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42 pages, 13951 KiB

Open AccessArticle

Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event

by Russell L. Elsberry, Joel W. Feldmeier, Hway-Jen Chen, Melinda Peng, Christopher S. Velden and Qing Wang

Atmosphere 2020, 11(11), 1200; https://doi.org/10.3390/atmos11111200 - 06 Nov 2020

Cited by 4 | Viewed by 1647

Abstract

This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The [...] Read more.

This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most important modifications are a more time-efficient dynamic initialization technique and adding a near-surface wind field adjustment as a low-level constraint on the distribution of deep convection relative to the translating center. With the new technique, the Coupled Ocean/Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model initial wind field at 12.86 km elevation quickly adjusts to the cirrus-level GOES-16 AMVs to better detect the Irma outflow magnitude and areal extent every 15 min, and predicts direct connections to adjacent synoptic circulations much better than a dynamic initialization with only lower-resolution hourly GOES-13 AMVs and also better than a cold-start COAMPS-TC initialization with a bogus vortex. Furthermore, only with the GOES-16 AMVs does the COAMPS-TC model accurately predict the timing of an intermediate 12 h constant-intensity period between two segments of the Irma RI. By comparison, HWRF model study of the Irma case that utilized the same GOES-16 AMV dataset predicted a continuous RI without the intermediate constant-intensity period, and predicted more limited outflow areal extents without strong direct connections with adjacent synoptic circulations. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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20 pages, 8072 KiB

Open AccessEditor’s ChoiceArticle

A Review and Evaluation of Planetary Boundary Layer Parameterizations in Hurricane Weather Research and Forecasting Model Using Idealized Simulations and Observations

by Jun A. Zhang, Evan A. Kalina, Mrinal K. Biswas, Robert F. Rogers, Ping Zhu and Frank D. Marks

Atmosphere 2020, 11(10), 1091; https://doi.org/10.3390/atmos11101091 - 13 Oct 2020

Cited by 28 | Viewed by 3283

Abstract

This paper reviews the evolution of planetary boundary layer (PBL) parameterization schemes that have been used in the operational version of the Hurricane Weather Research and Forecasting (HWRF) model since 2011. Idealized simulations are then used to evaluate the effects of different PBL [...] Read more.

This paper reviews the evolution of planetary boundary layer (PBL) parameterization schemes that have been used in the operational version of the Hurricane Weather Research and Forecasting (HWRF) model since 2011. Idealized simulations are then used to evaluate the effects of different PBL schemes on hurricane structure and intensity. The original Global Forecast System (GFS) PBL scheme in the 2011 version of HWRF produces the weakest storm, while a modified GFS scheme using a wind-speed dependent parameterization of vertical eddy diffusivity (K_m) produces the strongest storm. The subsequent version of the hybrid eddy diffusivity and mass flux scheme (EDMF) used in HWRF also produces a strong storm, similar to the version using the wind-speed dependent K_m. Both the intensity change rate and maximum intensity of the simulated storms vary with different PBL schemes, mainly due to differences in the parameterization of K_m. The smaller the K_m in the PBL scheme, the faster a storm tends to intensify. Differences in hurricane PBL height, convergence, inflow angle, warm-core structure, distribution of deep convection, and agradient force in these simulations are also examined. Compared to dropsonde and Doppler radar composites, improvements in the kinematic structure are found in simulations using the wind-speed dependent K_m and modified EDMF schemes relative to those with earlier versions of the PBL schemes in HWRF. However, the upper boundary layer in all simulations is much cooler and drier than that in dropsonde observations. This model deficiency needs to be considered and corrected in future model physics upgrades. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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20 pages, 4562 KiB

Open AccessArticle

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

by Shixuan Zhang and Zhaoxia Pu

Atmosphere 2020, 11(9), 1007; https://doi.org/10.3390/atmos11091007 - 21 Sep 2020

Cited by 2 | Viewed by 2420

Abstract

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of [...] Read more.

