阅读说明：本技术 一种高分辨率高空风的短期预报方法 (Short-term forecasting method for high-resolution high-altitude wind ) 是由 施萧 冯箫 聂于棚 甘思旧 郭学文 张滢 赵兴娜 贵志成 樊晶 赵小平 张晓杰 于 2020-12-10 设计创作，主要内容包括：本发明涉及一种高分辨率高空风的短期预报方法,属于航天气象保障中高分辨率高空风的预报技术领域。本发明针对航天气象保障中高分辨率高空风的预报保障需求,利用欧洲中期数值预报产品、北斗导航测风数据,通过WRF模式设计、高空风模式产品融合、风场概率分布订正、统计订正等方法实现了0.25～20km逐250m分层高空风预报。通过该方法可实现逐层高空风预报,提高分层高空风预报准确率,适用于高分辨率高空风预报。(The invention relates to a short-term forecasting method for high-resolution high-altitude wind, and belongs to the technical field of forecasting of high-resolution high-altitude wind in space weather guarantee. Aiming at the forecasting guarantee requirement of high-resolution high-altitude wind in space meteorological guarantee, the method realizes 0.25-20 km-250 m layered high-altitude wind forecasting by using European middle-term numerical forecasting products and Beidou navigation wind measurement data through WRF mode design, high-altitude wind mode product fusion, wind field probability distribution correction, statistical correction and other methods. The method can realize the layer-by-layer high-altitude wind forecast, improves the accuracy of the layer-by-layer high-altitude wind forecast, and is suitable for the high-resolution high-altitude wind forecast.)

1. A short-term forecasting method for high-resolution high-altitude wind is characterized by comprising the following steps:

s1, designing a WRF mode:

the WRF mode is vertically layered into 46 layers, and the WRF mode is integrated to generate 46 layers of wind field products 1 hour by 1 hour; in the post-processing of the wind field product, interpolating 46 layers into 82 layers to realize 250-20500m and 250 m-by-250 m WRF high-altitude wind products;

s2, fusing European numerical prediction and WRF high-altitude wind products:

dynamically matching the potential height of 17 layers within 250-20500m of European numerical prediction with the nearest height layer of 250m to obtain the corresponding integral multiple height of 250 m; calculating the trend change of the vertical wind profile of the WRF high-altitude wind product in a manner of 250m per minute, and feeding back the WRF trend to the wind profile of the European numerical prediction by adopting a method of piecewise interpolation;

s3, correcting the probability distribution of the wind field:

according to the structure of the rapid flow shaft, dividing a wind field into a wind speed increasing area, a strong wind area and a wind speed decreasing area; on the basis of high-altitude wind product fusion, adjusting wind fields of three areas, namely a wind speed increasing area, a high wind area and a wind speed decreasing area according to normal and skewed distribution;

s4, correcting statistics

And (3) correcting the rolling error of each layer of wind field after correcting the probability distribution of the wind field in the forecasting service based on an MOS principle according to the latest navigation wind measuring situation.

2. The method for short term prediction of high resolution high altitude winds as claimed in claim 1, wherein said european numerical prediction includes 19 layers of products, 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 20, 10 hPa; the two layers discarded in step S2 are 20hPa and 10 hPa.

3. The short-term prediction method of high-resolution high-altitude wind according to claim 1, characterized in that the WRF mode adopts terrain following vertical coordinates, the value of the lowest layer of the mode is 1, and the value of the top of the mode layer is 0; the WRF is vertically layered as 46 layers, 1.00, 0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76, 0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.57, 0.54, 0.51, 0.48, 0.45, 0.42, 0.39, 0.36, 0.33, 0.30, 0.27, 0.24, 0.21, 0.18, 0.15, 0.12, 0.09, 0.06, 0.03, 0.00, respectively.

4. The short-term prediction method for high-resolution high-altitude wind as claimed in claim 3, wherein the integration of the WRF mode in step S1 is performed to generate 46 layers of wind field products 1 hour by hour, and in the post-processing of the wind field products, the 46 layers are interpolated into 82 layers, so as to realize 250-20500m 250-m WRF high-altitude wind products specifically include: and performing integration operation for 72 hours in a WRF mode, generating 46 layers of high-resolution layer-by-layer wind field products once per hour by setting, and interpolating the WRF high-altitude wind products into 82 layers by using an ARWpost (Post-Processing of Advanced Research WRF, Post-Processing module of the WRF mode) in the Post-Processing of the wind field products, namely 250-reservoir 20500m 250 m-by-250 m WRF high-altitude wind products.

