A correction method for precipitation products that combines dry and wet season division and weight allocation

Abstract

This invention provides a precipitation product correction method based on climate adjustment, combining dry and wet season division and weight allocation. The method calculates adjustment amounts and scaling factors based on historical precipitation data of the region, generates a deviation adjustment value using the adjustment amount AA and scaling factor SF, and applies the deviation adjustment value to the corresponding IMERG precipitation estimate to obtain an adjusted and corrected precipitation estimate. This improves the accuracy of precipitation estimate correction, effectively corrects errors in satellite precipitation data, and enhances the reliability of satellite precipitation data.

Description

Technical Field

[0001] This invention relates to the field of rainfall prediction technology, and in particular to a correction method for rainfall products that combines dry and wet season division and weight allocation. Background Technology

[0002] Traditional precipitation data acquisition relies primarily on ground-based observation stations, such as rain gauges and weather radars. However, the coverage of these stations is limited, especially in geographically remote or complex areas, resulting in scarce and incomplete data.

[0003] The rapid development of satellite remote sensing technology has provided new avenues for acquiring large-scale, high spatiotemporal resolution precipitation data. Sensors mounted on satellites can acquire real-time global precipitation data. For example, the Integrated Multi-Satellite Inversion (IMERG) product for global precipitation measurement, developed jointly by NASA and the Japan Aerospace Exploration Agency (JAXA), is widely recognized for its excellent performance. The latest version, IMERGV06B, has extended its time coverage to 2000, now providing access to a 20-year dataset from 2000 to the present, thus enabling longer-term precipitation characteristic assessments. However, satellite observation data, limited by observation principles and data processing methods, still suffers from significant systematic and random errors. For instance, satellite-observed precipitation data may be affected by factors such as cloud thickness, surface reflectivity, and sensor performance, resulting in discrepancies between the observed data and actual ground precipitation.

[0004] The accuracy of IMERG-estimated precipitation data limits its application and development. Therefore, obtaining more accurate precipitation datasets through error correction is crucial for improving the quality of satellite precipitation products. Currently, the most widely used statistical error correction method for satellite precipitation data is the quantile mapping method. This method adjusts the probability distribution of the data to match that of the reference data, effectively reducing systematic bias. However, the accuracy of these methods for correcting errors in satellite precipitation data remains relatively low, and there is still significant room for improvement in overall effectiveness. To address these issues, this invention proposes a precipitation product correction method that combines dry and wet season division and weight allocation, aiming to further improve the accuracy and reliability of satellite-observed precipitation data to better serve fields such as meteorological forecasting, climate research, and water resource management. Summary of the Invention

[0005] To address the technical problems mentioned in the background section, this invention provides a precipitation product correction method based on climate adjustment that combines dry and wet season division and weight allocation. This method can improve the accuracy of precipitation estimate correction, effectively correct errors in satellite precipitation data, and enhance the reliability of satellite precipitation data.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A correction method for precipitation products that combines dry and wet season division and weight allocation includes the following steps:

[0008] S1, Historical data preprocessing: Obtain historical measured precipitation data and corresponding IMERG historical precipitation estimation data from ground meteorological stations. Divide the obtained historical measured precipitation data and corresponding IMERG historical precipitation estimation data according to dry and wet seasons to obtain historical datasets for the dry season and historical datasets for the wet season.

[0009] S2, Calculation of adjustment amount AA: Calculate the difference between the estimated historical precipitation data of each IMERG in the historical dataset of the dry season and the historical dataset of the wet season and the corresponding historical precipitation data, respectively, and calculate the probability density function of the difference. Then, use the probability density function as the weight to assign to the corresponding difference, so as to calculate the adjustment amount AA corresponding to the historical dataset of the dry season and the adjustment amount AA corresponding to the historical dataset of the wet season.

[0010] S3, Calculation of scaling factor SF: Set several value intervals to classify the IMERG historical precipitation estimation data in the historical datasets of the dry season and the historical datasets of the wet season respectively, and calculate the scaling factor SF according to the deviation type of the IMERG historical precipitation estimation data in each value interval.