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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42 pages, 13209 KiB

Open AccessArticle

Advanced Global Model Ensemble Forecasts of Tropical Cyclone Formation, and Intensity Predictions along Medium-Range Tracks

by Russell L. Elsberry, Hsiao-Chung Tsai, Wei-Chia Chin and Timothy P. Marchok

Atmosphere 2020, 11(9), 1002; https://doi.org/10.3390/atmos11091002 - 18 Sep 2020

Cited by 6 | Viewed by 2591

Abstract

Marchok vortex tracker outputs from the European Centre for Medium-Range Weather Forecasts ensemble (ECEPS) and National Centers for Environmental Prediction ensemble (GEFS) are utilized to provide the Time-to-Formation (T2F of 25 kt or 35 kt) timing and positions along the weighted-mean vector motion [...] Read more.

Marchok vortex tracker outputs from the European Centre for Medium-Range Weather Forecasts ensemble (ECEPS) and National Centers for Environmental Prediction ensemble (GEFS) are utilized to provide the Time-to-Formation (T2F of 25 kt or 35 kt) timing and positions along the weighted-mean vector motion (WMVM) track forecasts, and our weighted analog intensity Pacific (WAIP) technique provides 7-day intensity forecasts after the T2F. Example T2F(35) forecasts up to 5 days in advance of two typhoons and one non-developer in the western North Pacific are described in detail. An example T2F forecast of pre-Hurricane Kiko in the eastern North Pacific indicated that Hawaii would be under threat by the end of the 15-day ECEPS WMVM track forecast. An example T2F forecast of pre-Hurricane Lorenzo in the eastern Atlantic demonstrates that both the ECEPS and GEFS predict up to 5 days in advance that the precursor African wave will become a Tropical Storm off the west coast and will likely become a hurricane. Validations of the T2F(25) and T2F(35) timing and position errors are provided for all ECEPS and GEFS forecasts of the two typhoons and Hurricanes Kiko and Lorenzo. If the T2F timing errors are small (<1 day), the T2F position errors along the WMVM track forecasts will be small (<300 km). Although the primary focus is on the western North Pacific, the examples from the Atlantic and eastern/central North Pacific indicate the potential for future application in other basins. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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25 pages, 4164 KiB

Open AccessEditor’s ChoiceArticle

Vortex Initialization in the NCEP Operational Hurricane Models

by Qingfu Liu, Xuejin Zhang, Mingjing Tong, Zhan Zhang, Bin Liu, Weiguo Wang, Lin Zhu, Banglin Zhang, Xiaolin Xu, Samuel Trahan, Ligia Bernardet, Avichal Mehra and Vijay Tallapragada

Atmosphere 2020, 11(9), 968; https://doi.org/10.3390/atmos11090968 - 10 Sep 2020

Cited by 16 | Viewed by 3967

Abstract

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The [...] Read more.

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model). Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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17 pages, 5206 KiB

Open AccessArticle

Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

by Zaizhong Ma, Bin Liu, Avichal Mehra, Ali Abdolali, Andre van der Westhuysen, Saeed Moghimi, Sergey Vinogradov, Zhan Zhang, Lin Zhu, Keqin Wu, Roshan Shrestha, Anil Kumar, Vijay Tallapragada and Nicole Kurkowski

Atmosphere 2020, 11(9), 888; https://doi.org/10.3390/atmos11090888 - 22 Aug 2020

Cited by 8 | Viewed by 3242

Abstract

Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) [...] Read more.

Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) Hurricane Weather Research Forecast (HWRF) system has been widely used in both operational and research environments for studying hurricanes in different basins. In this study, high-resolution land–sea mask datasets are introduced to the nested domain of HWRF, for the first time, as an attempt to improve hurricane wind forecasts. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of extending the capability to HWRF, due to the introduction of the high-resolution land–sea masks into the nested domains for the first time. A preliminary assessment of the numerical experiments with HWRF shows that the introduction of high-resolution land–sea masks into the nested domains produce significantly more accurate definitions of coastlines, land-use, and soil types. Furthermore, the high-resolution land–sea mask not only improves the quality of simulated wind information along the coast, but also improves the hurricane track, intensity, and storm-size predictions. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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22 pages, 9223 KiB

Open AccessArticle

A Hydrodynamical Atmosphere/Ocean Coupled Modeling System for Multiple Tropical Cyclones

by Ghassan J. Alaka, Jr., Dmitry Sheinin, Biju Thomas, Lew Gramer, Zhan Zhang, Bin Liu, Hyun-Sook Kim and Avichal Mehra

Atmosphere 2020, 11(8), 869; https://doi.org/10.3390/atmos11080869 - 16 Aug 2020

Cited by 12 | Viewed by 2870

Abstract

The goal of this paper is to introduce a new multi-storm atmosphere/ocean coupling scheme that was implemented and tested in the Basin-Scale Hurricane Weather Research and Forecasting (HWRF-B) model. HWRF-B, an experimental model developed at the National Oceanic and Atmospheric Administration (NOAA) and [...] Read more.

