CN115903088A - Meteorological element nowcasting method and system based on advection diffusion model - Google Patents
Meteorological element nowcasting method and system based on advection diffusion model Download PDFInfo
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Abstract
The application discloses a meteorological element nowcasting method and system based on an advection diffusion model, wherein the method comprises the following steps: constructing an advection diffusion model, and calculating the backward trajectory of the previous N hours of the historical air mass arriving at the airport; extracting data of meteorological stations around the airport, calculating an observation station closest to the backward track position, and extracting backward track meteorological element values for the first N hours; and comparing the correlation between the meteorological elements observed on the front track and the rear track in real time in the front N hours and the airport to obtain a forecast result. According to the technical scheme of the application, different 'upstream stations' are flexibly utilized, the local terrain around the airport is considered to carry out meteorological element nowcasting, and a new method is provided for solving airport nowcasting.
Description
Technical Field
The application relates to the field of meteorological forecasting, in particular to a meteorological element nowcasting method and a meteorological element nowcasting system based on an advection diffusion model.
Background
At present, the nowcasting of airport meteorological elements mainly depends on the output result of a numerical mode or the release result of the mode. However, airport meteorological features under different terrains are different, and the numerical mode cannot depict the climatic features and differences under high-resolution local terrains, so that the result is usually different from the actual situation. Therefore, it is necessary to establish a new forecasting method based on the local terrain influence so as to provide more references for airport approach forecasting.
The upstream station is close to the airport in distance and is located on the terrain, and meteorological elements and changes of the elements have certain indication significance on meteorological elements of the airport. In the traditional analysis, an upstream station is usually a fixed station, and the future moving direction and the influence range of the air mass can be estimated by calculating the advection diffusion process of the air mass at the station. This method is therefore often used in pollution impact prediction. In practical situations, when forecasting is performed on one hand for a certain airport, the forecasted object is fixed, and the method is not applicable; on the other hand, the "upstream station" is a dynamic concept, and the peripheral stations can be the "upstream stations" of the airport under different circulation conditions. Therefore, in airport weather forecasting, an advection diffusion model with air mass trajectory tracking is needed.
At present, in the HYSPLIT (hybrid single particle Lagrange integrated trajectory) mode in the prior art, a Lagrange method is adopted, and the complete transportation and diffusion process of the gas cluster can be calculated by combining various meteorological element input fields and various physical processes. And therefore are also often used for the trace of the source of the contaminant and the study of the diffusion of the concentration of the contaminant. The mode assumes that the properties of the air mass are unchanged during the transmission of a certain time, so as to track the air mass track. However, the air mass track is a single position curve with longitude and latitude, and the meteorological element information on the air mass track cannot be directly acquired. The scholars interpolate the lattice background field data of the input HYSPLIT mode to the curve by an interpolation method. Because the spatial resolution of the lattice point data is high, and the influence of local terrain is not considered in the interpolation process, the current approach prediction method has certain limitation.
Therefore, how to flexibly utilize the upstream station and consider local terrain influence to carry out meteorological element proximity forecasting becomes a technical problem to be solved in the field.
Disclosure of Invention
In view of the above, the present application provides a meteorological element nowcasting method and system based on an advection diffusion model, which determine meteorological element properties of an air mass arriving at an airport by combining an advection diffusion model with meteorological element information of sites around the airport, so as to provide a theoretical basis for airport meteorological element nowcasting.
According to the application, a meteorological element nowcasting method based on an advection diffusion model is provided, and the method comprises the following steps:
step 1: constructing an advection diffusion model, and calculating the backward trajectory of the previous N hours of the historical air mass arriving at the airport;
step 2: extracting data of meteorological stations around the airport, calculating an observation station closest to the backward track position, and extracting backward track meteorological element values for the first N hours;
and step 3: and comparing the correlation between the meteorological elements observed on the front track and the rear track in real time in the front N hours and the airport to obtain a forecast result.
Based on a modification of the above method, the step 1 further includes: and acquiring historical data of meteorological factors in the area around the airport.
Based on an improvement of the above method, the step 1 specifically includes:
step 1-1: the estimated position coordinate P' (t + Δ t:
P′(t+Δt＝P(t+V(P,t*Δt
wherein, the initial position P (t is the airport position, namely the initial position P (x, y, z, x, y are the airport position coordinates at the time t, z is the airport elevation), V (P, t is the air mass initial speed; delta t is less than 0.75 grid distance/Umax;
step 1-2: correcting the predicted point P' (t + Δ t) to obtain a corrected predicted position P (t + Δ t), wherein the specific calculation formula is as follows:
P(t+Δt＝P(t+0.5V(P,t)+V(P′+Δt*Δt
where V (P '+ Δ t is the velocity of the air mass at the predicted position P' (t + Δ t;
step 1-3: taking P (t + delta t as a new starting point P (t, turning to step 1-1, repeatedly calculating the predicted position until the predicted position in the previous N hours is calculated, and turning to step 1-4;
step 1-4: and connecting the predicted positions to obtain a backward track.
