scholarly journals The New French Operational Radar Rainfall Product. Part II: Validation

2007 ◽  
Vol 22 (3) ◽  
pp. 409-427 ◽  
Author(s):  
P. Tabary ◽  
J. Desplats ◽  
K. Do Khac ◽  
F. Eideliman ◽  
C. Gueguen ◽  
...  
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Radiosonde Data ◽  
Radar Rainfall ◽  
Freezing Level ◽  
Ground Clutter ◽  
Precipitation Estimation ◽  
Upper Level ◽  
Quality Indexes ◽  
Precipitation Events

Abstract A new operational radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational network. The new quantitative precipitation estimation (QPE) product is based entirely on radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brightband peak, brightband thickness, and upper-level decreasing rate) is established and the radar-derived freezing-level heights are shown to be in good agreement with radiosonde data. A total of 27 precipitation events observed by three S-band radars of the network during the winter of 2005 and the autumns of 2002 and 2003 are used to compare the new radar QPE to the old one. Results are stratified according to the distance to the radar and according to the height of the freezing level. The Nash criterion is increased from 0.23 to 0.62 at close range (below 50 km) and from 0.35 to 0.42 at long range (between 100 and 150 km). The relevance of the proposed quality indexes is assessed by examining their statistical relationship with long-term radar–rain gauge statistics. Mosaics of QPE and quality indexes are also illustrated.

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

2011 ◽  
Vol 12 (6) ◽  
pp. 1414-1431 ◽  
Author(s):  
David Kitzmiller ◽  
Suzanne Van Cooten ◽  
Feng Ding ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
...  
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Freezing Level ◽  
Distributed Hydrologic Model ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

scholarly journals Polarimetric Signatures of Midlatitude Warm-Rain Precipitation Events

2017 ◽  
Vol 56 (3) ◽  
pp. 697-711 ◽  
Author(s):  
N. Carr ◽  
P. E. Kirstetter ◽  
J. J. Gourley ◽  
Y. Hong
Keyword(s):  
Three Dimensional ◽  
Radar Data ◽  
Identification Algorithm ◽  
Microphysical Process ◽  
Freezing Level ◽  
Warm Rain ◽  
Precipitation Estimation ◽  
Rain Events ◽  
Precipitation Events ◽  
Rain Precipitation

AbstractPrecipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, ZDR, and KDP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level ZDR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm.

scholarly journals Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model

2019 ◽  
Vol 11 (14) ◽  
pp. 1632 ◽  
Author(s):  
Johanna Orellana-Alvear ◽  
Rolando Célleri ◽  
Rütger Rollenbeck ◽  
Jörg Bendix
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Radar Data ◽  
Rain Gauge ◽  
High Mountain ◽  
Radar Rainfall ◽  
Site Specific ◽  
High Attenuation ◽  
X Band ◽  
Precipitation Estimation

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars.

scholarly journals Radar-Based Quantitative Precipitation Estimation for the Identification of Debris-Flow Occurrence over Earthquake affected Region in Sichuan, China

2017 ◽  
Author(s):  
Zhao Shi ◽  
Fangqiang Wei ◽  
Chandrasekar Venkatachalam
Keyword(s):  
Debris Flow ◽  
Radar Data ◽  
Rain Gauge ◽  
Small Scale ◽  
Mountainous Area ◽  
Radar Rainfall ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
New Generation ◽  
Frequentist Method

Abstract. Both of Ms8.0 Wenchuan earthquake on May 12, 2008 and Ms7.0 Lushan earth quake on April 20, 2013 occurred in Sichuan Province of China. In the earthquake affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall Intensity–Duration (I-D) threshold of the debris flow in the earthquake-affected area, and for filling up the observational gaps caused by the relatively scarce and low altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler weather radar (CINRAD) were captured for six rainfall events which triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous area, a series of improving measures are considered including the hybrid scan mode, the vertical reflectivity profile (VPR) correction, the mosaic of reflectivity, a merged rainfall-reflectivity(R-Z) relationship for convective and stratiform rainfall and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the Frequentist method is I = 10.1D−0.52, and it's also found that the I-D threshold is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D threshold and likewise underestimate I-D thresholds owing to under shooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the small-scale strong rainfall process in the study area.

scholarly journals Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

2018 ◽  
Vol 18 (3) ◽  
pp. 765-780 ◽  
Author(s):  
Zhao Shi ◽  
Fangqiang Wei ◽  
Venkatachalam Chandrasekar
Keyword(s):  
Debris Flow ◽  
Radar Data ◽  
Rain Gauge ◽  
Small Scale ◽  
Mountainous Area ◽  
Radar Rainfall ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Improvement Measures ◽  
Frequentist Method

Abstract. Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

scholarly journals Close-range radar rainfall estimation and error analysis

2016 ◽  
Vol 9 (8) ◽  
pp. 3837-3850 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. Hazenberg ◽  
R. Uijlenhoet
Keyword(s):  
Positive Impact ◽  
Rain Gauge ◽  
Minor Role ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Sources Of Error ◽  
Ground Clutter ◽  
Received Power ◽  
Radar Calibration ◽  
Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

scholarly journals Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 306 ◽  
Author(s):  
Dominique Faure ◽  
Guy Delrieu ◽  
Nicolas Gaussiat
Keyword(s):  
Ground Level ◽  
Radar Data ◽  
Rain Gauge ◽  
Altitudinal Gradients ◽  
French Alps ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

scholarly journals Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1340
Author(s):  
Woodson ◽  
Adams ◽  
Dymond
Keyword(s):  
Hydrological Modeling ◽  
Mean Squared Error ◽  
Uniform Estimate ◽  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Urban Watersheds ◽  
Precipitation Estimation

Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.

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.

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