CN101655568A - Four-dimensional interpolation method of high-altitude grid point meteorological data - Google Patents

Four-dimensional interpolation method of high-altitude grid point meteorological data Download PDF

Info

Publication number
CN101655568A
CN101655568A CN200910088653A CN200910088653A CN101655568A CN 101655568 A CN101655568 A CN 101655568A CN 200910088653 A CN200910088653 A CN 200910088653A CN 200910088653 A CN200910088653 A CN 200910088653A CN 101655568 A CN101655568 A CN 101655568A
Authority
CN
China
Prior art keywords
point
calculate
data value
data
altitude
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN200910088653A
Other languages
Chinese (zh)
Other versions
CN101655568B (en
Inventor
朱衍波
金开研
张军
唐金翔
许有臣
兆珺
吕嘉川
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
AVIATION DATA COMMUNICATION Corp
Beihang University
Original Assignee
AVIATION DATA COMMUNICATION Corp
Beihang University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by AVIATION DATA COMMUNICATION Corp, Beihang University filed Critical AVIATION DATA COMMUNICATION Corp
Priority to CN2009100886539A priority Critical patent/CN101655568B/en
Publication of CN101655568A publication Critical patent/CN101655568A/en
Application granted granted Critical
Publication of CN101655568B publication Critical patent/CN101655568B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a four-dimensional interpolation method of high-altitude grid point meteorological data, comprising the steps: (1) computing the interpolation of practical releasing height among isotonic layers; (2) computing bilinear interpolation among appointed latitude and longitude grid points; and (3) computing the linear interpolation of appointed time point. By analyzing and computing the four-dimensional interpolation of the high-altitude grid point meteorological data, the method can obtain the atmospheric pressure height data of random flying height layer, random latitude and longitude and random time point at a rated high altitude under the condition of standard atmosphere in practical atmospheric environment, and provides effective data source for the subsequent data process.

