CN109375294A - A kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data - Google Patents
A kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data Download PDFInfo
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Abstract
The present invention discloses a kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data, comprising: the reading of TRMM 3B42.V7 satellite precipitation data and monthly total precipitation statistics；The fusion of correcting variable and space scale are unified；Return NO emissions reduction model foundation；Cross validation and NO emissions reduction correction execute.Meteorological station observation precipitation data in mountain area is dissolved into the NO emissions reduction correction course of satellite precipitation data by the present invention, it is preferred that method is carried out with Cross-Validation technique, it has given full play to the advantage of mountain area finite observation data, the precision of the precipitation after NO emissions reduction correction and its has been substantially improved with the consistency of measured data series；Further it is proposed that the NO emissions reduction alignment technique of multi-method comparison review, tentatively solves the problems, such as the system deviation of single NO emissions reduction bearing calibration, enriches satellite precipitation data NO emissions reduction bearing calibration system, improve the confidence level of result.This method is good in the upper applicability of NO emissions reduction correction of typical mountain region satellite Precipitation Products, and related ends can grasp Precipitation in Mountain Area spatial-temporal distribution characteristic for system and provide strong support.
Description
Technical field
The present invention relates to the NO emissions reduction bearing calibrations of hydraulic engineering technical field more particularly to satellite precipitation data, specifically
For a kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data.
Background technique
Total input of the precipitation as water cycle process is that water cycle process is most important, one of most active element, in substance
Key player is play in movement and energy exchange.Precisely parse and paddle affairs of the accurate estimation of precipitation to water cycle process
Science decision it is most important.By the multifactor impacts such as mountain range trend, terrain slope and moisture source, mountainous region water cycle process tool
There is vertical zonality.However, existing ground observation website is unevenly distributed, be laid in low altitude area region more, economic condition it is poor,
Unfrequented High aititude mountain area, website is rare, and data is deficient.The Precipitation Distribution in Time and Space information obtained by interpolation algorithm, by
It is verified in the observational data for lacking High aititude mountain area, spread result is difficult to accurately hold the spatial distribution characteristic of precipitation.
Continue to bring out in recent years satellite precipitation data (including CMAP, TMPA, GPCP, CMORPH, PERSIANN-CDR,
NRL-Blend and GPM etc.) support can be provided to lack the hydrological analysis calculating in data mountain area, but there are spaces point for these data
Relatively thick and precision deficiency the problem of resolution, it is still necessary to carry out space NO emissions reduction and accuracy correction to meet application demand.
Present satellites precipitation data carries out NO emissions reduction timing in mountain area and has the following problems: (1) using single method more
NO emissions reduction correction, the NO emissions reduction knot when laying less High aititude mountain area for Rainfall Monitoring website are carried out to satellite precipitation data
Fruit verifying is insufficient, and error is larger；(2) existing method does not consider the actual measurement precipitation data monitored by laying website, only by it
For the verifying and assessment of NO emissions reduction result, fails joint and play on the accuracy benefits of ground observation and the face of satellite remote sensing precipitation
Advantage.
Summary of the invention
The technical problems to be solved by the invention are that existing satellite precipitation data NO emissions reduction bearing calibration is overcome to lack
Existing defect when the High aititude mountain area application of field data, propose blended based on ground observation and moonscope it is multi-party
The mountain area satellite precipitation data NO emissions reduction bearing calibration of method comparison review.This method melts mountain area meteorological station observation precipitation data
Enter into the NO emissions reduction correction course of satellite precipitation data, effectively reduce systematic error, improves correction result and actual measurement number
According to the goodness of fit and consistency, for system grasp Precipitation in Mountain Area spatial and temporal patterns provide science and technology support, enrich satellite
The method system of precipitation data NO emissions reduction correction.
The object of the present invention is achieved like this: a kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data, the side
Method includes four parts: the reading of I .TRMM 3B42.V7 satellite remote sensing precipitation and monthly total precipitation statistics；II, correcting variable is melted
It closes unified with space scale；III, returns NO emissions reduction model foundation；IV, cross validation and NO emissions reduction correction execute.Steps are as follows:
The specific steps of the reading of I .TRMM 3B42.V7 satellite remote sensing precipitation and monthly total precipitation statistics:
Step 1: according to the vector boundary in research area, obtain the research upper left of area's rectangular space range, upper right, lower-left and
The space coordinate Geo on the vertex of bottom right 4[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]。
Step 2: according to Geo[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]Four vertex institutes
Determining square boundary to make full use of measured data, and preferably reflects the influence that landform is distributed Precipitation in Mountain Area, along research
Area's outer boundary expands 0.5 ° outward and establishes buffer area, and TRMM 3B42.V7 precipitation is shown in Table with HDF stored in file format, arrangement mode
1, the TRMM precipitation information within the scope of buffer area is read, research area's time interval 3h, 0.25 ° of spatial resolution of TRMM drop are obtained
Water number is according to A.
