CN109325540A - A kind of space NO emissions reduction method for the daily precipitation data of remote sensing - Google Patents
A kind of space NO emissions reduction method for the daily precipitation data of remote sensing Download PDFInfo
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Abstract
A kind of space NO emissions reduction method for the daily precipitation data of remote sensing, this method comprises the following steps: first, NO emissions reduction is carried out to the daily precipitation data of remote sensing to realize indirectly using a space-time NO emissions reduction scheme, obtains the preliminary NO emissions reduction result of the daily precipitation of remote sensing；Secondly, based on the moon, merging all preliminary NO emissions reduction results of the daily precipitation of remote sensing and the observation of daily website in the moon using a kind of set fusion method, obtaining the daily precipitation fusion results of remote sensing；Then, the daily precipitation fusion results of remote sensing are accumulated, generates remote sensing monthly or annual precipitation fusion results.The present invention can overcome the limitation of prior art, comprehensively consider the time change of the space non-stationary relationship and daily precipitation between precipitation and the auxiliary environment factor, sufficiently take the entire error field of the preliminary NO emissions reduction result of the daily precipitation of remote sensing into account simultaneously, the spatial resolution and precision of the daily precipitation of remote sensing can be increased substantially, in addition, being obviously improved it in the ability in detection precipitation and non-precipitation region.
Description
Technical field
It is especially a kind of for the daily precipitation data of remote sensing the present invention relates to a kind of remote sensing precipitation data processing method
Space NO emissions reduction method.
Background technique
Though meteorological site provides accurately precipitation measurement, the high change in time and space characteristic of precipitation, the lower density of website with
And serious uneven distribution etc. seriously limits the precision of space interpolation result.The development of remote sensing technology brings for precipitation measurement
A series of unprecedented opportunities, in recent decades, with the transmitting exclusively for Rainfall Monitoring design satellite, remote sensing Precipitation Products
It is obtained.By the improvement persistently to remote sensing precipitation inversion algorithm, these data have been able to provide Global Scale, Gao Shi at present
Between resolution ratio Precipitation Products, such as: TRMM and PERSIANN etc., however, spatial resolution that these data are thicker (such as
0.25 °) seriously constrain their application depth and range.
Space NO emissions reduction can effectively solve the problems, such as this, and NO emissions reduction has been cited as remote sensing Precipitation Products in engineering and decision
An important research field.Current remote sensing precipitation NO emissions reduction research is concentrated mainly on year and moon precipitation；And it is directed to remote sensing
The NO emissions reduction research of daily precipitation is then less, and only the heavy rain to mountain area and heavy rain carry out.Itself the reason is as follows that: 1) precipitation with
Significant relation between environmental factor there's almost no on day scale, directly can not carry out remote sensing using the relationship in this way and drop daily
The space NO emissions reduction of water；2) precipitation is spatially discrete daily；3) it is different from the every monthly total precipitation product of remote sensing, remote sensing
Daily precipitation is not integrated with the Precipitation Products based on site analysis, and therefore, precision is relatively low；4) precipitation data has
Have Multiple Time Scales characteristic, this is required while obtaining accurate NO emissions reduction result daily, it should also ensure that the accumulation of its moon and
Year accumulation also has significant precision improvement.In short, existing space NO emissions reduction method can not carry out sky to the daily precipitation of remote sensing
Between NO emissions reduction, and significantly improve its precision and promote it in the monitoring precipitation and regional ability of non-precipitation.
Summary of the invention
The purpose of the present invention is to provide a kind of space NO emissions reduction method for the daily precipitation data of remote sensing, its energy gram
The limitation for taking prior art, comprehensively consider space non-stationary relationship between precipitation and the auxiliary environment factor and daily precipitation when
Between change, while sufficiently taking the entire error field of the preliminary NO emissions reduction result of the daily precipitation of remote sensing into account, can increase substantially distant
Feel the spatial resolution and precision of daily precipitation, and is obviously improved final result in the energy in detection precipitation and non-precipitation region
Power.
