CN105160192A - TRMM (Tropical Rainfall Measuring Mission) satellite rainfall data downscaling method based on M5-Local - Google Patents
TRMM (Tropical Rainfall Measuring Mission) satellite rainfall data downscaling method based on M5-Local Download PDFInfo
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Abstract
The invention discloses a TRMM (Tropical Rainfall Measuring Mission) satellite rainfall product downscaling method based on M5-Local and a multi-environmental factor variable. An M5-Local regression thought is adopted, and a plurality of environment stress factors with high spatial resolutions are fully utilized to improve the spatial resolution of the product. Firstly, 1km environmental variable factors, such as eight pieces of data including a vegetation index, a digital elevation model, a daytime surface temperature, a night surface temperature, a terrain moisture index, a gradient, a slope aspect and a slope length gradient, are subjected to aggregate calculating to obtain 25km which serves as an independent variable, corresponding 25km-resolution TRMM data is used as a dependent variable, an M5-Local method is adopted for modeling, the intercept of a regression modeling equation of 1km spatial resolution and a slope parameter corresponding to each environment factor variable are predicted, and a 1kmTRMM rainfall value is calculated. A downscaling result based on an M5-Local model is obviously superior to a downscaling result of a conventional regression model.
Description
Technical field
The present invention relates to the method for TRMM rainfall data NO emissions reduction, be specifically related to a kind of TRMM satellite rainfall data NO emissions reduction method based on M5-Local.
Technical background
Rainfall has served as key player, a particularly important component part of Exchange of material and energy conservation in fields such as hydrology, meteorology, ecology and agricultural researches.Surface-based observing station is a kind of widely used rainfall measurement means, and has the feature of precision height and technology maturation.But the rainfall amount of surface-based observing station monitoring only represents the precipitation situation of research station, earth's surface and periphery certain distance, be therefore difficult to statement widespread rain distribution characteristics, especially in the highlands that surface-based observing station cloth reticular density is sparse.And satellite remote sensing technology can provide the rainfall data compared with high-spatial and temporal resolution, covering space is wider, well overcomes the limitation of rainfall observation station, ground and rain detection radar, for global rainfall monitoring provides strong data supporting.
In recent years, along with the development of weather satellite technology, the survey rain Satellite Product of Global Scale high-spatial and temporal resolution is arisen at the historic moment, as tropical rainfall measurement setup (TropicalRainfallMeasuringMission, TRMM) surveys rain product 3B42 and U.S. climates prediction precipitation core integration technology Precipitation Products (ClimatePredictionCenterMorphingTechnique).TRMM rainfall satellite is first weather satellite being specifically designed to the quantitative observation torrid zone, subtropics rainfall, with higher spatial and temporal resolution, can provide the rainfall data in the region within 50 ° of S ~ 50 ° N covering the whole world.But, the original resolution of TRMM satellite is lower, and (spatial resolution is 0.25 °, about 25km), in the yardstick rainfall of estimation range, there is certain limitation and deviation, therefore need the spatial scaling carrying out spatially for TRMM data, thus obtain the higher rainfall measured value of resolution.
