A coastal fog forecasting method and system

Abstract

The present application belongs to the technical field of weather forecast, and discloses a kind of coast fog forecast method and system, the method is by obtaining the numerical weather prediction data of grid in target forecast area, coast fog key forecast factor is calculated based on forecast data;According to the nonlinear influence function of each key forecast factor, the influence index corresponding to each key forecast factor is calculated;Multiply all influence indexes, obtain coast fog forecast index;Historical forecast data and visibility observation data are matched, and the forecast index interval corresponding to each visibility influence grade is calibrated;For future target forecast time, the coast fog forecast index of each grid is calculated;Coast fog forecast index is compared with the calibrated each forecast index interval, the coast fog influence grade of this grid at this forecast time is determined, and the grid-based coast fog forecast product is generated.The present application has both professional and universality, and can provide important reference for the fine prediction of the occurrence and development trend of local coast fog.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of weather forecast, and particularly relates to a coastal fog forecast method and system. BACKGROUND

[0002] Coastal fog refers to the fog formed by the condensation of the warm and humid air around the coast flowing to the sea surface due to the cooling and humidification of the sea surface. Unlike the sea fog formed in the open sea, the coastal fog is mostly formed in the gulf and has a small formation range, with a horizontal scale of several kilometers to tens of kilometers. From the weather scale to the small-scale weather system, they all have important influences on the formation, development and dissipation process of the local coastal fog, and the strong temperature gradient is easy to cause the local coastal fog to form on the cold coastal sea surface.

[0003] At present, the research on the local coastal fog is less in China, there is no forecast reference product for the coastal fog, and the local coastal fog is generally formed on the small range of the coastal sea surface, and under the influence of the sea-land wind, it develops into the gulf or retreats and dissipates to the open sea, which is more difficult to predict than the large range of sea fog process.

[0004] Through the above analysis, the problems and defects of the prior art are:

[0005] (1) Insufficient prediction accuracy and lack of targeted products: the existing meteorological forecast business system is mainly designed for large-scale weather phenomena or general sea fog, and lacks fine prediction reference products specifically for the specific meteorological phenomenon of "coastal fog". The output prediction field has a rough spatial resolution, which cannot effectively capture and depict the formation and dissipation process of the coastal fog with a horizontal scale of only several kilometers to tens of kilometers.

[0006] (2) Spatial resolution and phenomenon scale do not match: the coastal fog has the typical characteristics of small range and small scale. However, the output product of the existing numerical prediction model or empirical prediction method usually has a low horizontal spatial resolution (for example, greater than 10 kilometers), which is difficult to accurately describe the boundary of the coastal fog formed in the small-scale geographical unit such as the gulf and estuary, resulting in serious deviation of the prediction position and range.

[0007] (3) Unable to effectively quantify key local physical processes: the formation and dissipation of the coastal fog are strongly dependent on the sea-land wind circulation, strong temperature gradient (horizontal temperature gradient) and other small-scale local physical processes. The existing prediction method cannot effectively extract and quantify the contribution of these key factors from the prediction data, and lacks a prediction criterion index system with clear physical meaning and specific to the coastal fog.

[0008] (4) The forecasting method is highly subjective and lacks objectivity: At present, the forecasting of small-scale coastal fog mainly relies on the personal experience of forecasters to make subjective judgments and inferences. The lack of objective, quantitative and automated forecasting methods will lead to poor consistency and low repeatability of forecast results, and make it difficult to promote and apply in operations.

[0009] (5) Poor adaptability and weak generalization ability: Existing sporadic studies or methods are mostly aimed at specific cases or specific regions, with poor universality and portability. Once the forecast area or the source of the numerical forecast model used changes, the original method becomes invalid and cannot quickly adapt to the needs of shore fog forecasting in different geographical environments. Summary of the Invention

[0010] To overcome the problems existing in related technologies, the present invention discloses a method and system for forecasting shore fog, particularly relating to a method for calculating and applying shore fog forecasting indices based on high-precision forecast data. The technical solution is as follows:

[0011] This invention is implemented as follows: a shoreline fog forecasting method, comprising the following steps:

[0012] S1, Key Forecast Factor Acquisition: Obtain gridded numerical weather forecast data within the target forecast area, and calculate a set of predefined key forecast factors for coastal fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity;

[0013] S2, Shoreline Fog Forecast Index Calculation: Based on the predefined nonlinear influence functions of each key forecast factor based on meteorological principles, calculate the influence index corresponding to each key forecast factor; multiply all influence indices to obtain the shoreline fog forecast index for each grid point in the target forecast area;

[0014] S3, Calibrating the range of impact levels of the shore fog forecast index: Match historical forecast data with visibility observation data, and statistically analyze the numerical distribution range of the corresponding shore fog forecast index for different visibility levels, so as to calibrate the forecast index range corresponding to each visibility impact level.

[0015] S4, Shoreline Fog Forecast: For future target forecast times, calculate the shoreline fog forecast index for each grid point, compare the shoreline fog forecast index with the calibrated forecast index intervals, determine the shoreline fog impact level of the grid point at the forecast time, and generate a gridded shoreline fog forecast product with high spatiotemporal resolution that is updated according to the numerical forecast update frequency for publication and display.

[0016] In step S1, the time resolution of the numerical weather forecast data is not less than 3 hours, and the horizontal spatial resolution is not less than 15 km.

[0017] If multiple layers of vertical height forecast data are included, the vertical spatial resolution shall be no less than 250m or 25hPa within 1000m of the ground.

[0018] In step S1, the relative humidity at a height of 2 meters is calculated as follows:

[0019] If the forecast data includes relative humidity at a height of 2 meters, then obtain it directly;

[0020] If the forecast data does not include relative humidity at a height of 2 meters, but includes specific humidity at a height of 2 meters and sea level pressure, then it is calculated using specific humidity, sea level pressure, and saturated specific humidity.

[0021] If the forecast data does not include relative humidity, specific humidity, and sea level pressure at a height of 2 meters, but does include dew point temperature at a height of 2 meters, it is calculated from the dew point temperature and the air temperature.

