Ship emission estimation method based on WRF model wind field and AIS data coupling

Abstract

According to the ship emission estimation method based on WRF model wind field and AIS data coupling, a high-resolution regional meteorological numerical simulation result is effectively combined with traditional AIS ship data, the influence of factors such as the wind direction and the wind speed on the actual power requirement and emission of a ship is quantitatively evaluated, and therefore deviation caused by the fact that the ship is only based on the AIS ground speed (SOG) is overcome, and the ship emission estimation accuracy is improved. And the accuracy of ship emission estimation under various wind conditions and different time periods is improved. The method further supports the fine division of ship activity states, and the discharge amount under various working conditions such as high-speed navigation, low-speed berthing, acceleration and deceleration can be more accurately calculated by matching with differential discharge factors. Accurate technical support is provided for maritime affair competent departments, port managers and shipping enterprises in the aspects of emission control, energy consumption optimization and green shipping decision making.

Description

Technical Field

[0001] The present invention relates to the field of ship emission measurement, and in particular to a ship emission estimation method based on the coupling of WRF model wind field and AIS data, which utilizes regional high-resolution meteorological numerical simulation (WRF model) in combination with AIS data to correct the influence of wind power on the actual power demand of the ship, thereby achieving a more accurate ship emission estimation method; the present invention belongs to the cross-technical field of environmental science, ocean and shipping engineering, and numerical meteorological simulation. Background Art

[0002] In the study of ship emissions, the traditional method based on AIS (Automatic Identification System) data usually only regards the ship's speed over ground (SOG) recorded by AIS as the main basis for determining the ship's operating conditions and power requirements. However, environmental factors such as sea breezes have an important impact on ship propulsion and resistance, resulting in greater uncertainty when estimating power and emissions based on SOG alone. When the wind direction is positive or reverse to the ship's heading, the higher wind speed will significantly boost or resist the ship's propulsion, and using only SOG is difficult to correctly reflect the actual output power requirements of the ship's main engine, causing deviations in the emission results.

[0003] In recent years, with the mature application of mesoscale numerical meteorological models (such as the WRF model) in the simulation of key regional or offshore meteorological elements, the use of the high-resolution wind field output of the WRF model combined with the AIS heading and speed information for wind force correction has become a new trend to improve the accuracy of refined estimation of ship emissions. However, there is still a lack of systematic methods for how to dynamically couple the WRF model wind field with AIS data to correct the actual power of the ship and accurately evaluate emissions. The present invention proposes a comprehensive method for vector decomposing the high-resolution wind speed and wind direction information of the WRF model with the AIS heading and ground speed and correcting the power demand, which can improve the calculation accuracy of ship emissions under different wind conditions. Summary of the invention

[0004] The main purpose of the present invention is to provide a refined estimation method for ship emissions based on the coupling of WRF model wind field and AIS data, which vector decomposes the high-resolution wind speed and wind direction information of the WRF model with the AIS heading and ground speed and corrects the power demand, quantitatively evaluates the impact of factors such as wind direction and wind speed on the actual power demand and emissions of the ship, thereby overcoming the deviation caused by only based on the AIS ground speed (SOG), and improving the accuracy of ship emission estimation under various wind conditions and different time periods. The present invention further supports the fine division of ship activity status, with differentiated emission factors, more accurately calculates emissions under various working conditions such as high-speed navigation, low-speed berthing, acceleration and deceleration, and provides accurate technical support for maritime authorities, port management parties and shipping companies in emission control, energy consumption optimization, and green shipping decision-making.

[0005] The present invention discloses a ship emission estimation method based on the coupling of WRF model wind field and AIS data, comprising the following steps: Step 1: Use the WRF (Weather Research and Forecasting) model to perform mesoscale or multi-grid nested high-resolution numerical simulations on the target sea area to obtain wind speed and direction distribution with high temporal and spatial resolution; Step 2: Acquire and parse AIS (Automatic Identification System) data, extract the ground speed (SOG) and heading (COG) of each ship at each sampling time, and pre-process the extracted data; wherein the pre-processing includes data cleaning, interpolation and other quality control; Step 3: Match the wind field with the AIS position and time, and use the vector relationship between the ship heading and the WRF wind direction to establish the wind force correction coefficient; Step 4: Correct the ship's main engine power inferred from the AIS ground speed according to the wind power correction coefficient to obtain an "effective speed" or actual power that better reflects the actual texture (propulsion load) of the ship; Step 5: Based on the corrected power or speed, classify the ship's activity status, and use the corresponding emission factors or emission models to calculate the ship's emissions, so as to achieve refined real-time or quasi-real-time emission estimates under different wind conditions and different operating conditions.