5. The short-term prediction method for high-resolution high-altitude wind as claimed in any one of claims 1 to 3, wherein the step S2 specifically comprises the following steps:

s21, converting the adjacent layer potential height gh of 17 layers with the European numerical prediction within 250-20500m into the corresponding vertical layer f with the potential height of 250m_lev1 and f_lev2；

S22, the sequence f of the latitudinal wind or the latitudinal wind of WRF at the height_lev1 to f_lev2, carrying out normalization processing to obtain a normalized sequence value;

s23, forecasting the European numerical value in the latitudinal wind field or the latitudinal wind field of the adjacent layerAs the head and tail values of the sequence value, carrying out interpolation according to the normalized sequence to obtain f_lev1 to f_lev2, a new latitudinal wind field or a longitudinal wind field.

6. The method for short-term prediction of high-resolution high-altitude wind as claimed in claim 5, wherein the step S21 specifically comprises: the potential height gh of the adjacent layer of 17 layers within 250-20500m of European numerical prediction is obtained by adopting a formula (1) to obtain the corresponding integral multiple height f of 250m_lev1 and f_lev2，

7. The method for short-term prediction of high-resolution high-altitude wind as claimed in claim 5, wherein the step S22 specifically comprises:

for a latitudinal wind, the latitudinal wind of the WRF is in the sequence f of the heights_lev1 to f_lev2, carrying out normalization processing by adopting a formula (2) to obtain a sequence value after normalization;

wherein u is_minMinimum wind field, u, of the sequence of heights for the latitudinal wind of WRF_maxThe maximum wind field of the latitudinal wind of the WRF at the height sequence is defined, u is the wind field of the latitudinal wind of the WRF at the current height, and u' is the normalization value of the latitudinal wind of the WRF at the current height;

for the transoceanic wind, the transoceanic wind of the WRF is at the altitude sequence f_lev1 to f_lev2, carrying out normalization processing by adopting a formula (3) to obtain a sequence value after normalization;

wherein v is_minMinimum wind field, v, of the sequence of heights for the transverse wind of WRF_maxThe wind field is the maximum wind field of the WRF at the sequence of heights, v is the wind field of the WRF at the current height, and v' is the normalized value of the WRF at the current height.

8. The method for short-term prediction of high-resolution high-altitude wind as claimed in claim 1, wherein the step S3 of adjusting the wind fields of the three regions of the wind speed increasing region, the high wind region and the wind speed decreasing region according to the normal and skewed distributions specifically comprises: and smoothing the wind fields of three areas, namely a wind speed increasing area, a strong wind area and a wind speed decreasing area according to normal and skewed distribution.

9. The short-term prediction method for high-resolution high-altitude wind as claimed in claim 1, wherein the step S4 specifically comprises the following steps:

s41, rolling and correcting by using the forecast wind and the actual wind measuring situation of the previous 2 days, and solving the error coefficient w_t；

y_t'＝y_t+w_t×e_t-1 (4)

Error e_t-1Taking the running average of the errors in the first 2 days, y_tTo predict value, y_t' calculating error coefficient w according to least square method as live value_t；

S42, correcting the rolling error of each layer of wind field corrected in the step S3 in the forecasting service;

and (4) performing secondary correction on each layer of wind field corrected in the step S3 according to the formula (4), wherein y is_tFor the corrected forecast value, y, of step S3_t' is the predicted value after rolling error correction.

10. The method for short-term prediction of high-resolution high-altitude wind according to claim 9, wherein the wind measurement scene is a beidou navigation wind measurement scene.

Technical Field

The invention belongs to the technical field, and particularly relates to a short-term forecasting method for high-resolution high-altitude wind.

