Improvement in rainfall estimates using bias correction with the IAP-DCP Global Model

2020 ◽  
Vol 171 ◽  
pp. 26-35
Author(s):  
Usa Humphries ◽  
Pramet Kaewmesri ◽  
Pariwate Varnakovida ◽  
Prungchan Wongwises
Keyword(s):  
Bias Correction ◽  
Global Model ◽  
Rainfall Estimates

scholarly journals Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

2012 ◽  
Vol 13 (1) ◽  
pp. 338-350 ◽  
Author(s):  
Menberu M. Bitew ◽  
Mekonnen Gebremichael ◽  
Lula T. Ghebremichael ◽  
Yared A. Bayissa
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Mountainous Watershed ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

scholarly journals Bias correction of radar rainfall estimates based on a geostatistical technique

ScienceAsia ◽  
2012 ◽  
Vol 38 (4) ◽  
pp. 373 ◽  
Author(s):  
Ratchawatch Hanchoowong ◽  
Uruya Weesakul ◽  
Siriluk Chumchean
Keyword(s):  
Bias Correction ◽  
Radar Rainfall ◽  
Rainfall Estimates

Correcting position error in rainfall estimates using temporal and spatial warping

2020 ◽  
Author(s):  
Camille Le Coz ◽  
Arnold Heemink ◽  
Martin Verlaan ◽  
Marie-claire ten Veldhuis ◽  
Nick van de Giesen
Keyword(s):  
Bias Correction ◽  
Monsoon Season ◽  
Position Error ◽  
Rainfall Event ◽  
Sub Saharan Africa ◽  
Registration Method ◽  
Rainfall Events ◽  
Meteorological Observatory ◽  
Sub Saharan ◽  
Rainfall Estimates

<p>An increasing number of satellite-based rainfall estimates, with ever finer resolution, are becoming available. They are particularly valuable in regions with sparse radar and gauge networks. For example, in most of sub-Saharan Africa, the gauge network is not dense enough to represent the high variability of the rainfall during the monsoon season. However, satellite-based estimates can be subject to errors in position and/or timing of the rainfall events, in addition to errors in the intensity.<br>Many satellite-based estimates use gauge measurements for bias correction. Bias correction methods focus on the intensity errors, and do not correct the position error explicitly. We propose to gauge-adjust the satellite-based estimates with respect to the position and time. We investigate two approaches: spatial and temporal warping. The first one is based on a spatial mapping and correct the spatial position while keeping the time constant. The second uses a temporal mapping and keeps the spatial domain unchanged. The mappings are derived through a fully automatic registration method. That is, only the gauge and satellite-based estimates are needed as inputs. There is no need to manually predefine the rain features.<br>The spatial and temporal approaches are both applied to a rainfall event during the monsoon season in southern Ghana. The Trans-African Hydro-Meteorological Observatory (TAHMO) gauge network is used to gauge-adjust the IMERG-Late (Integrated Multi-Satellite Retrievals for GPM) satellite-based estimates. The two approaches are evaluated with respect to the timing, the location and the intensity of the rainfall event.</p>

scholarly journals Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies

2018 ◽  
Vol 10 (7) ◽  
pp. 1074 ◽  
Author(s):  
Margaret Kimani ◽  
Joost Hoedjes ◽  
Zhongbo Su
Keyword(s):  
Bias Correction ◽  
Satellite Rainfall ◽  
Climate Studies ◽  
Rainfall Estimates

scholarly journals Bias correction of satellite rainfall estimation using a radar-gauge product

2010 ◽  
Vol 7 (6) ◽  
pp. 8913-8945 ◽  
Author(s):  
K. Tesfagiorgis ◽  
S. E. Mahani ◽  
R. Khanbilvardi
Keyword(s):  
Bias Correction ◽  
Bias Field ◽  
Systematic Errors ◽  
Daily Basis ◽  
Random Errors ◽  
Field Generation ◽  
Shuffled Complex Evolution ◽  
Hydrologic Prediction ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.

scholarly journals Bias correction schemes for CMORPH satellite rainfall estimates in the Zambezi River Basin

2016 ◽  
Author(s):  
W. Gumindoga ◽  
T. H. M. Rientjes ◽  
A. T. Haile ◽  
H. Makurira ◽  
P. Reggiani
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Large Scale ◽  
Statistical Error ◽  
Correction Method ◽  
Correction Scheme ◽  
Satellite Rainfall ◽  
Zambezi River Basin ◽  
Rainfall Estimates ◽  
Zambezi River

Abstract. Obtaining reliable records of rainfall from satellite rainfall estimates (SREs) is a challenge as SREs are an indirect rainfall estimate from visible, infrared (IR), and/or microwave (MW) based information of cloud properties. SREs also contain inherent biases which exaggerate or underestimate actual rainfall values hence the need to apply bias correction methods to improve accuracies. We evaluate the performance of five bias correction schemes for CMORPH satellite-based rainfall estimates. We use 54 raingauge stations in the Zambezi Basin for the period 1998–2013 for comparison and correction. Analysis shows that SREs better match to gauged estimates in the Upper Zambezi Basin than the Lower and Middle Zambezi basins but performance is not clearly related to elevation. Findings indicate that rainfall in the Upper Zambezi Basin is best estimated by an additive bias correction scheme (Distribution transformation). The linear based (Spatio-temporal) bias correction scheme successfully corrected the daily mean of CMORPH estimates for 70 % of the stations and also was most effective in reducing the rainfall bias. The nonlinear bias correction schemes (Power transform and the Quantile based empirical-statistical error correction method) proved most effective in reproducing the rainfall totals. Analyses through bias correction indicate that bias of CMORPH estimates has elevation and seasonality tendencies across the Zambezi river basin area of large scale.

scholarly journals Bias Correction of Satellite-Based Rainfall Estimates for Modeling Flash Floods in Semi-Arid regions: Application to Karpuz River, Turkey

2016 ◽  
Author(s):  
Mohamed Saber ◽  
Koray Yilmaz
Keyword(s):  
Bias Correction ◽  
Flash Flood ◽  
Flash Floods ◽  
Rainfall Rate ◽  
Assessment Model ◽  
Model Parameters ◽  
Flood Events ◽  
Rain Gauges ◽  
Semi Arid ◽  
Rainfall Estimates

Abstract. This study investigates the utility of gauge-corrected satellite-based rainfall estimates in simulating flash floods at Karpuz River - a semi-arid basin in Turkey. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at monthly and daily time-scales considering various time periods and rainfall rate thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges however suffers from significant underestimation bias. A rainfall rate threshold of 1 mm/month was the best choice to improve the match between GSMaP and rain gauges implying that appropriate threshold selection is critically important for the bias correction. Multiplicative bias correction was applied to GSMaP data using the bias factors calculated between GSMaP and observed rainfall. Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was used to simulate flash floods at the hourly time scale driven by the corrected GSMaP rainfall data. The model parameters were calibrated for flash flood events during October-December 2007 and then validated for flash flood events during October-December 2009. The results show that the simulated surface runoff hydrographs reasonably coincide with the observed hydrographs.

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