Search Results

You are looking at 1 - 10 of 16 items for :

  • Model performance/evaluation x
  • IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission x
  • All content x
Clear All
Huan Wu, Robert F. Adler, Yudong Tian, Guojun Gu, and George J. Huffman

United Nations World Food Programme (WFP) and International Federation of Red Cross and Red Crescent Societies. A global evaluation of the aforementioned DRIVE hydrologic model of the GFMS has been reported by W2014 , with generally good model performance in both streamflow simulation and flood event detection. The accuracy of the GFMS in flood prediction is determined by model inputs, particularly precipitation input, and the DRIVE model physics and parameterization. However, accurate quantitative

Full access
Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Kuolin Hsu, and Amir AghaKouchak

preclassified product depicts flooded areas that are noisy and disconnected from the main channel. While these classifications may be correct (i.e., small ponds), both the preflood and postflood images were manually cleaned such that only pixels of the main river channel and its tributaries were left. This allows for a more straightforward analysis of the model’s performance of flooding the actual river by negating any influence the isolated ponds (not connected to the main river) would have on performance

Full access
Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

1. Introduction Global-scale flood forecasting systems are currently being developed, evaluated, and improved ( Wu et al. 2014 ). These forecasting systems rely on two separate but equally important components: 1) global-scale, near-real-time precipitation estimates and 2) hydrological models that partition rainfall into runoff components and route runoff to predict streamflow fluctuations at various basin outlets. Multiple papers in the literature over the past 30 years document efforts to

Full access
Andrea Thorstensen, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

traditionally used and is discussed in NWS (2011) , this distribution method will see no changes in the description of basin heterogeneity beyond what has already been established in the a priori parameter grids. c. Performance metrics The model was evaluated using five metrics: RMSE, bias, correlation (CORR), coefficient of determination R 2 , and Nash–Sutcliffe efficiency (NSE). RMSE is defined by where n is the total number of observations, o is the observed variable, and s is the

Full access
Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

) conducted real-time forecasting with the NASA-Unified Weather Research and Forecasting (NU-WRF) Model, which was delivered daily to support 0900 local time forecast briefings to the campaign personnel. This effort required not only dedicated computational resources, but also a robust modeling system that is capable of simulating severe convective episodes typical of eastern Iowa during the active spring period. In this work, we provide a comprehensive evaluation of the precipitation forecasts from real

Full access
Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Mohamed ElSaadani, Radoslaw Goska, Luciana K. Cunha, Brenda Dolan, David B. Wolff, James A. Smith, Steven A. Rutledge, and Walter A. Petersen

RR estimates in this study because the comparison between individual (e.g., NPOL) and composite products would not be fair, and individual radar products are often affected by significant range effects (e.g., Fabry et al. 1992 ) that are less impactful for composite products. A detailed evaluation and the performance of NPOL estimates are documented in Chen et al. (2017) . We also drive the IFC hillslope-link model (HLM) using the reference product over the Turkey River basin in Iowa and assess

Full access
Haonan Chen, V. Chandrasekar, and Renzo Bechini

Iowa Flood Studies (IFloodS) field experiment in central and northeastern Iowa, especially for intense rainfall estimation. Chen and Chandrasekar (2015a) have implemented and evaluated DROPS1.0 in an urban environment in Dallas–Fort Worth, Texas, which showed better performance than both WSR-88D single- and dual-polarization rainfall products for the precipitation events presented in their study. However, the bin-by-bin-based fuzzy-logic hydrometeor classification algorithm implemented in DROPS1

Full access
Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

valid sensors The diagnostic soil moisture equation ( Pan et al. 2003 ; Pan 2012 ) was calibrated at each of the 18 valid sensors using each of the four precipitation products. The resulting performance was then used to determine which precipitation product was suitable for broader precipitation estimation over the larger spatial areas required for SMAP. Figure 2 presents a generalized flowchart of the process by which each individual sensor (marked locations in Fig. 1 ) is evaluated for its

Full access
Robert M. Beauchamp, V. Chandrasekar, Haonan Chen, and Manuel Vega

dataset is used to evaluate the model error. The simulated specific attenuation is compared to the model’s estimated specific attenuation [Eqs. (1) and (2) ] from the simulated K dp . Figures 5c and 5d show scattergrams of the model’s estimated specific attenuation versus the intrinsic specific attenuation from simulation. To quantitatively evaluate the accuracy of Eqs. (1) and (2) , the mean absolute percentage error (MAPE) is used, defined as where is the -based estimate of specific

Full access
Luciana K. Cunha, James A. Smith, Witold F. Krajewski, Mary Lynn Baeck, and Bong-Chul Seo

each event or basin region, but to evaluate uncertainties based on the comparison of different state-of-the-art QPE estimates and how they differ. This approach allows us to spatially describe the scale and pattern of QPE uncertainty for different rainfall events. This information can guide the use of QPE for hydrological studies (e.g., water balance) or as input to hydrological forecasting models. We evaluate uncertainties in the estimation of two hydrologically relevant variables that are the

Full access