The goal of this paper is to introduce a new multi-storm atmosphere/ocean coupling scheme that was implemented and tested in the Basin-Scale Hurricane Weather Research and Forecasting (HWRF-B) model. HWRF-B, an experimental model developed at the National Oceanic and Atmospheric Administration (NOAA) and supported by the Hurricane Forecast Improvement Program, is configured with multiple storm-following nested domains to produce high-resolution predictions for several tropical cyclones (TCs) within the same forecast integration. The new coupling scheme parallelizes atmosphere/ocean interactions for each nested domain in HWRF-B, and it may be applied to any atmosphere/ocean coupled system. TC forecasts from this new hydrodynamical modeling system were produced in the North Atlantic and eastern North Pacific from 2017–2019. The performance of HWRF-B was evaluated, including forecasts of TC track, intensity, structure (e.g., surface wind radii), and intensity change, and simulated sea-surface temperatures were compared with satellite observations. Median forecast skill scores showed significant improvement over the operational HWRF at most forecast lead times for track, intensity, and structure. Sea-surface temperatures cooled by 1–8 °C for the five HWRF-B case studies, demonstrating the utility of the model to study the impact of the ocean on TC intensity forecasting. These results show the value of a multi-storm modeling system and provide confidence that the multi-storm coupling scheme was implemented correctly. Future TC models within NOAA, especially the Unified Forecast System’s Hurricane Analysis and Forecast System, would benefit from the multi-storm coupling scheme whose utility and performance are demonstrated in HWRF-B here. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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20 pages, 5734 KiB

Open AccessArticle

The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System

by Zhan Zhang, Mingjing Tong, Jason A. Sippel, Avichal Mehra, Banglin Zhang, Keqin Wu, Bin Liu, Jili Dong, Zaizhong Ma, Henry Winterbottom, Weiguo Wang, Lin Zhu, Qingfu Liu, Hyun-Sook Kim, Biju Thomas, Dmitry Sheinin, Li Bi and Vijay Tallapragada

Atmosphere 2020, 11(8), 801; https://doi.org/10.3390/atmos11080801 - 29 Jul 2020

Cited by 7 | Viewed by 2615

Abstract

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone [...] Read more.

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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11 pages, 1310 KiB

Open AccessArticle

Coastal Resilience Against Storm Surge from Tropical Cyclones

by Robert Mendelsohn and Liang Zheng

Atmosphere 2020, 11(7), 725; https://doi.org/10.3390/atmos11070725 - 07 Jul 2020

Cited by 8 | Viewed by 4643

Abstract

It is well known that seawalls are effective at stopping common storm surges in urban areas. This paper examines whether seawalls should be built to withstand the storm surge from a major tropical cyclone. We estimate the extra cost of building the wall [...] Read more.

It is well known that seawalls are effective at stopping common storm surges in urban areas. This paper examines whether seawalls should be built to withstand the storm surge from a major tropical cyclone. We estimate the extra cost of building the wall tall enough to stop such surges and the extra flood benefit of this additional height. We estimate the surge probability distribution from six tidal stations spread along the Atlantic seaboard of the United States. We then measure how valuable the vulnerable buildings behind a 100 m wall must be to justify such a tall wall at each site. Combining information about the probability distribution of storm surge, the average elevation of protected buildings, and the damage rate at each building, we find that the value of protected buildings behind this 100 m wall must be in the hundreds of millions to justify the wall. We also examine the additional flood benefit and cost of protecting a km² of land in nearby cities at each site. The density of buildings in coastal cities in the United States are generally more than an order of magnitude too low to justify seawalls this high. Seawalls are effective, but not at stopping the surge damage from major tropical cyclones. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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18 pages, 5836 KiB

