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 Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain. Part II: Scale Dependence

2009 ◽  
Vol 24 (3) ◽  
pp. 800-811 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Strong Dependence ◽  
Large Dataset ◽  
Radar Rainfall ◽  
Sources Of Uncertainty ◽  
Quantitative Precipitation Estimation ◽  
Error Covariance ◽  
Precipitation Estimation ◽  
Grid Points ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground has been recently quantified at a resolution typically used in schemes assimilating rainfall at the ground onto mesoscale models. Here, the contribution of the two most important sources of uncertainty at nonattenuating wavelengths (the range-dependent error and the uncertainty due to the Z–R transformation) and their interaction are studied as a function of the resolution of radar observations. The analysis is carried out using a large dataset of collocated reflectivity profiles from the McGill S-band radar and disdrometric measurements obtained in stratiform rainfall at resolutions of 1 × 1, 5 × 5, and 15 × 15 km2. Results show that the errors affecting radar quantitative precipitation estimation (QPE) have a strong dependence with range, and that their structure is scale dependent. At the analyzed resolutions, QPE errors are significantly correlated in time and over several grid points.

scholarly journals A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain

2008 ◽  
Vol 23 (6) ◽  
pp. 1085-1101 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Covariance Matrix ◽  
Drop Size ◽  
Size Distributions ◽  
Radar Rainfall ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
Drop Size Distributions ◽  
The Impact ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground is quantified toward deriving and understanding the error covariance matrix of these estimates. The focus here is on stratiform precipitation at a resolution of 15 km, which is most relevant for data assimilation onto mesoscale numerical models. In the characterization of the error structure, the following contributions are considered: (i) the individual effect of the range-dependent error (associated with beam broadening and increasing height of radar measurements with range), (ii) the error associated with the transformation from reflectivity to rain rate due to the variability of drop size distributions, and (iii) the interaction of the first two, that is, the term resulting from the cross correlation between the effects of the range-dependent error and the uncertainty related to the variability of drop size distributions (DSDs). For this purpose a large database of S-band radar observations at short range (where reflectivity near the ground is measured and the beam is narrow) is used to characterize the range-dependent error within a simulation framework, and disdrometric measurements collocated with the radar data are used to assess the impact of the variability of DSDs. It is noted that these two sources of error are well correlated in the vicinity of the melting layer as result of the physical processes that determine the density of snow (e.g., riming), which affect both the DSD variability and the vertical profile of reflectivity.

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

Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

Quantifying the potential of AQPI gap-filling radar network for streamflow simulation through a WRF-Hydro experiment

2021 ◽  
Author(s):  
Yingzhao Ma ◽  
V. Chandrasekar ◽  
Haonan Chen ◽  
Robert Cifelli
Keyword(s):  
Water Model ◽  
Urban Region ◽  
Gap Filling ◽  
Radar Rainfall ◽  
Bay Area ◽  
Radar Network ◽  
X Band ◽  
The Impact ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractIt remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

scholarly journals Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier
Keyword(s):  
Weather Prediction ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Intensity Scale ◽  
Radar Rainfall ◽  
Scale Method ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
The Uk ◽  
Rainfall Estimates

Abstract. A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

A preliminary appraisal of Thurnham dual polarisation radar in the context of hydrological modelling structure

2012 ◽  
Vol 43 (5) ◽  
pp. 736-752 ◽  
Author(s):  
D. Zhu ◽  
I. D. Cluckie
Keyword(s):  
Direct Consequence ◽  
Hydrological Models ◽  
Short Term ◽  
Radar Rainfall ◽  
Mathematical Structures ◽  
Extreme Flood ◽  
Clutter Removal ◽  
The Impact ◽  
Further Development ◽  
Rainfall Estimates

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.

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