CN112632791B - Turbulence dissipation ratio forecasting method and device, electronic equipment and storage medium - Google Patents
Turbulence dissipation ratio forecasting method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a turbulence dissipation ratio forecasting method, a turbulence dissipation ratio forecasting device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining a forecast result of the bumpy index of the target position according to the 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 forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves. According to the turbulence dissipation rate forecasting method, the turbulence dissipation rate forecasting device, the electronic equipment and the storage medium, through combining the clear sky bumpy index and the turbulence dissipation rate corresponding to mountain waves, the forecast dispersion is increased, a result superior to single index forecast can be obtained, the hit rate can be improved, the sky report rate can be reduced, and the forecasting accuracy is higher.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and apparatus for forecasting turbulent dissipation ratio, an electronic device, and a storage medium.
Background
When the aircraft encounters jolt, the shaking of the aircraft body can influence the safe operation of the aircraft and cause certain damage, and even personnel injury can be caused, so jolt is a dangerous weather of a route which is concerned. The jolts are mainly classified into clear sky jolts, mountain waves, convective jolts and cloud nearby jolts. According to the vertical distribution characteristics of bump forecast on any two-point navigation route, the aircraft can be guided to run in a targeted manner.
Turbulence dissipation ratio EDR (Energy Dissipation Rate), which represents the rate at which turbulent energy is converted to heat. Turbulence dissipation ratio data acquired by an on-board detection device is used to characterize the aircraft pitch live. The existing turbulence dissipation ratio forecast mainly comprises the steps of calculating Ellord index, dutton index, brown index, and Manchesten number, and the like, and comparing with a live condition to form a forecast with a slight, medium or serious grade after correction, wherein the subjective jolt grade forecast has the limitation of difference among different machine types and is inconsistent with a jolt live condition unit obtained by airborne detection. Therefore, the existing turbulence dissipation ratio forecasting method has the defects of poor accuracy and poor precision.
Disclosure of Invention
The invention provides a turbulence dissipation ratio forecasting method, a device, electronic equipment and a storage medium, which are used for solving the defect of poor accuracy of turbulence dissipation ratio forecasting in the prior art and realizing more accurate turbulence dissipation ratio forecasting matched with bumpy live conditions.
The invention provides a turbulence dissipation ratio forecasting method, which comprises the following steps:
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 forecast result of the bumpy index of the target position comprises at least one corresponding forecast result of a single clear sky bumpy index and at least one forecast result of mountain waves.
According to the turbulence dissipation ratio forecasting method provided by the invention, the specific steps of acquiring the forecasting result of the bump index of the target position according to the numerical weather forecasting data of the target position comprise the following steps:
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.
According to the turbulence dissipation ratio forecasting method provided by the invention, the specific steps of obtaining the forecasting result of the turbulence dissipation ratio of the target position according to the forecasting result of the bump index of the target position comprise the following steps:
respectively inputting the at least one clear sky bumpy index and 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;
acquiring a first forecasting result according to a single forecasting result corresponding to each clear sky bumpy index, and acquiring a second forecasting result according to a forecasting result of each mountain wave corresponding to the bumpy index;
according to the first forecasting result and the second forecasting result, a third forecasting result is obtained, and the first forecasting result, the second forecasting result and the third forecasting result are used as forecasting results of turbulence dissipation ratios of the target positions;
Wherein 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 wave.
According to the turbulence dissipation ratio forecasting method provided by the invention, the conversion model is that
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.
According to the method for forecasting the turbulence dissipation ratio provided by the invention, the forecasting result of the at least one clear sky bumpy index and the forecasting result of the at least one mountain wave are respectively input into corresponding conversion models, and before outputting a single forecasting result corresponding to each clear sky bumpy index and a single forecasting result corresponding to each mountain wave, the method 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.
According to the turbulence dissipation ratio forecasting method provided by the invention, the first forecasting result, the second forecasting result and the third forecasting result comprise deterministic forecasting results and/or probability forecasting results.
According to the turbulence dissipation ratio forecasting method provided by the invention, the clear sky bumpy index comprises Ellrod, NGM1, IAWIND and V.T/Ri, and the mountain waves comprise MWT4, MWT6, MWT9 and MWT12.
