Sand, dust and haze multivariable collaborative regional dynamic comprehensive evaluation method based on site observation and grid live analysis products

Abstract

The invention discloses a dust and haze multivariable collaborative regional dynamic comprehensive evaluation method based on site observation and grid live analysis products, which combines the difference of physical and chemical properties of meteorological elements and particulate matters in dust, fog and haze weather, and introduces a Mie scattering theory to calculate an aerosol extinction coefficient to diagnose fog and haze. A scientific key characteristic index threshold value is adopted, and a multivariable cooperation and regional dynamic comprehensive evaluation method is used for diagnosis; finally, the three diagnosis results of direct diagnosis and site and grid point indirect diagnosis are comprehensively judged, the uncertainty of single data source diagnosis is reduced to the maximum extent, the evolution process, the influence range and the intensity change characteristics of sand, dust, fog and haze weather can be well represented, the temporal-spatial resolution is 5 km / 1 h, and the time efficiency lags for 55 min.

Description

Technical Field

[0001] This invention relates to the field of dust and haze discrimination technology. Specifically, it is a multi-variable collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products. Background Technology

[0003] The hourly, 5km resolution real-time analysis test product for dust, fog, and haze weather in China currently used by the National Meteorological Information Center can diagnose the spatial distribution characteristics of most dust, fog, and haze weather, especially showing good diagnostic effects on the spatial distribution and intensity of fog.

[0004] However, this real-time analysis test product has several limitations in diagnosing dust storms, fog, and haze: Firstly, the test product only uses a gridded product model that integrates meteorological and atmospheric composition elements, failing to fully utilize the advantages of direct site observation data. Furthermore, due to the timeliness of the data source, the diagnosis of dust storms, fog, and haze based on gridded data has a long lag time. Secondly, when diagnosing dust storms, fog, and haze, the same threshold is used nationwide, without considering PM2.5 levels in different regions. 2.5 / PM 10 Differences in relative humidity characteristics can lead to inaccuracies in the diagnosis of dust, fog, and haze in some areas. Furthermore, the test products only differentiate between floating dust, blowing sand, and dust storms in dusty weather, resulting in a rather coarse classification. Therefore, this work mainly aims to optimize the test products for dust, fog, and haze to address the limitations mentioned above. Summary of the Invention

[0005] Therefore, the technical problem to be solved by this invention is to provide a multivariate collaborative regional dynamic comprehensive evaluation method for dust, fog and haze based on site observation and grid real-time analysis products. It designs a method that combines the application of site observation data and grid real-time analysis product data, comprehensively considers the performance of key characteristic indicators affecting dust, fog and haze in different regions of China, and upgrades the dust, fog and haze diagnostic algorithm to a multivariate collaborative regional dynamic comprehensive evaluation method.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products includes the following steps:

[0008] P1. Using weather phenomena observed at the stations as the data source, and based on the weather phenomenon codes, a direct diagnosis of sandstorms, fog, and haze is performed, yielding direct diagnosis result 1; P2. Using meteorological elements and PM2.5 observed at the stations... 2.5 and PM 10Using the data source, multivariate collaborative and regional dynamic indirect diagnosis is performed to obtain indirect diagnosis results 2; P3, meteorological elements and PM2.5 of the grid-based real-time analysis product are used. 2.5 and PM 10 Using the data source, multivariate collaborative and regional dynamic indirect diagnosis is performed to obtain indirect diagnosis result 3; P4, based on direct diagnosis result 1, indirect diagnosis result 2 and indirect diagnosis result 3, a comprehensive evaluation of sandstorm, fog and haze weather is made.

[0009] Based on the aforementioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze using station observations and grid-based real-time analysis products, the station observation data includes hourly weather phenomena, visibility, relative humidity, wind speed, precipitation, and PM2.5 on the Chinese surface. 2.5 and PM 10 Observational data for a total of 7 elements;

[0010] The grid-based real-time analysis products include the national smart grid real-time fusion analysis product and the China regional atmospheric chemistry real-time analysis product. The national smart grid real-time fusion analysis product provides meteorological data, including visibility, relative humidity, wind speed, and precipitation. The China regional atmospheric chemistry analysis product provides PM2.5 data. 2.5 and PM 10 data.

[0011] The aforementioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on station observations and grid-based real-time analysis products includes dust weather diagnosis and haze weather diagnosis in step P1, including:

[0012] Step P1-1, Determination of dusty weather: Distinguish the correspondence between the observation codes of different weather phenomena observed at the station and dusty weather. When the observation code value of the weather phenomenon is 6, 7, 8, 9, 30, 31, 32, 33, 34, 35, 208, 209, or 230, it is determined to be dusty weather.

[0013] Step P1-2, Determination of Dust Level: Classify dust weather levels based on visibility and wind speed; Step P1-3, Determination of Haze Weather: Distinguish the correspondence between different weather phenomenon observation codes observed at stations and fog and haze weather; When the weather phenomenon observation code value is 10, 11, 12, 28, 40-49, 110, 120, 130-135, 241-249, it is determined to be fog; When the weather phenomenon observation code value is 4, 5, 104, 105, 204, 206, 220, 22, it is determined to be haze;

[0014] Step P1-4, Determining the Fog Level: Classify fog weather levels based on visibility.

[0015] The above-mentioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on station observation and grid real-time analysis products, in steps P2 and P3, although the data sources are different, the methods of multivariate collaboration and indirect regional dynamic diagnosis are the same, including the following steps:

[0016] P2-1, Data Processing and Filtering;

[0017] P2-2, Determination of Sandstorm Weather and Classification of Sandstorm Levels;

[0018] P2-3, Determination of smoggy weather and classification of fog levels.

[0019] The above-mentioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on station observations and grid-based real-time analysis products, in step P2-1,

[0020] Interpolation processing of observation station data: PM at the station 2.5 PM 10 The observation data is not located at the meteorological observation station, so it needs to be uniformly interpolated to the meteorological observation station. The nearest neighbor interpolation method is adopted. For the sparsely populated areas of Northwest my country, Inner Mongolia, Heilongjiang, Jilin and Liaoning, the interpolation distance threshold is selected as 200km, and the interpolation distance threshold is selected as 50km for other areas.

[0021] Data filtering: exclude the influence of precipitation and filter non-precipitation data.

[0022] The aforementioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products includes the following steps in step P2-2:

[0023] P2-21. Filter data with visibility less than 10km for the next judgment;

[0024] P2-22, When the relative humidity is less than the regional relative humidity threshold, PM 2.5 / PM 10 The ratio is less than the regional PM ratio threshold, and PM 10 >300μg m -3 At that time, it is determined to be sandstorm weather;

[0025] P2-23. Classify dust storm levels based on visibility and wind speed; when wind speed < 5.0 m / s... -1 When horizontal visibility is <10.0 km, it is judged as dust; when wind speed is ≥5.0 m / s... -1 When horizontal visibility is ≥1.0 and <10.0 km, it is judged as dust; when wind speed is ≥5.0 m / s... -1A sandstorm is defined as a situation where horizontal visibility is ≥0.5km and <1.0km; a sandstorm is defined as a situation where wind speed is ≥5.0m / s. -1 A strong sandstorm is defined as a horizontal visibility of ≥0.05km and <0.5km; a wind speed of ≥5.0m / s² is also considered a strong sandstorm. -1 When the horizontal visibility is less than 0.05 km, it is judged as an extremely strong sandstorm.

[0026] The above-mentioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on station observation and grid real-time analysis products has the following relative humidity thresholds: 24% for western Northwest China, 27% for eastern Northwest China, 23% for Tibet Autonomous Region, 52% for Southwest China, 60% for South China, 36% for Northeast China, 36% for North China, and 56% for eastern China.

