AOML's Hurricane Modeling & Prediction Program

Creating Next Generation Hurricane Modeling Systems

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Who We Are

AOML’s Hurricane Modeling Group was founded in 2007 to advance hurricane forecast models through development and targeted research. Our scientists are from the diverse fields of meteorology, hurricane modeling, and computer science. Since its inception, the team has worked to consistently improve NOAA’s Hurricane Weather Research Forecast (HWRF) model and is continuing to advance NOAA’s next generation hurricane modeling systems.

AOML’s Hurricane Modeling Group specializes in developing and evaluating experimental and operational hurricane forecast models for transitions from research to operations. Improvements to these hurricane models, once transitioned to NOAA’s Environmental Modeling Center, provide better forecast guidance on tropical cyclone structure, intensity and track to the National Hurricane Center.

Such improvements have the potential to save lives and reduce property damage. Read more about the value of investment in hurricane research and how numerical modeling contributes in our Hurricane Research Flyer. 

Our Objectives

The primary objective of AOML’s Hurricane Modeling Group is to develop and further advance NOAA hurricane research and forecast modeling systems. The program’s efforts aim to:

  • Develop hurricane research and forecast models
  • Advance our understanding of hurricane processes using high-resolution numerical modeling systems
  • Utilize observations from the Hurricane Field Program to improve physical parameterizations in modeling systems
  • Improve vortex-scale data assimilation techniques
  • Transition model research and developments into operations

Sundararaman Gopalakrishnan
305.361.4357

| Sundararaman Gopalakrishnan, Ph.D (Gopal)

Lead Scientist

Xuejin Zhang, Ph.D
305.361.4558

| Xuejin Zhang, Ph.D

Gus Alaka, Ph.D
305.361.4409

| Gus Alaka, Ph.D

Jason Sippel, Ph.D
305.361.4368

| Jason Sippel, Ph.D

Robert A. Black
305.361.4314

| Robert Black

Hua Leighton, Ph.D
305.361.4559

| Hua Leighton, Ph.D

Russell St. Fleur
305.361.4503

| Russell St. Fleur

Mu-Chieh Ko
305.361.4551

| Mu-Chieh Ko

Andrew Hazelton, Ph.D
305.361.4317

| Andrew Hazelton, Ph.D

William Ramstrom
305.361.4433

| William Ramstrom

Lew Gramer, Ph.D
305.361.4554

| Lew Gramer, Ph.D

Alrick Green

| Alrick Green

John Kaplan
305.361.4506

| John Kaplan

hwrfoperations

Forecast Improvements

From a Successful Transition to Operations

As a result of the these diligent efforts, unparalleled support by NOAA’s Hurricane Forecast Improvement Project, and close collaborative research work with the Environmental Modeling Center, the improved HWRF model was incorporated as one of the National Hurricane Center’s operational hurricane forecasting models beginning with the 2012 season.

Improved forecasts benefit emergency managers along coastal areas of the US, reducing warning times and the extent of evacuation areas, thus saving lives, helping to mitigate property damage and increasing the public’s confidence in NOAA’s official hurricane forecasts and warnings.

Rapid Intensification Prediction

HWRF had the best intensity forecast performance in 2018, even outperforming the National Hurricane Center’s official forecasters for some of the forecast intervals. Some cycles of HWRF forecasts captured the rapid intensification of Hurricane Michael (2018) at least four days in advance. Although the rapid intensification of a system is very difficult for models and forecasters to predict, it is incredibly important information for them to have.

Performance In Global Basins

In the East Pacific, HWRF was the best dynamical model with the lowest intensity errors for the 2018 hurricane season. In the Atlantic Basin, HWRF was the best dynamical model with the lowest intensity errors prior to Day Three. During that period, its intensity errors were comparable to those of the official forecasts from the National Hurricane Center.

Track Guidance

During Hurricane Lane (2018), the P-3 aircraft was flown for the first time in the Central Pacific for assimilating inner core winds in the HWRF model. Post-analysis of model forecasts indicated an average of 20% track improvements, with a maximum of 35% at 96 hours, with the inclusion of the Tail Doppler Radar data for initializing the HWRF system.

Data Assimilation System

The state-of-the-art inner-core Data Assimilation system was developed for HWRF in 2017. Several upgrades were made to the system in 2018, including the implementation of additional data sets for assimilating radial velocity from the Tail Doppler Radar and surface wind speeds derived from the Stepped-Frequency Microwave Radiometer. In addition to these changes, a significant change was made to how HWRF uses dropsonde data. Improvements in data assimilation, particularly for the inner core, mean that inner-core reconnaissance is becoming increasingly important for improving intensity forecasts. HWRF now uses all reconnaissance data that is transmitted in the operational data stream, and is the only operational model in the world to do so.

Evolution of the Hurricane Modeling System

HWRF

A Foundational Model

HWRF was transitioned to operations at NOAA’s National Centers for Environmental Prediction in 2007. From 2008-2011, scientists at AOML developed an experimental HWRF to target the intensity change problem. The experimental HWRF incorporated a movable multilevel nesting algorithm with planetary boundary layer and surface physics, carefully calibrated by in situ observations obtained from the hurricane inner-core region. These developments formed the backbone of the operational HWRF and resulted in continuous improvements to its track, intensity and structure predictions in the following years.