[0022] In step S1, the key forecasting factors also include the air-sea temperature difference and / or adiabatic subsidence term;

[0023] If the forecast data includes sea surface temperature, the air-sea temperature difference is calculated.

[0024] If the forecast data includes air temperature, horizontal zonal wind, and horizontal meridional wind at the vertical height level, then the adiabatic subsidence term is calculated based on the air temperature, horizontal zonal wind, and horizontal meridional wind at the vertical height level.

[0025] If the forecast data includes temperature, horizontal wind direction, and horizontal wind speed at the vertical height level, then calculate the horizontal zonal wind and horizontal meridional wind first, and then calculate the adiabatic subsidence term.

[0026] In step S2, the calculation process of the shore fog forecast index includes:

[0027] (1) Set the influence index of each forecast factor on the occurrence and development of shore fog;

[0028] By conducting statistical analysis on historical cases of shore fog, variables that affect the physical processes of fog formation, development, and dissipation were selected for statistical comparative analysis. Representative variables were chosen as forecasting factors affecting shore fog processes. Based on the comparative analysis of actual and forecast data for each time period throughout the entire process, the expression and parameters were determined.

[0029] ① Horizontal temperature gradient at a height of 2 meters Impact Index The calculation expression is:

[0030] ;

[0031] The low-level horizontal temperature gradient reflects the strength of the horizontal circulation in the secondary circulation within the boundary layer. Stronger onshore advection is conducive to the convergence and accumulation of water vapor in the coastal region. Based on the intensity of the anomalous horizontal circulation under the influence of the horizontal temperature gradient, the influence index of this forecasting factor is determined.

[0032] ② Wind direction at a height of 2 meters Impact Index The calculation expression is:

[0033] ;

[0034] Low-level horizontal winds at specific angles are conducive to the convergence and accumulation of water vapor in the lower atmosphere, and also to the condensation of water vapor through cooling. Therefore, for the orientation of my country's coastline and the prevailing wind direction of cold air, southerly to southeasterly winds and easterly to northeasterly winds are more conducive to the accumulation and condensation of water vapor. After a large amount of data statistics, the influence index of this forecast factor was determined.

[0035] ③ Wind speed at a height of 2 meters Impact Index The calculation expression is:

[0036] ;

[0037] Wind speeds of a specific intensity are conducive to the accumulation of water vapor in a specific area, and also to the thickening of the wet layer and the development and intensification of shore fog due to low-level turbulent activity. Based on the statistical results of the influence of wind speed on the intensity of shore fog, the influence index of this forecasting factor is determined.

[0038] ④ Relative humidity at a height of 2 meters Impact Index The calculation expression is:

[0039] ;

[0040] Lower-level relative humidity can directly reflect the water vapor content and condensation conditions in the air. Higher relative humidity is more conducive to water vapor condensing into fog. Based on the influence of relative humidity on the formation and development of coastal fog, the influence index of this forecasting factor is determined.

[0041] ⑤ Temperature difference between the atmosphere and the sea Impact Index The calculation expression is:

[0042] ;

[0043] The cooling effect of the ocean on the surface atmosphere is a favorable condition for fog formation on the sea surface. At the same time, the evaporation of water vapor from the ocean is also conducive to the humidification of the surface atmosphere. In long-term statistics, a relatively weak air-sea temperature difference is conducive to fog formation. Based on this, the influence index of this forecasting factor was determined.

[0044] ⑥ Adiabatic subsidence Impact Index The calculation expression is:

[0045] ;

[0046] The adiabatic subsidence term is a term in the atmospheric thermodynamic equation that represents the influence of vertical motion on atmospheric heat. It also reflects the intensity of vertical atmospheric motion and can represent the intensity of the subsidence branch in the vertical circulation of secondary circulation. Adiabatic subsidence is conducive to the formation of an inversion layer above the atmospheric boundary layer top and fog top, and promotes the formation and development of coastal fog. Therefore, when this term is significantly negative, the output index is less than 1; when it is positive, the influence index of this forecasting factor is determined based on the statistical results of its influence on the coastal fog process.

[0047] (2) For all forecast data grid points within the forecast area, calculate the shore fog forecast index using the product of the influence indices of all forecast factors at that point. For coordinates... The shoreline fog forecast index The calculation expression is:

[0048] ;

[0049] In the formula, Coordinates are Horizontal temperature gradient at a height of 2 meters The impact index, Coordinates are Wind direction at a height of 2 meters The impact index, Coordinates are Relative humidity at a height of 2 meters The impact index, Coordinates are Wind speed at a height of 2 meters The impact index, Coordinates are Temperature difference between the sea and the atmosphere The impact index, Coordinates are Insulation sinking The impact index;

[0050] ① If the forecast data lacks sea surface temperature, then delete it from the calculation expression. item;

[0051] ② If the forecast data lacks at least one meteorological element from the vertical height layer, namely temperature and horizontal wind, then delete it from the calculation expression. item.

[0052] In step S3, the calculation process for the impact level of the shore fog forecast index difference includes:

[0053] (1) Based on the observation data in the forecast area, construct a historical case dataset of shore fog, which includes location, time and visibility;

[0054] (2) Based on the start time of the grid forecast, the grid forecast points in the forecast area are matched with the observation stations. For each grid point in the forecast area... Match the nearest visibility observation station to the grid point with that grid point; if for a grid point At grid points Grid points Grid points Grid points If there are no visibility observation stations within the connected area, then this grid point... No matching is performed;

[0055] (3) For all matching data within the time range of the dataset, classify them according to the visibility of the observation station and determine the range of forecast index values ​​for each influence level of the grid point.

