Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

Xu, Guanghua; Xu, Xianli; Liu, Meixian; Sun, Alexander Y.; Wang, Kelin

doi:10.3390/w7063083

Cited by 28 publications

(31 citation statements)

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“…When applying the component decomposition models to spatial downscaling, many regression models have been applied to estimate the trend components by considering quantitative relationships between the target attribute and auxiliary variables (Immerzeel et al, 2009;Jia et al, 2011;Xu et al, 2015). For example, multiple linear regression (MLR), exponential models, geographically weighted regression (GWR), random forests and support vector machines have been applied for downscaling satellite-based precipitation products (Immerzeel et al, 2009;Chen et al, 2014;Chen et al, 2015;Jing et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

Kim¹,

Park²

2017

Korean Journal of Remote Sensing

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: Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models, residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

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Section: Introductionmentioning

confidence: 99%

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

Kim¹,

Park²

2017

Korean Journal of Remote Sensing

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“…However, the use of satellite-based precipitation observations in hydrological and environmental applications is often limited by coarse spatial resolutions. Various downscaling models have been developed for mapping precipitation with fine resolution from satellite-based precipitation datasets [14][15][16][17][18][36][37][38]. In this study, we downscaled the annual total TRMM 3B43 V7 precipitation from the 25-km scale to 1-km spatial resolution over the Tibetan Plateau with integration of MODIS NDVI, LST, and DEM data using machine learning algorithms.…”

Section: Value Of Spatial Downscalingmentioning

confidence: 99%

A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature

et al. 2016

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Abstract:Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation-land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation-land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation-LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale.

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“…However, since the model cannot well explain the actual spatial variability of precipitation at fine-scale, the residual correction technique at coarse scale is also utilized (i.e., interpolated to fine-scale and added up to the predicted precipitation at fine-scale), which produces the final downscaled precipitation. This method has been used in various studies [22,25,[29][30][31][32].…”

Section: Methodsmentioning

confidence: 99%

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Salimi

Samakosh

Sharifi

et al. 2019

Water

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Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R 2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.As precipitation varies greatly in space and time, gridded precipitation data in high spatiotemporal resolution is a considerable requirement as input for hydrometeorological and water resources management applications. This type of data is highly important for timely action (e.g., the initiation of landslide and mudslide movement) and decision making, such as evacuating an area with high potential for flooding, or to secure food and water supply [1]. However, high spatiotemporal resolution datasets are usually available only on a country level or cover a specific geographical region. These type of datasets might be obtained by interpolation of in situ observations and reanalysis products, or derived through remote sensing observations [2].The monitoring of precipitation from space opens a new era of precipitation observation. Hence, precipitation and hydrologic cycle studies are a hot topic in atmospheric science. High-resolution satellite precipitation estimates (SPEs) provide an effective global source of uninterrupted data for various water-related applications, especially over regions where ground-based observations are often lacking or sparse. These factors are underlying reasons why the increasing use of satellite data, which provide much better geographical coverage despite the potential limited accuracy, depends on precipitation products. The limited accuracy could be caused by various reasons e.g., algorithm, orographic effects, cloudy and/or snowy conditions, the relatively s...

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Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

Cited by 28 publications

References 70 publications

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

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