CN115903088B - Weather element proximity forecasting method and system based on advection diffusion model - Google Patents
Weather element proximity forecasting method and system based on advection diffusion model Download PDFInfo
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Abstract
The application discloses a weather element proximity forecasting method and a weather element proximity forecasting system based on a advection diffusion model, wherein the method comprises the following steps: constructing a advection diffusion model, and calculating a backward track of the previous N hours of the historical air mass reaching the airport; extracting airport peripheral weather observation site data, calculating the observation site closest to the backward track position, and extracting backward track weather element values in the previous N hours according to the observation site data; and (5) comparing the correlation between the forward track for N hours and the weather elements observed in real time at the airport to obtain a forecast result. According to the technical scheme, different upstream stations are flexibly utilized, and weather element approach forecast is carried out by considering local terrains around an airport, so that a new method is provided for solving the approach forecast of the airport.
Description
Technical Field
The application relates to the field of weather forecast, in particular to a weather element proximity forecast method and system based on a advection diffusion model.
Background
The approach forecast of the meteorological elements of the airport at present mainly depends on the output result of a numerical mode or the release result of the mode. However, the characteristics of the meteorological elements of the airport under different terrains are different, and the numerical mode cannot be used for etching the climatic characteristics and differences of the local terrains with high resolution, so that the result is generally different from the actual situation. Therefore, it is necessary to build a new forecasting method based on local terrain influence to provide more references for airport approach forecasting.
The upstream station is close to the airport, the terrain is close to the airport, and the meteorological elements and the change of the elements have certain indication significance on the airport meteorological elements. In conventional analysis, the upstream station is usually a fixed station, and the future movement direction and influence range of the air mass can be calculated by calculating the advection diffusion process of the air mass of the station. This method is therefore often used in pollution impact forecasting. In actual situations, when the method is used for forecasting an airport, the forecasting object is fixed, and the method is not applicable; on the other hand, "upstream station" is a dynamic concept, and in different circulation situations, the peripheral stations may be "upstream stations" of the airport. Therefore, in airport weather forecast, there is a need for a advection diffusion model with air mass trajectory tracking.
Currently, in the prior art, a hybrid single-particle Lagrange comprehensive track mode adopts a Lagrange method, and a plurality of meteorological element input fields and a plurality of physical processes are combined, so that a complete conveying and diffusing process of the gas particles can be calculated. And therefore are also often used in contaminant source tracking and contaminant concentration diffusion studies. This model assumes that the mass is of constant nature during the transfer over time to track the mass trajectory. However, the air mass track is a single position curve with longitude and latitude, so that the meteorological element information on the air mass track cannot be directly obtained. The learner interpolates the lattice background field data of the input HYSPLIT mode to the curve by interpolation. Because the spatial resolution of the lattice point data is higher, and the local terrain influence is not considered in the interpolation process, the current approach prediction method also has a certain limitation.
Therefore, how to flexibly utilize an "upstream station" and consider local terrain effects to perform weather element proximity prediction is a technical problem that needs to be solved in the art.
Disclosure of Invention
In view of this, the application provides a weather element proximity prediction method and system based on a advection diffusion model, so as to determine weather element properties of air masses reaching an airport by combining weather element information of peripheral stations of the airport through the advection diffusion model, thereby providing a theoretical basis for weather element proximity prediction of the airport.
According to the application, a weather element proximity forecasting method based on a advection diffusion model is provided, and the method comprises the following steps:
step 1: constructing a advection diffusion model, and calculating a backward track of the previous N hours of the historical air mass reaching the airport;
step 2: extracting airport peripheral weather observation site data, calculating the observation site closest to the backward track position, and extracting backward track weather element values in the previous N hours according to the observation site data;
step 3: and (5) comparing the correlation between the forward track for N hours and the weather elements observed in real time at the airport to obtain a forecast result.
An improvement based on the above method, the step 1 further includes: and collecting weather factor historical data of the surrounding area of the airport.