Description

The four-dimensional interpolation method of high-altitude grid point meteorological data
Technical field
The present invention relates to a kind of data analysis and interpolation computing method, particularly a kind of four-dimensional interpolation method that meets high-altitude grid point meteorological data.
Background technology
Grid point meteorological data is U.S. FAA (Federal Aviation Administration, US Federal Aviation Administration) a exclusive data, the weather data that wherein comprises some every day 0,6 points, and at 12 at 18, data on each time point comprise barometer altitude and the temperature data in the real atmosphere environment again, for each height or temperature data piece, again according to the series arrangement of 1000mb, 850mb, 700mb, 500mb, 400mb, 300mb, 250mb, 200mb, 150mb and 100mb.For the data of each millibar layer, it is included as the whole world is the trapeze dot matrix data at interval with 1.25 degree.
If wish to utilize these data to obtain any flight level in high-altitude of regulation under the standard atmosphere condition, any longitude and latitude and random time point barometer altitude (the letting pass highly) data in the real atmosphere environment, just need utilize existing accurate data to carry out interpolation calculation and obtain hereinafter to be referred as reality.
This actual clearance highly can be used widely at aspects such as upper airspace safety assessment, air route safety assessment and aircraft performance assessments.
Summary of the invention
For overcoming the defective of prior art, the technical problem to be solved in the present invention is: a kind of four-dimensional interpolation method that can access the high-altitude grid point meteorological data of any flight level in high-altitude of regulation under the standard atmosphere condition, any longitude and latitude and the barometer altitude of random time point in the real atmosphere environment is provided.
Technical scheme of the present invention is: the four-dimensional interpolation method of this high-altitude grid point meteorological data may further comprise the steps: the actual clearance highly carried out interpolation calculation between (1) reciprocity barosphere; (2) specify the bilinear interpolation between the longitude and latitude lattice point to calculate; (3) linear interpolation of carrying out the fixed time point is calculated.
The present invention has obtained following technique effect:
(1) the aerological data is analyzed, finished the actual clearance height interpolation on the high-altitude distribution height layer;
(2) utilize bilinear interpolation and linear interpolation method, finished the true altitude interpolation of any longitude and latitude, random time point.
Description of drawings
Figure 1 shows that the process flow diagram of step of the present invention (1);
Figure 2 shows that the synoptic diagram of the bilinear interpolation of data between the longitude and latitude lattice point;
Figure 3 shows that the process flow diagram of step of the present invention (2);
Figure 4 shows that the process flow diagram of step of the present invention (3).
Embodiment
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Figure 1 shows that the process flow diagram of step of the present invention (1).This step is utilized true temperature and the true altitude in the aerological lattice point data, calculates the true altitude data that height layer is distributed in the high-altitude, at first provides the parameters symbol and the meaning thereof that use in the calculating:
P 1And P 2: the clear for flying height layer the corresponding mb value (known empirical value) of mb barosphere up and down;
h 1And h 2: the clear for flying height layer the corresponding true altitude (being derived from weather data) of mb barosphere up and down;
t 1And t 2: the clear for flying height layer the corresponding true temperature (being derived from weather data) of mb barosphere up and down;
H 1And H 2: under the normal atmospheric environment clear for flying height layer the corresponding calibrated altitude (known empirical value) of mb barosphere up and down;
FL: hundred feet height layers of standard (known empirical value);
p Fl: the standard atmospheric pressure of height layer correspondence under the normal atmospheric environment (known empirical value);
T: the clear for flying height layer the corresponding true medial temperature of mb barosphere up and down, t ‾ = t 1 + t 2 2 ;
t Fi: by relevant barosphere up and down and hundred feet average ambient temperatures that height layer is determined;
H Fl: difference formula net result, the clearance height under the real atmosphere environment.
Table 1 has provided the every experience value between the FL280 to FL450 of high-altitude:
??FL ??p fl ??P 1 ??P 2 ??H 1 ??H 2
??280 ??329.323 ??400 ??300 ??23574.2 ??30065.4
??290 ??314.849 ??400 ??300 ??23574.2 ??30065.4
??300 ??300.895 ??300 ??250 ??30065.4 ??33999.1
??310 ??287.446 ??300 ??250 ??30065.4 ??33999.1
??320 ??274.488 ??300 ??250 ??30065.4 ??33999.1
??330 ??262.007 ??300 ??250 ??30065.4 ??33999.1
??340 ??249.99 ??250 ??200 ??33999.1 ??38661.3
??350 ??238.422 ??250 ??200 ??33999.1 ??38661.3
??360 ??227.293 ??250 ??200 ??33999.1 ??38661.3
??370 ??216.625 ??250 ??200 ??33999.1 ??38661.3
??380 ??206.461 ??250 ??200 ??33999.1 ??38661.3
??390 ??196.771 ??200 ??150 ??38661.3 ??44646.8
??400 ??187.539 ??200 ??150 ??38661.3 ??44646.8
??410 ??178.737 ??200 ??150 ??38661.3 ??44646.8
??420 ??170.351 ??200 ??150 ??38661.3 ??44646.8
??430 ??162.355 ??200 ??150 ??38661.3 ??44646.8
??440 ??154.738 ??150 ??100 ??44646.8 ??53082.8