The invention also provides a turbulent dissipation ratio forecasting device, which comprises:
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 forecast result of the bumping index of the target position comprises at least one forecast result of a sunny bumping index and at least one forecast result of mountain 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, the processor implementing the steps of any of the turbulence dissipation factor forecasting methods described above when executing the computer program.
The invention also provides 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 ratio forecasting method as described in any one of the above.
According to the turbulence dissipation rate forecasting method, the turbulence dissipation rate forecasting device, the electronic equipment and the storage medium, the forecasting result of the bumping index of the target position is obtained based on the numerical weather forecasting data of the target position, the forecasting result of the turbulence dissipation rate of the target position is obtained according to the forecasting result of the bumping index of the target position, the bumping conditions of different areas and types can be reflected, the forecasting dispersion is increased by combining the sunny bumping index and the turbulence dissipation rate corresponding to mountain waves, the result superior to the single index forecasting can be obtained, the hit rate can be improved, the air reporting rate can be reduced, the bumping forecasting effect above the light degree is better, and the forecasting accuracy is higher.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a turbulence dissipation ratio forecasting method provided by the invention;
FIG. 2 is a schematic diagram of a turbulence dissipation ratio forecasting device provided by the invention;
fig. 3 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the 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 or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the embodiments of the present invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, 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 describing embodiments of the present invention, it should be noted that, unless explicitly stated and limited otherwise, the terms "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in embodiments of the present invention will be understood in detail by those of ordinary skill in the art.
In order to overcome the problems in the prior art, the invention provides a turbulence dissipation ratio forecasting method, a device, electronic equipment and a storage medium, and the invention has the advantages that the forecasting indexes such as clear sky bumping indexes, mountain waves and the like are converted into the forecasting values of turbulence dissipation ratio units in real-time numerical forecasting and form an aggregate forecasting, so that the limitation that differences exist among different machine types in subjective bumping grade forecasting is avoided, and the accuracy of a forecasting result is higher, and therefore, the method can be used for forecasting the bumping phenomenon of an airplane.
Fig. 1 is a schematic flow chart of a turbulence dissipation ratio forecasting method provided by the invention. The turbulence dissipation ratio forecasting method of the embodiment of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: and 101, obtaining a forecast result of the bump index of the target position according to the numerical weather forecast data of the target position.
The forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves.
Specifically, the target location may be a certain location on the way, determined by longitude and latitude and altitude.
The numerical weather forecast data can comprise basic physical quantities such as horizontal wind, temperature, potential height, terrain height and the like 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 GRAPES area numerical mode forecast products independently developed by Chinese weather bureau.
The horizontal spatial resolution of GRAPES is 0.1 DEG, the vertical height is 20 layers from 1000hPa to 100hPa, the time resolution is 1 hour, and the forecast aging is 36 hours.
The bump index of the target location may include at least one clear sky bump index and at least one mountain wave.
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 bumpy index and a forecast result of at least one mountain wave of the target position can be obtained.
The at least one clear sky bumpy index and the at least one mountain wave of the obtained target position may be both used as prediction indexes.
And 102, obtaining a forecast result of the turbulence dissipation rate of the target position according to the forecast result of the bump index of the target position.
Specifically, the obtained forecast results of the bump indexes of the target positions comprise at least one forecast result of a clear sky bump index and at least one forecast result of mountain waves, and a single forecast result of turbulent dissipation rates corresponding to the bump indexes can be obtained according to the forecast results of the bump indexes; after obtaining single forecast results of the turbulent dissipation ratios corresponding to the bump indexes, the obtained single forecast results of the turbulent dissipation ratios of the target positions can be fused, and forecast results of the turbulent dissipation ratios of the final target positions can be obtained.
And fusing all the forecasting results, and adopting data analysis methods such as mathematical statistics and the like.
EDR values of 0.15-0.21m 2/3s-1 are slightly bumpy, 0.22-0.34m 2/3s-1 are moderately bumpy, and more than 0.34 are severely bumpy.