Based on an improvement of the above method, the step 1 specifically includes:
step 1-1: calculating the predicted position coordinate P' (t+Δt) of the air mass reached from the initial position P (t) under the advection of the initial speed V (P, t) of the point P (t) after the Δt time passes:
P'(t+Δt)＝P(t)+V(P,t)*Δt
the initial position P (t) is an airport position, namely, the initial position P (x, y, z) at the moment t, wherein x, y are airport position coordinates, and z is an airport elevation; v (P, t) is the initial velocity of the air mass; Δt <0.75 gauge/Umax;
step 1-2: correcting the predicted point P' (t+Δt) to obtain a corrected predicted position P (t+Δt), wherein the specific calculation formula is as follows:
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P'+Δt)]*Δt
wherein V (P '+Δt) is the velocity of the air mass at the predicted position P' (t+Δt);
step 1-3: taking P (t+Deltat) as a new starting point P (t), turning to the step 1-1, repeatedly calculating a predicted position until the predicted position of the previous N hours is calculated, and turning to the step 1-4;
step 1-4: and connecting the predicted positions to obtain a backward track.
Based on a modification of the above method, the Δt takes a value of 1 hour.
Based on an improvement of the above method, the step 2 specifically includes:
extracting site observation data before N hours around an airport, and extracting longitude and latitude of a historical air mass before N hours according to the air mass backward track data obtained in the step 1; calculate the position of the air mass before the N hourSetting the relative distance between the observation stations and the surrounding observation stations, and selecting the observation station closest to the historical air mass; the meteorological element value N hours before the nearest observation station is recorded as a variable Parcel -N 。
Based on an improvement of the above method, the step 3 specifically includes:
the weather elements are directly forecasted: obtaining a value Parcel of a certain meteorological element of a historical air mass arriving at an airport from step 2 -N The method comprises the steps of carrying out a first treatment on the surface of the Then extracting a certain meteorological element value observed at an airport and marking the meteorological element value as OBS; calculating Parcel over a period of time -N Correlation coefficient with OBS R:
wherein n represents the number of values of a certain meteorological element obtained within a period of time;a value representing a certain meteorological element of the historical air mass of the arrival airport is obtained in a period t; />An average value representing a certain meteorological element value of an airport historical air mass reached within a period of time; OBS (on-Board diagnostics) t A certain meteorological element value observed by an airport in a t period; />An average value representing a certain meteorological element value observed by an airport for a period of time;
when R is greater than the set threshold, parcel -N Near to the live condition, the air mass historical value before N hours is effective for forecasting the current moment and is used as a direct forecasting value of the current airport meteorological element.
Based on an improvement of the method, when R is larger than the set threshold, extracting the air mass element value before N-1 hours according to the step 3, and recording as Parcel -(N-1) In Parcel -(N-1) As a result of forecasting the future 1 hour of the airport, the result is marked as OBS +1 The method comprises the steps of carrying out a first treatment on the surface of the And similarly, forecasting results of the airport for the next N hours are obtained.
Based on an improvement of the above method, the step 4 further includes:
trend forecast is carried out on meteorological elements: observe value OBS at a certain moment in airport -0 Subtracting the observed value OBS- N Observing a change trend Δobs in N hours as an airport; meteorological element value Parcel of the air mass N hours ago -N Meteorological element value Parcel of the air mass 2N hours ago was subtracted -2N As a trend of change in the air mass factor value ΔParcel;
calculating a correlation coefficient R1 of the DeltaOBS and DeltaParcel in a period of time:
wherein ΔParcel t The variation trend of the air mass element value in the t period is represented;an average value of ΔParcel showing a time variation trend; ΔOBS t The change trend of the observation value of the time field in the t period is represented; />An average value representing a time-varying trend Δobs;
when R1 is larger than a set threshold, the weather element change trend of the historical air mass is consistent with the airport live change trend; and taking the weather element change trend of the historical air mass as a forecast result of the airport change trend.
Based on a modification of the above method, the set threshold is 0.6.
Based on a modification of the above method, the period of time is 1 month.
Based on a modification of the above method, the N takes a value of 3.
The invention also provides a weather element proximity forecasting system based on the advection diffusion model, which comprises the following steps:
the backward track calculating module is used for constructing a advection diffusion model and calculating a backward track of the previous N hours of the historical air mass reaching the airport;
the meteorological element value extraction module is used for extracting airport peripheral meteorological observation site data, calculating an observation site closest to the backward track position, and extracting backward track meteorological element values for the previous N hours according to the observation site;
and the forecasting module is used for comparing the correlation between the forward track of the previous N hours and the weather elements observed in real time at the airport to obtain a forecasting result.
An improvement based on the above system, the system further comprising:
and the data preparation module is used for collecting weather factor historical data of the airport peripheral area.