[0027] Regional PM2.5 threshold: PM2.5 ratio in the Northwest region 2.5 / PM 10 The PM2.5 concentration in the middle reaches of the Yellow River was 0.398, compared to a threshold of 0.398. 2.5 / PM 10 PM2.5 concentration in the Northeast region was 0.413, compared to the threshold. 2.5 / PM 10 The PM2.5 concentration in the Beijing-Tianjin-Hebei region and surrounding areas was measured at a threshold of 0.504. 2.5 / PM 10 The PM2.5 concentration in the southwest region was 0.507, compared to the threshold of 0.507. 2.5 / PM 10 PM2.5 in the middle reaches of the Yangtze River, with a threshold of 0.570. 2.5 / PM 10 PM2.5 concentration in the Yangtze River Delta region was 0.622, compared to a threshold of 0.622. 2.5 / PM 10 PM2.5 concentration in South China was 0.614, compared to a threshold of 0.614. 2.5 / PM 10 The threshold is 0.556.

[0028] The aforementioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products includes the following steps in steps P2-3:

[0029] P2-31. When visibility is less than 10km and relative humidity is less than 80%, it is preliminarily determined to be haze.

[0030] P2-32. When visibility is less than 10km and relative humidity is greater than or equal to 95%, it is judged as fog.

[0031] P2-33. When visibility is less than 10 km and relative humidity is greater than or equal to 80% and less than 95%, use the ratio of the extinction coefficient of the aerosol after hygroscopic growth to the extinction coefficient of the actual atmosphere and PM2.5. 2.5 PM 10 Make a comprehensive judgment on fog and haze;

[0032] P2-34. For weather conditions initially identified as haze, utilize PM2.5... 2.5 and PM 10 Make a final judgment;

[0033] When PM 2.5 >75μg m -3 or PM 10 >150μg m -3 At that time, it was ultimately determined to be smog;

[0034] P2-35. Classify fog levels based on visibility: when visibility is [1km, 10km), it is classified as light fog; when visibility is [0.5km, 1km), it is classified as dense fog; when visibility is [0.2km, 0.5km), it is classified as heavy fog; when visibility is [0.05km, 0.2km), it is classified as very dense fog; when visibility is less than 0.05km, it is classified as extremely dense fog.

[0035] The above-mentioned multi-variable collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products, if the aerosol extinction coefficient σ after hygroscopic growth... ex total Compared with the actual atmospheric extinction coefficient σ' ex When the ratio is greater than or equal to 0.8, it is judged as haze; when the ratio is less than 0.8, it is judged as fog.

[0036] That is, σ ex total / σ' ex If the concentration is ≥0.8, it is preliminarily determined to be haze;

[0037] σ ex total / σ' ex If the value is less than 0.8, it is determined to be fog.

[0038] The aforementioned multivariate collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observations and grid-based real-time analysis products includes the following steps in step P3:

[0039] If at least one of the three diagnostic results (P3-1, direct diagnostic result 1, indirect diagnostic result 2, and indirect diagnostic result 3) indicates the presence of sandstorm weather, then the integrated diagnosis will be classified as sandstorm weather.

[0040] P3-2. If at least two of the three diagnostic results (direct diagnostic result 1, indirect diagnostic result 2, and indirect diagnostic result 3) indicate fog or haze, and one of them is a grid point diagnostic result, then the integrated result is determined to be fog or haze.

[0041] The technical solution of the present invention achieves the following beneficial technical effects:

[0042] This application comprehensively utilizes observations from ground-based automatic weather stations (including visibility, relative humidity, wind speed, precipitation, and PM2.5). 2.5 PM 10 The analysis includes seven elements such as weather phenomena, hourly products of national smart grid real-time data fusion analysis (including visibility, relative humidity, wind speed, and precipitation), and atmospheric chemical data analysis products of China region (including PM2.5). 2.5 and PM 10 This system utilizes multiple data sources, including those from the Meteorological Data Center (MDC) and the Meteorological Data Center (MDC) and the Physical and Chemical Properties of Particulate Matter under Dust, Fog, and Haze conditions. It also incorporates Mie scattering theory to calculate aerosol extinction coefficients for diagnosing fog and haze. Employing a multi-variable collaborative and regional dynamic comprehensive evaluation method, it achieves the diagnostic identification of dust, fog, and haze weather conditions and their severity levels, resulting in a real-time data product. This product includes 11 elements: light fog, dense fog, heavy fog, very heavy fog, extremely heavy fog, haze, floating dust, blowing sand, dust storm, strong dust storm, and extremely strong dust storm. The spatiotemporal resolution is 5 km / 1 h, with a lead time of 55 minutes.

[0043] (1) Abundant and reliable data sources are used: The station observation products include five meteorological elements (visibility, relative humidity, wind speed, precipitation, and weather phenomena) and two atmospheric chemical elements (PM2.5). 2.5 PM 10 All data products or business platforms have been approved for business access; the grid analysis products used (national smart grid real-time fusion analysis product and China regional atmospheric chemistry real-time analysis product) have all been approved for business access.

[0044] (2) Scientific and comprehensive technical solution: Combining the differences in meteorological elements and particle physical and chemical properties under sandstorm, fog and haze weather, the Mie scattering theory is introduced to calculate the aerosol extinction coefficient to diagnose fog and haze; scientific key characteristic index thresholds are adopted, and multi-variable collaborative and regional dynamic comprehensive evaluation methods are used for diagnosis; finally, three diagnostic results, including direct diagnosis, station and grid indirect diagnosis, are comprehensively judged to minimize the uncertainty of diagnosis from a single data source.

[0045] (3) The product diagnostic elements are complete: The real-time product includes 11 types of weather phenomena, including haze, 5 types of fog (light fog, heavy fog, dense fog, strong dense fog, and extremely dense fog) and 5 types of dust (floating dust, blowing sand, dust storm, strong dust storm, and extremely strong dust storm), which is the highest number of types among similar products in China.

[0046] (4) Product quality is reliable: The evaluation results of 10 typical sandstorm, fog and haze cases from 2021 to 2023 show that the real-time products can reproduce the evolution process, impact range and intensity change characteristics of sandstorm, fog and haze weather very well; the comparison evaluation results of the products throughout 2022 with the calibration products of the meteorological center show that the consistency rate of the real-time products in the diagnosis of sandstorm weather with the calibration products reached 98.7%, the consistency rate in the diagnosis of haze reached 95.02%, and the consistency rate in the diagnosis of fog reached 95.06%; the quantitative evaluation results of weather phenomena observed by 7 stations show that the average hit rate of sandstorm and haze weather identification can reach more than 85%, and the average hit rate of fog weather is about 75%. Attached Figure Description

[0047] Figure 1 This application provides a technical solution process for real-time dust, fog, and haze data products in the Chinese region.

[0048] Figure 2 A direct diagnostic method for dust, fog, and haze weather based on observations at weather phenomenon stations;

[0049] Figure 3 It is a multivariate collaborative and regional dynamic comprehensive diagnostic method;

[0050] Figure 4 This is an indirect weather diagnosis method for dust, fog, and haze based on meteorological elements (visibility, relative humidity, wind speed, and precipitation), PM2.5, and PM10 station observations.

