Precipitation: Measurement, remote sensing, climatology and modeling

Michaelides, Silas; Levizzani, Vincenzo; Anagnostou, Emmanouil N.; Bauer, Péter; Kasparis, Takis; Lane, John E.

doi:10.1016/j.atmosres.2009.08.017

Cited by 390 publications

(238 citation statements)

References 111 publications

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“…Recent developments make satellite-based rainfall more accurate, accessible, consistent, and reliable than before (Boushaki et al 2009;Michaelides et al 2009). Various approaches have been developed to derive rainfall from satellites such as the Tropical Rainfall Measuring Mission MultiSatellite Precipitation Analysis (TRMM TMPA) by Simpson et al (1988) and Huffman et al (2007), Climate Prediction Center Morphing Method (CMORPH) by Joyce et al (2004), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) by Sorooshian et al (2000).…”

Section: Introductionmentioning

confidence: 99%

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Rahmawati

Lubczynski

2017

Theor Appl Climatol

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Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH 25 , CMORPH 8 , TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH 25 , CMORPH 8 , and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH 25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH 25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day −1 rain events. Lastly, the CMORPH 25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH 8 .

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Section: Introductionmentioning

confidence: 99%

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Rahmawati

Lubczynski

2017

Theor Appl Climatol

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“…Rainfall is an essential part of the global water cycle, and measurements are especially crucial for predicting extreme weather events, such as floods, high water and droughts. Precipitation can be measured on the ground using several instruments, such as rain gauges, radar and disdrometers [1,2]. Gauges are the major instruments used to build reference precipitation datasets.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

et al. 2015

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Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.

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“…Based on the Immerzeel's method, we built an exponential regression model to downscale the TRMM 3B43 data of Mainland China (Equation (7)). bx y a e c  (7) where a, b, c are the fitting coefficients of the regression model.…”

Section: Exponential Regression Modelmentioning

confidence: 99%

“…Precipitation dataset used in recent ecological and hydrological researches is mainly derived from three sources: (a) outputs from various numerical climate/weather models; (b) rain gauge observations; and (c) estimates from space-based observing systems [5][6][7]. For example, the Global Climate Models (GCMs) can simulate the changes of precipitation at large scales [5]; the Regional Climate Models (RCMs) can be used to predict the distribution of precipitation at a regional scale [8].…”

Section: Introductionmentioning

confidence: 99%

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

et al. 2015

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Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km 2 ) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

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Precipitation: Measurement, remote sensing, climatology and modeling

Cited by 390 publications

References 111 publications

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

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