Table 1TRMM 3B42.V7 satellite Precipitation Products arrangement mode
Annotation: table middle latitude negative value indicates that south latitude, positive value indicate north latitude；Longitude negative value indicates west longitude, and positive value indicates east
Through.
Step 3: the satellite precipitation information extracted is counted by pixel, obtains the TRMM precipitation number in each 1~December of pixel
According to B.
The fusion and the unified specific steps of space scale of II, correcting variable:
Step 1: the Daily rainfall amount that meteorological station monitors in Revision area, and count each website month by month
Precipitation data C.
Step 2: the TRMM precipitation grid where meteorological station is determined according to geographical coordinate, with the actual measurement precipitation of weather station
Obs is measured instead of the satellite remote sensing precipitation on the TRMM grid, the TRMM precipitation data B moon for being modified to " star-ground " fusion is dropped
Water number is according to D.
Step 3: digital elevation (DEM) and normalized differential vegetation index (NDVI) data are cut according to research area's range, are obtained
The NDVI data F after dem data E and cutting after cutting.
Step 4: dem data E is subjected to resampling, obtains the dem data G of 0.25 ° of spatial resolution, and according to DEM
Data G calculates the Gradient H and slope aspect data J on the research each pixel in area.
Step 5: the NDVI data F after cutting is subjected to resampling, obtains 0.25 ° of spatial resolution, NDVI number month by month
According to K.
Step 6: moon precipitation data D, dem data G, Gradient H, slope aspect data J, NDVI data K spatial and temporal scales are united
After one, the centroid point of 0.25 ° of spatial resolution grid is calculated, obtains the longitude data L of each grid, latitude data M.
The specific steps of III, recurrence NO emissions reduction model foundation:
Step 1: determining the independent variable and dependent variable for returning NO emissions reduction model, and the D of precipitation data month by month that fusion is obtained makees
For dependent variable, by the dem data G that spatial and temporal scales are unified, Gradient H, slope aspect data J, NDVI data K, longitude data L, latitude
Degree is according to M as independent variable.
Step 2: dem data E is subjected to resampling, obtains the dem data N of 0.05 ° of spatial resolution, is counted according to data N
Calculation obtains the Gradient O of 0.05 ° of spatial resolution, slope aspect data P.
Step 3: NDVI data F is subjected to resampling, obtains the NDVI data Q of 0.05 ° of spatial resolution.
Step 4: the Gradient O of 0.05 ° of spatial resolution grid, the space lattice of slope aspect data P, NDVI data Q are
Completely the same, the centroid of NO emissions reduction grid is calculated in an optional raster data, is calculated under the spatial resolution each
The longitude data R of grid, latitude data S.
Step 5: using multiple linear regression analysis method, establishes the multiple regression relationship between precipitation data D and independent variable month by month
MLR。
Step 6: using partial least-square regression method, establishes the offset minimum binary between precipitation data D and independent variable month by month
Regression relation PLSR.
Step 7: using Geographically weighted regression procedure, establishes the Geographical Weighted Regression between precipitation data D and independent variable month by month
Relationship GWR.
Step 8: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into polynary time
Return in relationship MLR, executes NO emissions reduction model, the precipitation T1 month by month after obtaining NO emissions reduction correction.
Step 9: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into partially minimum
Two multiply in regression relation PLSR, execute NO emissions reduction model, the precipitation T2 month by month after obtaining NO emissions reduction correction.
Step 10: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into geographical add
It weighs in regression relation GWR, executes NO emissions reduction model, the precipitation T3 month by month after obtaining NO emissions reduction correction.
The specific steps that IV, cross validation and NO emissions reduction correction execute:
Step 1: assuming that meteorological station number is Count, reduce by 1 meteorological station every time, II, executes correcting variable
It merges the step two unified with space scale to blend TRMM precipitation and ground observation precipitation, obtains " the star-not comprising the point
The moon precipitation data V of ground " fusion.