[0051] Figure 5 A comprehensive diagnostic method for multiple product types;

[0052] Figure 6a This is a live product taken at 01:00 (UTC) on February 13, 2021, during the peak of haze. Figure 6b Indirect diagnostic results 3 (grid-based indirect diagnostic results) for the period of intense haze at 01:00 (UTC) on February 13, 2021; Figure 6c The direct diagnostic result 1 (based on the weather phenomenon at the station) is for the period of 01:00 (UTC) on February 13, 2021, during the period of severe haze. Figure 6d Indirect diagnostic results 2 (site-based indirect diagnostic results) for the period of 01:00 (UTC) on February 13, 2021, during the peak of haze. Figure 6e For the Ministry of Environmental Protection PM 2.5 Spatial distribution map of mass concentration;

[0053] Figure 7a This is a live product taken at 01:00 (UTC) on February 14, 2021, during the haze dissipation phase. Figure 7bThe indirect diagnosis result 3 (grid-based indirect diagnosis result) for the haze dissipation phase at 01:00 (UTC) on February 14, 2021; Figure 7c The direct diagnostic result 1 (based on the weather phenomena at the station) is as of 01:00 (UTC) on February 14, 2021, during the haze dissipation phase. Figure 7d Indirect diagnostic results 2 (site-based indirect diagnostic results) for the haze dissipation phase at 01:00 (UTC) on February 14, 2021; Figure 7e For the Ministry of Environmental Protection PM 2.5 Spatial distribution map of mass concentration;

[0054] Figure 8a This is a live image taken at 05:00 (UTC) on March 15, 2021, during the peak of the dust storm. Figure 8b Indirect diagnostic results 3 (grid-based indirect diagnostic results) for the peak dust storm phase at 05:00 (UTC) on March 15, 2021; Figure 8c The direct diagnostic result 1 (based on the weather phenomenon at the station) is for the period of 05:00 (UTC) on March 15, 2021, during the peak of the dust storm. Figure 8d Indirect diagnostic results 2 (site-based indirect diagnostic results) for the peak of the dust storm at 05:00 (UTC) on March 15, 2021; Figure 8e For the Ministry of Environmental Protection PM 10 Spatial distribution map of mass concentration; Figure 8f The spatial distribution range characterized by the FY-4A dust diagnostic product (DST);

[0055] Figure 9a This is a live image taken at 04:00 (UTC) on April 11, 2023, during the peak of the dust storm. Figure 9b Indirect diagnostic results 3 (grid-based indirect diagnostic results) for the peak dust storm phase at 04:00 (UTC) on April 11, 2021; Figure 9c The direct diagnostic result 1 (based on the weather phenomenon at the station) is for the period of 04:00 (UTC) on April 11, 2021, during the peak of the dust storm. Figure 9d Indirect diagnostic results 2 (site-based indirect diagnostic results) for the peak of the dust storm at 04:00 (UTC) on April 11, 2021; Figure 9e For the Ministry of Environmental Protection PM 10 Spatial distribution map of mass concentration; Figure 9f The spatial distribution range characterized by the FY-4A dust diagnostic product (DST);

[0056] Figure 10a This is a live image taken at 00:00 (UTC) on November 23, 2022, during the period of intense fog. Figure 10bIndirect diagnostic results 3 (grid-based indirect diagnostic results) for the period of intense fog at 00:00 (UTC) on November 23, 2022; Figure 10c The direct diagnostic result 1 (based on the weather phenomenon at the station) is for the period of intense fog at 00:00 (UTC) on November 23, 2022. Figure 10d Indirect diagnosis result 2 (site-based indirect diagnosis result) for the period of intense fog at 00:00 (UTC) on November 23, 2022. Detailed Implementation

[0057] I. Data Source

[0058] This application utilized site data sources during its development process (including five meteorological elements: visibility, relative humidity, wind speed, precipitation, and weather phenomena, as well as PM2.5). 2.5 PM 10 Two atmospheric component elements and the element grid fusion analysis product were used as input sources. Detailed attributes of each data source (such as spatiotemporal resolution, spatial coverage, etc.) are shown in Table 1. Spatial coverage includes the spatial distribution of visibility from the national smart grid real-time fusion analysis product, the spatial distribution of relative humidity from the national smart grid real-time fusion analysis product, the spatial distribution of wind speed from the national smart grid real-time fusion analysis product, and the PM2.5 concentration from the China regional atmospheric chemistry real-time analysis product. 2.5 Spatial distribution of PM2.5 in China's atmospheric chemistry real-time analysis products 10 The spatial distribution of precipitation in the national smart grid real-time fusion analysis product, the spatial distribution of data sources from ground-based hourly data stations in China (approximately 2400 stations), and the spatial distribution of data sources from ground-based atmospheric composition observation basic dataset (V1.0) in China (approximately 1800 stations).

[0059] Element grid fusion analysis products: National Meteorological Information Center's nationwide smart grid real-time fusion analysis product (elements: visibility, relative humidity, wind speed, precipitation), China regional atmospheric chemistry real-time analysis product (elements: PM2.5). 2.5 PM 10 ).

[0060] Table 1 Data Source Attributes

[0061]

[0062] II. A multi-variable collaborative regional dynamic comprehensive evaluation method for dust and haze based on site observation and grid real-time analysis products.

[0063] like Figure 1As shown in the technical solution flow of real-time dust, fog, and haze data for China, this solution categorizes the diagnostic methods for dust, fog, and haze into two types: one type directly diagnoses the corresponding weather based on the classification of dust, fog, and haze according to weather phenomenon observations, resulting in a direct diagnostic result 1; the other type is based on meteorological elements (visibility, relative humidity, wind speed, and precipitation) and PM2.5. 2.5 and PM 10 Indirect diagnosis of dust storms, fog, and haze is performed simultaneously on both the station observation data dimension and the grid fusion analysis product dimension, yielding indirect diagnosis result 2 and indirect diagnosis result 3, respectively. Finally, by comprehensively considering the direct diagnosis results and the indirect diagnosis results from both dimensions, a final identification of dust storms, fog, and haze on each grid is obtained. The goal is to minimize the uncertainty of single-type diagnosis results and reduce the probability of deviations in diagnosis results due to quality issues with different types of observation data.

[0064] The technical process for classifying and directly diagnosing the corresponding weather conditions based on weather phenomenon observations, such as dust storms, fog, and haze, is as follows: Figure 2 As shown.

[0065] Based on meteorological elements (visibility, relative humidity, wind speed, and precipitation), PM 2.5 and PM 10 When indirectly diagnosing sandstorms, fog, and haze, a multivariate collaborative and regional dynamic comprehensive diagnostic method is used. Figure 3 Multivariate synergy refers to the comprehensive diagnosis of dust storms, fog, and haze by considering meteorological element observations, ambient air quality observations, and weather phenomenon observations. Regional dynamics refers to the dynamic setting of diagnostic thresholds for key characteristic indicators based on the climate characteristics of different regions during the diagnosis of dust storms, fog, and haze. The specific technical process of the multivariate synergy and regional dynamic integrated diagnostic method is as follows: Figure 4 As shown.

[0066] The following section details the direct diagnostic method and the two-dimensional indirect diagnostic method for identifying the three types of weather.

[0067] 1. Data processing

[0068] In terms of diagnostic dimensions of site observation data, due to PM 2.5 and PM 10 The number and location of atmospheric composition observation stations and meteorological element observation stations are inconsistent, therefore it is necessary to combine atmospheric composition (PM) with other meteorological elements. 2.5 and PM 10 Station observations are uniformly interpolated to the meteorological observation station data, using the nearest neighbor interpolation method. When using the nearest neighbor interpolation method to calculate PM2.5... 2.5 and PM 10 When interpolated to meteorological observation stations, PM2.5 concentrations in Northwest my country, Inner Mongolia, Heilongjiang, Jilin, and Liaoning are included.2.5 In areas with sparse observation stations, the interpolation distance threshold is set at 200km, while in other areas it is set at 50km. This method is also to avoid false interpolation in areas with sparse stations.

[0069] 2. Using weather phenomena observed at stations as the data source, and based on the classification standards of on-site weather observations, direct diagnosis of sandstorms, fog, and haze is performed to obtain direct diagnosis results 1;

[0070] P2-1. Determining Sandstorm Weather

[0071] Diagnosing dust storms based on weather phenomenon station observations requires first distinguishing the correspondence between different weather phenomenon observation codes and dust storms, because multiple codes are used to represent weather phenomena in weather phenomenon observations.

[0072] Table 2 shows the correspondence between different weather phenomenon codes and dusty weather.

[0073]

[0074] P2-2 Determination of Sandstorm Levels: Sandstorm weather levels are classified based on visibility and wind speed;

[0075] After identifying dusty weather, based on visibility and wind speed, dusty weather is divided into five levels: floating dust, blowing dust, dust storm, severe dust storm, and extremely severe dust storm (Table 3).