According to the embodiment of the invention, based on numerical weather forecast data of the target position, a forecast result of the bump index of the target position is obtained, and according to the forecast result of the bump index of the target position, a forecast result of the turbulence dissipation rate of the target position is obtained, so that bump conditions of different areas and types can be reflected, the forecast dispersion is increased by combining the clear sky bump index and the turbulence dissipation rate corresponding to mountain waves, a result superior to single index forecast can be obtained, the hit rate can be improved, the air report rate can be reduced, wherein the slightly more bump forecast effect is better, and the forecast accuracy is higher.
Based on the foregoing any one of the embodiments, the specific step of obtaining, according to the numerical weather forecast data of the target location, a forecast result of a bump index of the target location includes: and obtaining a forecast result of at least one clear sky bumpy index and a forecast result of at least one mountain wave according to the numerical weather forecast data of the target position.
Specifically, according to the above-mentioned basic physical quantity in the numerical weather forecast data of the target position, a forecast result of at least one clear sky bumpy index and a forecast result of at least one mountain 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 the more accurate prediction result of the turbulence 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 above description of any one of the embodiments, the specific steps of obtaining the forecast result of the turbulence dissipation ratio of the target location according to the forecast result of the bump index of the target location include: and respectively inputting the forecast result of at least one clear sky bumpy index and the forecast result of 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.
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.
Specifically, the live EDR and the clear sky bumpy index with contemporaneous sample position histories can be used as the turbulence dissipation rate of the sample position and the historical data of the clear sky bumpy index, and back calculation is carried out according to the turbulence dissipation rate of the sample position and the historical data of each clear sky bumpy index, so that a conversion model corresponding to the clear sky bumpy index is obtained.
And the conversion model corresponding to the clear sky bumpy index is used for describing the relation between the clear sky bumpy index and the EDR.
The live EDR and the mountain wave with contemporaneous sample position histories can be used as the turbulence dissipation rate of the sample position and the historical data of the mountain wave, and the conversion model corresponding to the mountain wave is obtained by back calculation according to the turbulence dissipation rate of the sample position and the historical data of each mountain wave.
The conversion model corresponding to the mountain wave is used for describing the relation between the mountain wave and the EDR.
Therefore, according to the obtained conversion model corresponding to the forecast result of each clear sky bumpy index of the target position and the forecast result of the clear sky bumpy index, a single forecast result corresponding to the clear sky bumpy 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.
According to the single forecast result corresponding to each clear sky bumpy index, a first forecast result is obtained, and according to the forecast result of the bumpy index corresponding to each mountain wave, a second forecast result is obtained.
Specifically, single prediction results corresponding to each clear sky bumpy index can be fused according to data analysis methods such as mathematical statistics and the like to obtain a first prediction result.
For example: the method of obtaining an average value, a weighted average value and the like can be adopted, and a single forecast result corresponding to each clear sky bumpy index is fused to obtain a first forecast 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 the like, and a second prediction result is obtained.
For example: the second prediction result can be obtained by fusing prediction results of the bump indexes corresponding to the mountain waves by adopting methods of obtaining an average value, a weighted average value and the like.
And obtaining 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 turbulence dissipation ratio of the target position.
Specifically, as the clear sky bumpy index and the mountain wave describe the turbulence dissipation ratio from different dimensions, the first prediction result and the second prediction result can be fused to obtain a third prediction result.
The maximum value of the first and second predictors may be taken as the third predictor.
According to the embodiment of the invention, a conversion model is established according to historical data of the forecast indexes and the turbulence dissipation ratios, the forecast results of the turbulence dissipation ratios of the target positions are obtained based on the conversion model and the forecast indexes of the target positions, more accurate forecast results of the turbulence dissipation ratios can be obtained, the first forecast result and the second forecast result are obtained according to the forecast results of the clear sky bump indexes and the forecast results of the mountain waves of at least one corresponding single forecast result respectively, the third forecast result is obtained according to the first forecast result and the second forecast result, and the forecast dispersion is increased by combining the clear sky bump indexes and the turbulence dissipation ratios corresponding to the mountain waves, so that the forecast result superior to the single index forecast can be obtained, the hit rate can be improved, the air report rate can be reduced, and the forecast accuracy is higher.
Based on the content of any of the above embodiments, the conversion model is as follows
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.
Specifically, the conversion model may be expressed as lnD * =a+b× lnD
For each clear sky bumpy index, coefficients a and b in a conversion model corresponding to the clear sky bumpy index are obtained according to turbulence dissipation rates of sample positions and historical data of the clear sky bumpy index.