Step 2: moon precipitation data V obtained using step 1 as dependent variable, by dem data G, Gradient H, slope aspect number
It repeats III, as independent variable according to J, NDVI data K, longitude data L, latitude data M and returns NO emissions reduction model foundation part
The step of five~step 10 Count times.
Step 3: the MLR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains multiple linear and returns
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W1；
Step 4: the PLSR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains geographical weight back
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W2；
Step 5: the GWR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains geographical weight back
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W3；
Step 6: the precipitation number generated according to the geographical coordinate of meteorological station, matching step three, step 4 and step 5
According to W1、W2、W3The monthly total precipitation Y corrected with meteorological station spatial position X, the NO emissions reduction of grid where extracting meteorological station,
Calculate the coefficient of determination R surveyed between precipitation data obs and data Y month by monthj 2, root-mean-square error RMSEjAnd average relative error
AREj。
Step 7: the cross validation of multiple linear regression and Geographically weighted regression procedure is completed.
Step 8: according to cross validation results, using moon precipitation data D of " star-ground " fusion as dependent variable, with dem data
G, Gradient H, slope aspect data J, NDVI data K, longitude data L and latitude data M are independent variable, use optimal method pair
It studies area's monthly total precipitation and carries out NO emissions reduction correction, the monthly total precipitation data Z after being corrected, and extract in optimum regression relationship
The regression coefficient AA of dependent variable and elevation.
Step 9: the regression coefficient AA of the Z of precipitation data month by month, precipitation and elevation after correction are converted into grid map
Piece obtains research area and corrects the gradient grid map that precipitation and monthly total precipitation change along elevation month by month.
Step 10: gradient grid of the precipitation with it along elevation variation is cut month by month with the vector boundary batch in research area
Figure obtains research average precipitation in 1~December of area (see Figure 24), and the research monthly precipitation in area along the change of gradient of elevation
(see Figure 25).
Further, IV, cross validation and NO emissions reduction correction execute, coefficient of determination R in step 6j 2, root-mean-square error
RMSEjWith average relative error AREjCalculation formula be respectively as follows:
In formula:CountTo survey website number, obsiFor the actual measurement precipitation of i-th of website,For all actual measurement websites
Average precipitation, YiPrecipitation, R are corrected for the NO emissions reduction of i-th of websitej 2、RMSEjAnd AREjWhat respectively jth time was verified determines
Determine coefficient, root-mean-square error and mean relative deviation.
Further, IV, cross validation and NO emissions reduction correction execute, the principle that cross validation is deferred in step 7 are as follows:
The beneficial effect comprise that:
(1) by the NO emissions reduction correction course of the precipitation measurement data fusion of meteorological stations to satellite precipitation data,
By cross validation, increases the number of numerical experiment to ensure the stability of analysis result, actual measurement number is farthest utilized
According to advantage；
(2) the synchronous recurrence NO emissions reduction for carrying out satellite precipitation datas using three kinds of methods correct and to carry out method preferred, logical
The comparison review for crossing a variety of methods, effectively reduces systematic error.
Satellite precipitation data NO emissions reduction correction upper applicability of this method in High aititude mountain area is good, and correlated results can be to be
System grasps Precipitation in Mountain Area spatial and temporal patterns and provides science and technology support.
