scholarly journals Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

2011 ◽  
Vol 11 (2) ◽  
pp. 587-595 ◽  
Author(s):  
J. Hosek ◽  
P. Musilek ◽  
E. Lozowski ◽  
P. Pytlak
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Storm Event ◽  
Rain Event ◽  
Ice Accretion ◽  
Ice Storm ◽  
Forecast Horizon ◽  
Freezing Rain ◽  
Numerical Weather ◽  
Ice Storms

Abstract. The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event. The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

scholarly journals Improving the Explicit Prediction of Freezing Rain in a Kilometer-Scale Numerical Weather Prediction Model

2018 ◽  
Vol 33 (3) ◽  
pp. 767-782 ◽  
Author(s):  
Agnieszka Barszcz ◽  
Jason A. Milbrandt ◽  
Julie M. Thériault
Keyword(s):  
Numerical Weather Prediction ◽  
Collection Efficiency ◽  
Weather Prediction ◽  
Temperature Inversion ◽  
Temperature Threshold ◽  
Rain Event ◽  
Prediction System ◽  
Freezing Rain ◽  
Microphysics Scheme ◽  
Numerical Weather

Abstract A freezing rain event, in which the Meteorological Centre of Canada’s 2.5-km numerical weather prediction system significantly underpredicted the quantity of freezing rain, is examined. The prediction system models precipitation types explicitly, directly from the Milbrandt–Yau microphysics scheme. It was determined that the freezing rain underprediction for this case was due primarily to excessive refreezing of rain, originating from melting snow and graupel, in and under the temperature inversion of the advancing warm front ultimately depleting the supply of rain reaching the surface. The refreezing was caused from excessive collisional freezing between rain and graupel. Sensitivity experiments were conducted to examine the effects of a temperature threshold for collisional freezing and on varying the values of the collection efficiencies between rain and ice-phase hydrometeors. It was shown that by reducing the rain–graupel collection efficiency and by imposing a temperature threshold of −5°C, above which collisional freezing is not permitted, excessive rain–graupel collection and graupel formation can be controlled in the microphysics scheme, leading to an improved simulation of freezing rain at the surface.

scholarly journals Evolutionary Optimization of an Ice Accretion Forecasting System

2010 ◽  
Vol 138 (7) ◽  
pp. 2913-2929 ◽  
Author(s):  
Pawel Pytlak ◽  
Petr Musilek ◽  
Edward Lozowski ◽  
Dan Arnold
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Rain Event ◽  
Ice Accretion ◽  
Numerical Weather ◽  
Industrial Sectors ◽  
Type Algorithm ◽  
Precipitation Type ◽  
Forecasting System

Abstract The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on overhead conductors and other components of power networks. This paper presents an extension to the ice accretion forecasting system (IAFS) that is comprised of a numerical weather prediction model, a precipitation-type algorithm, and an ice accretion model. To optimize the performance of IAFS, the parameters of the precipitation-type algorithm are estimated using a genetic algorithm. The system is developed by hindcasting a well-documented freezing-rain event and calibrated using four additional ice storms. Subsequently, the system is tested using three independent storms. The results show a significant improvement in consistency, accuracy, and skill of IAFS. The methodology described in this contribution is not limited to ice accretion modeling—it provides a general approach for setting operational parameters of data-processing algorithms to achieve interoperability of numerical weather prediction models with add-on applications based on empirical observations.

scholarly journals Antarctic Verification of the Australian Numerical Weather Prediction Model

2019 ◽  
Vol 34 (4) ◽  
pp. 1081-1096
Author(s):  
Benjamin J. E. Schroeter ◽  
Phil Reid ◽  
Nathaniel L. Bindoff ◽  
Kelvin Michael
Keyword(s):  
Numerical Weather Prediction ◽  
Performance Metrics ◽  
Weather Prediction ◽  
Model Performance ◽  
Weather Conditions ◽  
Forecast Model ◽  
Forecast Horizon ◽  
Positive Screen ◽  
Numerical Weather ◽  
The Antarctic

Abstract The Australian Community Climate and Earth-System Simulator-Global (ACCESS-G) features an atmosphere-only numerical weather prediction (NWP) suite used operationally by the Australian Bureau of Meteorology to forecast weather conditions for the Antarctic. The current operational version of the forecast model, the Australian Parallel Suite v2 (APS2), has been used operationally since early 2016. To date, the performance of the model has been largely unverified for the Antarctic and anecdotal reports suggest challenges for model performance in the region. This study investigates the performance of ACCESS-G south of 50°S over 2017 and finds that model performance degrades toward the poles and in proportion to forecast horizon against a range of performance metrics. The model exhibits persistent negative surface and mean sea level pressure biases around the Adelie Land coast, which is linked to the underrepresentation of model winds to the west, and driven by positive screen temperature biases that inhibit modeled katabatic outflow. These results suggest that an improved representation of boundary layer parameterization could be implemented to improve model performance in the region.

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