[0006] As a further improvement of the present invention, the WRF model numerical simulation in step 1 includes: A multiple nesting scheme of large-scale background and local fine grids is set up, with the outer grid used to provide boundary conditions and the inner grid used to depict the target sea area and nearshore area with high resolution; By assimilating multi-source observation data, the initial field or boundary conditions of WRF are corrected; the assimilated multi-source observation data include but are not limited to ground meteorological stations, offshore buoy observations, satellite wind field inversion data, etc. Select appropriate physical parameterization schemes to improve the simulation accuracy of the sea-land boundary and offshore wind field characteristics; the physical parameterization schemes include but are not limited to boundary layer, microphysics, surface processes, convection parameterization, etc.

[0007] As a further improvement of the present invention, the AIS data preprocessing in step 2 includes: The AIS data is interpolated and resampled in time and space to ensure that a set of matching data is formed with the wind field output by WRF at the same or similar time; where similar time refers to two time points within a preset time range; Remove missing values, erroneous values, and AIS records with obvious logical errors; The ship's geographic coordinate system in the AIS is converted to a coordinate system that is consistent or comparable with the WRF grid coordinate system, and the timestamps are aligned with a unified time zone or moment reference.

[0008] As a further improvement of the present invention, the calculation method of the wind force correction coefficient in step 3 includes: Using the x, y coordinate system, the x' axis and y' axis are due east and due north respectively; , and represents the ground ship speed, wind-induced drift speed and ship engine output speed respectively; α represents the wind direction angle predicted by the WRF model, β is the ship heading angle to the ground analyzed from AIS, and θ is and The relative wind direction between and Respectively represented by The two vectors along the x-axis and y-axis are analyzed. and Respectively represented by The two vectors along the x-axis and y-axis are analyzed and the formula is as follows: ; The wind-induced drift speed is a function of the ship's sail area, wind speed and direction, and ship load, and is highly correlated with the ship's speed. Here, the calculation model proposed by the China Maritime Service Center is used to obtain the ship's wind-induced drift speed, and the formula is as follows: ; Where K is the drift coefficient related to the ship load, B a and B w are the windward areas above and below the waterline of the hull, respectively. Represents the actual wind speed value predicted by the WRF model.

[0009] Combining the above two formulas, the actual output speed of the ship engine can be calculated.

[0010] As a further improvement of the present invention, the fluid mechanics empirical formula or test data includes: Drag coefficient or wind assistance coefficient calibrated according to different ship types and sizes (such as bulk carriers, container ships, tankers, etc.); Correction factors for wind loads on the hull and structures above the waterline under different sea conditions; The ship propulsion characteristic curve (power-speed characteristic) is used to map the range of changes in wind power to the actual main engine power.

[0011] As a further improvement of the present invention, in step 5, the corrected “effective speed” or actual power is applied to the ship operating condition classification, including: Apply the corrected “effective speed” or actual power to the ship’s operating conditions, including: When the actual power or effective speed is lower than the first threshold and the rate of change is lower than the second threshold, it is determined to be in a "slow sailing" or "berthing" state; When the rate of increase of the actual power or effective speed exceeds the third threshold, it is determined to be in the "acceleration" or "anchoring" state; When the actual power or effective speed is greater than the fourth threshold and is stable within the first preset range, it is determined to be in a "cruise" or "normal sailing" state; Additional identification rules can be set for extreme situations such as emergency navigation and special operations.