[0056] Furthermore, the numerical distribution interval is determined based on the quantile method of historical data statistics, and the calibration process for the forecast index interval corresponding to each visibility impact level includes:

[0057] ① For matching data with visibility less than 200m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Arrange all calculation results in terms of numerical value, and take the values ​​at the 25% to 75% positions as the numerical range for influence level 5. ;

[0058] ② For matching data with visibility ranging from 200m to 500m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in numerical order, and the values ​​at the 25% to 75% percentile are taken as the numerical range for impact level 4. ;

[0059] ③ For matching data with visibility in the range of 500m to 1000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of numerical value, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 3. ;

[0060] ④ For matching data with visibility in the range of 1000m to 2000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of magnitude, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 2. ;

[0061] ⑤ For matching data with visibility in the range of 2000m to 5000m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Sort all calculation results by numerical value and take the values ​​at the 25% to 75% positions as the numerical range for impact level 1. ;

[0062] ⑥ For matching data with visibility above 5000m, the shore fog forecast index is not calculated.

[0063] In step S4, the shore fog forecast includes:

[0064] Based on the forecast area and forecast model data determined by the differential impact level of the Haze Forecast Index, the calculation is performed for each grid point within the area at future forecast times. The shoreline fog forecast index ;

[0065] Determine each forecast index At grid points The level of influence at the location;

[0066] For each forecast time and each matched grid point within the forecast area, the impact level is output, thus generating a multi-time gridded shore fog forecast index product.

[0067] If the forecast area and forecast model data change, the method is reused to obtain shore fog forecast index products based on different forecast models for different application areas.

[0068] Furthermore, each forecast index At grid points The levels of influence at the location include:

[0069] ①If Values ​​in the range If the output level is 5, it means that there is a very high probability that the shore fog will appear at this location at this time.

[0070] ②If Values ​​in the range If the output level is 4, it means that there is a high probability that shore fog will occur at this location at this time.

[0071] ③If Values ​​in the range If the output is within the range, the impact level is 3, indicating that shore fog may occur at this location at this time.

[0072] ④If Values ​​in the range If the output level is 2, it means that there is a high probability of light fog appearing at this location at this time.

[0073] ⑤If Values ​​in the range If the output level is 1, it means that light fog may appear at that location at that time.

[0074] Another object of the present invention is to provide a shoreline fog forecasting system, which is implemented by the aforementioned shoreline fog forecasting method, and the system includes:

[0075] The data acquisition and processing module is configured to acquire gridded numerical weather forecast data within the target forecast area and calculate a set of predefined key forecast factors for shore fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity;

[0076] The forecast index calculation module is configured to calculate the influence index corresponding to each key forecast factor based on the predefined nonlinear influence function of each key forecast factor based on meteorological principles; and multiply all the influence indices to obtain the shore fog forecast index of each grid point in the target forecast area.

[0077] The level interval calibration module is configured to match historical forecast data with visibility observation data, and for different visibility levels, to statistically analyze the numerical distribution interval of the corresponding shore fog forecast index, so as to calibrate the forecast index interval corresponding to each visibility impact level.

[0078] The forecast product generation module is configured to calculate the shore fog forecast index for each grid point for a future target forecast time, compare the shore fog forecast index with the calibrated forecast index intervals, determine the shore fog impact level of the grid point at the forecast time, and generate a gridded shore fog forecast product.

[0079] The data acquisition and processing module, the forecast index calculation module, the level interval calibration module, and the forecast product generation module are implemented by the processor executing computer program instructions stored in the memory.

[0080] Combining all the above technical solutions, the beneficial effects of this invention are as follows:

[0081] First, this invention provides a method for calculating the shore fog forecast index based on high-precision forecast data. By analyzing the forecast results of various meteorological elements and characteristic quantities within the forecast area using a high-precision numerical forecast model, and considering their statistical characteristics during the occurrence of shore fog and their degree of influence on the occurrence and development of shore fog, the method comprehensively calculates the shore fog forecast index and its comprehensive impact level within the area. By retrospectively analyzing historical shore fog processes and forecast data for the area, the method provides differentiated impact level judgment criteria and forecast applications for the shore fog forecast index, which can provide important reference for the refined forecasting of the occurrence and development trend of local shore fog.

[0082] Secondly, this invention innovatively provides a method for calculating the forecast index of Kishihama fog. This method is both professional and universal, and is applicable to the secondary processing and application of various high-precision forecast model data. At the same time, it provides a forecast application method for the calculated Kishihama fog forecast index. The forecast application method can be used in different regions, and provides targeted adjustment procedures for the application method in different application areas, dynamically adapting to regional differences.

[0083] Third, this invention can directly provide gridded shore fog forecast products with high spatiotemporal resolution and rapid updates based on numerical forecast update frequency, which can be published and displayed for commercial application. This is of significant value for high-precision dense fog forecasting and early warning for nearshore and port operations and coastal transportation affected by shore fog. This invention provides a method for calculating the shore fog forecast index, which is both professional and universally applicable, suitable for the secondary processing and application of various high-precision forecast model data. This method also provides directly applicable products and methods for the calculated shore fog forecast index. The forecast application method can be used in different regions, and targeted adjustment procedures are provided for different application areas, dynamically adapting to regional differences, which is also one of the key points of this method.

[0084] Fourth, targeted fog forecasts for specific regions, such as patchy fog on highways or thin to dense fog in port lifting operation areas, can provide rapidly updated, high-precision, and intuitive forecast products, providing a basis for command and dispatch. While fog forecasts typically consider meteorological elements and physical processes directly affecting the near-surface layer, this technical solution comprehensively considers the interaction between surface and vertical layer elements. By introducing surface temperature gradients and adiabatic subsidence terms, it incorporates atmospheric boundary layer stability and secondary circulation intensity into shoreline fog forecasts, improving forecast accuracy and refining the representation of physical processes. Attached Figure Description

[0085] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0086] Figure 1 This is a flowchart of the shore fog forecasting method provided in an embodiment of the present invention;

[0087] Figure 2 This is a matching map of grid forecast points and observation stations within the forecast area provided in an embodiment of the present invention;

[0088] Figure 3 This is an embodiment of the present invention, showing the atmospheric horizontal visibility and shore fog impact level forecast maps for Qingdao area at 17:00 and 20:00 on April 1, 2018, and 00:00, 03:00, 07:00 and 08:00 on April 2, 2018. The circles in the map represent atmospheric horizontal visibility, and the locations of Qingdao Station (QD), People's Square Station (RM), and Pingdu Station (PD) are marked with black circles. The grid borders are filled with color to represent the shore fog forecast index and impact level forecast. Detailed Implementation

[0089] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0090] The innovation of this invention lies in its pioneering method for calculating the forecast index of kishina fog. This method combines professionalism and universality, and is applicable to the secondary processing and application of various high-precision forecast model data. Furthermore, this method provides a forecast application method for the calculated kishina fog forecast index, which can be used in different regions. It also provides targeted adjustment procedures for different application areas, dynamically adapting to regional differences, which is one of the key aspects of this method. In this invention, step S1 is a unique and innovative calculation method, step S2 utilizes unique scientific research results, step S3 makes targeted supplementary adjustments to the method, and step S4 makes innovative dynamic adjustments. The combined application of these four steps is also an innovation and therefore requires protection.

