Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California

2020 ◽  
Vol 58 (3) ◽  
pp. 1821-1832 ◽  
Author(s):  
Haonan Chen ◽  
Robert Cifelli ◽  
Allen White
2009 ◽  
Vol 10 (6) ◽  
pp. 1507-1520 ◽  
Author(s):  
Jonathan J. Gourley ◽  
David P. Jorgensen ◽  
Sergey Y. Matrosov ◽  
Zachary L. Flamig

Abstract Advanced remote sensing and in situ observing systems employed during the Hydrometeorological Testbed experiment on the American River basin near Sacramento, California, provided a unique opportunity to evaluate correction procedures applied to gap-filling, experimental radar precipitation products in complex terrain. The evaluation highlighted improvements in hourly radar rainfall estimation due to optimizing the parameters in the reflectivity-to-rainfall (Z–R) relation, correcting for the range dependence in estimating R due to the vertical variability in Z in snow and melting-layer regions, and improving low-altitude radar coverage by merging rainfall estimates from two research radars operating at different frequencies and polarization states. This evaluation revealed that although the rainfall product from research radars provided the smallest bias relative to gauge estimates, in terms of the root-mean-square error (with the bias removed) and Pearson correlation coefficient it did not outperform the product from a nearby operational radar that used optimized Z–R relations and was corrected for range dependence. This result was attributed to better low-altitude radar coverage with the operational radar over the upper part of the basin. In these regions, the data from the X-band research radar were not available and the C-band research radar was forced to use higher-elevation angles as a result of nearby terrain and tree blockages, which yielded greater uncertainty in surface rainfall estimates. This study highlights the challenges in siting experimental radars in complex terrain. Last, the corrections developed for research radar products were adapted and applied to an operational radar, thus providing a simple transfer of research findings to operational rainfall products yielding significantly improved skill.


2018 ◽  
Vol 10 (8) ◽  
pp. 1258 ◽  
Author(s):  
Marios Anagnostou ◽  
Efthymios Nikolopoulos ◽  
John Kalogiros ◽  
Emmanouil Anagnostou ◽  
Francesco Marra ◽  
...  

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.


2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov

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.


Author(s):  
Yingzhao Ma ◽  
V. Chandrasekar ◽  
Haonan Chen ◽  
Robert Cifelli

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.


2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier

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.


2017 ◽  
Vol 18 (5) ◽  
pp. 1247-1269 ◽  
Author(s):  
Peter Speirs ◽  
Marco Gabella ◽  
Alexis Berne

Abstract The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR’s operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain.


2012 ◽  
Vol 43 (5) ◽  
pp. 736-752 ◽  
Author(s):  
D. Zhu ◽  
I. D. Cluckie

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.


2017 ◽  
Vol 38 (18) ◽  
pp. 5106-5126 ◽  
Author(s):  
Esmail Ghaemi ◽  
Mohammadreza Kavianpour ◽  
Saber Moazami ◽  
Yang Hong ◽  
Hooman Ayat

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