For each mountain wave, coefficients a, b in the conversion model corresponding to the mountain wave are obtained according to the turbulence dissipation ratio of the sample position and the historical data of the mountain wave.
According to the embodiment of the invention, according to the conversion model, the clear sky bumpy index and mountain wave of the target position are converted into a single forecast result of the turbulence dissipation rate, and the more accurate forecast result of the turbulence dissipation rate of the target position can be obtained.
Based on the foregoing any one of the embodiments, before inputting the forecast result of the at least one clear sky-jolt index and the forecast result of the at least one mountain wave into the corresponding conversion models, respectively, outputting the single forecast result corresponding to each clear sky-jolt index and the single forecast result corresponding to each mountain wave, the method further includes: according to historical data of turbulence dissipation rates of sample positions, obtaining expected and standard deviations of turbulence dissipation rates, and according to historical data of each clear sky bump index and each mountain wave of the sample positions, obtaining expected and logarithmic standard deviations of each clear sky bump index and expected and logarithmic standard deviations of each mountain wave.
In particular, from historical data of turbulent dissipation rates at sample locations, probability density functions of turbulent dissipation rates can be obtained.
From the probability density function of the turbulent dissipation ratio, the turbulent dissipation ratio expectations and standard deviations can be obtained.
For each clear sky bumpy index, a probability density function of the clear sky bumpy index may be obtained from historical data of the clear sky bumpy index for a sample location.
And according to the probability density function of the clear sky bumpy index, the standard deviation of the logarithm of the clear sky bumpy index and the standard deviation of the clear sky bumpy index can be obtained.
For each mountain wave, a probability density function of the mountain wave may be obtained from historical data of the mountain wave at a sample location.
The standard deviation of the logarithm of the mountain wave and the standard deviation of the mountain wave can be obtained from the probability density function of the mountain wave.
The probability density function of the turbulence dissipation ratio, the probability density function of the clear sky bumpy index and the probability density function of the mountain wave all accord with normal distribution characteristics.
According to the expected standard deviation and the standard deviation of the turbulence 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 turbulence 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.
In particular, the coefficients in the conversion model corresponding to each predictive index can be determined from the expected and standard deviation of the turbulent dissipation ratio, the expected and logarithmic standard deviation of that predictive index.
a＝<lnD*>-b*<lnD>＝C1-b*<lnD>
C1＝<lnD*>
C2＝SDlnD*
Wherein SD represents standard deviation and < represents expectations.
By the above formula, the coefficients a and b in each conversion model can be determined, thereby obtaining the conversion models.
According to the embodiment of the invention, the conversion model is built according to the historical data of the forecast indexes and the turbulence dissipation ratios, so that a single forecast result of the turbulence dissipation ratio of the target position can be obtained based on the conversion model and each forecast index of the target position, and a more accurate single forecast result of the turbulence dissipation ratio can be obtained, so that a more accurate forecast result of the turbulence dissipation ratio of the target position can be obtained based on each single turbulence dissipation ratio.
Based on any of the above embodiments, the first, second and third forecasted results include deterministic forecasted results and/or probabilistic forecasted results.
Specifically, the first prediction result may be a deterministic prediction result, i.e. a prediction value of a turbulent dissipation ratio is directly output, or may be a probabilistic prediction result, i.e. a turbulent dissipation ratio under a preset confidence level is output.
The second prediction result may be a deterministic prediction result, i.e. a prediction value of a 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 a 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, the blank report rate can be reduced, and the prediction accuracy is higher by acquiring the deterministic prediction result and/or the probability prediction result of the turbulence dissipation rate of the target position.
Based on any of the above embodiments, the clear sky pitch index includes Ellrod, NGM1, IAWIND, and |·t|/Ri, and the mountain wave includes MWT4, MWT6, MWT9, and MWT12.
Specifically, the aforementioned at least one clear sky pitch index may include Ellrod, NGM1, IAWIND, and |· t|/Ri.
The calculation formula of the clear sky bumpy index is that
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; f represents a ground rotation parameter; v represents the horizontal wind speed, which is the vector sum of u and V; g represents a gravitational constant; t represents temperature; θ represents the bit temperature; ri represents a Lechassen number; t represents temperature.
