CN112632791B - Turbulence dissipation rate prediction method, device, electronic equipment and storage medium - Google Patents

Abstract

The present invention provides a turbulence dissipation rate prediction method, device, electronic device and storage medium, wherein the method comprises: obtaining the prediction result of the turbulence index of the target location according to the numerical weather forecast data of the target location; obtaining the prediction result of the turbulence dissipation rate of the target location according to the prediction result of the turbulence index of the target location; wherein the prediction result of the turbulence index of the target location includes at least one prediction result of the clear sky turbulence index and at least one prediction result of the mountain wave. The turbulence dissipation rate prediction method, device, electronic device and storage medium provided by the embodiments of the present invention increase the discreteness of the forecast by combining the clear sky turbulence index and the turbulence dissipation rate corresponding to the mountain wave, can obtain a result that is better than a single index forecast, can improve the hit rate and reduce the false alarm rate, and has a higher forecast accuracy.

Description

Turbulence dissipation rate prediction method, device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of computer technology, and in particular to a turbulence dissipation rate prediction method, device, electronic equipment and storage medium.

Background technique

When an aircraft encounters turbulence, the shaking of the aircraft body may affect the safe operation of the aircraft and cause certain damage, or even cause injuries to personnel. Therefore, turbulence is a dangerous weather condition on the route that is of great concern. Turbulence is mainly divided into clear air turbulence, mountain waves, convective turbulence and turbulence near clouds. According to the vertical distribution characteristics of turbulence forecast between any two points on the route, aircraft operation can be guided in a targeted manner.

The turbulence dissipation rate (EDR) indicates the rate at which turbulent kinetic energy is converted into heat. The turbulence dissipation rate data obtained by airborne detection equipment is used to characterize the actual aircraft turbulence. The existing turbulence dissipation rate forecast is mainly formed by calculating parameters such as the Ellord index, Dutton index, Brown index and Richardson number, and then comparing and correcting them with the actual situation to form a mild, moderate or severe level forecast. However, the subjective turbulence level forecast has the limitation of being different between different aircraft models, and is inconsistent with the actual turbulence units obtained by airborne detection. Therefore, the existing turbulence dissipation rate forecast method has the shortcomings of poor accuracy and precision.

Summary of the invention

The present invention provides a turbulence dissipation rate prediction method, device, electronic device and storage medium, which are used to solve the defect of poor accuracy of turbulence dissipation rate prediction in the prior art and realize more accurate turbulence dissipation rate prediction matching the actual turbulence situation.

The present invention provides a turbulent dissipation rate prediction method, comprising:

Obtaining a forecast result of a turbulence index at the target location according to numerical weather forecast data at the target location;

According to the prediction result of the turbulence index of the target position, obtaining the prediction result of the turbulence dissipation rate of the target position;

The forecast result of the turbulence index of the target location includes at least one corresponding single forecast result of the clear sky turbulence index and at least one forecast result of the mountain wave.

According to a turbulence dissipation rate forecasting method provided by the present invention, the specific steps of obtaining the forecast result of the turbulence index of the target location according to the numerical weather forecast data of the target location include:

According to the numerical weather forecast data of the target location, the forecast result of the at least one clear sky turbulence index and the forecast result of the at least one mountain wave are obtained.

According to a turbulence dissipation rate prediction method provided by the present invention, the specific steps of obtaining the prediction result of the turbulence dissipation rate at the target position according to the prediction result of the turbulence index at the target position include:

Inputting the at least one clear-air turbulence index and the at least one mountain wave into corresponding conversion models respectively, and outputting a single forecast result corresponding to each clear-air turbulence index and a single forecast result corresponding to each mountain wave;

Obtaining a first forecast result according to a single forecast result corresponding to each of the clear sky turbulence indices, and obtaining a second forecast result according to the forecast results of the turbulence indices corresponding to each of the mountain waves;

According to the first forecast result and the second forecast result, a third forecast result is obtained, and the first forecast result, the second forecast result and the third forecast result are used as the forecast result of the turbulence dissipation rate at the target position;

The conversion model is obtained based on historical data of turbulence dissipation rate and clear air turbulence index at the sample location, or historical data of turbulence dissipation rate and mountain waves.

According to a turbulence dissipation rate prediction method provided by the present invention, the conversion model is:

lnD ^* ＝a+b*lnD

Where D ^* represents a single forecast result; D represents the clear air turbulence index or mountain wave; a and b represent pre-acquired coefficients.

According to a turbulence dissipation rate prediction method provided by the present invention, before the prediction result of the at least one clear-air turbulence index and the prediction result of the at least one mountain wave are respectively input into corresponding conversion models and a single prediction result corresponding to each clear-air turbulence index and a single prediction result corresponding to each mountain wave are output, the method further includes:

According to the historical data of the turbulence dissipation rate at the sample position, the expectation and standard deviation of the turbulence dissipation rate are obtained, and according to the historical data of each clear sky turbulence index and each mountain wave at the sample position, the expectation and standard deviation of the logarithm of each clear sky turbulence index, and the expectation and standard deviation of the logarithm of each mountain wave are obtained;

According to the expectation and standard deviation of the turbulence dissipation rate and the expectation and logarithmic standard deviation of each of the clear sky turbulence indices, a conversion model corresponding to each of the clear sky turbulence indices is obtained, and according to the expectation and standard deviation of the historical data of the turbulence dissipation rate and the expectation and logarithmic standard deviation of each of the mountain waves, a conversion model corresponding to each of the mountain waves is obtained.

