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
initial time. Uncertainties in the model and lower boundary conditions are not considered in MEPS. In this study, the MEPS forecasts were only used every 3 h because of data size limitations. b. Clustering technique and its temporal connection This study used principal component analysis and the fuzzy c -means method as employed by Zheng et al. (2017 , 2019 ) to diverse Z500 scenarios. The specific procedures were as follows: Perform principal component analysis by computing the covariance
initial time. Uncertainties in the model and lower boundary conditions are not considered in MEPS. In this study, the MEPS forecasts were only used every 3 h because of data size limitations. b. Clustering technique and its temporal connection This study used principal component analysis and the fuzzy c -means method as employed by Zheng et al. (2017 , 2019 ) to diverse Z500 scenarios. The specific procedures were as follows: Perform principal component analysis by computing the covariance
rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain
rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain
rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain
rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain
involvement. Another component that could be improved in the current GTG algorithm is the selection of the diagnostics that constitute the final ensemble combination. The current method employs a forward-selection optimization technique that maximizes the skill of the ensemble prediction for a given statistical metric of relevance. As the forecasting skill metric, the area under the receiving operating characteristic curve (AUC) is typically used, which represents the degree or measure of separability of
involvement. Another component that could be improved in the current GTG algorithm is the selection of the diagnostics that constitute the final ensemble combination. The current method employs a forward-selection optimization technique that maximizes the skill of the ensemble prediction for a given statistical metric of relevance. As the forecasting skill metric, the area under the receiving operating characteristic curve (AUC) is typically used, which represents the degree or measure of separability of
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
meteorologists yet has shown promise in several other complex weather prediction applications, as described below. Statistical models have long been a part of weather forecasting. For example, model output statistics (MOS) based on multiple linear regressions are routinely used to compensate for systematic model biases and to generate reliable probabilistic forecasts of precipitation, cloud cover, and other variables ( Glahn and Lowry 1972 ). Analog statistical techniques identify similar past weather
meteorologists yet has shown promise in several other complex weather prediction applications, as described below. Statistical models have long been a part of weather forecasting. For example, model output statistics (MOS) based on multiple linear regressions are routinely used to compensate for systematic model biases and to generate reliable probabilistic forecasts of precipitation, cloud cover, and other variables ( Glahn and Lowry 1972 ). Analog statistical techniques identify similar past weather
instability, too many local minimum points, and nonpositive definite features of the background error covariance. 5. Conclusions and discussion In this study, we employ the adjoint technique to adjust the parameters of wind stress drag coefficient in the three-dimensional POM for improving storm surge forecasts. The identical twin experiments are performed by assigning different error sources. The twin experimental results indicate that it is an efficient and practical way to reduce errors in storm
instability, too many local minimum points, and nonpositive definite features of the background error covariance. 5. Conclusions and discussion In this study, we employ the adjoint technique to adjust the parameters of wind stress drag coefficient in the three-dimensional POM for improving storm surge forecasts. The identical twin experiments are performed by assigning different error sources. The twin experimental results indicate that it is an efficient and practical way to reduce errors in storm
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
storm, Tsai and Elsberry (2014) gave a higher weight for those analogs that better matched the 3–5-day tracks, because they hypothesized that the track was a primary determinant of the intensity changes in that time interval. Tsai and Elsberry (2016) demonstrated that this simple analog technique, which can be calculated in a few minutes on a desktop computer, was more accurate than the regional numerical model intensity guidance in the 3–5-day forecast intervals. Tsai and Elsberry (2015) then
storm, Tsai and Elsberry (2014) gave a higher weight for those analogs that better matched the 3–5-day tracks, because they hypothesized that the track was a primary determinant of the intensity changes in that time interval. Tsai and Elsberry (2016) demonstrated that this simple analog technique, which can be calculated in a few minutes on a desktop computer, was more accurate than the regional numerical model intensity guidance in the 3–5-day forecast intervals. Tsai and Elsberry (2015) then
