CN111582547B - Method for acquiring wind field distribution in different places by using wind field data set - Google Patents
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Abstract
The invention discloses a method for acquiring wind field distribution in different places by using a wind field data set, which comprises the following steps: collecting and summarizing the wind field data set to obtain global wind field data taking the world time as a reference; converting global wind field data based on the world time into global wind field data based on the designated local time; global wind field data taking the world time as a reference of any adjacent three days is taken as an original data set, global rainfall data of one hour interval of the next day is obtained by adopting adjacent point interpolation, and thus, the gridded global wind field data of one hour interval taking the time of a designated place as a reference is obtained; the wind field distribution at the place is calculated from global wind field data of all days at the place. The method can acquire local time-varying characteristics of the global wind speed and the wind direction by utilizing the existing various long-term wind field data sets based on UTC time under the condition of not increasing any cost and observation.
Description
Technical Field
The invention relates to the field of global climate research, in particular to a method for acquiring wind field distribution in different places by using a wind field data set.
Background
Wind is an important parameter for studying aerodynamics and climate as an essential element describing the movement of the atmosphere. For wind field research, the method is widely applied to the fields of soil wind erosion evaluation, sand storm prediction, ecological environment improvement, atmospheric pollution evaluation, wind energy resource evaluation and the like, and is a research hotspot of students. The daily variation of the wind, especially the local time variation thereof, is studied, and has important significance for researching material circulation, regional climate, weather forecast and the like.
Current wind measuring means are various, such as wind towers, scatterometers, radiometers, synthetic aperture radars, and the like. But each of these approaches has its drawbacks: the sites of the anemometer tower are unevenly distributed, and data blank appears in part of areas; the resolution of the inversion results of the scatterometer and the radiometer is insufficient, and some of the inversion results cannot realize global coverage; the SAR inversion method is complex. To solve these problems, there is a gradual advent of analysis of wind farm data such as NCEP, CCMP, ERA-inteim, CFSR, etc. However, the time base of these data sets is world time, and local time-varying research on wind farms cannot be satisfied. Current local time-of-day change studies are limited to localized areas, while global wind farms are less studied.
Disclosure of Invention
The invention aims to overcome the technical defects, and provides a method for researching wind field distribution in different places by utilizing a wind field data set, which has the characteristics of globalization, modularization, compatibility with various wind field data sets and the like. The method can acquire the climate characteristics of the global wind fields with different isobaric surfaces on the basis of the existing data set and on the basis of not adding any equivalent and observation conditions, and has important contribution to the investigation and evaluation of wind field forecast and wind energy resources.
To achieve the above object, embodiment 1 of the present invention provides a method for acquiring wind farm distribution at different places using a wind farm data set, the method including:
collecting and summarizing the wind field data set to obtain global wind field data taking the world time as a reference;
converting global wind field data based on the world time into global wind field data based on the designated local time;
global wind field data taking the world time as a reference of any adjacent three days is taken as an original data set, global rainfall data of one hour interval of the next day is obtained by adopting adjacent point interpolation, and thus, the gridded global wind field data of one hour interval taking the time of a designated place as a reference is obtained;
the wind field distribution at the place is calculated from global wind field data of all days at the place.
As an improvement of the above method, the world time based global wind field data includes an east-west wind speed vector U10 component and a north-south wind speed vector V10 component of each grid point 10m from the ground, wherein the grid points are divided by 0.25 ° x 0.25 ° in resolution.
As an improvement of the above method, the global wind field data based on the world time is converted into global wind field data based on the designated local time; the method comprises the following steps:
LT＝(UTC-12)+Lon/15°
where LT stands for local time, UTC stands for universal time, lon stands for longitude of the geographic location where the grid point is located.
As an improvement of the method, the wind field distribution at the place is calculated according to global wind field data of all days at the place; the method specifically comprises the following steps:
u10 component U10 of ith lattice point when place is to be designated i,j And V10 component V10 i,j Synthesized as wind speed data:
wherein j=1, 2,3, …, N is the time series, N is the total number of days, W ij The synthesized wind speed value;
averaging the wind speed value of the ith grid point in the appointed place according to the total days:
wherein ,
representing the average wind speed value of the ith grid point, wherein the unit is m/s;
calculating the average wind direction of the ith lattice point by adopting a vector method:
wherein ,Ai The average wind direction for the ith grid point,
for the average component of the i-th grid point wind speed in the east-west direction, +.>
Is the average component of the wind speed of the ith grid point in the north-south direction.