The calculation formula of mountain wave is
mws＝Vs×min(hi,j,2750)
MWT4＝mws×V
MWT6＝mws×NGM1
MWT9＝mws×IAWIND
MWT12＝mws×|▽·T|
Wherein Vs represents a maximum wind speed below an altitude of 1500 meters; h i,j represents altitude; v represents the horizontal wind speed.
According to the embodiment of the invention, 4 clear sky bumpy indexes such as Ellrod < 2>, NGM1, IAWIND and I < V >. T >/Ri and 4 mountain waves such as MWT4, MWT6, MWT9 and MWT12 are selected as the forecast indexes, so that a more accurate turbulence dissipation rate forecast result can be obtained according to the forecast indexes.
To facilitate an understanding of the turbulence dissipation factor forecasting method provided by the above embodiments of the present invention, an example is described below.
And selecting GRAPES region numerical mode forecasting products to obtain numerical weather forecast data of the target position.
The historical data of the turbulence dissipation rate of the sample position is EDR live data obtained by calculating on-board data in the order of seconds one by one in 2018-2019, and mainly comprises time, longitude, latitude, altitude, EDR peak value, EDR median value, confidence interval and other information, wherein the threshold value of the EDR data is between 0 and 1, the unit is m 2/3s-1, and the average daily bump live quantity is about 600-1000.
The 11-hour forecast of the report of the 11 th month 1 th day 08 in 2018 shows that the 300hPa bump forecast better reflects main weather systems such as the high altitude tank in the north of Xinjiang, the western wind zone rapid current near the Japanese sea, typhoons in the southeast coast and the like. According to the preliminary test result, the method can reflect various bump characteristics, and when the selected forecast index is more and the related bump type is wider, the forecast effect is better.
As the bump live data collected by the aircraft are mainly concentrated in the middle eastern region of China, the north China, the south and the southwest regions are selected respectively, and the bump set forecasting products integrated by a plurality of algorithms are checked respectively to compare the single bump index and the forecasting effect of different types of bumps.
The average forecasting result of the EDR set fused with a plurality of algorithms is better than that of a single index. In the evening from 26 days of 6 months in 2019 to night, the north China is influenced by high altitude rapid flow, and near 300hPa, clear sky jolts occur, live EDR values are between 0.1 and 0.34m 2/3s-1, with a light to medium bump strength. In general, the 23-hour bump set forecasting product and Ellord index (algorithm used by world meteorological center bump forecasting) forecasting product which are reported at the time of 25 days of 6 months and 20 show that slight bumps with EDR more than or equal to 0.15m 2/3s-1 exist in North China, and a certain forecasting effect is achieved; the overall forecast value of the index is low compared with Ellord, the moderate bump forecast with EDR more than or equal to 0.22m 2/3s-1 is lacked, and the empty report appears in the northeast of China; the EDR set forecast integrating a plurality of algorithms shows that the EDR value exceeds 0.22m 2/3s-1, is slightly more southwest than the live condition, reduces the empty report condition of the northeast of China, and has obviously better forecast effect on jolt than Ellord index.
The EDR bump set forecast product can forecast different types of bump conditions such as clear sky bump and mountain wave. The pitching caused by mountain waves occurs in the middle of Guizhou around 21 days in 2020, the EDR live conditions near 750hPa height exceed 0.15m 2/3s-1, and the maximum median and peak values are respectively 0.22m 2/3s-1 and 0.38m 2/3s-1, so that the moderate to severe pitching grade is achieved. The 14-time bump forecast reported at the time of day 08 of 21 months shows that the noble state forecast has more than slight bump, and the EDR forecast value near the actual bump occurrence position exceeds 0.22m 2/3s-1, which shows that the bump forecast effect caused by mountain waves with more than medium intensity is relatively good. In the period of 9 days of 3 months and 12 days in 2020, a plurality of jolts appear near 300hPa in the west and south eastern coasts of Zhejiang, the jolts of Zhejiang are mainly light to medium, and the median value and the peak value of EDR are respectively 0.2m 2/3s-1 and 0.36m 2/3s-1 detected in the south eastern coasts, so that the jolts are more than medium; the 09-time bump forecast reported at the time of 02-day of 12 months has better effect on the light bump forecast of Zhexi, the bump forecast with more than middle degree is positioned on the southeast coast and is more consistent with the live condition, but the bump strength forecast at the maximum value is slightly weaker than the live condition, and the positions are slightly deviated.