According to the technical scheme, different upstream stations are flexibly utilized, and weather element approach forecast is carried out by considering local terrains around an airport, so that a new method is provided for solving the approach forecast of the airport.
Additional features and advantages of the present application will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a weather element proximity forecasting method based on a advection diffusion model.
Detailed Description
According to the weather element proximity forecasting method and system based on the advection diffusion model, after the air mass historical track is obtained by using the advection diffusion model, the purpose of considering the influence of terrain is achieved by combining information of observation sites around an airport, so that a novel forecasting method is further provided for the weather element proximity forecasting of the airport.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in combination with embodiments.
As shown in FIG. 1, the weather element proximity prediction method based on the advection diffusion model comprises the following 4 steps:
step 1: and collecting weather factor historical data of the surrounding area of the airport.
Collecting GRIB2 format data of analysis data FNL of NCEP (national environmental forecast center) and converting the data into ARL format data;
FNL (Final Operational GlobalAnalysis) is a set of analytical data products provided by NCEP covering the global atmosphere (e.g., wind farm, temperature, altitude, etc.), spatially uniform distribution (1 x1 degree horizontal resolution), time-continuous (4 times daily at UTC 00,06,12 and 18, respectively).
The ARL format is a specific meteorological data file format constructed in U.S. Air Resources Laboratory, and is a customized binary format.
The original data used in the invention is FNL data in NCEP analysis data, the time resolution is 6 hours, the spatial horizontal resolution is 0.25 degrees multiplied by 0.25 degrees, and the vertical resolution is 1000-100hPa for 21 layers. The two-dimensional variables used are ground air pressure, ground temperature and 2 m height horizontal wind (latitudinal wind and longitudinal wind), and the three-dimensional variables used are potential height, temperature, vertical speed, relative humidity and horizontal wind (latitudinal wind and longitudinal wind).
The data format conversion method comprises the following steps: GRIB2 data was read, written and saved as ARL data by means of the Python program.
Step 2: constructing a advection diffusion model, and calculating a backward track of the previous N hours of the historical air mass reaching the airport;
constructing a advection diffusion model: assuming that the particles move with the wind field, the trajectory is the integral of the particles in space and time. The vector velocity of the location of the particle is calculated by linear interpolation both in time and in space.
Step 2-1: the first predicted position P' (t+Deltat) is calculated according to the airport position P (t) (namely P (x, y, z) at the moment t, wherein x, y are airport position coordinates, z is airport elevation, airport elevation is elevation of the highest point on the central line of the main runway), the initial velocity V (P, t) of the air mass and the time variation. The specific calculation formula is as follows:
P'(t+Δt)＝P(t)+V(P,t)*Δt
p' (t+Δt) represents the predicted position coordinates obtained at the initial position P (t) at the initial velocity V (P, t) of the air mass at point P (t) by advection conveyance over Δt time. And wherein the integration time step deltat is varied (1 minute-1 hour), but it is required to meet deltat <0.75 grid distance/Umax (Umax is the maximum wind speed in the FNL data), i.e. the movement of the air mass does not exceed 0.75 grid distances in one time step (deltat). Theoretically, the smaller Δt is, the closer the point interval between tracks is, but at the same time, the complexity of data processing is also high, and therefore Δt takes 1 hour in the present invention.
Step 2-2: correcting the predicted point P' (t+deltat), wherein the specific calculation formula is as follows:
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P'+Δt)]*Δt
where V (P '+Δt) is the velocity of the air mass at the first predicted position P' (t+Δt).
Step 2-3: and (3) taking the P (t+deltat) as a new starting point P (t), repeatedly calculating predicted positions, and connecting the predicted positions to obtain a backward track.
Since the model assumes that the particles do not change over time, in practice, the properties of the air mass change as the underlying surface passes, the longer the model is followed, the greater the likelihood of air mass change, and the less the reference to the downstream airport. Therefore, the invention selects the backward track tracking result for 3 hours.
Step 3: extracting airport peripheral weather observation site data, calculating the observation site closest to the backward track position, and extracting backward track weather element values in the previous N hours according to the observation site data;
extracting site observation data before N hours around an airport, and extracting longitude and latitude of the air mass before N hours according to the air mass backward track data obtained in the step 2; calculating the relative distance between the position of the air mass and the surrounding observation sites before the N hour, and selecting the observation site closest to the historical air mass; the meteorological element value N hours before the nearest observation station is recorded as a variable Parcel -N 。
Taking N as an example for 3 hours, extracting site observation data before 3 hours around an airport, and simultaneously extracting longitude and latitude of the air mass before 3 hours according to the air mass backward track data obtained in the step 2. And calculating the relative distance between the position of the air mass and the surrounding observation sites 3 hours before, and selecting the observation site closest to the historical air mass. The meteorological element value 3 hours before the nearest observation site is recorded as a variable Parcel -3 。
Step 4: and (5) comparing the correlation between the forward track for N hours and the weather elements observed in real time at the airport to obtain a forecast result.
