Search Results
resolution-dependent initial condition perturbations but easily could if the adjoint of a nested model was available. The breeding ensemble generation technique ( Toth and Kalnay 1993 , 1997 ) when applied to the mesoscale ( Stensrud et al. 1999 ) creates mesoscale analysis perturbations from mesoscale forecast perturbations and hence, in principle, provides initial condition perturbations at all scales resolved by the limited-area model (LAM). However, as pointed out by Wang and Bishop (2003) , the
resolution-dependent initial condition perturbations but easily could if the adjoint of a nested model was available. The breeding ensemble generation technique ( Toth and Kalnay 1993 , 1997 ) when applied to the mesoscale ( Stensrud et al. 1999 ) creates mesoscale analysis perturbations from mesoscale forecast perturbations and hence, in principle, provides initial condition perturbations at all scales resolved by the limited-area model (LAM). However, as pointed out by Wang and Bishop (2003) , the
in the forecast. Ideal seamlessness would involve a way to maintain such classical weather forecasts for some period beyond the forecast initial date, and then seamlessly transition them to time averages so as to maintain useful skill and slow the growth of uncertainty with lead time. Within such a framework, the probabilistic attributes afforded by ensemble forecasts should also be fully accommodated. In this study, we have refined the technique described in ( Ford et al. 2018 ) to retain a
in the forecast. Ideal seamlessness would involve a way to maintain such classical weather forecasts for some period beyond the forecast initial date, and then seamlessly transition them to time averages so as to maintain useful skill and slow the growth of uncertainty with lead time. Within such a framework, the probabilistic attributes afforded by ensemble forecasts should also be fully accommodated. In this study, we have refined the technique described in ( Ford et al. 2018 ) to retain a
value and cost exponentially more in terms of computational resources ( Kain et al. 2008 ). Thus, horizontal grid spacings of 2–4 km are common in operational ensemble systems to provide valuable probabilistic forecast guidance for severe convection. While model physics improvements, high resolution, and advancements in data assimilation techniques benefit the predictability of the atmosphere generally, other postprocessing techniques that harness ensemble information specific to various high
value and cost exponentially more in terms of computational resources ( Kain et al. 2008 ). Thus, horizontal grid spacings of 2–4 km are common in operational ensemble systems to provide valuable probabilistic forecast guidance for severe convection. While model physics improvements, high resolution, and advancements in data assimilation techniques benefit the predictability of the atmosphere generally, other postprocessing techniques that harness ensemble information specific to various high
produced using the ensemble prediction system (EPS) technique ( Heaps 1983 ; Buizza and Palmer 1995 ; Buizza et al. 1999 ). Based on the chaos theory describing systems’ behavior that are highly sensitive to the initial conditions, the method assesses uncertainty in forecasts by considering a set of different forecasts based on a set of different initial conditions, instead of a single “deterministic” forecast ( Mel and Lionello 2014a , b , 2016 ). These initial conditions are designed to include
produced using the ensemble prediction system (EPS) technique ( Heaps 1983 ; Buizza and Palmer 1995 ; Buizza et al. 1999 ). Based on the chaos theory describing systems’ behavior that are highly sensitive to the initial conditions, the method assesses uncertainty in forecasts by considering a set of different forecasts based on a set of different initial conditions, instead of a single “deterministic” forecast ( Mel and Lionello 2014a , b , 2016 ). These initial conditions are designed to include
howthe synoptic analysis of these vectors may be used for 'quantitative precipitation forecasting. An example of thevector field and the precipitation forecast is given. Although the prognosticformuladoes not give correct pointvalues of the precipitation, reasonably good agreement is found between the distributions of forecast and observedprecipitation. The technique is probably too laborious for daily forecasting routine but may be useful in the evalu-ation of rainmaking experiments
howthe synoptic analysis of these vectors may be used for 'quantitative precipitation forecasting. An example of thevector field and the precipitation forecast is given. Although the prognosticformuladoes not give correct pointvalues of the precipitation, reasonably good agreement is found between the distributions of forecast and observedprecipitation. The technique is probably too laborious for daily forecasting routine but may be useful in the evalu-ation of rainmaking experiments
