Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands

2008 ◽  
Vol 23 (6) ◽  
pp. 1253-1267 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang ◽  
Rudolf M. van Westrhenen
Keyword(s):  
The Netherlands ◽  
Weather Prediction ◽  
Limited Area ◽  
Regression Equations ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorms ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Total Lightning

Abstract The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km × 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

scholarly journals Probabilistic forecasts of winter thunderstorms around Amsterdam Airport Schiphol

2009 ◽  
Vol 3 (1) ◽  
pp. 39-43
Author(s):  
A. B. A. Slangen ◽  
M. J. Schmeits
Keyword(s):  
Weather Prediction ◽  
Convective Available Potential Energy ◽  
Radar Data ◽  
Primary Objective ◽  
Probabilistic Forecast ◽  
Convective Precipitation ◽  
Limited Area ◽  
Regression Equations ◽  
Forecast System ◽  
Probabilistic Forecasts

Abstract. The development and verification of a probabilistic forecast system for winter thunderstorms around Amsterdam Airport Schiphol is described. We have used Model Output Statistics (MOS) to develop the probabilistic forecast equations. The MOS system consists of 32 logistic regression equations, i.e. for two forecast periods (0–6 h and 6–12 h), four 90×80 km2 regions around Amsterdam Airport Schiphol, and four 6-h time periods. For the predictand quality-controlled Surveillance et Alerte Foudre par Interférométrie Radioélectrique (SAFIR) total lightning data were used. The potential predictors were calculated from postprocessed output of two numerical weather prediction (NWP) models – i.e. the High-Resolution Limited-Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model – and from an ensemble of advected lightning and radar data (0–6 h projections only). The predictors that are selected most often are the HIRLAM Boyden index, the square root of the ECMWF 3-h and 6-h convective precipitation sum, the HIRLAM convective available potential energy (CAPE) and two radar advection predictors. An objective verification was done, from which it can be concluded that the MOS system is skilful. The forecast system runs at the Royal Netherlands Meteorological Institute (KNMI) on an experimental basis, with the primary objective to warn aircraft pilots for potential aircraft induced lightning (AIL) risk during winter.

Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

2005 ◽  
Vol 20 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang
Keyword(s):  
The Netherlands ◽  
Important Predictor ◽  
Convective Precipitation ◽  
Square Root ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorm ◽  
Severe Thunderstorms ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract The derivation and verification of logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands is described. For 12 regions of about 90 km × 80 km each, and for projections out to 48 h in advance (with 6-h periods), these equations have been derived using model output statistics (MOS). As a source for the predictands, lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset mainly consisted of the combined (postprocessed) output from two numerical weather prediction (NWP) models. It contained 15 traditional thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (postprocessed) output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The most important predictor in the thunderstorm forecast system is the square root of the ECMWF 6-h convective precipitation sum, and the most important predictor in the severe thunderstorm forecast system is the HIRLAM Boyden index. The success of the square root of the ECMWF 6-h convective precipitation sum as a thunderstorm predictor indicates that there is a strong relation between the forecast convective precipitation by the ECMWF model and the occurrence of thunderstorms, at least in the Netherlands up to 3 days in advance. The overall verification results for the 0000, 0600, 1200, and 1800 UTC runs of the MOS (severe) thunderstorm forecast system are good, and, therefore, the system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in April 2004.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

2020 ◽  
Vol 148 (7) ◽  
pp. 2645-2669
Author(s):  
Craig S. Schwartz ◽  
May Wong ◽  
Glen S. Romine ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell
Keyword(s):  
United States ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Limited Area ◽  
Grid Spacing ◽  
Forecast System ◽  
Skill Scores ◽  
Ensemble Mean ◽  
Horizontal Grid ◽  
Better Than

Abstract Five sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

scholarly journals Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles

2007 ◽  
Vol 22 (3) ◽  
pp. 580-595 ◽  
Author(s):  
Chungu Lu ◽  
Huiling Yuan ◽  
Barry E. Schwartz ◽  
Stanley G. Benjamin
Keyword(s):  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Cycle Model ◽  
Forecast System ◽  
Multilinear Regression ◽  
Probabilistic Forecasts ◽  
Regression Approach ◽  
Ensemble Mean ◽  
Time Lagged

Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

scholarly journals Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

2018 ◽  
Vol 146 (8) ◽  
pp. 2361-2379 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Object Oriented ◽  
Comprehensive Analysis ◽  
Initial Condition ◽  
Forecast System ◽  
Model Assumption ◽  
Ensemble Forecasts ◽  
Severe Thunderstorms ◽  
Perfect Model

Abstract As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

scholarly journals An online trajectory module (version 1.0) for the non-hydrostatic numerical weather prediction model COSMO

2013 ◽  
Vol 6 (1) ◽  
pp. 1223-1257
Author(s):  
A. K. Miltenberger ◽  
S. Pfahl ◽  
H. Wernli
Keyword(s):  
High Resolution ◽  
Wind Field ◽  
Model Simulation ◽  
Weather Prediction ◽  
Limited Area ◽  
Model Output ◽  
Wind Fields ◽  
Time Step ◽  
Flow Distortion ◽  
Cosmo Model

