling information from satellite observations and numerical prediction model variables, such as atmospheric instability and water vapour content, with convection storm occurrence and intensity. Changes in these factors closely relate to the future development intensity of local convection. The Storm Warning In Pre-convective Environment Version 2.0 (SWIPE-V2.0) system aims to predict the occurrence and intensity (weak, medium, and strong) of local severe storms in China. Established using machine-learning techniques, SWIPE-V2.0 employs the brightness temperature threshold method and area threshold method to identify the local convective cloud. Meanwhile, it uses the overlapping and optical flow methods to track the movement of the local convective cloud. The machine-learning model uses the CLDAS (Global Land Data Assistance System) data of the multi-source precipitation fusion dataset, obtained half an hour to one hour after the cloud cluster, as the training tag. Independent validation results reveal SWIPE-V2.0’s strong performance in early warning for local convective storms, with recognition rates of 0.5-0.85 for cases with precipitation below 8 mm/h and 0.69-0.91 for cases with precipitation above 8 mm/h in the rainy season across six different regions. In the non-rainy seasons, across the same regional spread, recognition rates are 0.53-0.98 for cases with precipitation below 8 mm/h and 0.77-0.99 for cases with precipitation above 8 mm/h. Early warning results from SWIPE-V2.0 on real-time local convection systems demonstrate its potential for near real-time applications, while also indicating its useful role in understanding the environmental factors associated with local severe storms across various weather regimes. Currently, we are utilising SWIPE-V2.0 in real-time applications.
Local severe convective storms significantly impact people’s lives and socio-economic development. Understanding the mechanism of severe convective storms and predicting the occurrence and development of local severe storms remains challenging. We investigate local severe convective storms’ environmental and thermodynamic characteristics in pre-convection environments by combining observations from China’s new-generation geostationary satellites (FY-4 series), which offer high spatial-temporal resolution, with numerical weather forecast products from the China Meteorological Administration (CMA) global forecast system (CMA-GFS). Furthermore, we explore how their changes impact the future development intensity of local convection. Results show a close association between cloud top cooling information from satellite observations and numerical prediction model variables, such as atmospheric instability and water vapour content, with convection storm occurrence and intensity. Changes in these factors closely relate to the future development intensity of local convection. The Storm Warning In Pre-convective Environment Version 2.0 (SWIPE-V2.0) system aims to predict the occurrence and intensity (weak, medium, and strong) of local severe storms in China. Established using machine-learning techniques, SWIPE-V2.0 employs the brightness temperature threshold method and area threshold method to identify the local convective cloud. Meanwhile, it uses the overlapping and optical flow methods to track the movement of the local convective cloud. The machine-learning model uses the CLDAS (Global Land Data Assistance System) data of the multi-source precipitation fusion dataset, obtained half an hour to one hour after the cloud cluster, as the training tag. Independent validation results reveal SWIPE-V2.0’s strong performance in early warning for local convective storms, with recognition rates of 0.5-0.85 for cases with precipitation below 8 mm/h and 0.69-0.91 for cases with precipitation above 8 mm/h in the rainy season across six different regions. In the non-rainy seasons, across the same regional spread, recognition rates are 0.53-0.98 for cases with precipitation below 8 mm/h and 0.77-0.99 for cases with precipitation above 8 mm/h. Early warning results from SWIPE-V2.0 on real-time local convection systems demonstrate its potential for near real-time applications, while also indicating its useful role in understanding the environmental factors associated with local severe storms across various weather regimes. Currently, we are utilising SWIPE-V2.0 in real-time applications.
2023, 51(6):785-793.
DOI: 10.19517/j.1671-6345.20220389
Abstract:
The stratosphere is the layer of the atmosphere extending upwards about 50 km from the tropopause. The vertical stability of the atmosphere in the stratosphere is relatively high, and the vertical motion is relatively weak, primarily dominated by large-scale horizontal motion. Additionally, there is less dust in the stratosphere, the atmosphere is more transparent, and the content of water vapour is lower, so there are fewer phase transitions of water vapour. Moreover, given the large distance between the stratosphere and the ground, it is unaffected by complex underlying surface effects, and concurrently, there are fewer aircraft in this area, ensuring no interference by other aircraft. Precisely because of the peculiarity of the stratospheric atmospheric environment, its applications in the fields of high-resolution real-time reconnaissance and surveillance, missile early warning, navigation and positioning, rapid communication reconstruction, atmospheric environment monitoring, disaster prevention and mitigation, homeland security surveillance and defense, and anti-terrorism are considerable. This potential has attracted widespread attention globally. This paper proposes a detection system predicated on in-situ detection of stratospheric atmospheric wind field undertaken by a stratospheric aircraft, extensively introducing its working principle, computation formula, and wind tunnel test results. In the laboratory test, the theoretical measurement error value of the detection system is ≤2.5 m/s. In the wind tunnel test at room temperature, the system has illustrated exemplary working performance, system stability, and measurement accuracy. The correlation between wind direction and wind speed exceeds 0.99, the average absolute deviation of wind speed is 0.67 m/s, and the absolute average deviation of wind direction is 2.4°. In 2022, the Meteorological Observation Centre of the China Meteorological Administration equipped a stratospheric overpressure balloon to conduct an in-situ detection test in the stratosphere atmosphere at an altitude of 19 km, determining the wind speed and direction around the platform, and proceeding with a detailed analysis of the detection results. The comparison with the platform trajectory calculation results indicates that the system has good consistency; the comparison with the NCEP_FNL reanalysis data discloses a good correlation of east-west wind speed, reaching 0.795, while the correlation of north-south wind speed is relatively poor, only 0.33. The test results affirm that the detection system mounted on the stratospheric aircraft can effectively detect the stratospheric atmospheric wind field, and possesses vast application potential in areas such atmosphere environment planning, flight route planning, and regional stay.
The stratosphere is the layer of the atmosphere extending upwards about 50 km from the tropopause. The vertical stability of the atmosphere in the stratosphere is relatively high, and the vertical motion is relatively weak, primarily dominated by large-scale horizontal motion. Additionally, there is less dust in the stratosphere, the atmosphere is more transparent, and the content of water vapour is lower, so there are fewer phase transitions of water vapour. Moreover, given the large distance between the stratosphere and the ground, it is unaffected by complex underlying surface effects, and concurrently, there are fewer aircraft in this area, ensuring no interference by other aircraft. Precisely because of the peculiarity of the stratospheric atmospheric environment, its applications in the fields of high-resolution real-time reconnaissance and surveillance, missile early warning, navigation and positioning, rapid communication reconstruction, atmospheric environment monitoring, disaster prevention and mitigation, homeland security surveillance and defense, and anti-terrorism are considerable. This potential has attracted widespread attention globally. This paper proposes a detection system predicated on in-situ detection of stratospheric atmospheric wind field undertaken by a stratospheric aircraft, extensively introducing its working principle, computation formula, and wind tunnel test results. In the laboratory test, the theoretical measurement error value of the detection system is ≤2.5 m/s. In the wind tunnel test at room temperature, the system has illustrated exemplary working performance, system stability, and measurement accuracy. The correlation between wind direction and wind speed exceeds 0.99, the average absolute deviation of wind speed is 0.67 m/s, and the absolute average deviation of wind direction is 2.4°. In 2022, the Meteorological Observation Centre of the China Meteorological Administration equipped a stratospheric overpressure balloon to conduct an in-situ detection test in the stratosphere atmosphere at an altitude of 19 km, determining the wind speed and direction around the platform, and proceeding with a detailed analysis of the detection results. The comparison with the platform trajectory calculation results indicates that the system has good consistency; the comparison with the NCEP_FNL reanalysis data discloses a good correlation of east-west wind speed, reaching 0.795, while the correlation of north-south wind speed is relatively poor, only 0.33. The test results affirm that the detection system mounted on the stratospheric aircraft can effectively detect the stratospheric atmospheric wind field, and possesses vast application potential in areas such atmosphere environment planning, flight route planning, and regional stay.