The limitation of a single algorithm can be made up by a plurality of algorithms, the forecasting effect of the EDR objective forecasting product is better than that of a single index forecasting, and a better probability forecasting result can be provided; after the algorithm fuses mountain wave and clear sky jolt forecast indexes, the forecast of mountain areas is obviously enhanced, and the method is applicable to forecast of different types of jolts.
As the high-altitude rapid flow in autumn and winter is obviously enhanced in summer, the samples of high-altitude jolt are more in summer, and therefore 12 months in 2019 winter are selected as long-time sequences for objective inspection.
Comparing the box whisker graph distribution of the single-point live EDR and the corresponding forecast EDR site values, wherein the distribution range of the live EDR and all the forecast values is between 0 and 0.6, the average value is between 0.05 and 0.1, the maximum value of the EDR forecast product is slightly smaller than that of the live EDR, the values of 25 minutes and 75 minutes are respectively below 0.05 and above 0.1, and the forecast phase difference with the live EDR is smaller; because more numerical values in the forecast values are 0, the statistical result after being removed is larger than the live difference, and therefore the deviation of the median is considered to not influence the overall forecast effect. In addition, the median and average value of the live and forecast deviation are about 0.05, the deviation value between 25-75 minutes is below 0.1, and the comprehensive result shows that the magnitude of the jolt forecast value is more consistent with that of the live.
The relative action characteristic test is carried out on 36-hour bump forecast reported in 12 months 1-31 days 2019, and the result shows that the forecast of more than slight bump and more than moderate bump has relatively higher hit rate and lower empty report rate, the AUC area enclosed by ROC curve is larger than 0.5, the hit rate POD of more than slight bump reaches 0.75, TSS score exceeds 0.2, and the EDR aggregate forecast product has a certain forecast skill.
The novel forecasting result can be used for directly comparing with objective airborne bump live, and the limitation of the original subjective grade forecasting on different airplane types and configuration differences is avoided. The bump forecast product developed by utilizing the GRAPES area numerical mode of the China weather department at present operates in real time and provides relevant aviation weather services.
Statistical analysis results show that the novel turbulence dissipation rate-based jolt live data can reflect jolt characteristics and can be used for forecasting development and testing of products. The jolt live samples are more in number and are mainly distributed in the middle eastern region of China, and the seasonal change and other characteristics correspond to weather systems: the high-altitude rapid flow in winter is strong to make the high-rise bump more, and the heat convection in summer is increased to make the medium-low rise bump obviously enhanced. The probability density function distribution characteristics are not obvious along with seasonal differences, so that the influence on the forecasting result of the new algorithm by using live data recalculation of different periods is relatively small.
The turbulence dissipation ratio forecasting device provided by the invention is described below, and the turbulence dissipation ratio forecasting device and the turbulence dissipation ratio forecasting method described below can be correspondingly referred to each other.
Fig. 2 is a schematic structural diagram of a turbulence dissipation ratio forecasting device provided according to an embodiment of the present invention. Based on the foregoing in any of the embodiments, as shown in fig. 2, the apparatus includes a parameter forecasting module 201 and an integrated forecasting module 202, where:
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 configured to obtain a forecasting result of the turbulence dissipation ratio of the target location according to the forecasting result of the bump index of the target location;
The forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves.
Specifically, the parameter forecasting module 201 and the integrated forecasting module 202 are electrically connected.
The parameter forecasting module 201 can obtain the forecasting result of at least one clear sky bumpy index and the forecasting result of at least one mountain wave of the target position according to the basic physical quantities such as horizontal wind, temperature, potential height, terrain height and the like on the altitude layer in the numerical weather forecast data of the target position
The integrated forecasting module 202 can obtain a single forecasting result of the turbulence dissipation ratio corresponding to each bump index according to the forecasting result of each bump index; after obtaining single forecast results of the turbulent dissipation ratios corresponding to the bump indexes, the obtained single forecast results of the turbulent dissipation ratios of the target positions can be fused, and forecast results of the turbulent dissipation ratios of the final target positions can be obtained.