[0011] S4, Correction of IMERG precipitation estimates during the correction period: The correction method for the IMERG precipitation estimates of the rainfall area during the correction period is as follows: A deviation adjustment value is generated by using the adjustment amount AA corresponding to the season in which the correction period is located and the scaling factor SF corresponding to the value interval of the IMERG precipitation estimate. The generated deviation adjustment value is applied to the IMERG precipitation estimate to obtain the adjusted and corrected precipitation estimate.

[0012] Furthermore, the difference is calculated according to the following formula:

[0013] Let x be the historical precipitation estimate data of IMERG. IMERG Historical measured precipitation data from ground meteorological stations are denoted as x. ground Then the difference D is:

[0014] Bias = x IMERG -x ground (1)

[0015] D = |Bias| = |x IMERG -x ground | (2)

[0016] In the formula, Bias represents the deviation between the historical precipitation estimates and the historical measured precipitation data of IMERG.

[0017] Furthermore, the historical data preprocessing in step S1 specifically includes the following steps:

[0018] S11, determine the rainfall area to be studied, and determine the historical time period for correcting the precipitation estimate of the rainfall area;

[0019] S12, obtain the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations in the rainfall area for each day within the historical time period, and arrange the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations within the historical time period in chronological order to obtain the total historical dataset;

[0020] S13. The historical measured precipitation data and the corresponding IMERG historical precipitation estimation data in the total historical dataset are divided according to the dry and wet seasons to obtain the historical datasets for the dry season and the historical datasets for the wet season.

[0021] Furthermore, the calculation of the adjustment amount in step S2 specifically includes the following steps:

[0022] S21, During the historical time period, calculate the difference between the IMERG historical precipitation estimation data in the historical dataset of the dry season and the historical dataset of the wet season and the historical precipitation data of the corresponding ground meteorological station, and arrange the calculated differences in order of their smallest value to obtain a difference sequence.

[0023] S22, Calculate the probability density function for each difference in the difference sequence;

[0024] S23, assign a weight to each difference. For each difference, the assigned weight is the corresponding probability density function.

[0025] S24, the adjustment amount AA is obtained by adding the products of each difference and its corresponding weight.

[0026] Furthermore, the weight ω i The formula for calculating the adjustment amount AA is as follows:

[0027] ω i =PDF i (3)

[0028]

[0029] In the formula, i is a number from 1 to n, n is the total number of differences in the difference sequence, and ω iPDF represents the weight of the i-th difference in the difference sequence. i Let represent the probability density function of the i-th difference in the difference sequence.

[0030] Furthermore, the calculation of the scaling factor SF in step S3 specifically includes the following steps:

[0031] S31. Based on the size of the IMERG historical precipitation estimation data, set several value ranges to classify the IMERG historical precipitation estimation data in the dry season historical dataset and the wet season historical dataset respectively.

[0032] S32, For all IMERG historical precipitation estimation data in each value interval, the deviation type of the IMERG historical precipitation estimation data is determined by comparing the IMERG historical precipitation estimation data with the corresponding historical measured precipitation data;

[0033] S33, Calculate the percentage of the underestimated deviation type in each value interval based on the number of underestimated deviation types in each value interval;

[0034] S34, calculate the scaling factor SF for the corresponding value range based on the percentage of the underestimated deviation type.

[0035] Furthermore, the scaling factor SF is calculated using the following formula:

[0036] SF = a - (1 - a) (5)

[0037] In the formula, 'a' is the percentage of the underestimation of the deviation type within a certain value range; the value of SF ranges from -1 to 1.

[0038] Furthermore, the formula for calculating the percentage of underestimated deviation types is as follows:

[0039]

[0040] In the formula, n is the number of events of the underestimation deviation type within a certain value range; N is the number of all types of events within that range.

[0041] Furthermore, when correcting a certain IMERG precipitation estimate, the corresponding adjustment amount AA and the scaling factor SF of the value interval where the IMERG precipitation estimate falls are correlated according to the following formula to obtain the deviation adjustment value BA required for correction:

[0042] BA=AA×ST (7)

[0043] Furthermore, in step S4,

[0044] When the IMERG precipitation estimate is <1mm, BA will not be applied to the IMERG estimate;

[0045] When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is less than the estimated precipitation value of IMERG, the calculated corresponding deviation adjustment value is added to the estimated precipitation value of IMERG to obtain the corrected estimated precipitation value.