2023, 51(6):794-804.
DOI: 10.19517/j.1671-6345.20220450
Abstract:
In order to more effectively apply the globally shared AMDAR (Aircraft Meteorological Data Relay) data in local meteorological operations and address the challenge stemming from the uneven spatio-temporal distribution of AMDAR data, this paper initially conducts quality control processing using AMDAR data from April 2019 to May 2020 around Hangzhou. This is carried out with reference to the aircraft meteorological observation quality control scheme of the NOAA in the United States and the National Meteorological Information Centre. Following this, a new method is proposed for extracting AMDAR profile data, taking into consideration the determination of the temporal, spatial representation, and vertical resolution of AMDAR data. This method views AMDAR data within a specific temporal and spatial range around the airport as analogous to the observations of a weather balloon drifting to different positions, thereby extracting temperature and wind vertical profiles based on specified temporal and spatial representativeness. In the vertical direction, the interpolation algorithm is utilised to achieve a uniform distribution of the profile, and median filtering algorithm is carried out on the obtained profile data for additional quality control. Our results from comparing the AMDAR profile data with Hangzhou radiosonde data demonstrate that the overall average differences in temperature, wind speed, and wind direction between the AMDAR data and radiosonde data in Hangzhou are -0.83 ℃, 0.02 m/s, and 0.47°respectively. The root mean square errors amount to 1.93 ℃ for temperature, 2.02 m/s for wind speed, and 25.05° for wind direction. There is a trend toward the AMDAR temperature profile data being smaller than the radiosonde temperature data, as a result of the systematic error of aircraft detection. It should be noted that this is more evident in relatively warm and wet seasons compared to relatively dry and cold seasons. Notably, the AMDAR wind profile data do not exhibit clear systematic error, which leaves the data quality in a satisfactory state. The comparison errors of temperature and wind speed are slightly realigned in the boundary layer height range of 0-1000 m compared to 1000-2000 m, which increase with the increase in height in the 2000 m and above range. However, the comparison error of wind direction drastically diminishes with the increase in height in the whole comparison height range. Furthermore, the higher the ambient wind speed, the greater the comparison error of wind speed, but the smaller the comparison error of wind direction. The AMDAR profile data and radiosonde data show a good level of agreement, although in terms of data integrity, there appear to be numerous missing measurements in 02:00-06:00 and above 5000 m. This is attributed to the limitations of aircraft detection influenced by specific flight times and routes. In conclusion, the AMDAR profile extraction method proposed in this paper elucidates the temporal and spatial representation of AMDAR profile data. Furthermore, by ensuring it is evenly distributed in time and height, this contributes to convenience in meteorological operations. This new AMDAR profile extraction method indeed holds certain application value and can offer a reference point for local application of AMDAR data in different regions.
In order to more effectively apply the globally shared AMDAR (Aircraft Meteorological Data Relay) data in local meteorological operations and address the challenge stemming from the uneven spatio-temporal distribution of AMDAR data, this paper initially conducts quality control processing using AMDAR data from April 2019 to May 2020 around Hangzhou. This is carried out with reference to the aircraft meteorological observation quality control scheme of the NOAA in the United States and the National Meteorological Information Centre. Following this, a new method is proposed for extracting AMDAR profile data, taking into consideration the determination of the temporal, spatial representation, and vertical resolution of AMDAR data. This method views AMDAR data within a specific temporal and spatial range around the airport as analogous to the observations of a weather balloon drifting to different positions, thereby extracting temperature and wind vertical profiles based on specified temporal and spatial representativeness. In the vertical direction, the interpolation algorithm is utilised to achieve a uniform distribution of the profile, and median filtering algorithm is carried out on the obtained profile data for additional quality control. Our results from comparing the AMDAR profile data with Hangzhou radiosonde data demonstrate that the overall average differences in temperature, wind speed, and wind direction between the AMDAR data and radiosonde data in Hangzhou are -0.83 ℃, 0.02 m/s, and 0.47°respectively. The root mean square errors amount to 1.93 ℃ for temperature, 2.02 m/s for wind speed, and 25.05° for wind direction. There is a trend toward the AMDAR temperature profile data being smaller than the radiosonde temperature data, as a result of the systematic error of aircraft detection. It should be noted that this is more evident in relatively warm and wet seasons compared to relatively dry and cold seasons. Notably, the AMDAR wind profile data do not exhibit clear systematic error, which leaves the data quality in a satisfactory state. The comparison errors of temperature and wind speed are slightly realigned in the boundary layer height range of 0-1000 m compared to 1000-2000 m, which increase with the increase in height in the 2000 m and above range. However, the comparison error of wind direction drastically diminishes with the increase in height in the whole comparison height range. Furthermore, the higher the ambient wind speed, the greater the comparison error of wind speed, but the smaller the comparison error of wind direction. The AMDAR profile data and radiosonde data show a good level of agreement, although in terms of data integrity, there appear to be numerous missing measurements in 02:00-06:00 and above 5000 m. This is attributed to the limitations of aircraft detection influenced by specific flight times and routes. In conclusion, the AMDAR profile extraction method proposed in this paper elucidates the temporal and spatial representation of AMDAR profile data. Furthermore, by ensuring it is evenly distributed in time and height, this contributes to convenience in meteorological operations. This new AMDAR profile extraction method indeed holds certain application value and can offer a reference point for local application of AMDAR data in different regions.
2023, 51(6):805-814.
DOI: 10.19517/j.1671-6345.20220426
Abstract:
The accuracy of precipitation monitoring carries significant importance and impact on weather forecasting, climate change, service decision-making, disaster prevention and mitigation. The scientific and rational layout of the precipitation station network directly affects the precision of precipitation monitoring. Selecting Ya’an, Sichuan Province as the research object, based on the existing station network layout, we are analysing the control range of stations, the distance and the precipitation correlation between the closest stations, and on the principle of “no loss of precipitation information”. We are proposing an optimisation method for station network layout based on the reduction in stations and evaluating the rationality of station network layout by the changes in area rainfall before and after the reduction. The results show that there are 265 precipitation stations in Ya’an, with a control area of 2.64-510.78 km2 for each station, a distance between the nearest stations of 0.3-12.28 km, and a correlation coefficient of precipitation between the nearest stations of 0.62-0.993. Stations with smaller control areas and shorter distances between the nearest stations have relatively higher correlation coefficients of precipitation between the nearest stations. After each reduction, the control area of precipitation stations and the distance between the nearest stations gradually increase, and the precipitation correlation coefficient between the nearest stations gradually decreases. From the mutual information amount and the distribution of stations, the layout of the station network is becoming more uniform and reasonable. The monthly and daily area rainfall before and after reduction do not linearly increase with the increase of station reduction times, but the changes are not significant. The area rainfall and variation amplitude of the Tyson polygon method are both smaller than those of the arithmetic mean method. The area rainfall after the previous reduction aligns with that of the original network, with a smaller error. From the six precipitation processes in Ya’an, the heavy rainstorms of the six precipitation processes are mainly concentrated in the central part of Ya’an. After each reduction, the rain area and range are essentially consistent with the original station network, and the rain centre and region of heavy rain are able to be captured, as well as the extreme value of precipitation. However, with the reduction of stations, the granularity of precipitation morphology description has declined. Overall, after each reduction, the station network maintains a good ability to detect and capture precipitation, indicating that the optimization method for station network layout proposed in this article is reasonable.
