Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification

Brown, James Dean; He, Minxue; Regonda, Satish Kumar; Wu, Limin; Lee, Haksu; Seo, Dong‐Jun

doi:10.1016/j.jhydrol.2014.05.030

Cited by 31 publications

(35 citation statements)

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“…In sum, the multimodel forecasts reveal skill improvements relative to API, which may be considered here the best performing model in terms of the overall simulation and raw forecasts results; the optimal weights from QR‐BMA result in more skillful multimodel forecasts than using equal weights, particularly at the later lead times (>3 days); and increasing the ensemble size of the multimodel forecasts results in relatively mild skill gains. We also computed reliability diagrams, as determined by Brown et al (), for the single‐model and 9‐member multimodel forecasts (see Figures S2 and S3). The reliability diagrams show that the multimodel forecasts tend, for the most part, to display better reliability than the single‐model forecasts.…”

Section: Resultsmentioning

confidence: 99%

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Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Sharma

Siddique

Reed

et al. 2019

Water Resources Research

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We investigate the ability of hydrological multimodel ensemble predictions to enhance the skill of streamflow forecasts at short-to medium-range timescales. To generate the multimodel ensembles, we implement a new statistical postprocessor, namely, quantile regression-Bayesian model averaging (QR-BMA). Quantile regression-Bayesian model averaging uses quantile regression to bias correct the ensemble streamflow forecasts from the individual models and Bayesian model averaging to optimally combine their probability density functions. Additionally, we use an information-theoretic measure, namely, conditional mutual information, to quantify the skill enhancements from the multimodel forecasts. We generate ensemble streamflow forecasts at lead times from 1 to 7 days using three hydrological models: (i) Antecedent Precipitation Index-Continuous, (ii) Hydrology Laboratory-Research Distributed Hydrologic Model, and (iii) Weather Research and Forecasting Hydrological modeling system. As forcing to the hydrological models, we use weather ensemble forecasts from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2. The forecasting experiments are performed for four nested basins of the North Branch Susquehanna River, USA. We find that after bias correcting the streamflow forecasts from each model, their skill performance becomes comparable. We find that the multimodel ensemble forecasts have higher skill than the best single-model forecasts. Furthermore, the skill enhancements obtained by the multimodel ensemble forecasts are found to be dominated by model diversity, rather than by increased ensemble size alone. This result, obtained using conditional mutual information, indicates that each hydrological model contributes additional information to enhance forecast skill. Overall, our results highlight benefits of hydrological multimodel forecasting for improving streamflow predictions.

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Section: Resultsmentioning

confidence: 99%

“…Besides using CMI to measure skill improvements, we used the mean Continuous Ranked Probability Skill Score ( CRPSS ; Hersbach, ) since this is a commonly used verification metric to assess the quality of ensemble forecasts (Brown et al, ). The CRPSS is derived from the Continuous Ranked Probability Skill Score ( CRPS ).…”

Section: Methodsmentioning

confidence: 99%

Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Sharma

Siddique

Reed

et al. 2019

Water Resources Research

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“…EPSs in flood forecasting are now widely regarded as the state-of-the-art technique in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al, 2005;Gneiting and Raftery, 2005). And as discussed by Seo et al (2014), the recent researches on flood forecasting have been investigated and found that ensemble forecasts in hydrological fields increase accuracy and allow for skillful predictions with extended lead time (Xuan et al, 2009;Roulin and Vannitsem, 2005;Yu et al, 2013Yu et al, , 2014Bennet et al, 2014;Brown et al, 2014;Fan et al, 2014;Noh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall

Wang

Nakakita

Kim

et al. 2015

Journal of Hydrology

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“…Numerical simulations conducted suggest that, while the parameter tuning results in limited improvements in goodness-of-fit (GOF) for the BMGD as a bivariate distribution model, better results may be achieved by tuning the parameter for the one-dimensional conditional distribution of the observed given the forecast greater than a certain large value.Forecasting 2020, 2 2 producing precipitation and temperature ensemble forecasts based on a number of forecast products from numerical weather prediction models [13]. Validation results indicate that the BMGD is a suitable model for use in producing reliable precipitation and temperature ensemble forecasts for hydrologic ensemble forecasting [14,15]. The BMGD is extended in [16] to a wider class of probability distributions with given marginals, known as meta-elliptical distributions.…”

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confidence: 99%

“…Forecasting 2020, 2 2 producing precipitation and temperature ensemble forecasts based on a number of forecast products from numerical weather prediction models [13]. Validation results indicate that the BMGD is a suitable model for use in producing reliable precipitation and temperature ensemble forecasts for hydrologic ensemble forecasting [14,15]. The BMGD is extended in [16] to a wider class of probability distributions with given marginals, known as meta-elliptical distributions.…”

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Tuning the Bivariate Meta-Gaussian Distribution Conditionally in Quantifying Precipitation Prediction Uncertainty

2020

Forecasting

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One of the ways to quantify uncertainty of deterministic forecasts is to construct a joint distribution between the forecast variable and the observed variable; then, the uncertainty of the forecast can be represented by the conditional distribution of the observed given the forecast. The joint distribution of two continuous hydrometeorological variables can often be modeled by the bivariate meta-Gaussian distribution (BMGD). The BMGD can be obtained by transforming each of the two variables to a standard normal variable and the dependence between the transformed variables is provided by the Pearson correlation coefficient of these two variables. The BMGD modeling is exact provided that the transformed joint distribution is standard normal. In real-world applications, however, this normality assumption is hardly fulfilled. This is often the case for the modeling problem we consider in this paper: establish the joint distribution of a forecast variable and its corresponding observed variable for precipitation amounts accumulated over a duration of 24 h. In this case, the BMGD can only serve as an approximate model and the dependence parameter can be estimated in a variety of ways. In this paper, the effect of tuning this parameter is studied. Numerical simulations conducted suggest that, while the parameter tuning results in limited improvements in goodness-of-fit (GOF) for the BMGD as a bivariate distribution model, better results may be achieved by tuning the parameter for the one-dimensional conditional distribution of the observed given the forecast greater than a certain large value.

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Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification

Cited by 31 publications

References 31 publications

Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall

Tuning the Bivariate Meta-Gaussian Distribution Conditionally in Quantifying Precipitation Prediction Uncertainty

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