[0046] When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is greater than the estimated precipitation value of IMERG, the estimated precipitation value of IMERG is corrected to 0.1mm, as shown in formula (8):

[0047]

[0048] In the formula, x originalIMERG and x adjustedIMERG These represent the original and adjusted IMERG precipitation estimates during the correction period, respectively.

[0049] By adopting the above technical solution, the present invention has the following beneficial effects:

[0050] The aforementioned precipitation product correction method based on climate adjustment, combining dry and wet season division and weight allocation, calculates adjustment amounts and scaling factors based on historical precipitation data of the region. It generates a bias adjustment value using the adjustment amount (AA) and scaling factor (SF), and applies this value to the corresponding IMERG precipitation estimate to obtain an adjusted and corrected precipitation estimate. This improves the accuracy of precipitation estimate correction, effectively corrects errors in satellite precipitation data, and enhances the reliability of satellite precipitation data. Furthermore, since the distribution of satellite precipitation bias is sensitive to time and period, with different bias amplitudes and frequencies between dry and wet seasons, the aforementioned precipitation product correction method based on climate adjustment, combining dry and wet season division and weight allocation, divides the obtained measured precipitation data and corresponding IMERG precipitation estimates according to dry and wet seasons. This facilitates the acquisition of bias distribution characteristics, resulting in higher accuracy in bias correction. Attached Figure Description

[0051] Figure 1 This is an overall flowchart of a precipitation product correction method that combines dry and wet season division and weight allocation proposed in this invention;

[0052] Figure 2 This is a box plot of RB and RMSE between the original IMERG estimate and the adjusted IMERG estimate;

[0053] Figure 3 This is a comparison chart showing the effectiveness of the precipitation product correction method proposed in this invention, which combines dry and wet season division and weight allocation, and the traditional quantile mapping method in reducing RB and RMSE in each climate and topographic zone. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0056] Please see Figure 1 A preferred embodiment of the present invention provides a correction method for precipitation products that combines dry and wet season division and weight allocation, comprising the following steps:

[0057] S1, Historical Data Preprocessing: Obtain historical measured precipitation data and corresponding IMERG historical precipitation estimation data from ground meteorological stations. Divide the obtained historical measured precipitation data and corresponding IMERG historical precipitation estimation data according to dry and wet seasons to obtain historical datasets for the dry season and historical datasets for the wet season.

[0058] In this embodiment, the historical data preprocessing in step S1 specifically includes the following steps:

[0059] S11, Identify the rainfall area to be studied and determine the historical time period for correcting the precipitation estimates for the rainfall area. The rainfall data within the historical time period is used to analyze the deviation distribution characteristics of the measured precipitation data from surface meteorological stations in the rainfall area and the IMERG precipitation estimate data. The period should be more than 15 years to ensure a relatively stable deviation distribution.

[0060] S12, obtain the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations in the rainfall area for each day within the historical time period, and arrange the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations within the historical time period in chronological order to obtain the total historical dataset.

[0061] S13. The historical measured precipitation data and the corresponding IMERG historical precipitation estimation data in the total historical dataset are divided according to the dry and wet seasons to obtain the historical datasets for the dry season and the historical datasets for the wet season.

[0062] Specifically, the year can be divided into wet and dry seasons according to the seasonal characteristics of precipitation in different areas. Taking Guangxi Zhuang Autonomous Region as an example, the wet season is usually from April to September, and the dry season is from October to March of the following year. The data in the total historical dataset is divided into wet and dry seasons according to time, resulting in historical datasets for the dry season and wet season, which are then used for subsequent calculations.

[0063] S2, Calculation of adjustment amount AA: Calculate the difference between the estimated historical precipitation data and the corresponding historical measured precipitation data for each IMERG in the historical dataset of the dry season and the historical dataset of the wet season, respectively, and calculate the probability density function of the difference. Then, use the probability density function as a weight to assign to the corresponding difference, so as to calculate the adjustment amount AA corresponding to the historical dataset of the dry season and the historical dataset of the wet season, respectively.

