CN112632791A - Turbulent dissipation rate forecasting method and device, electronic equipment and storage medium - Google Patents
Turbulent dissipation rate forecasting method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a turbulent dissipation rate forecasting method, a turbulent dissipation rate forecasting device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position; acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position; the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves. According to the turbulent dissipation rate forecasting method and device, the electronic equipment and the storage medium, the forecasting dispersion is increased by combining the clear sky jolt index and the turbulent dissipation rate corresponding to the mountain waves, the result superior to that of single index forecasting can be obtained, the hit rate can be improved, the empty forecasting rate can be reduced, and the forecasting accuracy is higher.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a turbulent dissipation rate forecasting method and device, electronic equipment and a storage medium.
Background
When the airplane encounters bumping, the body shaking generated by bumping can affect the safe operation of the airplane and cause certain damage, even can cause personal injury, and thus bumping is a dangerous weather of the air route which is concerned more. Jounce is mainly classified into clear sky jounce, mountain land wave, convective jounce, and jounce near clouds. The aircraft can be guided to operate in a targeted manner according to the vertical distribution characteristics of the bump prediction on any two-point air routes.
Turbulent Dissipation rate edr (energy Dissipation rate), represents the rate at which turbulent kinetic energy is converted into heat. And turbulent dissipation rate data acquired by the onboard detection device is used for representing the airplane bump condition. The conventional turbulent dissipation rate forecast is characterized in that after parameters such as an Ellord index, a Dutton index, a Brown index and a Richcson number are calculated and corrected, a mild, moderate or severe grade forecast is formed by comparing and correcting with a real situation, but the subjective jolt grade forecast has the limitation of difference among different models and is inconsistent with a jolt situation unit obtained by airborne detection. Therefore, the existing turbulent dissipation rate forecasting method has the defects of poor accuracy and poor precision.
Disclosure of Invention
The invention provides a turbulent dissipation rate forecasting method, a turbulent dissipation rate forecasting device, electronic equipment and a storage medium, which are used for solving the defect of poor accuracy of turbulent dissipation rate forecasting in the prior art and realizing more accurate turbulent dissipation rate forecasting matched with a jolt scene.
The invention provides a turbulent dissipation rate forecasting method, which comprises the following steps:
acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position;
acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position;
the forecasting results of the bump indexes of the target position comprise at least one corresponding forecasting result of the single forecasting result of the clear-sky bump indexes and at least one forecasting result of the mountain and land waves.
According to the turbulent dissipation rate forecasting method provided by the invention, the concrete step of acquiring the forecasting result of the bump index of the target position according to the numerical weather forecasting data of the target position comprises the following steps:
and acquiring a forecast result of the at least one clear-sky bump index and a forecast result of the at least one mountain and land wave according to the numerical weather forecast data of the target position.
According to the turbulent dissipation rate forecasting method provided by the invention, the concrete step of obtaining the forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bump index of the target position comprises the following steps:
respectively inputting the at least one clear sky bump index and the at least one mountain land wave into corresponding conversion models, and outputting a single forecasting result corresponding to each clear sky bump index and a single forecasting result corresponding to each mountain land wave;
obtaining a first forecasting result according to the single forecasting result corresponding to each clear-sky bump index, and obtaining a second forecasting result according to the forecasting result of the bump index corresponding to each mountain and land wave;
acquiring a third forecasting result according to the first forecasting result and the second forecasting result, and taking the first forecasting result, the second forecasting result and the third forecasting result as forecasting results of the turbulent dissipation rate of the target position;
the conversion model is obtained according to historical data of the turbulent dissipation rate and the clear sky bump index of the sample position, or historical data of the turbulent dissipation rate and the mountain wave.
According to the method for forecasting the turbulent dissipation rate, the conversion model is
lnD*＝a+b*lnD
Wherein D is*Representing a single forecast result; d represents the clear sky bump index or mountain wave; a. b denotes a coefficient acquired in advance.
According to the method for forecasting the turbulent dissipation rate provided by the present invention, before the forecasting result of the at least one clear sky jolt index and the forecasting result of the at least one mountain land wave are respectively input into the corresponding conversion models and the single forecasting result corresponding to each clear sky jolt index and the single forecasting result corresponding to each mountain land wave are output, the method further includes:
acquiring expectation and standard deviation of the turbulent dissipation rate according to historical data of the turbulent dissipation rate of the sample position, and acquiring the expectation and logarithmic standard deviation of each clear-sky bump index and the expectation and logarithmic standard deviation of each mountain land wave according to each clear-sky bump index of the sample position and the historical data of each mountain land wave;
and acquiring a conversion model corresponding to each clear-sky bump index according to the expectation and standard deviation of the turbulent dissipation rate and the expectation and standard deviation of the logarithm of each clear-sky bump index, and acquiring a conversion model corresponding to each mountain-ground wave according to the expectation and standard deviation of the historical data of the turbulent dissipation rate and the standard deviation of the expectation and logarithm of each mountain-ground wave.