Detailed description of the invention
In the following with reference to the drawings and specific embodiments, invention is further described in detail；
Fig. 1 is the flow chart of the embodiment of the present invention the method；
Fig. 2 is Wild jujube in Taihang Mountain Area boundary, buffer area boundary, original TRMM raster data centroid point and the meteorology that the present invention uses
Station distribution figure；
Fig. 3 be present invention determine that 0.25 ° of buffer area spatial resolution digital elevation data；
Fig. 4 be present invention determine that the 0.25 ° of spatial resolution in buffer area Gradient；
Fig. 5 be present invention determine that 0.25 ° of buffer area spatial resolution slope aspect data；
Fig. 6 be present invention determine that 0.25 ° of buffer area spatial resolution NDVI data；
Fig. 7 be present invention determine that 0.05 ° of buffer area spatial resolution digital elevation data；
Fig. 8 be present invention determine that the 0.05 ° of spatial resolution in buffer area Gradient；
Fig. 9 be present invention determine that 0.05 ° of buffer area spatial resolution slope aspect data；
Figure 10 be present invention determine that 0.05 ° of buffer area spatial resolution NDVI data；
Figure 11 be present invention determine that NO emissions reduction to after 0.05 ° precipitation raster data centroid point distribution；
Figure 12 be present invention determine that cross validation after January average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 13 be present invention determine that cross validation after February average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 14 be present invention determine that cross validation after March average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 15 be present invention determine that cross validation after April average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 16 be present invention determine that cross validation after May average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 17 be present invention determine that cross validation after June average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 18 be present invention determine that cross validation after July average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 19 be present invention determine that cross validation after August average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 20 be present invention determine that cross validation after September average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 21 be present invention determine that cross validation after October average original TRMM precipitation, the correction of MLR NO emissions reduction
The comparison diagram of precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 22 be present invention determine that cross validation after November average original TRMM precipitation, MLR NO emissions reduction school
The comparison diagram of positive precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 23 be present invention determine that cross validation after December average original TRMM precipitation, MLR NO emissions reduction school
The comparison diagram of positive precipitation, PLSR NO emissions reduction correction precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation；
Figure 24 a~Figure 24 l be present invention determine that the research Qu Yueping that corrects of NO emissions reduction is carried out using best practice
The spatial distribution of equal precipitation；
Figure 25 a~Figure 25 b be present invention determine that the research Qu Nianping that corrects of NO emissions reduction is carried out using best practice
The spatial distribution and isopleth of equal precipitation；
Figure 26 a~Figure 26 l be present invention determine that the research Qu Yueping that corrects of NO emissions reduction is carried out using best practice
The spatial distribution for the gradient value that equal precipitation changes along elevation.
Specific embodiment
In the following with reference to the drawings and specific embodiments, the present invention is described in further details:
Taihang mountain range extends to the Wangwu Shan Mountain of Shanxi, Henan border land North gets Beijing's Western Hills southwards, and west connects Shanxi plateau,
The North China Plain is faced in east, and in northeast~southwest trend, it is the second ladder of China's landform east edge that be continuous more than 400 kilometers.The present invention chooses
Wild jujube in Taihang Mountain Area is as research area.It is as follows to study area's overview: about 12.78 ten thousand km of the gross area2, height above sea level section is -65~3059m, is indulged
Across Beijing, Hebei, Shanxi and Henan Si Sheng (city), Haihe River, two, the Yellow River level-one basin are traversed；In China's subhumid and half
Evergreen conifruticeta, theropencedrymion, broad-leaved deciduous forest, artificial forest, fallen leaves shrubbery and Cao Po is distributed in arid biogeographic zone intermediate zone
Etc. vegetation patterns；Belong to monsoon climate of medium latitudes, summer is burning hot and rainy by wet southeast wind effect is warmed up, and winter is by dry and cold northwester
It influences and cold short of rain, mean annual precipitation is in 400~600mm；Main River Systems include Yellow River basin the Yellow River mainstream and Qin He,
The tributaries such as Dan He, and belong to the rivers such as Caobai River, the Yongdinghe River, Daqinghe River, the Zhanghe River of Haihe basin.Taihang Mountain is the allusion quotation of monsoon region
Type mountain range has preferable representative using Wild jujube in Taihang Mountain Area as research object.
The reading of I .TRMM 3B42.V7 satellite remote sensing precipitation and monthly total precipitation statistics；
90 × 90m used from NASA (NASA) and U.S. National Imagery and Mapping Agency (NIMA) joint publication is empty
Between resolution ratio digital elevation (DEM) data set, 137 meteorological site Daily rainfall data of selection are from China national gas
Basic data as 2000~2011 years meteorological data data sets that office reorganizes, as the expansion of this example.Example with
ArcGIS10.2 software is display platform, and program calculation is realized by Python and MATLAB language.
Step 1: according to the vector boundary in research area, obtain the research upper left of area's rectangular space range, upper right, lower-left and
The space coordinate Geo on the vertex of bottom right 4[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]。
Step 2: according to Geo[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]Four vertex institutes
Determining square boundary to make full use of measured data, and preferably reflects the influence that landform is distributed Precipitation in Mountain Area, along research
Area's outer boundary expands 0.5 ° outward and establishes buffer area, and TRMM 3B42.V7 precipitation is shown in Table with HDF stored in file format, arrangement mode
1.Using the hdfread function of MATLAB software, the TRMM precipitation information within the scope of buffer area is read, is obtained between research area's time
Every 3h, 0.25 ° of spatial resolution of TRMM precipitation data A.