[0012] As a further improvement of the present invention, in step 5, differentiated emission factors and emission models are adopted for different working conditions, including: For berthing or slow sailing conditions, introduce emission factors at lower loads; For cruising or high-speed navigation, introduce emission factors under high load or rated load; Under acceleration conditions, the emission factor corresponding to the instantaneous power increase shall be selected as appropriate; For special operations or emergency situations, corresponding simulation models or calculation formulas can be applied.

[0013] As a further improvement of the present invention, the types of emissions emitted by the ship include at least carbon dioxide ( ), sulfur dioxide (SOx), nitrogen oxides (NOx) and particulate matter (PM), and can be extended to other components of greenhouse gases and atmospheric pollutants.

[0014] As a further improvement of the present invention, it also includes: summarizing and comparing the emission calculation results of different time periods, different ship types or different routes, so as to: Establish an inventory of ship emissions by time, geographic area, ship type or ship traffic; The differences in the estimation results before and after the introduction of wind power correction are analyzed, and the improvement of the emission calculation error by the correction method is quantitatively evaluated.

[0015] As a further improvement of the present invention, it further comprises: Dynamically adjust or optimize WRF wind field resolution and assimilation strategy to continuously improve the accuracy of wind dynamic correction; The method is integrated into the ship emission monitoring and big data analysis platform to achieve real-time estimation and time-series playback of emissions in the target sea area or port area, providing decision support for maritime management departments or shipping companies.

[0016] As a further improvement of the present invention, the method can also be combined with tidal field, wave field or tide level observation results to achieve a more comprehensive power field correction; under high wind speed, strong tidal or large wave conditions, the comprehensive correction of the actual power demand of the ship is more significant, thereby improving the accuracy of ship emission calculations under complex sea conditions.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention quantifies the effect of wind on ship propulsion by coupling WRF high-resolution wind field with AIS data, effectively avoiding the power and emission deviation caused by SOG alone. In strong winds or in specific wind directions, traditional methods tend to significantly overestimate or underestimate the actual emissions of ships, while the present invention can correct the deviation more reasonably.

[0018] The traditional SOG-based activity mode division is simple and rough, and cannot accurately capture the impact of wind on low-speed, accelerated and high-speed sailing conditions. The present invention determines the ship's activity by actual power or "effective speed", which can more accurately select emission factors that are sensitive to working conditions, thereby making the emission calculation closer to the actual situation.

[0019] The invention has a wide range of applications and strong versatility. It can be used in different types of navigation areas such as nearshore, inland rivers, and oceans; it can estimate emissions for various types of ships such as bulk carriers, container ships, passenger ships, and tankers; and it can be implemented by modularly upgrading and transforming the existing AIS emission inventory model.

[0020] Governments or maritime-related units can use this method to more accurately grasp regional ship emissions and strengthen emission control in periods or sea areas with complex winds; shipping companies can optimize speed and emission performance and reduce fuel consumption and emission costs by monitoring the impact of wind power; and provide a quantitative basis for the development of green shipping, emission compliance assessment, emission trading or the formulation and implementation of other environmental regulatory policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the ship emission estimation method based on the coupling of WRF model wind field and AIS data disclosed in the present invention; Figure 2 An estimation model for the effect of wind on ship speed established for the present invention; Figure 3 Flowchart of ship emission scenarios for this study; DETAILED DESCRIPTION

[0022] like Figure 1 As shown, the present invention provides a ship emission estimation method based on the coupling of WRF model wind field and AIS data, comprising the following steps: Step 1: Use the WRF (Weather Research and Forecasting) model to perform mesoscale or multi-grid nested high-resolution numerical simulations on the target sea area to obtain wind speed and direction distribution with high temporal and spatial resolution; Step 2: Acquire and parse AIS (Automatic Identification System) data, extract the ground speed (SOG) and heading (COG) of each ship at each sampling time, and pre-process the extracted data; wherein the pre-processing includes data cleaning, interpolation and other quality control; Step 3: Match the wind field with the AIS position and time, and use the vector relationship between the ship heading and the WRF wind direction to establish the wind force correction coefficient; Step 4: Correct the ship's main engine power inferred from the AIS ground speed according to the wind power correction coefficient to obtain an "effective speed" or actual power that better reflects the actual texture (propulsion load) of the ship; Step 5: Based on the corrected power or speed, classify the ship's activity status, and use the corresponding emission factors or emission models to calculate the ship's emissions, so as to achieve refined real-time or quasi-real-time emission estimates under different wind conditions and different operating conditions.