Based on a modification of the above method, Δ t is 1 hour.
Based on an improvement of the above method, the step 2 specifically includes:
extracting station observation data of the airport in N hours ago, and extracting longitude and latitude of a historical air mass in N hours ago according to the air mass backward trajectory data obtained in the step 1; calculating the relative distance between the position of the air mass and peripheral observation stations before the Nth hour, 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:
directly forecasting meteorological elements: the value Parcel of a certain meteorological element of the historical air mass arriving at the airport is obtained in the step 2 -N (ii) a Then extracting a certain meteorological element value observed at the airport and recording 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 meteorological element values obtained within a period of time;
representing t-time-interval arrival machineThe value of a certain meteorological element of a field history air mass; />
An average of values of a weather element representing a historical mass of air arriving at the airport over a period of time; OBS t A certain meteorological element value representing airport observation in a time period t; />
An average value representing a value of a weather element observed at an airport over a period of time;
when R is larger than the set threshold, parcel -N And the historical value of the air mass before N hours is effective to forecast at the current moment and is used as a direct forecast value of meteorological elements of the current airport.
Based on an improvement of the method, when R is larger than a set threshold value, extracting an air mass element value before N-1 hours according to step 3, and recording the value as Parcel -(N-1) In Parcel (Radcel) -(N-1) As the forecast result of airport 1 hour in the future, it is recorded as OBS +1 (ii) a By analogy, the forecast result of the airport in the future N hours is obtained.
Based on a modification of the above method, the step 4 further includes:
trend forecasting is carried out on meteorological elements: OBS observed value at a certain time of airport -0 Subtracting the observed value OBS of N hours before a certain moment of the airport -N Observing a change trend delta OBS in N hours as an airport; the meteorological element value Parcel of the air mass before N hours -N Subtracting the meteorological element value Parcel of the air mass 2N hours before -2N The change trend delta Parcel of the value of the air mass element;
calculating a correlation coefficient R1 of delta OBS and delta Parcel in a period of time:
wherein, delta Parcel t Representing the change trend of the gas mass element value in the t period;
the average value of change trend delta Parcel in a period of time is shown; delta OBS t Representing the change trend of the airport observation values in the t period; />
An average value representing a time-lapse trend Δ OBS;
when the R1 is larger than a set threshold value, the meteorological element variation trend of the historical air mass is consistent with the airport live variation trend; and taking the meteorological element variation trend of the historical air mass as a forecast result of the airport variation 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 an improvement of the above method, the value of N is 3.
The invention also provides a meteorological element nowcasting system based on the advection diffusion model, which comprises:
the backward trajectory calculation module is used for constructing an advection diffusion model and calculating the backward trajectory of the previous N hours of the historical air mass arriving at the airport;
the meteorological element value extracting module is used for extracting data of meteorological observation stations around an airport, calculating an observation station closest to a backward track position, and extracting backward track meteorological element values of the previous N hours;
and the forecasting module is used for comparing the correlation between the meteorological elements observed on the track and the airport in real time in the front N hours and the rear N hours to obtain a forecasting result.
An improvement based on the above system, said system further comprising:
and the data preparation module is used for acquiring meteorological factor historical data of the airport surrounding area.
According to the technical scheme of the application, different 'upstream stations' are flexibly utilized, the local terrain around the airport is considered to carry out meteorological element nowcasting, and a new method is provided for solving airport nowcasting.
Additional features and advantages of the present application will be described in detail in the detailed description which follows.
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The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application, and the illustrative embodiments and descriptions thereof are used to explain the application. In the drawings:
FIG. 1 is a flow chart of a meteorological element nowcasting method based on an advection diffusion model.
Detailed Description
According to the meteorological element nowcasting method and system based on the advection diffusion model, after the advection diffusion model is used for obtaining the historical track of the air mass, the purpose of considering the terrain influence is achieved by combining the information of observation stations around the airport, and therefore a new forecasting method is further provided for airport meteorological element nowcasting.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in FIG. 1, the meteorological element nowcasting method based on advection diffusion model of the present invention comprises the following 4 steps:
step 1: and acquiring historical data of meteorological factors in the area around the airport.