The weather elements are directly forecasted: obtaining a value Parcel of a certain meteorological element of a historical air mass reaching an airport through the calculation of the steps 1-3 -N The method comprises the steps of carrying out a first treatment on the surface of the Then extracting a certain meteorological element value observed at an airport and marking the meteorological element value as OBS; calculating Parcel over a period of time -N Correlation coefficient with OBS R:
wherein n represents the number of values of a certain meteorological element obtained within a period of time;a value representing a certain meteorological element of the historical air mass of the arrival airport is obtained in a period t; />An average value representing a certain meteorological element value of an airport historical air mass reached within a period of time; OBS (on-Board diagnostics) t A certain meteorological element value observed by an airport in a t period; />An average value representing a certain meteorological element value observed by an airport for a period of time;
parcel is considered when R is greater than 0.6 -N Near to the live condition, the air mass historical value before N hours is effective for forecasting the current moment and is used as a direct forecasting value of the current airport meteorological element.
When R is greater than 0.6, extracting air mass factor value before N-1 hr, and recording as Parcel -(N-1) In Parcel -(N-1) As a result of forecasting the future 1 hour of the airport, the result is marked as OBS +1 The method comprises the steps of carrying out a first treatment on the surface of the And similarly, forecasting results of the airport for the next N hours are obtained.
Taking 3 hours as N and taking one month data as an example, the direct forecast of meteorological elements is that: obtaining Parcel reaching a certain meteorological element of the airport historical air mass through the calculation of the steps 1-3 -3 . And then extracting a certain meteorological element value observed in real time at the airport and marking the meteorological element value as OBS. Parcel was calculated over a month -3 Correlation coefficient with OBS R:
where n is all time instances of the OBS. Parcel is considered when R is greater than 0.6 -3 Close to the live, the air mass history value 3 hours ago was verified to be valid for the forecast at the current time. Then the air mass factor value before 2 hours is extracted in the step 3 is recorded as Parcel -2 In Parcel -2 As a result of forecasting the future 1 hour of the airport, the result is marked as OBS +1 . By analogy, parcel -1 As a result of future 2 hours forecast of airport (OBS) +2 )。
Trend forecast is carried out on meteorological elements: observe value OBS at a certain moment in airport -0 Subtracting the observed value OBS- N Observing a change trend Δobs in N hours as an airport; meteorological element value Parcel of the air mass N hours ago -N Meteorological element value Parcel of the air mass 2N hours ago was subtracted -2N As a trend of change in the air mass factor value ΔParcel;
calculating a correlation coefficient R1 of the DeltaOBS and DeltaParcel in a period of time:
wherein ΔParcel t Representing the air mass element value at t time intervalIs a trend of change in (2);an average value of ΔParcel showing a time variation trend; ΔOBS t The change trend of the observation value of the time field in the t period is represented; />An average value representing a time-varying trend Δobs;
when R1 is more than 0.6, the weather element change trend of the historical air mass is consistent with the airport live change trend; and taking the weather element change trend of the historical air mass as a forecast result of the airport change trend.
Taking N as an example for 3 hours and taking one month data, trend forecast for meteorological elements: airport current Observation (OBS) -0 ) Minus Observations (OBS) 3 hours before airport -3 ) I.e. airport observations of trend of change (Δobs) over 3 hours. Similarly, the meteorological element value (Parcel) of the air mass 3 hours ago -3 ) The meteorological element value (Parcel) of the air mass before 6 hours was subtracted -6 ) And obtaining the change trend (delta Parcel) of the air mass element value. The ΔOBS and ΔParcel correlation coefficient R1 was calculated over a month. When R1 is greater than 0.6, the weather element change trend of the historical air mass is considered to be consistent with the airport live change trend. Similarly, to obtain the trend of the future 3-hour observation value and the current time (i.e. OBS) +3 -OBS -0 ) Then Parcel needs to be calculated -0 -Parcel -3 。
Compared with the prior art, the method has the advantages that the historical air mass track reaching the airport is tracked through the advection diffusion model, the weather element values of the historical air mass are obtained by combining the data of the observation points around the airport, the weather element values of the historical air mass and the weather element values observed in real time by the airport are compared, the forecast quantity is selected, and a new method is provided for solving the problem of airport approach forecast.