The accuracy of precipitation monitoring carries significant importance and impact on weather forecasting, climate change, service decision-making, disaster prevention and mitigation. The scientific and rational layout of the precipitation station network directly affects the precision of precipitation monitoring. Selecting Ya’an, Sichuan Province as the research object, based on the existing station network layout, we are analysing the control range of stations, the distance and the precipitation correlation between the closest stations, and on the principle of “no loss of precipitation information”. We are proposing an optimisation method for station network layout based on the reduction in stations and evaluating the rationality of station network layout by the changes in area rainfall before and after the reduction. The results show that there are 265 precipitation stations in Ya’an, with a control area of 2.64-510.78 km2 for each station, a distance between the nearest stations of 0.3-12.28 km, and a correlation coefficient of precipitation between the nearest stations of 0.62-0.993. Stations with smaller control areas and shorter distances between the nearest stations have relatively higher correlation coefficients of precipitation between the nearest stations. After each reduction, the control area of precipitation stations and the distance between the nearest stations gradually increase, and the precipitation correlation coefficient between the nearest stations gradually decreases. From the mutual information amount and the distribution of stations, the layout of the station network is becoming more uniform and reasonable. The monthly and daily area rainfall before and after reduction do not linearly increase with the increase of station reduction times, but the changes are not significant. The area rainfall and variation amplitude of the Tyson polygon method are both smaller than those of the arithmetic mean method. The area rainfall after the previous reduction aligns with that of the original network, with a smaller error. From the six precipitation processes in Ya’an, the heavy rainstorms of the six precipitation processes are mainly concentrated in the central part of Ya’an. After each reduction, the rain area and range are essentially consistent with the original station network, and the rain centre and region of heavy rain are able to be captured, as well as the extreme value of precipitation. However, with the reduction of stations, the granularity of precipitation morphology description has declined. Overall, after each reduction, the station network maintains a good ability to detect and capture precipitation, indicating that the optimization method for station network layout proposed in this article is reasonable.
2023, 51(6):815-823.
DOI: 10.19517/j.1671-6345.20220261
Abstract:
The mean generation function, standard normal uniformity test, correlation analysis and other methods are being used to interpolate, check, correct, and analyse the long-term summer precipitation data of 96 observation stations in eastern China for the period from 1931 to 2020. The results show that: (1) The overall trends and extreme values of the summer precipitation data of all stations from 1931 to 2020, fitted by the mean generating function, are in good agreement with the observed values. The consistency rate of six stations without missing data between the observed and fitted values reaches 86.1%, which can meet the needs of the interpolation work. (2) As for the 1931-1950 period with missing records and the 1951-2020 period without missing records, summer precipitation is being tested for differences using the mean and variance statistics. Only eight observation stations have showing significant differences. (3) There are 13 non-homogeneity stations of the interpolated data in the homogeneous testing and checking of the summer precipitation data from 1931 to 2020. (4) The spatial distribution similarity analysis is being conducted between the corrected station data and the grid data of the CRU_TS4.05 database. The spatial correlation coefficients of the two sets of data during 1931-1950, 1951-2020 and 1931-2020 have reached 0.90, 0.92 and 0.92, respectively. The spatial distribution is consistent, and the corrected station data show certain reliability.
The mean generation function, standard normal uniformity test, correlation analysis and other methods are being used to interpolate, check, correct, and analyse the long-term summer precipitation data of 96 observation stations in eastern China for the period from 1931 to 2020. The results show that: (1) The overall trends and extreme values of the summer precipitation data of all stations from 1931 to 2020, fitted by the mean generating function, are in good agreement with the observed values. The consistency rate of six stations without missing data between the observed and fitted values reaches 86.1%, which can meet the needs of the interpolation work. (2) As for the 1931-1950 period with missing records and the 1951-2020 period without missing records, summer precipitation is being tested for differences using the mean and variance statistics. Only eight observation stations have showing significant differences. (3) There are 13 non-homogeneity stations of the interpolated data in the homogeneous testing and checking of the summer precipitation data from 1931 to 2020. (4) The spatial distribution similarity analysis is being conducted between the corrected station data and the grid data of the CRU_TS4.05 database. The spatial correlation coefficients of the two sets of data during 1931-1950, 1951-2020 and 1931-2020 have reached 0.90, 0.92 and 0.92, respectively. The spatial distribution is consistent, and the corrected station data show certain reliability.
2023, 51(6):824-834.
DOI: 10.19517/j.1671-6345.20220409
Abstract:
Fine observation of precipitation in mountainous areas is limited by many factors. To understand the distribution characteristics of precipitation in the Helan Mountains region and its relationship with the terrain, we are applying the hourly CLDAS integrated grid precipitation data from May to September, 2008 to 2016, based on availability, evaluating and verifying, to analyse the spatial and temporal distribution characteristics of precipitation in the Helan Mountains region. The results show that: (1) The CLDAS precipitation data are basically consistent with the measured precipitation, can be applied to analyse the precipitation characteristics in the Helan Mountains region. The CLDAS precipitation data tend to underestimate precipitation when heavy rainfall events occur. (2) Precipitation in the Helan Mountains is more abundant in the eastern and southern regions and less in the western and northern regions. The maximum precipitation centre exceeding 240 mm is located at the 0.1 longitude position west from the main peak of the Helan Mountains, and the maximum daily precipitation tends to be higher in the western region than in the eastern region. (3) Most precipitation tends to occur in August, with precipitation in August accounting for 25.6% of the total precipitation from May to September, with July and September being the second and third highest months for precipitation. Short-term heavy precipitation mostly occurs in August, constituting up to 56.4% of the total number of short-duration heavy precipitation events from May to September. Most precipitation occurs from 11:00 to 18:00 on a daily basis, and most short-term heavy precipitation occurs in the period from noon to midnight. (4) The spatial distribution of heavy and moderate rainfall days in the Helan Mountains is basically consistent with the distribution of total precipitation, indicating an overall spatial distribution of high in the east and low in the west, and high in the south and low in the north. The predominant precipitation process in the Helan Mountain is the light rain, then followed by moderate rain. The proportion of moderate rain and light rain processes accounts for 85% of the total regional precipitation processes, and the contribution of heavy rain process is significant. (5) The precipitation in the Helan Mountains tends to increase with the increase of altitude, and the precipitation increasing rate is 5.1 mm/hm in the west slope and 2.1 mm/hm in the east slope. The precipitation increasing rate along with altitude is evidently higher on the west slope than that on the east slope; the correlation between the number of moderate rain days and the terrain height is good, while the correlation between the number of rainy days at other levels and the terrain is relatively weak.