The present invention is achieved through the following technical solutions:
Step I. data prediction
Precipitation data (DailyPre daily to remote sensinglow) carry out accumulation generation remote sensing annual precipitation (AnnPrelow), on
Marking low indicates low spatial resolution；Monthly all NDVI data are synthesized using synthetic method is maximized, are generated monthly
NDVI data, then to 12 months monthly NDVI data averagely generate annual NDVI data；It is flat to year using the pixel method of average
Equal NDVI data and dem data carry out a liter scale, them is made to have spatial resolution identical with remote sensing annual precipitation data,
That is the annual NDVI data of low spatial resolution, are expressed as NDVIlowAnd the dem data of low spatial resolution, it is expressed as
DEMlow；In addition, annual NDVI data and dem data are risen scale to desired high-space resolution using the pixel method of average
Rate, such as 1km or 0.01 °, i.e. the annual NDVI data of high spatial resolution, are expressed as NDVIhighAnd high spatial point
The dem data of resolution, is expressed as DEMhigh, subscript
High indicates high spatial resolution.
Step II. remote sensing annual precipitation space-time NO emissions reduction
1, remote sensing annual precipitation space NO emissions reduction
Remote sensing annual precipitation space NO emissions reduction is based primarily upon between annual precipitation and vegetation and landform that there is significant phases
Pass relationship constructs NO emissions reduction model using the relationship.In view of between annual precipitation and vegetation and landform relationship it is non-stationary,
AnnPre is explored using Geographically weighted regression procedurelowWith NDVIlowAnd DEMlowBetween relationship, it is specific as follows:
In formula,For the parameter changed with spatial variations, u is position.By these parameter weights
Sampling is to desired high spatial resolution, i.e.,Next to residual epsilonlow(u) batten letter is carried out
Number interpolation, generates the residual error item ε of high spatial resolutionhigh(u).Finally, the preliminary NO emissions reduction result of remote sensing annual precipitation calculates such as
Under:
In formula,For the preliminary NO emissions reduction of remote sensing annual precipitation as a result, subscript high indicates high-space resolution
Rate.
(2) the preliminary NO emissions reduction result time NO emissions reduction of remote sensing annual precipitation
Using the daily precipitation of remote sensing specific gravity information shared in accumulation in its year to the preliminary NO emissions reduction of remote sensing annual precipitation
As a result time NO emissions reduction is carried out.Firstly, the daily precipitation of remote sensing specific gravity shared in accumulation in its year is calculated, it is specific as follows:
In formula,It is i-th day remote sensing precipitation,For its shared ratio in annual precipitation
Weight.
It, will in order to be consistent with the high spatial resolution of the preliminary NO emissions reduction result of remote sensing annual precipitation
It is resampled to desired high spatial resolution, i.e.,In this way, the preliminary NO emissions reduction result meter of the daily precipitation of remote sensing
It calculates as follows:
In formula,It is i-th day preliminary NO emissions reduction result of remote sensing precipitation.
Step III. merges the preliminary NO emissions reduction result of the daily precipitation of remote sensing and daily website is observed
As unit of monthly, using set fusion method to the preliminary NO emissions reduction knot of the daily precipitation of remote sensing all in this month
Fruit and the observation of daily website are merged, and the daily precipitation fusion results of remote sensing in the moon are generated.First by the daily precipitation of remote sensing
It measures preliminary NO emissions reduction result serializing and generates 1 dimensional vector x, then using day as sequence generator matrix
N is the number of days of this month in formula, and n is the pixel number of the preliminary NO emissions reduction result of the daily precipitation of remote sensing；Daily website is observed to stand
Dot sequency generates 1 dimensional vector y, then using day as sequence then generator matrixN is the moon in formula
Number of days, m be daily website observe data number.It is as follows to gather fusion calculation:
Xf=X+K (Y-HX) (5)
In formula, XfIt is the daily precipitation fusion results of remote sensing, H is mapping matrix, it is by the remote sensing of site location in matrix X
The daily preliminary NO emissions reduction result of precipitation projects to corresponding site location in observing matrix Y.K is weight matrix, is calculated such as
Under:
K=(C ο P) HT(H(CοP)HT+R)-1 (6)
Wherein, P and R is error co-variance matrix respectively, and approximate calculation is as follows respectively:
Wherein, X ' is the set exception matrix of X, is defined as:Y ' is the set exception matrix of Y, is determined
Justice are as follows:N is number of days monthly, and diag () is the diagonal line of one matrix of extraction to construct diagonal matrix.