(the ImmerzeelWW such as Immerzeel, RuttenMM, DroogersP.SpatialdownscalingofTRMMprecipitationusingvege tativeresponseontheIberianPeninsula [J] .RemoteSensingofEnvironment, 2009,113 (2): 362-370.) there is based on rainfall and different spaces yardstick vegetative coverage the hypothesis of correlativity, propose utilize rainfall and normalized differential vegetation index index (NDVI) between funtcional relationship NO emissions reduction is carried out to satellite rainfall data.Jia etc. (JiaS, ZhuW,
a, etal.AstatisticalspatialdownscalingalgorithmofTRMMprecip itationbasedonNDVIandDEMintheQaidamBasinofChina [J] .RemotesensingofEnvironment, 2011,115 (12): 3069-3079.) based on multiple linear regression model, utilize NDVI and dem data, NO emissions reduction is carried out to 1km to 0.25 ° of TRMM rainfall data in the Caidamu Basin.Duan and Bastiaanssen (DuanZ, BastiaanssenWGM.FirstresultsfromVersion7TRMM3B43precipit ationproductincombinationwithanewdownscaling – calibrationprocedure [J] .RemoteSensingofEnvironment, 2013, geographical difference analysis (GDA) and geographical ratio analysis (GRA) calibration steps 131:1-13.) is utilized to further improve NO emissions reduction algorithm, thus the error of rainfall data after decreasing NO emissions reduction.But current researcher thinks: adopt polyfactorial NO emissions reduction algorithm not improve precision, polyfactorial precision on the contrary can lower than monofactor.As (XuS such as XuS, WuC, WangL, etal.Anewsatellite-basedmonthlyprecipitationdownscalinga lgorithmwithnon-stationaryrelationshipbetweenprecipitati onandlandsurfacecharacteristics [J] .RemoteSensingofEnvironment, 2015, 162:119-140) Geographical Weighted Regression analytic approach is applied to the NO emissions reduction research of TRMM satellite Rainfall Products, utilize environmental variance as DEM respectively, NDVI and simultaneously use DEM and NDVI modeling return, result shows only good than using the modeling effect of DEM and NDVI simultaneously by the effect of DEM or NDVI, and other researchers also have similar conclusion.
M5-LocalR is a kind of method based on local weighted regression modeling, it combines M5 principal component analytical method and LocalR Geographically weighted regression procedure, extend traditional recurrence framework, allow the parameter estimation of local instead of the overall situation, after expansion, the parameter of model is a function of position k, and the parameter namely in model is different at each regression point.
Summary of the invention
The object of the invention is to solve problems of the prior art, and a kind of TRMM satellite rainfall data NO emissions reduction method based on M5-Local is provided.Concrete technical scheme is as follows:
Based on a method for the TRMM satellite Rainfall Products NO emissions reduction of M5-LocalR and Multi-environment factor variable, comprise the following steps:
Step 1) data acquisition: obtain the TRMM meteorological satellite remote sensing image data in region to be measured, MODIS satellite remote-sensing image data and ASTERGDEM satellite remote-sensing image data, collect the daily rainfall observed reading of ground observation website in this region to be measured simultaneously; Wherein MODIS satellite remote-sensing image data comprise MOD11A2 data product and MOD13A2 data product;
Step 2) data prediction: by step 1) temporal resolution of TRMM meteorological satellite remote sensing image data that obtains is treated to the moon; ASTERGDEM satellite remote-sensing image data are carried out polymerization calculating and obtain the dem data that spatial resolution is 1km and 25km respectively; Extract from MOD11A2 data product daytime surface temperature and evening surface temperature parameter, and by polymerization calculate obtain respectively spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be the surface temperature data in evening of 1km and 25km; From MOD13A2 data product, extract vegetation index parameter, after abnormality value removing process, calculated by polymerization and obtain the vegetation index data that spatial resolution is 1km and 25km respectively; Carry out being polymerized the Gradient, Topographic Wetness Index data, Barrier facility data and the slope aspect data that calculate and obtain 1km and 25km respectively from the ASTERGDEM satellite remote-sensing image extracting data gradient, Topographic Wetness Index, Barrier facility and slope aspect 4 parameters;
Step 3) carry out M5-LocalR regression modeling: using step 2) process after TRMM meteorological satellite remote sensing image data as dependent variable, the surface temperature data on daytime of 25km are with spatial resolution, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out Geographical Weighted Regression as between independent variable and 1km latitude and longitude coordinates point, thus obtain the rainfall regression residuals value that regression equation that spatial resolution is each grid point in the raster data of 1km and spatial resolution are 25km, the latitude and longitude coordinates of described 1km latitude and longitude coordinates point to be spatial resolution be each grid point in the raster data of 1km,
Step 4) NO emissions reduction prediction: based on step 3) spatial resolution of gained is the regression equation of each grid point in the raster data of 1km, with spatial resolution be the surface temperature data on daytime of 1km, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data be as input independent variable, calculate, obtaining spatial resolution is 1km ground prediction of precipitation Value Data; Be that the rainfall regression residuals value of 25km is carried out resampling and obtained the rainfall regression residuals value that spatial resolution is 1km by spatial resolution simultaneously, and be that 1km ground prediction of precipitation Value Data is added by itself and spatial resolution, obtain the TRMM weather satellite rainfall data that spatial resolution is 1km.
