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Spatial downscaling method and system for remote sensing precipitation data

A downscaling and data technology, applied in the field of satellite remote sensing data processing, can solve problems such as inability to apply precipitation downscaling, inability to use hourly average or daily average precipitation downscaling, etc., to achieve the effect of improving spatial resolution

Active Publication Date: 2020-05-19
INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The above scheme is only suitable for downscaling of annual average precipitation data, and cannot be used for downscaling of hourly or daily average precipitation
In addition, variables such as vegetation index, surface temperature, elevation, slope, and aspect are all relevant geographical parameters on land, so the above-mentioned precipitation downscaling scheme cannot be applied to the downscaling of precipitation on the ocean

Method used

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  • Spatial downscaling method and system for remote sensing precipitation data
  • Spatial downscaling method and system for remote sensing precipitation data
  • Spatial downscaling method and system for remote sensing precipitation data

Examples

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Embodiment 1

[0055] Such as figure 1 , 2 As shown, step 100 is executed, and the data import module 200 imports geostationary satellite fusion data, and the fusion data is geostationary satellite infrared band fusion data with high temporal and spatial resolution.

[0056] Step 110 is executed, and the precipitation calculation module 210 performs high spatial resolution precipitation calculation based on the infrared cloud top brightness temperature of the geostationary satellite. In step 110, execute step 111, data preprocessing. The data used include TMPA-3B42 precipitation products with a spatial resolution of 0.25°×0.25° and a temporal resolution of 3 hours and / or NCEP-IR with a spatial resolution of 4km×4km and a temporal resolution of 30 minutes. For the NCEP-IR that matches TPMA-3B42 in time, the selected NCEP-IR is resampled into data with two equal longitude and latitude spatial resolutions: 0.25°×0.25° and 0.05°×0.05°.

[0057] Step 112 is executed to construct a cumulative h...

Embodiment 2

[0064] At present, the spatial resolution of long-term global precipitation fusion products is mostly 0.25°. The precipitation downscaling method proposed in the patent of the present invention can increase the spatial resolution of precipitation data on land and oceans worldwide from 0.25° to 0.05°. It enables users to obtain more refined precipitation spatial distribution data on a global scale, and provides richer information for global flood disaster monitoring, drought monitoring, and water resource distribution investigation. Compared with the existing downscaling method, the precipitation downscaling method provided by the patent of the present invention can improve the spatial resolution of the data while ensuring that the downscaled precipitation data is comparable to the original data in terms of accuracy and time resolution. reduce while retaining the spatial distribution trend of the original precipitation data.

[0065] In this example, if image 3 As shown, the...

Embodiment 3

[0088] In this example, the TMPA-3B42 precipitation data in July 2017 were downscaled. Such as Figure 4 Shown is the comparison of the spatial distribution of daily cumulative precipitation before and after downscaling calculated based on TMPA-3B42 on July 16, 2017. It can be seen from the comparison results that the downscaled precipitation data is more delicate in spatial distribution and can better display the spatial distribution of precipitation. In addition, the present invention also uses the daily accumulated precipitation total amount observed by the surface rain gauge in Thailand to verify the precipitation data before and after downscaling, as Figure 5 shown. It can be seen from the figure that compared with the original data, the downscaled precipitation data has improved both in terms of correlation and root mean square error RMSE.

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Abstract

The invention provides a spatial downscaling method and system for remote sensing precipitation data, and the method comprises the steps: introducing stationary satellite fusion data, and also comprises the following steps: carrying out the high-spatial-resolution precipitation calculation based on the infrared cloud top brightness temperature of a stationary satellite; and downscaling the coarse-resolution precipitation data. Based on the spatial downscaling method and system for remote sensing rainfall data, by introducing fusion data NCEP-IR of stationary satellite infrared band brightnesstemperature with high temporal-spatial resolution, establishing a downscaling method, spatial downscaling of coarse-resolution (0.25 degree*0.25 degree) rainfall data on land and sea is realized in aglobal medium and low latitude range (50N-50S, 180W-180E); and precipitation data with the spatial resolution of 0.05 degree*0.05 degree is obtained, and it is ensured that the precision and the timeresolution of the downscaled precipitation data are not reduced compared with those of the original precipitation data.

Description

technical field [0001] The invention relates to the technical field of satellite remote sensing data processing, in particular to a space downscaling method and system for remote sensing precipitation data. Background technique [0002] Precipitation is one of the key parameters of the global water cycle process. Accurate precipitation data can not only predict and evaluate flood disasters caused by precipitation, but also affect the distribution of terrestrial water resources due to the temporal and spatial distribution of precipitation. At present, precipitation fusion products widely used at home and abroad mainly include TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-ClimateData Record (PERSIANN-CDR) and other products , these products have accumulated nearly 20 years of global precipitation data. These precipitation fusion produc...

Claims

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Application Information

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IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 姬大彬施建成王天星李小涛雷添杰
Owner INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI
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