Rainfall nowcasting by at site stochastic model P.R.A.I.S.E.

Sirangelo, B.; Versace, Pasquale; Luca, Davide Luciano De

doi:10.5194/hess-11-1341-2007

Cited by 20 publications

(2 citation statements)

References 19 publications

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“…This limitation has been noted before (e.g. Richardson 1981, Dibike & Couli baly 2005, Sirangelo et al 2007. Our case study location is characterized by an arid climate, and it has been af fected by climate change, drought, and water scarcity.…”

Section: Comparison Of Simulation and Observation Outputs In The Evalmentioning

confidence: 80%

Prediction of climate variables by comparing the knearest neighbor method and MIROC5 outputs in an arid environment

Yazd¹,

Salehnia²,

Kolsoumi³

et al. 2019

Clim. Res.

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The goal of this study was to compare the ability of the k-nearest neighbors (k-NN) approach and the downscaled output from the MIROC5 model for generating daily precipitation (mm) and daily maximum and minimum temperature (T max and T min ; °C) for an arid environment. For this study, data from the easternmost province of Iran, South Khorasan, were used for the period 1986 to 2015. We also used an ensemble method to decrease the uncertainty of the k-NN approach. Although, based on an initial evaluation, MIROC5 had better results, we also used the output results of k-NN alongside the MIROC5 data to generate future weather data for the period 2018 to 2047. Nash-Sutcliffe efficiency (NSE) between MIROC5 estimates and observed monthly T max ranged from 0.86 to 0.92, and from 0.89 to 0.93 for T min over the evaluation period (2006− 2015). k-NN performed less well, with NSE between k-NN estimates and observed T max ranging from 0.54 to 0.64, and from 0.75 to 0.78 for T min. The MIROC5 simulated precipitation was close to observed historical values (−0.06 < NSE < 0.07), but the k-NN simulated precipitation was less accurate (−0.36 < NSE < −0.14). For the studied arid regions, the k-NN precipitation results compared poorly to the MIROC5 downscaling results. MIROC5 predicts increases in monthly T min and T max in summer and autumn and decreases in winter and spring, and decreases in winter monthly precipitation under RCP4.5 over the 2018−2047 period of this study. This study showed that the k-NN method should be expected to have inaccurate results for generating future data in comparison to the out puts of the MIROC5 model for arid environments.

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Section: Comparison Of Simulation and Observation Outputs In The Evalmentioning

confidence: 80%

Prediction of climate variables by comparing the knearest neighbor method and MIROC5 outputs in an arid environment

Yazd¹,

Salehnia²,

Kolsoumi³

et al. 2019

Clim. Res.

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show abstract

“…With rapid developments in atmospheric science and computer technology, numerical weather prediction models can now provide short-term rainfall forecasts. These models can generate 1-3-day-ahead valid rainfall forecasts based on the current weather conditions on a considerable spatial scale (Sirangelo et al 2007;Shahrban et al 2016). However, these prediction models cannot reliably describe and simulate the small-scale evolution of the atmosphere because of spin-up problems and coarse spatial resolutions (Mecklenburg et al 2000).…”

Section: Introductionmentioning

confidence: 99%

New Flood Early Warning and Forecasting Method Based on Similarity Theory

Xiao

Liang

et al. 2019

J. Hydrol. Eng.

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The challenge of achieving reliable flood forecasting results in semiarid regions remains stark. We developed a flood early warning and forecasting method based on similarity theory and a hydrological model to extend the lead time and achieve dynamic rolling forecasting. A multimeasure-based rainfall event similarity analysis (MRESA) method was proposed for rainfall forecasting based on the similarity evaluation between two rainstorms from multiple perspectives (including the quantity similarity, pattern similarity, earth mover's distance, and rainstorm spatial distribution similarity). Moreover, an ideal sample experiment was conducted to verify the method's rationality and feasibility. The MRSA method for rainfall prediction and the vertically mixed runoff model were applied to the Beiniuchuan River located in the middle Yellow River basin. Results showed that the flood forecast would be continuously updated and the prediction accuracy gradually increases with the increase of rainstorm and flood information. Therefore, in this study the proposed flood forecasting method based on the similarity analysis is effective and applicable.

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