According to a turbulent dissipation rate prediction method provided by the present invention, the first prediction result, the second prediction result and the third prediction result include deterministic prediction results and/or probabilistic prediction results.

According to a turbulence dissipation rate prediction method provided by the present invention, the clear air turbulence index includes Ellrod2, NGM1, IAWIND and |▽·T|/Ri, and the mountain waves include MWT4, MWT6, MWT9 and MWT12.

The present invention also provides a turbulent dissipation rate prediction device, comprising:

A parameter forecasting module, used for obtaining a forecast result of a turbulence index of a target location according to numerical weather forecast data of the target location;

An integrated prediction module, used for obtaining a prediction result of a turbulence dissipation rate at the target location according to a prediction result of a turbulence index at the target location;

The forecast result of the turbulence index of the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave.

The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned turbulence dissipation rate prediction methods when executing the computer program.

The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned turbulent dissipation rate prediction methods are implemented.

The turbulence dissipation rate prediction method, device, electronic device and storage medium provided by the present invention obtain the forecast result of the turbulence index of the target location based on the numerical weather forecast data of the target location, and obtain the forecast result of the turbulence dissipation rate of the target location according to the forecast result of the turbulence index of the target location, which can reflect the turbulence conditions of different regions and types, and increase the discreteness of the forecast by combining the clear sky turbulence index and the turbulence dissipation rate corresponding to mountain waves, and can obtain results that are better than those of a single index forecast, which can improve the hit rate and reduce the false alarm rate, wherein the forecast effect of turbulence above mild is better and has a higher forecast accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a schematic flow diagram of a method for predicting turbulent dissipation rate provided by the present invention;

FIG2 is a schematic diagram of the structure of a turbulent dissipation rate prediction device provided by the present invention;

FIG. 3 is a schematic diagram of the structure of an electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

In the description of the embodiments of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the embodiments of the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance, and do not involve order.

In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

In order to overcome the above-mentioned problems of the prior art, the present invention provides a turbulence dissipation rate prediction method, device, electronic device and storage medium. The inventive concept is to convert forecast indices such as clear air turbulence index and mountain wave into forecast values of turbulence dissipation rate units in real-time numerical forecasting and form an ensemble forecast, thereby avoiding the limitation that the subjective turbulence level forecast varies among different aircraft models, and the prediction results are more accurate, so they can be used to predict aircraft turbulence.

Fig. 1 is a schematic flow chart of a turbulence dissipation rate prediction method provided by the present invention. The turbulence dissipation rate prediction method of an embodiment of the present invention is described below in conjunction with Fig. 1. As shown in Fig. 1, the method comprises: step 101, obtaining a prediction result of a turbulence index at a target location according to numerical weather forecast data at the target location.

The forecast result of the turbulence index at the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave.

Specifically, the target location may be a certain location on the route, which is determined by latitude, longitude and altitude.

Numerical weather forecast data may include basic physical quantities such as horizontal wind, temperature, potential temperature, geopotential height and terrain height at the altitude layer.

Before step 101, the process may include obtaining numerical weather forecast data of the target location.

The GRAPES regional numerical model forecast product independently developed by the China Meteorological Administration can be used to obtain numerical weather forecast data for the target location.

The horizontal spatial resolution of GRAPES is 0.1°*0.1°, the vertical height is from 1000hPa to 100hPa, with a total of 20 layers, the time resolution is 1 hour, and the forecast time is 36 hours.

The turbulence index of the target location may include at least one clear air turbulence index and at least one mountain wave.

According to the above basic physical quantities in the numerical weather forecast data of the target location, a forecast result of at least one clear sky turbulence index and a forecast result of at least one mountain wave at the target location can be obtained.

It should be noted that the at least one clear air turbulence index and the at least one mountain wave index of the target location obtained above can both be used as forecast indices.

Step 102: Obtain the forecast result of the turbulence dissipation rate at the target position according to the forecast result of the turbulence index at the target position.

Specifically, the forecast result of the turbulence index of the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave. According to the forecast results of each turbulence index, a single forecast result of the turbulence dissipation rate corresponding to each turbulence index can be obtained; after obtaining the single forecast result of the turbulence dissipation rate corresponding to each turbulence index, the single forecast results of the turbulence dissipation rate of the target location can be fused to obtain the final forecast result of the turbulence dissipation rate of the target location.

To integrate the forecast results, data analysis methods such as mathematical statistics can be used.

An EDR value of 0.15-0.21m ^2/3 s ^-1 is considered mild turbulence, 0.22-0.34m ^2/3 s ^-1 is considered moderate turbulence, and greater than 0.34 is considered severe turbulence.