[0076] Table 3. Classification Standards for Dust Weather Levels

[0077]

[0078] P2-3, Determining Haze Weather

[0079] Because weather phenomena are represented by multiple codes in weather observation, methods for diagnosing fog and haze based on weather phenomena also need to distinguish the correspondence between different weather phenomenon observation codes and fog and haze weather. Tables 4 and 5 show the correspondence between different weather phenomenon codes and fog and haze weather, respectively.

[0080] Table 4. Correspondence between weather phenomenon codes and foggy weather

[0081]

[0082]

[0083] Table 5. Correspondence between weather phenomenon codes and haze weather

[0084]

[0085] P2-4. Determination of Fog Level: Fog weather levels are classified based on visibility.

[0086] Based on visibility, foggy weather is divided into five levels: light fog, dense fog, heavy fog, very heavy fog, and extremely heavy fog (Table 6).

[0087] Table 6 Fog Weather Levels and Classification Standards

[0088] grade Visibility (km) Light mist [1，10) Dense fog [0.5，1) Dense fog [0.2，0.5) Dense fog [0.05，0.2) Extremely dense fog <0.05

[0089] 3. Based on meteorological elements (visibility, relative humidity, wind speed, and precipitation), PM 2.5 and PM 10 Indirect diagnostic results 2 and 3 are presented in two dimensions: site observation and grid analysis products.

[0090] P3-1. Determination of dusty weather and classification of dust levels;

[0091] Based on meteorological elements, PM 2.5 and PM 10 Indirect diagnostic methods for dust storms take into account the conditions for their formation. These methods can be based on meteorological factors such as visibility, relative humidity, and precipitation, as well as PM2.5 levels. 2.5 PM 10 Confirming the dust storm weather.

[0092] However, considering the characteristics of sandstorm weather, a unified approach is based on relative humidity <50% and PM2.5 concentration. 10 Hourly concentration >300 μgm -3 And PM 2.5 Hourly concentration and PM 10 The rule of defining dust storms as an hourly concentration ratio <0.5 is not applicable to all regions of my country. Firstly, the main source areas of dust storms in my country are Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region, and Mongolia. Therefore, dust storms typically originate in the north and spread southwards to other parts of my country. Secondly, the complex topography and varying climate characteristics across different regions of my country lead to different characteristics of dust storms in different areas. Based on these two considerations, key characteristic indicators of dust storms in different regions were investigated during the development of the real-time product, and regional dynamic key characteristic indicators were adopted in the diagnostic algorithm.

[0093] Considering the applicability of key characteristic indicators during sandstorms in different regions, this work uses different PM2.5 concentrations in different regions of my country. 2.5 / PM 10 The ratio threshold was optimized for diagnosing dust storms (Table 7).

[0094] and PM 2.5 / PM 10Similarly, during the dust storm season, the relative humidity thresholds in different regions of my country also show a trend of gradually increasing from northwest to southeast. Considering the applicability of key characteristic indicators during dust storms in different regions, this work optimizes the diagnosis of dust storms by using different relative humidity thresholds in different regions of my country (Table 8).

[0095] Table 7. PM2.5 concentrations in eight major regions of my country and their spring averages. 2.5 / PM 10 ratio

[0096]

[0097] Table 8. Relative Humidity of Eight Major Regions in my country and Their Average Relative Humidity in Spring

[0098]

[0099]

[0100] Finally, after determining the dust weather, based on visibility and wind speed, the dust weather was divided into five levels: floating dust, blowing dust, dust storm, severe dust storm, and extremely severe dust storm. The method is the same as step P2-2 above, as detailed in Table 3.

[0101] P3-2, Determination of Haze and Classification of Fog Levels

[0102] Considering the physicochemical properties of fog and haze formation, based on meteorological elements and PM2.5... 2.5 and PM 10 Indirect diagnosis of fog and haze.

[0103] Fog and haze are both types of weather phenomena that impair visibility (less than 10 km), but they are different weather phenomena. The key difference between them lies in the composition and water content of atmospheric aerosols.

[0104] However, currently, both domestically and internationally, operational observations and scientific research typically distinguish between fog and haze using relative humidity or a combination of visibility and relative humidity. However, these diagnostic methods primarily differentiate fog and haze from a meteorological perspective, thus having certain limitations in their diagnostic capabilities.

[0105] This application, based on the differences in the physicochemical properties of fog and haze, considers the extinction effect of aerosols in distinguishing between fog and haze. When visibility is less than 10 km, and visibility impairment caused by weather phenomena such as precipitation, sandstorms, blowing sand, dust, smoke, blowing snow, and snowstorms is excluded:

[0106] The relative humidity is less than 80%, which is preliminarily determined to be haze.

[0107] When visibility is less than 10km and relative humidity is greater than or equal to 95%, it is judged as fog;

[0108] Relative humidity greater than or equal to 80% and less than 95% needs to be combined with PM2.5. 2.5 Information such as concentration is used to determine whether it is fog or haze: when the ratio of the extinction coefficient of aerosol after hygroscopic growth to the actual atmospheric extinction coefficient is greater than or equal to 0.8, it is initially determined to be haze.

[0109] To determine whether something is fog or haze when visibility is less than 10 km and relative humidity is between 80% and 95%, it is necessary to calculate both the extinction coefficient of the aerosol after hygroscopic growth and the extinction coefficient of the actual atmosphere. The extinction coefficient of the actual atmosphere is σ'. ex It can be given by Formula 1:

[0110]

[0111] Where Vis represents the measured meteorological visibility, in meters (m). -1 or km -1 .

[0112] The extinction coefficient of aerosols after hygroscopic growth was calculated based on Mie scattering theory. First, it was assumed that the aerosol particles were uniformly mixed spheres, and the measured PM2.5 concentration was used as the basis for this calculation. 2.5 The mass concentration (M), combined with the aerosol particle density (r, Table 9), is given by Equation 2 for PM. 2.5 Total volume V:

[0113]

[0114] Dry aerosol PM 2.5 The volume spectrum distribution of the particles is given by Equation 3:

[0115]

[0116] Where: V represents aerosol PM 2.5 Total volume concentration, in mm 3 ·m -3 D represents the dry particle size of the aerosol particles, in mm. g The geometric mean particle size is a log-normal distribution, expressed in mm; s g M represents the geometric standard deviation of the log-normal distribution, in mm; M represents the aerosol PM2.5 concentration. 2.5 The mass concentration, in gm -3 r represents the aerosol particle density, in kg·m³. -3 N represents PM aerosols. 2.5 Total concentration, in units of cells per cubic meter. -3 .

[0117] The volume spectrum of dry aerosols can be calculated using Formula 3. Then, the particle size D of each particle size range after hygroscopic growth of the aerosol can be calculated using the single-parameter k-Coraline equation (Formula 4). wet :

[0118]

[0119] Where: S is the water vapor saturation ratio; in the unsaturated state, S is equivalent to relative humidity (RH); D we The particle size of the aerosol after hygroscopic growth is shown in mm; k is the hygroscopicity parameter of the aerosol; s sol The surface tension coefficient of water is expressed in N·m. -1 M w This is the molar mass of water, expressed in g·mol⁻¹. -1 R is the universal gas constant, with units of J·mol⁻¹. -1 ·K -1 T represents ambient temperature, in Kelvin (K); r w The density of water is expressed in kg·m³. -3 .

[0120] The complex refractive index m of the aerosol after hygroscopic growth is calculated by formula 5 based on the volume-weighted average of the complex refractive index of dry aerosol and the complex refractive index of water:

[0121]

[0122] Where: m water m is the complex refractive index of water. dry The complex refractive index of dry aerosol;

[0123] aerosol extinction efficiency factor Q after hygroscopic growth ex According to the Mie scattering principle, the result is calculated using Equation 6:

[0124]

[0125] Where: α is the size number of the aerosol particles, given by Equation 7 based on the aerosol radius (r) and the incident wavelength (l); (a n +b n ) is the Mie scattering function, a n and b n Both are complex functions, given by the half-integer order Bessel function and the second kind of Becker function, and their first derivatives; Re(a n +b n ) is to take (a n +b n The real part of ).