Background

High-altitude wind is an important factor for aerospace weather guarantee. When the carrier rocket passes through the atmosphere, the flight attitude of the rocket is influenced by the wind load brought by high-altitude strong wind, and certain influence is caused on the structural reliability of the rocket. The new generation carrier rocket in China puts higher requirements on high altitude wind weather guarantee, and high altitude wind forecast is more precise. There is currently no relevant research to address this problem. The high-altitude wind forecasting method provided by the text can well solve the problem.

Disclosure of Invention

Technical problem to be solved

The invention aims to solve the technical problem of how to provide a high-resolution short-term high-altitude wind forecasting method so as to solve the problem of high-altitude wind fine forecasting.

(II) technical scheme

In order to solve the technical problem, the invention provides a short-term forecasting method of high-resolution high-altitude wind, which comprises the following steps:

s1, designing a WRF mode:

the WRF mode is vertically layered into 46 layers, the WRF mode is integrated to generate 46 layers of wind field products 1 hour by hour, and in the post-processing of the wind field products, the 46 layers are interpolated into 82 layers to realize 250-250 m WRF high altitude wind products 20500m by 250 m;

s2, fusing European numerical prediction and WRF high-altitude wind products:

dynamically matching the potential height of 17 layers within 250-20500m of European numerical prediction with the nearest height layer of 250m to obtain the corresponding integral multiple height of 250 m; calculating the trend change of the vertical wind profile of the WRF high-altitude wind product in a manner of 250m per minute, and feeding back the WRF trend to the wind profile of the European numerical prediction by adopting a method of piecewise interpolation;

s3, correcting the probability distribution of the wind field:

according to the structure of the rapid flow shaft, dividing a wind field into a wind speed increasing area, a strong wind area and a wind speed decreasing area; the wind field in the big wind area is normally distributed in the vertical direction, and the wind field in the wind speed descending area and the wind speed increasing area is distributed in a left skewed state and a right skewed state. On the basis of high-altitude wind product fusion, adjusting wind fields of three areas, namely a wind speed increasing area, a high wind area and a wind speed decreasing area according to normal and skewed distribution;

s4, correcting statistics

And (3) correcting the rolling error of each layer of wind field after correcting the probability distribution of the wind field in the forecasting service based on an MOS principle according to the latest navigation wind measuring situation.

Further, the european numerical forecast comprises 19 layers of products, 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 20, 10hPa, respectively; the two layers discarded in step S2 are 20hPa and 10 hPa.

Furthermore, the WRF mode adopts a terrain following vertical coordinate, the value of the lowest layer of the mode is 1, and the value of the top of the mode layer is 0; the WRF is vertically layered as 46 layers, 1.00, 0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76, 0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.57, 0.54, 0.51, 0.48, 0.45, 0.42, 0.39, 0.36, 0.33, 0.30, 0.27, 0.24, 0.21, 0.18, 0.15, 0.12, 0.09, 0.06, 0.03, 0.00, respectively.

Further, the integrating the WRF mode in step S1 generates 46 layers of wind farm products 1 hour by hour, and in the post-processing of the wind farm products, the 46 layers are interpolated into 82 layers, so as to realize a WRF high altitude wind product 250m by 250m with 20500m specifically includes: and performing integration operation for 72 hours on each layer of data in the WRF mode, generating 46 layers of high-resolution layer-by-layer wind field products once per hour through setting, and interpolating the WRF high-altitude wind products into 82 layers by using ARWpost (Post-Processing of Advanced Research WRF, Post-Processing module in the WRF mode) in the Post-Processing of the wind field products, namely 250-reservoir 20500m WRF high-altitude wind products with 250 m.

Further, the step S2 specifically includes the following steps:

s21, converting the adjacent layer potential height gh of 17 layers with the European numerical prediction within 250-20500m into the corresponding vertical layer f with the potential height of 250m_lev1 and f_lev2；

S22, the sequence f of the latitudinal wind or the latitudinal wind of WRF at the height_lev1 to f_lev2, carrying out normalization processing to obtain a normalized sequence value;

s23, taking the value of the European numerical forecast in the latitudinal wind field or the latitudinal wind field of the adjacent layer as the head and tail values of the sequence value, and carrying out interpolation according to the normalized sequence to obtain f_lev1 to f_lev2, a new latitudinal wind field or a longitudinal wind field.

