CN114020725B - Window sliding GPM data correction method considering spatial distribution - Google Patents

Window sliding GPM data correction method considering spatial distribution Download PDF

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CN114020725B
CN114020725B CN202111333763.4A CN202111333763A CN114020725B CN 114020725 B CN114020725 B CN 114020725B CN 202111333763 A CN202111333763 A CN 202111333763A CN 114020725 B CN114020725 B CN 114020725B
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杨明祥
南林江
刘珂
董宁澎
王贺佳
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention discloses a window sliding GPM data correction method considering spatial distribution, which comprises the following steps of S1, preprocessing satellite raster data and ground station data; s2, setting an initial window according to the preprocessed satellite raster data and ground station data; s3, correcting the satellite raster data in the initial window, and moving the initial window to correct all the satellite raster data in sequence; and S4, evaluating the correction result. The advantages are that: the space-time distribution characteristics of the satellite precipitation data can be considered, the actually measured precipitation data of the ground station is used as a reference, the satellite raster data is locally corrected, the correction error is reduced, and the correction result has relatively high precision.

Description

Window sliding GPM data correction method considering spatial distribution
Technical Field
The invention relates to the technical field of satellite precipitation data correction, in particular to a window sliding GPM data correction method considering spatial distribution.
Background
The existing technology is satellite precipitation data fusion technology, and the adopted method can be divided into a global type and a local type. The global fusion method comprises an average deviation correction method, a linear regression method, a dual-core smoothing method and the like; the local fusion method mainly comprises collaborative kriging, geographic weighted regression, Bayesian fusion and the like. However, for satellite precipitation data with low precision and errors, fusion is directly performed, so that the product quality cannot be improved, larger errors are introduced instead, and the reliability of results is difficult to ensure. In addition, the common satellite precipitation data fusion technology does not generally consider the difference of precipitation in space distribution and time, so that the fusion result can generate great uncertainty. Therefore, the prior art has certain limitations, and the reasonable and efficient correction of satellite data is particularly important.
Disclosure of Invention
The present invention aims to provide a window sliding GPM data correction method considering spatial distribution, so as to solve the aforementioned problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a window-sliding GPM data correction method considering spatial distribution comprises the following steps,
s1, preprocessing the satellite raster data and the ground station data;
s2, setting an initial window according to the preprocessed satellite raster data and ground station data;
s3, correcting the satellite raster data in the initial window, and moving the initial window to correct all the satellite raster data in sequence;
and S4, evaluating the correction result.
Preferably, step S1 specifically includes the following steps,
reading an original satellite nc file, processing satellite raster data into a matrix form, and respectively processing longitude and latitude coordinates corresponding to each grid of the satellite raster data into the matrix form;
and respectively processing the actually measured precipitation data of each ground station under a certain time scale and the longitude and latitude coordinates corresponding to the ground stations into a matrix form.
Preferably, step S2 specifically includes the following steps,
s21, starting from the satellite raster data initial grid, fixing the vertex, and taking 1 grid unit as the side length of a rectangular window;
s22, judging whether the rectangular window simultaneously contains satellite raster data and at least one ground station data, if so, entering S23, otherwise, increasing the side length of the rectangular window by 1 grid unit, and judging again;
and S23, selecting the rectangular window with the side length as an initial window.
Preferably, step S22 determines whether the rectangular window contains both the satellite grid data and at least one ground station data according to the correlation between the longitude and latitude coordinates of the satellite grid and the ground station coordinates, wherein the specific formula is,
|lonSatellite-longround surface|<N
|latSatellite-latGround surface|<T
Wherein, lonSatelliteGrid longitude for satellite raster data; lonGround surfaceFor each ground site longitude; latSatelliteGrid latitude of the satellite grid data is obtained; latGround surfaceThe latitude of each ground station; n, T are each the spatial resolution of the selected satellite precipitation data.