Acquiring GRIB2 format data of re-analysis data FNL of the NCEP (national environmental prediction center) and converting the data into ARL format data;
FNL (Final Operational Global Analysis) is a set of multivariate (e.g., wind field, temperature, altitude, etc.), spatially uniform (horizontal resolution 1x1 degree), time continuous (4 hours per day, UTC 00,06,12 and 18 respectively) analytical data products provided by the NCEP covering the Global atmosphere.
The ARL format is a specific meteorological data file format constructed by Air Resources Laboratory in the United states, and is a self-defined binary format.
The original data used in the invention is FNL data in the NCEP reanalysis 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 a total of 21 layers. The two-dimensional variables are ground air pressure, ground temperature and horizontal wind (latitude wind and longitude wind) with the height of 2 meters, and the three-dimensional variables are potential height, temperature, vertical speed, relative humidity and horizontal wind (latitude wind and longitude wind).
The data format conversion method comprises the following steps: and reading GRIB2 data by a Python program, writing the GRIB2 data into the Python program and storing the GRIB2 data as ARL data.
And 2, step: constructing an advection diffusion model, and calculating a backward trajectory of the previous N hours of the historical air mass arriving at the airport;
constructing an advection diffusion model: the trajectory is the integral of the particle in space and time, assuming that the particle moves with the wind field. The vector velocity of the position of the particle is calculated by linear interpolation in both time and space.
Step 2-1: a first predicted position P' (t + Δ t. The specific calculation formula is:
P′(t+Δt＝P(t+V(P,t*Δt
p' (t + Δ t represents the predicted position coordinates obtained under advection at an initial position P (t, the initial velocity V (P, t) of the air mass at point P (t) over the time Δ t.) and wherein the integration time step Δ t is varied (1 minute-1 hour), but it is required to satisfy Δ t < 0.75 grid/Umax (Umax is the maximum wind speed in FNL data), i.e. (Δ t) within one time step, the movement of the air mass does not exceed 0.75 grid intervals.
Step 2-2: the predicted point P' (t + Δ t) is corrected by the specific calculation formula:
P(t+Δt＝P(t+0.5V(P,t)+V(P′+Δt*Δt
where V (P '+ Δ t is the velocity of the air mass at the first predicted position P' (t + Δ t).
Step 2-3: and taking P (t + delta t) as a new starting point P (t, repeatedly calculating the predicted positions, and connecting the predicted positions to obtain the backward track.
Since the particle is assumed to be invariable in the model for a certain time, in actual conditions, the property of the air mass changes with the passing underlying surface, so the longer the forward tracing is, the higher the possibility of the air mass degeneration is, and the less the reference significance is on the downstream airport. Therefore, the present invention selects the 3-hour backward trajectory tracking result.
And step 3: extracting data of meteorological observation stations around the airport, calculating observation stations closest to the backward track position, and extracting backward track meteorological element values in the first N hours according to the data;
extracting station observation data of the airport before N hours, and extracting the longitude and latitude of the air mass before N hours according to the air mass backward trajectory data obtained in the step 2; calculating the relative distance between the position of the air mass and peripheral observation stations before the Nth hour, 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 。
Taking N for 3 hours as an example, extracting station observation data of the airport in 3 hours before, and extracting the longitude and latitude of the air mass in 3 hours before according to the backward trajectory data of the air mass obtained in the step 2. And calculating the relative distance between the position of the air mass and the peripheral observation stations before 3 hours, and selecting the observation station closest to the historical air mass. The meteorological element value before 3 hours of the nearest observation site is recorded as a variable Parcel -3 。
And 4, step 4: and comparing the correlation between the meteorological elements observed on the front track and the rear track in real time in the front N hours and the airport to obtain a forecast result.
Directly forecasting meteorological elements: calculating through the steps 1-3 to obtain a value Parcel of a certain meteorological element of a historical air mass arriving at an airport -N (ii) a Then extracting a certain meteorological element value observed at the airport and recording 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 meteorological element values obtained within a period of time; parcel t -N Representing that a certain meteorological element value of a historical air mass arriving at the airport is obtained in the time t;
an average of values of a weather element representing a historical mass of air arriving at the airport over a period of time; OBS t A certain meteorological element value representing airport observation in t time period; />
An average value representing a value of a weather element observed at an airport over a period of time;
when R is greater than 0.6, consider Parcel -N And the air mass historical value N hours ago is close to the actual situation, and the air mass historical value is effective to forecast at the current moment and is used as a direct forecast value of the meteorological elements of the current airport.