Fine observation of precipitation in mountainous areas is limited by many factors. To understand the distribution characteristics of precipitation in the Helan Mountains region and its relationship with the terrain, we are applying the hourly CLDAS integrated grid precipitation data from May to September, 2008 to 2016, based on availability, evaluating and verifying, to analyse the spatial and temporal distribution characteristics of precipitation in the Helan Mountains region. The results show that: (1) The CLDAS precipitation data are basically consistent with the measured precipitation, can be applied to analyse the precipitation characteristics in the Helan Mountains region. The CLDAS precipitation data tend to underestimate precipitation when heavy rainfall events occur. (2) Precipitation in the Helan Mountains is more abundant in the eastern and southern regions and less in the western and northern regions. The maximum precipitation centre exceeding 240 mm is located at the 0.1 longitude position west from the main peak of the Helan Mountains, and the maximum daily precipitation tends to be higher in the western region than in the eastern region. (3) Most precipitation tends to occur in August, with precipitation in August accounting for 25.6% of the total precipitation from May to September, with July and September being the second and third highest months for precipitation. Short-term heavy precipitation mostly occurs in August, constituting up to 56.4% of the total number of short-duration heavy precipitation events from May to September. Most precipitation occurs from 11:00 to 18:00 on a daily basis, and most short-term heavy precipitation occurs in the period from noon to midnight. (4) The spatial distribution of heavy and moderate rainfall days in the Helan Mountains is basically consistent with the distribution of total precipitation, indicating an overall spatial distribution of high in the east and low in the west, and high in the south and low in the north. The predominant precipitation process in the Helan Mountain is the light rain, then followed by moderate rain. The proportion of moderate rain and light rain processes accounts for 85% of the total regional precipitation processes, and the contribution of heavy rain process is significant. (5) The precipitation in the Helan Mountains tends to increase with the increase of altitude, and the precipitation increasing rate is 5.1 mm/hm in the west slope and 2.1 mm/hm in the east slope. The precipitation increasing rate along with altitude is evidently higher on the west slope than that on the east slope; the correlation between the number of moderate rain days and the terrain height is good, while the correlation between the number of rainy days at other levels and the terrain is relatively weak.
7 Analysis of Intensification of Precipitation in Central Jiangsu after Weakening of Typhoon “InFa”
2023, 51(6):835-847.
DOI: 10.19517/j.1671-6345.20220232
Abstract:
After the No. 6 typhoon “In-Fa” in 2021 decayed to a tropical depression on July 28, the spiral rainbands around the typhoon caused a sudden increase in precipitation in central Jiangsu Province. Based on the conventional observation data, numerical forecast products and ERA5 reanalysis data, this paper analyzes the process of heavy rainfall. The results show that the ECMWF-FINE numerical forecast product was more accurate for forecasting the situation field. However, the mesoscale CMA models could reflect the magnitude and the extreme precipitation area. Analysis of the reasons for the intensification of precipitation shows that the movement of “In-Fa” was little affected by the guiding air from high altitude; the weak cold air carried by the westerly wind trough reduced the atmospheric stability and forced the warm and humid air to the east of the typhoon to lift when it moved around in Anhui Province. The high and low spatial divergence fields and vorticity fields were concentrated in a relatively narrow area, which constituted an advantageous configuration of vertical mutual coupling. It was conducive to the formation of strong and deep vertical motion, which was an important dynamic mechanism for the generation and persistence of heavy rainfall. The heavy precipitation area was in the warm and humid air flow area of the negative centre of the wet potential vortex (MPV). The negative area of MPV had indicative significance for the direction of the heavy precipitation zone. The distribution of MPV1 and MPV2 reflected the characteristics of convective precipitation caused by the invasion of cold air. In addition, the central part of Jiangsu was located in the highhumidity and highenergy region to the right of the spiral cloud belt. The northeast and southwest airflow converged to form strong linear convective cloud clusters, along with the mesoscale system, which triggered and maintained with the ground convergence line, resulting in extremely heavy precipitation.
After the No. 6 typhoon “In-Fa” in 2021 decayed to a tropical depression on July 28, the spiral rainbands around the typhoon caused a sudden increase in precipitation in central Jiangsu Province. Based on the conventional observation data, numerical forecast products and ERA5 reanalysis data, this paper analyzes the process of heavy rainfall. The results show that the ECMWF-FINE numerical forecast product was more accurate for forecasting the situation field. However, the mesoscale CMA models could reflect the magnitude and the extreme precipitation area. Analysis of the reasons for the intensification of precipitation shows that the movement of “In-Fa” was little affected by the guiding air from high altitude; the weak cold air carried by the westerly wind trough reduced the atmospheric stability and forced the warm and humid air to the east of the typhoon to lift when it moved around in Anhui Province. The high and low spatial divergence fields and vorticity fields were concentrated in a relatively narrow area, which constituted an advantageous configuration of vertical mutual coupling. It was conducive to the formation of strong and deep vertical motion, which was an important dynamic mechanism for the generation and persistence of heavy rainfall. The heavy precipitation area was in the warm and humid air flow area of the negative centre of the wet potential vortex (MPV). The negative area of MPV had indicative significance for the direction of the heavy precipitation zone. The distribution of MPV1 and MPV2 reflected the characteristics of convective precipitation caused by the invasion of cold air. In addition, the central part of Jiangsu was located in the highhumidity and highenergy region to the right of the spiral cloud belt. The northeast and southwest airflow converged to form strong linear convective cloud clusters, along with the mesoscale system, which triggered and maintained with the ground convergence line, resulting in extremely heavy precipitation.
2023, 51(6):848-857.
DOI: 10.19517/j.1671-6345.20220247
Abstract:
Based on the data from conventional ground observation, wind profiler radar, and microwave radiometer data, we analyse the heavy precipitation weather process of Typhoon Hagupit at Pudong Airport during the 4-5th of August 2020. The results show that: (1) The strong and stable subtropical high pressure and the typhoon’s compression led to the long time maintained strong winds at Pudong Airport. (2) After 12:00 on the 4th, the average wind speed stayed stable at over 10 m/s with gusts. Approximately 9 hours before and after reaching the same latitude as the typhoon at Pudong Airport, the wind direction shifted from southeast to south after 21:00 on the 4th, and the cumulative precipitation for 12 hours (from 21:00 on the 4th to 09:00 on the 5th) reached 57.9 mm. (3) Before it landed, the cloud structure primarily maintained a 9-shape, with the spiral cloud belt of Hagupit mainly situated in the southeast quadrant of the typhoon. The northern cloud belt’s boundary was distinct, first moving northwest, gradually affecting the southeast coast of Zhejiang, and then migrating north, impacting Shanghai and Jiangsu. It arrived at the west side of Shanghai at the same latitude at 21:00 on the 4th, and the typhoon’s main structure began to degrade and loosen. (4) When the significant precipitation process took place, the vertical speed below 5 km correlated with a corresponding large value area, and the height of the large vertical velocity area even approached upwards of 5 km. The vertical velocity value also correlated positively with the intensity of precipitation, with the maximum value nearing 9 m/s. (5) When at an altitude below 1 km, a high-value area of the atmospheric refractive index structure constant Cn2 was present. The second-highest value area reached between 2 and 6 km, corresponding with heavy precipitation. As the high-value area gradually weakened, it signalled the tapering or cessation of heavy precipitation. (6) Throughout the continuous heavy precipitation period, the maximum height of the signal-to-noise ratio (SNR) can reached between 5 and 6 km, suggesting that the SNR could serve as an indicator of rainfall intensity. (7) Before the onset of heavy precipitation, the whole atmosphere began to get moist from the middle layer. When precipitation manifested at 21:30, the liquid water content first increased markedly, followed by a significant decrease. When the liquid water content reached 1.25 g/m3, it coincided with the actual heavy rain period at Pudong Airport, and the height was primarily between 700 m and 2200 m. (8) The shortest period of convective effective potential energy (CAPE) overlapped with the period of heavy rainfall, while the KI value remained at 37 ℃ stably, with a negative lifting index. These findings help improve our understanding of the complex meteorological conditions leading to heavy rainfall, and they can aid in the development of more accurate forecasting models.