The embodiment of the present invention obtains the forecast result of the turbulence index of the target location based on the numerical weather forecast data of the target location, and obtains the forecast result of the turbulence dissipation rate of the target location according to the forecast result of the turbulence index of the target location, which can reflect the turbulence conditions of different regions and types, and increases the discreteness of the forecast by combining the clear sky turbulence index and the turbulence dissipation rate corresponding to mountain waves, and can obtain results that are better than those of a single index forecast, which can improve the hit rate and reduce the false alarm rate, and the forecast effect of turbulence above mild is better and has a higher forecast accuracy.

Based on the content of any of the above embodiments, the specific steps of obtaining the forecast result of the turbulence index of the target location according to the numerical weather forecast data of the target location include: obtaining at least one forecast result of the clear sky turbulence index and at least one forecast result of the mountain wave according to the numerical weather forecast data of the target location.

Specifically, based on the above basic physical quantities in the numerical weather forecast data of the target location, a forecast result of at least one clear sky turbulence index and a forecast result of at least one mountain wave at the target location can be obtained.

The embodiment of the present invention predicts the turbulence index based on the numerical weather forecast data of the target location, and can obtain a more accurate forecast result of the turbulence index, thereby obtaining a more accurate forecast result of the turbulence dissipation rate of the target location based on the forecast result of the turbulence index of the target location.

Based on the content of any of the above embodiments, the specific steps of obtaining the forecast results of the turbulence dissipation rate of the target location according to the forecast results of the turbulence index of the target location include: inputting the forecast results of at least one clear sky turbulence index and the forecast results of the at least one mountain wave into corresponding conversion models respectively, and outputting a single forecast result corresponding to each clear sky turbulence index and a single forecast result corresponding to each mountain wave.

The conversion model is obtained based on the historical data of turbulence dissipation rate and clear air turbulence index at the sample location, or the historical data of turbulence dissipation rate and mountain waves.

Specifically, the actual EDR and clear air turbulence index of the sample position during the same period of history can be used as the historical data of the turbulence dissipation rate and clear air turbulence index of the sample position. Back-calculation is performed based on the historical data of the turbulence dissipation rate of the sample position and each clear air turbulence index to obtain the conversion model corresponding to the clear air turbulence index.

The conversion model corresponding to the clear air turbulence index is used to describe the relationship between the clear air turbulence index and EDR.

The actual EDR and mountain waves of the sample position in the same historical period can be used as the historical data of the turbulence dissipation rate and mountain waves at the sample position. Back-calculation is performed based on the turbulence dissipation rate at the sample position and the historical data of each mountain wave to obtain the conversion model corresponding to the mountain wave.

The conversion model corresponding to the mountain wave is used to describe the relationship between the mountain wave and the EDR.

Therefore, according to the forecast result of each clear sky turbulence index of the target location and the conversion model corresponding to the forecast result of the clear sky turbulence index, a single forecast result corresponding to the clear sky turbulence index of the target location can be obtained.

According to the obtained forecast result of each mountain wave at the target location and the conversion model corresponding to the forecast result of the mountain wave, a single forecast result corresponding to the mountain wave at the target location can be obtained.

A first forecast result is obtained based on a single forecast result corresponding to each clear sky turbulence index, and a second forecast result is obtained based on the forecast result of the turbulence index corresponding to each mountain wave.

Specifically, the single forecast results corresponding to each clear sky turbulence index may be fused according to data analysis methods such as mathematical statistics to obtain a first forecast result.

For example, the single forecast results corresponding to each clear sky turbulence index may be integrated by taking the average value, the weighted average value, etc., to obtain the first forecast result.

The forecast results of the turbulence index corresponding to each mountain wave can be fused according to data analysis methods such as mathematical statistics to obtain a second forecast result.

For example, the second forecast result can be obtained by using methods such as obtaining an average value or a weighted average value to fuse the forecast results of the turbulence index corresponding to each mountain wave.

A third forecast result is obtained according to the first forecast result and the second forecast result, and the first forecast result, the second forecast result and the third forecast result are used as the forecast result of the turbulent dissipation rate of the target position.

Specifically, since the clear air turbulence index and mountain wave describe the turbulence dissipation rate from different dimensions, the first forecast result and the second forecast result can be fused to obtain the third forecast result.

The maximum value of the first forecast result and the second forecast result may be used as the third forecast result.

The embodiment of the present invention establishes a conversion model according to historical data of forecast indices and turbulence dissipation rates, obtains a forecast result of the turbulence dissipation rate of the target position based on the conversion model and the forecast indices of the target position, and can obtain a more accurate forecast result of the turbulence dissipation rate. In addition, a first forecast result and a second forecast result are obtained according to at least one corresponding single forecast result, a forecast result of a clear sky turbulence index and a forecast result of at least one mountain wave, respectively. A third forecast result is obtained according to the first forecast result and the second forecast result. By combining the clear sky turbulence index and the turbulence dissipation rate corresponding to the mountain wave, the discreteness of the forecast is increased, and a result that is better than a single index forecast can be obtained. The hit rate can be improved and the false alarm rate can be reduced, and the forecast accuracy is higher.