According to the method for forecasting the turbulent dissipation rate, the first forecasting result, the second forecasting result and the third forecasting result comprise a certainty forecasting result and/or a probability forecasting result.
According to the method for forecasting the turbulent dissipation rate, the clear sky bump index comprises Ellrod2, NGM1, IAWIND and |. T |/Ri, and the mountain land waves comprise MWT4, MWT6, MWT9 and MWT 12.
The invention also provides a turbulent dissipation rate forecasting device, comprising:
the parameter forecasting module is used for acquiring a forecasting result of the bump index of the target position according to the numerical weather forecasting data of the target position;
the integrated forecasting module is used for acquiring a forecasting result of the turbulent dissipation rate of the target position according to a forecasting result of the bumping index of the target position;
and the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for forecasting turbulent dissipation rate as described in any of the above.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of turbulent dissipation ratio forecasting as any of the above.
According to the method, the device, the electronic equipment and the storage medium for forecasting the turbulent dissipation rate, the forecasting result of the bump index of the target position is obtained based on the numerical weather forecasting data of the target position, the forecasting result of the turbulent dissipation rate of the target position is obtained according to the forecasting result of the bump index of the target position, bumping conditions of different areas and types can be reflected, the forecasting dispersion degree is increased by combining the bump index of clear sky and the turbulent dissipation rate corresponding to mountain waves, the result superior to that of single index forecasting can be obtained, the hit rate can be improved, the empty forecasting rate can be reduced, the effect of forecasting the bump above slight degree is better, and the forecasting accuracy rate is higher.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for forecasting turbulent dissipation rate according to the present invention;
FIG. 2 is a schematic structural diagram of a turbulent dissipation ratio predictor provided by the present invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope 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", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the embodiments of the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have specific orientations, be configured in specific orientations, and operate, and thus, should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and not order.
In the description of the embodiments of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. Specific meanings of the above terms in the embodiments of the present invention can be understood in specific cases by those of ordinary skill in the art.
In order to overcome the problems in the prior art, the invention provides a method, a device, electronic equipment and a storage medium for forecasting the turbulent dissipation rate.
Fig. 1 is a schematic flow chart of a method for forecasting a turbulent dissipation ratio according to the present invention. A method for forecasting a turbulent dissipation ratio according to an embodiment of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: step 101, obtaining a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position.
The prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
Specifically, the target position may be a certain position on the air route, which is determined by latitude and longitude and height.
The numerical weather forecast data can comprise basic physical quantities such as horizontal wind, temperature, potential height and terrain height on the height layer.
Prior to step 101, obtaining numerical weather forecast data for the target location may be included.
The numerical weather forecast data of the target position can be obtained by adopting a GRAPES regional numerical mode forecast product independently developed by the China weather service.
GRAPES horizontal spatial resolution of 0.1 degree, vertical height from 1000hPa to 100hPa total 20 layers, time resolution of 1 hour, forecast aging of 36 hours.
The bump index of the target position may include at least one clear sky bump index and at least one mountain land wave.
According to the basic physical quantity in the numerical weather forecast data of the target position, at least one clear sky bump index forecast result and at least one mountain land wave forecast result of the target position can be obtained.
It should be noted that, the at least one clear sky jolt index and the at least one mountain land wave at the target position may be both used as forecast indexes.
And 102, acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumpiness index of the target position.
Specifically, the obtained prediction results of the bump indexes of the target position include at least one prediction result of a clear-sky bump index and at least one prediction result of a mountain land wave, and a single prediction result of a turbulent dissipation rate corresponding to each bump index can be obtained according to the prediction results of each bump index; after the single prediction results of the turbulent dissipation rates corresponding to the respective bumpiness indexes are obtained, the obtained single prediction results of the turbulent dissipation rates of the target position can be fused to obtain a final prediction result of the turbulent dissipation rates of the target position.
And the forecasting results are fused, and data analysis methods such as mathematical statistics and the like can be adopted.
EDR value of 0.15-0.21m2/3s-1Slight bump of 0.22-0.34m2/3s-1Moderate jounce was attributed and severe jounce was attributed to greater than 0.34.
