基于GWR模型的典型區(qū)域GPM數(shù)據(jù)降尺度研究——以浙江省為例

doi:10.12306/2022jms.0003

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基于GWR模型的典型區(qū)域GPM數(shù)據(jù)降尺度研究——以浙江省為例
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                        10.12306/2022jms.0003
                    
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中圖分類號:P426.6
基金項(xiàng)目:國家自然科學(xué)基金資助項(xiàng)目(41871285)

Downscaling research of GPM data in typical region based on GWR model: a case study of Zhejiang Province

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為提高研究區(qū)域的降尺度效果,基于地理加權(quán)回歸法(Geographically Weighted Regression,GWR),選取全球降水計(jì)劃(Global Precipitation Measurement, GPM)3IMERGM產(chǎn)品,以數(shù)字高程模型(Digital Elevation Model, DEM)作為控制解釋變量,將其分別與解釋變量水汽通量散度、氣溫構(gòu)建兩個降尺度模型、與解釋變量歸一化植被指數(shù)(Normalized Difference Vegetation Index, NDVI)構(gòu)建對照降尺度模型,對浙江省進(jìn)行降尺度研究。利用研究區(qū)域內(nèi)氣象站點(diǎn)的實(shí)測數(shù)據(jù),對由不同解釋變量構(gòu)建的3個降尺度模型的降尺度結(jié)果進(jìn)行對比分析及精度驗(yàn)證。結(jié)果表明:構(gòu)建的3個降尺度模型中,引入解釋變量水汽通量散度構(gòu)建的降尺度模型的綜合效果優(yōu)于其余兩種模型,水汽通量散度較NDVI、氣溫更適合作為解釋變量。構(gòu)建的降尺度模型有效地提高了GPM數(shù)據(jù)的空間分辨率(由0.1°提升至1 km),降尺度數(shù)據(jù)維持了精度且能夠更真實(shí)反映研究區(qū)域內(nèi)的降水量分布情況。

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In order to explore precipitation explanatory variables which better conform to regional typical characters, on the basis of GPM 3IMERGM precipitation product in Zhejiang Province, three Geographically Weighted Regression (GWR) models for downscaling were constructed taking advantage of Digital Elevation Model (DEM). Compared with Normalized Difference Vegetation Index (NDVI) which was commonly used as explanatory variable in downscaling research, divergence of moisture flux and air temperature were innovatively introduced in models respectively. In light of measured data from Zhejiang meteorological stations, precision verification and comparison between the models were taken for further analysis. The results show that the downscaling model with divergence of moisture flux as explanatory variable has better fitting effect and precision than others among the three models constructed. Divergence of moisture flux is more suitable than NDVI and air temperature as explanatory variable. Meanwhile, the constructed downscaling model effectively improves the spatial resolution of GPM data (from 0.1° to 1 km). The downscaling data maintains the precision and reflects the precipitation distribution more accurately in study region.

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林書睿,顧恒竹,路明月.基于GWR模型的典型區(qū)域GPM數(shù)據(jù)降尺度研究——以浙江省為例.氣象科學(xué),2022,42(6):793-803 LIN Shurui, GU Hengzhu, LU Mingyue. Downscaling research of GPM data in typical region based on GWR model: a case study of Zhejiang Province. Journal of the Meteorological Sciences,2022,42(6):793-803

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收稿日期:2021-06-05
最后修改日期:2022-01-15
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在線發(fā)布日期: 2023-03-03
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