Further, the step S21 specifically includes: the potential height gh of the adjacent layer of 17 layers within 250-20500m of European numerical prediction is obtained by adopting a formula (1) to obtain the corresponding integral multiple height f of 250m_lev1 and f_lev2，

Further, the step S22 specifically includes:

for a latitudinal wind, the latitudinal wind of the WRF is in the sequence f of the heights_lev1 to f_lev2, carrying out normalization processing by adopting a formula (2) to obtain a sequence value after normalization;

wherein u is_minIs the weft direction of WRFMinimum wind field of wind in the sequence of heights, u_maxThe maximum wind field of the latitudinal wind of the WRF at the height sequence is defined, u is the wind field of the latitudinal wind of the WRF at the current height, and u' is the normalization value of the latitudinal wind of the WRF at the current height;

for the transoceanic wind, the transoceanic wind of the WRF is at the altitude sequence f_lev1 to f_lev2, carrying out normalization processing by adopting a formula (3) to obtain a sequence value after normalization;

wherein v is_minMinimum wind field, v, of the sequence of heights for the transverse wind of WRF_maxThe wind field is the maximum wind field of the WRF at the sequence of heights, v is the wind field of the WRF at the current height, and v' is the normalized value of the WRF at the current height.

Further, the adjusting of the wind field of the three areas, i.e., the wind speed increasing area, the high wind area and the wind speed decreasing area according to the normal and the skewed distribution in step S3 specifically includes: and smoothing the wind fields of three areas, namely a wind speed increasing area, a strong wind area and a wind speed decreasing area according to normal and skewed distribution.

Further, the step S4 specifically includes the following steps:

s41, rolling and correcting by using the forecast wind and the actual wind measuring situation of the previous 2 days, and solving the error coefficient w_t；

y_t'＝y_t+w_t×e_t-1 (4)

Error e_t-1Taking the running average of the errors in the first 2 days, y_tFor the first prediction value, y_t' calculating error coefficient w according to least square method as live value_t；

S42, correcting the rolling error of each layer of wind field corrected in the step S3 in the forecasting service;

and (4) performing secondary correction on each layer of wind field corrected in the step S3 according to the formula (4), wherein y is_tFor the corrected forecast value, y, of step S3_t' after correction for rolling errorThe predicted value of (a).

Further, the wind measuring live scene is a Beidou navigation wind measuring live scene.

(III) advantageous effects

The invention provides a high-resolution high-altitude wind short-term forecasting method which realizes 0.25-20 km high-altitude wind forecasting layer by 250m by utilizing European middle-term numerical forecasting products and Beidou navigation wind measurement data and through WRF mode design, high-altitude wind mode product fusion, wind field probability distribution correction, statistical correction and other methods. The method can realize the layer-by-layer high-altitude wind forecast, improves the accuracy of the layer-by-layer high-altitude wind forecast, and is suitable for the high-resolution high-altitude wind forecast.

Drawings

FIG. 1 is a diagram of the effect of forecasting high altitude wind in a certain day;

Detailed Description

In order to make the objects, contents and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.

The WRF (weather Research and Forecasting model) model is a unified, mid-scale weather Forecasting model developed by the American environmental Forecasting center (NCEP) American atmospheric Research center (NCAR) and a combination of multiple universities, institutes and business departments.

According to the method, the probability distribution correction and the statistical correction of the wind field are carried out on the basis of probability distribution characteristics and the actual sounding situation through WRF regional mode design, European numerical prediction and WRF mode product fusion, and the high-altitude wind prediction of 80 layers is realized by adding 250m by 250m below the high-altitude wind of 20 km. The method is suitable for high-resolution high-altitude wind forecasting.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

s1, designing a WRF mode:

and (3) the WRF mode is vertically layered into 46 layers, the WRF mode is integrated to generate 46 layers of wind field products 1 hour by hour, and in the post-processing of the wind field products, the 46 layers are interpolated into 82 layers to realize 250-20500m WRF high-altitude wind products 250m by 250 m.