[0023] The technical points of the present invention include: 1. High-resolution wind field acquisition based on WRF model WRF model settings and grid nesting: A set of nested grids is laid out in the research sea area. The outer grid provides the meteorological background of a larger area, and the inner grid focuses on the target sea area or port area to achieve wind field simulation with a resolution of kilometers or below. According to the simulation objectives and the characteristics of air-sea coupling, select appropriate physical parameterization schemes (such as boundary layer physics, microphysical processes, convection parameterization, surface processes, etc.); Set the simulation period to cover the time range covered by the ship's AIS data.

[0024] Data Assimilation and Observation Correction: Incorporate multi-source data such as ground observation stations, offshore buoy observations, and satellite-derived wind speed and direction into the assimilation process to improve the accuracy of the starting field or boundary conditions of the WRF simulation; If the target area has data sources with higher temporal and spatial resolution, such as radar wind profilers and coastal meteorological stations, they can be integrated into the WRF assimilation process according to actual conditions.

[0025] Wind farm output and precision control: Output three-dimensional spatiotemporal data including wind speed, wind direction and other meteorological elements; By comparing with measured or other reanalysis data, the WRF simulation bias is evaluated to ensure that the wind field ultimately used for ship emission calculations has sufficient accuracy.

[0026] 2. AIS data acquisition and preprocessing AIS Data Collection: The location information (latitude and longitude), ground speed (SOG), heading (COG) and timestamp of the ship in the target area are obtained from the ship automatic identification system.

[0027] Data cleaning and interpolation: Delete or correct AIS records that contain abnormal values, missing values, or obvious deviations from logic; To ensure that the wind field output by WRF matches in time and space, linear interpolation or spline interpolation is performed on the AIS data to provide continuous and effective speed and position information in a unified time series.

[0028] Ship information association: If it is necessary to distinguish between ship types (such as bulk carriers, container ships, tankers, etc.), the ship identification code (MMSI) or IMO number, ship static information and other related data can be combined to subsequently formulate more accurate emission factors or power correction models for different ship types.

[0029] 3. Wind force vector decomposition and power correction Space-time matching: At the time and location of each AIS record, the corresponding grid point is extracted from the WRF wind field or the wind speed and direction of the adjacent grid are obtained by interpolation; Align or interpolate WRF wind data based on timestamps to ensure temporal synchronization with AIS data.

[0030] Vector decomposition and relative wind direction calculation: Using the x, y coordinate system, the x' axis and y' axis are due east and due north respectively; , and represents the ground ship speed, wind-induced drift speed and ship engine output speed respectively; α represents the wind direction angle predicted by the WRF model, β is the ship heading angle to the ground analyzed from AIS, and θ is and The relative wind direction between and Respectively represented by The two vectors along the x-axis and y-axis are analyzed. and Respectively represented by The two vectors along the x-axis and y-axis are analyzed.

[0031] Power correction and "effective speed": The corrected “effective speed” can be regarded as the corresponding ground speed under the same thrust demand after taking into account the influence of wind power.

[0032] 4. Ship operating condition determination and emission calculation Working condition identification: Traditional methods often use SOG thresholds to determine high-speed sailing, low-speed sailing, acceleration / deceleration, berthing and other operating conditions. However, under the action of wind, the actual output power level of the ship may not match the SOG; The present invention determines the current state of the ship based on the corrected effective speed; when the effective speed increases significantly, it is regarded as acceleration or departure; when it is at a low level and stable, it is regarded as slow sailing or berthing; when it is relatively high and stable, it is regarded as cruising, etc.

[0033] Emission factors and emission models: Select different emission factors (such as g / kWh) or more complex emission models for different operating conditions; include , SOx, NOx, PM and other conventional pollutants and greenhouse gas types, and can also be expanded to methane, CO, VOCs, etc. as needed; The corresponding emission factors or empirical formulas can be refined according to the ship type, engine type, and fuel type, and can be switched between high load and low load states.

