Search Results
significant ice events for areas in which there will be active shipping. A 30-day outlook is then issued and reassessed every 15 days throughout the Arctic summer. The new statistical events forecasting model (SEF) presented herein uses the dates on which these various events have occurred each year since the Canadian Ice Service first began detecting them by aerial or satellite reconnaissance almost 46 years ago. These dates form a continuous series for each arctic summer year. The SEF uses the series
significant ice events for areas in which there will be active shipping. A 30-day outlook is then issued and reassessed every 15 days throughout the Arctic summer. The new statistical events forecasting model (SEF) presented herein uses the dates on which these various events have occurred each year since the Canadian Ice Service first began detecting them by aerial or satellite reconnaissance almost 46 years ago. These dates form a continuous series for each arctic summer year. The SEF uses the series
. 1 ’s annual management cycles can be simulated over similar periods if daily meteorological data are available to drive crop simulation models. By contrast, retrospective forecast records derived from the National Centers for Environmental Prediction (NCEP) climate forecast system, although potentially more skillful, are available for only 24 yr ( Saha et al. 2006 ). Thus basing management simulations on these simple prediction schemes might provide better statistical sampling of the production
. 1 ’s annual management cycles can be simulated over similar periods if daily meteorological data are available to drive crop simulation models. By contrast, retrospective forecast records derived from the National Centers for Environmental Prediction (NCEP) climate forecast system, although potentially more skillful, are available for only 24 yr ( Saha et al. 2006 ). Thus basing management simulations on these simple prediction schemes might provide better statistical sampling of the production
Colucci 1997 ; Grimit and Mass 2002 ; Scherrer et al. 2004 ; Eckel and Mass 2005 ). Hence, to realize the full potential of an ensemble forecast it is necessary to apply some form of statistical postprocessing, with the goal of generating probabilistic forecasts that are calibrated and yet sharp. In the spirit of the pioneering work of Glahn and Lowry (1972) , who introduced regression-type model output statistics approaches to a meteorological audience, various statistically based ensemble
Colucci 1997 ; Grimit and Mass 2002 ; Scherrer et al. 2004 ; Eckel and Mass 2005 ). Hence, to realize the full potential of an ensemble forecast it is necessary to apply some form of statistical postprocessing, with the goal of generating probabilistic forecasts that are calibrated and yet sharp. In the spirit of the pioneering work of Glahn and Lowry (1972) , who introduced regression-type model output statistics approaches to a meteorological audience, various statistically based ensemble
1. Introduction Considerable progress has been made in numerical weather prediction (NWP) during the past decades due partly to a steady accumulation of scientific knowledge and partly to technological advances in utilizing a variety of observations and gaining computing power ( Bauer et al. 2015 ). Despite the steady progress, significant forecast errors in today’s NWP models are still present, especially in processing atmospheric statistical properties that are not directly available from the
1. Introduction Considerable progress has been made in numerical weather prediction (NWP) during the past decades due partly to a steady accumulation of scientific knowledge and partly to technological advances in utilizing a variety of observations and gaining computing power ( Bauer et al. 2015 ). Despite the steady progress, significant forecast errors in today’s NWP models are still present, especially in processing atmospheric statistical properties that are not directly available from the
trees (CART) (e.g., Livingston et al. 1996 ; Mazany et al. 2002 ; Burrows et al. 2004 ; Brenner 2004 ; Lambert et al. 2005 ). These methods attempt to quantify the relationship between a set of predictors and the outcome of interest such as thunderstorm probability or lightning frequency (e.g., Neumann and Nicholson 1972 ; Reap 1994 ). The present study develops a statistical scheme that provides improved forecast guidance of warm season afternoon and evening lightning for 11 areas of the
trees (CART) (e.g., Livingston et al. 1996 ; Mazany et al. 2002 ; Burrows et al. 2004 ; Brenner 2004 ; Lambert et al. 2005 ). These methods attempt to quantify the relationship between a set of predictors and the outcome of interest such as thunderstorm probability or lightning frequency (e.g., Neumann and Nicholson 1972 ; Reap 1994 ). The present study develops a statistical scheme that provides improved forecast guidance of warm season afternoon and evening lightning for 11 areas of the