Step 3: the satellite precipitation information extracted is counted by pixel, obtains the TRMM precipitation number in each 1~December of pixel
According to B, Fig. 2 is shown in the distribution of TRMM precipitation grid central point.
The fusion and the unified specific steps of space scale of II, correcting variable:
Step 1: the Daily rainfall amount of meteorological station monitoring within the scope of buffer area is arranged, and counts each website
Precipitation data C month by month, research area's range, the range of buffer area are shown in Fig. 2.
Step 2: the TRMM precipitation grid where meteorological station is determined according to geographical coordinate, with the actual measurement precipitation of weather station
Amount obs replaces the satellite remote sensing precipitation on the TRMM grid, the TRMM precipitation data B moon for being modified to " star-ground " fusion is dropped
Water number is according to D.
Step 3: the Extract by under 10.2 tool box platform Spatial Analyst tools Arcgis is used
Mask tool cuts digital elevation (DEM) and normalized differential vegetation index (NDVI) data according to buffer area range, after obtaining cutting
Dem data E and cut after NDVI data F.
Step 4: the Resample tool in the tool box Data Management Tools of 10.2 platform of Arcgis is used
Dem data E is subjected to resampling, obtains the dem data G (see Fig. 3) of 0.25 ° of spatial resolution, and calculate and delay according to data G
Area is rushed by the Gradient H (see Fig. 4) and slope aspect data J of pixel (see Fig. 5).
Step 5: the Resample tool in the tool box Data Management Tools of 10.2 platform of Arcgis is used
NDVI data F after cutting is subjected to resampling, obtains 0.25 ° of spatial resolution, NDVI data K month by month (see Fig. 6).
Step 6: moon precipitation data D, dem data G, Gradient H, slope aspect data J, NDVI data K spatial and temporal scales are united
After one, calculated using the Raster To Point tool under 10.2 tool box platform Conversion tools Arcgis
The centroid of each raster data under 0.25 ° of spatial resolution obtains the longitude data L (can obtain from Fig. 2) of each grid, latitude
Data M (can be obtained) from Fig. 2.
The specific steps of III, recurrence NO emissions reduction model foundation:
Step 1: determining the independent variable and dependent variable for returning NO emissions reduction model, and the D of precipitation data month by month that fusion is obtained makees
For dependent variable, by the dem data G that spatial and temporal scales are unified, Gradient H, slope aspect data J, NDVI data K, longitude data L, latitude
Degree is according to M as independent variable.
Step 2: the Resample work in the tool box Data Management Tools of 10.2 platform of Arcgis is used
Dem data E is carried out resampling, obtains the dem data N (see Fig. 7) of 0.05 ° of spatial resolution, use Arcgis 10.2 by tool
The Gradient of 0.05 ° of spatial resolution is calculated in Slope tool under the tool box platform Spatial Analyst tools
O (see Fig. 8) is calculated 0.05 ° using the Aspect tool under 10.2 tool box platform Spatial Analysis Arcgis
The slope aspect data P of spatial resolution (see Fig. 9).
Step 3:, will using the Resample tool under 10.2 tool box platform Spatial Analysis Arcgis
NDVI data F carries out resampling, obtains the NDVI data Q (example is shown in Figure 10) of 0.05 ° of spatial resolution.
Step 4: Gradient O, the space lattice of slope aspect data P, NDVI data Q of 0.05 ° of spatial resolution grid are complete
It is complete consistent, use the Raster To Point tool optional one under 10.2 tool box platform Conversion tools Arcgis
The centroid point (see Figure 11) of NO emissions reduction grid is calculated in a raster data, and each grid under the spatial resolution is calculated
Longitude data R (can be read) from Figure 11, and latitude data S (can be read) from Figure 11.
Step 5: using multiple linear regression analysis method, writes MATLAB program, establishes precipitation data D and independent variable month by month
Multiple regression relationship MLR.
Step 6: using partial least-square regression method, writes MATLAB program, establishes precipitation data D month by month and becomes certainly
The Partial Least Squares Regression relationship PLSR of amount.
