scholarly journals Real-Time Adjustment of Range-Dependent Biases in WSR-88D Rainfall Estimates due to Nonuniform Vertical Profile of Reflectivity

2000 ◽  
Vol 1 (3) ◽  
pp. 222-240 ◽  
Author(s):  
Dong-Jun Seo ◽  
Jay Breidenbach ◽  
Richard Fulton ◽  
Dennis Miller ◽  
Timothy O’Bannon
Keyword(s):  
Real Time ◽  
Vertical Profile ◽  
Rainfall Estimates

Radar-based rainfall nowcasting for flash flood hazard assessment: recent results at regional and Continental scales

2020 ◽  
Author(s):  
Marc Berenguer ◽  
Shinju Park ◽  
Daniel Sempere-Torres
Keyword(s):  
Real Time ◽  
Hazard Assessment ◽  
Flash Flood ◽  
Flood Hazard ◽  
Spatial Scales ◽  
Flash Floods ◽  
Radar Rainfall ◽  
European Projects ◽  
Rainfall Estimates ◽  
Ne Spain

<p>Radar rainfall estimates and nowcasts have been used in Catalonia (NE Spain) for real-time flash flood hazard nowcasting based on the basin-aggregated rainfall for several years. This approach has been further developed within the European Projects ERICHA (www.ericha.eu) and ANYWHERE (www.anywhere-h2020.eu), where it has been demonstrated to monitor flash floods in real time in several locations and at different spatial scales (from regional to Continental coverage).</p><p>The work summarizes the main results of the recent projects, analysing the performance of the flash flood nowcasting system. The results obtained on recent events  show the main advantages and some of the limitations of the system.</p>

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 Evaluating TMPA Rainfall over the Sparsely Gauged East African Rift

2018 ◽  
Vol 19 (9) ◽  
pp. 1507-1528 ◽  
Author(s):  
Elise Monsieurs ◽  
Dalia Bach Kirschbaum ◽  
Jackson Tan ◽  
Jean-Claude Maki Mateso ◽  
Liesbet Jacobs ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
East African Rift ◽  
East African ◽  
High Rainfall ◽  
Two Factors ◽  
African Rift ◽  
Rainfall Estimates

Abstract Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability.

Statistical model of the range-dependent error in radar-rainfall estimates due to the vertical profile of reflectivity

2011 ◽  
Vol 402 (3-4) ◽  
pp. 306-316 ◽  
Author(s):  
Witold F. Krajewski ◽  
Bertrand Vignal ◽  
Bong-Chul Seo ◽  
Gabriele Villarini
Keyword(s):  
Statistical Model ◽  
Vertical Profile ◽  
Radar Rainfall ◽  
Dependent Error ◽  
Rainfall Estimates

A climatological approach for operational radar rainfall bias correction 

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Training Dataset ◽  
Correction Factors ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Rainfall Estimates

<p>Most radar quantitative precipitation estimation (QPE) products systematically deviate from the true rainfall amount. This makes radar QPE adjustments unavoidable for operational use in hydro-meteorological (forecasting) models. Most correction methods require a timely available, high-density network of quality-controlled rain gauge observations. Here, we introduce a set of fixed bias reduction factors for the Netherlands, which vary per grid cell and day of the year. With this approach, we aim to provide an alternative to current practice, because the climatological factors are both operationally available and independent of the real-time rain gauge availability.</p><p>The correction factors were based on 10 years of 5-min radar QPE and reference rainfall data. We tested this method on the resulting rainfall estimates and subsequent discharge simulations for twelve Dutch catchment and polder areas. In addition, we compared the results to the operational mean field bias (MFB) corrected rainfall estimates and a reference dataset. This reference consisted of the radar QPE, spatially adjusted with a network of 356 validated rain gauge observations. Of this network, only 31 are automatic gauges. Hence, only these were available in real-time for the operational MFB corrections.</p><p>The climatological correction factors show clear spatial and temporal patterns. The factors are higher far from the radars and higher during winter than in summer. The latter pattern is likely a result of sampling above the melting layer during the months December–March, which causes higher underestimations. Estimated yearly rainfall sums are generally comparable to the reference and outperform the MFB corrected rainfall estimates for catchments far from the radars (south and east of the country). This difference is absent for catchments closer to the radars, where both products tend to marginally overestimate the rainfall sums. The differences amplify when both QPE products are used to force the hydrologic models. Discharge simulations based on the proposed QPE product outperform the MFB corrected rainfall estimates for all but one basin. Moreover, the climatological factor derivation shows little sensitivity to the moving window length and to leaving individual years out of the training dataset. The presented method provides a robust and straightforward operational alternative. It can serve as a benchmark for further QPE algorithm development in the Netherlands and elsewhere.</p>

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

scholarly journals Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

2017 ◽  
Vol 21 (12) ◽  
pp. 6559-6572 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger ◽  
Jackson Tan ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Gauge ◽  
Time Data ◽  
Hourly Rainfall ◽  
Precipitation Estimates ◽  
Climate Station ◽  
Using Data ◽  
Global Precipitation ◽  
Early Late ◽  
Rainfall Estimates

Abstract. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1°  ×  0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

Sign in / Sign up

Export Citation Format

Share Document