Based on the content of any of the above embodiments, the conversion model is:

lnD ^* ＝a+b*lnD

Where D ^* represents a single forecast result; D represents the clear air turbulence index or mountain wave; a and b represent pre-acquired coefficients.

Specifically, the conversion model can be expressed as lnD ^* = a+b*lnD

For each clear air turbulence index, the coefficients a and b in the conversion model corresponding to the clear air turbulence index are obtained based on the historical data of the turbulence dissipation rate and the clear air turbulence index at the sample location.

For each mountain wave, the coefficients a and b in the conversion model corresponding to the mountain wave are obtained according to the turbulence dissipation rate at the sample location and the historical data of the mountain wave.

The embodiment of the present invention converts the clear sky turbulence index and mountain wave at the target location into a single prediction result of the turbulence dissipation rate according to the conversion model, so as to obtain a more accurate prediction result of the turbulence dissipation rate at the target location.

Based on the content of any of the above embodiments, the forecast results of at least one clear air turbulence index and the forecast results of at least one mountain wave are respectively input into the corresponding conversion models, and before the single forecast result corresponding to each clear air turbulence index and the single forecast result corresponding to each mountain wave are output, it also includes: according to the historical data of the turbulence dissipation rate at the sample position, the expectation and standard deviation of the turbulence dissipation rate are obtained, and according to the historical data of each clear air turbulence index and each mountain wave at the sample position, the expectation and standard deviation of the logarithm of each clear air turbulence index, as well as the expectation and standard deviation of the logarithm of each mountain wave are obtained.

Specifically, according to the historical data of the turbulence dissipation rate at the sample position, the probability density function of the turbulence dissipation rate can be obtained.

According to the probability density function of the turbulent dissipation rate, the expectation and standard deviation of the turbulent dissipation rate can be obtained.

For each clear-air turbulence index, a probability density function of the clear-air turbulence index may be obtained based on historical data of the clear-air turbulence index at a sample location.

According to the probability density function of the clear air turbulence index, the standard deviation of the logarithm of the clear air turbulence index and the standard deviation of the clear air turbulence index can be obtained.

For each mountain wave, the probability density function of the mountain wave can be obtained according to the historical data of the mountain wave at the sample position.

According to the probability density function of the mountain wave, the standard deviation of the logarithm of the mountain wave and the standard deviation of the mountain wave can be obtained.

It should be noted that the probability density functions of the turbulence dissipation rate, the clear air turbulence index and the mountain wave all conform to the normal distribution characteristics.

According to the expectation and standard deviation of the turbulence dissipation rate, the expectation and logarithmic standard deviation of each clear-air turbulence index, the conversion model corresponding to each clear-air turbulence index is obtained, and according to the expectation and standard deviation of the historical data of the turbulence dissipation rate, the expectation and logarithmic standard deviation of each mountain wave, the conversion model corresponding to each mountain wave is obtained.

Specifically, according to the expectation and standard deviation of the turbulence dissipation rate, and the expectation and standard deviation of the logarithm of each forecast index, the coefficient in the conversion model corresponding to the forecast index can be determined.

a＝<lnD ^* >-b*<lnD>＝ _C1 -b*<lnD>

C ₁ = <lnD ^* >

C ₂ = SDlnD ^*

Among them, SD represents standard deviation and <> represents expectation.

Through the above formula, the coefficients a and b in each conversion model can be determined, thereby obtaining each conversion model.

The embodiment of the present invention establishes a conversion model according to the forecast index and the historical data of the turbulence dissipation rate, so that based on the conversion model and the forecast indexes of the target position, a single forecast result of the turbulence dissipation rate at the target position can be obtained, and a more accurate single forecast result of the turbulence dissipation rate at the target position can be obtained.

Based on the content of any of the above embodiments, the first forecast result, the second forecast result and the third forecast result include deterministic forecast results and/or probabilistic forecast results.

Specifically, the first forecast result may be a deterministic forecast result, that is, directly outputting a forecast value of the turbulence dissipation rate, or a probabilistic forecast result, that is, outputting the turbulence dissipation rate under a preset confidence level.

The second forecast result may be a deterministic forecast result, that is, directly outputting a forecast value of the turbulence dissipation rate, or a probabilistic forecast result, that is, outputting the turbulence dissipation rate under a preset confidence level.

The third forecast result can be a deterministic forecast result, that is, directly outputting a forecast value of the turbulence dissipation rate, or a probabilistic forecast result, that is, outputting the turbulence dissipation rate under a preset confidence level.

The embodiments of the present invention can improve the hit rate and reduce the false alarm rate by obtaining the deterministic forecast result and/or the probabilistic forecast result of the turbulence dissipation rate at the target position, thereby having a higher forecast accuracy.

Based on the content of any of the above embodiments, the clear air turbulence index includes Ellrod2, NGM1, IAWIND and |▽·T|/Ri, and the mountain wave includes MWT4, MWT6, MWT9 and MWT12.

Specifically, the aforementioned at least one clear air turbulence index may include Ellrod2, NGM1, IAWIND and |▽·T|/Ri.