According to the embodiment of the invention, the prediction result of the bump index of the target position is obtained based on the numerical weather prediction data of the target position, the prediction result of the turbulent dissipation rate of the target position is obtained according to the prediction result of the bump index of the target position, the bump conditions of different areas and types can be reflected, the prediction dispersion is increased by combining the bump index of clear sky and the turbulent dissipation rate corresponding to mountain waves, the result of prediction superior to a single index can be obtained, the hit rate can be improved, the air report rate can be reduced, wherein the effect of predicting the bump above slight degree is better, and the prediction accuracy is higher.
Based on the content of any of the above embodiments, the specific step of obtaining the prediction result of the bump index of the target location according to the numerical weather prediction data of the target location includes: and acquiring a forecast result of at least one clear-sky bump index and a forecast result of at least one mountain and land wave according to the numerical weather forecast data of the target position.
Specifically, according to the basic physical quantity in the numerical weather forecast data of the target position, a forecast result of at least one clear sky bump index and a forecast result of at least one mountain and land wave of the target position can be obtained.
According to the embodiment of the invention, the prediction of the bump index is carried out according to the numerical weather prediction data of the target position, and a more accurate prediction result of the bump index can be obtained, so that a more accurate prediction result of the turbulent dissipation rate of the target position can be obtained according to the prediction result of the bump index of the target position.
Based on the content of any of the above embodiments, the specific step of obtaining the prediction result of the turbulent dissipation rate of the target location according to the prediction result of the pitch index of the target location includes: and respectively inputting the forecasting result of at least one clear sky bump index and the forecasting result of at least one mountain land wave into corresponding conversion models, and outputting a single forecasting result corresponding to each clear sky bump index and a single forecasting result corresponding to each mountain land wave.
The conversion model is obtained according to historical data of the turbulent dissipation rate and the clear sky bump index of the sample position, or historical data of the turbulent dissipation rate and the mountain wave.
Specifically, live EDRs and clear sky jolt indexes of the sample positions in the same period of the history of the sample positions can be used as historical data of the turbulent dissipation rate and the clear sky jolt index of the sample positions, and a conversion model corresponding to the clear sky jolt index is obtained by performing back calculation according to the turbulent dissipation rate of the sample positions and the historical data of each clear sky jolt index.
And the conversion model corresponding to the clear sky bump index is used for describing the relationship between the clear sky bump index and the EDR.
Live EDR and mountain waves in the same period of the sample position history can be used as the turbulent dissipation rate of the sample position and the historical data of the mountain waves, back calculation is carried out according to the turbulent dissipation rate of the sample position and the historical data of each mountain wave, and a conversion model corresponding to the mountain waves is obtained.
And the conversion model corresponding to the mountain wave is used for describing the relationship between the mountain wave and the EDR.
Therefore, according to the obtained forecasting result of each clear-sky jolting index of the target position and the conversion model corresponding to the forecasting result of the clear-sky jolting index, a single forecasting result corresponding to the clear-sky jolting index of the target position can be obtained.
According to the obtained forecasting result of each mountain wave of the target position and the conversion model corresponding to the forecasting result of the mountain wave, a single forecasting result corresponding to the mountain wave of the target position can be obtained.
And acquiring a first prediction result according to the single prediction result corresponding to each clear sky bump index, and acquiring a second prediction result according to the prediction result of the bump index corresponding to each mountain wave.
Specifically, the single prediction results corresponding to each clear sky jolt index may be fused according to data analysis methods such as mathematical statistics, and the like, so as to obtain a first prediction result.
For example: the method of obtaining the average value, weighting the average value and the like can be adopted, and the single forecasting results corresponding to all the clear sky bump indexes are fused to obtain the first forecasting result.
The prediction results of the bump indexes corresponding to the mountain waves can be fused according to data analysis methods such as mathematical statistics, and a second prediction result can be obtained.
For example: the method of obtaining the average value, the weighted average value and the like can be adopted, the prediction results of the bump indexes corresponding to the mountain waves are fused, and the second prediction result is obtained.
And acquiring a third forecasting result according to the first forecasting result and the second forecasting result, and taking the first forecasting result, the second forecasting result and the third forecasting result as forecasting results of the turbulent dissipation rate of the target position.
Specifically, since the clear sky bump index and the mountain wave describe the turbulent dissipation rate from different dimensions, the first prediction result and the second prediction result may be fused to obtain the third prediction result.
The maximum value of the first forecast result and the second forecast result may be used as the third forecast result.
According to the embodiment of the invention, a conversion model is established according to historical data of the prediction indexes and the turbulent dissipation rate, the prediction result of the turbulent dissipation rate of the target position is obtained based on the conversion model and each prediction index of the target position, a more accurate prediction result of the turbulent dissipation rate can be obtained, the first prediction result and the second prediction result are obtained according to the prediction result of at least one corresponding single prediction result of the clear-sky jolting index and the prediction result of at least one mountain land wave, the third prediction result is obtained according to the first prediction result and the second prediction result, the dispersion degree of the prediction is increased by combining the clear-sky jolting index and the turbulent dissipation rate corresponding to the mountain waves, the result of the prediction is superior to the single index can be obtained, the hit rate can be improved, the air prediction rate can be reduced, and the higher prediction accuracy rate can be achieved.