[0034] Emissions calculation and spatial and temporal distribution: Taking more accurate power or effective speed as input, the total amount of emissions from ships at different times or in each period of time is calculated. The calculation formula for pollutant emissions is: ; In the formula, i , j , k , l and n They represent pollution category, emission equipment, fuel type, operating condition and total time interval of AIS messages respectively; E represents the emission of pollutants from the ship, P represents the rated power of the ship, LF represents the load factor, T Represents the interval between two consecutive AIS messages for each ship. EF represents the ship pollutant emission factor, LLAF Stands for low load adjustment factor.

[0035] The actual output power of the engine during ship operation is quite different from the rated power. The actual power of the ship's main engine is the product of the main engine load factor and the main engine rated power (MCR), which changes with environmental conditions. When establishing a high-resolution ship emission inventory, the actual pollutant emissions must be considered according to the actual power of the ship's main engine. It cannot be estimated based solely on the rated power of the ship's main engine, otherwise the result of the ship's atmospheric pollutant share will be significantly larger.

[0036] If spatial distribution analysis is required, the emission results can be mapped to the ship's position trajectory to achieve a detailed characterization of the emissions in the time-space dimension.

[0037] 5. Results summary, visualization and accuracy assessment Error analysis and comparison: Quantify the improvement in emissions estimation accuracy resulting from wind power corrections compared to emissions calculations based solely on AIS surface speed; Comparison indicators may include total emissions during the voyage, peak emission rate, spatial distribution error, etc.

[0038] Result visualization: The revised emission estimation results can be visualized through a GIS platform or other visualization tools, including heat maps of ship route emissions, emission overlay fields for specified areas and times, etc.

[0039] Uncertainty Analysis: Analyze the impact of WRF simulation errors, AIS data sampling frequency, wind power correction model accuracy, etc. on the final emission results; When more observation data or more accurate dynamic models are available, the method of the present invention can be iteratively optimized or updated. Example

[0040] (1) Study area selection and WRF model setting: Regional selection: It is assumed that the study area is the eastern region of my country, which covers the entire eastern coastline. The wind direction in this area is changeable and the seasonal sea and land breeze effects are obvious.

[0041] WRF model grid: A double-layer nested grid is used, with the horizontal resolution set to 27 km and 9 km, and several levels (35 layers) are divided in the vertical direction to balance the computational effort and simulation accuracy; The points period covers one full month.

[0042] Physical parameterization scheme: Select boundary layer schemes (YSU), microphysical processes (Lin scheme), convection parameterization schemes (Kain-Fritsch), etc. that are suitable for offshore simulation.

[0043] Run and output: After the simulation is completed, the wind field data (including wind speed and direction) are output at 1-hour intervals, and the information of all grid points in the calculation domain and time period is stored.

[0044] (2) AIS data collection and processing AIS Source: The AIS data of the ship during the study period were obtained from the port management department or the commercial AIS service platform, including fields such as MMSI, latitude and longitude, SOG, COG, and timestamp.

[0045] Data cleaning and screening: Remove records with significant missing, duplicate, or erroneous values ​​(such as negative or non-numeric values) in timestamps or longitude and latitude; If the AIS signal of a ship is completely lost for a period of time, it can be supplemented by interpolation or data from nearby moments (or the severely missing part can be discarded directly).

[0046] Interpolation resampling: Interpolate the cleaned AIS data according to a preset time step (such as 5 minutes or 10 minutes) to make each ship have a more uniform and continuous speed and position in time series; To ensure a certain correspondence with the WRF output (1 hour interval), time interpolation (linear or spline) can be used to align to the time nodes of the WRF data.

[0047] (3) Wind force vector decomposition and main engine power correction WRF wind field data extraction: At the time t of each AIS record, find the wind field at the same or nearest time in the corresponding WRF output, and then locate the ship to the (i, j) cell of the WRF grid according to the longitude and latitude of the ship to obtain the wind speed and direction at that point (or after interpolation of that point).