Embodiment 2 of the present invention provides a method for acquiring wind farm distribution at different places using a wind farm data set, the method comprising:
collecting and summarizing the wind field data set to obtain global wind field data taking the world time as a reference;
performing time weight linear interpolation on global wind field data with time resolution of not 1 hour, so as to obtain weft and warp wind component data of each hour in the whole day;
converting global wind field data based on the world time into global wind field data based on the designated local time;
global wind field data taking the world time as a reference of any adjacent three days is taken as an original data set, global rainfall data of one hour interval of the next day is obtained by adopting adjacent point interpolation, and thus, the gridded global wind field data of one hour interval taking the time of a designated place as a reference is obtained;
the wind field distribution at the place is calculated from global wind field data of all days at the place.
As an improvement of the method, the global wind field data based on the world time comprises a weft wind component u-wind and a warp wind quantity v-wind of each grid point, wherein the grid points are divided according to the resolution of 2.5 degrees multiplied by 2.5 degrees; the total four observation times per day were 00, 06, 12, and 18UTC, respectively.
As an improvement of the above method, the global wind field data with time resolution of not 1 hour is subjected to time weight linear interpolation, so that weft and warp wind component data of each hour in the whole day are obtained; the method comprises the following steps:
carrying out time weight interpolation on the wind field data of each grid point, wherein the specific formula is as follows:
u interp ＝P before *u before +P after *u after
v interp ＝P before *v before +P after *v after
wherein ,uinterp and vinterp The weft wind component and the warp wind component after linear interpolation are respectively, P before and Pafter Respectively represent the required interpolation time points t interp The previous known time t before And a later known time t after Time weights of u before and vbefore Respectively the time point t before Weft wind division of (2)Quantity and warp wind component, u after and vafter Respectively the time point t after A weft wind component and a warp wind component.
As an improvement of the method, the wind field distribution at the place is calculated according to global wind field data of all days at the place; the method specifically comprises the following steps:
the u-wind component u of the ith lattice point when the place is designated i,j And v-wind component v i,j Synthesizing wind speed data at the local moment:
wherein j=1, 2,3, …, N is the time series, N is the total number of days, W ij The wind speed value is synthesized;
averaging the wind speed value of the ith grid point at the moment of the appointed place according to the total days:
wherein ,
representing the average wind speed value of the ith grid point, wherein the unit is m/s;
calculating the average wind direction of the ith lattice point by adopting a vector method:
wherein ,Ai The average wind direction for the ith grid point,
for the average component of the i-th grid point wind speed in the dimension,
is the average component of the wind speed of the ith grid point in terms of longitude.
The invention has the technical advantages that:
1. the method has the advantages of simplicity and easiness in operation, compatibility with various wind field data sets and the like, and can meet the requirements of wind field information extraction of different pressure surfaces at any place and time;
2. the method has the advantages of globalization and regional modularization, and can be compatible with various wind field data sets;
3. the method adopts a time weight interpolation method, can improve the resolution of the data set with the time resolution less than 1 hour, and is convenient for calculating the wind field data at any appointed place moment;
4. the method adopts the nearest neighbor interpolation method, and is simple, convenient and easy to implement;
5. the method can acquire local time-varying characteristics of the global wind speed and the wind direction by utilizing the existing various long-term wind field data sets based on UTC time under the condition of not increasing any cost and observation, and has important contribution to the field of wind climate research.
Drawings
FIG. 1 is a flow chart of a method of acquiring global wind farm distribution at different locations using a wind farm dataset according to embodiment 1 of the present invention;
fig. 2 is a flowchart of a method for acquiring global wind farm distribution at different locations using a wind farm data set according to embodiment 2 of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1, embodiment 1 of the present invention provides a method for acquiring wind field distribution at different places by using a wind field data set, and a specific embodiment thereof mainly comprises five steps of raw data preparation, place-time conversion, nearest neighbor interpolation, years of average processing and data visualization. In this embodiment, since the time resolution of the downloaded data set is 1 hour, no time weight interpolation is required, and this step is omitted.