[0091] Example 1, such as Figure 1 As shown, the shore fog forecasting method provided in this embodiment of the invention includes the following steps:

[0092] S1, Key Forecast Factor Acquisition: Obtain gridded numerical weather forecast data within the target forecast area, and calculate a set of predefined key forecast factors for coastal fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity;

[0093] Step 1.1: Select the land and sea areas for which shoreline fog forecasting is required, and obtain high-precision forecast model grid forecast data within this geographical range. The temporal resolution should be no less than 3 hours, and the horizontal spatial resolution should be no less than 15 km. If multiple vertical height forecast data are included, the vertical spatial resolution should be no less than 250 m or 25 hPa within 1000 m of the ground. The meteorological elements in the forecast data should include at least one of the following at a height of 2 meters: air temperature, wind direction, wind speed, and at least one of the following: ① relative humidity at a height of 2 meters, ② specific humidity and sea level pressure at a height of 2 meters, ③ dew point temperature at a height of 2 meters, as well as optional sea surface temperature, air temperature at the vertical height level, and horizontal wind.

[0094] Step 1.2: Adjust the wind direction (Unit: °), Wind speed (Unit: m / s) was directly selected as the forecast factor.

[0095] Step 1.3: Use the air temperature at a height of 2 meters. (Unit: °C) Calculate coordinates Horizontal temperature gradient :

[0096] ;

[0097] In the formula, coordinates Adjacent to the east Temperature at a height of 2 meters (Unit: °C) coordinates The western adjacent coordinates Temperature at a height of 2 meters (Unit: °C) The interval is the latitudinal coordinate. coordinates The adjacent coordinates on the north side Temperature at a height of 2 meters (Unit: °C) coordinates The adjacent coordinates on the south side Temperature at a height of 2 meters (Unit: °C) Radial coordinate interval;

[0098] The direction pointing towards the ocean is taken as positive and selected as the forecast factor.

[0099] Step 1.4: ① If the meteorological elements in the forecast data include relative humidity at a height of 2 meters... (Unit is %), then directly use Selected as a forecasting factor.

[0100] ② If the meteorological elements in the forecast data do not include And including the specific humidity at a height of 2 meters (Unit: kg / kg) and sea level pressure (Unit: Pa), then calculate :

[0101] ;

[0102] In the formula, Natural bottom;

[0103] And selected as a forecasting factor.

[0104] ③ If the meteorological elements in the forecast data do not include And including the dew point temperature at a height of 2 meters (Unit: °C), then calculate :

[0105] ;

[0106] And selected as a forecasting factor.

[0107] Step 1.5: If the meteorological elements in the forecast data include sea surface temperature (Unit: °C), then calculate the air-sea temperature difference. :

[0108] ;

[0109] And selected as a forecasting factor.

[0110] Step 1.6: ① If the meteorological elements in the forecast data include temperature at vertical altitude levels... (Unit: °C) Horizontal Zonal Wind (Units of magnitude are m / s) and horizontal meridional wind (The unit of size is m / s), then for a time interval of... The latitudinal coordinate interval is The radial coordinate interval is The height is calculated from the forecast data. Layer, at that moment ,coordinate Insulation sinking item :

[0111] ;

[0112] In the formula, For this height Layer, the next moment after that moment and the previous moment ,coordinate Temperature at the location , For this height Layer, at that moment ,coordinate The eastern adjacent coordinates and adjacent coordinates on the west side Temperature at the location , For this height Layer, at that moment ,coordinate The adjacent coordinates on the north side and adjacent coordinates on the south side Temperature at the location ;

[0113] And selected as a forecasting factor.

[0114] ② If the meteorological elements in the forecast data include temperature at vertical altitude levels (Unit: °C), Horizontal wind direction (Unit: °) Horizontal wind speed (Unit: m / s), then calculate the horizontal zonal wind. and horizontal meridional wind :

[0115] ;

[0116] ;

[0117] Substitute this back into ① to calculate the adiabatic settlement term. And selected as a forecasting factor.

[0118] Step 1.7: Determine all forecast factors and complete the calculations, including the temperature gradient at a height of 2 meters. (Unit: ℃ / 10) 5 m), wind direction (Unit: °), Wind speed (Unit: m / s) Relative humidity at a height of 2 meters (Unit: %), optional to include air-sea temperature difference (Unit: °C), Adiabatic Settlement Item (Unit is K / (10)) 4 s)).

[0119] S2, Shoreline Fog Forecast Index Calculation: Based on the predefined nonlinear influence functions of each key forecast factor based on meteorological principles, calculate the influence index corresponding to each key forecast factor; multiply all influence indices to obtain the shoreline fog forecast index for each grid point in the target forecast area;

[0120] Step 2.1: Based on the long-term statistical characteristics of coastal fog in a certain coastal area from 2015 to 2022, set the impact index of each forecasting factor on the occurrence and development of coastal fog.

[0121] By statistically analyzing historical coastal fog cases from 2016 to 2022, 48 variables that affect the physical processes of fog formation, development, and dissipation were selected from the European Centre for Medium-Range Weather Forecasting (ECMWF) numerical weather prediction model results for statistical comparative analysis. Six representative variables were selected as forecasting factors affecting the coastal fog process. Based on the comparative analysis of actual and forecast data for each time period in the entire process, the following expressions and parameters were determined.