Step 7: using Geographically weighted regression procedure, write MATLAB program, determines geographical power using Gaussian function method
Weight establishes month by month the Geographical Weighted Regression relationship GWR of precipitation data D and independent variable.
Step 8: using the geotiffread function of MATLAB software, read and by Gradient O, slope aspect data P,
NDVI data Q, longitude data R, latitude data S are brought into respectively in multiple regression relationship MLR, are executed NO emissions reduction model, are dropped
Precipitation T month by month after dimension correction1。
Step 9: using the geotiffread function of MATLAB software, read and by Gradient O, slope aspect data P,
NDVI data Q, longitude data R, latitude data S are brought into respectively in Partial Least Squares Regression relationship PLSR, execute NO emissions reduction model,
Precipitation T month by month after obtaining NO emissions reduction correction2。
Step 10: using the geotiffread function of MATLAB software, read and by Gradient O, slope aspect data P,
NDVI data Q, longitude data R, latitude data S are brought into respectively in Geographical Weighted Regression relationship GWR, are executed NO emissions reduction model, are obtained
Precipitation T month by month to after NO emissions reduction correction3。
The specific steps of IV, cross validation and NO emissions reduction correction:
Step 1: assuming that meteorological station number is Count, reduce by 1 meteorological station every time, execution II be " correcting variable
The step of fusion and space scale unification " part two, blends TRMM precipitation and ground observation precipitation, obtains not including the point
" star-ground " fusion moon precipitation data V.
Step 2: moon precipitation data V obtained using step 1 as dependent variable, by dem data G, Gradient H, slope aspect number
III " returning NO emissions reduction model foundation " portion is repeated as independent variable according to J, NDVI data K, longitude data L, latitude data M
The step of dividing, five~step 10 was Count times total.
Step 3: the MLR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains multiple linear and returns
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W1；
Step 4: the PLSR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains geographical weight back
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W2；
Step 5: the GWR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains geographical weight back
The NO emissions reduction of method is returned to correct monthly total precipitation raster data W3；
Step 6: the precipitation number generated according to the geographical coordinate of meteorological station, matching step three, step 4 and step 5
According to W1、W2、W3The monthly total precipitation corrected with the spatial position X of meteorological station, the NO emissions reduction of grid where extracting meteorological station
Y calculates the coefficient of determination R surveyed between precipitation data obs and data Y month by monthj 2, root-mean-square error RMSEjAnd average relative error
AREj.2~Figure 23 of the result is shown in Figure 1 of each moon, summarizing for comparing result are shown in Table 2 and table 3.
In formula:CountTo survey website number, obsiFor the actual measurement precipitation of i-th of website,For all actual measurement websites
Average precipitation, YiPrecipitation, R are corrected for the NO emissions reduction of i-th of websitej 2、RMSEjAnd AREjWhat respectively jth time was verified determines
Determine coefficient, root-mean-square error and mean relative deviation.
Table 2 be after the embodiment of the present invention cross validation each month original TRMM precipitation, MLR NO emissions reduction correction precipitation
Amount；PLSR NO emissions reduction corrects precipitation, the coefficient of determination of GWR NO emissions reduction correction precipitation and website actual measurement precipitation series and equal
The statistical form of square error average value；
Table 2R2Result is cross-checked with RMSE
Table 3 be after the embodiment of the present invention cross validation each month original TRMM precipitation, MLR NO emissions reduction correction precipitation
Amount；PLSR NO emissions reduction corrects the mean relative deviation of precipitation, GWR NO emissions reduction correction precipitation and website actual measurement precipitation series
The statistical form of mean value.
Table 3ARE cross-checks result
Step 7: according to principle listed by formula (4), multiple linear regression is completed, minimum two partially adds at recurrence and geography
Weigh the cross validation of homing method.
Step 8: according to cross validation results, using moon precipitation data D of " star-ground " fusion as dependent variable, with dem data
G, Gradient H, slope aspect data J, NDVI data K, longitude data L, latitude data M are independent variable, using best practice to grinding
Study carefully area's monthly total precipitation carry out NO emissions reduction correction, the monthly total precipitation data Z after being corrected, and extract in optimum regression relationship because
The regression coefficient AA of variable and elevation.
Step 9: it using the geotiffwrite function of MATLAB software, obtains research area and corrects precipitation and the moon month by month
The gradient grid map that precipitation changes along elevation.