??450 ??147.475 ??150 ??100 ??44646.8 ??53082.8
Table 1: wait the actual height interpolation calculation empirical parameter value of letting pass between barosphere
Described step (1) comprises step by step following:
(1.1) true altitude of beginning interpolation calculation distribution height layer;
(1.2) get fixed each known parameters numerical value according to the numerical value of FL;
(1.3) calculate the second auxiliary parameter K 2, wherein K 2 = t 2 - t 1 H 2 - H 1 ;
(1.4) judge whether current height layer is higher than FL370, if execution in step (1.6), otherwise execution in step (1.5);
(1.5) by low spatial domain computing formula, calculate t ‾ fl = t 1 + K 2 2 · ( FL - H 1 ) , Low spatial domain comprises FL280 to FL370 totally 10 height layers, execution in step (1.7);
(1.6), calculate by higher spatial domain computing formula t ‾ fl = t 2 + K 2 2 · ( H 2 - FL ) , Higher spatial domain comprises FL380 to FL450 totally 8 height layers, execution in step (1.7);
(1.7) calculate the first auxiliary parameter K 1, wherein K 1 = t ‾ fl t ‾ · ln ( P 1 p fl ) / ln ( P 1 P 2 ) ;
(1.8) judge whether current height layer is higher than FL370, if execution in step (1.10), otherwise execution in step (1.9);
(1.9) by low spatial domain computing formula, calculate H Fl=h 1+ K 1(h 2-h 1), low spatial domain comprises FL280 to FL370 totally 10 height layers, execution in step (1.11);
(1.10), calculate H by higher spatial domain computing formula Fl=h 2+ K 1(h 2-h 1), higher spatial domain comprises FL380 to FL450 totally 8 height layers, execution in step (1.11);
(1.11) return result of calculation H FlValue, the true altitude data of distributing height layer for the high-altitude;
(1.12) etc. the actual height interpolation calculation of letting pass finishes between barosphere.
This step has been carried out interpolation calculation to the data of different barometer altitude layers, has obtained the true altitude that height layer is distributed in the high-altitude on the lattice point longitude and latitude, for next step bilinear interpolation of carrying out on longitude and latitude provides the active data source.
Figure 2 shows that longitude and latitude lattice point bilinear interpolation process synoptic diagram among the present invention, according to known point P (1,1), P (1,2), the true altitude data of P (2,1) and P (2,2) point, calculate arbitrfary point M (x by interpolation method, y) true altitude, Fig. 3 has provided the process flow diagram of step (2), is specially:
(2.1) the beginning bilinear interpolation is calculated;
(2.2) give the attached initial value of every variable, make the longitude and latitude of P (1,1) point be respectively x 1, y 1, data value is f (1,1), the longitude and latitude of P (1,2) point is respectively x 1, y 2, data value is f (1,2), the longitude and latitude of P (2,1) point is respectively x 2, y 1, data value is f (2,1), the longitude and latitude of P (2,2) point is respectively x 2, y 2, data value is f (2,2), M (x, longitude and latitude y) is x, y, data value be f (x, y);
(2.3) calculate Q 1(x, y 1) point data value, promptly
f ( x , y 1 ) ≈ x 2 - x x 2 - x 1 f ( x 1 , y 1 ) + x - x 1 x 2 - x 1 f ( x 2 , y 1 ) ;
(2.4) calculate Q 2(x, y 2) point data value, promptly
f ( x , y 2 ) ≈ x 2 - x x 2 - x 1 f ( x 1 , y 2 ) + x - x 1 x 2 - x 1 f ( x 2 , y 2 ) ;
(2.5) calculate M (x, data value y), promptly f ( x , y ) ≈ y 2 - y y 2 - y 1 f ( x , y 1 ) + y - y 1 y 2 - y 1 f ( x , y 2 ) , Return f (x, y) data value;
(2.6) the bilinear interpolation process finishes.
This step will be specified between the longitude and latitude lattice point arbitrarily the longitude and latitude data value to carry out interpolation and be found the solution, and obtain some preset time, and the true altitude data of longitude and latitude arbitrarily are for next step linear interpolation of carrying out between the time lattice point provides the active data source.
Figure 4 shows that the process flow diagram of step of the present invention (3).Since the weather data of high lattice vacancy by every day 0 point, 6 points, 12 points, 18 provide, utilize the method can obtain the true altitude data value of random time point, be specially:
(3.1) begin linear interpolation calculation;
(3.2) give the attached initial value of every variable, the time that makes the T moment point is t 1, data value is f (1), the time of T+6 moment point is t 2, data value is f (2), waits that finding the solution the time constantly is t, data value is f (t);
(3.3) calculate t data value constantly, promptly f ( t ) ≈ f ( 1 ) + f ( 2 ) - f ( 1 ) t 2 - t 1 ( t - t 1 ) , Return f (t) data value;
(3.4) the linear interpolation process finishes.
This step has been finished the lattice point data linear interpolation of random time point, has finished the data parsing process, has obtained the final data analysis result.
The present invention is by four-dimensional interpolation analysis and calculating with high-altitude grid point meteorological data, the standard atmosphere condition any flight level in high-altitude, any longitude and latitude and the barometer altitude of random time point in the real atmosphere environment of regulation have down been obtained, for follow-up data processing provides the active data source.
The above; it only is preferred embodiment of the present invention; be not that the present invention is done any pro forma restriction, every foundation technical spirit of the present invention all still belongs to the protection domain of technical solution of the present invention to any simple modification, equivalent variations and modification that above embodiment did.