In step one of this embodiment, raw data is prepared. The data mainly adopts ERA5 re-analysis grid data estimated in hours in 2013-2018, and comprises U-component (east-west direction) and V-component (north-south direction) data which are 10m away from the ground, wherein the horizontal resolution is 0.25 degrees multiplied by 0.25 degrees. ERA5 analysis data is the fifth generation global re-analysis data of ERA5 issued by the middle-term weather forecast center (ECMWF) in europe, and is developed in near real time and continuously through the golombian climate change service (C3S), covering the data of the period of time to date (three months later) in 1950, and is the most advanced analysis product in the current technology. ERA5 data is processed by using an Integrated Forecasting System (IFS) with a period of 41r2 developed by ECMWF, adopting a four-dimensional variation assimilation (4D-Var) method, wherein the global spatial resolution is 31km, the ERA5 data is vertically divided into 137 mode layers, the top layer is 0.01hPa, the ERA5 data is analyzed once per hour, and the rapid radiation transmission mode is RTTOV-11, so that the ERA5 data can be suitable for all-weather variables. In addition to satellite radiation data, ozone, aircraft and ground air pressure data are included for correction in terms of variation deviation correction. In addition, ERA5 data contains 240 variables in total, and can be downloaded and used for free by people. The main variables used in this example were 10m U-component and 10V-component ofbands (abbreviated as U10 and V10, respectively), and the file format was experimental NetCDF format in m/s. Where U10 represents the east vector wind speed 10 meters above the ground and V10 represents the north vector wind speed 10 meters above the ground.
In the second embodiment, the local time conversion is performed. When converting the downloaded ERA5 analysis data U10 and V10 data in 2013-2018 from world time to local time, the local time conversion formula adopted is as follows:
LT＝(UTC-12)+Lon/15°
where LT stands for local time, UTC stands for universal time, lon stands for longitude of the geographic location where the grid point is located.
In the third embodiment, the nearest-neighbor interpolation processing refers to selecting the U10 component and V10 component data for 72 hours before and after three days (yesterday, the same day and the same day) taking the universal time as the reference time for the area where the wind field information at a certain place is required to be known, and performing nearest-neighbor interpolation. Firstly, 0000LT, 0300LT, 0600LT, 0900LT, 1200LT, 1500LT, 1800LT and 2100LT when typical places are selected, and secondly, the nearest neighbor interpolation method is used for combining a place-time conversion formula to calculate the U10 component and the V10 component values of each lattice point at each typical place time. The specific implementation process is as follows: since ERA5 analyzes data with a horizontal resolution of 0.25 °, the world is divided into 721 longitudinal bands at intervals of 0.25 °; next, when each longitude zone of the world is unified into one place, for different longitude zones, the U10 component and V10 component data of the corresponding place moment are found by adopting adjacent point interpolation three days before and after the corresponding original data set is selected (yesterday, the current day and the tomorrow) at certain UTC of the current day.
Step four in this example, a multi-year average process. Firstly, combining U10 component and V10 component wind speed data of 0000LT, 0300LT, 0600LT, 0900LT, 1200LT, 1500LT, 1800LT and 2100LT respectively obtained in the third step into wind speed data of each local moment, wherein the specific formulas are as follows:
wherein j=1, 2,3, …, N is the time series, N is the total number of days, W j U10 j and V10j And (5) synthesizing a wind speed value. Secondly, carrying out average treatment on the obtained wind speed value of each grid point at each local moment according to the total number of days, wherein the formula is as follows:
wherein j=1, 2,3, …, N is the time series, N is the total number of days，
Represents the average wind speed value of the ith grid point, and the unit is m/s. For the calculation of the average wind direction at eight places at moment, a vector method is adopted, and the specific formula is as follows:
wherein ,Ai The average wind direction for the ith grid point,
for the average component of the i-th grid point wind speed in the east-west direction, +.>
Is the average component of the wind speed of the ith grid point in the north-south direction.
In the fifth embodiment, the data is visualized. The method is characterized in that the data of the average wind speed and the average wind direction in eight places obtained in the step four are visualized by using a computer program so as to be convenient for people to intuitively read and analyze.
The method can obtain the distribution results of the average wind speed and the average wind direction at 1038240 points worldwide in 2013-2018 when 0000LT, 0300LT, 0600LT, 0900LT, 1200LT, 1500LT, 1800LT and 2100 LT.
Example 2
As shown in fig. 2, embodiment 2 of the present invention provides a method for acquiring wind field distribution at different places by using a wind field data set, and a specific implementation manner of the method mainly includes six steps of raw data preparation, time weight interpolation, place-time conversion, nearest neighbor interpolation, years of average processing and data visualization.