The calculation formula of the clear air turbulence index is:

Among them, u represents the horizontal wind speed in the east-west direction; v represents the horizontal wind speed in the north-south direction; x represents the coordinate in the east-west direction, y represents the coordinate in the north-south direction, and z represents the height coordinate; f represents the geostrophic parameter; V represents the horizontal wind speed, which is the vector sum of u and v; g represents the gravity constant; t represents temperature; θ represents potential temperature; Ri represents Richardson number; and T represents temperature.

The calculation formula for mountain waves is:

mws＝Vs×min( _hi,j ,2750)

MWT4＝mws×V

MWT6＝mws×NGM1

MWT9＝mws×IAWIND

MWT12＝mws×|▽·T|

Wherein, Vs represents the maximum wind speed below 1500 meters above sea level; hi _,j represents the altitude; and V represents the horizontal wind speed.

The embodiment of the present invention selects four clear air turbulence indices, namely Ellrod2, NGM1, IAWIND and |▽·T|/Ri, and four mountain waves, namely MWT4, MWT6, MWT9 and MWT12, as forecast indices, so that more accurate turbulence dissipation rate forecast results can be obtained according to the forecast indices.

In order to facilitate the understanding of the turbulence dissipation rate prediction method provided by the above-mentioned embodiments of the present invention, an example is used to illustrate it below.

The GRAPES regional numerical model forecast product is selected to obtain numerical weather forecast data for the target location.

The historical data of turbulence dissipation rate at the sample location is the EDR real-time data calculated using the second-by-second airborne data from 2018 to 2019, which mainly includes information such as time, longitude, latitude, altitude, EDR peak, EDR median and confidence interval. The threshold of EDR data is between 0 and 1, and the unit is m ^2/3 s ^-1 . The average daily turbulence real-time volume is about 600-1000.

The 11:00 forecast reported from 08:00 on November 1, 2018 shows that the 300hPa turbulence forecast reflects the main weather systems such as the high-altitude trough in northern Xinjiang, the westerly jet stream near the Sea of Japan, and the typhoon in the southeast coast. According to preliminary test results, this method can reflect the characteristics of various turbulences. The more forecast indices are selected and the wider the turbulence types involved, the better the forecast effect.

Since the actual turbulence data collected by aircraft are mainly concentrated in the central and eastern regions of my country, individual cases in North China, eastern Jiangnan and eastern Southwest China were selected to test the forecast effect of turbulence ensemble forecast products integrated with multiple algorithms compared with a single turbulence index and different types of turbulence.

The EDR ensemble average forecast results that integrate multiple algorithms are better than single index forecasts. From the evening to the night of June 26, 2019, clear-air turbulence occurred near 300hPa in the northern part of North China due to the influence of the high-altitude jet stream. The actual EDR value was between 0.1-0.34m ^2/3 s ^-1 , and the turbulence intensity was mild to moderate. In general, the 23-hour turbulence ensemble forecast product and the Ellord index (the algorithm used by the World Meteorological Center for turbulence forecast) forecast product reported from 20:00 on June 25th both showed that there was mild turbulence with EDR≥0.15m ^2/3 s ^-1 in North China, which had a certain forecast effect. Compared with the Ellord index, the overall forecast value was low, there was a lack of moderate turbulence forecast with EDR≥0.22m ^2/3 s ^-1 , and there were false reports in eastern North China. The EDR ensemble forecast based on a combination of multiple algorithms showed that the EDR value exceeded 0.22m ^2/3 s ^-1 , which was only slightly southwesterly than the actual situation. At the same time, the number of false reports in eastern North China was reduced, and the forecast effect of turbulence was significantly better than that of the Ellord index.

The EDR turbulence ensemble forecast product can forecast different types of turbulence such as clear-air turbulence and mountain waves. Around 14:00 on January 21, 2020, a turbulence caused by mountain waves occurred in central Guizhou. Multiple EDR actual conditions near the 750hPa altitude exceeded 0.15m ^2/3 s ^-1 , and the maximum median and peak values were 0.22m ^2/3 s ^-1 and 0.38m ^2/3 s ^-1 , reaching moderate to severe turbulence levels. The 14:00 turbulence forecast reported from 08:00 on January 21 predicted mild or above turbulence in Guizhou, and the EDR forecast value near the actual location of the turbulence exceeded 0.22m ^2/3 s ^-1 , indicating that the forecast effect for turbulence caused by mountain waves of moderate or above intensity is relatively good. At 09:00 on March 12, 2020, multiple turbulences occurred near the 300hPa altitude in the western and southeastern coastal areas of Zhejiang. The turbulence in western Zhejiang was mainly light to moderate, while the median and peak EDR values monitored in the southeastern coast were 0.2m ^2/3 s ^-1 and 0.36m ^2/3 s ^-1 , respectively, which were moderate or above turbulence. The turbulence forecast at 09:00 reported from 02:00 on March 12 was more effective for forecasting light turbulence in western Zhejiang, and the forecast of moderate or above turbulence was located in the southeastern coast, which was consistent with the actual situation, but the turbulence intensity forecast at the maximum value was slightly weaker than the actual situation, and the position was slightly deviated.