The parameter forecasting module 201 is specifically configured to obtain a forecasting result of at least one clear sky bumpy index and a forecasting result of at least one mountain wave according to the numerical weather forecast data of the target location.
The integrated forecasting module 202 may include:
The model conversion unit is used for respectively inputting the forecast result of at least one clear sky bumpy index and the forecast result of at least one mountain wave into the corresponding conversion model and outputting a single forecast result corresponding to each clear sky bumpy index and a single forecast result corresponding to each mountain wave; 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 first integration unit is used for obtaining a first forecasting result according to the single forecasting result corresponding to each clear sky bumpy index.
The second integration unit is used for obtaining a second forecasting result according to the forecasting result of the bumping index corresponding to each mountain wave.
And the third integrated 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 the forecasting result of the turbulence dissipation ratio of the target position.
The device can also comprise a model building module for obtaining each conversion model according to historical data of turbulence dissipation rate and clear sky bumpiness index of the sample position and historical data of turbulence dissipation rate and mountain wave.
The model building module is specifically used for obtaining turbulence dissipation rate expectations and standard deviations according to historical data of turbulence dissipation rates of sample positions, and obtaining standard deviations of expectations and logarithms of each clear sky bump index and standard deviations of expectations and logarithms 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 turbulence 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 turbulence 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.
The turbulence dissipation ratio forecasting device provided by the embodiment of the invention is used for executing the turbulence dissipation ratio forecasting method provided by the invention, the implementation mode of the turbulence dissipation ratio forecasting device is consistent with the implementation mode of the turbulence dissipation ratio forecasting method provided by the invention, and the same beneficial effects can be achieved, and the description is omitted here.
The turbulence dissipation ratio forecasting device is used for the turbulence dissipation ratio forecasting method of each embodiment. Thus, the descriptions and definitions in the turbulence dissipation ratio forecasting method in the foregoing embodiments may be used for understanding the various execution modules in the embodiments of the present invention.
According to the embodiment of the invention, based on numerical weather forecast data of the target position, a forecast result of the bump index of the target position is obtained, and according to the forecast result of the bump index of the target position, a forecast result of the turbulence dissipation rate of the target position is obtained, so that bump conditions of different areas and types can be reflected, the forecast dispersion is increased by combining the clear sky bump index and the turbulence dissipation rate corresponding to mountain waves, a result superior to single index forecast can be obtained, the hit rate can be improved, the air report rate can be reduced, wherein the slightly more bump forecast effect is better, and the forecast accuracy is higher.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320 and memory 330 communicate with each other via communication bus 340. Processor 310 may invoke logic instructions stored in memory 330 and executable on processor 310 to perform the turbulence dissipation ratio forecasting method provided by the method embodiments described above, the method comprising: obtaining a forecast result of the bumpy index of the target position according to the 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 forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The processor 310 in the electronic device provided by the embodiment of the present invention may call the logic instruction in the memory 330, and its implementation manner is consistent with the implementation manner of the turbulence dissipation ratio forecasting method provided by the present invention, and may achieve the same beneficial effects, which are not described herein.
In another aspect, embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the turbulence dissipation ratio forecasting method provided by the above method embodiments, the method comprising: obtaining a forecast result of the bumpy index of the target position according to the 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 forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves.
When the computer program product provided by the embodiment of the invention is executed, the turbulence dissipation ratio forecasting method is realized, the specific implementation manner of the method is consistent with the implementation manner recorded in the embodiment of the method, and the same beneficial effects can be achieved, and the description is omitted here.
In yet another aspect, embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the turbulence dissipation ratio forecasting method provided by the above embodiments, the method comprising: obtaining a forecast result of the bumpy index of the target position according to the 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 forecast result of the bumpy index of the target position comprises at least one forecast result of a clear sky bumpy index and at least one forecast result of mountain waves.
When the computer program stored on the non-transitory computer readable storage medium provided by the embodiment of the invention is executed, the turbulence dissipation ratio forecasting method is realized, and the specific implementation manner is consistent with the implementation manner recorded in the embodiment of the method, and the same beneficial effects can be achieved, and the description is omitted here.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart 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.
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