In formula (6), operator ο is Schur product.For the related coefficient square based on distance
Battle array, in which: m is the number that daily website observes data；N is the pixel number of the preliminary NO emissions reduction result of the daily precipitation of remote sensing；ci
It is each grid in the preliminary NO emissions reduction result of the daily precipitation of remote sensing to i-th website apart from associated vector, the value range of i
For [1, m], it is similarly 1 dimensional vector after serializing, wherein as follows apart from relevant calculation:
In formula, s is defined as d*0.5752/r, and d is each grid in the preliminary NO emissions reduction result of the daily precipitation of remote sensing to i-th
The Euclidean distance of a website, r are the radius of setting.
Step IV. accumulation generates remote sensing monthly and annual precipitation fusion results
The daily precipitation fusion results of remote sensing are obtained to step III to accumulate, and generate remote sensing monthly and annual precipitation
Fusion results.
In above step, the daily precipitation data (DailyPre of the remote sensinglow) it is the daily precipitation of TRMM or IMERG
Measure data；NDVI is vegetation index data, derives from PROBA-V S10TOC or MODIS MOD13A2, they are respectively 10 days
Or the NDVI data of synthesis in 16 days；DEM is the digital elevation model from SRTM data, the spatial resolution with 90m；It is described
Resampling is closest resampling or bilinear interpolation or Spline interpolation.
The present invention is by adopting the above technical scheme.Firstly, every to remote sensing to realize indirectly using a space-time NO emissions reduction scheme
Its precipitation data carries out space NO emissions reduction, obtains the preliminary NO emissions reduction result of the daily precipitation of remote sensing；Secondly, using a kind of set
Fusion method is merged all preliminary NO emissions reduction results of the daily precipitation of remote sensing and daily website in the moon and is observed based on the moon,
Obtain the daily precipitation fusion results of remote sensing；Then, the daily precipitation fusion results of remote sensing are accumulated, generates remote sensing monthly
Or annual precipitation fusion results.The present invention, which can be realized effectively, carries out NO emissions reduction to the daily precipitation data of remote sensing, not only significantly
Its spatial resolution and precision are improved, while promoting the monitoring capability to precipitation and non-precipitation region；Furthermore it is ensured that remote sensing is every
The moon, annual precipitation fusion results equally have significant precision improvement；Therefore, the present invention has broad application prospects.
Detailed description of the invention
Attached drawing 1 is a kind of flow chart of space NO emissions reduction method for the daily precipitation data of remote sensing
Specific embodiment
It describes the specific embodiments of the present invention in detail with reference to the accompanying drawings and examples
The remote sensing precipitation data of selection is the daily precipitation data of IMERG in 2015, with 0.1 ° of spatial resolution；DEM
Data come from SRTM, with 90 meters of spatial resolution；NDVI data (are closed for 10 days from PROBA-V S10TOC NDVI
At), the spatial resolution with 1km.Research area is central China and western some areas, and longitude and latitude range is 25 ° N-38 °
N and 105 ° of E-118.5 ° of E；The daily precipitation measurement data that meteorological site obtains are studied within the scope of area from National Meteorological Bureau
There are 289 weather stations, wherein daily website observes 80% for model construction, remaining 20% is used for final precision test.
The specific embodiment of the method for the present invention includes the following steps:
Step I. data prediction
The daily precipitation of IMERG in 2015 is accumulated and generates IMERG annual precipitation, is expressed as AnnIMERG0.1°；To 2015
The NDVI data of year monthly 3 synthesis in 10 days generate monthly NDVI data using synthetic method is maximized, then every to 12 months
Month NDVI data take average generations annual NDVI data, and annual NDVI data are risen ruler by the use pixel method of average respectively
For degree to 0.1 ° and 0.01 °, 0.1 ° and 0.01 ° of annual NDVI data are expressed as NDVI0.1°And NDVI0.01°；This
Outside, DEM is risen to scale respectively using the pixel method of average to 0.1 ° and 0.01 °, 0.1 ° and 0.01 ° of dem data respectively indicates
For DEM0.1°And DEM0.01°.0.01 ° and 0.1 ° of the subscript spatial resolution for respectively indicating the data.