Preferably, step S3 specifically includes the following steps,
s31, respectively calculating the arithmetic mean of the data of the rainfall observed by the ground stations in the initial window and the arithmetic mean of the data of the contained satellite grids, and calculating the ratio of the arithmetic mean of the data of the rainfall observed by the ground stations in the initial window to the arithmetic mean of the data of the contained satellite grids; multiplying the ratio by the satellite grid data in the initial window to obtain a correction result of the satellite grid data in the initial window;
s32, moving the initial window according to the step length of 1 grid unit, and correcting the satellite raster data in the window in sequence according to the method of the step S31;
s33, for a plurality of correction results existing in a single grid, an arithmetic mean calculation is performed to obtain the correction result of the entire satellite grid data.
Preferably, in step S4, with the ground station observation precipitation data as a reference, the corrected satellite grid data is evaluated by using nine evaluation indexes including a correlation coefficient, a relative deviation, a root mean square error, an average absolute error, a detection rate, a false alarm rate, a critical success index, a frequency deviation, and a fair foreboding score;
the closer the correlation coefficient is to 1, the smaller the relative deviation is, the smaller the root mean square error is, the smaller the average absolute error is, the larger the detection rate is, the smaller the false alarm rate is, the larger the critical success index is, the smaller the frequency deviation is and the larger the fairness foreboding score is, the better the correction effect of the satellite raster data is represented.
The invention has the beneficial effects that: 1. the method can consider the space-time distribution characteristics of the satellite precipitation data, take the actually measured precipitation data of the ground station as a reference, and perform local correction on the raster data, thereby reducing correction errors and enabling correction results to have relatively high precision. 2. The invention can consider the relative position relation between the satellite grid and the ground observation station, and correct different regions with relatively high pertinence, thereby effectively solving the problems of uneven ground station distribution and lack of precipitation data in partial regions to a certain extent. 3. The invention obtains a set of high-precision precipitation data set through correction, thereby better serving the works such as local precipitation forecast, hydrological simulation and the like.
Drawings
FIG. 1 is a schematic flow chart of a method of correction in an embodiment of the present invention;
FIG. 2 is a flow diagram of data pre-processing in an embodiment of the invention;
FIG. 3 is a flow chart of setting an initial window in an embodiment of the present invention;
FIG. 4 is a flow chart of satellite raster data correction in an embodiment of the present invention;
FIG. 5 is a flow chart of the correction effect evaluation in the embodiment of the present invention;
FIG. 6 is a schematic diagram of a ground observation site in the northwest region of an embodiment of the present invention;
FIG. 7 is a RMSE contour plot before and after window shift correction in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example one
In the embodiment, as shown in fig. 1, a window-sliding GPM data correction method considering spatial distribution is provided, which includes the following steps,
s1, preprocessing the satellite raster data and the ground station data;
s2, setting an initial window according to the preprocessed satellite raster data and ground station data;
s3, correcting the satellite raster data in the initial window, and moving the initial window to correct all the satellite raster data in sequence;
and S4, evaluating the correction result.
In this embodiment, the correction method mainly includes four parts of contents, which are data preprocessing, setting of an initial window, correction of the satellite raster data, and evaluation of the correction result, and the following is set forth in detail for the four parts of contents.
First, data preprocessing
This section corresponds to step S1, and step S1 specifically includes, with reference to fig. 2,
preprocessing satellite data: in consideration of the characteristics of the satellite precipitation data form, reading an original satellite nc file, processing satellite raster data into a matrix form, and respectively processing longitude and latitude coordinates corresponding to each grid of the satellite raster data into the matrix form;
preprocessing ground data: and respectively processing the actually measured precipitation data of each ground station under a certain time scale and the longitude and latitude coordinates corresponding to the ground stations into a matrix form.
Setting an initial window
This section corresponds to step S2, and step S2 specifically includes, referring to fig. 3,
s21, starting from the satellite raster data initial grid, fixing the vertex, and taking 1 grid unit as the side length of a rectangular window;
s22, judging whether the rectangular window simultaneously contains satellite raster data and at least one ground station data, if so, entering S23, otherwise, increasing the side length of the rectangular window by 1 grid unit, and judging again;
and S23, selecting the rectangular window with the side length as an initial window.
In this embodiment, step S22 determines whether the rectangular window contains both the satellite grid data and at least one ground station data according to the correlation between the longitude and latitude coordinates of the satellite grid and the ground station coordinates, where the specific formula is,
|lonSatellite-longround surface|<N
|latSatellite-latGround surface|<T
Wherein, lonSatelliteGrid longitude for satellite raster data; lonGround surfaceFor each ground site longitude; latSatelliteGrid latitude of the satellite grid data is obtained; latGround surfaceThe latitude of each ground station; n, T are each the spatial resolution of the selected satellite precipitation data. The rectangular window satisfying the above formula is determined as the initial window.