As preferably, described step 1) in, the spatial resolution of TRMM meteorological satellite remote sensing image data is 0.25 ° × 0.25 °, and temporal resolution is 3 hours; The spatial resolution of described ASTERGDEM satellite remote-sensing image data is 30m; The spatial resolution of described MODIS satellite remote-sensing image data is 1km, and temporal resolution is 8 days.
As preferably, described step 2) in the concrete steps of abnormality value removing process as follows: using the vegetation index that extracts in MOD13A2 data product as initial vegetation index, first the part that in initial vegetation index, grid point value is less than 0 is deleted, again with 11 × 11 window gliding smoothing vegetation index, then the vegetation index is smoothly deducted with initial vegetation index,-0.15 to 0.15 is selected to screen the result after subtracting each other as threshold range again, cast out the grid exceeding threshold range, all the other are as normal vegetation index point.
As preferably, described step 3) in, Geographical Weighted Regression adopts M5-LocalR regression modeling, the concrete steps that M5-LocalR regression modeling adopts are as follows: first by step 2) surface temperature data on daytime after process, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out principal component analysis (PCA), thus obtain the main-control factors of each grid point; So with the main-control factors of each grid point for independent variable, using step 2) TRMM meteorological satellite remote sensing image data after process carries out Geographical Weighted Regression as dependent variable.
As further preferred, described principal component analysis (PCA) adopts M5 method.The invention has the beneficial effects as follows that carrying out modeling in conjunction with multiple-factor carries out NO emissions reduction prediction to TRMM data, wherein M5-LocalR returns and extends traditional recurrence framework, allow the parameter estimation of local instead of the overall situation, after expansion, the parameter of model is the function of position, and the parameter namely in model is different at each regression point.Therefore carry out Geographical Weighted Regression modeling by multiple-factor and can carry out precipitation predicting to complex area more accurately, and greatly improve the spatial resolution of precipitation predicting.There is important theory, practice significance and application value.
The present invention is based on M5-LocalR homing method, carry out modeling in conjunction with multiple-factor and NO emissions reduction research is carried out to TRMM data.M5-LocalR returns and extends traditional recurrence framework, allows the parameter estimation of local instead of the overall situation, and after expansion, the parameter of model is the function of position k, and the parameter namely in model is different at each regression point.Therefore carry out Geographical Weighted Regression modeling by multiple-factor and can carry out precipitation predicting to complex area more accurately, and greatly improve the spatial resolution of precipitation predicting.There is important theory, practice significance and application value.
Accompanying drawing explanation
Fig. 1 is the 25kmTRMM rainfall amount spatial distribution characteristic figure adopted in embodiment 1 or 2.
Fig. 2 is the rainfall amount spatial distribution characteristic figure based on 1km after polyfactorial Geographically weighted regression procedure NO emissions reduction in embodiment 1.
Fig. 3 is the screening distribution plan carrying out main-control factors in embodiment 2 based on M5 method.
Fig. 4 is the rainfall amount spatial distribution characteristic figure based on 1km after polyfactorial M5-LocalR homing method NO emissions reduction in embodiment 2.
Fig. 5 is based on the prediction of precipitation value of 1km after polyfactorial Geographically weighted regression procedure NO emissions reduction and the correlation analysis figure of ground observation website in embodiment 1.
Fig. 6 is based on the prediction of precipitation value of 1km and the correlation analysis figure of ground observation website after polyfactorial M5-LocalR homing method NO emissions reduction in embodiment 2.
Embodiment
Below in conjunction with drawings and Examples, the present invention is further described.
Embodiment 1:
Carry out NO emissions reduction prediction with Geographical Weighted Regression in the present embodiment, concrete steps are as follows:
Choose Tibet region as survey region, forecasting research is carried out to the moon rainfall amount of 2003-2009 rainy season (annual May-October), finally obtains the rainfall amount distribution plan of monthly 1km spatial resolution.
