Combining probabilistic precipitation forecasts from a nowcasting technique with a time-lagged ensemble

Scheufele, Katrin; Kober, Kirstin; Craig, George C.; Keil, Christian

doi:10.1002/met.1381

Cited by 17 publications

(22 citation statements)

References 35 publications

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“…(, ) and Scheufele et al . (). The UK Met Office combines extrapolated radar data with downscaled NWPs to their nowcasting tool called STEPS (Bowler et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…A blending of nowcasts based on extrapolated radar data with probabilistic high resolution forecasts for precipitation forecasts has already been used by, for example, Kober (2010), Kober et al (2012Kober et al ( , 2014 and Scheufele et al (2014). The UK Met Office combines extrapolated radar data with downscaled NWPs to their nowcasting tool called STEPS (Bowler et al, 2006).…”

Section: Blending With Deterministic and Probabilistic Forecastsmentioning

confidence: 99%

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Short‐range probabilistic forecasting of convective risks for aviation based on a lagged‐average‐forecast ensemble approach

Osinski

Bouttier

2017

Meteorological Applications

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An hourly initialized numerical weather prediction model, AROME-NWC, optimized for nowcasting purposes was used in this study to predict the probabilities of occurrence of convective aviation risks by generating an ensemble of time-lagged forecasts. The objective is the prediction of echotop and reflectivity maximum based on simulated 3D radar reflectivity columns. Forecasts were postprocessed using an upscaling of the model output fields in order to account for uncertainties in horizontal positions. Simulated radar reflectivities were bias corrected using a quantile-to-quantile mapping resulting in an improvement of the ensemble performance. A lagged-average-forecast ensemble was then constructed in order to blend mesoscale deterministic and ensemble forecasts, using numerical weather prediction systems that will soon be available in real time. The probabilities of reflectivities predicted by the ensemble are shown to have objective value at thresholds that are meaningful for air traffic control. Possible applications for aviation management purposes are discussed.

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“…(, ) and Scheufele et al . (). The UK Met Office combines extrapolated radar data with downscaled NWPs to their nowcasting tool called STEPS (Bowler et al ., ).…”

Section: Methodsmentioning

confidence: 97%

Section: Blending With Deterministic and Probabilistic Forecastsmentioning

confidence: 99%

Short‐range probabilistic forecasting of convective risks for aviation based on a lagged‐average‐forecast ensemble approach

Osinski

Bouttier

2017

Meteorological Applications

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“…We will evaluate whether calibration of the NWP forecasts can be improved by considering equilibrium and non‐equilibrium convective regimes separately and whether further improvement can be obtained by blending nowcast and forecast probabilities with regime‐dependent weights. A secondary aim of this work is to confirm the results of Kober et al (2012) and Scheufele et al (2013) for a longer time period.…”

Section: Introductionmentioning

confidence: 87%

“…While most of these methods seek to combine forecasts of meteorological fields, it is appealing to blend probability forecasts, which allows a consistent and smooth transition from one forecast component to the other. Kober et al (2012) and Scheufele et al (2013) demonstrate that an additive blending of radar‐ and model‐derived probabilities maintains or may even exceed the predictive skill of each component.…”

Section: Introductionmentioning

confidence: 99%

Aspects of short‐term probabilistic blending in different weather regimes

Kober

Craig

Keil

2013

Quart J Royal Meteoro Soc

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This study explores the potential of regime‐dependent approaches to improve short‐range precipitation forecasts. Probabilistic forecasts have been generated from the radar nowcaster Radar Tracking and Monitoring (Rad‐TRAM) and the convection‐permitting weather prediction model of the Consortium for Small‐scale Modeling, Deutscher Wetterdienst (COSMO‐DE) using the neighbourhood method for a 99 day period during summer 2009. The convective adjustment time‐scale was used to classify the days of the investigated period into stratiform, equilibrium and non‐equilibrium convection regimes. The COSMO‐DE forecasts were calibrated using the reliability diagram method and blended with the nowcasts using an additive weighting, where the weighting function varies with lead time according to the time evolution of the conditional square root of ranked probability score (CSRR). The examination of two case studies showed large differences in the calibration and weighting functions for different regimes. Over the entire period, regime‐dependent calibration was found to produce large improvements in reliability in comparison with the uncalibrated forecasts; however, the results were only modestly better than with a single calibration function. The blending procedure successfully combined the nowcast and forecast information, in the sense that the blended forecast was as good as either of the two components, but there was no further gain expected from regime‐dependent weighting if regime‐dependent calibration had already been performed.

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“…First, it is possible to control the input data. The observed data can be varied by introducing time lag [45][46][47][48]. It is also possible to introduce some amount of perturbation to the observed input data [49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

Na¹,

Yoo²

2019

Water

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The rainfall forecasts currently available in Korea are not sufficiently accurate to bedirectly applied to the flash flood warning system or urban flood warning system. As the lead timeincreases, the quality becomes even lower. In order to overcome this problem, this study proposesan ensemble forecasting method. The proposed method considers all available rainfall forecasts asensemble members at the target time. The ensemble members are combined based on the weightedaverage method, where the weights are determined by applying the two conditions of theunbiasedness and minimum error variance. The proposed method is tested with McGill Algorithmfor Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four stormevents that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts aregenerated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts.As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast.Considering additional ensemble members beyond six does not significantly improve the quality ofthe ensemble forecast. The quality of the ensemble forecast is also found to be better than that of thesingle forecast, and the weighted average method is found to be better than the simple arithmeticaverage method.

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Combining probabilistic precipitation forecasts from a nowcasting technique with a time-lagged ensemble

Cited by 17 publications

References 35 publications

Short‐range probabilistic forecasting of convective risks for aviation based on a lagged‐average‐forecast ensemble approach

Short‐range probabilistic forecasting of convective risks for aviation based on a lagged‐average‐forecast ensemble approach

Aspects of short‐term probabilistic blending in different weather regimes

Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

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