Use of Weather Surveillance Radars—88 Doppler Data in Hydrologic Modeling

1998 ◽  
Vol 1647 (1) ◽  
pp. 61-66
Author(s):  
David C. Curtis
Keyword(s):  
Coming Out ◽  
Hydrologic Modeling ◽  
Federal Aviation Administration ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Data Sets ◽  
Radar Rainfall ◽  
Meteorological Observations ◽  
Rainfall Estimates

Successful hydrologic modeling depends heavily on high-quality rainfall data sets. If hydrologists cannot determine what is coming into a watershed, there is little chance that any hydrologic model will accurately estimate what is coming out on a consistent basis. Hydrologists are frequently forced to use rainfall data sets derived from sparse rain gauge networks that poorly resolve critical rainfall features, leading to inadequate model results. Over the past several years, the modernizing National Weather Service, the Federal Aviation Administration, and the Department of Defense have installed a new nationwide network of weather radars, providing a rich suite of real-time meteorological observations. Radar rainfall estimates from the new radars cover vast areas at a spatial and temporal resolution that would be impossibly expensive to match with a conventional rain gauge network. Hydrologists can now literally see between the gauges and view truer representations of the spatial distribution of rainfall than ever before. Results from the analysis of the January 9-10, 1995, storms in Sacramento, California, show that gauge-adjusted radar rainfall estimates help resolve rainfall features that could not have been inferred from rain gauge analysis alone. Accurate estimates of the volume, timing, and distribution of rainfall helped create excellent modeling results. In Waco, Texas, radar rainfall estimates were used to improve the analysis of excess inflow and infiltration into city storm sewers. The radar rainfall analyses enabled modelers to account for inflow/infiltration variations down to the neighborhood level.

Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging

2019 ◽  
Vol 14 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Roby Hambali ◽  
Djoko Legono ◽  
Rachmad Jayadi ◽  
Satoru Oishi ◽  
◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Early Warning ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Value Distribution ◽  
Rainfall Data ◽  
Warning Systems ◽  
Radar Rainfall ◽  
X Band ◽  
Rainfall Estimates

Rainfall monitoring is important for providing early warning of lahar flow around Mt. Merapi. The X-band multi-parameter radar developed to support these warning systems provides rainfall information with high spatial and temporal resolution. However, this method underestimates the rainfall compared with rain gauge measurements. Herein, we performed conditional radar-rain gauge merging to obtain the optimal rainfall value distribution. By using the cokriging interpolation method, kriged gauge rainfall, and kriged radar rainfall data were obtained, which were then combined with radar rainfall data to yield the adjusted spatial rainfall. Radar-rain gauge conditional merging with cokriging interpolation provided reasonably well-adjusted spatial rainfall pattern.

scholarly journals Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation

2018 ◽  
Vol 20 (4) ◽  
pp. 784-797 ◽  
Author(s):  
Marija Ivković ◽  
Andrijana Todorović ◽  
Jasna Plavšić
Keyword(s):  
Daily Rainfall ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Rain Gauges ◽  
Observation Network ◽  
Hourly Rainfall ◽  
Extreme Flood ◽  
Flood Simulations

Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project

2015 ◽  
Vol 16 (4) ◽  
pp. 1658-1675 ◽  
Author(s):  
Bong-Chul Seo ◽  
Brenda Dolan ◽  
Witold F. Krajewski ◽  
Steven A. Rutledge ◽  
Walter Petersen
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Superior Performance ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Precipitation Estimation ◽  
Improved Performance ◽  
Rainfall Estimates

Abstract This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (Z–R) relation that might lead to substantial underestimation for the presented case.

scholarly journals An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

2013 ◽  
Vol 28 (6) ◽  
pp. 1478-1497 ◽  
Author(s):  
Luciana K. Cunha ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Witold F. Krajewski
Keyword(s):  
Kansas City ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Urban Flood ◽  
Temporal And Spatial ◽  
Single Polarization ◽  
Early Performance ◽  
Rainfall Estimates

Abstract Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.

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.

Hydrologic modeling Of New England river basins using radar rainfall data

1990 ◽  
Vol 95 (D3) ◽  
pp. 2143 ◽  
Author(s):  
Jonathan Wyss ◽  
Earle R. Williams ◽  
Rafael L. Bras
Keyword(s):  
New England ◽  
Hydrologic Modeling ◽  
River Basins ◽  
Rainfall Data ◽  
Radar Rainfall

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals Doppler radar rainfall prediction and gauge data

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jesse W. Lansford ◽  
Tyson H. Walsh ◽  
T. V. Hromadka ◽  
P. Rao
Keyword(s):  
Doppler Radar ◽  
Weather Forecasting ◽  
Rain Gauge ◽  
Data Sets ◽  
Multiple Sources ◽  
Data Set ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Doppler Data ◽  
Precipitation Estimates

Abstract Objective The data herein represents multiple gauge sets and multiple radar sites of like-type Doppler data sets combined to produce populations of ordered pairs. Publications spanning decades yet specific to Doppler radar sites contain graphs of data pairs of Doppler radar precipitation estimates versus rain gauge precipitation readings. Data description Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 8830 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar and precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.

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