Step 1) data acquisition: obtain the TRMM meteorological satellite remote sensing image data in region, Tibet, MODIS satellite remote-sensing image data and ASTERGDEM satellite remote-sensing image data, collect the daily rainfall observed reading of ground observation website in region, Tibet simultaneously; Wherein MODIS satellite remote-sensing image data comprise MOD11A2 data product and MOD13A2 data product.Wherein: the spatial resolution of TRMM meteorological satellite remote sensing image data is 0.25 ° × 0.25 °, and temporal resolution is 3 hours; The spatial resolution of described ASTERGDEM satellite remote-sensing image data is 30m; The spatial resolution of described MODIS satellite remote-sensing image data is 1km, and temporal resolution is 8 days.
Step 2) data prediction: by step 1) temporal resolution of TRMM meteorological satellite remote sensing image data that obtains is treated to the moon, as shown in Figure 1; ASTERGDEM satellite remote-sensing image data are carried out polymerization calculating and obtain the dem data that spatial resolution is 1km and 25km respectively; Extract from MOD11A2 data product daytime surface temperature and evening surface temperature parameter, and by polymerization calculate obtain respectively spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be the surface temperature data in evening of 1km and 25km; From MOD13A2 data product, extract vegetation index parameter, after abnormality value removing process, calculated by polymerization and obtain the vegetation index data that spatial resolution is 1km and 25km respectively; Wherein, the concrete steps of abnormality value removing process are as follows: using the vegetation index that extracts in MOD13A2 data product as initial vegetation index, first the part that in initial vegetation index, grid point value is less than 0 is deleted, again with 11 × 11 window gliding smoothing vegetation index, then the vegetation index is smoothly deducted with initial vegetation index, select-0.15 to 0.15 to screen the result after subtracting each other as threshold range again, cast out the grid exceeding threshold range, all the other are as normal vegetation index point.Finally, carry out being polymerized the Gradient, Topographic Wetness Index data, Barrier facility data and the slope aspect data that calculate and obtain 1km and 25km respectively from the ASTERGDEM satellite remote-sensing image extracting data gradient, Topographic Wetness Index, Barrier facility and slope aspect 4 parameters;
Step 3) carry out Geographical Weighted Regression modeling: using step 2) process after TRMM meteorological satellite remote sensing image data as dependent variable, the surface temperature data on daytime of 25km are with spatial resolution, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out Geographical Weighted Regression as between independent variable and 1km latitude and longitude coordinates point, thus obtain the rainfall regression residuals value that regression equation that spatial resolution is each grid point in the raster data of 1km and spatial resolution are 25km, the latitude and longitude coordinates of described 1km latitude and longitude coordinates point to be spatial resolution be each grid point in the raster data of 1km,
Step 4) NO emissions reduction prediction: based on step 3) spatial resolution of gained is the regression equation of each grid point in the raster data of 1km, with spatial resolution be the surface temperature data on daytime of 1km, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data be as input independent variable, calculate, obtaining spatial resolution is 1km ground prediction of precipitation Value Data; Be that the rainfall regression residuals value of 25km is carried out resampling and obtained the rainfall regression residuals value that spatial resolution is 1km by spatial resolution simultaneously, and be that 1km ground prediction of precipitation Value Data is added by itself and spatial resolution, obtain TRMM weather satellite rainfall data that spatial resolution is 1km and import in ArcGIS charting, after playing up as shown in Figure 2.
Step 5) precision analysis of prediction of precipitation value: utilize the method for crosscheck to step 4) in the prediction of precipitation value of 1km spatial resolution carry out precision of prediction check analysis, crosscheck selects root-mean-square error, mean absolute error and related coefficient as evaluation points.As shown in Figure 5, coefficient R
2be 0.651, root-mean-square error RMSE be 39.578mm, mean absolute error MEA be 29.611mm.
The computing formula of each index is as follows:
In formula, MAE representative is mean absolute error, and what RMSE represented is root-mean-square error, R
2what represent is regression correlation coefficient, Y
kthe observed reading of ground observation website k, O
kbe by after model NO emissions reduction in the predicted value at site k place,
the mean value of all ground rainfall observation station data,
the mean value of the model predication value at all websites.