Based on the data from conventional ground observation, wind profiler radar, and microwave radiometer data, we analyse the heavy precipitation weather process of Typhoon Hagupit at Pudong Airport during the 4-5th of August 2020. The results show that: (1) The strong and stable subtropical high pressure and the typhoon’s compression led to the long time maintained strong winds at Pudong Airport. (2) After 12:00 on the 4th, the average wind speed stayed stable at over 10 m/s with gusts. Approximately 9 hours before and after reaching the same latitude as the typhoon at Pudong Airport, the wind direction shifted from southeast to south after 21:00 on the 4th, and the cumulative precipitation for 12 hours (from 21:00 on the 4th to 09:00 on the 5th) reached 57.9 mm. (3) Before it landed, the cloud structure primarily maintained a 9-shape, with the spiral cloud belt of Hagupit mainly situated in the southeast quadrant of the typhoon. The northern cloud belt’s boundary was distinct, first moving northwest, gradually affecting the southeast coast of Zhejiang, and then migrating north, impacting Shanghai and Jiangsu. It arrived at the west side of Shanghai at the same latitude at 21:00 on the 4th, and the typhoon’s main structure began to degrade and loosen. (4) When the significant precipitation process took place, the vertical speed below 5 km correlated with a corresponding large value area, and the height of the large vertical velocity area even approached upwards of 5 km. The vertical velocity value also correlated positively with the intensity of precipitation, with the maximum value nearing 9 m/s. (5) When at an altitude below 1 km, a high-value area of the atmospheric refractive index structure constant Cn2 was present. The second-highest value area reached between 2 and 6 km, corresponding with heavy precipitation. As the high-value area gradually weakened, it signalled the tapering or cessation of heavy precipitation. (6) Throughout the continuous heavy precipitation period, the maximum height of the signal-to-noise ratio (SNR) can reached between 5 and 6 km, suggesting that the SNR could serve as an indicator of rainfall intensity. (7) Before the onset of heavy precipitation, the whole atmosphere began to get moist from the middle layer. When precipitation manifested at 21:30, the liquid water content first increased markedly, followed by a significant decrease. When the liquid water content reached 1.25 g/m3, it coincided with the actual heavy rain period at Pudong Airport, and the height was primarily between 700 m and 2200 m. (8) The shortest period of convective effective potential energy (CAPE) overlapped with the period of heavy rainfall, while the KI value remained at 37 ℃ stably, with a negative lifting index. These findings help improve our understanding of the complex meteorological conditions leading to heavy rainfall, and they can aid in the development of more accurate forecasting models.
2023, 51(6):858-866.
DOI: 10.19517/j.1671-6345.20220360
Abstract:
The observed process constitutes a combination of systemic cold air gale and thunderstorm gale. The convective system is induced and moves along with the front, resulting in sudden and extreme gale conditions akin to thunderstorm gales. Upon transitioning, the convective system mirrors the persistent traits of a systemic cold air gale. This study uses NCEP-FNL data, FY-4 satellite infrared cloud data, radar data, wind profile data, and automatic weather station data to investigate the characteristics and causes of the extreme gales. The principal findings are as follows: (1) The eastward progression of high trough at 500 hPa and low vortex at 850 hPa established a widened warm region at 850 hPa, as well as an unstable stratified complex along the west coast of the Bohai Sea. The transition of the front triggered convection, leading to extreme windy weather along the west coast. The reflectivity factor revealed that two bands of weak echoes along the west coast of the Bohai Sea were converging and intensifying into a single band, which then developed from a band to a bow echo with a gap in the front, as the system moved eastward. Notably, substantial velocity blurring and mid-level radial convergence emerged from the velocity diagram. (2) The gale event occurred in early spring when shortterm forecasts did not accommodate convective weather, accounting only for the manifestation of frontal precipitation and cold air gales. Prior to the emergence of convection, cold advection at 500 hPa and pronounced warm advection at 700-925 hPa were present over the west coast of the Bohai Sea. The unstable stratum between the upper-level cold and lower-level warm layers furnished favourable environmental conditions for the frontal transition to induce convection. In this context, the CAPE and DCAPE values derived from the NCEP forecast model served as a reference for thunderstorm and gale forecasting. (3) The gale progression was influenced by the downward momentum transfer of large-scale cold air, variable pressure winds, gradient winds induced by large-scale cold air, and the effects of mesoscale thunderstorm cold pool outflow invoked by convective systems. The systemic cold air gales, propagated due to the downward momentum transfer of largescale cold air, variable pressure winds and gradient winds, exerted an augmenting impact on the mesoscale thunderstorm outflow.
The observed process constitutes a combination of systemic cold air gale and thunderstorm gale. The convective system is induced and moves along with the front, resulting in sudden and extreme gale conditions akin to thunderstorm gales. Upon transitioning, the convective system mirrors the persistent traits of a systemic cold air gale. This study uses NCEP-FNL data, FY-4 satellite infrared cloud data, radar data, wind profile data, and automatic weather station data to investigate the characteristics and causes of the extreme gales. The principal findings are as follows: (1) The eastward progression of high trough at 500 hPa and low vortex at 850 hPa established a widened warm region at 850 hPa, as well as an unstable stratified complex along the west coast of the Bohai Sea. The transition of the front triggered convection, leading to extreme windy weather along the west coast. The reflectivity factor revealed that two bands of weak echoes along the west coast of the Bohai Sea were converging and intensifying into a single band, which then developed from a band to a bow echo with a gap in the front, as the system moved eastward. Notably, substantial velocity blurring and mid-level radial convergence emerged from the velocity diagram. (2) The gale event occurred in early spring when shortterm forecasts did not accommodate convective weather, accounting only for the manifestation of frontal precipitation and cold air gales. Prior to the emergence of convection, cold advection at 500 hPa and pronounced warm advection at 700-925 hPa were present over the west coast of the Bohai Sea. The unstable stratum between the upper-level cold and lower-level warm layers furnished favourable environmental conditions for the frontal transition to induce convection. In this context, the CAPE and DCAPE values derived from the NCEP forecast model served as a reference for thunderstorm and gale forecasting. (3) The gale progression was influenced by the downward momentum transfer of large-scale cold air, variable pressure winds, gradient winds induced by large-scale cold air, and the effects of mesoscale thunderstorm cold pool outflow invoked by convective systems. The systemic cold air gales, propagated due to the downward momentum transfer of largescale cold air, variable pressure winds and gradient winds, exerted an augmenting impact on the mesoscale thunderstorm outflow.
2023, 51(6):867-878.
DOI: 10.19517/j.1671-6345.20220492
Abstract:
Due to the lack of radar data in the Tibet Plateau region, lightning nowcasting has met certain difficulties. In order to solve this problem, the FY-4A satellite, the convection index of ERA5 reanalysis data and lightning location information are being used to propose 18 prediction factors in accordance with the mechanism of formation and development of lightning. A lightning nowcasting model is being established based on the random forest algorithm for Tibet’s Shannan region. By statistically analysing the probability density distribution of each prediction factor in the lightning and non-lightning samples, and comparing with the feature importance from the random forest model, it is demonstrated that the statistical analysis results fit well with the conclusion from the important feature. Therefore the proposed prediction factors have a relatively clear physical meaning and the established model is of high reliability. The results also reveal that the difference between the infrared brightness temperature and land surface temperature, the lightning location data of the past 10 minutes, the K-index and the infrared brightness temperature of channels 11 and 12 have significant contributions to the lightning nowcasting model. Analysing the prediction ability of the random forest model at different development stages of lightning, through two cases, the results show that the model can effectively predict the lightning location for the next 30 minutes. The lightning forecasting location is in good consistency with the observation data, especially at the stage of strong convective development. However, at the early stages of the convective development and dissipation, due to the model limitations in predicting the evolution of convection, the model has a relatively high false alarm ratio (FAR) and miss alarm ratio (MAR), so the prediction effect is relatively poor. To find the best predictable time scale, the lightning nowcasting models have been trained separately for the next 10, 20 and 30 minutes by using the random forest algorithm. The validation results show that with the increase of predictable time, the FAR of the random forest model gradually decreases, and the MAR gradually increases. Hence, the model for the next 20 minutes has the highest critical success index (CSI), and the overall prediction effect is the best. In order to further test the forecast effects of the models, the traditional optical flow extrapolation method has been selected for a contrast test. The results show that the random forest models perform better than the optical flow extrapolation method for all three predictable time scales. These three random forest models all have a better probability of detection (POD), CSI, and a relatively lower FAR. As a result, the CSI of the random forest model has reached above 0.70.
