A review of downscaling methods of satellite-based precipitation estimates

Abdollahipour, Arman; Ahmadi, Hassan; Aminnejad, Babak

doi:10.1007/s12145-021-00669-4

Cited by 30 publications

(13 citation statements)

References 95 publications

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“…Our study shows that GWR with VP but without RC has the best performance, and RC is unnecessary for GWRs without VP, or even destructive for GWRs with VP. GWR itself cannot choose the optimal combinations of predictors, and the introduction of more predictors can therefore be detrimental to GWR modeling [11], [46]. Compared with RC, more information beneficial for GWR downscaling is included in VP.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that coarseresolution predictors were obtained by spatial upscaling of fine-resolution predictors [18]. If only an average operator is applied, which is common in existing research [3], [10], [46], [47], [48], information loss is inevitable [1], [11], [18]. Variations in the fine-resolution predictors at the spatial scale of the target variable to be downscaled can have an underlying value for the spatial downscaling of the target variable.…”

Section: Introductionmentioning

confidence: 99%

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Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the ''standard deviation'' operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination(R 2 ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.INDEX TERMS Variation predictor, spatial downscaling, disaggregation, unmixing, scaling, geographically weighted regression (GWR), tropical rainfall measuring mission (TRMM), precipitation, north-south transitional zone of China.The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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“…Statistical downscaling methods are mainly conducted by building the explanatory ability of the precipitation spatial distribution with fine-scale predictors, including topographic, geographic, atmospheric and vegetation variables, with the use of traditional regression methods (Xu et al, 2015;Ma et al, 2019b;Mei et al, 2020), optimal interpolation techniques (Shen et al, 2014;Chao et al, 2018), multidata fusion (Rozante et al, 2020;Ma et al, 2021), spatial data mining algorithm (called cubist) (Ma et al, 2017a, b), geographical ratio analysis (Duan and Bastiaanssen, 2013;Ma et al, 2019a) and machine learning algorithms (He et al, 2016;Baez-Villanueva et al, 2020;Min et al, 2020). Due to their convenience and efficiency, these approaches are dominant in precipitation spatial downscaling research (Abdollahipour et al, 2021). Comparatively, dynamical downscaling refers to the use of regional climate models driven by global climate model output or reanalysis data to generate regional precipitation information (Rockel, 2015), which requires more information on internal mechanisms related to complex physical processes of precipitation, such as atmospheric, oceanic and surface information (Tang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…However, the availability of high-resolution SSM data is very limited and most of the current SSM products have a spatial resolution of more than 10 km (Peng et al, 2021), placing significant restrictions on these applications. Furthermore, affected by the indirect physical connection between topographic and vegetation factors and precipitation at a coarse temporal scale, a large amount of downscaling works have been conducted at monthly or annual scales (Abdollahipour et al, 2021). In addition, although daily high-resolution precipitation data have been produced by different methods (Brocca et al, 2019;Hong et al, 2021), the use of high-resolution SSM data to improve the spatial resolution of satellite precipitation products for generating daily-scale high-resolution precipitation data based on physical mechanisms is less studied.…”

Section: Introductionmentioning

confidence: 99%

SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

Zhao

Brocca

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. As a key component in the water and energy cycle, estimates of precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. However, current satellite-based precipitation products have a coarse spatial resolution (from 10 to 50 km) not meeting the needs of several applications (e.g., flash floods and landslides). The implementation of spatial downscaling methods can be a suitable approach to overcome this shortcoming. In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the integrated multisatellite retrievals for global precipitation measurement (IMERG) V06B daily precipitation product over a complex topographic and climatic area in southwestern Europe (Iberian Peninsula) in the period 2016–2018. By exploiting the soil-water balance equation, high-resolution surface soil moisture (SSM) and normalized difference vegetation index (NDVI) products were used as auxiliary variables. The spatial resolution of the IMERG daily precipitation product was downscaled from 10 to 1 km. An evaluation using 1027 rain gauge stations highlighted the good performance of the downscaled 1 km IMERG product compared to the original 10 km product, with a correlation coefficient of 0.61, root mean square error (RMSE) of 4.83 mm and a relative bias of 5 %. Meanwhile, the 1 km downscaled results can also capture the typical temporal and spatial variation behaviors of precipitation in the study area during dry and wet seasons. Overall, the SMPD method greatly improves the spatial details of the original 10 km IMERG product also with a slight enhancement of accuracy. It shows good potential to be applied for the development of high-quality and high-resolution precipitation products in any region of interest.

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“…A downscaled urban precipitation product at high spatiotemporal scales is necessary to capture the different active processes. To circumvent the coarse-scale issue for impact and assessment studies, downscaling approaches have been employed (Abdollahipour et al., 2022 ). Downscaling operator improves the resolution of the coarse grid and sampling frequency datasets to higher resolution outputs.…”

Section: Introductionmentioning

confidence: 99%

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

et al. 2023

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Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

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A review of downscaling methods of satellite-based precipitation estimates

Cited by 30 publications

References 95 publications

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

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