An Examination of Radar and Rain Gauge–Derived Mean Areal Precipitation over Georgia Watersheds

Stellman, Keith; Fuelberg, Henry E.; Garza, Reggina; Mullusky, Mary

doi:10.1175/1520-0434(2001)016<0133:aeorar>2.0.co;2

Cited by 49 publications

(41 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These data problems led some DMIP 1 participants to report problems with calibration . The quality of these multisensor data has been much studied in the DMIP 2 basins and elsewhere (e.g., Westcott et al, 2008;Xie et al, 2006;Jayakrishnan et al, 2004;Stellman et al, 2001;Young et al, 2000;Wang et al, 2000;Smith et al, 1999;Johnson et al, 1999) and researchers have identified problems such as underestimation and non-stationarity resulting from changes in the raw data processing algorithms (Young et al, 2000; see also 'About the Multisensor (NEXRAD and gauge) Data', http://www.nws.noaa.gov/oh/hrl/dmip/2/docs/about_mult-isensor.pdf). These known deficiencies were exacerbated by the typically short period of record of the multisensor precipitation products.…”

Section: Scientific Backgroundmentioning

confidence: 99%

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

Smith

Koren

Reed

et al. 2012

Journal of Hydrology

View full text Add to dashboard Cite

s u m m a r yThe Office of Hydrologic Development (OHD) of the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) conducted the second phase of the Distributed Model Intercomparison Project (DMIP 2). After DMIP 1, the NWS recognized the need for additional science experiments to guide its research-to-operations path towards advanced hydrologic models for river and water resources forecasting. This was accentuated by the need to develop a broader spectrum of water resources forecasting products (such as soil moisture) in addition to the more traditional river, flash flood, and water supply forecasts. As it did for DMIP 1, the NWS sought the input and contributions from the hydrologic research community. DMIP 1 showed that using operational precipitation data, some distributed models could indeed perform as well as lumped models in several basins and better than lumped models for one basin. However, in general, the improvements were more limited than anticipated by the scientific community. Models combining so-called conceptual rainfall-runoff mechanisms with physically-based routing schemes achieved the best overall performance. Clear gains were achieved through calibration of model parameters, with the average performance of calibrated models being better than uncalibrated models. DMIP 1 experiments were hampered by temporally-inconsistent precipitation data and few runoff events in the verification period for some basins. Greater uncertainty in modeling small basins was noted, pointing to the need for additional tests of nested basins of various sizes.DMIP 2 experiments in the Oklahoma (OK) region were more comprehensive than in DMIP 1, and were designed to improve our understanding beyond what was learned in DMIP 1. Many more stream gauges were located, allowing for more rigorous testing of simulations at interior points. These included two new gauged interior basins that had drainage areas smaller than the smallest in DMIP 1. Soil moisture and routing experiments were added to further assess if distributed models could accurately model basininterior processes. A longer period of higher quality precipitation data was available, and facilitated a test to note the impacts of data quality on model calibration. Moreover, the DMIP 2 calibration and verification periods contained more runoff events for analysis. Two lumped models were used to define a robust benchmark for evaluating the improvement of distributed models compared to lumped models. Fourteen groups participated in DMIP 2 using a total of sixteen models. Ten of these models were not in DMIP 1. This paper presents the motivation for DMIP 2 Oklahoma experiments, discusses the major project elements, and describes the data and models used. In addition, the paper introduces the findings, which are covered in a companion results paper (Smith et al., this issue). Lastly, the paper summarizes the DMIP 1 and 2 experiments with commentary from the NWS perspective. Future papers will cover the DMIP 2 experiments in the western...

show abstract

Section: Scientific Backgroundmentioning

confidence: 99%

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

Smith

Koren

Reed

et al. 2012

Journal of Hydrology

View full text Add to dashboard Cite

show abstract

“…Model under-estimation can also be attributed to uncertainties resulting from using interpolated precipitation estimates. This is also noted by Smith et al (1996) and Stellman et al (2001). Thus it is wise to conduct further analysis to evaluate the influence of rainfall data sources on hydrological model performance.…”

Section: Calibration Periodmentioning

confidence: 91%

Application of the Distributed Hydrological Model, TOPNET, to the Big Darby Creek Watershed, Ohio, USA

Guzha

Hardy

2009

Water Resour Manage

View full text Add to dashboard Cite

Evaluation of the applicability and utility of watershed hydrologic models in different hydro-geologic and soil conditions is necessary for a range of spatial scales and to assess the utility of these models as watershed water resources management tools. This study presents the application of the hydrological model TOPNET to the Big Darby Creek watershed, Ohio, United States. It focuses on the simulation modeling of stream flow in the watershed based on meteorological data for the eight year period of 1992-1999. Visual comparison of time series plots and statistical measures namely, Nash-Sutcliffe efficiency (NS), coefficient of correlation (R 2 ), and the percent bias (PBIAS) were used to assess the model performance. The statistical model evaluation results indicated that the model has a relatively high confidence and can give a good representation of the flow hydrographs for the watershed. For the calibration period simulations of annual stream flow were accurate with a mean R 2 and NS of 86% and 85% for the Big Darby at Darbyville gaging station. For the little Darby at West Jefferson gaging station a mean R 2 of 81% was obtained while the NS averaged 78%. Further analysis based on the aggregation of the water years into wet seasons and dry seasons, the model was also able to adequately simulate stream flow for both gaging stations and for both low flow periods and high flow periods. Statistical analysis for the validation period also yielded high R 2 values of 88% and 83% for the Darby at Big Darby at Darbyville gaging station and Little Darby at West Jefferson gaging station respectively. The worst PBIAS obtained for both calibration and validation period was 18% and this is better than recommended values for satisfactory daily simulations of ±25% for PBIAS. The encouraging simulation results obtained in this study shows the utility and usefulness of the TOPNET A.C. Guzha, T.B. Hardy model in hydrological modeling and ultimately as a water resources management tool.

show abstract

“…However, their precipitation measurements are affected by several factors, such as a varying Z-R relationship, topography blockage, evaporation of rain falling from relatively higher altitudes, and, for radars with nondual polarization, overestimation of precipitation due to the bright band (Crosson et al 1996;Fulton 1999;Krajewski and Smith 2002;Morin et al 2005). Several authors have argued for, and used, a combination of both rain gauge and weather radar (Stellman et al 2001;Xie and Arkin 1995;Kursinski and Zeng 2006;Kitzmiller et al 2013). Recent work by Zhang et al (2014) has shown the value of adding an orographic precipitation climatology to this mix of information.…”

Section: Introductionmentioning

confidence: 99%

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

Minjarez-Sosa

Castro

Cummins

et al. 2017

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

A lightning-precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial-and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

show abstract

An Examination of Radar and Rain Gauge–Derived Mean Areal Precipitation over Georgia Watersheds

Cited by 49 publications

References 19 publications

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

Application of the Distributed Hydrological Model, TOPNET, to the Big Darby Creek Watershed, Ohio, USA

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

Contact Info

Product

Resources

About