Very short period quantitative precipitation forecasting

Fox, Neil I.; Wilson, James W.

doi:10.1002/asl.83

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…ValverdeRamirez et al (2006) developed linear and nonlinear downscaling to establish empirical relationships between the synoptic-scale circulation and observed rainfall over southeastern Brazil. Fox and Wilson (2005) presented an overview of the state of very short period quantitative precipitation forecasting (QPF). What they demonstrated is that there are a number of powerful and practical approaches to the problems of very short-period (QPF), and, as these methods reach maturity, they can be applied in an operational setting.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

Moustris

Larissi

Nastos

et al. 2011

Water Resour Manage

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In recent years, significant changes in precipitation regimes have been observed and these manifest in socio economic and ecological problems especially in regions with increased vulnerability such as the Mediterranean region. For this reason, it is necessary to estimate the future projected precipitation on short and long-term basis by analyzing long time series of observed station data. In this study, an effort was made in order to forecast the monthly maximum, minimum, mean and cumulative precipitation totals within a period of the next four consecutive months, using Artificial Neural Networks (ANNs). The precipitation datasets concern monthly totals recorded at four meteorological stations (Alexandroupolis, Thessaloniki, Athens, and Patras), in Greece. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indexes such as the coefficient of determination (R 2 ), the index of agreement (IA) and and P. Ralli Str., 122 44 Athens, Greece 1980 K.P. Moustris et al.the root mean square error (RMSE) were used. The findings from this analysis showed that the ANN's methodology provides satisfactory precipitation totals in four consecutive months and these results are better results, than those obtained using classical statistical methods. A fairly good consistency between the observed and the predicted precipitation totals at a statistical significance level of p < 0.01 for the most of the examined cases has been revealed. More specifically, the Index of Agreement (IA) ranges between 0.523 and 0.867 and the coefficient of determination (R 2 ) ranges between 0.141 and 0.603. The most accurate forecasts concern the mean monthly and the cumulative precipitation for the next four consecutive months.

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Section: Introductionmentioning

confidence: 99%

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

Moustris

Larissi

Nastos

et al. 2011

Water Resour Manage

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“…Schmid et al, 2002), but also the uncertainty in the development of the precipitation field. Pierce et al (2005), as well as Fox et al (2005), attempt to determine more explicit measures of uncertainty by running multiple realizations of their nowcast schemes.…”

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confidence: 99%

Improving QPF by blending techniques at the Meteorological Service of Catalonia

Atencia

Rigo

Sairouni

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The current operational very short-term and shortterm quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of Correspondence to: A. Atencia (aatencia@meteo.cat) episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

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“…There is a very high chance that the ensemble members from the rainfall nowcast may be able to reduce the uncertainties in predictions, especially for higher rainfall; however, dealing with the ensemble members remains a challenging problem. The skill of a radar advection nowcast decreases rapidly with lead time (Dixon and Wiener, 1993;Mecklenburg et al, 2000;Fox and Wilson, 2005). STEPS is based on the idea that the temporal development in Lagrangian coordinates is scale dependent and this can be modelled using a combination of a multiplicative cascade with autoregressive (AR) updates (Seed et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…The generation of short-term (i.e., 0-3 hr) future precipitation distributions from a sequence of radar images is known as precipitation nowcasting, but this approach is based solely on a simple extrapolation technique and its accuracy rapidly decreases with increasing lead time (Dixon and Wiener, 1993;Mecklenburg et al, 2000;Fox and Wilson, 2005). This is because storm initiation, growth, and subsequent dissipation of the echo are not included in the process.…”

Section: Introductionmentioning

confidence: 99%

Comparison of rainfall nowcasting derived from the STEPS model and JMA precipitation nowcasts

Shakti

Misumi

Nakatani

et al. 2015

Hydrological Research Letters

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Abstract:Quantitative precipitation estimation and precipitation nowcasting are important components of systems that aim at minimizing or managing flash flooding. This study used the Short Term Ensemble Prediction System (STEPS), one of the most advanced Quantitative Precipitation Forecast (QPF) systems currently available. The Japan Meteorological Agency (JMA) radar rainfall data (1-km resolution) from the Kanto region, Japan, covering various periods, were used in STEPS to generate ensemble nowcasts of rainfall. Hourlong 30-member-ensemble rainfall nowcasts were generated for five separate rainfall events using 5-minute time steps. The ensemble nowcasts were verified using radar rainfall data, and the results showed that the STEPS forecasts are in good agreement with the observed data for forecast periods of <1 hour. To check the performance of the STEPS model output in more detail, it was compared with JMA precipitation nowcast data, and both nowcasting datasets were also compared separately with rain gauge data. The skill scores suggest that STEPS generates more accurate nowcasts, especially for higher-intensity rainfall events. Combining all members of the STEPS nowcasting results appears to improve the reliability of short-term rainfall prediction, and the output of such ensemble nowcasts could be used in hydrological models to generate probabilistic forecasts in the future.

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Very short period quantitative precipitation forecasting

Cited by 10 publications

References 20 publications

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

Improving QPF by blending techniques at the Meteorological Service of Catalonia

Comparison of rainfall nowcasting derived from the STEPS model and JMA precipitation nowcasts

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