Claims (4)

1, the four-dimensional interpolation method of high-altitude grid point meteorological data is characterized in that, may further comprise the steps:
(1) the actual clearance highly carried out interpolation calculation between reciprocity barosphere;
(2) specify the bilinear interpolation between the longitude and latitude lattice point to calculate;
(3) linear interpolation of carrying out the fixed time point is calculated.
2, the four-dimensional interpolation method of high-altitude grid point meteorological data according to claim 1 is characterized in that, described step (1) comprises step by step following:
(1.1) true altitude of beginning interpolation calculation distribution height layer;
(1.2) get fixed each known parameters numerical value according to the numerical value of FL;
(1.3) calculate the second auxiliary parameter K 2, wherein K 2 = t 2 - t 1 H 2 - H 1 ;
(1.4) judge whether current height layer is higher than FL370, if execution in step (1.6), otherwise execution in step (1.5);
(1.5) by low spatial domain computing formula, calculate t ‾ fl = t 1 + K 2 2 · ( FL - H 1 ) , Low spatial domain comprises FL280 to FL370 totally 10 height layers, execution in step (1.7);
(1.6), calculate by higher spatial domain computing formula t ‾ fl = t 2 + K 2 2 · ( H 2 - FL ) , Higher spatial domain comprises FL380 to FL450 totally 8 height layers, execution in step (1.7);
(1.7) calculate the first auxiliary parameter K 1, wherein K 1 = t ‾ fl t ‾ · ln ( P 1 p fl ) / ln ( P 1 P 2 ) ;
(1.8) judge whether current height layer is higher than FL370, if execution in step (1.10), otherwise execution in step (1.9);
(1.9) by low spatial domain computing formula, calculate H Fl=h 1+ K 1(h 2-h 1), low spatial domain comprises FL280 to FL370 totally 10 height layers, execution in step (1.11);
(1.10), calculate H by higher spatial domain computing formula Fl=h 2+ K 1(h 2-h 1), higher spatial domain comprises FL380 to FL450 totally 8 height layers, execution in step (1.11);
(1.11) return result of calculation H FlValue, the true altitude data of distributing height layer for the high-altitude;
(1.12) etc. the actual height interpolation calculation of letting pass finishes between barosphere.
3, the analytic method of ATB form weather data according to claim 1 and 2 is characterized in that, described step (2) comprises step by step following:
(2.1) the beginning bilinear interpolation is calculated;
(2.2) give the attached initial value of every variable, make the longitude and latitude of P (1,1) point be respectively x 1, y 1, data value is f (1,1), the longitude and latitude of P (1,2) point is respectively x 1, y 2, data value is f (1,2), the longitude and latitude of P (2,1) point is respectively x 2, y 1, data value is f (2,1), the longitude and latitude of P (2,2) point is respectively x 2, y 2, data value is f (2,2), M (x, longitude and latitude y) is x, y, data value be f (x, y);
(2.3) calculate Q 1(x, y 1) point data value, promptly
f ( x , y 1 ) ≈ x 2 - x x 2 - x 1 f ( x 1 , y 1 ) + x - x 1 x 2 - x 1 f ( x 2 , y 1 ) ;
(2.4) calculate Q 2(x, y 2) point data value, promptly
f ( x , y 2 ) ≈ x 2 - x x 2 - x 1 f ( x 1 , y 2 ) + x - x 1 x 2 - x 1 f ( x 2 , y 2 ) ;
(2.5) calculate M (x, data value y), promptly f ( x , y ) ≈ y 2 - y y 2 - y 1 f ( x , y 1 ) + y - y 1 y 2 - y 1 f ( x , y 2 ) , Return f (x, y) data value;
(2.6) the bilinear interpolation process finishes.
4, the analytic method of ATB form weather data according to claim 1 and 2 is characterized in that, described step (3) comprises step by step following:
(3.1) begin linear interpolation calculation;
(3.2) give the attached initial value of every variable, the time that makes the T moment point is t 1, data value is f (1), the time of T+6 moment point is t 2, data value is f (2), waits that finding the solution the time constantly is t, data value is f (t);
(3.3) calculate t data value constantly, promptly f ( t ) ≈ f ( 1 ) + f ( 2 ) - f ( 1 ) t 2 - t 1 ( t - t 1 ) , Return f (t) data value;
(3.4) the linear interpolation process finishes.
CN2009100886539A 2009-07-06 2009-07-06 Four-dimensional interpolation method of high-altitude grid point meteorological data Expired - Fee Related CN101655568B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2009100886539A CN101655568B (en) 2009-07-06 2009-07-06 Four-dimensional interpolation method of high-altitude grid point meteorological data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2009100886539A CN101655568B (en) 2009-07-06 2009-07-06 Four-dimensional interpolation method of high-altitude grid point meteorological data