Open AccessArticle

Performance of Forecasts of Hurricanes with and without Upper-Level Troughs over the Mid-Latitudes

by Kazutoshi Sato, Jun Inoue and Akira Yamazaki

Atmosphere 2020, 11(7), 702; https://doi.org/10.3390/atmos11070702 - 01 Jul 2020

Cited by 4 | Viewed by 3520

Abstract

We investigated the accuracy of operational medium-range ensemble forecasts for 29 Atlantic hurricanes between 2007 and 2019. Upper-level troughs with strong wind promoted northward movement of hurricanes over the mid-latitudes. For hurricanes with upper-level troughs, relatively large errors in the prediction of troughs [...] Read more.

We investigated the accuracy of operational medium-range ensemble forecasts for 29 Atlantic hurricanes between 2007 and 2019. Upper-level troughs with strong wind promoted northward movement of hurricanes over the mid-latitudes. For hurricanes with upper-level troughs, relatively large errors in the prediction of troughs result in large ensemble spreads, which result in failure to forecast hurricane track. In contrast, for hurricanes without upper-level troughs, mean central position errors are relatively small in all operational forecasts because of the absence of upper-level strong wind around troughs over the mid-latitudes. Hurricane Irma in September 2017 was accompanied by upper-level strong wind around a trough; errors and ensemble spreads for the predicted upper-level trough are small, contributing to smaller errors and small ensemble spreads in the predicted tracks of Irma. Our observing system experiment reveals that inclusion of additional Arctic radiosonde observation data obtained from research vessel Mirai in 2017 improves error and ensemble spread in upper-level trough with strong wind at initial time for forecast, increasing the accuracy of the forecast of the track of Irma in 2017. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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14 pages, 5106 KiB

Open AccessArticle

Strategies for Assimilating High-Density Atmospheric Motion Vectors into a Regional Tropical Cyclone Forecast Model (HWRF)

by William E. Lewis, Christopher S. Velden and David Stettner

Atmosphere 2020, 11(6), 673; https://doi.org/10.3390/atmos11060673 - 26 Jun 2020

Cited by 12 | Viewed by 2599

Abstract

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted [...] Read more.

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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17 pages, 3400 KiB

Open AccessArticle

Evaluation of Hurricane Harvey (2017) Rainfall in Deterministic and Probabilistic HWRF Forecasts

by Mu-Chieh Ko, Frank D. Marks, Ghassan J. Alaka, Jr. and Sundararaman G. Gopalakrishnan

Atmosphere 2020, 11(6), 666; https://doi.org/10.3390/atmos11060666 - 22 Jun 2020

Cited by 13 | Viewed by 3136

Abstract

Rainfall forecast performance was evaluated for the first time for the Hurricane Weather Research and Forecasting (HWRF) model. This study focused on HWRF performance in predicting rainfall from Hurricane Harvey in 2017. In particular, two configurations of the 2017 version of HWRF were [...] Read more.

Rainfall forecast performance was evaluated for the first time for the Hurricane Weather Research and Forecasting (HWRF) model. This study focused on HWRF performance in predicting rainfall from Hurricane Harvey in 2017. In particular, two configurations of the 2017 version of HWRF were investigated: a deterministic version of the Basin-scale HWRF (HB17) and an ensemble version of the operational HWRF (H17E). This study found that HB17 generated reasonable rainfall patterns and rain-rate distributions for Hurricane Harvey, in part due to accurate track forecasts. However, the estimated rain rates near the storm center (within 50 km) were slightly overestimated. In the rainband region (150 to 300 km), HB17 reproduced heavy rain rates and underestimated light rain rates. The accumulated rainfall pattern successfully captured Harvey’s intense outer rainband with adequate spatial displacement. In addition, the performance of H17E on probabilistic rainfall has shown that the ensemble forecasts can potentially increase the accuracy of the predicted locations for extreme rainfall. Moreover, the study also indicated the importance of high-resolution dynamical models for rainfall predictions. Although statistical models can generate the overall rainfall patterns along a track, extreme rainfall events produced from outer rainbands can only be forecasted by numerical models, such as HWRF. Accordingly, the HWRF models have the capability of simulating reasonable quantitative precipitation forecasts and providing essential rainfall guidance in order to further reduce loss of life and cost to the economy. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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15 pages, 8113 KiB