Step 10: being based on Python translation and compiling environment, calls the arcpy secondary development bag of Arcgis 10.2, according to research area
Vector boundary (see Fig. 2), using arcpy.sa.Extractbymask function batch cut precipitation month by month and its along elevation
The gradient grid map of variation obtains research average precipitation in 1~December of area (see Figure 24), and the research monthly precipitation edge in area
The change of gradient of elevation (see Figure 25).
Finally it should be noted that being only used to illustrate the technical scheme of the present invention and not to limit it above, although referring to preferable cloth
The scheme of setting describes the invention in detail, those skilled in the art should understand that, it can be to technology of the invention
Scheme (such as utilization, sequencing of step of various formula etc.) is modified or replaced equivalently, without departing from the present invention
The spirit and scope of technical solution.
Claims (4)
1. a kind of NO emissions reduction bearing calibration of mountain area satellite precipitation data, which is characterized in that the precipitation NO emissions reduction method includes
Four parts: the reading of I .TRMM 3B42.V7 satellite remote sensing precipitation and monthly total precipitation statistics；The fusion of II, correcting variable and sky
Between scale it is unified；III, returns NO emissions reduction model foundation；IV, cross validation and NO emissions reduction correction execute.
2. the NO emissions reduction bearing calibration of satellite precipitation data in mountain area according to claim 1, which is characterized in that its specific step
Suddenly it is:
The reading of I .TRMM 3B42.V7 satellite remote sensing precipitation and monthly total precipitation statistics:
Step 1: according to the vector boundary in research area, upper left, upper right, lower-left and the bottom right 4 of research area's rectangular space range are obtained
The space coordinate Geo on a vertex[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]；
Step 2: according to Geo[top,left], Geo[top,right], Geo[bottom,left], Geo[bottom,right]Four vertex are determined
Square boundary, expand 0.5 ° outward along research area's outer boundary and establish buffer area, read the TRMM precipitation letter within the scope of buffer area
Breath obtains research area's time interval 3h, 0.25 ° of spatial resolution of TRMM precipitation data A；
Step 3: the satellite precipitation information extracted is counted by pixel, obtains the TRMM precipitation data B in each 1~December of pixel；
The fusion of II, correcting variable and space scale are unified:
Step 1: the Daily rainfall amount that meteorological station monitors in Revision area, and count the precipitation month by month of each website
Measure data C；
Step 2: the TRMM precipitation grid where meteorological station is determined according to geographical coordinate, with the actual measurement precipitation obs of weather station
Instead of the satellite remote sensing precipitation on the TRMM grid, TRMM precipitation data B is modified to the moon precipitation data of " star-ground " fusion
D；
Step 3: digital elevation and normalized differential vegetation index data are cut according to research area's range, the dem data after being cut
The E and NDVI data F after cutting；
Step 4: dem data E is subjected to resampling, obtains the dem data G of 0.25 ° of spatial resolution, and according to dem data
G calculates the Gradient H and slope aspect data J on the research each pixel in area；
Step 5: the NDVI data F after cutting is subjected to resampling, obtains 0.25 ° of spatial resolution, NDVI data K month by month；
Step 6: moon precipitation data D, dem data G, Gradient H, slope aspect data J, NDVI data K spatial and temporal scales are unified
Afterwards, the centroid point for calculating 0.25 ° of spatial resolution grid obtains the longitude data L of each grid, latitude data M；
III, returns NO emissions reduction model foundation:
Step 1: determining the independent variable and dependent variable for returning NO emissions reduction model, using the obtained D of precipitation data month by month of fusion as because
Variable, by the dem data G that spatial and temporal scales are unified, Gradient H, slope aspect data J, NDVI data K, longitude data L, latitude number
According to M as independent variable；
Step 2: dem data E is subjected to resampling, the dem data N of 0.05 ° of spatial resolution is obtained, is calculated according to data N
To the Gradient O of 0.05 ° of spatial resolution, slope aspect data P；
Step 3: NDVI data F is subjected to resampling, obtains the NDVI data Q of 0.05 ° of spatial resolution；
Step 4: Gradient O, the space lattice of slope aspect data P, NDVI data Q of 0.05 ° of spatial resolution are completely the same,
The centroid of NO emissions reduction grid is calculated in an optional raster data, and the warp of each grid under the spatial resolution is calculated
Degree is according to R, latitude data S；
Step 5: using multiple linear regression analysis method, establishes the multiple regression relationship MLR between precipitation data D and independent variable month by month；
Step 6: using partial least-square regression method, establishes the Partial Least Squares Regression between precipitation data D and independent variable month by month