[0122] ① Horizontal temperature gradient at a height of 2 meters Impact Index The calculation expression is:

[0123] ;

[0124] The low-level horizontal temperature gradient can reflect the strength of the horizontal circulation in the secondary circulation within the boundary layer. Stronger onshore advection is conducive to the convergence and accumulation of water vapor in the coastal region. Based on the intensity of the anomalous horizontal circulation under the influence of the horizontal temperature gradient, the influence index of this forecasting factor is determined.

[0125] ② Wind direction at a height of 2 meters Impact Index The calculation expression is:

[0126] ;

[0127] Low-level horizontal winds at specific angles are conducive to the convergence and accumulation of water vapor in the lower atmosphere, and also to the condensation of water vapor through cooling. Therefore, for the orientation of my country's coastline and the prevailing wind direction of cold air, southerly to southeasterly winds and easterly to northeasterly winds are more conducive to the accumulation and condensation of water vapor. After a large amount of data statistics, the influence index of this forecast factor was determined.

[0128] ③ Wind speed at a height of 2 meters Impact Index The calculation expression is:

[0129] ;

[0130] Wind speeds of a specific intensity are conducive to the accumulation of water vapor in a specific area, and also to the thickening of the wet layer and the development and intensification of shore fog due to low-level turbulent activity. Based on the statistical results of the influence of wind speed on the intensity of shore fog, the influence index of this forecasting factor is determined.

[0131] ④ Relative humidity at a height of 2 meters Impact Index The calculation expression is:

[0132] ;

[0133] Lower-level relative humidity can directly reflect the water vapor content and condensation conditions in the air. Higher relative humidity is more conducive to water vapor condensing into fog. Based on the influence of relative humidity on the formation and development of coastal fog, the influence index of this forecasting factor is determined.

[0134] ⑤ Temperature difference between the atmosphere and the sea Impact Index The calculation expression is:

[0135] ;

[0136] The cooling effect of the ocean on the surface atmosphere is a favorable condition for fog formation on the sea surface. At the same time, the evaporation of water vapor from the ocean is also conducive to the humidification of the surface atmosphere. In long-term statistics, a relatively weak air-sea temperature difference is conducive to fog formation. Based on this, the influence index of this forecasting factor was determined.

[0137] ⑥ Adiabatic subsidence Impact Index The calculation expression is:

[0138] ;

[0139] The adiabatic subsidence term is a term in the atmospheric thermodynamic equation that represents the influence of vertical motion on atmospheric heat. It also reflects the intensity of vertical atmospheric motion and can represent the intensity of the subsidence branch in the vertical circulation of secondary circulation. Adiabatic subsidence is conducive to the formation of an inversion layer above the atmospheric boundary layer top and fog top, and promotes the formation and development of coastal fog. Therefore, when this term is significantly negative, the output index is less than 1; when it is positive, the influence index of this forecasting factor is determined based on the statistical results of its influence on the coastal fog process.

[0140] Step 2.2: For all forecast data grid points within the forecast area, calculate the shore fog forecast index using the product of the influence indices of all forecast factors at that point. For coordinates... The shoreline fog forecast index The calculation expression is:

[0141] ;

[0142] In the formula, Coordinates are Horizontal temperature gradient at a height of 2 meters The impact index, Coordinates are Wind direction at a height of 2 meters The impact index, Coordinates are Relative humidity at a height of 2 meters The impact index, Coordinates are Wind speed at a height of 2 meters The impact index, Coordinates are Temperature difference between the sea and the atmosphere The impact index, Coordinates are Insulation sinking The impact index;

[0143] ① If the forecast data lacks sea surface temperature, then delete it from the calculation expression. item;

[0144] ② If the forecast data lacks at least one meteorological element from the vertical height layer, namely temperature and horizontal wind, then delete it from the calculation expression. item.

[0145] S3, Calibrating the range of impact levels of the shore fog forecast index: Match historical forecast data with visibility observation data, and statistically analyze the numerical distribution range of the corresponding shore fog forecast index for different visibility levels, so as to calibrate the forecast index range corresponding to each visibility impact level.

[0146] Step 3.1: Using observational data within the forecast area, compile a long-term historical dataset of coastal fog cases, including location, time, visibility, etc.

[0147] Step 3.2: As Figure 2 As shown, based on the start time of the grid forecast, the grid forecast points within the forecast area are matched with the observation stations. For each grid point within the forecast area... Match the nearest visibility observation station to the grid point with that grid point; if for a grid point At grid points Grid points Grid points Grid points If there are no visibility observation stations within the connected area, then this grid point... No matching will be performed.

[0148] Step 3.2 uses grid forecast start time data to match observation data. Typically, data from the 20:00 start time within the 24 hours prior to observation, forecasted up to the observation time, is used for this matching process. However, this method directly uses the start time data for two reasons: First, current and developing forecast models can update at the hourly / minute level. To better suit most current and future forecast model data, using only a single start time data point is insufficient. Therefore, this method uses forecast start time data from the same time period as the observation data, fully considering the application of high-resolution models and the expansion of data volume. Second, the model's assimilation of observation data for the start field reflects the model's algorithmic foundation and is representative of the model's forecast data, thus replacing existing methods that use forecast data for matching.

[0149] Step 3.3: For all matching data within the time range of the dataset, classify them according to the visibility of the observation stations, and determine the forecast index value range for each influence level of the grid point:

[0150] ① For matching data with visibility less than 200m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Arrange all calculation results in terms of numerical value, and take the values ​​at the 25% to 75% positions as the numerical range for influence level 5. .

[0151] ② For matching data with visibility ranging from 200m to 500m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in numerical order, and the values ​​at the 25% to 75% percentile are taken as the numerical range for impact level 4. (The range of values ​​affecting level 5 should be excluded from this range) ).

[0152] ③ For matching data with visibility in the range of 500m to 1000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of numerical value, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 3. (The range of values ​​affecting level 4 should be excluded from this range) ).