Open AccessArticle

Improvement of the Numerical Tropical Cyclone Prediction System at the Central Weather Bureau of Taiwan: TWRF (Typhoon WRF)

by Ling-Feng Hsiao, Der-Song Chen, Jing-Shan Hong, Tien-Chiang Yeh and Chin-Tzu Fong

Atmosphere 2020, 11(6), 657; https://doi.org/10.3390/atmos11060657 - 19 Jun 2020

Cited by 14 | Viewed by 4209

Abstract

Typhoon WRF (TWRF) based on the Advanced Research Weather Research and Forecasting Model (ARW WRF) was operational at the Central Weather Bureau (CWB) for tropical cyclone (TC) predictions since 2010 (named TWRF V1). CWB has committed to improve this regional model, aiming to [...] Read more.

Typhoon WRF (TWRF) based on the Advanced Research Weather Research and Forecasting Model (ARW WRF) was operational at the Central Weather Bureau (CWB) for tropical cyclone (TC) predictions since 2010 (named TWRF V1). CWB has committed to improve this regional model, aiming to increase the model predictability toward typhoons over East Asia. In 2016, an upgraded version designed to replace TWRF V1 became operational (named TWRF V2). Compared with V1, which has triple-nested meshes with coarser resolution (45/15/5 km), V2 increased the model resolution to 15/3 km. Since V1 and V2 were maintained in parallel from 2016 to 2018, this study utilized the real-time forecasts to investigate the impact of model resolution on TC prediction. Statistical measures pointed out the superiority of the high-resolution model on TC prediction. The forecast performance was also found competitive with that of two leading global models. The case study further pointed out, with the higher resolution, the model not only advanced the prediction on the TC track and inner core structure but also improved the representativeness of the complex terrain. Overall, the high-resolution model can better handle the so-called terrain phase-lock effect and, therefore, improve the TC quantitative precipitation forecast over the complex Taiwanese terrain. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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16 pages, 18518 KiB

Open AccessArticle

The Evaluation of Real-Time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) Model Performance for the 2019 Atlantic Hurricane Season

by Jili Dong, Bin Liu, Zhan Zhang, Weiguo Wang, Avichal Mehra, Andrew T. Hazelton, Henry R. Winterbottom, Lin Zhu, Keqin Wu, Chunxi Zhang, Vijay Tallapragada, Xuejin Zhang, Sundararaman Gopalakrishnan and Frank Marks

Atmosphere 2020, 11(6), 617; https://doi.org/10.3390/atmos11060617 - 11 Jun 2020

Cited by 27 | Viewed by 5293

Abstract

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based [...] Read more.

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based convection-allowing HAFS Stand-Alone Regional model (HAFS-SAR) was successfully implemented during Hurricane Forecast Improvement Project (HFIP) real-time experiments for the 2019 Atlantic TC season. HAFS-SAR has a single large 3-km horizontal resolution regional domain covering the North Atlantic basin. A total of 273 cases during the 2019 TC season are systematically evaluated against the best track and compared with three operational forecasting systems: Global Forecast System (GFS), Hurricane Weather Research and Forecasting model (HWRF), and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model (HMON). HAFS-SAR has the best performance in track forecasts among the models presented in this study. The intensity forecasts are improved over GFS, but show less skill compared to HWRF and HMON. The radius of gale force wind is over-predicted in HAFS-SAR, while the hurricane force wind radius has lower error than other models. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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20 pages, 9431 KiB

Open AccessArticle

Assimilation of Himawari-8 Rapid-Scan Atmospheric Motion Vectors on Tropical Cyclone in HWRF System

by Masahiro Sawada, Zaizhong Ma, Avichal Mehra, Vijay Tallapragada, Ryo Oyama and Kazuki Shimoji

Atmosphere 2020, 11(6), 601; https://doi.org/10.3390/atmos11060601 - 05 Jun 2020

Cited by 6 | Viewed by 2797

Abstract

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers [...] Read more.

