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- Author or Editor: Yi Jin x
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Major damage caused by hurricanes occurs over land during and after landfall. Accurate predictions of winds and precipitation in and around hurricanes at or near landfall are therefore of vital importance for hurricane preparation and damage mitigation, yet they continue to present a challenge for the hurricane research and numerical weather prediction (NWP) communities. This is, in part, due to rapid changes in hurricane intensity and structure during landfall associated with multiscale dynamical and physical interactions in the hurricane core regions and outer spiral rainbands, and also associated with sudden changes of surface conditions.
In this study, we demonstrate the capability of improving predictions of hurricane intensity and structures near landfall by assimilating high-resolution, three-dimensional observations from land-based radars in the landfall regions into a mesoscale NWP model. The landfall of Hurricane Isabel on the east coast of the United States in 2003 is the focus of this study. Observations of Doppler radial velocity and reflectivity from five Doppler radars in the landfall region were collected and assimilated into the Navy's Coupled Ocean-Atmosphere Mesoscale Prediction System in a variational data assimilation framework. Four cycles of hourly radar reflectivity data assimilation effectively correct the overprediction of hydrometeor fields by the model, and move the maximum reflectivity regions to the observed locations. Better hurricane structures, including increased maximum wind speed, a tighter inner core, and better organized outer rainbands, are obtained by the radar radial velocity assimilation. Much-improved forecasts of 24-h accumulated precipitation during and after hurricane landfall have also been achieved by the radar data assimilation. The positive results from this study indicate the potential for improving hurricane intensity and structure forecasts by assimilating radar observations into NWP models.
Major damage caused by hurricanes occurs over land during and after landfall. Accurate predictions of winds and precipitation in and around hurricanes at or near landfall are therefore of vital importance for hurricane preparation and damage mitigation, yet they continue to present a challenge for the hurricane research and numerical weather prediction (NWP) communities. This is, in part, due to rapid changes in hurricane intensity and structure during landfall associated with multiscale dynamical and physical interactions in the hurricane core regions and outer spiral rainbands, and also associated with sudden changes of surface conditions.
In this study, we demonstrate the capability of improving predictions of hurricane intensity and structures near landfall by assimilating high-resolution, three-dimensional observations from land-based radars in the landfall regions into a mesoscale NWP model. The landfall of Hurricane Isabel on the east coast of the United States in 2003 is the focus of this study. Observations of Doppler radial velocity and reflectivity from five Doppler radars in the landfall region were collected and assimilated into the Navy's Coupled Ocean-Atmosphere Mesoscale Prediction System in a variational data assimilation framework. Four cycles of hourly radar reflectivity data assimilation effectively correct the overprediction of hydrometeor fields by the model, and move the maximum reflectivity regions to the observed locations. Better hurricane structures, including increased maximum wind speed, a tighter inner core, and better organized outer rainbands, are obtained by the radar radial velocity assimilation. Much-improved forecasts of 24-h accumulated precipitation during and after hurricane landfall have also been achieved by the radar data assimilation. The positive results from this study indicate the potential for improving hurricane intensity and structure forecasts by assimilating radar observations into NWP models.
Abstract
Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.
Abstract
Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.
Abstract
Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.
Abstract
Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.
Abstract
El Niño–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.
Abstract
El Niño–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.