Multiple algorithms can make up for the limitations of a single algorithm. The forecast effect of EDR objective forecast products is generally better than that of a single index forecast, and can provide better probabilistic forecast results. After the algorithm integrates the mountain wave and clear air turbulence forecast index, the forecast for mountainous areas is significantly enhanced, and it is applicable to the forecast of different types of turbulence.

Since the high-altitude jet stream in autumn and winter is significantly stronger than in summer, the samples of high-altitude turbulence are more than in summer. Therefore, this section selects December 2019 as a long time series for objective testing.

Comparing the box-whisker plots of the actual EDR of a single point and its corresponding predicted EDR station value, the distribution range of the actual and all predicted values is between 0-0.6, and the average value is between 0.05-0.1. The maximum value of the EDR forecast product is slightly smaller than the actual value, and the 25th and 75th percentile values are both below 0.05 and above 0.1, and the difference between the forecast and the actual value is also small. Since many of the forecast values are 0, the statistical results after elimination are more different from the actual value, so it is believed that the deviation of the median does not affect the overall forecast effect. In addition, the median and average values of the deviation between the actual and the forecast are around 0.05, and the deviation values between the 25th and 75th percentiles are below 0.1. The comprehensive results show that the magnitude of the turbulence forecast value is consistent with the actual value.

A relative effect characteristic test was conducted on the 36-hour turbulence forecast reported from December 1 to 31, 2019. The results showed that the forecasts of turbulence above mild and above and turbulence above moderate have relatively high hit rates and low false alarm rates. The AUC area enclosed by the ROC curve is greater than 0.5, the hit rate POD of turbulence above mild and above reaches 0.75, and the TSS score exceeds 0.2, indicating that the EDR ensemble forecast product has certain forecasting skills.

The algorithm combines different types of turbulence forecast index methods to form an ensemble forecast product based on the turbulence dissipation rate (EDR) unit standard. This new forecast result can be used to directly compare with the objective airborne turbulence reality, avoiding the limitations of the original subjective grade forecast for different aircraft types and configurations. At present, the turbulence forecast product developed using the GRAPES regional numerical model of the China Meteorological Administration has been running in real time and providing relevant aviation meteorological services.

The statistical analysis results show that this new turbulence data based on turbulence dissipation rate can reflect the characteristics of turbulence and can be used for the development and testing of forecast products. The number of turbulence samples is large and they are mainly distributed in the central and eastern regions of my country. Their seasonal changes correspond to the weather system: the strong high-altitude jet stream in winter causes more turbulence in the upper layers, and the increased thermal convection in summer significantly enhances the turbulence in the middle and lower layers. The distribution characteristics of the probability density function do not differ significantly with the seasonality, so the impact of back-calculating the actual data from different periods on the forecast results of the new algorithm is relatively small.

The turbulence dissipation rate prediction device provided by the present invention is described below. The turbulence dissipation rate prediction device described below and the turbulence dissipation rate prediction method described above can be referred to each other.

Fig. 2 is a schematic diagram of the structure of a turbulent dissipation rate prediction device provided according to an embodiment of the present invention. Based on the content of any of the above embodiments, as shown in Fig. 2, the device includes a parameter prediction module 201 and an integrated prediction module 202, wherein:

The parameter forecasting module 201 is used to obtain the forecast result of the turbulence index of the target location according to the numerical weather forecast data of the target location;

The integrated prediction module 202 is used to obtain the prediction result of the turbulence dissipation rate at the target location according to the prediction result of the turbulence index at the target location;

The forecast result of the turbulence index at the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave.

Specifically, the parameter prediction module 201 and the integrated prediction module 202 are electrically connected.

The parameter forecast module 201 can obtain at least one forecast result of the clear air turbulence index and at least one forecast result of the mountain wave at the target location according to the basic physical quantities such as horizontal wind, temperature, potential temperature, geopotential height and terrain height at the altitude layer in the numerical weather forecast data of the target location.

The integrated prediction module 202 can obtain a single prediction result of the turbulence dissipation rate corresponding to each turbulence index based on the prediction results of each turbulence index; after obtaining the single prediction result of the turbulence dissipation rate corresponding to each turbulence index, the obtained single prediction results of the turbulence dissipation rate of the target position can be fused to obtain the final prediction result of the turbulence dissipation rate of the target position.

The parameter forecasting module 201 is specifically used to obtain at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave according to the numerical weather forecast data of the target location.

The integrated forecast module 202 may include:

A model conversion unit, used to input the forecast result of at least one clear air turbulence index and the forecast result of at least one mountain wave into corresponding conversion models respectively, and output a single forecast result corresponding to each clear air turbulence index and a single forecast result corresponding to each mountain wave; wherein the conversion model is obtained based on the historical data of the turbulence dissipation rate and the clear air turbulence index at the sample position, or the historical data of the turbulence dissipation rate and the mountain wave;

The first integration unit is used to obtain a first forecast result according to a single forecast result corresponding to each clear sky turbulence index.

The second integrated unit is used to obtain a second forecast result according to the forecast result of the turbulence index corresponding to each mountain wave.

The third integrated unit is used to obtain a third forecast result according to the first forecast result and the second forecast result, and use the first forecast result, the second forecast result and the third forecast result as the forecast result of the turbulence dissipation rate at the target position.