Embodiment 2
Select in the present embodiment to carry out regression modeling with M5-LocalR method, concrete steps are: carry out NO emissions reduction prediction with Geographical Weighted Regression in the present embodiment, concrete steps are as follows:
Choose Tibet region as survey region, forecasting research is carried out to the moon rainfall amount of 2003-2009 rainy season (annual May-October), finally obtains the rainfall amount distribution plan of monthly 1km spatial resolution.
Step 1) data acquisition: obtain the TRMM meteorological satellite remote sensing image data in region, Tibet, MODIS satellite remote-sensing image data and ASTERGDEM satellite remote-sensing image data, collect the daily rainfall observed reading of ground observation website in this region, Tibet simultaneously; Wherein MODIS satellite remote-sensing image data comprise MOD11A2 data product and MOD13A2 data product.Wherein: the spatial resolution of TRMM meteorological satellite remote sensing image data is 0.25 ° × 0.25 °, and temporal resolution is 3 hours; The spatial resolution of described ASTERGDEM satellite remote-sensing image data is 30m; The spatial resolution of described MODIS satellite remote-sensing image data is 1km, and temporal resolution is 8 days.
Step 2) data prediction: by step 1) temporal resolution of TRMM meteorological satellite remote sensing image data that obtains is treated to the moon; ASTERGDEM satellite remote-sensing image data are carried out polymerization calculating and obtain the dem data that spatial resolution is 1km and 25km respectively; Extract from MOD11A2 data product daytime surface temperature and evening surface temperature parameter, and by polymerization calculate obtain respectively spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be the surface temperature data in evening of 1km and 25km; From MOD13A2 data product, extract vegetation index parameter, after abnormality value removing process, calculated by polymerization and obtain the vegetation index data that spatial resolution is 1km and 25km respectively; Wherein: the concrete steps of abnormality value removing process are as follows: using the vegetation index that extracts in MOD13A2 data product as initial vegetation index, first the part that in initial vegetation index, grid point value is less than 0 is deleted, again with 11 × 11 window gliding smoothing vegetation index, then the vegetation index is smoothly deducted with initial vegetation index,-0.15 to 0.15 is selected to screen the result after subtracting each other as threshold range again, cast out the grid exceeding threshold range, all the other are as normal vegetation index point.Finally, carry out being polymerized the Gradient, Topographic Wetness Index data, Barrier facility data and the slope aspect data that calculate and obtain 1km and 25km respectively from the ASTERGDEM satellite remote-sensing image extracting data gradient, Topographic Wetness Index, Barrier facility and slope aspect 4 parameters;
Step 3) carry out M5-LocalR regression modeling: using step 2) process after TRMM meteorological satellite remote sensing image data as dependent variable, the surface temperature data on daytime of 25km are with spatial resolution, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data adopt M5-LocalR method to carry out Geographical Weighted Regression as between independent variable and 1km latitude and longitude coordinates point, thus obtain the rainfall regression residuals value that regression equation that spatial resolution is each grid point in the raster data of 1km and spatial resolution are 25km, wherein: the latitude and longitude coordinates of 1km latitude and longitude coordinates point to be spatial resolution be each grid point in the raster data of 1km,
The concrete steps that M5-LocalR regression modeling adopts are as follows: first by step 2) surface temperature data on daytime after process, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data separate M5 method carry out principal component analysis (PCA), thus obtain the main-control factors of each grid point, as shown in Figure 3.M5 method in the present embodiment also can adopt other principal component analytical methods to carry out, as long as the principal component analysis (PCA) on energy implementation space.Then using the main-control factors of each grid point as input independent variable, using step 2) process after TRMM meteorological satellite remote sensing image data carry out Geographical Weighted Regression as dependent variable.
Step 4) NO emissions reduction prediction: based on step 3) spatial resolution of gained is the regression equation of each grid point in the raster data of 1km, with spatial resolution be the surface temperature data on daytime of 1km, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data be as input independent variable, calculate, obtaining spatial resolution is 1km ground prediction of precipitation Value Data; Be that the rainfall regression residuals value of 25km is carried out resampling and obtained the rainfall regression residuals value that spatial resolution is 1km by spatial resolution simultaneously, and be that 1km ground prediction of precipitation Value Data is added by itself and spatial resolution, obtain TRMM weather satellite rainfall data that spatial resolution is 1km and import in ArcGIS charting, after playing up as shown in Figure 4.