Step II. remote sensing annual precipitation space-time NO emissions reduction
IMERG annual precipitation space NO emissions reduction
In view of the space non-stationary relationship between annual precipitation and vegetation and elevation, using Geographically weighted regression procedure come
Explore AnnIMERG0.1°And NDVI0.1°And DEM0.1°Between relationship, it is specific as follows:
In formula,The parameter changed for spatial variations, u are position；Then by these parameters
It is resampled to 0.01 °, i.e.,The method for resampling used is closest resampling or bilinearity
Interpolation or Spline interpolation method.It should be noted that in three kinds of methods, using any one method, specifically optionally
Depending on, now by taking closest resampling as an example；Next to residual epsilon0.1°(u) Spline interpolation is carried out, 0.01 ° of residual error is generated
Item ε0.01°(u).Finally, the preliminary NO emissions reduction result of IMERG annual precipitation calculates as follows:
In formula,The preliminary NO emissions reduction result of remote sensing annual precipitation for being 0.01 ° for spatial resolution.
The preliminary NO emissions reduction result time NO emissions reduction of IMERG annual precipitation
Ruler is tentatively dropped to IMERG annual precipitation using the daily precipitation of IMERG specific gravity information shared in accumulation in its year
It spends result and carries out time NO emissions reduction.Firstly, the daily precipitation of IMERG specific gravity shared in accumulation in its year is calculated, it is specific as follows:
In formula,It is i-th day IMERG precipitation,For its in annual precipitation it is shared
Specific gravity；It, will in order to be consistent with the spatial resolution of the preliminary NO emissions reduction result of IMERG annual precipitationIt adopts again
Sample is at 0.01 °, i.e.,The method for resampling used is closest resampling or bilinear interpolation or batten letter
Number interpolation method, it should be noted that in three kinds of methods, using any one method, specifically depend on the circumstances, now with most adjacent
For nearly resampling.In this way, the preliminary NO emissions reduction result calculating of the daily precipitation of IMERG is as follows:
In formula,It is i-th day IMERG precipitation fusion results.
Step III. merges the preliminary NO emissions reduction result of the daily precipitation of IMERG and daily website is observed
As unit of monthly, using set fusion method to the preliminary NO emissions reduction of the daily precipitation of IMERG all in this month
As a result and website observation daily is merged, and generates the daily precipitation fusion results of this month IMERG, specific as follows: by IMERG
The daily preliminary NO emissions reduction result serializing of precipitation data generates 1 dimensional vector x, then using day as sequence generator matrixN is the number of days of this month in formula, and n is the pixel of the preliminary NO emissions reduction result of the daily precipitation of IMERG
Number；Daily website is observed, 1 dimensional vector y is sequentially generated with website, then using day as sequence then generator matrixN is the number of days of this month in formula, and m is the number that daily website observes data.Set fusion
It calculates as follows:
Xf=X+K (Y-HX) (5)
In formula, XfIt is the daily precipitation fusion results of IMERG, H is mapping matrix, it is by the drop of site location in matrix X
Scale result projects to corresponding observation position in observing matrix Y.K is weight matrix, is calculated as follows:
K=(C ο P) HT(H(CοP)HT+R)-1 (6)
Wherein, P and R is error co-variance matrix respectively, and difference approximate calculation is as follows:
In formula, X ' is the set exception matrix of X, is defined as:Y ' is the set exception matrix of Y, definition
Are as follows: its is defined as:N is number of days monthly, and diag () is the diagonal line of one matrix of extraction to construct to angular moment
Battle array.
In formula (6), operator ο is Schur product.For the related coefficient square based on distance
Battle array, in which: m is the number that daily website observes data；N is the pixel number of the preliminary NO emissions reduction result of the daily precipitation of IMERG；
ciIt is each grid in the preliminary NO emissions reduction result of the daily precipitation of IMERG to i-th website apart from associated vector, the value of i
Range is [1, m], it is similarly 1 dimensional vector after serializing, wherein as follows apart from relevant calculation:
In formula, s is defined as d*0.5752/r, wherein d is each grid in the preliminary NO emissions reduction result of the daily precipitation of IMERG
Lattice are to the Euclidean distance of i-th website, and r is the radius of setting, and radius r is set as 0.5 ° in this research.
Step IV. accumulation generates IMERG monthly and annual precipitation fusion results
The every intra day ward fusion results of IMERG are obtained to step III to accumulate, and generate remote sensing monthly and annual precipitation
Fusion results.
Finally, verifying the precision of IMERG precipitation fusion results using residue 20%, the evaluation index used is root mean square
Error.The result shows that: original I MERG data are compared, IMERG is daily, and monthly and annual precipitation fusion results reduce respectively
Square error is 20%, 10% and 15%；In addition, their significantly improving due to spatial resolution, are capable of providing more drops
Water detailed information.
