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- Author or Editor: Yi Jin x
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Abstract
When consulting a forecast, users often ask some variant of the following questions: Will an event of interest occur? If so, when will it occur? How long will it last? How intense will it be? Standard verification measures often do not directly communicate the ability of a forecast to answer these questions. Instead, quantitative scores typically address them indirectly or in some combined form. A more direct performance measure grew from what started as a project for a high-school intern. The challenge was to evaluate aspects of forecast quality from a set of convection-allowing (1.67 km) precipitation forecasts over Florida. Although the output was highly detailed, evaluation became manageable by simply adding a series of static landmarks with range rings and radials. Using the “targets” as a guide, the student and the two authors successfully obtained quantitative estimates of model tendencies that had heretofore only been reported anecdotally. What follows is a description of the method as well as the results from the analysis. It is hoped that this work will stimulate a broader discussion about how to extract performance information from very complex forecasts and present that information in terms that humans can readily perceive.
Abstract
When consulting a forecast, users often ask some variant of the following questions: Will an event of interest occur? If so, when will it occur? How long will it last? How intense will it be? Standard verification measures often do not directly communicate the ability of a forecast to answer these questions. Instead, quantitative scores typically address them indirectly or in some combined form. A more direct performance measure grew from what started as a project for a high-school intern. The challenge was to evaluate aspects of forecast quality from a set of convection-allowing (1.67 km) precipitation forecasts over Florida. Although the output was highly detailed, evaluation became manageable by simply adding a series of static landmarks with range rings and radials. Using the “targets” as a guide, the student and the two authors successfully obtained quantitative estimates of model tendencies that had heretofore only been reported anecdotally. What follows is a description of the method as well as the results from the analysis. It is hoped that this work will stimulate a broader discussion about how to extract performance information from very complex forecasts and present that information in terms that humans can readily perceive.
Abstract
A series of experiments have been conducted using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) to assess the impact of horizontal resolution on hurricane intensity prediction for 10 Atlantic storms during the 2005 and 2007 hurricane seasons. The results of this study from the Hurricane Katrina (2005) simulations indicate that the hurricane intensity and structure are very sensitive to the horizontal grid spacing (9 and 3 km) and underscore the need for cloud microphysics to capture the structure, especially for strong storms with small-diameter eyes and large pressure gradients. The high resolution simulates stronger vertical motions, a more distinct upper-level warm core, stronger upper-level outflow, and greater finescale structure associated with deep convection, including spiral rainbands and the secondary circulation. A vortex Rossby wave (VRW) spectrum analysis is performed on the simulated 10-m winds and the NOAA/Hurricane Research Division (HRD) Real-Time Hurricane Wind Analysis System (H*Wind) to evaluate the impact of horizontal resolution. The degree to which the VRWs are adequately resolved near the TC inner core is addressed and the associated resolvable wave energy is explored at different grid resolutions. The fine resolution is necessary to resolve higher-wavenumber modes of VRWs to preserve more wave energy and, hence, to attain a more detailed eyewall structure. The wind–pressure relationship from the high-resolution simulations is in better agreement with the observations than are the coarse-resolution simulations for the strong storms. Two case studies are analyzed and overall the statistical analyses indicate that high resolution is beneficial for TC intensity and structure forecasts, while it has little impact on track forecasts.
Abstract
A series of experiments have been conducted using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) to assess the impact of horizontal resolution on hurricane intensity prediction for 10 Atlantic storms during the 2005 and 2007 hurricane seasons. The results of this study from the Hurricane Katrina (2005) simulations indicate that the hurricane intensity and structure are very sensitive to the horizontal grid spacing (9 and 3 km) and underscore the need for cloud microphysics to capture the structure, especially for strong storms with small-diameter eyes and large pressure gradients. The high resolution simulates stronger vertical motions, a more distinct upper-level warm core, stronger upper-level outflow, and greater finescale structure associated with deep convection, including spiral rainbands and the secondary circulation. A vortex Rossby wave (VRW) spectrum analysis is performed on the simulated 10-m winds and the NOAA/Hurricane Research Division (HRD) Real-Time Hurricane Wind Analysis System (H*Wind) to evaluate the impact of horizontal resolution. The degree to which the VRWs are adequately resolved near the TC inner core is addressed and the associated resolvable wave energy is explored at different grid resolutions. The fine resolution is necessary to resolve higher-wavenumber modes of VRWs to preserve more wave energy and, hence, to attain a more detailed eyewall structure. The wind–pressure relationship from the high-resolution simulations is in better agreement with the observations than are the coarse-resolution simulations for the strong storms. Two case studies are analyzed and overall the statistical analyses indicate that high resolution is beneficial for TC intensity and structure forecasts, while it has little impact on track forecasts.