[0153] ④ For matching data with visibility in the range of 1000m to 2000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of magnitude, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 2. (The range of values ​​affecting level 3 should be excluded from this range) ).

[0154] ⑤ For matching data with visibility in the range of 2000m to 5000m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Sort all calculation results by numerical value and take the values ​​at the 25% to 75% positions as the numerical range for impact level 1. (The range of values ​​affecting level 3 should be excluded from this range) ).

[0155] ⑥ For matching data with visibility above 5000m, the shore fog forecast index is not calculated.

[0156] Step 3.3 referenced the visibility grading standard for fog, but also considered the actual impact of each visibility level on production and daily life found by the creators in their statistical analysis of shore fog processes. Therefore, three grades from the grading standard were selected for application (200m, 500m, 1000m), and two more grades (2000m, 5000m) were added to account for the impact of shore fog visibility in coastal areas. In addition, the quartile method for selecting thresholds is also commonly used in index threshold selection, but it is usually applied once for each meteorological element to select upper and lower thresholds. In this method, a single complex variable (shore fog forecast index) was applied multiple times in a graded manner to obtain five threshold intervals. The five intervals contain each other but do not overlap, which is a targeted adjustment to the calculation method of the shore fog forecast index that is better applied to shore fog forecasting.

[0157] Meanwhile, forecast indices are often used for single-point calculations, that is, when determining the presence or absence of a weather phenomenon at a single location, indices are calculated and analyzed to determine whether a certain weather phenomenon is forecast. Step 3 of this method performs individual threshold calculations for each matching grid point within the forecast area. This serves two purposes: firstly, it provides a reference for the presence or absence of shore fog at each grid point; secondly, it allows for more intuitive and refined references for forecasting the development direction, trend, and intensity changes of shore fog, including its regional and intensity ranges, through index calculations of grid points where shore fog has already occurred and surrounding grid points.

[0158] S4, Shoreline Fog Forecast: For future target forecast times, calculate the shoreline fog forecast index for each grid point, compare the shoreline fog forecast index with the calibrated forecast index intervals, determine the shoreline fog impact level of the grid point at the forecast time, and generate a gridded shoreline fog forecast product with high spatiotemporal resolution that is updated according to the numerical forecast update frequency for publication and display.

[0159] Step 4.1: For the forecast area and forecast model data determined in Step S3) of the calculation of the impact level of the difference in the haze forecast index, calculate the value of each grid point in the area at future forecast times. The shoreline fog forecast index .

[0160] Step 4.2: Determine each forecast index At grid points The level of influence at the location.

[0161] ①If Values ​​in the range If the output level is 5, it means that there is a very high probability that the shore fog will appear at this location at this time.

[0162] ②If Values ​​in the range If the output level is 4, it means that there is a high probability that shore fog will occur at this location at this time.

[0163] ③If Values ​​in the range If the output is within the range, the impact level is 3, indicating that shore fog may occur at this location at this time.

[0164] ④If Values ​​in the range If the output level is 2, it means that there is a high probability of light fog appearing at this location at this time.

[0165] ⑤If Values ​​in the range If the output level is 1, it means that light fog may appear at that location at that time.

[0166] Step 4.3: Output the impact level for each forecast time and each matched grid point within the forecast area to generate a multi-time gridded shore fog forecast index product.

[0167] Step 4.4: If the forecast area and forecast model data change, the method can be reused to obtain shore fog forecast index products based on different forecast models for different application areas.

[0168] Step S4 is a practical application guide for this method: Steps 4.1 and 4.3 reflect the creative adjustments to Step S3—the index calculation for each matching grid point and the application of the index in multiple ways; Step 4.2 is an extension of Step 3.3, which also divides the five levels into two categories: influence levels 3, 4, and 5 are existing fog classification standards, while influence levels 1 and 2 are further refined classifications of existing light fog standards, with the improvement approach being the same as in Step 3.3; Step 4.4 demonstrates the wide applicability of this method in different forecast models and different regions.

[0169] Example 2: The shoreline fog forecasting system provided in this embodiment of the invention includes:

[0170] The data acquisition and processing module is configured to acquire gridded numerical weather forecast data within the target forecast area and calculate a set of predefined key forecast factors for shore fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity;

[0171] The forecast index calculation module is configured to calculate the influence index corresponding to each key forecast factor based on the predefined nonlinear influence function of each key forecast factor based on meteorological principles; and multiply all the influence indices to obtain the shore fog forecast index of each grid point in the target forecast area.

[0172] The level interval calibration module is configured to match historical forecast data with visibility observation data, and for different visibility levels, to statistically analyze the numerical distribution interval of the corresponding shore fog forecast index, so as to calibrate the forecast index interval corresponding to each visibility impact level.

[0173] The forecast product generation module is configured to calculate the shore fog forecast index for each grid point for a future target forecast time, compare the shore fog forecast index with the calibrated forecast index intervals, determine the shore fog impact level of the grid point at the forecast time, and generate a gridded shore fog forecast product.

[0174] To further demonstrate the positive effects of the above embodiments, the present invention conducts the following experiments based on the above technical solutions.

[0175] Taking the coastal fog event that occurred in Qingdao from April 1st to April 2nd, 2018 as an example, the coastal fog event started in the southeastern coastal area and near-shore waters of Qingdao, and was distributed in a narrow strip. The horizontal visibility in the coastal area was less than 200m. The coastal fog further developed and intensified in the coastal area of ​​Qingdao at night and extended inland. By 03:00 on the 2nd, the fog area had extended to the northern inland area of ​​Qingdao, and a large area of ​​dense fog had appeared along the coast and around Jiaozhou Bay. By 07:00 on the 2nd, the fog area in the northeastern land of Qingdao had dissipated, while the fog area in the northwestern land continued to intensify. After 08:00 on the 2nd, the fog area in the inland and near-shore areas gradually weakened, while the near-shore fog area continued to persist.