When R is more than 0.6, extracting the air mass element value before N-1 hour, and recording as Parcel -(N-1) In Parcel -(N-1) As the forecast result of airport 1 hour in the future, it is recorded as OBS +1 (ii) a And by analogy, the forecast result of the airport in the next N hours is obtained.
Taking N as an example, taking 3 hours and one month data, directly forecasting meteorological elements: the Parcel of a certain meteorological element reaching the historical air mass of the airport is obtained through calculation in the step 1-3 -3 . And extracting a certain meteorological element value observed in real time in the airport and recording the meteorological element value as OBS. Calculate Parcel within one month -3 Correlation coefficient with OBS R:
wherein n is the total time number of the OBS. When R is greater than 0.6, it is considered Parcel -3 And (5) verifying that the historical value of the air mass before 3 hours is effective on forecasting at the current moment, close to the actual situation. Extracting the air mass element value before 2 hours according to the step 3 and recording as Parcel -2 In Parcel -2 As a 1 hour future forecast node for airportsFruit, marked as OBS +1 . By analogy, parcel -1 As a result of airport 2 hour future forecasts (OBS) +2 )。
Trend forecasting is carried out on meteorological elements: OBS observed value of airport at a certain time -0 Subtracting an observed value OBS N hours before a certain moment of the airport -N Observing a change trend delta OBS in N hours as an airport; the meteorological element value Parcel of the air mass before N hours -N The meteorological element value Parcel of the air mass before 2N hours is subtracted -2N The change trend delta Parcel is taken as the value change trend of the air mass element;
calculating a correlation coefficient R1 of the delta OBS and the delta Parcel in a period of time:
wherein, delta Parcel t Representing the change trend of the gas mass element value in the t period;
the mean value of the variation trend delta Parcel in a period of time is shown; delta OBS t Representing the variation trend of the airport observation values in the t time period; />
An average value representing a time-lapse trend Δ OBS;
when R1 is larger than 0.6, the meteorological element variation trend of the historical air mass is consistent with the airport live variation trend; and taking the meteorological element variation trend of the historical air mass as a forecast result of the airport variation trend.
Taking N as an example, taking 3 hours and one month data as examples, forecasting the trend of meteorological elements: comparing airport current Observations (OBS) -0 ) Subtract the observed value 3 hours ago at airport (OBS) -3 ) I.e. the trend of change (Δ OBS) observed at the airport for 3 hours. Similarly, the meteorological element value (Parcel) of the air mass before 3 hours is calculated -3 ) Subtract the meteorological element value (Parcel) of the air mass 6 hours ago -6 ) And obtaining the change trend (delta Parcel) of the factor value of the air mass. Calculating Delta O within one monthAnd the correlation coefficient R1 of the BS and the delta Parcel. When R1 is larger than 0.6, the meteorological element variation trend of the historical air mass is considered to be consistent with the airport live variation trend. By analogy, the change trend of the future 3-hour observation value and the current time (namely OBS) is obtained +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 meteorological element values of the historical air mass are obtained by combining the data of the observation points around the airport, the historical air mass and the meteorological element values observed in the airport in real time are compared, and the forecast amount is selected, so that a new method is provided for solving the 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 can be made to the technical solution of the present application within the technical idea of the present application, and these simple modifications all belong to the protection scope of the present application.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in the present application.
In addition, any combination of the various embodiments of the present application can be made, and the same should be considered as the disclosure of the present invention as long as the idea of the present application is not violated.
Claims (13)
1. A meteorological element nowcasting method based on an advection diffusion model, the method comprising:
step 1: constructing an advection diffusion model, and calculating a backward trajectory of the previous N hours of the historical air mass arriving at the airport;
step 2: extracting data of meteorological stations around the airport, calculating an observation station closest to the backward track position, and extracting backward track meteorological element values for the first N hours;
and step 3: and comparing the correlation between the meteorological elements observed on the front track and the rear track in real time in the front N hours and the airport to obtain a forecast result.
2. The meteorological element nowcasting method based on an advection diffusion model according to claim 1, further comprising, before the step 1: and acquiring historical data of meteorological factors in the area around the airport.