Based on the contents of any of the above embodiments, the model is converted into
lnD*＝a+b*lnD
Wherein D is*Representing a single forecast result; d represents the clear sky bump index or mountain wave; a. b denotes a coefficient acquired in advance.
Specifically, the conversion model may be represented as lnD*＝a+b*lnD
For each clear sky bump index, the coefficients a and b in the conversion model corresponding to the clear sky bump index are obtained according to the turbulent flow dissipation rate of the sample position and the historical data of the clear sky bump index.
For each mountain land wave, the coefficients a and b in the conversion model corresponding to the mountain land wave are obtained according to the turbulent dissipation rate of the sample position and the historical data of the mountain land wave.
According to the embodiment of the invention, the clear sky jolt index and the mountain wave of the target position are converted into the single forecasting result of the turbulent dissipation rate according to the conversion model, so that the more accurate forecasting result of the turbulent dissipation rate of the target position can be obtained.
Based on the content of any of the above embodiments, before the step of respectively inputting the prediction result of at least one clear sky jolt index and the prediction result of at least one mountain land wave into the corresponding conversion models and outputting the single prediction result corresponding to each clear sky jolt index and the single prediction result corresponding to each mountain land wave, the method further includes: and obtaining the expectation and standard deviation of the turbulent dissipation rate according to the historical data of the turbulent dissipation rate of the sample position, and obtaining the expectation and logarithmic standard deviation of each clear sky bump index and the expectation and logarithmic standard deviation of each mountain land wave according to each clear sky bump index of the sample position and the historical data of each mountain land wave.
Specifically, from historical data of the turbulent dissipation ratio at the sample location, a probability density function of the turbulent dissipation ratio may be obtained.
According to the probability density function of the turbulent dissipation ratio, the expectation and the standard deviation of the turbulent dissipation ratio can be obtained.
For each clear sky bump index, a probability density function of the clear sky bump index can be obtained according to historical data of the clear sky bump index of the sample position.
According to the probability density function of the clear sky bump index, the standard deviation of the logarithm of the clear sky bump index and the standard deviation of the clear sky bump index can be obtained.
For each mountain wave, a probability density function of the mountain wave may be obtained according to historical data of the mountain wave at the sample location.
According to the probability density function of the mountain land wave, the standard deviation of the logarithm of the mountain land wave and the standard deviation of the mountain land wave can be obtained.
It should be noted that the probability density function of the turbulent dissipation ratio, the probability density function of the clear sky bump index, and the probability density function of the mountain land wave all conform to the normal distribution characteristic.
And obtaining a conversion model corresponding to each clear sky bump index according to the expectation and standard deviation of the turbulent dissipation rate and the expectation and standard deviation of the logarithm of each clear sky bump index, and obtaining a conversion model corresponding to each mountain land wave according to the expectation and standard deviation of the historical data of the turbulent dissipation rate and the standard deviation of the expectation and logarithm of each mountain land wave.
Specifically, according to the expectation and standard deviation of the turbulent dissipation rate, the expectation and standard deviation of the logarithm of each prediction index, the coefficient in the conversion model corresponding to the prediction index can be determined.
a＝<lnD*>-b*<lnD>＝C1-b*<lnD>
C1＝<lnD*>
C2＝SDlnD*
Where SD denotes the standard deviation and < > denotes the expectation.
By the above formula, the coefficients a and b in each conversion model can be determined, thereby obtaining each conversion model.
According to the embodiment of the invention, the conversion model is established according to the forecast indexes and the historical data of the turbulent dissipation rate, so that a single forecast result of the turbulent dissipation rate of the target position can be obtained based on the conversion model and each forecast index of the target position, a more accurate single forecast result of the turbulent dissipation rate can be obtained, and a more accurate forecast result of the turbulent dissipation rate of the target position can be obtained based on each single forecast of the turbulent dissipation rate.
Based on the content of any of the above embodiments, the first, second and third forecast results comprise deterministic forecast results and/or probabilistic forecast results.
Specifically, the first prediction result may be a deterministic prediction result, i.e., a prediction value of the turbulent dissipation ratio is directly output, or may be a probabilistic prediction result, i.e., a turbulent dissipation ratio at a preset confidence level is output.
The second prediction result may be a deterministic prediction result, i.e., a prediction value of the turbulent dissipation ratio is directly output, or a probabilistic prediction result, i.e., a turbulent dissipation ratio under a preset confidence level is output.