[0126] aerosol PM after hygroscopic growth 2.5The extinction coefficient is given by formula 8:

[0127]

[0128] Where: s ex为 aerosol PM after hygroscopic growth 2.5 Extinction coefficient, in m -1 or km -1 Q ex (D wet ) is D wet The particle extinction efficiency factor for particle size is given by Equation 6; DN(D wet (D) represents the particle size. wet To D wet +dD wet The aerosol number concentration within the range is calculated using the volume spectrum formula for dry aerosols (Formula 3), i.e., Formula 9, with units of particles per m³. -3 ;

[0129]

[0130] Where: ΔlnD is the width of each spectral range, in mm.

[0131] When visibility is <10km and relative humidity is <80%, it is assumed that the actual atmospheric extinction is entirely contributed by the total aerosol extinction, i.e., the calculated actual atmospheric extinction coefficient s' ex Equivalent to the total aerosol extinction coefficient, therefore, using the actual atmospheric extinction coefficient s' ex and aerosol PM 2.5 Extinction coefficient s after hygroscopic growth ex Perform linear regression, that is:

[0132] σ' ex =A×σ ex +B Formula 10

[0133] Therefore, using linear regression coefficients A and B, and based on the conditions of visibility <10km and relative humidity <95%, aerosol PM2.5 was calculated. 2.5 The extinction coefficient is used to obtain the total aerosol extinction coefficient s under the conditions of visibility <10km and relative humidity <95%. ex total Therefore, in conditions where visibility is <10km and relative humidity is greater than or equal to 80% and less than 95%, if the aerosol extinction coefficient (σ) increases after moisture absorption, the following conditions will be met. ex total ) and the actual atmospheric extinction coefficient (σ' ex When the ratio of the two parameters is greater than or equal to 0.8, it is preliminarily determined to be haze; when the ratio is less than 0.8, it is determined to be fog. The parameters used to determine fog and haze using the above method are shown in Table 9.

[0134] Table 9 Recommended values ​​for relevant parameters

[0135] Parameter symbol Parameter name Recommended value limit unit <![CDATA[D g ]]> Log-normal distribution geometric mean particle size 0.4mm <![CDATA[s g ]]> Geometric standard deviation of log-normal distribution 1.8mm <![CDATA[m dry ]]> Dry aerosol complex refractive index 1.55-0.04i <![CDATA[m water ]]> Complex refractive index of water 1.33-0.0i r aerosol particle density <![CDATA[1500kg·m -3 ]]> k aerosol hygroscopic parameters 0.3 <![CDATA[M w ]]> Molar mass of water <![CDATA[18g·mol -1 ]]> <![CDATA[r w ]]> density of water <![CDATA[1000kg·m -3 ]]> R Universal gas constant <![CDATA[8.3145J·mol -1 ·K -1 ]]> T Ambient temperature 283.15K <![CDATA[s s ol]]> surface tension coefficient of water <![CDATA[7.42×10 -2 N·m -1 ]]>

[0136] The above methods only preliminarily consider the extinction effect of aerosols, but in reality, they still differentiate between fog and haze based on meteorological conditions. This can lead to some misjudgment in the diagnosis of haze, which is mainly caused by aerosol extinction.

[0137] P3-4. For weather conditions initially identified as haze, utilize PM2.5... 2.5 and PM 10 Make a final judgment;

[0138] Taking into full account that haze is mainly caused by the extinction contribution of aerosols, this work adds PM2.5 to the haze diagnosis algorithm. 2.5 >75μg m -3 or PM 10 >150μg m -3 The criteria for determining ambient air pollution are diagnostic methods that jointly identify haze formation from meteorological and environmental perspectives.

[0139] When PM 2.5 >75μg m -3 or PM 10 >150μg m -3 At that time, it was ultimately determined to be smog.

[0140] P3-5. Classify fog levels based on visibility;

[0141] After determining whether it is fog or haze, fog weather is divided into five levels based on visibility: light fog, heavy fog, dense fog, very dense fog, and extremely dense fog. The classification method is the same as that in steps P2-4 (see Table 6 for details).

[0142] 4. Sandstorms, fog, and haze are comprehensively evaluated based on direct diagnostic results 1, indirect diagnostic results 2, and indirect diagnostic results 3.

[0143] Based on the above methods for diagnosing dust storms, fog, and haze, three preliminary diagnostic results were obtained: direct diagnostic results based on weather phenomenon station observations, and diagnostic results based on meteorological elements and PM2.5. 2.5 / PM 10 Indirect diagnostic results from site observations 2 and meteorological elements and PM 2.5 / PM 10 Grid analysis product indirect diagnostic result 3. The final real-time product integrates and interprets the three diagnostic results. This process involves combining direct diagnostic results based on weather phenomenon station observations with diagnostic results based on meteorological elements and PM2.5. 2.5 / PM 10Indirect diagnostic results from site observations were interpolated to the results from both types of sites and those based on meteorological elements and PM2.5. 2.5 / PM 10 Grid analysis results are consistent with the indirect diagnostic results of the products.

[0144] This report uses a comprehensive assessment of multiple product types to integrate the three diagnostic results into a real-world product analysis. Figure 5 ).according to Figure 5 The multi-type product integrated diagnostic method integrates two scenarios: dust storms, fog, and haze. Scenario 1 integrates scenarios for dust storms, while Scenario 2 integrates scenarios for fog and haze. Scenario 1: If at least one of the three diagnostic results indicates dust storm conditions, the integrated product is determined to have experienced dust storms. Scenario 2: If at least two of the three diagnostic results indicate fog or haze conditions, and one of these is a gridded diagnostic result, the integrated product is determined to have experienced fog or haze. This integration method primarily considers that dust storms are typically large-scale pollution events caused by weather patterns.

[0145] III. Quality Assessment of this Method

[0146] 1. Case Assessment

[0147] Based on the "Atmospheric Environment Meteorological Bulletin" (China Meteorological Administration, 2021, 2022), the National Meteorological Center's record of dust and heavy pollution weather processes from 2021 to 2023, and news reports, this study conducted a detailed assessment of 10 typical dust, fog, and haze weather cases from 2021 to 2023. These included 6 dust cases (1 case of blowing dust, 2 cases of dust storms, and 3 cases of severe dust storms), 2 haze cases, and 2 fog cases.

[0148] This application selects serial numbers 1 (haze), 3 (severe sandstorm), 7 (sandstorm), and 9 (fog) as individual examples for illustration.

[0149] 1.1. February 10-14, 2021 (haze), affecting Beijing, Tianjin, Hebei, Henan, Guanzhong Plain in Shaanxi, Shanxi, Sichuan, and Liaoning.

[0150] Figures 6a-6e This is a live video recording of a peak smog event during the 2021 Spring Festival. Figure 6a ) and grid point indirect diagnostic results ( Figure 6b Indirect diagnostic results 3) Direct diagnostic results from weather phenomenon station observations ( Figure 6c ) as well as meteorological elements and PM 2.5 and PM 10 Indirect diagnostic results from site observations ( Figure 6dThe indirect diagnostic results 2) were compared and evaluated. The "2021 Heavy Air Pollution Process Summary Table" records that the affected area of ​​this process was Beijing, Tianjin, Hebei, Henan, Guanzhong Plain in Shaanxi, Shanxi, Sichuan, and Liaoning, and the distribution was in the northeast-southwest direction. Figure 6a The real-time product accurately reproduces the impact range and distribution characteristics of the peak haze phase, namely affecting Beijing, Tianjin, Hebei, Henan, Guanzhong Plain in Shaanxi, Shanxi, Sichuan, and Liaoning, with a distribution trend of northeast to southwest. Figure 6b The indirect diagnostic result 3 is largely consistent with the actual product performance; Figure 6c The direct diagnostic results 1 show that the haze is concentrated in Shanxi, Hebei, Beijing and other places, and sporadically distributed in other northern provinces; Figure 6d Indirect diagnosis result 2 shows that haze has the widest distribution range. Except for Yunnan, Guangdong, Fujian, Hunan and Jiangxi, where there is no haze, all other provinces are affected by haze. Among them, the haze is most concentrated in Shaanxi, Hebei, Tianjin, Beijing, Shandong and Jiangsu. Figure 6e Indicates PM 2.5 More than 75 μg m -3 The spatial distribution of the sites is largely consistent with the actual product, exhibiting a northeast-southwest orientation from Liaoning to Sichuan; from Figure 6a and Figure 6e The comparison shows that the real-time product can reproduce the impact range and distribution characteristics of the strong haze phase very well. This is mainly because the real-time product's haze diagnosis scheme requires at least two of the three diagnostic results to show haze weather phenomena, and one of them is a grid point diagnosis result. Therefore, the real-time product absorbs the spatial continuity advantage of grid point diagnosis results and reduces the uncertainty of misjudgment caused by using only a single criterion through the comprehensive identification of two or more criteria.