Due to the lack of radar data in the Tibet Plateau region, lightning nowcasting has met certain difficulties. In order to solve this problem, the FY-4A satellite, the convection index of ERA5 reanalysis data and lightning location information are being used to propose 18 prediction factors in accordance with the mechanism of formation and development of lightning. A lightning nowcasting model is being established based on the random forest algorithm for Tibet’s Shannan region. By statistically analysing the probability density distribution of each prediction factor in the lightning and non-lightning samples, and comparing with the feature importance from the random forest model, it is demonstrated that the statistical analysis results fit well with the conclusion from the important feature. Therefore the proposed prediction factors have a relatively clear physical meaning and the established model is of high reliability. The results also reveal that the difference between the infrared brightness temperature and land surface temperature, the lightning location data of the past 10 minutes, the K-index and the infrared brightness temperature of channels 11 and 12 have significant contributions to the lightning nowcasting model. Analysing the prediction ability of the random forest model at different development stages of lightning, through two cases, the results show that the model can effectively predict the lightning location for the next 30 minutes. The lightning forecasting location is in good consistency with the observation data, especially at the stage of strong convective development. However, at the early stages of the convective development and dissipation, due to the model limitations in predicting the evolution of convection, the model has a relatively high false alarm ratio (FAR) and miss alarm ratio (MAR), so the prediction effect is relatively poor. To find the best predictable time scale, the lightning nowcasting models have been trained separately for the next 10, 20 and 30 minutes by using the random forest algorithm. The validation results show that with the increase of predictable time, the FAR of the random forest model gradually decreases, and the MAR gradually increases. Hence, the model for the next 20 minutes has the highest critical success index (CSI), and the overall prediction effect is the best. In order to further test the forecast effects of the models, the traditional optical flow extrapolation method has been selected for a contrast test. The results show that the random forest models perform better than the optical flow extrapolation method for all three predictable time scales. These three random forest models all have a better probability of detection (POD), CSI, and a relatively lower FAR. As a result, the CSI of the random forest model has reached above 0.70.
2023, 51(6):879-887.
DOI: 10.19517/j.1671-6345.20220428
Abstract:
Based on the GB/T 36743—2018 national standard for forest fire risk meteorological level, we have analyzed the characteristics of forest fire risk meteorological levels at 91 national meteorological stations in Guangxi from 1981 to 2020. We also verified our results using actual forest fire conditions. The areas with high forest fire risk are located in Baise and Chongzuo in the west of Guangxi, and Beihai in the coastal region. The average number of days with high fire risk is less in the northeast of Guangxi. The high fire risk period is from October to December, with the number of high fire risk days during this period accounting for 45.3% of the entire year. Among the four meteorological factors affecting the meteorological level of forest fire risk in Guangxi, precipitation has the greatest impact, followed by temperature. Wind speed and relative humidity have a comparatively lesser impact. We have studied the abnormal characteristics of sea surface temperature and atmospheric circulation of sea level pressure, geopotential height, wind field, and vertical velocity in the middle and lower troposphere using NCEP/NCAR monthly average reanalysis data, NOAA monthly sea surface temperature data, correlation coefficient analysis method, and composite analysis method. We also have examined the causes of the differences in the number of high fire risk days during the high forest fire risk period in Guangxi. The results reveal that the variant interactions between the ocean and the atmosphere, induced by the sea surface temperature in key areas in autumn, lead to differences in precipitation during the high fire risk period in Guangxi. This in turn results in differences in the number of high fire risk days. In autumn, the cold sea surface temperature in the equatorial Middle East Pacific triggers the abnormal cyclone circulation in the Philippines from October to December. The East Asian winter monsoon becomes stronger, and the western Pacific subtropical high is weaker than normal and shifts to the east. Consequently, Guangxi is in a sinking dry air flow area with less precipitation, leading to a higher number of high fire risk days during the high fire risk period. Conversely, the warm sea surface temperature in the equatorial Middle East Pacific in autumn gives rise to the abnormal anticyclone circulation in the South China Sea. The western Pacific subtropical high is stronger, and its ridge point extends westward. The warm and humid southwest airflow is stronger, and Guangxi is in the area of rising humid airflow. This results in more precipitation, thereby reducing the number of high fire risk days during the high fire risk period.
Based on the GB/T 36743—2018 national standard for forest fire risk meteorological level, we have analyzed the characteristics of forest fire risk meteorological levels at 91 national meteorological stations in Guangxi from 1981 to 2020. We also verified our results using actual forest fire conditions. The areas with high forest fire risk are located in Baise and Chongzuo in the west of Guangxi, and Beihai in the coastal region. The average number of days with high fire risk is less in the northeast of Guangxi. The high fire risk period is from October to December, with the number of high fire risk days during this period accounting for 45.3% of the entire year. Among the four meteorological factors affecting the meteorological level of forest fire risk in Guangxi, precipitation has the greatest impact, followed by temperature. Wind speed and relative humidity have a comparatively lesser impact. We have studied the abnormal characteristics of sea surface temperature and atmospheric circulation of sea level pressure, geopotential height, wind field, and vertical velocity in the middle and lower troposphere using NCEP/NCAR monthly average reanalysis data, NOAA monthly sea surface temperature data, correlation coefficient analysis method, and composite analysis method. We also have examined the causes of the differences in the number of high fire risk days during the high forest fire risk period in Guangxi. The results reveal that the variant interactions between the ocean and the atmosphere, induced by the sea surface temperature in key areas in autumn, lead to differences in precipitation during the high fire risk period in Guangxi. This in turn results in differences in the number of high fire risk days. In autumn, the cold sea surface temperature in the equatorial Middle East Pacific triggers the abnormal cyclone circulation in the Philippines from October to December. The East Asian winter monsoon becomes stronger, and the western Pacific subtropical high is weaker than normal and shifts to the east. Consequently, Guangxi is in a sinking dry air flow area with less precipitation, leading to a higher number of high fire risk days during the high fire risk period. Conversely, the warm sea surface temperature in the equatorial Middle East Pacific in autumn gives rise to the abnormal anticyclone circulation in the South China Sea. The western Pacific subtropical high is stronger, and its ridge point extends westward. The warm and humid southwest airflow is stronger, and Guangxi is in the area of rising humid airflow. This results in more precipitation, thereby reducing the number of high fire risk days during the high fire risk period.
2023, 51(6):888-897.
DOI: 10.19517/j.1671-6345.20220443
Abstract:
Based on the observation data from 142 national meteorological stations in Hebei Province and the case data of conductor icing provided by the electric power department, the spatial characteristics of the historical conductor icing in Hebei Province are being analysed. It is found that the southern coast of Hebei Province and the northeast of Zhangjiakou mainly experience rime icing, and there is a small amount of glaze icing in the northeast of Xingtai and the east of Cangzhou. The weather circulation of 15 typical cases in November 2015 is systematically analysed. The cold air which causes the disaster is spreading from north to south, and the airflow moving from west to east is getting superimposed in the middle and late stages. In the key region, there is a mixture of snow and water as well as obvious wind shear in the lower troposphere, and a "negative-position-negative” temperature anomaly stratification distribution in the middle troposphere. The upward movement in the thermal circulation provides favourable conditions for ice covering. Based on meteorological observation data, a prediction model of hourly ice thickness is established, which comprehensively considers the growth, maintenance and ablation processes of wire ice coating. It is found that the model can better capture the influence of the meteorological conditions on the icecovering process of conductors, and can objectively reflect the spatial and temporal distribution characteristics of ice-covering thickness, thus providing refined data support for ice-covering monitoring and early warning.