Abstract. A module to calculate online trajectories has been implemented into the non-hydrostatic limited-area weather prediction and climate model COSMO. Whereas offline trajectories are calculated with wind fields from model output, which is typically available every one to six hours, online trajectories use the simulated wind field at every model time step (typically less than a minute) to solve the trajectory equation. As a consequence, online trajectories much better capture the short-term temporal fluctuations of the wind field, which is particularly important for mesoscale flows near topography and convective clouds, and they do not suffer from temporal interpolation errors between model output times. The numerical implementation of online trajectories in the COSMO model is based upon an established offline trajectory tool and takes full account of the horizontal domain decomposition that is used for parallelization of the COSMO model. Although a perfect workload balance cannot be achieved for the trajectory module (due to the fact that trajectory positions are not necessarily equally distributed over the model domain), the additional computational costs are fairly small for high-resolution simulations. Various options have been implemented to initialize online trajectories at different locations and times during the model simulation. As a first application of the new COSMO module an Alpine North Föhn event in summer 1987 has been simulated with horizontal resolutions of 2.2 km, 7 km, and 14 km. It is shown that low-tropospheric trajectories calculated offline with one- to six-hourly wind fields can significantly deviate from trajectories calculated online. Deviations increase with decreasing model grid spacing and are particularly large in regions of deep convection and strong orographic flow distortion. On average, for this particular case study, horizontal and vertical positions between online and offline trajectories differed by 50–190 km and 150–750 m, respectively, after 24 h. This first application illustrates the potential for Lagrangian studies of mesoscale flows in high-resolution convection-resolving simulations using online trajectories.

scholarly journals Statistical post-processing of wind speed forecasts using convolutional neural networks

2021 ◽  
Author(s):  
Simon Veldkamp ◽  
Kirien Whan ◽  
Sjoerd Dirksen ◽  
Maurice Schmeits
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Convolutional Neural Networks ◽  
Weather Prediction ◽  
Estimation Methods ◽  
Post Processing ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Truncated Normal ◽  
Nwp Model

AbstractCurrent statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI’s deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best.

Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

2021 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Francesca Di Giuseppe ◽  
Christopher Barnard ◽  
Thomas M. Hamill ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Post Processing ◽  
Medium Range Weather Forecast ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Systematic Biases ◽  
Processing Techniques

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

scholarly journals An online trajectory module (version 1.0) for the nonhydrostatic numerical weather prediction model COSMO

2013 ◽  
Vol 6 (6) ◽  
pp. 1989-2004 ◽  
Author(s):  
A. K. Miltenberger ◽  
S. Pfahl ◽  
H. Wernli
Keyword(s):  
High Resolution ◽  
Wind Field ◽  
Weather Prediction ◽  
Limited Area ◽  
Model Output ◽  
Wind Fields ◽  
Time Step ◽  
Flow Distortion ◽  
Computational Costs ◽  
Cosmo Model

Abstract. A module to calculate online trajectories has been implemented into the nonhydrostatic limited-area weather prediction and climate model COSMO. Whereas offline trajectories are calculated with wind fields from model output, which is typically available every one to six hours, online trajectories use the simulated resolved wind field at every model time step (typically less than a minute) to solve the trajectory equation. As a consequence, online trajectories much better capture the short-term temporal fluctuations of the wind field, which is particularly important for mesoscale flows near topography and convective clouds, and they do not suffer from temporal interpolation errors between model output times. The numerical implementation of online trajectories in the COSMO-model is based upon an established offline trajectory tool and takes full account of the horizontal domain decomposition that is used for parallelization of the COSMO-model. Although a perfect workload balance cannot be achieved for the trajectory module (due to the fact that trajectory positions are not necessarily equally distributed over the model domain), the additional computational costs are found to be fairly small for the high-resolution simulations described in this paper. The computational costs may, however, vary strongly depending on the number of trajectories and trace variables. Various options have been implemented to initialize online trajectories at different locations and times during the model simulation. As a first application of the new COSMO-model module, an Alpine north foehn event in summer 1987 has been simulated with horizontal resolutions of 2.2, 7 and 14 km. It is shown that low-tropospheric trajectories calculated offline with one- to six-hourly wind fields can significantly deviate from trajectories calculated online. Deviations increase with decreasing model grid spacing and are particularly large in regions of deep convection and strong orographic flow distortion. On average, for this particular case study, horizontal and vertical positions between online and offline trajectories differed by 50–190 km and 150–750 m, respectively, after 24 h. This first application illustrates the potential for Lagrangian studies of mesoscale flows in high-resolution convection-resolving simulations using online trajectories.

scholarly journals Performance of probabilistic forecast of the onset of the rainy season over Java Island based on the application of Constructed Analogue (CA) method on Climate Forecast System Version 2 (CFSV2) model output

2021 ◽  
Vol 893 (1) ◽  
pp. 012037
Author(s):  
F Lubis ◽  
I J A Saragih
Keyword(s):  
Rainy Season ◽  
Climate Model ◽  
Rainfall Variability ◽  
Probabilistic Forecast ◽  
Brier Score ◽  
Climate Forecast ◽  
Model Output ◽  
Forecast System ◽  
Climate Forecast System ◽  
Skill Scores

Abstract The onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.

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