FEBRUARY 1991 LANCE M. LESLIE AND GREG J. HOLLAND 425Predicting Regional Forecast Skill Using Single and Ensemble Forecast Techniques LANCE M. LESLIE AND GREG J. HOLLANDBureau of Meteorology Research Centre, Melbourne, Australia(Manuscript received 4 May 1990, in final form 25 August 1990) ABSTRACT The potential for predicting the skill of 36-h forecasts from the Australian region limited
FEBRUARY 1991 LANCE M. LESLIE AND GREG J. HOLLAND 425Predicting Regional Forecast Skill Using Single and Ensemble Forecast Techniques LANCE M. LESLIE AND GREG J. HOLLANDBureau of Meteorology Research Centre, Melbourne, Australia(Manuscript received 4 May 1990, in final form 25 August 1990) ABSTRACT The potential for predicting the skill of 36-h forecasts from the Australian region limited
perfect forecasting models and perfect ensembles, observations behave like draws from the ensemble distribution and relative frequencies will make good forecasts. In practice, however, models are imperfect ( Ferranti et al. 2002 ) and ensemble generation techniques do not sample randomly from the probability distribution of initial-condition uncertainty ( Hamill et al. 2000 , 2003 ; Wang and Bishop 2003 ). Various techniques have therefore been proposed for improving such probabilistic forecasts
perfect forecasting models and perfect ensembles, observations behave like draws from the ensemble distribution and relative frequencies will make good forecasts. In practice, however, models are imperfect ( Ferranti et al. 2002 ) and ensemble generation techniques do not sample randomly from the probability distribution of initial-condition uncertainty ( Hamill et al. 2000 , 2003 ; Wang and Bishop 2003 ). Various techniques have therefore been proposed for improving such probabilistic forecasts
VOL. IO9~NO. 7 MONTHLY WEATHER REVIEW JULY 1981Averaging Techniques in Long-Range Weather Forecasting A. N. SEIDMANThe Aerospace Corporation, P.O. Box 92937, Los Angeles, CA 90009(Manuscript received 3 June 1980, in final form 30 December 1980)ABSTRACT A method is investigated for increasing the length of prediction time for intermediate-range forecasting(up to 30 days). The method
VOL. IO9~NO. 7 MONTHLY WEATHER REVIEW JULY 1981Averaging Techniques in Long-Range Weather Forecasting A. N. SEIDMANThe Aerospace Corporation, P.O. Box 92937, Los Angeles, CA 90009(Manuscript received 3 June 1980, in final form 30 December 1980)ABSTRACT A method is investigated for increasing the length of prediction time for intermediate-range forecasting(up to 30 days). The method
procedure A variety of methods exists for statistical adaptation of the direct model output of ensemble forecasts. Focusing especially on severe weather, the parameters of primary interest for statistical calibration are precipitation, 10-m wind speed, and 2-m temperature. As the observed relative frequency of precipitation is characterized by a high degree of skewness, logistic regression techniques are found to be adequate for many applications ( Hamill et al. 2008 ). In case of wind speed
procedure A variety of methods exists for statistical adaptation of the direct model output of ensemble forecasts. Focusing especially on severe weather, the parameters of primary interest for statistical calibration are precipitation, 10-m wind speed, and 2-m temperature. As the observed relative frequency of precipitation is characterized by a high degree of skewness, logistic regression techniques are found to be adequate for many applications ( Hamill et al. 2008 ). In case of wind speed
et al. (2006) is adopted for calculating verification scores in this study. MODE is a typical feature-based displacement approach and an example for a spatial diagnostic technique. MODE attempts to mimic the way a human would subjectively evaluate a forecast via setting a precipitation threshold and spatially convoluting (scale-dependent averaging) the precipitation field. The median of maximum interest (MMI; Davis et al. 2009 ) and the object-based threat score (OTS; Johnson et al. 2011a
et al. (2006) is adopted for calculating verification scores in this study. MODE is a typical feature-based displacement approach and an example for a spatial diagnostic technique. MODE attempts to mimic the way a human would subjectively evaluate a forecast via setting a precipitation threshold and spatially convoluting (scale-dependent averaging) the precipitation field. The median of maximum interest (MMI; Davis et al. 2009 ) and the object-based threat score (OTS; Johnson et al. 2011a