[0064] In this embodiment, the basic principle for calculating the adjustment amount AA is that the higher the frequency of a certain difference occurring in a historical time period, the higher the weight of that difference in the adjustment. Specifically, the calculation of the adjustment amount AA in step S2 includes the following steps:

[0065] S21, within the historical time period, calculate the difference between the IMERG historical precipitation estimation data and the corresponding historical measured precipitation data from the historical datasets of the dry season and the wet season, respectively. Arrange the calculated differences in ascending order to obtain a difference sequence. Specifically, the differences are calculated according to the following formula:

[0066] Let x be the historical precipitation estimate data of IMERG. IMERG Historical measured precipitation data from ground meteorological stations are denoted as x. ground Then the difference D is:

[0067] Bias = x IMERG -x ground (1)

[0068] D = |Bias| = |x IMERG -x ground | (2)

[0069] In the formula, Bias represents the deviation between the historical precipitation estimates and the historical measured precipitation data of IMERG.

[0070] S22, calculate the probability density function for each difference in the difference sequence.

[0071] S23, assign a weight to each difference. For each difference, the assigned weight is the corresponding probability density function.

[0072] S24, the adjustment amount AA is obtained by adding the products of each difference and its corresponding weight, specifically, the weight ω i The formula for calculating the adjustment amount AA is as follows:

[0073] ω i =PDF i (3)

[0074]

[0075] In the formula, i is a number from 1 to n, n is the total number of differences in the difference sequence, and ω i PDF represents the weight of the i-th difference in the difference sequence. i Let represent the probability density function of the i-th difference in the difference sequence. The calculation of the probability density function is a prior art and will not be elaborated here for the sake of brevity.

[0076] S3, Calculation of scaling factor SF: Several value intervals are set to classify the IMERG historical precipitation estimation data in the historical datasets of the dry season and the wet season respectively. The scaling factor SF is calculated according to the bias type of the IMERG historical precipitation estimation data in each value interval. Specifically, the calculation of scaling factor SF in step S3 includes the following steps:

[0077] S31. Based on the size of the IMERG historical precipitation estimation data, several value ranges are set to classify the IMERG historical precipitation estimation data in the dry season historical dataset and the wet season historical dataset respectively.

[0078] Specifically, in this embodiment, based on the magnitude of the IMERG estimated value for the entire Chinese mainland, five value intervals are proposed. The threshold values ​​for dividing these intervals are 5, 10, 25, and 50 mm, respectively, constituting five value intervals for rainfall: 0-5 mm, 6-10 mm, 11-25 mm, 26-50 mm, and above 50 mm. Different estimation intervals represent different rainfall intensities estimated by the IMERG product. For example, meteorological departments typically use 10, 25, and 50 mm of rainfall as threshold values ​​to classify light, moderate, heavy, and torrential rain. Subsequently, the IMERG historical precipitation estimation data in the historical datasets of the dry season and the wet season are classified according to these five value intervals. For example, if a historical IMERG precipitation estimation data in the historical datasets of several seasons is 4 mm, it is classified into the 0-5 mm value interval.

[0079] S32, for all IMERG historical precipitation estimates in each value interval, the bias type of the IMERG historical precipitation estimate is determined by comparing it with the corresponding historical measured precipitation data. Specifically, if the value of the IMERG historical precipitation estimate is less than the value of the corresponding historical measured precipitation data, it is determined to be an underestimation bias event; otherwise, it is classified as another bias event.

[0080] S33, calculate the percentage of the underestimated deviation type in each value interval based on the number of underestimated deviation types in each value interval, the percentage of which can be considered as the frequency of the underestimated event.

[0081] S34, calculate the scaling factor SF for the corresponding value range based on the percentage of the underestimated deviation type. Specifically, the calculation formula for the scaling factor SF is as follows:

[0082] SF = a - (1 - a) (5)

[0083] In the formula, 'a' is the percentage of the underestimation of the deviation type within a certain value range; the value of SF ranges from -1 to 1.

[0084] When SF equals -1 and 1, it means that the bias type of all IMERG estimates in that value range is either overestimation or underestimation, respectively. Within the rainfall range, if SF < 0, it indicates that the bias adjustment decreased the precipitation estimate of IMERG during the correction period; if SF > 0, it indicates that the bias adjustment increased the precipitation estimate of IMERG during the correction period. It can be seen that the greater the proportion of overestimation or underestimation events, the greater the decrease or increase in the precipitation estimate of IMERG.

[0085] Specifically, the formula for calculating the percentage of underestimated deviation types is as follows:

[0086]

[0087] In the formula, n is the number of events of the underestimation bias type within a certain value range; N is the number of all types of events within that range, i.e., the total number of IMERG historical precipitation estimation data or the total number of historical measured rainfall data.

[0088] S4, Correction of IMERG precipitation estimates during the correction period: The correction method for the IMERG precipitation estimates of the rainfall area during the correction period is as follows: A deviation adjustment value is generated by using the adjustment amount AA corresponding to the season in which the correction period is located and the scaling factor SF corresponding to the value interval of the IMERG precipitation estimate. The generated deviation adjustment value is applied to the IMERG precipitation estimate to obtain the adjusted and corrected precipitation estimate.

[0089] Specifically, when correcting a given IMERG precipitation estimate, firstly, determine whether the correction period for the IMERG precipitation estimate occurs during the dry or wet season; secondly, determine the value range within which the IMERG precipitation estimate falls; finally, correlate the seasonal adjustment AA with the scaling factor SF of the value range where the IMERG precipitation estimate falls according to the following formula to obtain the required deviation adjustment value BA:

[0090] BA=AA×SD (7)

[0091] After calculating the deviation adjustment value BA, the deviation adjustment value BA is applied to the corresponding IMERG precipitation estimate to obtain the adjusted and corrected precipitation estimate, specifically as follows:

[0092] When the IMERG precipitation estimate is <1 mm, the ground condition typically corresponds to very little precipitation or no rainfall. Therefore, when the IMERG precipitation estimate is <1 mm, BA will not be applied to the IMERG estimate.

[0093] When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is less than the estimated precipitation value of IMERG, the calculated corresponding deviation adjustment value is added to the estimated precipitation value of IMERG to obtain the corrected estimated precipitation value.

[0094] When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is greater than the estimated precipitation value of IMERG, the estimated precipitation value of IMERG is corrected to 0.1mm, as shown in formula (8):

[0095]

[0096] In the formula, x originalIMERG and x adjustedIMERG These represent the original and adjusted IMERG precipitation estimates during the correction period, respectively.

[0097] If multiple IMERG precipitation estimates need to be corrected within the correction period, after each IMERG precipitation estimate has been corrected, the IMERG precipitation estimates adjusted for dry and wet seasons are then re-integrated and merged in chronological order to obtain the complete IMERG estimate correction results for that correction period.

[0098] The effectiveness and reliability of this method will be verified through a specific embodiment and example experiment.

[0099] Using mainland China as the experimental area, which is located in East Asia and on the western coast of the Pacific Ocean, the longitude ranges from 73°41′E to 135°02′E, and the latitude ranges from 18°10′N to 53°33′N. Based on climatic characteristics, mainland China is divided into several climatic zones, as shown in Table 1. Furthermore, based on different topographic regions defined by altitude, mainland China is divided into seven topographic regions, as shown in Table 2.

[0100] During the experiment, the measured precipitation data from selected ground meteorological stations came from the China National Meteorological Center, and the satellite precipitation data came from three sub-products of IMERGV06: near-real-time products IMERG-E and IMERG-L, and post-real-time product IMERG-F. The historical period was from January 1, 2000 to December 31, 2017, and the verification period (i.e., the correction period) was from January 1, 2018 to December 31, 2019. To visually demonstrate the effect of this invention, the widely accepted mainstream method, quantile mapping, was used as a reference to compare the correction effects of this invention and the quantile mapping method on IMERG precipitation data. In the following description, the quantile mapping method will be abbreviated as QM (Quantile mapping) method, and this invention will be abbreviated as DW (Dry-wet seasondivision and weight allocation) method.

[0101] To demonstrate the effectiveness of this invention in correcting satellite precipitation estimates, several typical statistical indicators were selected for measurement: CC (correlation coefficient), RB (relative bias), and RMSE (root mean square error), as detailed in Table 3.

[0102] Figure 2 This is a box plot of the RB and RMSE values ​​for the original estimates and the corresponding adjusted estimates. For the QM method, the RB and RMSE values ​​at the 75th, 50th, and 25th percentiles of the adjusted IMERG estimates are higher than those of the original estimates. For the DW method, the RB and RMSE results are the opposite of those of the QM method described above. Overall, the QM method performs poorly, while the DW method is effective in reducing RB and RMSE.