Publications (2)

Publication Number Publication Date
CN101655568A true CN101655568A (en) 2010-02-24
CN101655568B CN101655568B (en) 2011-05-04

Family

ID=41709928

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2009100886539A Expired - Fee Related CN101655568B (en) 2009-07-06 2009-07-06 Four-dimensional interpolation method of high-altitude grid point meteorological data

Country Status (1)

Country Link
CN (1) CN101655568B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104897130A (en) * 2015-06-18 2015-09-09 广西壮族自治区气象减灾研究所 Method for calculating solar elevation angle by adopting space-based remote sensing, blocking and interpolation
CN106291756A (en) * 2016-08-02 2017-01-04 哈尔滨工业大学 The construction method of near space air virtual environment resource
CN112488382A (en) * 2020-11-27 2021-03-12 清华大学 ENSO forecasting method based on deep learning

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101349767B (en) * 2008-09-05 2012-06-13 国家卫星气象中心 High resolution precipitation data processing method

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104897130A (en) * 2015-06-18 2015-09-09 广西壮族自治区气象减灾研究所 Method for calculating solar elevation angle by adopting space-based remote sensing, blocking and interpolation
CN104897130B (en) * 2015-06-18 2017-11-14 广西壮族自治区气象减灾研究所 The method of space-based remote sensing piecemeal interpolation calculation sun altitude
CN106291756A (en) * 2016-08-02 2017-01-04 哈尔滨工业大学 The construction method of near space air virtual environment resource
CN106291756B (en) * 2016-08-02 2018-07-03 哈尔滨工业大学 The construction method of near space air virtual environment resource
CN112488382A (en) * 2020-11-27 2021-03-12 清华大学 ENSO forecasting method based on deep learning

Also Published As

Publication number Publication date
CN101655568B (en) 2011-05-04

Similar Documents

Publication Publication Date Title
Williams Increased light, moderate, and severe clear-air turbulence in response to climate change
Attema et al. Extreme precipitation response to climate perturbations in an atmospheric mesoscale model
CN111177851B (en) Assessment method for ground risk in unmanned aerial vehicle operation safety risk assessment
He et al. Greenland surface albedo changes in July 1981–2012 from satellite observations
Zhou et al. Decreased takeoff performance of aircraft due to climate change
CN104240541A (en) 4D track generating method
CN104406580A (en) Navigation method, device and system for general aviation aircraft
CN101634711A (en) Method for estimating temperature of near-surface air from MODIS data
Gilmore et al. Temporal and spatial variability in the aviation NOx-related O3 impact
CN101655568B (en) Four-dimensional interpolation method of high-altitude grid point meteorological data
Wang et al. Inter‐decadal variability of Tibetan spring vegetation and its associations with eastern China spring rainfall
Peng et al. Temporal and spatial variations of global deep cloud systems based on CloudSat and CALIPSO satellite observations
Sun et al. Distinct impacts of vapor transport from the tropical oceans on the regional glacier retreat over the Qinghai-Tibet Plateau
Yamada et al. Extreme precipitation intensity in future climates associated with the Clausius-Clapeyron-like relationship
Zhang et al. Wind erosion climate change in northern China during 1981–2016
Muhsin et al. Effect of convection on the thermal structure of the troposphere and lower stratosphere including the tropical tropopause layer in the South Asian monsoon region
CN114595876A (en) Regional wind field prediction model generation method and device and electronic equipment
Babaei et al. Impacts of orography on large-scale atmospheric circulation: application of a regional climate model
CN104036086A (en) MODIS (moderate resolution imaging spectroradiometer) data based relative atmosphere humidity estimation method
CN101739790A (en) Forecasting, early warning and emergency controlling method for a plurality of fixed chemical risk sources
You et al. Changes of summer cloud water content in China from ERA-Interim reanalysis
Dash et al. Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha
CN102254090B (en) Method for estimating lightning density distribution and annual average ground flash density (NG) value by kernel density estimation
Zhang et al. Atmospheric moisture budget and floods in the Yangtze River basin, China
Torres Trajectory accuracy sensitivity to modeling factors

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20110504

Termination date: 20210706

CF01 Termination of patent right due to non-payment of annual fee