Correction of satellite raster data
In this embodiment, step S3 specifically includes the following steps, referring to fig. 4,
respectively calculating an arithmetic mean value (marked as a) of satellite raster data contained in an initial window and an arithmetic mean value (marked as b) of ground station observation precipitation data in the initial window, and calculating a ratio (marked as c and used as a correction coefficient) between the arithmetic mean value of the ground station observation precipitation data in the initial window and the arithmetic mean value of the satellite raster data contained in the initial window, wherein the calculation formula is as follows:
Figure BDA0003349774280000051
multiplying the ratio by the satellite grid data in the initial window to obtain a correction result of the satellite grid data in the initial window;
then moving the initial window according to the step length of 1 grid unit, and correcting the satellite raster data in the window in sequence according to the method of the step S31;
and finally, performing arithmetic mean calculation on a plurality of correction results existing in a single grid to obtain the correction result of the whole satellite grid data.
Fourth, correction result evaluation
In this embodiment, step S4 is specifically, referring to fig. 5,
with the ground station observation precipitation data as reference, evaluating the corrected satellite grid data by respectively adopting nine evaluation indexes including correlation coefficients, relative deviation, root mean square error, average absolute error, detection rate, false alarm rate, critical success index, frequency deviation and fairness foreboding score;
the closer the correlation coefficient is to 1, the smaller the relative deviation is, the smaller the root mean square error is, the smaller the average absolute error is, the larger the detection rate is, the smaller the false alarm rate is, the larger the critical success index is, the smaller the frequency deviation is and the larger the fairness foreboding score is, the better the correction effect of the satellite raster data is represented. The nine evaluation indexes are specifically as follows:
1. pearson correlation coefficient (R)
The Pearson correlation coefficient reflects the strength of a linear relation between the satellite precipitation data and the ground observation precipitation data, the value range of the absolute value is 0-1, and the closer to 1, the more the satellite precipitation data is matched with the information of the ground observation precipitation data, the higher the reference value is.
In general, 0.8< R.ltoreq.1.0 means very strong correlation; 0.6< R ≦ 0.8 means a strong association; 0.4< R.ltoreq.0.6 means moderate correlation; 0.2< R.ltoreq.0.4 means weak correlation; r is 0.0-0.2, which is very weak or irrelevant; r0.0 represents a negative correlation.
Figure BDA0003349774280000061
2. Root Mean Square Error (RMSE)
The root mean square error is used for evaluating the deviation degree of the satellite precipitation data and the ground observation precipitation data, the value of the root mean square error is always non-negative, the smaller the value is, the smaller the observation error is, and otherwise, the larger the error is.
Figure BDA0003349774280000062
3. Relative deviation (BIAS)
The relative deviation is the percentage of the absolute deviation in the average value, and can be used for measuring the deviation degree of the satellite precipitation data and the ground observation station precipitation data.
Figure BDA0003349774280000063
4. Mean Absolute Error (MAE)
Mean Absolute Error (MAE), which is often used to describe the difference between satellite precipitation data and ground station observations, measures the magnitude of the mean error. The average absolute error can avoid the problem of mutual offset of errors, so that the size of the actual error can be accurately reflected.
Figure BDA0003349774280000064
In the above formulas 1 to 4, n represents the number of data pairs used for the accuracy evaluation. XiRepresenting precipitation observations at the ith ground station, YiPixel values representing a satellite precipitation data grid in which the ground station is located;
Figure BDA0003349774280000065
is XiThe average value of (a) of (b),
Figure BDA0003349774280000066
is YiIs measured.
5. Detectivity (detection probability POD)
The detection probability represents the proportion of the number of correctly detected events to the total number of detected events in the rainfall events detected by the satellite, and reflects the missing report degree of the satellite to the rainfall events. The value range of the POD is [0,1], and the larger the value is, the higher the successful detection degree of the satellite on precipitation events is.
