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 of satellite rainfall estimates using a radar-gauge product – a case study in Oklahoma (USA)

2011 ◽  
Vol 15 (8) ◽  
pp. 2631-2647 ◽  
Author(s):  
K. Tesfagiorgis ◽  
S. E. Mahani ◽  
N. Y. Krakauer ◽  
R. Khanbilvardi
Keyword(s):  
Bias Correction ◽  
Flash Flood ◽  
Stage Iv ◽  
Bias Field ◽  
Daily Basis ◽  
Performance Criteria ◽  
Absolute Difference ◽  
Random Errors ◽  
Flood Prediction ◽  
Uncertainty Sources

Abstract. Hourly Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. SPEs are prone to larger systematic errors and more uncertainty sources in comparison with ground based radar and gauge precipitation products. The present work develops an approach to seamlessly blend satellite, radar and gauge products to fill gaps in ground-based data. To mix different rainfall products, the bias of any of the products relative to each other should be removed. The study presents and tests a proposed ensemble-based method which aims to estimate spatially varying multiplicative biases in hourly SPEs using a radar-gauge rainfall product and compare it with previously used bias correction methods. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. Bias field parameters were determined on a daily basis using the shuffled complex evolution optimization algorithm. To include more error sources, ensembles of bias factors were generated and applied before bias field generation. We demonstrate this method using two satellite-based products, CPC Morphing (CMORPH) and Hydro-Estimator (HE), and a radar-gauge rainfall Stage-IV (ST-IV) dataset for several rain events in 2006 over Oklahoma. The method was compared with 3 simpler methods for bias correction: mean ratio, maximum ratio and spatial interpolation without ensembles. Bias ratio, correlation coefficient, root mean square error and mean absolute difference are used to evaluate the performance of the different methods. Results show that: (a) the methods of maximum ratio and mean ratio performed variably and did not improve the overall correlation with the ST-IV in any of the rainy events; (b) the method of interpolation was consistently able to improve all the performance criteria; (c) the method of ensembles outperformed the other 3 methods.

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 Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

2020 ◽  
Vol 24 (2) ◽  
pp. 615-631 ◽  
Author(s):  
Yixin Mao ◽  
Wade T. Crow ◽  
Bart Nijssen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Red River ◽  
Future Research ◽  
Surface Model ◽  
Random Errors ◽  
Rainfall Estimates

Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.

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 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 Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO<sub>2</sub> flux estimates

2019 ◽  
Author(s):  
Susan S. Kulawik ◽  
Sean Crowell ◽  
David Baker ◽  
Junjie Liu ◽  
Kathryn McKain ◽  
...  
Keyword(s):  
Bias Correction ◽  
Correction Term ◽  
Systematic Errors ◽  
Department Of Energy ◽  
Total Carbon ◽  
Quality Data ◽  
Random Errors ◽  
Carbon Dioxide Co2 ◽  
Bias Correction Term ◽  
Good Quality Data

Abstract. We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

K nearest neighbour in merging satellite rainfall estimates from diverse sources in sparsely gauged basins

2021 ◽  
Author(s):  
Biswa Bhattacharya ◽  
Junaid Ahmad
Keyword(s):  
Bias Correction ◽  
Spatial Scales ◽  
Error Variance ◽  
Indus Basin ◽  
Nearest Neighbour ◽  
Data Merging ◽  
Basin Scale ◽  
Satellite Rainfall ◽  
Merging Method ◽  
Rainfall Estimates

&lt;p&gt;Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot always be corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various satellite rainfall products to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges can be used to merge bias corrected SBREs using the minimisation of variance principle.&lt;/p&gt;&lt;p&gt;In this research we investigated the possibility of using K nearest neighbour as a data merging method. Four satellite rainfall products were used in this study namely CMORPH, PERSIANN CDR, TRMM 3B42 and MSWEP. MSWEP was used as a reference dataset for comparing the merged rainfall dataset since it is also a merged product. All these products were downloaded at 0.25&amp;#176; x 0.25&amp;#176; spatial scale and daily temporal scale. Satellite products are known to behave differently at different temporal and spatial scales. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonal scales whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the biasness of satellite products by correcting the products at monthly and seasonal scale. Wth bias correction the daily normalised root mean square error (NRMSE) was reduced up to 20% for CMORPH, 22% for PERSIANN CDR and 14% for TRMM at the Indus basin scale for monthly scale which is why the monthly bias corrected data was used for merging. Merging of satellite products can be fruitful to benefit from the strength of each product and minimize the weakness of products. Four different merging methods i.e. Arithmetic merging method (AMM), Inverse error squared weighting (IESW), Error variance (EV) and K Nearest Neighbour method (KNN) were used and performance was checked in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. Daily NRMSE was reduced to 0.3 at Indus basin scale with KNN method, AMM and EV reduced the error to 0.45 in comparison to error produced by CMORPH, PERSIANN CDR and TRMM of 0.8, 0.65 and 0.53 respectively in the wet season. KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging improves rainfall estimates in sparsely gauged basin.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words:&lt;/strong&gt; Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.&lt;/p&gt;

scholarly journals Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

2014 ◽  
Vol 6 (7) ◽  
pp. 6688-6708 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Haile ◽  
Nazmus Sazib ◽  
Yu Zhang ◽  
Tom Rientjes
Keyword(s):  
Bias Correction ◽  
Blue Nile ◽  
Satellite Rainfall ◽  
Rainfall Estimates

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

2018 ◽  
Author(s):  
Margaret Wambui Kimani ◽  
Joost C.B. Hoedjes ◽  
Zhongbo Su
Keyword(s):  
East Africa ◽  
Bias Correction ◽  
Large Scale ◽  
Lake Victoria ◽  
Systematic Errors ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall ◽  
Climate Studies

Advances in remote sensing have led to use of satellite-derived rainfall products to complement the sparse rain gauge data. Although globally derived and some regional bias corrected, these products often show large discrepancies with ground measurements attributed to local and external factors that require systematic consideration. Decreasing rain gauge network however inhibits continuous validation of these products. We propose to deal with this problem by the use of Bayesian approach to merge the existing historical rain gauge information to create a consistent satellite rainfall data that can be used for climate studies. Monthly Bayesian bias correction is applied to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS v2) data to reduce systematic errors using a corresponding gridded (0.05&deg;) rain gauge data over East Africa for a period of 33 (1981&ndash;2013) years of which 22 years are utilized to derive error fields which are then applied to an independent CHIRPS data for 11 years for validation. The bias correction is spatially and temporally assessed during the rainfall wet months of March-May (MAM), June-August (JJA) and October&ndash;December (OND) in East Africa. Results show significant reduction of systematic errors at both monthly and yearly scales and harmonization of their cumulative distributions. Monthly statistics showed a reduction of RMSD (29&ndash;56)% and MAE (28&ndash;60)% and an increase of correlations (2&ndash;32) %, while yearly ones showed reductions of RMSD (9-23)%, and MAE (7&ndash;27)% and increase of correlations (4&ndash;77)% for MAM months, reduction of RMSD (15&ndash;35)% and MAE (16&ndash;41)% and increase in correlations (5&ndash;16)% for JJA months, and reduction of RMSD (3&ndash;35)% and MAE (9&ndash;32)% and increase of correlations (3&ndash;65)% for OND months. Systematic errors of corrected data were influenced by local processes especially over Lake Victoria and high elevated areas. Large-scale circulations induced errors were mainly during JJA and OND rainfall seasons and were reduced by the separation of anomalous years during training. The proposed approach is recommended for generating long-term data for climate studies where consistencies of errors can be assumed.

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