Based on the observation data from 142 national meteorological stations in Hebei Province and the case data of conductor icing provided by the electric power department, the spatial characteristics of the historical conductor icing in Hebei Province are being analysed. It is found that the southern coast of Hebei Province and the northeast of Zhangjiakou mainly experience rime icing, and there is a small amount of glaze icing in the northeast of Xingtai and the east of Cangzhou. The weather circulation of 15 typical cases in November 2015 is systematically analysed. The cold air which causes the disaster is spreading from north to south, and the airflow moving from west to east is getting superimposed in the middle and late stages. In the key region, there is a mixture of snow and water as well as obvious wind shear in the lower troposphere, and a "negative-position-negative” temperature anomaly stratification distribution in the middle troposphere. The upward movement in the thermal circulation provides favourable conditions for ice covering. Based on meteorological observation data, a prediction model of hourly ice thickness is established, which comprehensively considers the growth, maintenance and ablation processes of wire ice coating. It is found that the model can better capture the influence of the meteorological conditions on the icecovering process of conductors, and can objectively reflect the spatial and temporal distribution characteristics of ice-covering thickness, thus providing refined data support for ice-covering monitoring and early warning.
2023, 51(6):898-906.
DOI: 10.19517/j.1671-6345.20220456
Abstract:
Spacebased earth observation has the advantages of extensive coverage and the potential for using multiple payloads for detection, offering technical support for increasingly frequent disaster emergencies. FY-4B, represents the first operational satellite of China’s second generation geostationary orbit quantitative remote sensing meteorological satellite. Its high-speed imager’s primary function is to conduct high-frequency emergency observations for sudden natural disasters such as severe convection, typhoons and earthquakes. Given the regular occurrence of various disasters, the response capability of current ground application systems fails to meet the needs for normalised effective observation. Therefore, to capitalise on the benefits of flexible and speedy observation performance, we adopt a mix of static and dynamic task planning observation strategies. We have designed, within a one-minute timeframe, an observation range of 2000 km (South-North)×1800 km (East-West) that aligns with emergency response and convention observation requirements. This is for a West-East single direction scanning imaging observation mode and a 15-minute period observation schedule. Included are areas, position, and calibration missions planned with 37 units in the China region and 119 units in the full disk region. Efficient observation is achievable only through the rapid and dynamic switching of payload areas. Furthermore, it is crucial to minimise the response time of the satellite-ground system as much as possible. To this end, we carry out a comprehensive conflict analysis according to the existing satellite platform and payload operation schedule. We establish a full process intelligent observation task decisionmaking and automatic switching system, adopting a dual-thread concurrent automatic operation. This aims to achieve unified command and scheduling of the entire business process, including high-speed imagers deployment observation and the data processing, product generation, and emergency response for the FY4-02 batch ground application system. This improves the high effectiveness, stability, and reliability of the unified command and scheduling of the normalised emergency response satelliteground system. The fully automatic FY-4B high-speed imager business system entered trial operation on 15 June 2022. Up until 20 October 2022, a total of 37 emergency responses have been completed with a success rate of 100% for satellite-ground measurement and control scheduling. While ensuring satellite safety, we achieved rapid deployment of payload emergency observation, with 950 regional observations within 24 hours and an average response time of no more than 30 minutes for the entire business process. This system effectively alleviates the pressure on satelliteground resources caused by frequent changes in observation tasks, ensures the coherence of remote sensing data, and plays an important data support role in flood season and various disaster meteorological support.
Spacebased earth observation has the advantages of extensive coverage and the potential for using multiple payloads for detection, offering technical support for increasingly frequent disaster emergencies. FY-4B, represents the first operational satellite of China’s second generation geostationary orbit quantitative remote sensing meteorological satellite. Its high-speed imager’s primary function is to conduct high-frequency emergency observations for sudden natural disasters such as severe convection, typhoons and earthquakes. Given the regular occurrence of various disasters, the response capability of current ground application systems fails to meet the needs for normalised effective observation. Therefore, to capitalise on the benefits of flexible and speedy observation performance, we adopt a mix of static and dynamic task planning observation strategies. We have designed, within a one-minute timeframe, an observation range of 2000 km (South-North)×1800 km (East-West) that aligns with emergency response and convention observation requirements. This is for a West-East single direction scanning imaging observation mode and a 15-minute period observation schedule. Included are areas, position, and calibration missions planned with 37 units in the China region and 119 units in the full disk region. Efficient observation is achievable only through the rapid and dynamic switching of payload areas. Furthermore, it is crucial to minimise the response time of the satellite-ground system as much as possible. To this end, we carry out a comprehensive conflict analysis according to the existing satellite platform and payload operation schedule. We establish a full process intelligent observation task decisionmaking and automatic switching system, adopting a dual-thread concurrent automatic operation. This aims to achieve unified command and scheduling of the entire business process, including high-speed imagers deployment observation and the data processing, product generation, and emergency response for the FY4-02 batch ground application system. This improves the high effectiveness, stability, and reliability of the unified command and scheduling of the normalised emergency response satelliteground system. The fully automatic FY-4B high-speed imager business system entered trial operation on 15 June 2022. Up until 20 October 2022, a total of 37 emergency responses have been completed with a success rate of 100% for satellite-ground measurement and control scheduling. While ensuring satellite safety, we achieved rapid deployment of payload emergency observation, with 950 regional observations within 24 hours and an average response time of no more than 30 minutes for the entire business process. This system effectively alleviates the pressure on satelliteground resources caused by frequent changes in observation tasks, ensures the coherence of remote sensing data, and plays an important data support role in flood season and various disaster meteorological support.
2023, 51(6):907-916.
DOI: 10.19517/j.1671-6345.20220423
Abstract:
The Climate Interactive Plotting and Analysis System (CIPAS) serves as the core business system for climate monitoring, diagnosis, and prediction at the National Climate Centre. The Meteorological Big Data Cloud Platform (Tianqing) functions as a digitised integration platform, founded on the meteorological proprietary cloud and public cloud. This includes exchange and quality control systems, product processing systems, mining, analysis systems, storage, and service systems. Leveraging various data sources, Tianqing conducts standardized quality controlling and processing to generate statistical, gridbased, and multisource fusion products, supporting the integration of national meteorological applications and shared services. As of 2021, CIPAS has established comprehensive and deep integration with Tianqing concerning data sources, algorithms, products and other dimensions, officially releasing the CIPAS 3.0 version based on the “Cloud+Client” business model. To ascertain the stable operation of CIPAS 3.0, a realtime monitoring application grounded on the Real-Time Monitoring System of Integrated Meteorological Operation (Tianjing) has been crafted and implemented. Tianjing supports an efficient distributed processing and storage system, encompassing information collection, preprocessing, and storage processes, and establishing various technical specifications. This provides a robust platform foundation for the construction of CIPAS 3.0 monitoring applications. This paper presents a detailed analysis of the technical formation and business process of CIPAS 3.0, proposing the monitoring requirements and introducing the architecture and process design of CIPAS 3.0 monitoring application. Additionally, it offers a comprehensive description of the product monitoring process and data source anomaly tracing algorithm, which provides innovative ideas for the successive development of a general monitoring system integrated into Tianqing. The CIPAS 3.0 monitoring application enables realtime monitoring and alerts for data source decoding and storage, product generation, as well as basic resource functioning. Further features include data source error tracing, onekey operation of data sources and product algorithms, automatic alarm recovery, and more. It resolves longstanding issues for CIPAS, such as opaque downloading and transmission of foreign source data, inconvenient manual operation of data sources and product algorithm, notably reducing the workload of operation and maintenance personnel in identifying and resolving failures. Having passed expert review, the system was operational as of December 2021. To date, it has monitored 87 data sources and 125 products, guaranteeing the stable operation of CIPAS 3.0.