In step one of this embodiment, raw data is prepared. The data acquisition mainly adopts wind field data of 20mb isopiestic surface of NCEP-DOE analysis 2 in 2013-2018, the horizontal resolution is 2.5 degrees multiplied by 2.5 degrees, the spatial coverage is 90N-90S, 0E-357.5E, and the file format is NetCDF. NCEP-DOE analysis 2 is a modified version of NCEP analysis 1 that modifies some inherent errors and updates parameterized physical processes, with the current data set coverage time period being 1979.01-2020.02. The main variables of the data set used in this example are u-wind and v-wind, where u-wind represents the weft component of the wind and v-wind represents the warp component of the wind, for a total of four observation times per day, 00, 06, 12, 18UTC respectively.
In step two of this embodiment, time weight interpolation is performed. Since the downloaded NCEP-DOE analysis 2 dataset has a temporal resolution of less than 1 hour, a temporal weight interpolation is required to increase the temporal resolution of the data to one hour. Performing time weight interpolation on the u-wind and v-wind data of each grid point, and firstly setting the time point required to perform interpolation as t interp The interpolated u-component and v-component values are set to u interp and vinterp . The specific calculation formula is as follows:
u interp ＝P before *u before +P after *u after
v interp ＝P before *v before +P after *v after
wherein ,uinterp and vinterp The weft and warp wind components, P, after linear interpolation respectively before and Pafter Respectively represent the required interpolation time points t interp The former known timeInterval (t) before ) And the latter known time (t after ) Time weights of u before and vbefore Respectively the time point t before Weft and warp wind components, u after and vafter Then respectively are the time points t after In the weft and warp direction. The u-wind and v-wind component values at unobserved hours are linearly interpolated according to the above formula and recombined with the original data into a new set of data with time resolution of 1 hour.
In step three of this embodiment, local time transition. Performing local time conversion on the u-wind and v-wind data sets with new time resolution obtained in the second step, wherein the specific conversion formula is as follows:
LT＝(UTC-12)+Lon/15°
where LT stands for local time, UTC stands for universal time, lon stands for longitude of the geographic location where each grid point is located.
In the fourth embodiment, the nearest-neighbor interpolation processing refers to selecting u-wind component and v-wind component data for 72 hours before and after three days (yesterday, the same day and the same day) taking the world time as the reference time for the area where the wind field information at a certain place is required to be known, and performing nearest-neighbor interpolation. When a place to be calculated is designated firstly, then the u-wind component and the v-wind component values of each lattice point at the designated place moment are calculated by combining a nearest neighbor interpolation method and a place-time conversion formula. The specific implementation process is as follows: the horizontal resolution of the data according to NCEP-DOE analysis 2 is 2.5 DEG, so the global is divided into 73 longitude zones with 2.5 DEG as intervals; next, when each longitude zone of the world is unified into one place, for different longitude zones, the u-wind component and v-wind component values of the appointed place moment are found by adopting adjacent point interpolation three days before and after the corresponding original data set (yesterday, the current day and the tomorrow) are selected at certain UTC of the current day.
Step five in this example, the process was averaged over many years. Firstly, synthesizing wind speed data of a u-wind component and a v-wind component of a designated local moment obtained in the step four into wind speed data of the local moment, wherein the specific formula is as follows:
where j=1, 2, 3..n is time series, N is total days, W j The wind speed values after the synthesis of u-wind and v-wind. Secondly, carrying out average treatment on the obtained wind speed value of each grid point at each local moment according to the total number of days, wherein the formula is as follows:
where j=1, 2,3,..n is the time series, N is the total number of days,
represents the average wind speed value of the ith grid point, and the unit is m/s. The calculation of the average wind direction at the moment of the appointed place adopts a vector method, and the specific formula is as follows:
wherein ,Ai The average wind direction for the ith grid point,
for the mean component of the wind speed of the ith grid point in the latitudinal direction, +.>
Is the average component of the i-th grid point wind speed in the longitudinal direction.
In step six of this embodiment, the data is visualized. The method specifically refers to the step five of obtaining the annual average wind speed and the average wind direction data of the appointed place and visualizing the annual average wind speed and the average wind direction data by using a computer program.
The method can obtain the distribution result of the annual average wind speed and the average wind direction of 10512 points worldwide from 2013 to 2018 at the 20mb isobaric surface at the moment of the appointed place.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the appended claims.