Abstract
The impact of dissipative heating on tropical cyclone (TC) intensity forecasts is investigated using the U.S. Navy’s operational mesoscale model (the Coupled Ocean/Atmosphere Mesoscale Prediction System). A physically consistent method of including dissipative heating is developed based on turbulent kinetic energy dissipation to ensure energy conservation. Mean absolute forecast errors of track and surface maximum winds are calculated for eighteen 48-h simulations of 10 selected TC cases over both the Atlantic basin and the northwest Pacific. Simulation results suggest that the inclusion of dissipative heating improves surface maximum wind forecasts by 10%–20% at 15-km resolution, while it has little impact on the track forecasts. The resultant improvement from the inclusion of the dissipative heating increases to 29% for the surface maximum winds at 5-km resolution for Hurricane Isabel (2003), where dissipative heating produces an unstable layer at low levels and warms a deep layer of the troposphere. While previous studies depicted a 65 m s−1 threshold for the dissipative heating to impact the TC intensity, it is found that dissipative heating has an effect on the TC intensity when the TC is of moderate strength with the surface maximum wind speed at 45 m s−1. Sensitivity tests reveal that there is significant nonlinear interaction between the dissipative heating from the surface friction and that from the turbulent kinetic energy dissipation in the interior atmosphere. A conceptualized description is given for the positive feedback mechanism between the two processes. The results presented here suggest that it is necessary to include both processes in a mesoscale model to better forecast the TC structure and intensity.
Abstract
The impact of dissipative heating on tropical cyclone (TC) intensity forecasts is investigated using the U.S. Navy’s operational mesoscale model (the Coupled Ocean/Atmosphere Mesoscale Prediction System). A physically consistent method of including dissipative heating is developed based on turbulent kinetic energy dissipation to ensure energy conservation. Mean absolute forecast errors of track and surface maximum winds are calculated for eighteen 48-h simulations of 10 selected TC cases over both the Atlantic basin and the northwest Pacific. Simulation results suggest that the inclusion of dissipative heating improves surface maximum wind forecasts by 10%–20% at 15-km resolution, while it has little impact on the track forecasts. The resultant improvement from the inclusion of the dissipative heating increases to 29% for the surface maximum winds at 5-km resolution for Hurricane Isabel (2003), where dissipative heating produces an unstable layer at low levels and warms a deep layer of the troposphere. While previous studies depicted a 65 m s−1 threshold for the dissipative heating to impact the TC intensity, it is found that dissipative heating has an effect on the TC intensity when the TC is of moderate strength with the surface maximum wind speed at 45 m s−1. Sensitivity tests reveal that there is significant nonlinear interaction between the dissipative heating from the surface friction and that from the turbulent kinetic energy dissipation in the interior atmosphere. A conceptualized description is given for the positive feedback mechanism between the two processes. The results presented here suggest that it is necessary to include both processes in a mesoscale model to better forecast the TC structure and intensity.
Abstract
The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.
Abstract
The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.
Abstract
This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.
Abstract
This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.
Abstract
This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.
Abstract
This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.
Abstract
In 1980 the Holland tropical cyclone (TC) wind profile model was introduced. This simple model was originally intended to estimate the wind profile based on limited surface pressure information alone. For this reason and its relative simplicity, the model has been used in many practical applications. In this paper the potential of a simplified version of the Holland B parameter, which is related to the shape of the tangential wind profile, is explored as a powerful diagnostic tool for monitoring TC structure. The implementation examined is based on the limited information (maximum wind, central pressure, radius and pressure of the outer closed isobar, radii of operationally important wind radii, etc.) that is typically available in operational models and routine analyses of TC structure. This “simplified Holland B” parameter is shown to be sensitive to TC intensity, TC size, and the rate of radial decay of the tangential winds, but relatively insensitive to the radius of maximum winds. A climatology of the simplified Holland B parameter based on historical best-track data is also developed and presented, providing the expected natural ranges of variability. The relative simplicity, predictable variability, and desirable properties of the simplified Holland B parameter make it ideal for a variety of applications. Examples of how the simplified Holland B parameter can be used for improving forecaster guidance, developing TC structure tools, diagnosing TC model output, and understanding and comparing the climatological variations of TC structure are presented.
Abstract
In 1980 the Holland tropical cyclone (TC) wind profile model was introduced. This simple model was originally intended to estimate the wind profile based on limited surface pressure information alone. For this reason and its relative simplicity, the model has been used in many practical applications. In this paper the potential of a simplified version of the Holland B parameter, which is related to the shape of the tangential wind profile, is explored as a powerful diagnostic tool for monitoring TC structure. The implementation examined is based on the limited information (maximum wind, central pressure, radius and pressure of the outer closed isobar, radii of operationally important wind radii, etc.) that is typically available in operational models and routine analyses of TC structure. This “simplified Holland B” parameter is shown to be sensitive to TC intensity, TC size, and the rate of radial decay of the tangential winds, but relatively insensitive to the radius of maximum winds. A climatology of the simplified Holland B parameter based on historical best-track data is also developed and presented, providing the expected natural ranges of variability. The relative simplicity, predictable variability, and desirable properties of the simplified Holland B parameter make it ideal for a variety of applications. Examples of how the simplified Holland B parameter can be used for improving forecaster guidance, developing TC structure tools, diagnosing TC model output, and understanding and comparing the climatological variations of TC structure are presented.
Abstract
High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.
Abstract
High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.