The device may also include a model building module for acquiring each conversion model based on historical data of turbulence dissipation rate and clear air turbulence index at the sample position, and historical data of turbulence dissipation rate and mountain waves.

The model building module is specifically used to obtain the expectation and standard deviation of the turbulence dissipation rate based on the historical data of the turbulence dissipation rate at the sample position, and to obtain the expectation and standard deviation of the logarithm of each clear sky turbulence index and the expectation and standard deviation of the logarithm of each mountain wave based on the historical data of each clear sky turbulence index and each mountain wave at the sample position; to obtain the conversion model corresponding to each clear sky turbulence index based on the expectation and standard deviation of the turbulence dissipation rate and the expectation and standard deviation of the logarithm of each clear sky turbulence index, and to obtain the conversion model corresponding to each mountain wave based on the expectation and standard deviation of the historical data of the turbulence dissipation rate and the expectation and standard deviation of the logarithm of each mountain wave.

The turbulence dissipation rate prediction device provided in an embodiment of the present invention is used to execute the turbulence dissipation rate prediction method of the present invention. Its implementation method is consistent with the implementation method of the turbulence dissipation rate prediction method provided by the present invention, and can achieve the same beneficial effects, which will not be repeated here.

The turbulence dissipation rate prediction device is used in the turbulence dissipation rate prediction method of the above-mentioned embodiments. Therefore, the description and definition in the turbulence dissipation rate prediction method of the above-mentioned embodiments can be used for understanding each execution module in the embodiments of the present invention.

The embodiment of the present invention obtains the forecast result of the turbulence index of the target location based on the numerical weather forecast data of the target location, and obtains the forecast result of the turbulence dissipation rate of the target location according to the forecast result of the turbulence index of the target location, which can reflect the turbulence conditions of different regions and types, and increases the discreteness of the forecast by combining the clear sky turbulence index and the turbulence dissipation rate corresponding to mountain waves, and can obtain results that are better than those of a single index forecast, which can improve the hit rate and reduce the false alarm rate, and the forecast effect of turbulence above mild is better and has a higher forecast accuracy.

FIG3 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG3, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions stored in the memory 330 and executable on the processor 310 to execute the turbulence dissipation rate prediction method provided by the above-mentioned method embodiments, the method comprising: obtaining the prediction result of the turbulence index of the target location according to the numerical weather forecast data of the target location; obtaining the prediction result of the turbulence dissipation rate of the target location according to the prediction result of the turbulence index of the target location; wherein the prediction result of the turbulence index of the target location includes at least one prediction result of the clear sky turbulence index and at least one prediction result of the mountain wave.

In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

The processor 310 in the electronic device provided in the embodiment of the present invention can call the logic instructions in the memory 330, and its implementation method is consistent with the implementation method of the turbulence dissipation rate prediction method provided by the present invention, and can achieve the same beneficial effects, which will not be repeated here.

On the other hand, an embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the turbulence dissipation rate prediction method provided by the above-mentioned method embodiments, the method including: obtaining a forecast result of a turbulence index at the target location based on numerical weather forecast data at the target location; obtaining a forecast result of a turbulence dissipation rate at the target location based on the forecast result of the turbulence index at the target location; wherein the forecast result of the turbulence index at the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave.

When the computer program product provided in the embodiment of the present invention is executed, the above-mentioned turbulence dissipation rate prediction method is implemented. Its specific implementation method is consistent with the implementation method recorded in the embodiment of the aforementioned method, and can achieve the same beneficial effects, which will not be repeated here.

On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the turbulence dissipation rate prediction method provided in the above-mentioned embodiments, the method comprising: obtaining a forecast result of a turbulence index at the target location based on numerical weather forecast data of the target location; obtaining a forecast result of a turbulence dissipation rate at the target location based on the forecast result of the turbulence index at the target location; wherein the forecast result of the turbulence index at the target location includes at least one forecast result of a clear sky turbulence index and at least one forecast result of a mountain wave.

When the computer program stored on the non-transitory computer-readable storage medium provided in the embodiment of the present invention is executed, the above-mentioned turbulence dissipation rate prediction method is implemented. Its specific implementation method is consistent with the implementation method recorded in the embodiment of the aforementioned method, and can achieve the same beneficial effects, which will not be repeated here.

The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1.A method of turbulence dissipation ratio prediction, comprising:

forecasting products by adopting a GRAPES area numerical mode, and acquiring numerical weather forecast data of a target position;

Obtaining a forecast result of a bump index of a target position according to numerical weather forecast data of the target position;

according to the forecast result of the bumpy index of the target position, obtaining the forecast result of the turbulence dissipation rate of the target position;

The prediction result of the bumpy index of the target position comprises at least one prediction result of a clear sky bumpy index and at least one prediction result of mountain waves;

The specific steps of obtaining the forecast result of the turbulence dissipation rate of the target position according to the forecast result of the bump index of the target position comprise the following steps:

Respectively inputting the forecast result of the at least one clear sky bumpy index and the forecast result of the at least one mountain wave into corresponding conversion models, and outputting a single forecast result corresponding to each clear sky bumpy index and a single forecast result corresponding to each mountain wave;