3. The meteorological element nowcasting method according to claim 1, wherein the step 1 specifically includes:
step 1-1: the time Δ t elapsed from the initial position Pt is calculated, and the predicted position coordinate P' (t + Δ t:
P′(t+Δt＝P(t+V(P,t*Δt
wherein the initial position Pt is an airport position, namely the initial position P (x, y, z, x, y are airport position coordinates at the time t, z is airport elevation, V (P, t is the initial speed of air mass; delta t is less than 0.75 grid distance/Umax;
step 1-2: correcting the predicted point P' (t + Δ t) to obtain a corrected predicted position P (t + Δ t), wherein the specific calculation formula is as follows:
Pt+Δt＝Pt+0.5V(P,t)+V(P′+Δt*Δt
where V (P '+ Δ t is the velocity of the air mass at the predicted position P' (t + Δ t;
step 1-3: taking Pt + delta t as a new starting point Pt, turning to the step 1-1, repeatedly calculating the 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 meteorological element nowcasting method based on the advection diffusion model, according to claim 3, wherein the Δ t value is 1 hour.
5. The meteorological element nowcasting method according to claim 1, wherein the step 2 specifically includes:
extracting station observation data of the airport periphery N hours ago, and extracting longitude and latitude of a historical air mass N hours ago according to the air mass backward trajectory data obtained in the step 1; calculating the relative distance between the position of the air mass and peripheral observation stations before the Nth hour, 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 。
6. The meteorological element nowcasting method based on the advection diffusion model according to claim 5, wherein the step 3 specifically comprises:
directly forecasting meteorological elements: the value Parcel of a certain meteorological element of the historical air mass arriving at the airport is obtained in the step 2 -N (ii) a Then extracting a certain meteorological element value observed at the airport and recording 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 meteorological element values obtained within a period of time;
representing that a certain meteorological element value of a historical air mass arriving at the airport is obtained in the time t; />
An average of values of a weather element representing a historical mass of air arriving at the airport over a period of time; OBS t A certain meteorological element value representing airport observation in a time period t; />
An average value representing a value of a weather element observed at an airport over a period of time;
when R is greater than the set thresholdWhen the value is positive, parcel -N And the historical value of the air mass before N hours is effective to forecast at the current moment and is used as a direct forecast value of meteorological elements of the current airport.
7. The meteorological element nowcasting method based on the advection diffusion model as claimed in claim 6, wherein when R is greater than a set threshold, the value of the air mass element N-1 hours ago is extracted with reference to step 3 and is recorded as Parcel -(N-1) In Parcel -(N-1) As the forecast result of airport 1 hour in the future, it is recorded as OBS +1 (ii) a By analogy, the forecast result of the airport in the future N hours is obtained.
8. The meteorological element nowcasting method according to claim 6, wherein the step 4 further includes:
trend forecasting is carried out on meteorological elements: OBS observed value at a certain time of airport -0 Subtracting an observed value OBS N hours before a certain moment of the airport -N Observing a change trend delta OBS in N hours as an airport; the meteorological element value Parcel of the air mass before N hours -N The meteorological element value Parcel of the air mass before 2N hours is subtracted -2N The change trend delta Parcel of the value of the air mass element;
calculating a correlation coefficient R1 of the delta OBS and the delta Parcel in a period of time:
wherein, delta Parcel t Representing the change trend of the gas mass element value in the t period;
the mean value of the variation trend delta Parcel in a period of time is shown; delta OBS t Representing the change trend of the airport observation values in the t period; />
An average value representing a time-lapse trend Δ OBS;
when R1 is larger than a set threshold value, the meteorological element variation trend of the historical air mass is consistent with the airport live variation trend; and taking the meteorological element variation trend of the historical air mass as a forecast result of the airport variation trend.
9. The meteorological element nowcasting method according to any one of claims 6 or 8, wherein the set threshold is 0.6.
10. The meteorological element nowcasting method based on the advection diffusion model according to claim 6 or 8, wherein the period of time is 1 month.
11. The meteorological element nowcasting method based on the advection diffusion model, according to claim 1, wherein N is 3.
12. A system for meteorological element nowcasting based on an advection-diffusion model, the system comprising:
the backward trajectory calculation module is used for constructing an advection diffusion model and calculating the backward trajectory of the previous N hours of the historical air mass arriving at the airport;
the meteorological element value extracting module is used for extracting data of meteorological observation stations around an airport, calculating an observation station closest to a backward track position, and extracting backward track meteorological element values of the previous N hours; and
and the forecasting module is used for comparing the correlation between the meteorological elements observed on the track and the airport in real time in the front N hours and the rear N hours to obtain a forecasting result.
13. The advection-diffusion-model-based meteorological element nowcasting system of claim 12, further comprising:
and the data preparation module is used for acquiring meteorological factor historical data of the airport surrounding area.
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| CN109948840A (en) * | 2019-03-08 | 2019-06-28 | 宁波市气象台 | A kind of Urban Air Pollution Methods |
| KR20210089982A (en) * | 2020-01-09 | 2021-07-19 | 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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