Figure BDA0003349774280000067
6. False alarm rate (false alarm index FAR)
The false alarm index reflects the proportion of the number of false detection events in the rainfall events detected by the satellite to the total number of detection events, and the index can reflect the false alarm degree of the satellite to the rainfall events, namely the null alarm rate. The value range of the false alarm index is [0,1], and the smaller the value is, the smaller the false alarm degree of the satellite on precipitation is.
Figure BDA0003349774280000071
7. Critical Success Index (CSI)
The critical success index represents the proportion of the number of correctly detected precipitation events of the satellite to the total number of events, and is capable of comprehensively reflecting the characteristics of the satellite precipitation data.
Figure BDA0003349774280000072
8. Deviation in frequency (B)
The frequency deviation is used for measuring whether the precipitation event is overestimated or underestimated, the value range is [0, + ∞ ], and B >1 indicates that the satellite overestimates the precipitation event; b <1, indicating that the satellite underestimates the precipitation event.
Figure BDA0003349774280000073
9. Just aura score (ETS)
The fair aura score is used to measure the overall detection of precipitation. A value range of
Figure BDA0003349774280000074
The larger the value, the stronger the comprehensive detection capability of the GPM product on precipitation.
Figure BDA0003349774280000075
Figure BDA0003349774280000076
In the formulas 5 to 9, H represents the number of precipitation events successfully captured by the ground observation station and the satellite at the same time under a specific threshold; m represents the number of precipitation events for which the ground observation station successfully captures and the satellite fails to capture under a specific threshold; f represents the number of precipitation events captured by the satellite but not observed by the ground observation station under a specific threshold; z is the number of events that the satellite precipitation and the ground observation data do not generate the intensity precipitation.
In step S4, the correction result evaluation of the satellite raster data includes evaluation of a daily scale, a monthly scale, a quarterly scale, and a yearly scale.
According to the relevant regulations of the national meteorological department on precipitation standards, the daily rainfall can be divided into light rain (<10mm), medium rain (10-24.9mm), heavy rain (25-49.9mm) and heavy rain (> 50 mm). In order to evaluate the capacity of GPM for capturing precipitation on a daily scale, 4 precipitation thresholds of 0.1 mm/d, 10 mm/d, 25 mm/d and 50mm/d are selected as standards for generating precipitation and generating light rain, medium rain and heavy rain respectively.
Outputting the corrected satellite raster data meeting the evaluation requirements as a data set. The evaluation requirements can be set according to specific conditions so as to better meet the actual requirements.
In order to compare the accuracy of the satellite raster data after correction with the accuracy of the satellite raster data before correction, the nine evaluation indexes are used for calculating the relevant index data of the ground station observation data and the satellite raster data after correction, the calculation results are compared, and the accuracy of the satellite raster data after correction and the accuracy of the satellite raster data before correction are determined.
Example two
In this embodiment, the northwest area (longitude range from 73 east longitude to 123 east longitude, and latitude range from 37 north latitude to 50 north latitude) is taken as the research area of the embodiment, and the ground observation station data is adopted to correct the satellite grid data (GPM IMERG Final Run), which illustrates the effectiveness of the invention.
The ground observation stations used in the northwest region of the study are 178 in total, and the spatial distribution of the ground observation stations is shown in fig. 6. Firstly, correcting and training satellite raster data of 7 months in 2018 by using 128 ground station measured precipitation data to obtain corrected raster data, then, verifying and evaluating correction results by using the remaining 50 ground station measured data, wherein the results are shown in table 1, and a contour map is drawn according to the results of the table 1, as shown in fig. 7, (a) is a RMSE contour map before correction, and (b) is the RMSE contour map after correction.