The preferred embodiments of the present application have been described in detail above, but the present application is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present application within the scope of the technical concept of the present application, and all the simple modifications belong to the protection scope of the present application.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in detail.
Moreover, any combination of the various embodiments of the present application may be made without departing from the spirit of the present application, which should also be considered as the disclosure of the present invention.
Claims (11)
1. A weather element approach prediction method based on a advection diffusion model, the method comprising:
step 1: constructing a advection diffusion model, and calculating a backward track of the previous N hours of the historical air mass reaching the airport;
step 2: extracting airport peripheral weather observation site data, calculating the observation site closest to the backward track position, and extracting backward track weather element values in the previous N hours according to the observation site data;
step 3: the correlation between the forward track of the previous N hours and the weather elements observed in real time at the airport is compared to obtain a forecast result;
the step 2 specifically includes:
extracting site observation data before N hours around an airport, and extracting longitude and latitude of a historical air mass before N hours according to the air mass backward track data obtained in the step 1; calculating the relative distance between the position of the air mass and the surrounding observation sites before the N hour, and selecting the observation site closest to the historical air mass; the meteorological element value N hours before the nearest observation station is recorded as a variable Parcel -N ；
The step 3 specifically includes:
the weather elements are directly forecasted: obtaining a value Parcel of a certain meteorological element of a historical air mass arriving at an airport from step 2 -N The method comprises the steps of carrying out a first treatment on the surface of the Then extracting a certain meteorological element value observed at an airport and marking the meteorological element value as OBS; calculating Parcel over a period of time -N Correlation coefficient with OBS R:
wherein n represents the number of values of a certain meteorological element obtained within a period of time;a value representing a certain meteorological element of the historical air mass of the arrival airport is obtained in a period t; />An average value representing a certain meteorological element value of an airport historical air mass reached within a period of time; OBS (on-Board diagnostics) t A certain meteorological element value observed by an airport in a t period; />An average value representing a certain meteorological element value observed by an airport for a period of time;
when R is greater than the set threshold, parcel -N Near to the live condition, the air mass historical value before N hours is effective for forecasting the current moment and is used as a direct forecasting value of the current airport meteorological element.
2. The weather element proximity prediction method based on the advection diffusion model according to claim 1, wherein before the step 1, further comprises: and collecting weather factor historical data of the surrounding area of the airport.
3. The weather element proximity prediction method based on the advection diffusion model according to claim 1, wherein the step 1 specifically includes:
step 1-1: calculating the predicted position coordinate P' (t+Δt) of the air mass reached from the initial position P (t) under the advection of the initial speed V (P, t) of the point P (t) after the Δt time passes:
P'(t+Δt)＝P(t)+V(P,t)*Δt
the initial position P (t) is an airport position, namely, the initial position P (x, y, z) at the moment t, wherein x, y are airport position coordinates, and z is an airport elevation; v (P, t) is the initial velocity of the air mass; Δt <0.75 gauge/Umax;
step 1-2: correcting the predicted point P' (t+Δt) to obtain a corrected predicted position P (t+Δt), wherein the specific calculation formula is as follows:
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P'+Δt)]*Δt
wherein V (P '+Δt) is the velocity of the air mass at the predicted position P' (t+Δt);
step 1-3: taking P (t+Deltat) as a new starting point P (t), turning to the step 1-1, repeatedly calculating a predicted position until the predicted position of the previous N hours is calculated, and turning to the step 1-4;
step 1-4: and connecting the predicted positions to obtain a backward track.
4. The weather element proximity prediction method based on the advection diffusion model according to claim 3, wherein the Δt takes a value of 1 hour.
5. The weather element proximity prediction method based on advection diffusion model according to claim 1, wherein when R is greater than a set threshold, extracting air mass element value N-1 hr before referring to step 3, and recording as Parcel -(N-1) In Parcel -(N-1) As a result of forecasting the future 1 hour of the airport, the result is marked as OBS +1 The method comprises the steps of carrying out a first treatment on the surface of the And similarly, forecasting results of the airport for the next N hours are obtained.