Fusing single forecast results corresponding to the clear sky bumpy indexes to obtain a first forecast result, and fusing forecast results corresponding to the mountain waves to obtain a second forecast result;

taking the maximum values of the first forecast result and the second forecast result as a third forecast result, and taking the first forecast result, the second forecast result and the third forecast result as forecast results of the turbulence dissipation rate of the target position;

the conversion model is obtained according to historical data of turbulence dissipation rate and clear sky bumpiness index of a sample position or historical data of turbulence dissipation rate and mountain waves;

the clear sky bumpy index comprises Ellrod, NGM1, IAWIND and |v.t|/Ri, and the mountain wave comprises MWT4, MWT6, MWT9 and MWT12;

Wherein,

Wherein u represents horizontal wind speed in the east-west direction; v represents the horizontal wind speed in the north-south direction; x represents the coordinates in the east-west direction, y represents the coordinates in the north-south direction, and z represents the height coordinates; g represents a gravitational constant; t represents temperature; θ represents the bit temperature; ri represents the Richson number.

2. The turbulence dissipation factor forecasting method of claim 1, wherein the specific step of obtaining a forecast result of a bump index of a target location according to numerical weather forecast data of the target location comprises:

and obtaining a forecast result of the at least one clear sky bumpy index and a forecast result of the at least one mountain wave according to the numerical weather forecast data of the target position.

3. The turbulence dissipation factor forecasting method of claim 1, wherein the conversion model is

lnD^*＝a+b*lnD

Wherein D ^* represents a single forecast result; d represents a clear sky bumpy index or mountain waves; a. b denotes a coefficient acquired in advance.

4. The method according to claim 1, wherein the step of inputting the forecast result of the at least one clear sky pitch index and the forecast result of the at least one mountain wave into the corresponding conversion model respectively, and outputting the single forecast result corresponding to each clear sky pitch index and the single forecast result corresponding to each mountain wave further comprises:

Acquiring a turbulence dissipation rate expectation and a standard deviation according to historical data of turbulence dissipation rates of the sample positions, and acquiring a standard deviation of expectation and logarithm of each clear sky bump index and a standard deviation of expectation and logarithm of each mountain wave according to historical data of each clear sky bump index and each mountain wave of the sample positions;

According to the expected standard deviation and the standard deviation of the turbulent dissipation rate, the expected standard deviation and the logarithmic standard deviation of each clear sky bumpy index are obtained, the conversion model corresponding to each clear sky bumpy index is obtained, and according to the expected standard deviation and the standard deviation of historical data of the turbulent dissipation rate, the expected standard deviation and the logarithmic standard deviation of each mountain wave are obtained, and the conversion model corresponding to each mountain wave is obtained.

5. The turbulence dissipation ratio forecasting method of claim 1, wherein the first, second, and third forecasting results include deterministic forecasting results and/or probabilistic forecasting results.

6. A turbulence dissipation ratio forecasting device, comprising:

The data acquisition module is used for forecasting products by adopting a GRAPES region numerical mode and acquiring numerical weather forecast data of a target position;

the parameter forecasting module is used for acquiring a forecasting result of the bumpy index of the target position according to the numerical weather forecast data of the target position;

The integrated forecasting module is used for acquiring a forecasting result of the turbulence dissipation rate of the target position according to the forecasting result of the bump index of the target position;

The prediction result of the bumpy index of the target position comprises at least one prediction result of a clear sky bumpy index and at least one prediction result of mountain waves;

The specific steps of obtaining the forecast result of the turbulence dissipation rate of the target position according to the forecast result of the bump index of the target position comprise the following steps:

Respectively inputting the forecast result of the at least one clear sky bumpy index and the forecast result of the at least one mountain wave into corresponding conversion models, and outputting a single forecast result corresponding to each clear sky bumpy index and a single forecast result corresponding to each mountain wave;

Fusing single forecast results corresponding to the clear sky bumpy indexes to obtain a first forecast result, and fusing forecast results corresponding to the mountain waves to obtain a second forecast result;

taking the maximum values of the first forecast result and the second forecast result as a third forecast result, and taking the first forecast result, the second forecast result and the third forecast result as forecast results of the turbulence dissipation rate of the target position;

the conversion model is obtained according to historical data of turbulence dissipation rate and clear sky bumpiness index of a sample position or historical data of turbulence dissipation rate and mountain waves;

the clear sky bumpy index comprises Ellrod, NGM1, IAWIND and |v.t|/Ri, and the mountain wave comprises MWT4, MWT6, MWT9 and MWT12;

Wherein,

Wherein u represents horizontal wind speed in the east-west direction; v represents the horizontal wind speed in the north-south direction; x represents the coordinates in the east-west direction, y represents the coordinates in the north-south direction, and z represents the height coordinates; g represents a gravitational constant; t represents temperature; θ represents the bit temperature; ri represents the Richson number.

7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the turbulence dissipation factor forecasting method of any of claims 1 to 5 when the program is executed by the processor.

8. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the turbulence dissipation factor forecasting method of any of claims 1 to 5.