The third prediction result may be a deterministic prediction result, i.e., a prediction value of the turbulent dissipation ratio is directly output, or a probabilistic prediction result, i.e., a turbulent dissipation ratio under a preset confidence level is output.
According to the embodiment of the invention, the hit rate can be improved and the empty report rate can be reduced by obtaining the deterministic forecast result and/or the probability forecast result of the turbulent dissipation rate of the target position, so that the forecasting accuracy is higher.
Based on the disclosure of any of the above embodiments, the clear sky pitch index includes Ellrod2, NGM1, IAWIND and |/Ri, and the mountain wave includes MWT4, MWT6, MWT9, and MWT 12.
Specifically, the aforementioned at least one clear sky pitch index may include Ellrod2, NGM1, IAWIND, and |. T |/Ri.
The calculation formula of the clear sky bump index is
Wherein 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 coordinates in the east-west direction, y represents the coordinates in the north-south direction, and z represents the height coordinate; f represents a ground parameter; v represents the horizontal wind speed, which is the vector sum of u and V; g represents a gravity constant; t represents a temperature; θ represents a temperature; ri represents a Richceb number; t represents temperature.
The formula for calculating the mountain land wave is
mws＝Vs×min(hi,j,2750)
MWT4＝mws×V
MWT6＝mws×NGM1
MWT9＝mws×IAWIND
MWT12＝mws×|▽·T|
Wherein Vs represents the maximum value of wind speed below an altitude of 1500 meters; h isi,jRepresenting an altitude; v represents the horizontal wind speed.
According to the embodiment of the invention, 4 clear-sky bump indexes such as Ellrod2, NGM1, IAWIND and |. T |/Ri and 4 mountain waves such as MWT4, MWT6, MWT9 and MWT12 are selected as forecast indexes, so that a more accurate turbulent dissipation rate forecast result can be obtained according to the forecast indexes.
To facilitate understanding of the method for forecasting the turbulent dissipation ratio provided by the above embodiments of the present invention, the following description is made by way of an example.
And selecting a GRAPES regional numerical mode forecast product to obtain numerical weather forecast data of a target position.
Selecting the historical data of the turbulent dissipation rate of the sample position, selecting the EDR live data obtained by calculating the airborne data of 2018-2019 year-by-second grade, wherein the EDR live data mainly comprises information such as time, longitude, latitude, altitude, EDR peak value, EDR median value, confidence interval and the like, the threshold value of the EDR data is between 0 and 1, and the unit is m2/3s-1The average daily average bumpiness amount is about 600-.
The 11-hour forecast from 11 months, 1 days and 08 hours in 2018 shows that the 300hPa jounce forecast better reflects the major weather systems such as the high empty slot in the northern part of Xinjiang, the west wind band rush current near the Japanese sea, the typhoon in the southeast coastal sea and the like. According to the preliminary test result, the method can reflect the characteristics of various kinds of jolts, and the forecasting effect is better when the more forecasting indexes are selected and the more wide the types of the jolts are involved.
As the actual data of the jolts collected by the airplane is mainly concentrated in the middle east area of China, the examples of the northChina area, the east area of the south of the Yangtze river and the east area of the southwest area are respectively selected, and the prediction effects of the jolt aggregate prediction products integrated by multiple algorithms in comparison with a single jolt index and different types of jolts are respectively tested.
The EDR ensemble average prediction result fused with multiple algorithms has better effect compared with the single index prediction result. In 2019, in 26 th evening after 6 th month and at night, the northern height of China is influenced by high altitude torrentClear air bump appears near the air 300hPa, and the live EDR value is 0.1-0.34m2/3s-1In between, the bump strength was mild to moderate. In general, the 23-hour bump ensemble forecasting product and the Ellord index (algorithm used by the bump forecasting of the world weather center) forecasting product which are reported at 25 days and 20 days of 6 months and all show that the EDR (electronic data rate) is more than or equal to 0.15m in North China2/3s-1The method has a certain forecasting effect due to slight jolt; compared with the Ellor index total forecast value, the total forecast value is lower, and the lack of EDR is more than or equal to 0.22m2/3s-1The moderate bump forecast, and the empty forecast appears in the northeast of north China; EDR ensemble prediction of a plurality of algorithms is integrated to show that the EDR value exceeds 0.22m2/3s-1The prediction method is only slightly southwest compared with the actual situation, meanwhile, the air report condition of northeast China is reduced, and the prediction effect on the jolt is obviously better than that of an Ellord index.