Claims (5)
1. A method of obtaining wind farm distribution at different locations using a wind farm dataset, the method comprising:
collecting and summarizing the wind field data set to obtain global wind field data taking the world time as a reference;
converting global wind field data based on the world time into global wind field data based on the designated local time;
global wind field data taking the world time as a reference of any adjacent three days is taken as an original data set, global rainfall data of one hour interval of the next day is obtained by adopting adjacent point interpolation, and thus, the gridded global wind field data of one hour interval taking the time of a designated place as a reference is obtained;
calculating the wind field distribution at the place according to the global wind field data of all days at the place;
the global wind field data taking the world time as a reference is converted into global wind field data taking the designated local time as a reference; the method comprises the following steps:
LT＝(UTC-12)+Lon/15°
where LT represents local time, UTC represents universal time, lon represents longitude of the geographic location where the grid point is located;
calculating the wind field distribution in the place according to the global wind field data of all days in the place; the method specifically comprises the following steps:
u10 component U10 of ith lattice point when place is to be designated i,j And V10 component V10 i,j Synthesized as wind speed data:
wherein j=1, 2,3, …, N is the time series, N is the total number of days, W ij The synthesized wind speed value;
averaging the wind speed value of the ith grid point in the appointed place according to the total days:
wherein ,
representing the average wind speed value of the ith grid point, wherein the unit is m/s;
calculating the average wind direction of the ith lattice point by adopting a vector method:
wherein ,Ai The average wind direction for the ith grid point,
wind speed is in east-west direction for the ith grid pointAverage component on>
Is the average component of the wind speed of the ith grid point in the north-south direction.
2. The method of claim 1, wherein the universal time based global wind field data includes an east-west wind speed vector U10 component and a north-south wind speed vector V10 component for each grid point 10m from the ground, wherein the grid points are divided by 0.25 ° x 0.25 ° in resolution.
3. A method of obtaining wind farm distribution at different locations using a wind farm dataset, the method comprising:
collecting and summarizing the wind field data set to obtain global wind field data taking the world time as a reference;
performing time weight linear interpolation on global wind field data with time resolution of not 1 hour, so as to obtain weft and warp wind component data of each hour in the whole day;
converting global wind field data based on the world time into global wind field data based on the designated local time;
global wind field data taking the world time as a reference of any adjacent three days is taken as an original data set, global rainfall data of one hour interval of the next day is obtained by adopting adjacent point interpolation, and thus, the gridded global wind field data of one hour interval taking the time of a designated place as a reference is obtained;
calculating the wind field distribution at the place according to the global wind field data of all days at the place;
the global wind field data taking the world time as a reference is converted into global wind field data taking the designated local time as a reference; the method comprises the following steps:
LT＝(UTC-12)+Lon/15°
where LT represents local time, UTC represents universal time, lon represents longitude of the geographic location where the grid point is located;
calculating the wind field distribution in the place according to the global wind field data of all days in the place; the method specifically comprises the following steps:
the u-wind component u of the ith lattice point when the place is designated i,j And v-wind component v i,j Synthesizing wind speed data at the local moment:
wherein j=1, 2,3, …, N is the time series, N is the total number of days, W ij The wind speed value is synthesized;
averaging the wind speed value of the ith grid point at the moment of the appointed place according to the total days:
wherein ,
representing the average wind speed value of the ith grid point, wherein the unit is m/s;
calculating the average wind direction of the ith lattice point by adopting a vector method:
wherein ,Ai The average wind direction for the ith grid point,
for the mean component of the wind speed of the ith grid point in latitude, +.>
Is the average component of the wind speed of the ith grid point in terms of longitude.
4. A method according to claim 3, wherein the world time based global wind field data comprises a weft wind component u-wind and a warp wind volume v-wind for each grid point, wherein grid points are divided by a resolution of 2.5 ° x 2.5 °; the total four observation times per day were 00, 06, 12, and 18UTC, respectively.
5. The method of claim 4, wherein the global wind field data with a time resolution other than 1 hour is time-weighted linearly interpolated to obtain the weft and warp wind component data for each hour throughout the day; the method comprises the following steps:
carrying out time weight interpolation on the wind field data of each grid point, wherein the specific formula is as follows:
u interp ＝P before *u before +P after *u after
v interp ＝P before *v before +P after *v after
wherein ,uinterp and vinterp The weft wind component and the warp wind component after linear interpolation are respectively, P before and Pafter Respectively represent the required interpolation timePoint t interp The previous known time t before And a later known time t agter Time weights of u before and vbefore Respectively the time point t before Weft-wise and warp-wise wind components, u after and vafter Respectively the time point t after A weft wind component and a warp wind component.
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