6. The weather element proximity prediction method based on the advection diffusion model according to claim 1, wherein the step 3 further comprises:
trend forecast is carried out on meteorological elements: observe value OBS at a certain moment in airport -0 Subtracting the observed value OBS of the airport before N hours at a certain moment -N Observing a change trend Δobs in N hours as an airport; meteorological element value Parcel of the air mass N hours ago -N Meteorological element value Parcel of the air mass 2N hours ago was subtracted -2N As an element of air massTrend of change in value Δparcel;
calculating a correlation coefficient R1 of the DeltaOBS and DeltaParcel in a period of time:
wherein ΔParcel t The variation trend of the air mass element value in the t period is represented;an average value of ΔParcel showing a time variation trend; ΔOBS t The change trend of the observation value of the time field in the t period is represented; />An average value representing a time-varying trend Δobs;
when R1 is larger than a set threshold, the weather element change trend of the historical air mass is consistent with the airport live change trend; and taking the weather element change trend of the historical air mass as a forecast result of the airport change trend.
7. The weather element approach forecast method based on the advection diffusion model according to one of claims 1 or 6, wherein the set threshold is 0.6.
8. The method of weather element proximity prediction based on advection diffusion model according to one of claims 1 or 6, wherein the period of time is 1 month.
9. The weather element proximity prediction method based on the advection diffusion model according to claim 1, wherein the value of N is 3.
10. A weather element proximity forecasting system based on a advection diffusion model, the system comprising:
the backward track calculating module is used for constructing a advection diffusion model and calculating a backward track of the previous N hours of the historical air mass reaching the airport;
the meteorological element value extraction module is used for extracting airport peripheral meteorological observation site data, calculating an observation site closest to the backward track position, and extracting backward track meteorological element values for the previous N hours according to the observation site; and
the forecasting module is used for comparing the correlation between the forward track of the previous N hours and the weather elements observed in real time at the airport to obtain a forecasting result;
the calculation method of the meteorological element value extraction module specifically comprises the following steps:
station observation data before N hours around an airport are extracted, and longitude and latitude of a historical air mass before N hours are extracted according to air mass backward track data obtained by calculating a backward track module; calculating the relative distance between the position of the air mass and the surrounding observation sites before the N hour, and selecting the observation site closest to the historical air mass; the meteorological element value N hours before the nearest observation station is recorded as a variable Parcel -N ；
The calculation method of the forecasting module specifically comprises the following steps:
the weather elements are directly forecasted: obtaining a value Parcel of a certain meteorological element of a historical air mass arriving at an airport from a module for extracting meteorological element values -N The method comprises the steps of carrying out a first treatment on the surface of the Then extracting a certain meteorological element value observed at an airport and marking the meteorological element value as OBS; calculating Parcel over a period of time -N Correlation coefficient with OBS R:
wherein n represents the number of values of a certain meteorological element obtained within a period of time;a value representing a certain meteorological element of the historical air mass of the arrival airport is obtained in a period t; />An average value representing a certain meteorological element value of an airport historical air mass reached within a period of time; OBS (on-Board diagnostics) t A certain meteorological element value observed by an airport in a t period; />An average value representing a certain meteorological element value observed by an airport for a period of time;
when R is greater than the set threshold, parcel -N Near to the live condition, the air mass historical value before N hours is effective for forecasting the current moment and is used as a direct forecasting value of the current airport meteorological element.
11. The advection diffusion model based weather element proximity forecast system of claim 10, further comprising:
and the data preparation module is used for collecting weather factor historical data of the airport peripheral area.
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| CN106339775B (en) * | 2016-08-23 | 2019-10-11 | 北京市环境保护监测中心 | The air heavily contaminated case method of discrimination clustered based on weather typing and meteorological element |
| US10989839B1 (en) * | 2017-08-29 | 2021-04-27 | University Of Hawai'i | Ground-based sky imaging and irradiance prediction system |
| CN109948840B (en) * | 2019-03-08 | 2020-02-21 | 宁波市气象台 | Air quality forecasting method |
| KR102350026B1 (en) * | 2020-01-09 | 2022-01-11 | 주식회사 에어텍 | Apparatus and method for predicting occurrence of odor based on weather conditions |
| CN111679345A (en) * | 2020-05-18 | 2020-09-18 | 上海眼控科技股份有限公司 | Method and equipment for extracting aeronautical meteorological element values |
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