The EDR (electronic data recorder) bump ensemble prediction product can predict different types of bump conditions such as clear sky bump and mountain land wave. In 14 days 21/1/2020, one bump caused by mountain land waves occurs in the middle of Guizhou, and a plurality of EDR live scenes around 750hPa height exceed 0.15m2/3s-1Maximum median and peak values of 0.22m, respectively2/3s-1And 0.38m2/3s-1Moderate to severe jounce ratings were achieved. The 14-hour bump forecast from 1 month and 21 days at 08 forecast has more than slight bump in Guizhou, and the EDR forecast value near the position where the bump actually occurs exceeds 0.22m2/3s-1The method shows that the effect of predicting the jolt caused by the mountain waves with the intensity higher than the medium intensity is relatively good. At 9 of 12 days 3 and 12 months in 2020, multiple real journeys occur in the west Zhejiang and the south east coastal areas at the height of 300hPa, the light to medium Zhejiang journeys are dominant, and the median and peak values of EDR (extractive solution) monitored in the south east coastal area are 0.2m respectively2/3s-1And 0.36m2/3s-1Pitch above moderate; the prediction of the slight jolt in West Zhejiang by 09 hours starting at 3 months and 12 days at 02 has a good effect, the jolt prediction in the southeast coast is more consistent with the actual situation, but the prediction of the jolt intensity in the maximum value is slightly weaker than the actual situation, and the position is slightly deviated.
The multiple algorithms can make up the limitation of a single algorithm, the EDR objective forecast product forecast effect is generally better than that of single index forecast, and a better probability forecast result can be provided; after the algorithm is fused with mountain ground wave and clear sky bump prediction indexes, the prediction of mountain areas is obviously enhanced, and the method is suitable for the prediction of different types of bumps.
As the high-altitude torrent in autumn and winter is obviously enhanced compared with summer, so that the high-altitude jolt samples are more than those in summer, the section is selected to take 12 months in winter in 2019 as a long-time sequence for objective inspection.
Comparing the single-point live EDR with the corresponding forecast EDR station value, wherein the distribution of the box whisker chart shows that the distribution ranges of the live EDR and all forecast values are between 0 and 0.6, the average value is between 0.05 and 0.1, the maximum value of an EDR forecast product is slightly smaller than the live EDR, the values of 25 min and 75 min are both below 0.05 and above 0.1, and the difference between the forecast and the live EDR is small; as more numerical values in the forecast values are 0, the difference between the eliminated statistical result and the actual situation is larger, and the deviation of the median is not considered to influence the overall forecast effect. In addition, the median and the average value of the actual condition and the forecast deviation are about 0.05, the deviation value between 25-75 quantiles is below 0.1, and the comprehensive result shows that the magnitude of the bump forecast value is more consistent with the actual condition.
The relative action characteristic test is carried out on the 36-hour bump forecast from 12, 1 to 31 days in 2019, the results show that the forecast of more than slight bump and more than moderate bump have relatively high hit rate and low empty report rate, the AUC area defined by the ROC curve is more than 0.5, the hit rate POD of more than slight bump reaches 0.75, the TSS score exceeds 0.2, and the EDR ensemble forecast product has certain forecast skill.
The method is an ensemble forecasting product based on the turbulent dissipation ratio (EDR) unit standard and formed by integrating different types of bump forecasting index methods, and the new forecasting result can be used for being directly compared with an objective airborne bump scene, so that the limitation of different airplane types and configuration differences existing in the original subjective grade forecasting is avoided. At present, a bump forecasting product researched and developed by utilizing a GRAPES regional numerical mode of the China weather service bureau runs in real time and provides related aeronautical weather services.
The statistical analysis result shows that the new turbulence dissipation rate-based bumpiness data can reflect the bumpiness characteristics and can be used for forecasting the development and testing of products. The number of bumpy scene samples is large and mainly distributed in the eastern region of China, and the characteristics of seasonal changes and the like correspond to the weather system: the high altitude torrent is strong in winter, so that the high-rise jolts are more, and the heat convection is increased in summer, so that the jolts of the middle-rise and the low-rise jolts are obviously enhanced. The distribution characteristics of the probability density function are not obvious along with seasonal differences, so that the influence of the calculation of live data in different periods on the prediction result of the new algorithm is relatively small.
The turbulent dissipation rate forecasting device provided by the present invention is described below, and the turbulent dissipation rate forecasting device described below and the turbulent dissipation rate forecasting method described above may be referred to correspondingly.
Fig. 2 is a schematic structural diagram of a turbulent dissipation ratio forecasting 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 apparatus includes a parameter forecasting module 201 and an integrated forecasting module 202, wherein:
the parameter forecasting module 201 is configured to obtain a forecasting result of the bump index of the target location according to the numerical weather forecast data of the target location;
the integrated forecasting module 202 is used for acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumpiness index of the target position;
the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
Specifically, the parameter forecasting module 201 is electrically connected with the integrated forecasting module 202.