[0048] Vector decomposition process: like Figure 2 As shown, an x ​​and y coordinate system is used, where the x' axis and the y' axis are due east and due north respectively; , and represents the ground ship speed, wind-induced drift speed and ship engine output speed respectively; α represents the wind direction angle predicted by the WRF model, β is the ship heading angle to the ground analyzed from AIS, and θ is and The relative wind direction between and Respectively represented by The two vectors along the x-axis and y-axis are analyzed. and Respectively represented by The two vectors along the x-axis and y-axis are analyzed and the formula is as follows: ; The wind-induced drift speed is a function of the ship's sail area, wind speed and direction, and ship load, and is highly correlated with the ship's speed. Here, the calculation model proposed by the China Maritime Service Center is used to derive the ship's wind-induced drift speed, and the formula is as follows: ; Where K is the drift coefficient related to the ship load. Ba and Bw are the windward areas above and below the waterline of the hull, respectively. In general, when the ship is running without load, K is 0.038 and Ba / Bw is about 1.8; when the ship is running with full load, K is 0.041 and Ba / Bw is about 0.8. Represents the actual wind speed value predicted by the WRF model.

[0049] Combining the above two formulas, the actual output speed of the ship engine can be calculated.

[0050] (4) Emission calculation The calculation process is as follows Figure 3 As shown in the figure, based on the ship activity level database and ship emission factor data, the base year ship air pollutant emissions are calculated, and then the base year ship emission inventory is established; the air pollutants include SO2, NO x 、PM 10 、PM 2.5, CO, HC and CO2; the calculation formula for each pollutant emission under each ship operating condition in the base year ship emission inventory is: ; In the formula, i , j , k , l and n They represent pollution category, emission equipment, fuel type, operating condition and total time interval of AIS messages respectively; E represents the emission of pollutants from ships, in tons; P represents the rated power of the ship, in kW; LF represents the load factor, dimensionless; T Represents the interval between two consecutive AIS messages of each ship, unit: h; EF Represents the ship pollutant emission factor, unit: g / kW·h; LLAF Represents the low load adjustment factor, unit: %, only applicable to the host.

[0051] The actual output power of the engine during ship operation is quite different from the rated power. The actual power of the ship's main engine is the product of the main engine load factor and the main engine rated power (MCR), which changes with environmental conditions. When establishing a high-resolution ship emission inventory, the actual pollutant emissions must be considered according to the actual power of the ship's main engine. It cannot be estimated based solely on the rated power of the ship's main engine, otherwise the result of the ship's atmospheric pollutant share will be significantly larger.

[0052] The Propeller formula is used to calculate the ship's main engine load factor based on the ship's real-time speed and the ship's maximum design speed during navigation. The formula is as follows: ; Wherein, LF represents the ship's main engine load factor (Load factor; dimensionless); AS represents the ship's actual speed (unit: knot); MS represents the ship's maximum designed speed (maximum designed speed; unit: knot).

[0053] If the ship engine type and fuel type are known, the corresponding emission factors (unit: g / kWh) can be used to calculate CO2, SOx, NOx, and PM emissions, taking into account the actual implementation of the ship emission control policy within the scope of the study.

[0054] (5) Summary and comparison of results: For each ship and each AIS record during the study period, emissions were calculated after power correction and the results were compared with the traditional values ​​calculated using only SOG: R_diff = (Emissions_Corrected - Emissions_Traditional) / Emissions_Traditional × 100% If it is found that the difference between the two is more obvious when the wind force is stronger, then the necessity of wind force correction is confirmed.

[0055] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A ship emission estimation method based on the coupling of WRF model wind field and AIS data, characterized in that: The following steps are involved: Step 1: Use the WRF model to implement high-resolution numerical simulation of mesoscale or multi-grid nesting in the target sea area to obtain the wind speed and wind direction distribution with high temporal and spatial resolution; Step 2: Acquire and parse AIS data, extract the ground speed and heading of each ship at each sampling time, and pre-process the extracted data; wherein the pre-processing includes data cleaning and interpolation; Step 3: Match the wind field with the AIS position and time, and use the vector relationship between the ship heading and the WRF wind direction to establish the wind force correction coefficient; Step 4: Correct the ship's main engine power inferred from the AIS ground speed according to the wind power correction coefficient to obtain an "effective speed" or actual power that better reflects the actual structure of the ship; Step 5: Based on the corrected "effective speed" or actual power, classify the activity status of the ship, and use the corresponding emission factors or emission models to calculate the ship emissions, so as to achieve refined emission estimates under different wind conditions and different operating conditions.

