scholarly journals Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

2021 ◽  
Vol 13 (15) ◽  
pp. 2890
Author(s):  
Dawit T. Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
High Resolution ◽  
Return Period ◽  
Extreme Values ◽  
Stage Iv ◽  
Radar Data ◽  
The State ◽  
Storm Events ◽  
High Resolution Radar ◽  
Idf Curves ◽  
Spatio Temporal

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.

scholarly journals Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 401
Author(s):  
Phoebe Hänsel ◽  
Stefan Langel ◽  
Marcus Schindewolf ◽  
Andreas Kaiser ◽  
Arno Buchholz ◽  
...  
Keyword(s):  
High Resolution ◽  
Interdisciplinary Approach ◽  
Agricultural Landscapes ◽  
Transport Infrastructure ◽  
Data Series ◽  
Storm Events ◽  
Erosion Modeling ◽  
Physically Based ◽  
High Resolution Radar ◽  
Forecast Modeling

The monitoring, modeling, and prediction of storm events and accompanying heavy rain is crucial for intensively used agricultural landscapes and its settlements and transport infrastructure. In Saxony, Germany, repeated and numerous storm events triggered muddy floods from arable fields in May 2016. They caused severe devastation to settlements and transport infrastructure. This interdisciplinary approach investigates three muddy floods, which developed on silty soils of loess origin tending to soil surface sealing. To achieve this, the study focuses on the test of a historical forecast modeling of three muddy floods in ungauged agricultural landscapes. Therefore, this approach firstly illustrates the reconstruction of the muddy floods, which was performed by high-resolution radar precipitation data, physically-based erosion modeling, and the qualitative validation by unmanned aerial vehicle-based orthophotos. Subsequently, historical radar precipitation forecasts served as input data for the physically-based erosion model to test the forecast modeling retrospectively. The model results indicate a possible warning for two of the three muddy floods. This method of a historical forecast modeling of muddy floods seems particularly promising. Naturally, the data series of three muddy floods should be extended to more reliable data and statistical statements. Finally, this approach assesses the feasibility of a real-time muddy flood early warning system in ungauged agricultural landscapes by high-resolution radar precipitation forecasts and physically-based erosion modeling.

Errors and Uncertainties in Microwave Link Rainfall Estimation Explored Using Drop Size Measurements and High-Resolution Radar Data

2010 ◽  
Vol 11 (6) ◽  
pp. 1330-1344 ◽  
Author(s):  
Hidde Leijnse ◽  
Remko Uijlenhoet ◽  
Alexis Berne
Keyword(s):  
High Resolution ◽  
Power Law ◽  
Radar Data ◽  
Mean Bias Error ◽  
Bias Error ◽  
Error Statistics ◽  
Link Length ◽  
Taylor’S Hypothesis ◽  
High Resolution Radar ◽  
Microwave Link

Abstract Microwave links can be used for the estimation of path-averaged rainfall by using either the path-integrated attenuation or the difference in attenuation of two signals with different frequencies and/or polarizations. Link signals have been simulated using measured time series of raindrop size distributions (DSDs) over a period of nearly 2 yr, in combination with wind velocity data and Taylor’s hypothesis. For this purpose, Taylor’s hypothesis has been tested using more than 1.5 yr of high-resolution radar data. In terms of correlation between spatial and temporal profiles of rainfall intensities, the validity of Taylor’s hypothesis quickly decreases with distance. However, in terms of error statistics, the hypothesis is seen to hold up to distances of at least 10 km. Errors and uncertainties (mean bias error and root-mean-square error, respectively) in microwave link rainfall estimates due to spatial DSD variation are at a minimum at frequencies (and frequency combinations) where the power-law relation for the conversion to rainfall intensity is close to linear. Errors generally increase with link length, whereas uncertainties decrease because of the decrease of scatter about the retrieval relations because of averaging of spatially variable DSDs for longer links. The exponent of power-law rainfall retrieval relations can explain a large part of the variation in both bias and uncertainty, which means that the order of magnitude of these error statistics can be predicted from the value of this exponent, regardless of the link length.

scholarly journals Precipitation and Fatal Motor Vehicle Crashes: Continental Analysis with High-Resolution Radar Data

2019 ◽  
Vol 100 (8) ◽  
pp. 1453-1461 ◽  
Author(s):  
Scott E. Stevens ◽  
Carl J. Schreck ◽  
Shubhayu Saha ◽  
Jesse E. Bell ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
High Resolution ◽  
Motor Vehicle ◽  
Radar Data ◽  
The United States ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
High Resolution Radar

AbstractMotor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months.

scholarly journals Some Considerations for the Use of High-Resolution Mobile Radar Data in Tornado Intensity Determination

2014 ◽  
Vol 29 (4) ◽  
pp. 799-827 ◽  
Author(s):  
Jeffrey C. Snyder ◽  
Howard B. Bluestein
Keyword(s):  
High Resolution ◽  
Rural Areas ◽  
Radar Data ◽  
Radial Velocities ◽  
Wind Speeds ◽  
Rapid Scan ◽  
X Band ◽  
Central United States ◽  
High Resolution Radar ◽  
Single Polarization

Abstract The increasing number of mobile Doppler radars used in field campaigns across the central United States has led to an increasing number of high-resolution radar datasets of strong tornadoes. There are more than a few instances in which the radar-measured radial velocities substantially exceed the estimated wind speeds associated with the enhanced Fujita (EF) scale rating assigned to a particular tornado. It is imperative, however, to understand what the radar data represent if one wants to compare radar observations to damage-based EF-scale estimates. A violent tornado observed by the rapid-scan, X-band, polarimetric mobile radar (RaXPol) on 31 May 2013 contained radar-relative radial velocities exceeding 135 m s−1 in rural areas essentially devoid of structures from which damage ratings can be made. This case, along with others, serves as an excellent example of some of the complications that arise when comparing radar-estimated velocities with the criteria established in the EF scale. In addition, it is shown that data from polarimetric radars should reduce the variance of radar-relative radial velocity estimates within the debris field compared to data from single-polarization radars. Polarimetric radars can also be used to retrieve differential velocity, large magnitudes of which are spatially associated with large spectrum widths inside the polarimetric tornado debris signature in several datasets of intense tornadoes sampled by RaXPol.

scholarly journals Processing of TerraSAR-X/TanDEM-X High Resolution Radar Data using Persistent Scatterers Method

2018 ◽  
Vol 11 (1) ◽  
pp. 86-94
Author(s):  
Anton V. Filatov ◽  
◽  
Arkadi V. Yevtyushkin ◽  
Yuri V. Vasilev ◽  
Peter V. Pogodin ◽  
...  
Keyword(s):  
High Resolution ◽  
Radar Data ◽  
Persistent Scatterers ◽  
High Resolution Radar

scholarly journals Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

2019 ◽  
Vol 23 (1) ◽  
pp. 207-224 ◽  
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Tirthankar Roy ◽  
Graham P. Weedon ◽  
Florian Pappenberger ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Weather Forecasting ◽  
System Modeling ◽  
Stage Iv ◽  
Radar Data ◽  
Fourth Generation ◽  
Retrieval Algorithm ◽  
Secondary Importance ◽  
Bias Score

Abstract. New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

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