Search Results
You are looking at 1 - 3 of 3 items for :
- Author or Editor: Eric S. Blake x
- Weather and Forecasting x
- Refine by Access: All Content x
Abstract
Although skillful seasonal hurricane forecasts for the Atlantic basin are now a reality, large gaps remain in our understanding of observed variations in the distribution of activity within the hurricane season. The month of August roughly spans the first third of the climatologically most active part of the season, but activity during the month is quite variable. This paper reports on an initial investigation into forecasting year-to-year variability of August tropical cyclone (TC) activity using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. It is shown that 55%–75% of the variance of August TC activity can be hindcast using a combination of 4–5 global predictors chosen from a 12-predictor pool with each of the predictors showing precursor associations with TC activity. The most prominent predictive signal is the equatorial July 200-mb wind off the west coast of South America. When this wind is anomalously strong from the northeast during July, Atlantic TC activity in August is almost always enhanced. Other July conditions associated with active Augusts include a weak subtropical high in the North Atlantic, an enhanced subtropical high in the northwest Pacific, and low pressure in the Bering Sea region.
The most important application of the August-only forecast is that predicted net tropical cyclone (NTC) activity in August has a significant relationship with the incidence of U.S. August TC landfall events. Better understanding of August-only TC variability will allow for a more complete perspective of total seasonal variability and, as such, assist in making better seasonal forecasts.
Abstract
Although skillful seasonal hurricane forecasts for the Atlantic basin are now a reality, large gaps remain in our understanding of observed variations in the distribution of activity within the hurricane season. The month of August roughly spans the first third of the climatologically most active part of the season, but activity during the month is quite variable. This paper reports on an initial investigation into forecasting year-to-year variability of August tropical cyclone (TC) activity using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. It is shown that 55%–75% of the variance of August TC activity can be hindcast using a combination of 4–5 global predictors chosen from a 12-predictor pool with each of the predictors showing precursor associations with TC activity. The most prominent predictive signal is the equatorial July 200-mb wind off the west coast of South America. When this wind is anomalously strong from the northeast during July, Atlantic TC activity in August is almost always enhanced. Other July conditions associated with active Augusts include a weak subtropical high in the North Atlantic, an enhanced subtropical high in the northwest Pacific, and low pressure in the Bering Sea region.
The most important application of the August-only forecast is that predicted net tropical cyclone (NTC) activity in August has a significant relationship with the incidence of U.S. August TC landfall events. Better understanding of August-only TC variability will allow for a more complete perspective of total seasonal variability and, as such, assist in making better seasonal forecasts.
Abstract
The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.
Significance Statement
NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.
Abstract
The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.
Significance Statement
NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.
Abstract
The National Hurricane Center issues analyses, forecasts, and warnings over large parts of the North Atlantic and Pacific Oceans, and in support of many nearby countries. Advances in observational capabilities, operational numerical weather prediction, and forecaster tools and support systems over the past 15–20 yr have enabled the center to make more accurate forecasts, extend forecast lead times, and provide new products and services. Important limitations, however, persist. This paper discusses the current workings and state of the nation’s hurricane warning program, and highlights recent improvements and the enabling science and technology. It concludes with a look ahead at opportunities to address challenges.
Abstract
The National Hurricane Center issues analyses, forecasts, and warnings over large parts of the North Atlantic and Pacific Oceans, and in support of many nearby countries. Advances in observational capabilities, operational numerical weather prediction, and forecaster tools and support systems over the past 15–20 yr have enabled the center to make more accurate forecasts, extend forecast lead times, and provide new products and services. Important limitations, however, persist. This paper discusses the current workings and state of the nation’s hurricane warning program, and highlights recent improvements and the enabling science and technology. It concludes with a look ahead at opportunities to address challenges.