[0151] Figures 7a-7e This is a comparative evaluation of real-time product data and intermediate diagnostic results during the dissipation phase of this haze event. From... Figure 7a It can be seen that the haze is mainly distributed in southern Hebei. Figure 7b In the indirect diagnosis results of the haze dissipation phase, haze was mainly distributed in southern Hebei and sporadically distributed in some provinces in central China. Figure 7c In the direct diagnostic results 1 during the haze dissipation phase, the haze stations were mainly distributed in Shanxi, Jiangsu, Qinghai and other places. Figure 7d In the indirect diagnosis results 2 of the haze dissipation stage, haze stations are widely distributed, mainly concentrated in provinces such as Shanxi and Hebei, and also distributed in other northern provinces of my country. Figure 7e Display PM 2.5 More than 75 μg m -3 The monitoring stations are mainly distributed in southern Hebei, consistent with the actual data. According to the Ministry of Ecology and Environment's report on urban air quality during the 2021 Spring Festival (New Year's Eve to the first day of the Lunar New Year), this haze event had the longest duration of impact on the Beijing-Tianjin-Hebei region. The actual data ( Figure 7a The study accurately reproduced the characteristics of haze mainly affecting the Beijing-Tianjin-Hebei region during the dissipation phase, while the direct diagnostic results from weather phenomenon stations and the indirect diagnostic results from meteorological element stations showed obvious overdiagnosis.

[0152] 1.2. From March 13 to March 18, 2021 (severe sandstorm), the affected areas include the Fenwei Plain, Henan, southern Shandong, Anhui, central and eastern Hubei, central and northern Hunan, central and southern Sichuan Basin, and the urban cluster on the northern slope of Tianshan Mountains.

[0153] This severe dust storm originated in southern Mongolia on the morning of March 14, 2021, and entered China on the night of the 14th. On the 15th, the Mongolian cyclone intensified and moved eastward and southward, transporting the dust to Ningxia, Gansu, and northern Shaanxi, successively affecting North China, Northeast China, and the Yangtze River Basin. The dust weather in eastern Northwest China persisted until the night of the 18th.

[0154] Figures 8a-8f This is a live product documenting the most intense phase of this sandstorm. Figure 8a ) and grid point indirect diagnostic results ( Figure 8b ), direct diagnostic results from weather phenomenon station observations ( Figure 8c Meteorological elements and PM 2.5 and PM 10 Indirect diagnostic results from site observations ( Figure 8d Comparative evaluation of FY-4A dust detection products Figure 8f ). Figure 8a The real-time data shows that during the peak of the sandstorm, sandstorms occurred in Xinjiang, Gansu, Inner Mongolia, the Beijing-Tianjin-Hebei region and Northeast China, with strong sandstorms in northwestern Inner Mongolia and sandstorms in Shanxi and Hebei. Figure 8b Indirect diagnosis result 3 shows that blowing sand is mainly occurring in parts of Heilongjiang, Jilin and Liaoning provinces; Figure 8c Direct diagnosis result 1 shows that the dust storm occurred in Xinjiang, Gansu, Inner Mongolia, the Beijing-Tianjin-Hebei region and the Northeast. Figure 8d The spatial distribution of dust weather in indirect diagnosis result 2 is largely consistent with that in direct diagnosis result 1, with a weaker intensity, mainly consisting of floating dust and blowing sand. Figure 8e PM 10 More than 150 μg m -3 The spatial distribution of the sites is consistent with the spatial distribution of the actual products, and the PM data is available in Gansu, Inner Mongolia, Shanxi, and Hebei. 2.5 More than 420 μg m -3 It reached a level of severe pollution; Figure 8f Spatial distribution range of dust storms and PM2.5 10The high-value monitoring data is relatively consistent with the impact range and intensity of the actual data products. The "2021 Dust Weather Process Summary Table" records that the impact range of this process included large-scale blowing dust or floating dust in eastern and southern Xinjiang, most of Gansu, northeastern Qinghai and the Qaidam Basin, most of Inner Mongolia, Ningxia, Shaanxi, Shanxi, Beijing, Tianjin, Hebei, central and western Heilongjiang, central and western Jilin, central Liaoning, Shandong, Henan, central and northern Jiangsu, central and northern Anhui, and western Hubei. Dust storms occurred in central and western Inner Mongolia, western Gansu, Ningxia, northern Shaanxi, northern Shanxi, northern Hebei, Beijing, and Tianjin, with strong dust storms occurring in parts of central and western Inner Mongolia, Ningxia, northern Shaanxi, northern Shanxi, northern Hebei, and Beijing. The diagnostic scheme for dust weather in the actual data products is that if at least one of the three diagnostic results indicates dust weather, then the integrated actual data product is considered to have dust weather. In other words, the determination of dust weather in the actual data products is the union of the three diagnostic results. (The actual data products...) Figure 8a It can capture the dust storms that occur in Xinjiang, Gansu, Inner Mongolia, the Beijing-Tianjin-Hebei region, and Northeast China during their strongest phases, and also characterize the severity of this strong dust storm, matching the spatial distribution range of the dust storm at that time as characterized by the FY-4A dust storm detection product. Figure 8f ) and the Ministry of Environmental Protection PM 10 High-value area monitoring Figure 8e The data is relatively consistent with the dust storm impact range and intensity level in the dust storm weather summary table.

[0155] 1.3. From April 9th ​​to April 13th, 2023 (dust storm), the affected areas will include eastern and southern Xinjiang, most of Inner Mongolia, Gansu, the Qaidam Basin in Qinghai, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Tianjin, Henan, Shandong, Jiangsu, Anhui, Hubei, Shanghai, Zhejiang, northern Hunan, northern Jiangxi, central and northern Fujian, Jilin, Liaoning, and Heilongjiang, where there will be blowing sand or dust. Among these areas, some parts of central and western Inner Mongolia, northwestern and central Hebei, Beijing, Tianjin, and southern Xinjiang will experience dust storms, with some areas experiencing severe dust storms.