The parameter forecasting module 201 can obtain a forecasting result of at least one clear sky jolt index and a forecasting result of at least one mountain and land wave of the target position according to basic physical quantities such as horizontal wind, temperature, potential height, terrain height and the like on the height layer where the target position is located in the numerical weather forecast data of the target position
The integrated forecasting module 202 can obtain a single forecasting result of the turbulent dissipation rate corresponding to each bump index according to the forecasting result of each bump index; after the single prediction results of the turbulent dissipation rates corresponding to the respective bumpiness indexes are obtained, the obtained single prediction results of the turbulent dissipation rates of the target position can be fused to obtain a final prediction result of the turbulent dissipation rates of the target position.
The parameter forecasting module 201 is specifically configured to obtain a forecasting result of at least one clear-sky bump index and a forecasting result of at least one mountain and land wave according to the numerical weather forecasting data of the target position.
The ensemble forecasting module 202 may include:
the model conversion unit is used for respectively inputting the prediction result of at least one clear sky bump index and the prediction result of at least one mountain land wave into corresponding conversion models and outputting a single prediction result corresponding to each clear sky bump index and a single prediction result corresponding to each mountain land wave; the conversion model is obtained according to historical data of a turbulent flow dissipation rate and a clear sky bump index of a sample position, or historical data of the turbulent flow dissipation rate and mountain waves;
and the first integration unit is used for acquiring a first forecasting result according to the single forecasting result corresponding to each clear-sky bump index.
And the second integration unit is used for acquiring a second forecasting result according to the forecasting result of the bump index corresponding to each mountain wave.
And the third integration unit is used for acquiring a third forecasting result according to the first forecasting result and the second forecasting result, and taking the first forecasting result, the second forecasting result and the third forecasting result as forecasting results of the turbulent dissipation rate of the target position.
The device also comprises a model establishing module which is used for obtaining each conversion model according to the historical data of the turbulent dissipation rate and the clear sky jolt index of the sample position, and the historical data of the turbulent dissipation rate and the mountain and ground waves.
The model establishing module is specifically used for acquiring expectation and standard deviation of the turbulent dissipation rate according to historical data of the turbulent dissipation rate of the sample position, and acquiring the standard deviation of the expectation and logarithm of each clear-sky bump index and the standard deviation of the expectation and logarithm of each mountain land wave according to each clear-sky bump index of the sample position and the historical data of each mountain land wave; and obtaining a conversion model corresponding to each clear sky bump index according to the expectation and standard deviation of the turbulent dissipation rate and the expectation and standard deviation of the logarithm of each clear sky bump index, and obtaining a conversion model corresponding to each mountain land wave according to the expectation and standard deviation of the historical data of the turbulent dissipation rate and the standard deviation of the expectation and logarithm of each mountain land wave.
The turbulent dissipation rate forecasting device provided by the embodiment of the invention is used for executing the turbulent dissipation rate forecasting method provided by the invention, and the implementation mode of the turbulent dissipation rate forecasting device is consistent with that of the turbulent dissipation rate forecasting method provided by the invention, and the same beneficial effects can be achieved, and the details are not repeated here.
The turbulent dissipation rate forecasting device is used for the turbulent dissipation rate forecasting method of the foregoing embodiments. Therefore, the description and definition in the turbulent dissipation ratio forecasting method in the foregoing embodiments can be used for understanding the execution modules in the embodiments of the present invention.
According to the embodiment of the invention, the prediction result of the bump index of the target position is obtained based on the numerical weather prediction data of the target position, the prediction result of the turbulent dissipation rate of the target position is obtained according to the prediction result of the bump index of the target position, the bump conditions of different areas and types can be reflected, the prediction dispersion is increased by combining the bump index of clear sky and the turbulent dissipation rate corresponding to mountain waves, the result of prediction superior to a single index can be obtained, the hit rate can be improved, the air report rate can be reduced, wherein the effect of predicting the bump above slight degree is better, and the prediction accuracy is higher.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)310, a communication Interface (communication Interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may invoke logic instructions stored in the memory 330 and executable on the processor 310 to perform the turbulent dissipation ratio forecasting method provided by the above-described method embodiments, the method comprising: acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position; acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position; the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The processor 310 in the electronic device provided in the embodiment of the present invention may call the logic instruction in the memory 330, and the implementation manner of the logic instruction is consistent with the implementation manner of the method for forecasting the turbulent dissipation rate provided in the present invention, and the same beneficial effects may be achieved, and details are not described here.