Claims (2)
1. a kind of space NO emissions reduction method for the daily precipitation data of remote sensing, which is characterized in that comprise the following steps:
Step I. data prediction
Precipitation data (DailyPre daily to remote sensinglow) accumulated, generate remote sensing annual precipitation data (AnnPrelow),
Wherein subscript low indicates low spatial resolution；Monthly all NDVI data are synthesized using synthetic method is maximized, are generated
Monthly NDVI data, then to 12 months monthly NDVI data averagely generate annual NDVI data；Using the pixel method of average pair
Annual NDVI data and dem data carry out a liter scale, make them with space identical with remote sensing annual precipitation data point
Resolution, i.e. the annual NDVI data of low spatial resolution, are expressed as NDVIlowAnd the dem data of low spatial resolution, table
It is shown as DEMlow；In addition, annual NDVI data and dem data are risen scale to desired high spatial using the pixel method of average
Resolution ratio, i.e. the annual NDVI data of high spatial resolution, are expressed as NDVIhighAnd the DEM number of high spatial resolution
According to being expressed as DEMhigh, wherein subscript high indicates high spatial resolution；
Step II. remote sensing annual precipitation space-time NO emissions reduction
(1) remote sensing annual precipitation space NO emissions reduction
AnnPre is explored using Geographically weighted regression procedurelowWith NDVIlowAnd DEMlowBetween relationship, it is specific as follows:
In formula,For the parameter changed with spatial variations, u is spatial position；By these parameter weights
Sampling is to desired high spatial resolution, i.e.,Next to residual epsilonlow(u) batten letter is carried out
Number interpolation, generates the residual error item ε of high spatial resolutionhigh(u), finally, the preliminary NO emissions reduction result of remote sensing annual precipitation calculates such as
Under:
In formula,For the preliminary NO emissions reduction of remote sensing annual precipitation as a result, subscript high indicates high spatial resolution；
(2) the preliminary NO emissions reduction result time NO emissions reduction of remote sensing annual precipitation
Using the daily precipitation of remote sensing specific gravity information shared in accumulation in its year to the preliminary NO emissions reduction result of remote sensing annual precipitation
Time NO emissions reduction is carried out, firstly, the daily precipitation of remote sensing specific gravity shared in accumulation in its year is calculated, it is specific as follows:
In formula,It is i-th day remote sensing precipitation,For its shared specific gravity in annual precipitation；
It, will in order to be consistent with the high spatial resolution of the preliminary NO emissions reduction result of remote sensing annual precipitationIt is resampled to
Desired high spatial resolution, i.e.,In this way, the preliminary NO emissions reduction result calculating of the daily precipitation of remote sensing is as follows:
In formula,It is i-th day preliminary NO emissions reduction result of remote sensing precipitation；
Step III. merges the preliminary NO emissions reduction result of the daily precipitation of remote sensing and daily website is observed
As unit of monthly, use set fusion method to the preliminary NO emissions reduction result of the daily precipitation of remote sensing all in this month with
And website observation daily is merged, and the daily precipitation fusion results of remote sensing in the moon are generated.First by the daily precipitation of remote sensing
Preliminary NO emissions reduction result serializing generates 1 dimensional vector x, then using day as sequence generator matrix
N is the number of days of this month in formula, and n is the pixel number of the preliminary NO emissions reduction result of the daily precipitation of remote sensing；Daily website is observed to stand
Dot sequency generates 1 dimensional vector y, then using day as sequence then generator matrixN is the moon in formula
Number of days, m be daily website observe data number, set fusion calculation it is as follows:
Xf=X+K (Y-HX) (5)
In formula, XfIt is the daily precipitation fusion results of remote sensing, H is mapping matrix, it is daily by the remote sensing of site location in matrix X
The preliminary NO emissions reduction result of precipitation projects to corresponding site location in observing matrix Y, and K is weight matrix, calculates as follows:
K=(C ο P) HT(H(CοP)HT+R)-1 (6)
Wherein, P and R is error co-variance matrix respectively, and it is as follows that they distinguish approximate calculation:
Wherein, X ' is the set exception matrix of X, is defined asY ' is the set exception matrix of Y, is defined asN is number of days monthly, and diag () is the diagonal line of one matrix of extraction to construct diagonal matrix；
In formula (6), operator ο is Schur product,For the correlation matrix based on distance,
In: m is the number that daily website observes data；N is the pixel number of the preliminary NO emissions reduction result of the daily precipitation of remote sensing；ciFor remote sensing
In the daily preliminary NO emissions reduction result of precipitation each grid to i-th website apart from associated vector, the value range of i be [1,
M], it is similarly 1 dimensional vector after serializing, wherein as follows apart from relevant calculation:
In formula, s is defined as d*0.5752/r, and d is each grid in the preliminary NO emissions reduction result of the daily precipitation of remote sensing to i-th of station
The Ou De distance of point, r are the radius of setting；
Step IV. accumulation generate remote sensing monthly, annual precipitation fusion results
The daily precipitation fusion results of remote sensing are obtained to step III to accumulate, and generate remote sensing monthly and annual precipitation merges
As a result.