[0156] Figures 9a-9f This is a live product documenting the most intense phase of this sandstorm. Figure 9a ) and grid point indirect diagnostic results ( Figure 9b ), direct diagnostic results from weather phenomenon station observations ( Figure 9c Meteorological elements and PM 2.5 and PM 10 Indirect diagnostic results from site observations ( Figure 9d ), FY-4A dust detection product ( Figure 9f Comparative evaluation of ). Figure 9aIn the live product data, most parts of Xinjiang, Inner Mongolia, Gansu, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Tianjin, Henan, Shandong, Jiangsu, Anhui, Jilin, Liaoning, and Heilongjiang experienced blowing sand or dust, while some areas of Inner Mongolia experienced sandstorms. Figure 9b The indirect diagnosis result 3 shows that Heilongjiang, Jilin, Liaoning, Hebei, Beijing, Tianjin, Shandong, Shanxi, Henan and other places have floating dust and blowing sand weather; Figure 9c The direct diagnostic results 1 show that dust and blowing sand weather stations appeared in Jilin, Liaoning, Inner Mongolia, Hebei, Shanxi, Shandong, Xinjiang and other regions; Figure 9d The indirect diagnosis results show that dust storm weather stations are distributed in Inner Mongolia, Heilongjiang, Jilin, Liaoning, Hebei, Beijing, Tianjin, Shanxi, Shaanxi, Ningxia, Gansu, Xinjiang and other places; Figure 9e The PM10 level exceeded 150 μg / m³. -3 The spatial distribution of the sites is consistent with the spatial distribution range of the actual products; Figure 9f This indicates that the spatial range of the FY-4A dust weather diagnostic product is largely consistent with the distribution of the actual weather products. The "2023 Dust Weather Process Summary Table" records that the affected area of ​​this process included eastern and southern Xinjiang, most of Inner Mongolia, Gansu, the Qaidam Basin in Qinghai, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Tianjin, Henan, Shandong, Jiangsu, Anhui, Hubei, Shanghai, Zhejiang, northern Hunan, northern Jiangxi, central and northern Fujian, Jilin, Liaoning, and Heilongjiang, experiencing blowing dust or floating dust. Dust storms occurred in parts of central and western Inner Mongolia, northwestern and central Hebei, Beijing, Tianjin, and southern Xinjiang, with some areas experiencing severe dust storms. Actual weather products (…) Figure 9a The diagnosis of sandstorm weather involves three intermediate products. Figure 9b , Figure 9c , Figure 9d The union of the data shows that the real-time product uses comprehensive identification, absorbing diagnostic results from different data types, and can well reproduce areas with severe dust storms such as central and western Inner Mongolia, northwestern and central Hebei, Beijing, Tianjin, and southern Xinjiang. Furthermore, it is comparable to the FY-4A dust storm detection product. Figure 9f The spatial distribution range of the dust storm at that time and the PM2.5 concentration of PM2.5 in the Ministry of Environmental Protection are represented. 10 High-value area monitoring Figure 9e They are fairly consistent.

[0157] 1.4. From November 23 to November 25, 2022 (fog), the affected area will be parts of central and southern North China and the Huanghuai region, with some areas experiencing dense fog with a visibility of less than 200 meters.

[0158] Live Product ( Figure 10a The characterization of the occurrence and evolution of this fog process was applied in the morning consultation of the Central Meteorological Observatory on November 23, 2022. Figures 10a-10d This is a real-time fog weather product showing the situation in central and southern North China and the Huanghuai region at 00:00 on November 23, 2022. Figure 10a ) and grid point indirect diagnostic results ( Figure 10b ), direct diagnostic results from weather phenomenon station observations ( Figure 10c Meteorological elements and PM 2.5 and PM 10 Indirect diagnostic results from site observations ( Figure 10d The comparison results are as follows. Figure 10a The live product data shows that heavy fog or fog of greater intensity has occurred in central and southern North China and the Huanghuai region. Figure 10b The indirect diagnostic result 3 is largely consistent with the actual product; Figure 10c Direct diagnosis result 1 shows that the fog covered a large area, with fog occurring not only in the central and southern parts of North China and the Huanghuai region, but also in parts of Xinjiang and Yunnan. Figure 10d Indirect diagnosis result 2 shows that the fog affected a large area, including parts of central and southern North China and the Huanghuai region, as well as parts of Xinjiang and Shaanxi. Since the real-time product's fog diagnosis scheme requires at least two of the three diagnostic results to indicate fog, with one being a grid point diagnosis result, the real-time product absorbs the spatial continuity advantage of grid point diagnostic results, but simultaneously reduces the weight of site-specific diagnostic results. Real-time product ( Figure 10a The diagnostic range for fog is closer to the results of grid-based indirect diagnosis. Figure 10b ), compared to the site diagnostic results ( Figure 10c , Figure 10d The scope should be small.

[0159] 2. Comparative evaluation with similar products

[0160] This application presents a comparative evaluation of the multivariate collaborative regional dynamic comprehensive evaluation method for dust, fog, and haze based on station observations and grid-based real-time analysis products (hereinafter referred to as the "real-time product") with the National Meteorological Center's "National-level Ground Station Hourly Dust, Fog, and Haze Weather Phenomenon Calibration Product" (hereinafter referred to as the "calibration product"). Table 10 compares the different attributes of the two sets of products. As can be seen, the two sets of products differ significantly in product elements and diagnostic schemes. The indicators for comparing and evaluating the two are the consistency rate and non-consistency rate of the weather phenomena diagnosed at corresponding grid points between the calibration product stations and the real-time product (Tables 11-13).

[0161] Table 10 Comparison of attributes between real-time products and calibration products for sandstorm, fog, and haze weather phenomena.

[0162]

[0163] Table 11. Consistency and Inconsistency Rates of Real-Time and Calibration Products in Diagnosing Dust Storms Throughout 2022

[0164]

[0165] Table 12. Consistency and Inconsistency Rates of Real-Time and Calibration Products in Diagnosing Haze Weather Throughout 2022

[0166]

[0167] Table 12 shows the consistency and inconsistency rates of the real-world and calibration products in diagnosing haze weather in 2022. It can be seen that the average consistency rate of the two sets of products in 2022 reached 95.02%, with an inconsistency rate of 4.98%. The consistency rates for January-February and December, when haze is most frequent, were between 83% and 92%, slightly lower than other months, while the consistency rates for other months remained around 93%.

[0168] Table 13. Consistency and Inconsistency Rates of Real-World and Calibration Products in Diagnosing Foggy Weather in 2022

[0169]

[0170] Table 13 shows the consistency and inconsistency rates of the real-world and calibration products in diagnosing foggy weather in 2022. It can be seen that the average consistency rate between the two sets of products in 2022 reached 95.06%, with an inconsistency rate of 4.94%. The consistency rates for January-February and November were slightly lower than other months, both above 90%. Further analysis revealed that the inconsistency mainly manifested in the real-world product diagnosing no fog phenomena while the calibration product diagnosed fog phenomena. This is primarily because the real-world product uses two or more criteria for fog diagnosis.

[0171] 3. Independent quantitative assessment compared with manually observed weather phenomenon data

[0172] Table 14 shows the quantitative evaluation results of real-time weather products for dust, fog, and haze based on manual observations of weather phenomena at seven stations. The accuracy rate of the real-time products for identifying no weather phenomena at the seven stations, except for Dianbai and Yinchuan, all exceeded 88%, with Altay, Zhangye, and Changchun achieving accuracy rates of over 95%. The accuracy rate for identifying dust was relatively high. Golmud station achieved accuracy rates of 80% for floating dust, 63% for blowing dust, and 67% for dust storms. Yinchuan station achieved accuracy rates of 85% for floating dust and 100% for blowing dust. It is worth noting that Zhangye station's manual observations were conducted during July-August 2022.

[0173] Table 14. Accuracy of Identification of Dust, Fog, and Haze Based on Manual Weather Phenomena Observed from 7 Stations (Hit Rate)

[0174]

[0175] Table 15 is a record of weather phenomena observed manually. This table details the dust storm at 07:00 on July 7, 2022, which dissipated at 08:00. The real-time weather product accurately captured the dust storm at 07:00 and the absence of dust storm at 08:00, perfectly matching the manual observations and records. The real-time weather product also showed a high accuracy rate in identifying haze, with 84% and 100% accuracy rates at Yinchuan and Guiyang stations, respectively. The accuracy rate for identifying fog or light fog varied significantly between stations. Guiyang station had a 95% accuracy rate for fog, while Dianbai station had 85% and 100% accuracy rates for light fog and fog, respectively. However, Zhangye and Changchun had 70% and 77% accuracy rates for light fog, respectively, Altay station had 67% and 50% accuracy rates for light fog and fog, and Yinchuan station had a 63% accuracy rate for fog. In summary, based on the quantitative assessment of weather phenomena observed manually at seven stations, the real-time products showed a high accuracy rate in identifying fog, dust storms, and haze. However, the accuracy rate for identifying fog or light fog varied significantly among different stations, and the overall accuracy rate was not very high. Further optimization of fog diagnosis methods and comprehensive identification will be conducted in the future.

