scholarly journals Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

1999 ◽  
Vol 35 (8) ◽  
pp. 2487-2503 ◽  
Author(s):  
Matthias Steiner ◽  
James A. Smith ◽  
Stephen J. Burges ◽  
Carlos V. Alonso ◽  
Robert W. Darden
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data

scholarly journals Difficulties with Correcting Radar Rainfall Estimates Based on Rain Gauge Data: A Case Study of Severe Weather in Montana on 16–17 June 2007

2009 ◽  
Vol 24 (5) ◽  
pp. 1334-1344 ◽  
Author(s):  
Steven V. Vasiloff ◽  
Kenneth W. Howard ◽  
Jian Zhang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Flash Flood ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Radar Calibration

Abstract The principal source of information for operational flash flood monitoring and warning issuance is weather radar–based quantitative estimates of precipitation. Rain gauges are considered truth for the purposes of validating and calibrating real-time radar-derived precipitation data, both in a real-time sense and climatologically. This paper examines various uncertainties and challenges involved with using radar and rain gauge data in a severe local storm environment. A series of severe thunderstorm systems that occurred across northeastern Montana illustrates various problems with comparing radar precipitation estimates and real-time gauge data, including extreme wind effects, hail, missing gauge data, and radar quality control. Ten radar–gauge time series pairs were analyzed with most found to be not useful for real-time radar calibration. These issues must be carefully considered within the context of ongoing efforts to develop robust real-time tools for evaluating radar–gauge uncertainties. Recommendations are made for radar and gauge data quality control efforts that would benefit the operational use of gauge data.

scholarly journals Multisensor Precipitation Reanalysis

2010 ◽  
Vol 11 (3) ◽  
pp. 666-682 ◽  
Author(s):  
Brian R. Nelson ◽  
D-J. Seo ◽  
Dongsoo Kim
Keyword(s):  
Quality Control ◽  
South Carolina ◽  
Stage Iv ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Rain Gauge Data ◽  
Wide Range ◽  
Gauge Data ◽  
Validation Experiments

Abstract Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

scholarly journals Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela

Atmósfera ◽  
2016 ◽  
Vol 29 (4) ◽  
pp. 323-342 ◽  
Author(s):  
Franklin Javier Paredes Trejo ◽  
◽  
Humberto Álvarez Barbosa ◽  
Marcos A. Peñaloza-Murillo ◽  
Maria Alejandra Moreno ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Rainfall Estimation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

scholarly journals Operational Implementation of Satellite-Rain Gauge Data Merging for Hydrological Modeling

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 533
Author(s):  
Alejandra De Vera ◽  
Pablo Alfaro ◽  
Rafael Terra
Keyword(s):  
Electricity Market ◽  
Hydrological Modeling ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Hydrological Simulation ◽  
Rain Gauge Data ◽  
Electric System ◽  
Hydroelectric Energy ◽  
Regression Residuals ◽  
Gauge Data

Systems exposed to hydroclimatic variability, such as the integrated electric system in Uruguay, increasingly require real-time multiscale information to optimize management. Monitoring of the precipitation field is key to inform the future hydroelectric energy availability. We present an operational implementation of an algorithm that merges satellite precipitation estimates with rain gauge data, based on a 3-step technique: (i) Regression of station data on the satellite estimate using a Generalized Linear Model; (ii) Interpolation of the regression residuals at station locations to the entire grid using Ordinary Kriging and (iii) Application of a rain/no rain mask. The operational implementation follows five steps: (i) Data download and daily accumulation; (ii) Data quality control; (iii) Merging technique; (iv) Hydrological modeling and (v) Electricity-system simulation. The hydrological modeling is carried with the GR4J rainfall-runoff model applied to 17 sub-catchments of the G. Terra basin with routing up to the reservoir. The implementation became operational at the Electricity Market Administration (ADME) on June 2020. The performance of the merged precipitation estimate was evaluated through comparison with an independent, dense and uniformly distributed rain gauge network using several relevant statistics. Further validation is presented comparing the simulated inflow to the estimate derived from a reservoir mass budget. Results confirm that the estimation that incorporates the satellite information in addition to the surface observations has a higher performance than the one that only uses rain gauge data, both in the rainfall statistical evaluation and hydrological simulation.

scholarly journals Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

2020 ◽  
Vol 12 (3) ◽  
pp. 363 ◽  
Author(s):  
Qingtai Qiu ◽  
Jia Liu ◽  
Jiyang Tian ◽  
Yufei Jiao ◽  
Chuanzhe Li ◽  
...  
Keyword(s):  
Northern China ◽  
Rain Gauge ◽  
Bayesian Regression ◽  
Small Scale ◽  
Regression Kriging ◽  
Storm Events ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

scholarly journals An Integrated Approach to Error Correction for Real-Time Radar-Rainfall Estimation

2006 ◽  
Vol 23 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Siriluk Chumchean ◽  
Ashish Sharma ◽  
Alan Seed
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Mean Field ◽  
Rain Gauge ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Bias Adjustment ◽  
Field Bias ◽  
The Mean ◽  
Correction Strategies

Abstract A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the sample of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation.

scholarly journals Estimation of Finescale Rainfall Fields Using Broadcast TV Satellite Links and a 4DVAR Assimilation Method

2015 ◽  
Vol 32 (10) ◽  
pp. 1709-1728 ◽  
Author(s):  
François Mercier ◽  
Laurent Barthès ◽  
Cécile Mallet
Keyword(s):  
Hydrological Cycle ◽  
Real Data ◽  
Rain Attenuation ◽  
Rain Gauge ◽  
Propagation Model ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Gauge Data

AbstractThis study proposes a method based on the use of a set of commercial satellite-to-Earth microwave links to rebuild finescale rainfall fields. Such microwave links exist all over the world and can be used to estimate the integrated rain attenuation over the links’ first 5–7 km with a very high temporal resolution (10 s in the present case). The retrieval algorithm makes use of a four-dimensional variational data assimilation (4DVAR) method involving a numerical advection scheme. The advection velocity is recovered from the observations or from radar rainfall fields at successive time steps.This technique has been successively applied to simulated 2D rain maps and to real data recorded in the autumn of 2013 during the Hydrological Cycle in the Mediterranean Experiment (HyMeX), with one sensor receiving microwave signals from four different satellites. The performance of this system is assessed and is compared to an operational Météo-France radar and a network of 10 rain gauges. Because of the limitations of the propagation model, this study is limited to the events with strong advective characteristics (four out of eight recorded events). For these events (only), the method produces rainfall fields that are highly correlated with the radar maps at spatial resolutions greater than . The point-scale results are also satisfactory for temporal resolutions greater than 10 min (mean correlation with rain gauge data equal to approximately 0.8, similar to the correlation between radar and rain gauge data).This method can also be adapted to the fusion of a rain gauge with microwave link measurements and, through the use of several sensors, it has the potential of being applied to larger areas.

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