TABLE 1 verification of RMSE indicators before and after site correction
Figure BDA0003349774280000081
Figure BDA0003349774280000091
As can be seen from table 1 and fig. 7, for the northwest region, before correction, the Root Mean Square Error (RMSE) high values of the satellite grid data and the ground station observation data are collectively distributed in the south of kansu, the large part of the autonomous region of the ningxia hui nationality, and the west of shanxi province. The method is characterized in that the original raster data are corrected by adopting a window sliding correction method, the Root Mean Square Error (RMSE) values of the corrected raster data and a verification station are calculated, and the RMSE values before and after correction are compared, so that the RMSE can be obviously reduced, and therefore, the precision of the satellite raster data is obviously improved. In sum, the method has a certain correction effect and can improve the precision of the satellite raster data.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a window sliding GPM data correction method considering spatial distribution, which can consider the space-time distribution characteristics of satellite precipitation data, take actually measured precipitation data of ground stations as reference, perform local correction on raster data, reduce correction errors and enable correction results to have relatively high precision. The invention can consider the relative position relation between the satellite grid and the ground observation station, and correct different regions with relatively high pertinence, thereby effectively solving the problems of uneven ground station distribution and lack of precipitation data in partial regions to a certain extent. The invention obtains a set of high-precision precipitation data set through correction, thereby better serving the works such as local precipitation forecast, hydrological simulation and the like.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.

Claims (5)

1. A window sliding GPM data correction method considering spatial distribution is characterized in that: comprises the following steps of (a) carrying out,
s1, preprocessing the satellite raster data and the ground station data;
s2, setting an initial window according to the preprocessed satellite raster data and ground station data;
s3, correcting the satellite raster data in the initial window, and moving the initial window to correct all the satellite raster data in sequence;
s4, evaluating the correction result;
the step S3 specifically includes the following contents,
s31, respectively calculating the arithmetic mean of the data of the rainfall observed by the ground stations in the initial window and the arithmetic mean of the data of the contained satellite grids, and calculating the ratio of the arithmetic mean of the data of the rainfall observed by the ground stations in the initial window to the arithmetic mean of the data of the contained satellite grids; multiplying the ratio by the satellite grid data in the initial window to obtain a correction result of the satellite grid data in the initial window;
s32, moving the initial window according to the step length of 1 grid unit, and correcting the satellite raster data in the window in sequence according to the method of the step S31;
s33, for a plurality of correction results existing in a single grid, an arithmetic mean calculation is performed to obtain the correction result of the entire satellite grid data.
2. The method of GPM data correction considering spatial distribution according to claim 1, wherein: the step S1 specifically includes the following contents,
reading an original satellite nc file, processing satellite raster data into a matrix form, and respectively processing longitude and latitude coordinates corresponding to each grid of the satellite raster data into the matrix form;
and respectively processing the actually measured precipitation data of each ground station under a certain time scale and the longitude and latitude coordinates corresponding to the ground stations into a matrix form.
3. The method of GPM data correction considering spatial distribution according to claim 2, wherein: the step S2 specifically includes the following contents,
s21, starting from the satellite raster data initial grid, fixing the vertex, and taking 1 grid unit as the side length of a rectangular window;
s22, judging whether the rectangular window simultaneously contains satellite raster data and at least one ground station data, if so, entering S23, otherwise, increasing the side length of the rectangular window by 1 grid unit, and judging again;
and S23, selecting the rectangular window with the side length as an initial window.
4. The method of GPM data correction considering spatial distribution, according to claim 3, characterized in that: step S22, judging whether the rectangular window contains satellite grid data and at least one ground station data at the same time by adopting the correlation between the longitude and latitude coordinates of the satellite grid and the coordinates of the ground station, the concrete formula is,
|lonSatellite-longround surface|<N
|latSatellite-latGround surface|<T
Wherein, lonSatelliteGrid longitude for satellite raster data; lonGround surfaceFor each ground site longitude; latSatelliteGrid latitude of the satellite grid data is obtained; latGround surfaceThe latitude of each ground station; n, T are each the spatial resolution of the selected satellite precipitation data.
5. The method of GPM data correction considering spatial distribution according to claim 1, wherein: step S4 is concretely, with ground station observation precipitation data as reference, nine evaluation indexes including correlation coefficient, relative deviation, root mean square error, average absolute error, detection rate, false alarm rate, critical success index, frequency deviation and fairness foreboding score are respectively adopted to evaluate corrected satellite grid data;
the closer the correlation coefficient is to 1, the smaller the relative deviation is, the smaller the root mean square error is, the smaller the average absolute error is, the larger the detection rate is, the smaller the false alarm rate is, the larger the critical success index is, the smaller the frequency deviation is and the larger the fairness foreboding score is, the better the correction effect of the satellite raster data is represented.
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