In another aspect, the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, the computer can execute the turbulent dissipation rate forecasting method provided by the above method embodiments, the method includes: acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position; acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position; the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
When the computer program product provided by the embodiment of the present invention is executed, the method for forecasting the turbulent dissipation rate is implemented, and the specific implementation manner of the method is consistent with the implementation manner described in the embodiment of the foregoing method, and the same beneficial effects can be achieved, and details are not repeated herein.
In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for forecasting the turbulent dissipation ratio provided by the foregoing embodiments, and the method includes: acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position; acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position; the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
When the computer program stored on the non-transitory computer-readable storage medium provided in the embodiments of the present invention is executed, the method for forecasting the turbulent dissipation ratio is implemented, and the specific implementation manner of the method is consistent with the implementation manner described in the embodiments of the method, and the same beneficial effects can be achieved, which is not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for forecasting turbulent dissipation rate, comprising:
acquiring a prediction result of the bump index of the target position according to the numerical weather prediction data of the target position;
acquiring a forecasting result of the turbulent dissipation rate of the target position according to the forecasting result of the bumping index of the target position;
and the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
2. The method for forecasting the turbulent dissipation ratio according to claim 1, wherein the specific step of obtaining the forecasting result of the bump index of the target location according to the numerical weather forecast data of the target location comprises:
and acquiring a forecast result of the at least one clear-sky bump index and a forecast result of the at least one mountain and land wave according to the numerical weather forecast data of the target position.
3. The method for forecasting the turbulent dissipation rate according to claim 1, wherein the specific step of obtaining the forecasting result of the turbulent dissipation rate of the target location according to the forecasting result of the pitch index of the target location comprises:
respectively inputting the forecasting result of the at least one clear sky bump index and the forecasting result of the at least one mountain land wave into corresponding conversion models, and outputting a single forecasting result corresponding to each clear sky bump index and a single forecasting result corresponding to each mountain land wave;
acquiring a first forecasting result according to the single forecasting result corresponding to each clear-sky bump index, and acquiring a second forecasting result according to the forecasting result corresponding to each mountain land wave;
acquiring a third forecasting result according to the first forecasting result and the second forecasting result, and taking the first forecasting result, the second forecasting result and the third forecasting result as forecasting results of the turbulent dissipation rate of the target position;
the conversion model is obtained according to historical data of the turbulent dissipation rate and the clear sky bump index of the sample position, or historical data of the turbulent dissipation rate and the mountain wave.
4. The method for forecasting turbulent dissipation rate according to claim 3, wherein the transformation model is
lnD*＝a+b*lnD
Wherein D is*Representing a single forecast result; d represents the clear sky bump index or mountain wave; a. b denotes a coefficient acquired in advance.
5. The method for forecasting the turbulent dissipation rate according to claim 3, wherein before the step of inputting the forecast result of the at least one clear sky jolt index and the forecast result of the at least one mountain land wave into the corresponding transformation models and outputting the single forecast result corresponding to each clear sky jolt index and the single forecast result corresponding to each mountain land wave, the method further comprises:
acquiring expectation and standard deviation of the turbulent dissipation rate according to historical data of the turbulent dissipation rate of the sample position, and acquiring the expectation and logarithmic standard deviation of each clear-sky bump index and the expectation and logarithmic standard deviation of each mountain land wave according to each clear-sky bump index of the sample position and the historical data of each mountain land wave;
and acquiring a conversion model corresponding to each clear-sky bump index according to the expectation and standard deviation of the turbulent dissipation rate and the expectation and standard deviation of the logarithm of each clear-sky bump index, and acquiring a conversion model corresponding to each mountain-ground wave according to the expectation and standard deviation of the historical data of the turbulent dissipation rate and the standard deviation of the expectation and logarithm of each mountain-ground wave.
6. The method for turbulent dissipation rate forecasting according to claim 3, wherein the first, second and third forecasts comprise deterministic and/or probabilistic forecasts.
7. The method for forecasting the turbulent dissipation rate according to any one of claims 1 to 6, wherein the clear sky bumpiness index includes Ellrod2, NGM1, IAWIND and |. T |/Ri, and the mountain wave includes MWT4, MWT6, MWT9 and MWT 12.
8. A turbulent dissipation ratio forecasting apparatus, comprising:
the parameter forecasting module is used for acquiring a forecasting result of the bump index of the target position according to the numerical weather forecasting data of the target position;
the integrated forecasting module is used for acquiring a forecasting result of the turbulent dissipation rate of the target position according to a forecasting result of the bumping index of the target position;
and the prediction result of the bump index of the target position comprises at least one prediction result of the clear sky bump index and at least one prediction result of the mountain and land waves.
9. 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 method for turbulent dissipation rate forecasting according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for turbulent dissipation ratio forecasting according to any one of claims 1 to 7.
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