Validation of the NEXRAD DSP Product with a Dense Rain Gauge Network

Mazari, Newfel; Xie, Hongjie; Zeitler, Jon W.; Sharif, Hatim O.

doi:10.1061/(asce)he.1943-5584.0000676

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Comparison between the half‐hourly Early IMERG product and rain gauge observations shows significant scatter at the single grid scale with a moderate value of the CCs. However, this scatter is similar to the scatter of uncalibrated weather radar estimates (Mazari et al ., ). The BIAS of the Early product is within 90%, which is in the same order of that of weather radar data.…”

Section: Discussionmentioning

confidence: 99%

“…The second set of measures is more probabilistic and describes the probability/accuracy of rain detection by satellite products based on ground observations. Probability of rainfall detection (POD), false alarm ratio (FAR), and critical success index (CSI) are widely used performance measures in this category (Wang et al, 2008;Mazari et al, 2013;Prakash et al, 2016a, b;Tang et al, 2016a, b). POD describes the probability of rainfall detection by satellite when it is observed by the gauge network.…”

Section: Performance Measuresmentioning

confidence: 99%

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Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Omranian

Sharif

2018

J American Water Resour Assoc

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Quality of precipitation products from the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) was evaluated over the Lower Colorado River Basin of Texas. Observations of several rainfall events of a wide range of magnitudes during May 2015 by a very dense network of 241 rain gauges over the basin were used as a reference. The impact of temporal and spatial downscaling of different satellite products (near/post‐real‐time) on their accuracy was studied. Generally, all IMERG products perform better when the temporal and spatial resolutions are downscaled. The Final product shows relatively better performance compared to the near‐real‐time products in terms of basic performance measures; however, regarding rainfall detection, all products show nearly similar performance. When considering rainfall detection, IMERG adequately captures the precipitation events; however, in terms of spatial patterns and accuracy, more improvements are needed. IMERG products analysis results may help developers gain insight into the regional performance of the product, improve the product algorithms, and provide information to end users on the products’ suitability for potential hydrometeorological applications. Overall, the IMERG products, even the uncalibrated product at its finest resolution, showed reasonable performance indicating their great potential for applications such as water resources management, prevention of natural disasters, and flood forecasting.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Measuresmentioning

confidence: 99%

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Omranian

Sharif

2018

J American Water Resour Assoc

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“…Due to the limitations inherent with gauge-measured and radar-derived data, it is important to understand the quality of the data (in terms of precipitation quantity), possible bias, and systematic offsets. Many authors have found that NEXRAD underestimated rain when compared to rain gauge measurements [4,9], whereas Mazari [10] concluded that radar data was comparable to rain gauge measurements. Other researchers recommend understanding the potential differences in precipitation values, quality of radar data, and the overall bias of radar generated rainfall compared to rain gauge rainfall quantities, as they have a direct impact on hydrologic simulations [11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Bredesen

Brown

2018

Ecws-3

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Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar-based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a subtropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash–Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

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“…Kitzmiller et al (2013) described various techniques utilized by the NWS to prepare gridded rainfall data for input into hydrologic forecasting models and decision-making systems for river forecasting, flood and flash flood warning and other hydrologic monitoring purposes. Mazari et al (2013) compared the NEXRAD Digital Storm-total Precipitation (DSP) products from two radar stations with an observation from a network of 50 rain gauges in the Upper Guadalupe River Basin located in Texas. The rainfall data comparisons were conducted at six minutes, one hour and storm-total accumulation temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Applicability of Doppler weather radar based rainfall data for runoff estimation in Indian watersheds – A case study of Chennai basin

Josephine

Mudgal

Thampi

2014

Sadhana

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Traditionally, India has been vulnerable to various hazards such as floods, droughts and cyclones. About 8% of the total Indian landmass is prone to cyclones. A number of Doppler weather radars are installed in India and their products are utilized for weather predictions and detection of cyclones approaching the Indian coast. Radar-based hydrological studies in various countries have proven that computation of runoff using radar rainfall data could outperform rain gauge network measurements. There are no reported studies on their utilization for hydrological modelling and/or flood-related studies in Indian river basins. A comparison study between Doppler weather radar (DWR) derived rainfall data and the conventional rain gauge data was carried out with hourly inputs at one of the watersheds of Chennai basin, Tamil Nadu, India using HEC-HMS model. The model calibration and validation were performed by comparing the simulated outflow with the observed daily outflow data. The calibrated model was used to predict runoff from two post-monsoon cyclonic storm events with hourly inputs. It was noticed that the discrepancy in the runoff volume was small, but the difference in the peak flow was substantial. Additionally, there was a variation at the time to peak flow using daily and hourly inputs. The results show that the use of radar data may be optional for runoff volume estimation for the watersheds with sufficient rain gauge density, but highly desirable for peak flow and time to peak estimation. Therefore, the DWR derived rainfall data is a promising input for runoff estimation, especially in urban flood modelling.

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Validation of the NEXRAD DSP Product with a Dense Rain Gauge Network

Cited by 6 publications

References 20 publications

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin, Texas

Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Applicability of Doppler weather radar based rainfall data for runoff estimation in Indian watersheds – A case study of Chennai basin

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