Satellite rainfall data correction method for judging rainfall events based on machine learning
A technology of rainfall data and correction method, applied in machine learning, instruments, calculation models, etc., can solve the problems of insufficient ability of satellite rainfall to capture rainfall, little consideration of rainfall event judgment and correction, unfavorable satellite rainfall deviation correction, etc. , to achieve the effect of increasing the false negative rate and false positive rate, improving the capture ability, and improving the accuracy
Active Publication Date: 2021-04-30
INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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Problems solved by technology
[0004] Although there are many methods for correcting rainfall deviation, most of the existing methods perform deviation correction on a certain aspect of rainfall, and do not consider much about the judgment and correction of whether a rainfall event occurs, which leads to the satellite rainfall’s ability to capture whether there is rain or not. Insufficient, high false positive rate and false negative rate
At the same time, in the existing application of distribution fitting to correct rainfall data, most methods use artificially given distribution for correction, so the given distribution will have the characteristics of subjectivity and time invariance, which is not conducive to the deviation correction of satellite rainfall in changing environments
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Embodiment 1
[0034] A satellite rainfall data correction method based on machine learning to distinguish rainfall events, such as figure 1 shown, including the following steps:
[0035] Shape correction for the rainfall process:
[0036] 1) Multi-source data collection: collect and download meteorological factor data within a watershed (take ERA5-Land reanalysis data as an example), elevation terrain factor data, satellite rainfall data (take IMERG satellite rainfall data as an example) and actual rainfall at meteorological stations Data, etc.; said meteorological factor data (including surface temperature, 2m temperature, 2m dew point temperature, soil temperature, horizontal wind speed, vertical wind speed, surface air pressure, surface soil humidity, surface evapotranspiration, surface net radiation, surface sensible heat flux and Surface latent heat flux and other indicators), elevation terrain factor data (elevation, slope, aspect), satellite rainfall data (such as IMERG satellite ra...
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The invention discloses a satellite rainfall data correction method for judging rainfall events based on machine learning, belongs to the technical field of satellite rainfall data precision correction, and relates to a correction method capable of concretizing data of each grid point at each moment. The invention aims to improve the capability of satellite rainfall data for capturing rainfall and improve the precision of satellite rainfall products. The method comprises the steps of 1) multi-source data collection, 2) data processing, 3) rainfall event discrimination model construction, 4) rainfall correction, 5) rainfall sequence segmentation, 6) distribution optimization and 7) rainfall data segmentation deviation correction. The method comprises the following steps: firstly, constructing a rainfall event discrimination model by adopting a machine learning method to correct whether rainfall exists in satellite rainfall data or not, and then correcting the total amount of satellite rainfall by adopting segmental suitable distribution, so as to obtain continuous satellite rainfall data with relatively high precision. According to the method, the false alarm rate and the missing report rate of the satellite rainfall data are effectively improved, and the precision of the satellite rainfall data is remarkably improved. Furthermore, the proper distribution of the rainfall sequence is preferably obtained based on various distributions, so that the subjectivity of artificially given distribution is avoided, and the errors of satellite rainfall data and actually measured rainfall can be better eliminated.
Description
technical field [0001] The invention relates to a method for correcting satellite rainfall data based on machine learning for distinguishing rainfall events, and belongs to the technical field of precision correction of satellite rainfall data. Background technique [0002] Rainfall is one of the key links in the hydrological cycle, and its accuracy directly affects the simulation accuracy of runoff process and the design of flood control and flood control projects. Accurate rainfall data play a crucial role in studying the response of hydrological processes in the context of climate change and in modeling runoff processes in undatad areas. With the intensification of global climate change, the temporal and spatial distribution of rainfall has been greatly affected. How to simulate rainfall and improve the accuracy of existing rainfall products has gradually become an issue that requires extensive attention in the fields of atmospheric science and geography. [0003] In rec...
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IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 邹磊肖帅王飞宇沈建明刘成建
Owner INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS