青海地区TRMM 3B43降水产品融合降尺度与时空特征分析

doi:10.12118/j.issn.1000-6060.2023.559

Abstract

Abstract:

Satellite remote sensing precipitation products have been widely used to estimate precipitation, especially in regions with sparse ground observation stations. However, the lower spatial resolution of these satellite products limits their application in localized regions and watersheds. This study proposes a fused downscaling framework based on the area-to-point kriging (ATPOK) algorithm and geographic weighted regression kriging (GWRK) algorithm to downscale TRMM 3B43 data for the Qinghai region of China from 2000 to 2019. The framework incorporates ground observation station data, normalized difference vegetation index (NDVI), digital elevation model, slope, and auxiliary factors for calibration, ultimately obtaining precipitation products at 1 km resolution. The results showed that: (1) The proposed fused downscaling framework can effectively improve the accuracy of TRMM 3B43 products; however, it cannot eliminate the overestimation of TRMM 3B43. (2) Compared to the ATPOK algorithm, the GWRK algorithm using data TRMM precipitation data and auxiliary factors can better estimate annual/monthly precipitation data. (3) Based on the study of the relationship between TRMM 3B43 and NDVI, it was found that NDVI responds to precipitation with a delay of 0-2 months. (4) Based on the spatiotemporal variation analysis of downscaled precipitation products, significant increases in monthly precipitation were observed in the Qinghai region, with an interannual change rate of 3.33% in dry month (December) and 1.79% in wet month (July).

Key words: spatial downscaling, TRMM 3B43, ATPOK, GWRK, downscaling-integration framework

WANG Peng, SHI Yuli. Fused-downscaling framework and spatiotemporal characteristics of TRMM 3B43 precipitation product in the Qinghai region[J].Arid Land Geography, 2024, 47(7): 1136-1146.

Figures/Tables 10

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Tab. 2

Max correlation coefficients between monthly NDVI and concurrent, previous 1 month, and previous 2 months TRMM 3B43 in the Qinghai region"

月份	$r N D V I - T 0$	$r N D V I - T 1$	$r N D V I - T 2$
1	0.464	0.459	0.605
2	0.557	0.474	0.461
3	0.727	0.640	0.543
4	0.802	0.745	0.643
5	0.818	0.825	0.776
6	0.773	0.827	0.831
7	0.778	0.774	0.822
8	0.795	0.776	0.770
9	0.802	0.754	0.726
10	0.659	0.686	0.672
11	0.610	0.702	0.737
12	0.425	0.584	0.662

Tab. 2

Fig. 8

References 39

[1]	Dobson A P, Bradshaw A D, Baker A J M. Hopes for the future: Restoration ecology and conservation biology[J]. Science, 1997, 277(5325): 515-522.
[2]	Immerzeel W, Lutz A F, Andrade M, et al. Importance and vulnerability of the world’s water towers[J]. Nature, 2020, 577(7790): 364-369.
[3]	Emanuel K. Increasing destructiveness of tropical cyclones over the past 30 years[J]. Nature, 2005, 436(7051): 686-688.
[4]	Goovaerts P. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall[J]. Journal of Hydrology, 2000, 228(1): 113-129.
[5]	Langella G, Basile A, Bonfante A, et al. High-resolution space-time rainfall analysis using integrated ANN inference systems[J]. Journal of Hydrology, 2010, 387(3): 328-342.
[6]	Kummerow C, Hong Y, Olson W S, et al. The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors[J]. Journal of Applied Meteorology, 2001, 40(11): 1801-1820.
[7]	Kummerow C, Simpson J, Thiele O, et al. The status of the tropical rainfall measuring mission (TRMM) after two years in orbit[J]. Journal of Applied Meteorology and Climatology, 2000, 39(12): 1965-1982.
[8]	Huffman G J, Bolvin D T, Nelkin E J, et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, 2007, 8(1): 38-55.
[9]	Kummerow C, Barnes W, Kozu T, et al. The tropical rainfall measuring mission (TRMM) sensor package[J]. Journal of Atmospheric and Oceanic Technology, 1998, 15(3): 809-817.
[10]	Huffman G J, Adler R F, Arkin P, et al. The global precipitation climatology project (GPCP) combined precipitation dataset[J]. Bulletin of the American Meteorological Society, 1997, 78(1): 5-20.
[11]	Adler R F, Huffman G J, Chang A, et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present)[J]. Journal of Hydrometeorology, 2003, 4(6): 1147-1167.
[12]	Huffman G J, Adler R F, Bolvin D T, et al. Improving the global precipitation record: GPCP version 2.1[J]. Geophysical Research Letters, 2009, 36(17): L17808, doi: 10.1029/2009GL040000.
[13]	Adler R F, Sapiano M R P, Huffman G J, et al. The global precipitation climatology project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation[J]. Atmosphere, 2018, 9(4): 138, doi: 10.3390/atmos9040138.
[14]	Backer D, Billing T. Validating famine early warning systems network projections of food security in Africa, 2009—2020[J]. Global Food Security, 2021, 29: 100510, doi: 10.1016/j.gfs.2021.100510.
[15]	Smith E A, Asrar G, Furuhama Y, et al. Measuring precipitation from space[M]. Dordrecht: Springer, 2007: 611-653.
[16]	Kubota T, Shige S, Hashizume H, et al. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(7): 2259-2275.
[17]	Huffman G J, Adler R F, Morrissey M M, et al. Global precipitation at one-degree daily resolution from multisatellite observations[J]. Journal of Hydrometeorology, 2001, 2(1): 36-50.
[18]	Xie P, Arkin P A. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs[J]. Bulletin of the American Meteorological Society, 1997, 78(11): 2539-2558.
[19]	Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications[J]. Earth System Science Data, 2021, 13(9): 4349-4383.
[20]	Okamoto K, Takahashi N, Iwanami K, et al. High precision and high resolution global precipitation map from satellite data[C]// IEEE. 2008 Microwave Radiometry and Remote Sensing of the Environment. Florence: IEEE, 2008: 1-4.
[21]	Joyce R J, Janowiak J E, Arkin P A, et al. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution[J]. Journal of Hydrometeorology, 2004, 5(3): 487-503.
[22]	Turk F J, Rohaly G D, Hawkins J, et al. Meteorological applications of precipitation estimation from combined SSM/I, TRMM and infrared geostationary satellite data[M]. London: CRC Press, 1999: 353-363.
[23]	Hsu K, Gao X, Sorooshian S, et al. Precipitation estimation from remotely sensed information using artificial neural networks[J]. Journal of Applied Meteorology and Climatology, 1997, 36(9): 1176-1190.
[24]	Huffman G J. Integrated multi-satellite retrievals for the global precipitation measurement (GPM) mission (IMERG)[M]. Cham: Springer, 2020: 343-353.
[25]	Immerzeel W, Rutten M, Droogers P. Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula[J]. Remote Sensing of Environment, 2009, 113(2): 362-370.
[26]	Park N W. Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables[J]. Advances in Meteorology, 2013, 2013(1): 237126, doi: 10.1155/2013/237126.
[27]	Shi Y L, Song L. Spatial downscaling of monthly TRMM precipitation based on EVI and other geospatial variables over the Tibetan Plateau from 2001 to 2012[J]. Mountain Research and Development, 2015, 35(2): 180-194.
[28]	Ma Z Q, Shi Z, Zhou Y, et al. A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai-Tibet Plateau with the effects of systematic anomalies removed[J]. Remote Sensing of Environment, 2017, 200: 378-395.
[29]	Mateus P, Borma L S, da Silva R D, et al. Assessment of two techniques to merge ground-based and TRMM rainfall measurements: A case study about Brazilian Amazon rainforest[J]. GIScience & Remote Sensing, 2016, 53(6): 689-706.
[30]	Duan Z, Bastiaanssen W G M. First results from version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure[J]. Remote Sensing of Environment, 2013, 131: 1-13.
[31]	Kyriakidis P C. A geostatistical framework for area-to-point spatial interpolation[J]. Geographical Analysis, 2004, 36(3): 259-289.
[32]	Goovaerts P. Combining areal and point data in geostatistical interpolation: Applications to soil science and medical geography[J]. Mathematical Geosciences, 2010, 42(5): 535-554. pmid: 21132098
[33]	Duan Z, Liu J Z, Tuo Y, et al. Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales[J]. Science of the Total Environment, 2016, 573: 1536-1553.
[34]	Chen Y Y, Huang J F, Sheng S X, et al. A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data[J]. Remote Sensing of Environment, 2018, 214: 154-172.
[35]	李运龙, 熊立华, 闫磊. 基于地理权重回归克里金的降水数据融合及其在水文预报中的应用[J]. 长江流域资源与环境, 2017, 26(9): 1360-1369.
	[Li Yunlong, Xiong Lihua, Yan Lei. A geographically weighted regression kriging approach for TRMM-rain gauge data merging and its application in hydrological forecasting[J]. Resources and Environment in the Yangtze Basin, 2017, 26(9): 1360-1369.]
[36]	Li X H, Zhang Q, Xu C Y. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang Lake Basin[J]. Journal of Hydrology, 2012, 426: 28-38.
[37]	韩雅, 朱文博, 李双成. 基于GWR模型的中国NDVI与气候因子的相关分析[J]. 北京大学学报(自然科学版), 2016, 52(6): 1125-1133.
	[Han Ya, Zhu Wenbo, Li Shuangcheng. Modelling Relationship between NDVI and climatic factors in China using geographically weighted regression[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 52(6): 1125-1133.]
[38]	Xu S G, Wu C Y, Wang L, et al. A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics[J]. Remote Sensing of Environment, 2015, 162: 119-140.
[39]	Foody G M. Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship[J]. Remote Sensing of Environment, 2003, 88(3): 283-293.

时间尺度	降水数据	精度评价指标
时间尺度	降水数据	R²	Bias/%	RMSE/mm	MAE/mm
年尺度	TRMM 3B43	0.86	4.93	73.16	59.50
	ATPOK_TRMM	0.89	4.78	64.64	51.85
	GWR_TRMM	0.91	3.13	57.34	46.26
	GWRK_TRMM	0.92	3.60	55.70	45.39
月尺度	TRMM 3B43	0.87	4.76	6.15	4.96
	ATPOK_TRMM	0.89	4.77	5.44	4.32
	GWR_TRMM	0.90	3.79	4.83	3.85
	GWRK_TRMM	0.91	4.32	4.68	3.78

Fused-downscaling framework and spatiotemporal characteristics of TRMM 3B43 precipitation product in the Qinghai region

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