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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22 pages, 3521 KiB

Open AccessArticle

Estimating Tropical Cyclone Intensity in the South China Sea Using the XGBoost Model and FengYun Satellite Images

by Qingwen Jin, Xiangtao Fan, Jian Liu, Zhuxin Xue and Hongdeng Jian

Atmosphere 2020, 11(4), 423; https://doi.org/10.3390/atmos11040423 - 22 Apr 2020

Cited by 16 | Viewed by 3367

Abstract

Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity [...] Read more.

Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity prediction, which remains challenging, as the internal convection and formation mechanisms of such storms are not fully understood. This study investigated the relationship between remote sensing data and TC intensity to improve the accuracy of TC intensity prediction. An intensity forecast model for the South China Sea was built using the eXtreme Gradient Boosting (XGBoost) model and FengYun-2 (FY-2) satellite data, environmental data, and best track datasets from 2006 to 2017. First, correlation analysis algorithms were used to extract the TC regions in which the satellite data were best correlated, with TC intensity at lead times of 6, 12, 18, and 24 h. Then, satellite, best track, and environmental data were used as source data to develop three different XGBoost models for predicting TC intensity: model A1 (climatology and persistence predictors + environmental predictors), model A2 (A1 + satellite-based predictors extracted as mean values), and model A3 (A1 + satellite-based predictors extracted by our method). Finally, we analyzed the impact of the FY-2 satellite data on the accuracy of TC intensity prediction using the forecast skill parameter. The results revealed that the equivalent blackbody temperature (TBB) of the FY-2 data has a strong correlation with TC intensity at 6, 12, 18, and 24 h lead times. The mean absolute error (MAE) of model A3 was reduced by 0.47%, 1.79%, 1.91%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A2, respectively, and by 2.73%, 7.58%, 7.64%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A1. Furthermore, the accuracy of TC intensity prediction is improved by FY-2 satellite images, and our extraction method was found to significantly improve upon the traditional extraction method. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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17 pages, 6904 KiB

Open AccessArticle

Sensitivity of the Intensity and Structure of Tropical Cyclones to Tropospheric Stability Conditions

by Tetsuya Takemi and Shota Yamasaki

Atmosphere 2020, 11(4), 411; https://doi.org/10.3390/atmos11040411 - 20 Apr 2020

Cited by 6 | Viewed by 3072

Abstract

The intensity of tropical cyclones (TCs) is controlled by their environmental conditions. In addition to the sea surface temperature, tropospheric temperature lapse rate and tropopause height are highly variable. This study investigates the sensitivity of the intensity and structure of TCs to environmental [...] Read more.

The intensity of tropical cyclones (TCs) is controlled by their environmental conditions. In addition to the sea surface temperature, tropospheric temperature lapse rate and tropopause height are highly variable. This study investigates the sensitivity of the intensity and structure of TCs to environmental static stability with a fixed sea surface temperature by conducting a large ensemble of axisymmetric numerical experiments in which tropopause height and tropospheric temperature lapse rate are systematically changed based on the observed environmental properties for TCs that occurred in the western North Pacific. The results indicate that the intensity of the simulated TCs changes more sharply with the increase in the temperature lapse rate than with the increase in the tropopause height. The increases in the intensity of TCs are 1.3–1.9 m s⁻¹ per 1% change of the lapse rate and 0.1–0.5 m s⁻¹ per 1% change of the tropopause height. With the increase in the intensity of TCs, supergradient wind at low levels and double warm core structures are evident. Specifically, the formation of the warm core at the lower levels is closely tied with the intensification of TCs, and the temperature excess of the lower warm core becomes larger in higher lapse rate cases. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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Review

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29 pages, 628 KiB

Open AccessReview

Machine Learning in Tropical Cyclone Forecast Modeling: A Review

by Rui Chen, Weimin Zhang and Xiang Wang

Atmosphere 2020, 11(7), 676; https://doi.org/10.3390/atmos11070676 - 27 Jun 2020

Cited by 100 | Viewed by 20507

Abstract

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, [...] Read more.

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors. Full article

(This article belongs to the Special Issue Modeling and Data Assimilation for Tropical Cyclone Forecasts)

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