[0176] Table 15 Record of Weather Phenomena Observed at Zhangye Station

[0177]

Claims

1. A dust haze multivariate collaborative regional dynamic comprehensive evaluation method based on site observation and grid live analysis product, characterized in that, Comprising the following steps: P1, taking the weather phenomenon observed by the station as the data source, directly diagnosing the dust, fog and haze weather based on the weather phenomenon code description, and obtaining a direct diagnosis result 1; P2, meteorological elements observed by stations, PM 2.5 and PM 10 are data sources, and the indirect diagnosis result 2 is obtained through multivariate coordination and regional dynamic indirect diagnosis. P3, analyze the meteorological elements and PM of the product with grid live data 2.5 and PM 10 as data source, carry out multivariate coordination, regional dynamic indirect diagnosis, and obtain indirect diagnosis result 3; P4, comprehensively judging the dust, fog and haze weather based on the direct diagnosis result 1, the indirect diagnosis result 2 and the indirect diagnosis result 3.

2. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 1, characterized in that, The site observation data include hourly observation data of weather phenomena, visibility, relative humidity, wind speed and precipitation, PM 2.5 and PM 10 in China, including 7 elements The grid live analysis product includes a national intelligent grid live fusion analysis product and a China regional atmospheric chemical live analysis product; the national intelligent grid live fusion analysis product provides meteorological element data, including visibility, relative humidity, wind speed and precipitation; the China regional atmospheric chemical live analysis product provides PM 2.5 and PM 10 data.

3. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 1, characterized in that, In step P1, dust weather diagnosis and fog and haze weather diagnosis are included, comprising: Step P1-1, determination of dust weather: distinguishing the corresponding relationship between different weather phenomenon observation codes observed by the station and dust weather, when the weather phenomenon observation code value is 6, 7, 8, 9, 30, 31, 32, 33, 34, 35, 208, 209, 230, it is determined as dust weather; Step P1-2, determination of dust grade: dividing the dust weather grade according to the visibility and wind speed; Step P1-3, determination of fog and haze weather: distinguishing the corresponding relationship between different weather phenomenon observation codes observed by the station and fog and haze weather; when the weather phenomenon observation code value is 10, 11, 12, 28, 40-49, 110, 120, 130-135, 241-249, it is determined as fog; when the weather phenomenon observation code value is 4, 5, 104, 105, 204, 206, 220, 22, it is determined as haze; Step P1-4, determination of fog grade: dividing the fog weather grade according to the visibility.

4. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 1, characterized in that, In steps P2 and P3, the data sources are different, the methods of multi-variable coordination and regional dynamic indirect diagnosis are the same, comprising the following steps: P2-1, data processing and screening; P2-2, determination of dust weather and division of dust grade; P2-3, determination of fog and haze weather and division of fog grade.

5. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 4, characterized in that, In step P2-1, Interpolation processing of observation station data: PM at the station 2.5 PM 10 The observation data is not located at the meteorological observation station, so it needs to be uniformly interpolated to the meteorological observation station. The nearest neighbor interpolation method is adopted. For the sparsely populated areas of Northwest my country, Inner Mongolia, Heilongjiang, Jilin and Liaoning, the interpolation distance threshold is selected as 200km, and the interpolation distance threshold is selected as 50km for other areas. Filtering and screening data: excluding the influence of precipitation and screening non-precipitation data.

6. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 5, characterized in that, In step P2-2, comprising the following steps: P2-21, screening data with visibility less than 10 km for the following determination; P2-22, when the relative humidity is less than the regional relative humidity threshold, the ratio of PM 2.5 / PM 10 is less than the regional PM ratio threshold, and the PM 10 > 300 μg m -3 , then the dust weather is determined; P2-23, dividing the dust grade of the dust weather determined according to the visibility and wind speed; when wind speed < 5.0 m s -1 and horizontal visibility < 10.0 km, it is judged to be floating dust; when wind speed ≥ 5.0 m s -1 and horizontal visibility ≥ 1.0 and < 10.0 km, it is judged to be blowing dust; when wind speed ≥ 5.0 m s -1 and horizontal visibility ≥ 0.5 km and < 1.0 km, it is judged to be sandstorm; when wind speed ≥ 5.0 m s -1 and horizontal visibility ≥ 0.05 km and < 0.5 km, it is judged to be strong sandstorm; when wind speed ≥ 5.0 m s -1 and horizontal visibility < 0.05 km, it is judged to be very strong sandstorm.

7. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 6, characterized in that, Regional relative humidity threshold: the relative humidity threshold of the western region of Northwest China is 24%, the relative humidity threshold of the eastern region of Northwest China is 27%, the relative humidity threshold of the Tibet Autonomous Region is 23%, the relative humidity threshold of the Southwest Region is 52%, the relative humidity threshold of South China is 60%, the relative humidity threshold of Northeast China is 36%, the relative humidity threshold of North China is 36%, and the relative humidity threshold of East China is 56%; Regional PM ratio threshold: PM of the northwest region 2.5 / PM 10 Ratio threshold 0.398, PM of the middle reaches of the Yellow River region 2.5 / PM 10 Ratio threshold 0.413, PM of the northeast region 2.5 / PM 10 Ratio threshold 0.504, PM of the Beijing-Tianjin-Hebei and surrounding region 2.5 / PM 10 Ratio threshold 0.507, PM of the southwest region 2.5 / PM 10 Ratio threshold 0.570, PM of the middle reaches of the Yangtze River region 2.5 / PM 10 Ratio threshold 0.622, PM of the Yangtze River Delta region 2.5 / PM 10 Ratio threshold 0.614, PM of the South China region 2.5 / PM 10 Ratio threshold 0.

556.

8. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 4, characterized in that, In step P2-3, comprising the following steps: P2-31, when the visibility is less than 10 km and the relative humidity is less than 80%, it is preliminarily determined as haze; P2-32, when the visibility is less than 10 km and the relative humidity is greater than or equal to 95%, it is determined as fog; P2-33, when the visibility is less than 10 km, and the relative humidity is greater than or equal to 80% and less than 95%, the extinction coefficient ratio of the hygroscopic growth aerosol to the actual atmospheric extinction coefficient and PM 2.5 are used to comprehensively judge the fog and haze; 10 ​ P2-34, for the weather preliminarily determined as haze, using PM 2.5 and PM 10 for final determination; When PM 2.5 > 75 μg m -3 or PM 10 > 150 μg m -3 a final decision is made that it is haze. P2-35, according to the visibility, the fog weather determined is classified; when the visibility is [1km, 10km), it is determined as light fog; when the visibility is [0.5km, 1km), it is determined as heavy fog; when the visibility is [0.2km, 0.5km), it is determined as dense fog; when the visibility is [0.05km, 0.2km), it is determined as strong dense fog; when the visibility is less than 0.05km, it is determined as extra strong dense fog.

9. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 8, characterized in that, If the ratio of the aerosol extinction coefficient σ ex total to the actual atmospheric extinction coefficient σ' ex is greater than or equal to 0.8, it is determined to be haze; if the ratio is less than 0.8, it is determined to be fog. i.e. σ ex total / σ' ex ≥ 0.8, then it is preliminarily determined as haze; σ ex total / σ' ex <0.8, then it is determined to be fog.

10. The dust haze multivariate synergistic regional dynamic comprehensive judgment method based on site observation and grid live analysis product according to claim 1, characterized in that, In step P3, the following steps are included: P3-1, among the three diagnostic results of direct diagnostic result 1, indirect diagnostic result 2 and indirect diagnostic result 3, at least one diagnostic result has sand-dust weather phenomenon, then the integrated comprehensive determination is sand-dust weather; P3-2, among the three diagnostic results of direct diagnostic result 1, indirect diagnostic result 2 and indirect diagnostic result 3, at least two diagnostic results have fog or haze weather phenomenon, and one of them is the diagnostic result of the grid, then the integrated comprehensive determination is fog or haze weather phenomenon.

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