its simplest form, we decompose forecast error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and is required to vary slowly and smoothly with position. FCA is a general method to compare two datasets. In this pilot study, we use FCA in reverse to dress individual dynamical forecasts statistically. Statistical dressing refers to the process of generating random but “statistically reasonable” differences and adding
its simplest form, we decompose forecast error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and is required to vary slowly and smoothly with position. FCA is a general method to compare two datasets. In this pilot study, we use FCA in reverse to dress individual dynamical forecasts statistically. Statistical dressing refers to the process of generating random but “statistically reasonable” differences and adding
1. Introduction Seasonal climate forecasts are in high demand in Australia, and seasonal rainfall forecasts in particular are sought by farmers, water managers, and others throughout the year. The Australian Bureau of Meteorology provides probabilistic forecasts of seasonal rainfall using a statistical prediction system based on sea surface temperature (SST) anomaly patterns over the Indian and Pacific Oceans ( Drosdowsky and Chambers 2001 ; Fawcett et al. 2005 ). Different forecast models are
1. Introduction Seasonal climate forecasts are in high demand in Australia, and seasonal rainfall forecasts in particular are sought by farmers, water managers, and others throughout the year. The Australian Bureau of Meteorology provides probabilistic forecasts of seasonal rainfall using a statistical prediction system based on sea surface temperature (SST) anomaly patterns over the Indian and Pacific Oceans ( Drosdowsky and Chambers 2001 ; Fawcett et al. 2005 ). Different forecast models are
decades. This technique describes the regional climate via a statistical relationship linked to large-scale climate signals that can be well simulated by GCMs ( Zorita and von Storch 1999 ; Fowler et al. 2007 ). The statistical downscaling technique has been widely applied to forecasting and predicting regional climate ( Charles et al. 1999 ; Benestad 2002 ; Landman and Goddard 2002 ; Oshima et al. 2002 ; Feddersen and Andersen 2005 ; Chu et al. 2008 ; Zhu et al. 2008 ; Li and Smith 2009
decades. This technique describes the regional climate via a statistical relationship linked to large-scale climate signals that can be well simulated by GCMs ( Zorita and von Storch 1999 ; Fowler et al. 2007 ). The statistical downscaling technique has been widely applied to forecasting and predicting regional climate ( Charles et al. 1999 ; Benestad 2002 ; Landman and Goddard 2002 ; Oshima et al. 2002 ; Feddersen and Andersen 2005 ; Chu et al. 2008 ; Zhu et al. 2008 ; Li and Smith 2009
to forecast, especially when it behaves abnormally ( Zhang et al. 2002 ; Grinsted and Moore 2004 ). Current forecasts for the WPSH can be divided into two categories: numerical forecasts and statistical forecasts. Numerical forecasts are widely used throughout the world; examples include the numerical forecast products of the European Centre for Medium-Range Weather Forecasts model ( Wu et al. 2009 ), the Japanese FUFE502 numerical forecast products ( Kug et al. 2007 ), and the T213 numerical
to forecast, especially when it behaves abnormally ( Zhang et al. 2002 ; Grinsted and Moore 2004 ). Current forecasts for the WPSH can be divided into two categories: numerical forecasts and statistical forecasts. Numerical forecasts are widely used throughout the world; examples include the numerical forecast products of the European Centre for Medium-Range Weather Forecasts model ( Wu et al. 2009 ), the Japanese FUFE502 numerical forecast products ( Kug et al. 2007 ), and the T213 numerical
.06 over these 35 years. In addition, the correlation between the basinwide Accumulated Cyclone Energy (ACE; Bell et al. 2000 ) and the number of storms crossing NYS is 0.20. Hence, as discussed in section 1 , even perfect seasonal forecasts of the basinwide tropical cyclone statistics will not be particularly useful for predicting the number of storms crossing NYS. b. Observation and reforecasts For statistical–dynamical hybrid prediction, seasonal reforecasts from NCEP Climate Forecast System
.06 over these 35 years. In addition, the correlation between the basinwide Accumulated Cyclone Energy (ACE; Bell et al. 2000 ) and the number of storms crossing NYS is 0.20. Hence, as discussed in section 1 , even perfect seasonal forecasts of the basinwide tropical cyclone statistics will not be particularly useful for predicting the number of storms crossing NYS. b. Observation and reforecasts For statistical–dynamical hybrid prediction, seasonal reforecasts from NCEP Climate Forecast System