S2, fusing European numerical prediction and WRF high-altitude wind products:

dynamically matching the potential height of 17 layers within 250-20500m of European numerical prediction with the nearest height layer of 250m to obtain the corresponding integral multiple height of 250 m; and calculating the trend change of the vertical wind profile of the WRF high-altitude wind product in a manner of 250m per minute, and feeding back the WRF trend to the wind profile of the European numerical prediction by adopting a method of piecewise interpolation.

S3, correcting the probability distribution of the wind field:

according to the structure of the rapid flow shaft, the wind field is divided into a wind speed increasing area, a strong wind area and a wind speed decreasing area.

Two assumptions were made: 1) the wind speed in the high wind area is normally distributed in the vertical direction; 2) for the high wind level with the wind speed falling or rising trend, the wind field is distributed in a left partial state or a right partial state. On the basis of high-altitude wind product fusion, wind fields of three areas, namely a wind speed increasing area, a high wind area and a wind speed decreasing area, are adjusted according to normal and skewed distribution.

S4, correcting statistics

And (4) according to the latest navigation wind measuring situation, based on an MOS principle, correcting the rolling error of each layer of wind field after correcting the probability distribution in the forecasting service.

And performing rolling correction by using the forecast wind and the difference between the actual forecasts of the first two days.

The short-term forecasting method of the high-resolution high-altitude wind specifically comprises the following steps:

s1 WRF mode design

The WRF numerical mode is driven according to the 19-tier product (1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 20, 10hPa, respectively) of the numerical forecast (hereinafter abbreviated as european numerical forecast) released by the european mid-term weather forecast center. Each layer of products comprises data on time, wind, temperature, humidity, potential height and the like.

The WRF (weather Research and Forecasting model) model is a unified, mid-scale weather Forecasting model developed by the American environmental Forecasting center (NCEP) American atmospheric Research center (NCAR) and a combination of multiple universities, institutes and business departments.

The WRF mode adopts a terrain following vertical coordinate, the value of the lowest layer of the mode is 1, and the value of the top of the mode layer is 0. The WRF is typically layered vertically in 46 layers, 1.00, 0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76, 0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.57, 0.54, 0.51, 0.48, 0.45, 0.42, 0.39, 0.36, 0.33, 0.30, 0.27, 0.24, 0.21, 0.18, 0.15, 0.12, 0.09, 0.06, 0.03, 0.00, respectively. Each layer includes data on time, wind, temperature, humidity, potential altitude, etc.

The WRF mode was run for 72 hours of integration, with a set-up of 46 layers of high-resolution, layer-by-layer wind farm product generation every hour. In the Post-Processing of the wind field product, the WRF high-altitude wind product is interpolated into 82 layers by using an ARWpost (Post-Processing of Advanced Research WRF, Post-Processing module of WRF mode), namely 250-20500m WRF high-altitude wind products one by one, wherein the WRF high-altitude wind product is obtained by interpolation.

S2, fusion of European numerical prediction and WRF high-altitude wind product

The potential heights of 20 and 10hPa in the European numerical forecast are too high, so the remaining 17 layers are usually selected to be fused with WRF high altitude wind products.

Dynamically matching the potential height (gh) of the 17 layers with the height layer nearest to 250m within the range of 250-20500m according to the European numerical prediction to obtain the integral multiple height (f) of 250m corresponding to the potential height of the 17 layers_lev)。

And calculating the trend change of the vertical wind profile predicted by WRF in a 250 m-by-250 m manner, and feeding back the WRF trend to the wind profile predicted by the European numerical value by adopting a piecewise interpolation method to obtain 82 layers of latitude wind (u) and longitude wind (v) predicted by the European numerical value.

Take 600-500hPa latitudinal wind field as an example

S21, converting the potential height (gh) of the adjacent layer of 17 layers within 250-20500m of the European numerical prediction into a corresponding vertical layer f with potential height of 250m by 250m_lev1 and f_lev2; for example, the potential heights of the adjacent layers 600-500hPa are converted into corresponding 250m potential height vertical layers;

specifically, the potential height (gh) of the adjacent layer of 17 layers within 250-20500m of European numerical prediction is obtained by adopting a formula (1) to obtain the corresponding integral multiple height f of 250m_lev1 and f_lev2; for example, a potential height of 600hpa translates into a corresponding integral multiple height f of 250m_lev1 is 4250m, the potential height of 500hpa is converted into the corresponding integral multiple height f of 250m_lev2 is 5750 m; then 4500, 4750, 5000, 5250 and 5500 latitudinal wind field and longitudinal wind field between 4250m-5750mm need to be obtained;