Step 5) precision analysis of prediction of precipitation value: utilize the method for crosscheck to step 4) in the prediction of precipitation value of 1km spatial resolution carry out precision of prediction check analysis, crosscheck selects root-mean-square error, mean absolute error and related coefficient as evaluation points.As shown in Figure 6, the coefficient R wherein obtained
2be 0.844, root-mean-square error RMSE be 25.04mm, mean absolute error is 17.17mm.Clearly, on precision of prediction, comparatively example 1 improves.
Claims (5)
1., based on a method for the TRMM satellite Rainfall Products NO emissions reduction of M5-LocalR and Multi-environment factor variable, it is characterized in that, comprise the following steps:
Step 1) data acquisition: obtain the TRMM meteorological satellite remote sensing image data in region to be measured, MODIS satellite remote-sensing image data and ASTERGDEM satellite remote-sensing image data, collect the daily rainfall observed reading of ground observation website in this region to be measured simultaneously; Wherein MODIS satellite remote-sensing image data comprise MOD11A2 data product and MOD13A2 data product;
Step 2) data prediction: the temporal resolution of TRMM meteorological satellite remote sensing image data step 1) obtained is treated to the moon; ASTERGDEM satellite remote-sensing image data are carried out polymerization calculating and obtain the dem data that spatial resolution is 1km and 25km respectively; Extract from MOD11A2 data product daytime surface temperature and evening surface temperature parameter, and by polymerization calculate obtain respectively spatial resolution be 1km and 25km daytime surface temperature data and spatial resolution be the surface temperature data in evening of 1km and 25km; From MOD13A2 data product, extract vegetation index parameter, after abnormality value removing process, calculated by polymerization and obtain the vegetation index data that spatial resolution is 1km and 25km respectively; Carry out being polymerized the Gradient, Topographic Wetness Index data, Barrier facility data and the slope aspect data that calculate and obtain 1km and 25km respectively from the ASTERGDEM satellite remote-sensing image extracting data gradient, Topographic Wetness Index, Barrier facility and slope aspect 4 parameters;
Step 3) carries out M5-LocalR regression modeling: using step 2) process after TRMM meteorological satellite remote sensing image data as dependent variable, the surface temperature data on daytime of 25km are with spatial resolution, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out Geographical Weighted Regression as between independent variable and 1km latitude and longitude coordinates point, thus obtain the rainfall regression residuals value that regression equation that spatial resolution is each grid point in the raster data of 1km and spatial resolution are 25km, the latitude and longitude coordinates of described 1km latitude and longitude coordinates point to be spatial resolution be each grid point in the raster data of 1km,
Step 4) NO emissions reduction is predicted: the spatial resolution based on step 3) gained is the regression equation of each grid point in the raster data of 1km, with spatial resolution be the surface temperature data on daytime of 1km, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index raster data be as input independent variable, calculate, obtaining spatial resolution is 1km ground prediction of precipitation Value Data; Be that the rainfall regression residuals value of 25km is carried out resampling and obtained the rainfall regression residuals value that spatial resolution is 1km by spatial resolution simultaneously, and be that 1km ground prediction of precipitation Value Data is added by itself and spatial resolution, obtain the TRMM weather satellite rainfall data that spatial resolution is 1km.
2. as claimed in claim 1 based on the TRMM satellite rainfall data NO emissions reduction Forecasting Methodology of M5-LocalR, it is characterized in that, in described step 1), the spatial resolution of TRMM meteorological satellite remote sensing image data is 0.25 ° × 0.25 °, and temporal resolution is 3 hours; The spatial resolution of described ASTERGDEM satellite remote-sensing image data is 30m; The spatial resolution of described MODIS satellite remote-sensing image data is 1km, and temporal resolution is 8 days.