[0176] 1) Key Forecast Factor Selection and Calculation Module: In this case, the forecast area is the Qingdao area and its nearshore waters (35.5°-37.125°N, 119.5°-121.125°E). The forecast model is the European Centre for Medium-Range (EC-thin) model, with a time resolution of 3 hours, a horizontal resolution of 0.125° (approximately 11-14 km), and a vertical resolution of 25 hPa. The forecast elements of this model include air temperature, wind direction, and wind speed at a 2-meter height, as well as relative humidity, sea surface temperature (ocean grid only), and air temperature and horizontal wind at the vertical height. Therefore, the air temperature gradient (unit: °C / 10⁵ m) and wind direction at a 2-meter height are selected. (Unit: °), Wind speed (Unit: m / s) Relative humidity at a height of 2 meters (Unit: %), Atmospheric-Sea Temperature Difference (Unit: °C, ocean grid only), adiabatic subsidence item (Unit: K / (104s)) is the forecast factor, and all factors are calculated.

[0177] 2) The Kishina fog forecast index calculation module calculates the Kishina fog forecast index by multiplying the influence indices of all forecast factors at all forecast data grid points within the forecast area. The index range is 0-15625.

[0178] 3) The module for calculating the impact level of coastal fog forecast index differences uses historical cases of coastal fog in Qingdao from 2016 to 2022 for statistical analysis. It classifies the impact levels based on visibility at observation stations and determines the forecast index value range for each grid point: ① For marine grid points, The range is 5120-15625. The value is 3072-5119. For 1728-3071, It is 144-1727. For 1-143; ② For land grid points: The range is 1280-3125. The range is 768-1279. The range is 432-767. It is 48-433. The range is 1-47.

[0179] 4) Forecasting Application Module: This module calculates forecast factors for the forecast data from 17:00 on April 1st to 08:00 on April 2nd, 2018, on the grid within the region. Results for some time periods are shown below. Figure 3 .

[0180] Comparing the visibility distribution and forecast impact level distribution in the Qingdao area, at 17:00 on the 1st, a relatively high impact level was forecast for the Yellow Sea coast, the Jiaozhou Bay coast, and the eastern nearshore areas, while the impact level for areas closer to the inland was relatively low. At this time, the Yellow Sea fog area only developed towards the coast, but the impact level around the northern Jiaozhou and People's Square stations also indicated that the coastal fog would develop inland and northward. At 20:00 on the 1st, a relatively high impact level was forecast over a large area of ​​land and inland areas northwest of Jiaozhou Bay. At this time, the fog area covered a large area around Jiaozhou Bay, and 2 The fog extended to areas with low visibility below 00m and spread to the southern coastal and central inland areas of Qingdao. At 00:00 on the 2nd, the fog extended to the People's Square station in northern Qingdao. At 08:00 on the 2nd, the impact level of the Jiaozhou Bay and coastal areas had significantly decreased, and the Pingdu station showed an impact level. Looking back, the Pingdu station had already shown coastal fog at 07:00, which also showed the effect of the impact forecast. The People's Square station, which had fog earlier to its east, saw the fog dissipate after the impact level decreased, further demonstrating that the forecast product has indicative significance for the forecast of coastal fog.

[0181] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for forecasting shoreline fog, characterized in that, The method includes the following steps: S1, Key Forecast Factor Acquisition: Obtain gridded numerical weather forecast data within the target forecast area, and calculate a set of predefined key forecast factors for coastal fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity; S2, Shoreline Fog Forecast Index Calculation: Based on the predefined nonlinear influence functions of each key forecast factor based on meteorological principles, calculate the influence index corresponding to each key forecast factor; multiply all influence indices to obtain the shoreline fog forecast index for each grid point in the target forecast area; S3, Calibrating the range of impact levels of the shore fog forecast index: Match historical forecast data with visibility observation data, and statistically analyze the numerical distribution range of the corresponding shore fog forecast index for different visibility levels, so as to calibrate the forecast index range corresponding to each visibility impact level. S4, Shoreline Fog Forecast: For future target forecast times, calculate the shoreline fog forecast index for each grid point, compare the shoreline fog forecast index with the calibrated forecast index intervals, determine the shoreline fog impact level of the grid point at the forecast time, and generate a gridded shoreline fog forecast product with high spatiotemporal resolution that is updated according to the numerical forecast update frequency for publication and display.

2. The shoreline fog forecasting method according to claim 1, characterized in that, In step S1, the time resolution of the numerical weather forecast data is not less than 3 hours, and the horizontal spatial resolution is not less than 15 km. If multiple layers of vertical height forecast data are included, the vertical spatial resolution shall be no less than 250m or 25hPa within 1000m of the ground.

3. The shoreline fog forecasting method according to claim 1, characterized in that, In step S1, the relative humidity at a height of 2 meters is calculated as follows: If the forecast data includes relative humidity at a height of 2 meters, then obtain it directly; If the forecast data does not include relative humidity at a height of 2 meters, but includes specific humidity at a height of 2 meters and sea level pressure, then it is calculated using specific humidity, sea level pressure, and saturated specific humidity. If the forecast data does not include relative humidity, specific humidity, and sea level pressure at a height of 2 meters, but does include dew point temperature at a height of 2 meters, it is calculated from the dew point temperature and the air temperature.

4. The shoreline fog forecasting method according to claim 1, characterized in that, In step S1, the key forecasting factors also include the air-sea temperature difference and / or adiabatic subsidence term; If the forecast data includes sea surface temperature, the air-sea temperature difference is calculated. If the forecast data includes air temperature, horizontal zonal wind, and horizontal meridional wind at the vertical height level, then the adiabatic subsidence term is calculated based on the air temperature, horizontal zonal wind, and horizontal meridional wind at the vertical height level. If the forecast data includes temperature, horizontal wind direction, and horizontal wind speed at the vertical height level, then calculate the horizontal zonal wind and horizontal meridional wind first, and then calculate the adiabatic subsidence term.