s22 weft wind of WRF in the height sequence f_lev1 to f_lev2, carrying out normalization processing by adopting a formula (2) to obtain a sequence value after normalization;

wherein u is_minMinimum wind field, u, of the sequence of heights for the latitudinal wind of WRF_maxThe maximum wind field of the latitudinal wind of the WRF at the height sequence is defined, u is the wind field of the latitudinal wind of the WRF at the current height, and u' is the normalization value of the latitudinal wind of the WRF at the current height; thereby obtaining a WRF weftwise wind at f_lev1 to f_lev2, normalized sequence value;

for example, the latitudinal wind fields of 4250, 4500, 4750, 5000, 5250 and 5500 within 4250m-5750mm of the latitudinal wind of the WRF are normalized to obtain sequence values which respectively represent the latitudinal wind fields at all heights.

S23, taking the latitude direction wind field value of the European numerical prediction in the adjacent layer as the head and tail values of the sequence value, and carrying out interpolation according to the normalized sequence to obtain f_lev1 to f_lev2, new latitudinal wind field.

Similarly, calculating the wind direction uses the following equation

Wherein v is_minMinimum wind field, v, of the sequence of heights for the transverse wind of WRF_maxThe wind field is the largest wind field of the warp wind of the WRF at the height sequence, v is the wind field of the warp wind of the WRF at the current height, and v' is the normalized value of the warp wind of the WRF at the current height; thereby obtaining the Trans-wind at f of the WRF_lev1 to f_lev2, normalized sequence value; the normalized sequence value is used for interpolating the warp wind field with European numerical prediction of 600hPa and 500hPa to obtain f_lev1 to f_lev2, new meridional wind field.

S3 correcting probability distribution of wind field

The high altitude torrent is a narrow and long airflow band above about 9km above the upper troposphere and with the wind speed greater than 30 m/s. The long axis of the high altitude torrent center is called the torrent axis and is distributed approximately in the latitudinal direction. Wind shear per hundred kilometers is about 5m/s in the horizontal direction and 5-10m/s in the vertical direction. The vertical wind shear area of the fast flow zone is treated as follows.

According to the structure of the rapid flow shaft, the wind field is divided into a wind speed increasing area, a strong wind area and a wind speed decreasing area. The wind speed increasing area is a transition area from low wind speed to rapid flow; the wind speed descending area is a transition area from rapid flow to low wind speed; the strong wind area is the main influence area of the torrent.

Two assumptions were made: 1) the wind speed in the high wind area is normally distributed in the vertical direction; 2) for the high wind level with the wind speed falling or rising trend, the wind field is distributed in a left partial state or a right partial state. And then performing trend adjustment according to normal and skewed distributions.

On the basis of high-altitude wind product fusion, wind fields of three areas, namely a wind speed increasing area, a strong wind area and a wind speed decreasing area, are smoothly processed according to normal and skewed distribution.

S4, correcting statistics

In weather forecasting, deviation between a live condition (actually detected high-altitude wind condition) and a forecast condition (forecasted high-altitude wind) needs to be considered, and actual forecast is guided through secondary correction of a deviation sequence. A MOS (Model output statistics method) is a commonly used dynamic statistics prediction method, which fully considers the deviation between prediction and actual conditions and can establish a linear or non-linear algorithm for specific implementation.

And (3) according to the latest Beidou navigation wind measuring situation, based on the MOS principle, correcting the rolling error of each layer of fused wind field in the forecasting service. The method comprises the following specific steps:

s41, performing rolling correction by only using the forecast wind of the first 2 days and the Beidou navigation wind measurement actual condition, and solving an error coefficient w_t；

y_t'＝y_t+w_t×e_t-1 (4)

Error e_t-1Taking the running average of the errors in the first 2 days, y_tTo predict value, y_t' calculating error coefficient w according to least square method as live value_t。

And S42, correcting the rolling error of each layer of wind field after the fusion and wind field probability distribution correction in the forecasting service.