The Climate Interactive Plotting and Analysis System (CIPAS) serves as the core business system for climate monitoring, diagnosis, and prediction at the National Climate Centre. The Meteorological Big Data Cloud Platform (Tianqing) functions as a digitised integration platform, founded on the meteorological proprietary cloud and public cloud. This includes exchange and quality control systems, product processing systems, mining, analysis systems, storage, and service systems. Leveraging various data sources, Tianqing conducts standardized quality controlling and processing to generate statistical, gridbased, and multisource fusion products, supporting the integration of national meteorological applications and shared services. As of 2021, CIPAS has established comprehensive and deep integration with Tianqing concerning data sources, algorithms, products and other dimensions, officially releasing the CIPAS 3.0 version based on the “Cloud+Client” business model. To ascertain the stable operation of CIPAS 3.0, a realtime monitoring application grounded on the Real-Time Monitoring System of Integrated Meteorological Operation (Tianjing) has been crafted and implemented. Tianjing supports an efficient distributed processing and storage system, encompassing information collection, preprocessing, and storage processes, and establishing various technical specifications. This provides a robust platform foundation for the construction of CIPAS 3.0 monitoring applications. This paper presents a detailed analysis of the technical formation and business process of CIPAS 3.0, proposing the monitoring requirements and introducing the architecture and process design of CIPAS 3.0 monitoring application. Additionally, it offers a comprehensive description of the product monitoring process and data source anomaly tracing algorithm, which provides innovative ideas for the successive development of a general monitoring system integrated into Tianqing. The CIPAS 3.0 monitoring application enables realtime monitoring and alerts for data source decoding and storage, product generation, as well as basic resource functioning. Further features include data source error tracing, onekey operation of data sources and product algorithms, automatic alarm recovery, and more. It resolves longstanding issues for CIPAS, such as opaque downloading and transmission of foreign source data, inconvenient manual operation of data sources and product algorithm, notably reducing the workload of operation and maintenance personnel in identifying and resolving failures. Having passed expert review, the system was operational as of December 2021. To date, it has monitored 87 data sources and 125 products, guaranteeing the stable operation of CIPAS 3.0.
2023, 51(6):917-926.
DOI: 10.19517/j.1671-6345.20220305
Abstract:
This paper focuses on the system architecture and overall design of the Climate Monitoring and Prediction Analysis System (CIPAS 3.0) data environment, proposing and providing an in-depth analysis of several key technologies for system implementation. As the core business system for climate monitoring, prediction, and diagnosis at the National Climate Centre, CIPAS 3.0 integrates its data support environment fully into the meteorological big data cloud platform “Tianqing,” supporting the “cloud+client” climate business system framework, providing stable and reliable support for various stages such as data collection, storage, processing and monitoring. To support the storage and management of highresolution grid data and longtime sequence data from the meteorological station, the CIPAS 3.0 data environment designs and applies a spatial analysis database and a historical analysis database based on PostgreSQL and GBASE. This fills the performance bottlenecks of traditional databases and ensures efficient data storage and management functions. By integrating the Tianqing data collection, storage and monitoring, the CIPAS 3.0 data environment adopts a pluggable data processing framework based on profile to achieve automatic operation and construction of the whole process. Based on the standard meteorological service interface specifications, a data access interface with integrated numerical computation capabilities is provided. This not only improve the efficiency of realtime interactive computation, but also enhances the sensitivity to information such as data reporting and storage. The CIPAS 3.0 data environment design adopts OBS (Object Storage Service) for product management, and concurrently, a data monitoring system based on Tianjing has been constructed, which resolves the difficulties encountered in the daily operation and maintenance of CIPAS 3.0 such as data integrity monitoring, oneclick supplementary recording, and data source tracing. It actualises the support role of data collection, storage, processing, access, monitoring, and other links. Currently, the CIPAS 3.0 data environment is in operation, and its construction achievements have demonstrated its good potential for supporting business applications and development prospects through testing in national-level business units and pilot provinces. The future development of the CIPAS 3.0 data environment will be driven by demand to dynamically expand the types of data in the data environment; it will develop a general configurable system page to enable rapid data configuration and storage as needed, utilise matrix arithmetic to optimise the performance of the algorithm and interfaces of the spatial analysis database, implement automated operation and maintenance processes based on data perception and trigger message mechanisms, optimise data registration and review processes, alarm mechanisms, and automatic recovery, etc., to further enhance the supporting role of the CIPAS 3.0 data environment for business application capabilities.
This paper focuses on the system architecture and overall design of the Climate Monitoring and Prediction Analysis System (CIPAS 3.0) data environment, proposing and providing an in-depth analysis of several key technologies for system implementation. As the core business system for climate monitoring, prediction, and diagnosis at the National Climate Centre, CIPAS 3.0 integrates its data support environment fully into the meteorological big data cloud platform “Tianqing,” supporting the “cloud+client” climate business system framework, providing stable and reliable support for various stages such as data collection, storage, processing and monitoring. To support the storage and management of highresolution grid data and longtime sequence data from the meteorological station, the CIPAS 3.0 data environment designs and applies a spatial analysis database and a historical analysis database based on PostgreSQL and GBASE. This fills the performance bottlenecks of traditional databases and ensures efficient data storage and management functions. By integrating the Tianqing data collection, storage and monitoring, the CIPAS 3.0 data environment adopts a pluggable data processing framework based on profile to achieve automatic operation and construction of the whole process. Based on the standard meteorological service interface specifications, a data access interface with integrated numerical computation capabilities is provided. This not only improve the efficiency of realtime interactive computation, but also enhances the sensitivity to information such as data reporting and storage. The CIPAS 3.0 data environment design adopts OBS (Object Storage Service) for product management, and concurrently, a data monitoring system based on Tianjing has been constructed, which resolves the difficulties encountered in the daily operation and maintenance of CIPAS 3.0 such as data integrity monitoring, oneclick supplementary recording, and data source tracing. It actualises the support role of data collection, storage, processing, access, monitoring, and other links. Currently, the CIPAS 3.0 data environment is in operation, and its construction achievements have demonstrated its good potential for supporting business applications and development prospects through testing in national-level business units and pilot provinces. The future development of the CIPAS 3.0 data environment will be driven by demand to dynamically expand the types of data in the data environment; it will develop a general configurable system page to enable rapid data configuration and storage as needed, utilise matrix arithmetic to optimise the performance of the algorithm and interfaces of the spatial analysis database, implement automated operation and maintenance processes based on data perception and trigger message mechanisms, optimise data registration and review processes, alarm mechanisms, and automatic recovery, etc., to further enhance the supporting role of the CIPAS 3.0 data environment for business application capabilities.
External LinksChina Meteorological AdministrationCMA Meteorological Observation CentreChinese Academy of Meteorological SciencesBeijing Meteorological ServiceNational Satellite Meteorological Center
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.