5. The shoreline fog forecasting method according to claim 1, characterized in that, In step S2, the calculation process of the shore fog forecast index includes: (1) Set the influence index of each forecast factor on the occurrence and development of shore fog; By conducting statistical analysis on historical cases of shore fog, variables that affect the physical processes of fog formation, development, and dissipation were selected for statistical comparative analysis. Representative variables were chosen as forecasting factors affecting shore fog processes. Based on the comparative analysis of actual and forecast data for each time period throughout the entire process, the expression and parameters were determined. ① Horizontal temperature gradient at a height of 2 meters Impact Index The calculation expression is: ； ② Wind direction at a height of 2 meters Impact Index The calculation expression is: ； ③ Wind speed at a height of 2 meters Impact Index The calculation expression is: ； ④ Relative humidity at a height of 2 meters Impact Index The calculation expression is: ； ⑤ Temperature difference between the atmosphere and the sea Impact Index The calculation expression is: ； ⑥ Adiabatic subsidence Impact Index The calculation expression is: ； (2) For all forecast data grid points within the forecast area, calculate the shore fog forecast index using the product of the influence indices of all forecast factors at that point. For coordinates... The shoreline fog forecast index The calculation expression is: ； In the formula, Coordinates are Horizontal temperature gradient at a height of 2 meters The impact index, Coordinates are Wind direction at a height of 2 meters The impact index, Coordinates are Relative humidity at a height of 2 meters The impact index, Coordinates are Wind speed at a height of 2 meters The impact index, Coordinates are Temperature difference between the sea and the atmosphere The impact index, Coordinates are Insulation sinking The impact index; ① If the forecast data lacks sea surface temperature, then delete it from the calculation expression. item; ② If the forecast data lacks at least one meteorological element from the vertical height layer, namely temperature and horizontal wind, then delete it from the calculation expression. item.

6. The shoreline fog forecasting method according to claim 1, characterized in that, In step S3, the calculation process for the impact level of the shore fog forecast index difference includes: (1) Based on the observation data in the forecast area, construct a historical case dataset of shore fog, which includes location, time and visibility; (2) Based on the start time of the grid forecast, the grid forecast points in the forecast area are matched with the observation stations. For each grid point in the forecast area... Match the nearest visibility observation station to the grid point with that grid point; if for a grid point At grid points Grid points Grid points Grid points If there are no visibility observation stations within the connected area, then this grid point... No matching is performed; (3) For all matching data within the time range of the dataset, classify them according to the visibility of the observation station and determine the range of forecast index values ​​for each influence level of the grid point.

7. The shoreline fog forecasting method according to claim 6, characterized in that, The numerical distribution intervals are determined based on the quantile method of historical data statistics. The calibration process for the forecast index intervals corresponding to each visibility impact level includes: ① For matching data with visibility less than 200m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Arrange all calculation results in terms of numerical value, and take the values ​​at the 25% to 75% positions as the numerical range for influence level 5. ; ② For matching data with visibility in the range of 200m to 500m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of magnitude, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 4. ; ③ For matching data with visibility in the range of 500m to 1000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of magnitude, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 3. ; ④ For matching data with visibility in the range of 1000m to 2000m, the shore fog forecast index for the time range is calculated using the forecast data of the corresponding grid point. All calculation results are arranged in order of magnitude, and the values ​​at the 25% to 75% positions are taken as the numerical range for impact level 2. ; ⑤ For matching data with visibility in the range of 2000m to 5000m, calculate the shore fog forecast index within the time range using the forecast data of the corresponding grid point. Sort all calculation results by numerical value and take the values ​​at the 25% to 75% positions as the numerical range for impact level 1. ; ⑥ For matching data with visibility above 5000m, the shore fog forecast index is not calculated.

8. The shoreline fog forecasting method according to claim 1, characterized in that, In step S4, the shore fog forecast includes: Based on the forecast area and forecast model data determined by the differential impact level of the Haze Forecast Index, the calculation is performed for each grid point within the area at future forecast times. The shoreline fog forecast index ; Determine each forecast index At grid points The level of influence at the location; For each forecast time and each matched grid point within the forecast area, the impact level is output, thus generating a multi-time gridded shore fog forecast index product. If the forecast area and forecast model data change, the method is reused to obtain shore fog forecast index products based on different forecast models for different application areas.

9. The shoreline fog forecasting method according to claim 8, characterized in that, Each forecast index At grid points The levels of influence at the location include: ①If Values ​​in the range If the output level is 5, it means that there is a very high probability that the shore fog will appear at this location at this time. ②If Values ​​in the range If the output level is 4, it means that there is a high probability that shore fog will occur at this location at this time. ③If Values ​​in the range If the output is within the range, the impact level is 3, indicating that shore fog may occur at this location at this time. ④If Values ​​in the range If the output level is 2, it means that there is a high probability of light fog appearing at this location at this time. ⑤If Values ​​in the range If the output level is 1, it means that light fog may appear at that location at that time.

10. A shoreline fog forecasting system, characterized in that, The system is implemented using the shore fog forecasting method according to any one of claims 1-9, and the system includes: The data acquisition and processing module is configured to acquire gridded numerical weather forecast data within the target forecast area and calculate a set of predefined key forecast factors for shore fog based on the forecast data; the key forecast factors include at least the horizontal temperature gradient at a height of 2 meters, wind direction, wind speed, and relative humidity; The forecast index calculation module is configured to calculate the influence index corresponding to each key forecast factor based on the predefined nonlinear influence function of each key forecast factor based on meteorological principles; and multiply all the influence indices to obtain the shore fog forecast index of each grid point in the target forecast area. The level interval calibration module is configured to match historical forecast data with visibility observation data, and for different visibility levels, to statistically analyze the numerical distribution interval of the corresponding shore fog forecast index, so as to calibrate the forecast index interval corresponding to each visibility impact level. The forecast product generation module is configured to calculate the shore fog forecast index for each grid point for a future target forecast time, compare the shore fog forecast index with the calibrated forecast index intervals, determine the shore fog impact level of the grid point at the forecast time, and generate a gridded shore fog forecast product. The data acquisition and processing module, the forecast index calculation module, the level interval calibration module, and the forecast product generation module are implemented by the processor executing computer program instructions stored in the memory.

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