[0103] Figure 3The effectiveness of two methods in reducing RB and RMSE in each climate and topographic region is described. In all regions, the DW method is more effective than or equal to the QM method. Regarding RB reduction, the DW method maintains high performance in regions 1, N, and NE, while the QM method maintains high performance in regions 6, 7, and QT. Regions S and N correspond to the climate regions with the worst performance under the DW and QM methods, respectively. For RMSE reduction, the DW method performs better in regions NW and N than in other regions, while the QM method performs best in region QT. In all topographic regions, the highest efficiency of the QM method is generally observed in regions 6 and 7.

[0104] Overall, the DW method described in this invention is significantly more effective than the QM method used for comparison in increasing CC, reducing RB and RMSE values. The DW method is significantly better than the QM method in almost all indicators, as shown in Table 3.

[0105] Table 1. Regions of Mainland China divided according to climatic conditions

[0106]

[0107] Table 2 Topographic regions of mainland China based on elevation

[0108] Terrain zoning Terrain type Altitude (E) / m 1 Plains E≤100 2 hills or plains 100<E≤250 3 hills 250<E≤500 4 Mountains or plateaus 500<E≤1000 5 Mountains or plateaus 1000<E≤2000 6 Mountains or plateaus 2000<E≤3000 7 plateau 3000<E

[0109] Table 3. Effectiveness of the two error adjustment methods in increasing CC value and decreasing RB and RMSE values.

[0110]

[0111] As can be seen from the above, the precipitation product correction method based on climate adjustment and combining dry and wet season division and weight allocation proposed in this embodiment of the invention has a significant improvement in correction accuracy compared with the traditional frequency correction method quantile mapping method. It can effectively correct satellite precipitation data and improve the accuracy and applicability of near real-time satellite precipitation products.

[0112] The aforementioned precipitation product correction method based on climate adjustment, combining dry and wet season division and weight allocation, captures the bias distribution characteristics by calculating the probability distribution from a large sample, uses the probability density function to determine the weight of each bias value in the bias adjustment, calculates the adjustment amount AA, then divides the IMERG precipitation estimation data into several value intervals, calculates the scaling factor SF based on the bias type of the IMERG precipitation estimation data in each value interval, and finally applies the adjustment amount and scaling factor to the initial IMERG precipitation estimation data to obtain the corrected satellite precipitation data. This method can improve the accuracy of precipitation estimation correction, effectively correct errors in satellite precipitation data, and improve the reliability of satellite precipitation data. Furthermore, since the satellite precipitation bias distribution is sensitive to time and period, the bias amplitude differs between dry and wet seasons, and the frequency of different bias values ​​also varies greatly. The aforementioned precipitation product correction method based on climate adjustment, combining dry and wet season division and weight allocation, divides the obtained measured precipitation data and corresponding IMERG precipitation estimation data according to dry and wet seasons, which is beneficial for obtaining bias distribution characteristics and resulting in higher accuracy of bias correction.

[0113] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A method for correcting precipitation products by combining dry and wet season division and weight allocation, characterized in that, Includes the following steps: S1, Historical data preprocessing: Obtain historical measured precipitation data and corresponding IMERG historical precipitation estimation data from ground meteorological stations. Divide the obtained historical measured precipitation data and corresponding IMERG historical precipitation estimation data according to dry and wet seasons to obtain historical datasets for the dry season and historical datasets for the wet season. S2, Calculation of adjustment amount AA: Calculate the difference between the estimated historical precipitation data of each IMERG in the historical dataset of the dry season and the historical dataset of the wet season and the corresponding historical precipitation data, respectively, and calculate the probability density function of the difference. Then, use the probability density function as the weight to assign to the corresponding difference, so as to calculate the adjustment amount AA corresponding to the historical dataset of the dry season and the adjustment amount AA corresponding to the historical dataset of the wet season. S3, Calculation of scaling factor SF: Set several value intervals to classify the IMERG historical precipitation estimation data in the historical datasets of the dry season and the historical datasets of the wet season respectively, and calculate the scaling factor SF according to the deviation type of the IMERG historical precipitation estimation data in each value interval. S4, Correction of IMERG precipitation estimates during the correction period: The correction method for IMERG precipitation estimates for rainfall areas during the correction period is as follows: A deviation adjustment value is generated by using the adjustment amount AA corresponding to the season in which the correction period is located and the scaling factor SF corresponding to the value interval of the IMERG precipitation estimate. The generated deviation adjustment value is applied to the IMERG precipitation estimate to obtain the adjusted and corrected precipitation estimate.

2. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 1, characterized in that, The difference is calculated according to the following formula: The historical precipitation estimates of IMERG are denoted as... Historical measured precipitation data from ground meteorological stations are recorded as follows: Then the difference D is: （1）； （2）； In the formula, This represents the discrepancy between IMERG's historical precipitation estimates and historical measured precipitation data.

3. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 1, characterized in that, Step S1, historical data preprocessing, specifically includes the following steps: S11, determine the rainfall area to be studied, and determine the historical time period for correcting the precipitation estimate of the rainfall area; S12, obtain the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations in the rainfall area for each day within the historical time period, and arrange the historical measured precipitation data and corresponding IMERG historical precipitation estimation data of the ground meteorological stations within the historical time period in chronological order to obtain the total historical dataset; S13. The historical measured precipitation data and the corresponding IMERG historical precipitation estimation data in the total historical dataset are divided according to the dry and wet seasons to obtain the historical datasets for the dry season and the historical datasets for the wet season.

4. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 3, characterized in that, The calculation of the adjustment amount in step S2 specifically includes the following steps: S21, During the historical time period, calculate the difference between the IMERG historical precipitation estimation data in the historical dataset of the dry season and the historical dataset of the wet season and the historical precipitation data of the corresponding ground meteorological station, and arrange the calculated differences in order of their smallest value to obtain a difference sequence. S22, Calculate the probability density function for each difference in the difference sequence; S23, assign a weight to each difference. For each difference, the assigned weight is the corresponding probability density function. S24, the adjustment amount AA is obtained by adding the products of each difference and its corresponding weight.

5. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 4, characterized in that, The weight The formula for calculating the adjustment amount AA is as follows: （3）； （4）； In the formula, It consists of numbers from 1 to n, where n is the total number of differences in the difference sequence. Represents the first value in the difference sequence. The weights of each difference Represents the first value in the difference sequence. The probability density function of the differences.

6. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 3, characterized in that, The calculation of the scaling factor SF in step S3 specifically includes the following steps: S31. Based on the size of the IMERG historical precipitation estimation data, set several value ranges to classify the IMERG historical precipitation estimation data in the dry season historical dataset and the wet season historical dataset respectively. S32, For all IMERG historical precipitation estimation data in each value interval, the deviation type of the IMERG historical precipitation estimation data is determined by comparing the IMERG historical precipitation estimation data with the corresponding historical measured precipitation data; S33, Calculate the percentage of the underestimated deviation type in each value interval based on the number of underestimated deviation types in each value interval; S34, calculate the scaling factor SF for the corresponding value range based on the percentage of the underestimated deviation type.

7. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 6, characterized in that, The scaling factor SF is calculated using the following formula: （5）； In the formula, a is the percentage of the underestimation of the deviation type within a certain value range; the value of SF ranges from −1 to 1.

8. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 7, characterized in that, The formula for calculating the percentage of underestimated deviation types is: （6）； In the formula, n is the number of events of the underestimation deviation type within a certain value range; N is the number of all types of events within that range.

9. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 1, characterized in that, When correcting a specific IMERG precipitation estimate, the corresponding adjustment AA and the scaling factor SF of the value interval containing the IMERG precipitation estimate are correlated according to the following formula to obtain the required deviation adjustment value BA for correction: （7）； 10. The correction method for precipitation products combining dry and wet season division and weight allocation as described in claim 9, characterized in that, In step S4 When the IMERG precipitation estimate is <1mm, BA will not be applied to the IMERG estimate; When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is less than the estimated precipitation value of IMERG, the calculated corresponding deviation adjustment value is added to the estimated precipitation value of IMERG to obtain the corrected estimated precipitation value. When the estimated precipitation value of IMERG is ≥1mm and the deviation adjustment value is greater than the estimated precipitation value of IMERG, the estimated precipitation value of IMERG is corrected to 0.1mm, as shown in formula (8): （8）； In the formula, and These represent the original and adjusted IMERG precipitation estimates during the correction period, respectively.

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