2. a kind of space NO emissions reduction method for the daily precipitation data of remote sensing according to claim 1, feature exist
In: the daily precipitation data of the remote sensing is the daily precipitation data of TRMM 3B42 or IMERG；NDVI is vegetation index, should
For data from PROBA-V S10TOC or MODIS MOD13A2, they are respectively the NDVI data of synthesis in 10 days or 16 days；
DEM is digital elevation model, and the data are from SRTM data, the spatial resolution with 90m；The method for resampling is
Closest resampling or bilinear interpolation or Spline interpolation.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110766229A (en) * | 2019-10-24 | 2020-02-07 | 河南大学 | High-resolution daily rainfall mapping method based on downscaling-fusion |
| CN112699959A (en) * | 2021-01-11 | 2021-04-23 | 中国科学院地理科学与资源研究所 | Multi-source multi-scale precipitation data fusion method and device based on energy functional model |
| CN114417646A (en) * | 2022-03-31 | 2022-04-29 | 中国科学院地理科学与资源研究所 | High-dimensional heterogeneous precipitation data fusion method and system |
| CN115659853A (en) * | 2022-12-28 | 2023-01-31 | 中国科学院地理科学与资源研究所 | Nonlinear mixed-effect strain coefficient downscaling method and system |
| CN116070792A (en) * | 2023-03-28 | 2023-05-05 | 中国科学院地理科学与资源研究所 | Fusion method, device, storage medium and equipment of multi-source precipitation data |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021872A (en) * | 2016-05-10 | 2016-10-12 | 浙江大学 | Dynamic filtering modeling downscaling method of environment variable on the basis of low-resolution satellite remote sensing data |
-
2018
- 2018-09-30 CN CN201811157032.7A patent/CN109325540B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021872A (en) * | 2016-05-10 | 2016-10-12 | 浙江大学 | Dynamic filtering modeling downscaling method of environment variable on the basis of low-resolution satellite remote sensing data |
Non-Patent Citations (2)
| Title |
|---|
| 孙云华: "《典型卫星影像数据反演降水产品精度分析与融合改进研究》", 《中国博士学位论文电子期刊网》 * |
| 蔡明勇等: "《雅鲁藏布江流域TRMM降水数据降尺度研究》", 《北京师范大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110766229A (en) * | 2019-10-24 | 2020-02-07 | 河南大学 | High-resolution daily rainfall mapping method based on downscaling-fusion |
| CN112699959A (en) * | 2021-01-11 | 2021-04-23 | 中国科学院地理科学与资源研究所 | Multi-source multi-scale precipitation data fusion method and device based on energy functional model |
| CN114417646A (en) * | 2022-03-31 | 2022-04-29 | 中国科学院地理科学与资源研究所 | High-dimensional heterogeneous precipitation data fusion method and system |
| CN115659853A (en) * | 2022-12-28 | 2023-01-31 | 中国科学院地理科学与资源研究所 | Nonlinear mixed-effect strain coefficient downscaling method and system |
| CN115659853B (en) * | 2022-12-28 | 2023-03-10 | 中国科学院地理科学与资源研究所 | Nonlinear mixed-effect strain coefficient downscaling method and system |
| CN116070792A (en) * | 2023-03-28 | 2023-05-05 | 中国科学院地理科学与资源研究所 | Fusion method, device, storage medium and equipment of multi-source precipitation data |
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