3. as claimed in claim 1 based on the TRMM satellite rainfall data NO emissions reduction Forecasting Methodology of M5-LocalR, it is characterized in that, described step 2) in the concrete steps of abnormality value removing process as follows: using the vegetation index that extracts in MOD13A2 data product as initial vegetation index, first the part that in initial vegetation index, grid point value is less than 0 is deleted, again with 11 × 11 window gliding smoothing vegetation index, then the vegetation index is smoothly deducted with initial vegetation index,-0.15 to 0.15 is selected to screen the result after subtracting each other as threshold range again, cast out the grid exceeding threshold range, all the other are as normal vegetation index point.
4. as claimed in claim 1 based on the TRMM satellite rainfall data NO emissions reduction Forecasting Methodology of M5-LocalR, it is characterized in that, in described step 3), Geographical Weighted Regression adopts M5-LocalR regression modeling, the concrete steps that M5-LocalR regression modeling adopts are as follows: first by step 2) surface temperature data on daytime after process, evening surface temperature data, vegetation index data, dem data, Gradient, slope aspect data, Barrier facility data and Topographic Wetness Index data carry out principal component analysis (PCA), thus obtain the main-control factors of each grid point; So with the main-control factors of each grid point for independent variable, using step 2) TRMM meteorological satellite remote sensing image data after process carries out Geographical Weighted Regression as dependent variable.
5. as claimed in claim 4 based on the TRMM satellite rainfall data NO emissions reduction Forecasting Methodology of M5-LocalR, it is characterized in that, described principal component analysis (PCA) adopts M5 method.
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| CN112836146A (en) * | 2021-03-09 | 2021-05-25 | 威创集团股份有限公司 | Geographic space coordinate information acquisition method and device based on network message |
| CN114330123A (en) * | 2021-12-29 | 2022-04-12 | 湖南省柘溪电力集团有限公司 | Cross-platform data processing method |
| CN116151483A (en) * | 2023-04-14 | 2023-05-23 | 山东理工大学 | Regional rainfall landslide probabilistic prediction method and prediction terminal |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120040463A (en) * | 2010-10-19 | 2012-04-27 | 부산대학교 산학협력단 | System and method for calculating quantitative of heavy rainfall amount using satellite images |
| CN102789004A (en) * | 2012-07-20 | 2012-11-21 | 南京大学 | Satellite retrieval method for night rainfall probability |
| CN103235890A (en) * | 2013-04-28 | 2013-08-07 | 南京信大高科技发展有限公司 | System and method for satellite short-time approaching rainfall forecasting |
| CN103810376A (en) * | 2014-01-17 | 2014-05-21 | 浙江大学 | Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging |
-
2015
- 2015-09-17 CN CN201510595009.6A patent/CN105160192B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120040463A (en) * | 2010-10-19 | 2012-04-27 | 부산대학교 산학협력단 | System and method for calculating quantitative of heavy rainfall amount using satellite images |
| CN102789004A (en) * | 2012-07-20 | 2012-11-21 | 南京大学 | Satellite retrieval method for night rainfall probability |
| CN103235890A (en) * | 2013-04-28 | 2013-08-07 | 南京信大高科技发展有限公司 | System and method for satellite short-time approaching rainfall forecasting |
| CN103810376A (en) * | 2014-01-17 | 2014-05-21 | 浙江大学 | Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging |
Non-Patent Citations (7)
| Title |
|---|
| QINGFANG HU等: "An Assessment on Geographically Weighted Regression-Based Merging Method of Satellite and Gauge Rainfall", 《APPLIED MECHANICS & MATERIALS》 * |
| Y. KAMARIANAKIS等: "Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression", 《ENVIRONMENTAL MODELLING& SOFTWARE》 * |
| 胡庆芳: "基于多源信息的降水空间估计及其水文应用研究", 《中国博士学位论文全文数据库基础科学辑》 * |
| 蔡研聪 等: "中高纬度地区TRMM卫星降雨数据的精度评价", 《应用生态学报》 * |
| 覃文忠: "地理加权回归基本理论与应用研究", 《中国博士学位论文全文数据库基础科学辑》 * |
| 马金辉 等: "2001-2010年石羊河流域上游TRMM降水资料的降尺度研究", 《地理科学进展》 * |
| 高洁: "基于TRMM卫星数据的降雨测量精度评价", 《水力发电》 * |
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