Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

Alharbi, Raied Saad; Hsu, Kuolin; Sorooshian, Soroosh

doi:10.1007/s12517-018-3860-4

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…However, for constructing CDFs in each CCS pixel (0.04 • ), or even a 1 • × 1 • box (as used by Yang et al [34]), sufficient samples are necessary, but rain gauges are sparse in mountainous and desert areas such as Iran. Therefore, using climate regions with homogenous precipitation patterns as employed in this study and other similar works [36] would be an appropriate method to overcome this restriction in similar areas. Furthermore, using climate regions revealed the distinguished effects of bias corrections in different climates.…”

Section: Discussionmentioning

confidence: 99%

“…However, the CDFs can be accurately approximated only for sufficiently large sample sizes. To enlarge the sample size in some gauge-scarce regions such as Saudi Arabia, which is an arid country with a sparse rain-gauge network, Alharbi et al [36] used the QM for climate regions instead of a 1 • × 1 • box region. Their results showed that using climate regions (CR) led to more accurate CDF matching and a better bias correction of the PERSIANN-CCS compared to cases without climate regions.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the PERSIANN-CCS is evaluated and bias-corrected over different parts of Iran, which include high mountains and extended deserts. For this purpose, the country is divided into different CRs, as recommended by Alharbi et al [36], and the empirical CDFs are derived for each CR. Then, we apply a filter with the QM method using the CDFs derived for each CR and season to reduce the systematic bias of the daily precipitation of the PERSIANN-CCS and generate a gridded bias-corrected PERSIANN-CCS.…”

Section: Introductionmentioning

confidence: 99%

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Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

et al. 2020

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High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

et al. 2020

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“…To ameliorate the uncertainties, both precipitation correction and multi-source estimation approaches have been explored and applied for different regions. Here, we distinguish between (1) the conventional approach of exclusively relying on rain gauge observations [6,18,19] and (2) the more recent approach of incorporating additional explanatory variables [20][21][22][23] to correct precipitation estimates. The latter approach is the focus of the current study.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

2020

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Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

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“…For PERSIANN, there is a consistent pattern of overestimation (Alharbi, Hsu, & Sorooshian, 2018;Behrangi et al, 2011;Y. Yang & Luo, 2014).…”

Section: Precipitation Estimation From Remotely Sensed Information Usingmentioning

confidence: 72%

Bias correction of satellite precipitation estimates over Thailand

Ventura¹

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Over the years, meteorological satellite instruments have produced Satellite Precipitation Estimates (SPEs) that can supply rainfall intensity rates globally. However, these datasets do not directly reflect the actual values of ground measurements so it is imperative to correct the systematic biases of SPEs to produce reliable hydrologic models. Thus, the aim of this study is to assess the effectiveness of bias correction of SPE products over Thailand. The Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS), Global Satellite Mapping of Precipitation - Near Real Time (GSMaP_NRT), and Integrated Multi-satellitE Retrievals for GPM (IMERG) Early version were evaluated in comparison to the Thai Meteorological Department (TMD) gauge measurements from 2003 to 2018. Subsequently, the SPEs were corrected by using Scaling, Quantile Mapping (QM), and an Artificial Neural Network (ANN) correction. Both the original PERSIANN-CCS and IMERG Early generally exhibit overestimation over Thailand while the GSMaP_NRT slightly underestimate rainfall. The original IMERG Early also shows the least RMSE overall, followed by GSMaP_NRT, then by PERSIANN-CCS. GSMaP_NRT shows the highest Equitable Threat Score (ETS) while IMERG Early has the lowest ETS because it has large amounts of false alarms. All products exhibit higher errors during the wet season, high underestimation during heavy and extreme rainfall, and higher errors near the coastal areas where high rainfall occurs. IMERG Early also shows the least RMSE in all river basins. After bias correction, the adjusted IMERG Early dataset still provides the least RMSE for all basins regardless of which correction method was applied. The ANN bias correction method performs the best among the three methods in terms of RMSE. However, it increases the underestimation and RMSE of extreme rainfall events and worsens ETS of PERSIANN-CCS and GSMaP_NRT. Only the QM bias correction is able to consistently reduce errors of extreme rainfall and improve ETS. Overall, the ANN adjusted IMERG Early dataset has the least RMSE in all river basins.

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Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

Cited by 9 publications

References 46 publications

Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

Bias correction of satellite precipitation estimates over Thailand

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