And (4) performing secondary correction on each layer of wind field corrected in the step S3 according to the formula (4), wherein y is_tFor the corrected forecast value, y, of step S3_t' is the predicted value after rolling error correction.

The high-resolution high-altitude wind short-term forecast calculation method mainly comprises the following steps: driving a WRF mode by using a European middle-term numerical forecast product to generate a regional mode product with higher vertical resolution; feeding back the trend of the wind profile forecasted by WRF to a European middle-term numerical forecasting product to realize the fusion of the two products; and then the final high-altitude wind product is obtained by utilizing a probability and power correction method.

1. WRF mode design:

and driving a WRF numerical mode according to a 19-layer product driving WRF numerical mode of European numerical prediction. The WRF numerical mode is driven according to the 19-tier product (1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 20, 10hPa, respectively) of the numerical forecast (hereinafter abbreviated as european numerical forecast) released by the european mid-term weather forecast center. The WRF is vertically layered as 46 layers, 1.00, 0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76, 0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.57, 0.54, 0.51, 0.48, 0.45, 0.42, 0.39, 0.36, 0.33, 0.30, 0.27, 0.24, 0.21, 0.18, 0.15, 0.12, 0.09, 0.06, 0.03, 0.00, respectively.

The WRF mode was run for 72 hours of integration, with a set-up of once per hour high resolution wind farm production.

In the WRF mode Post-Processing, the WRF high-altitude wind product is interpolated into 82 layers by using ARWpost (Post-Processing of Advanced Research WRF, Post-Processing module of WRF mode), namely 250 layers and 20500m, one by one.

2. The European mid-term numerical forecasting product is fused with the WRF high-altitude wind product:

and dynamically matching the potential height of 17 layers in 250-20500m of European numerical prediction with the nearest height layers of 250m to obtain the corresponding trans-wind and latitudinal wind with integral multiple height of 250 m.

And calculating the trend change of the vertical wind profile predicted by WRF from 250m to 250m, and feeding back the WRF trend to the European numerically-predicted wind profile by adopting a piecewise interpolation method.

Taking a 600-500hPa latitudinal wind field as an example:

1) the potential height of 600-500hPa is converted into a corresponding 250m potential height vertical layer;

2) normalizing the height sequence by the wind direction of the WRF to obtain a normalized sequence value;

3) and (3) taking the trans-wind field values with European numerical value forecasts at 600hPa and 500hPa as the head and tail values of the sequence values, and carrying out interpolation according to the normalized sequence to obtain a new sequence value as a new latitudinal wind field.

Similarly, a new warp wind direction may be calculated.

3. Correcting the probability distribution of the wind field:

according to the structure of the rapid flow shaft, the wind field is divided into a wind speed increasing area, a strong wind area and a wind speed decreasing area.

Two assumptions were made: 1) the wind speed in the high wind area is normally distributed in the vertical direction; 2) for the high wind level with the wind speed falling or rising trend, the wind field is distributed in a left partial state or a right partial state. And then performing trend adjustment according to normal and skewed distributions.

And correcting the wind fields of three areas, namely a wind speed increasing area, a strong wind area and a wind speed decreasing area according to normal and skewed distribution.

4. And (3) statistical correction: and (4) performing rolling error correction on each layer of the fused wind field in the forecasting service based on an MOS principle according to the latest navigation exploration situation. And performing rolling correction by using the difference between the forecast wind and the actual forecast in the previous 2 days.

y_t'＝y_t+w_t×e_t-1 (4)

Error e_t-1A running average of the errors was taken over the first 2 days. Calculating error coefficient w according to least square method_t。y_tTo predict value, y_t' is the predicted value after correction.

The prediction accuracy of the same layer of the latitudinal wind U and the longitudinal wind V is shown in the table 1.

TABLE 1 same layer U, V wind forecast accuracy

(accuracy is recorded if the forecast live deviation of the same layer wind, U and V are both within threshold values)

As shown in fig. 1, prediction and actual situation of the high altitude wind profile and wind plumes of 12 days 2 and month 2020 are compared (prediction is a predicted wind profile and vector wind plumes below 20000m, and actual wind profile and vector wind plumes are detected for beidou high altitude in actual situation).

The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.