2. The ship emission estimation method according to claim 1, characterized in that: The WRF model numerical simulation in step 1 includes: A multiple nesting scheme of large-scale background and local fine grids is set up, with the outer grid used to provide boundary conditions and the inner grid used to depict the target sea area and nearshore area with high resolution; Correct the initial field or boundary conditions of WRF by assimilating multi-source observation data; Select appropriate physical parameterization schemes to improve the simulation accuracy of the land-sea boundary and offshore wind field characteristics.

3. The ship emission estimation method according to claim 1, characterized in that: The AIS data preprocessing in step 2 includes: The AIS data is interpolated and resampled in time and space to ensure that a set of matching data is formed with the wind field output by WRF at the same or similar time; where similar time refers to two time points within a preset time range; Remove missing values, erroneous values, and AIS records with obvious logical errors; The ship's geographic coordinate system in the AIS is converted to a coordinate system that is consistent or comparable with the WRF grid coordinate system, and the timestamps are aligned with a unified time zone or moment reference.

4. The ship emission estimation method according to claim 1, characterized in that: The calculation method of the wind force correction coefficient in step 3 includes: Using the x, y coordinate system, the x' axis and y' axis are due east and due north respectively; 、 and are the ship speed on the ground, the wind-induced drift speed and the ship engine output speed respectively; α is the wind direction angle predicted by the WRF model, β is the ship heading angle to the ground analyzed from AIS, and θ is and The relative wind direction between and Respectively represented by The two vectors along the x-axis and y-axis are analyzed. and Respectively represented by The two vectors along the x-axis and y-axis are analyzed and the formula is as follows: ； The formula for calculating the wind-induced drift speed of a ship is as follows: ； Where K is the drift coefficient related to the ship load, B a and B w are the windward areas above and below the waterline of the hull, respectively. Represents the actual wind speed value predicted by the WRF model.

5. The ship emission estimation method according to claim 1, characterized in that: In step 5, the corrected "effective speed" or actual power is applied to the ship's operating conditions, including: When the actual power or effective speed is lower than the first threshold and the rate of change is lower than the second threshold, it is determined to be in "slow sailing" or "berthing" state; When the rate of increase of the actual power or effective speed exceeds the third threshold, it is determined to be in the "acceleration" or "anchoring" state; When the actual power or effective speed is greater than the fourth threshold and is stable within the first preset range, it is determined to be in a "cruise" or "normal navigation" state.

6. The ship emission estimation method according to claim 5, characterized in that: In step 5, differentiated emission factors and emission models are used for different operating conditions, including: For berthing or slow sailing conditions, introduce emission factors at lower loads; For cruising or high-speed navigation, introduce emission factors under high load or rated load; For the acceleration state, the emission factor corresponding to the instantaneous increase in power is selected.

7. The ship emission estimation method according to claim 1, characterized in that: Types of emissions from ships include carbon dioxide, sulfur dioxide, nitrogen oxides and particulate matter.

8. The ship emission estimation method according to claim 1, characterized in that: Also includes: Summarize and compare emission calculations for different time periods, different ship types or different routes to: Establish an inventory of ship emissions by time, geographic area, ship type or ship traffic; The differences in the estimation results before and after the introduction of wind power correction are analyzed, and the improvement of the emission calculation error by the correction method is quantitatively evaluated.

9. The ship emission estimation method according to claim 8, characterized in that: Also includes: Dynamically adjust or optimize WRF wind field resolution and assimilation strategy to continuously improve the accuracy of wind dynamic correction; The method is integrated into the ship emission monitoring and big data analysis platform to achieve real-time estimation and time-series playback of emissions in the target sea area or port area, providing decision support for maritime management departments or shipping companies.

Images