Relationship PLSR；
Step 7: using Geographically weighted regression procedure, establishes the Geographical Weighted Regression relationship between precipitation data D and independent variable month by month
GWR；
Step 8: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into multiple regression and closed
It is to execute NO emissions reduction model in MLR, the precipitation T month by month after obtaining NO emissions reduction correction1；
Step 9: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into offset minimum binary
In regression relation PLSR, NO emissions reduction model is executed, the precipitation T month by month after obtaining NO emissions reduction correction2；
Step 10: Gradient O, slope aspect data P, NDVI data Q, longitude data R, latitude data S are brought into geographical weight back
Return in relationship GWR, executes NO emissions reduction model, the precipitation T month by month after obtaining NO emissions reduction correction3；
IV, cross validation and NO emissions reduction correction execute:
Step 1: it assuming that meteorological station number is Count, reduces by 1 meteorological station every time, executes the fusion of II, correcting variable
Unified step two blends TRMM precipitation and ground observation precipitation with space scale, obtains " star-ground " not comprising the point
The moon precipitation data V of fusion；
Step 2: moon precipitation data V obtained using step 1 as dependent variable, by dem data G, Gradient H, slope aspect data J,
NDVI data K, longitude data L, latitude data M repeat the step that III, returns NO emissions reduction model foundation part as independent variable
Rapid five~step 10 Count times；
Step 3: the MLR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains multiple linear regression side
The NO emissions reduction of method corrects monthly total precipitation raster data W1；
Step 4: the PLSR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains Geographical Weighted Regression side
The NO emissions reduction of method corrects monthly total precipitation raster data W2；
Step 5: the GWR NO emissions reduction correction monthly total precipitation for calculating Count times is done sums average, obtains Geographical Weighted Regression side
The NO emissions reduction of method corrects monthly total precipitation raster data W3；
Step 6: the precipitation data W generated according to the geographical coordinate of meteorological station, matching step three, step 4 and step 51、
W2、W3The monthly total precipitation Y corrected with the spatial position X of meteorological station, the NO emissions reduction of grid where extracting meteorological station, meter
Calculate the coefficient of determination R surveyed between precipitation data obs and data Y month by monthj 2, root-mean-square error RMSEjWith average relative error AREj；
Step 7: the cross validation of multiple linear regression and Geographically weighted regression procedure is completed；
Step 8: according to cross validation results, using moon precipitation data D of " star-ground " fusion as dependent variable, with dem data G, slope
Degree is according to H, and slope aspect data J, NDVI data K, longitude data L, latitude data M are independent variable, using optimal method to research
Area's monthly total precipitation carries out NO emissions reduction correction, the monthly total precipitation data Z after being corrected, and extracts in optimum regression relationship because becoming
The regression coefficient AA of amount and elevation；
Step 9: being converted to grille picture for the regression coefficient AA of the Z of precipitation data month by month, precipitation and elevation after correction,
It obtains research area and corrects the gradient grid map that precipitation and monthly total precipitation change along elevation month by month；
Step 10: gradient grid map of the precipitation with it along elevation variation is cut month by month with the vector boundary batch in research area, is obtained
To research average precipitation in 1~December of area, and the research monthly precipitation in area is along the change of gradient of elevation.
3. the NO emissions reduction bearing calibration of satellite precipitation data in mountain area according to claim 2, which is characterized in that IV, intersects
Verifying is executed with NO emissions reduction correction, coefficient of determination R in step 6j 2, root-mean-square error RMSEjWith average relative error AREjMeter
Formula is calculated to be respectively as follows:
In formula:CountTo survey website number, obsiFor the actual measurement precipitation of i-th of website,For the flat of all actual measurement websites
Equal precipitation, YiPrecipitation, R are corrected for the NO emissions reduction of i-th of websitej 2、RMSEjAnd AREjThe respectively decision system of jth time verifying
Number, root-mean-square error and mean relative deviation.
4. the NO emissions reduction bearing calibration of satellite precipitation data in mountain area according to claim 3, which is characterized in that IV, intersects
Verifying is executed with NO emissions reduction correction, the principle that cross validation is deferred in step 7 are as follows:
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