Using cutting-edge numerical modeling and verification techniques, our research has helped accelerate the advancement of the model’s development and has played a key role in the success of the Hurricane Forecast Improvement Project. These achievements provide hurricane forecasters not only with improved forecast guidance, but also with critical information on the evolution of the three dimensional wind structure within the storm.

HWRF-B

Cutting Edge Capabilities

The Basin-scale HWRF modeling system (HWRF-B), an upgrade of the HWRF for potential Research-to-Operations transition, was developed at AOML in collaboration with other organizations under the  auspices of the Hurricane Forecast Improvement Project. HWRF-B includes two salient improvements over the operational HWRF system: a large, static outermost domain that spans a quarter of the tropical belt; and multiple sets of movable multi-level nests that can follow multiple storms simultaneously.

HWRF-B is currently utilized as a framework for innovative research and an experimental tool for research-to-operations implementations. This unique NOAA capacity will be the baseline for future transitions to next-generation hurricane modeling systems, e.g. the Hurricane Analysis and Forecast System (HAFS).

FV3 HAFS

Next Generation Modeling

The Hurricane Analysis and Forecast System (HAFS) is NOAA’s next-generation numerical model and data assimilation system developed within the framework of the Uniform Forecast System (UFS).  HAFS aims to provide reliable and skillful guidance on tropical cyclone track, intensity, and structure, including rapid intensity changes, genesis, and storm size.

AOML’s Hurricane Modeling group is actively involved in the development of HAFS, along with partners throughout NOAA. One important HAFS development will be the advancement of the model dynamical core to include high-resolution moving nests. Our group is also actively involved in the development and evaluation of changes to model physics by using the high-quality observations collected in the Hurricane Field Program.

hwrf

Hurricane Weather Research Forecast Model

“In 2007, NOAA’s HWRF model was officially adopted by the National Hurricane Center. Since then, the numerical modeling at AOML has greatly advanced NOAA’s HWRF model.”

HFIP Annual Report (2018)

The HWRF model is a storm-following, double-nested, high-resolution, grid modeling system designed to operate at a horizontal resolution of 2 km or less. It was designed for capturing tropical cyclone inner core processes, including interactions with the large-scale processes which have been proven to be critical for improving track, intensity, rainfall and size predictions. HWRF was initially developed for improving intensity guidance, but is also used to provide some reliable track guidance, together with the Global Forecasting System and other models.

HWRF’s development and its transition to operations is one of the major accomplishments of NOAA’s Hurricane Forecast Improvement Project. HWRF has become one of the top performing operational models for track and intensity predictions-in 2018, its average intensity error for tropical cyclones in the North Atlantic Ocean was less than 16 kt through five days (Read the Forecast Verification Report). HWRF is increasing the reliability of operational forecast guidance and is paving the way to next-generation model developments.

HWRF is the driving dynamical model of the Real-Time Hurricane Forecast Improvement Project Corrected Consensus Approach (HCCA) for Tropical Cyclone Intensity Guidance at the National Hurricane Center, and has become the flagship intensity prediction tool for hurricane forecasting at the National Weather Service. The High-resolution HWRF model was developed through state-of-the-art research involving the following key elements:

basinscalehwrf

Basin-scale HWRF

AOML’s Hurricane Modeling Group has upgraded the foundational HWRF model to include new capabilities like the production of simultaneous tropical cyclone forecasts and a much higher resolution environment. It also serves as a prototype for the development of multiple moving, multi-level nests within the global model. This model, called the “Basin-scale HWRF” (HWRF-B), is also a testbed for research aimed at improving predictions of storm-storm, land-storm, and other multi-scale interactions. HWRF-B has been a real-time demonstration project under HFIP since 2013 and is currently being vetted for transition to operations.

HWRF-B Video Screenshot

hafs

Hurricane Analysis and Forecast System

The Hurricane Analysis and Forecast System (HAFS) is NOAA’s next-generation numerical model and data assimilation system developed within the framework of the Uniform Forecast System (UFS).  HAFS aims to provide reliable and skillful guidance on tropical cyclone track, intensity, and structure, including rapid intensity changes, genesis, and storm size. The Hurricane Modeling group is actively involved in the development of HAFS, along with partners throughout NOAA. One important HAFS development will be the advancement of the model dynamical core to include high-resolution moving nests. Our group is also actively involved in the development and evaluation of changes to model physics by using the high-quality observations collected in HRD’s Hurricane Field Program.

As part of the ongoing HAFS development, AOML has maintained a real-time experimental HAFS that runs every 6 hours during the 2019 Atlantic hurricane season, from July through October.  This has allowed for real-time evaluation of the model’s performance in cases like Hurricanes Dorian and Humberto. These real-time runs also will form a basis for further model improvements, as well as research studies examining the details of the track, intensity, and structure evolution of tropical cyclones.

Image of the FV3 HAFS model showing multiple storm interactions.

Statistical Dynamical Modeling

Tropical Cyclone Rapid Intensification Prediction

Rapid intensification occurs in over 80% of major hurricanes and all category four and five hurricanes in the Atlantic Basin. The largest operational intensity forecast errors occur due to the difficulty in forecasting processes relating to rapid intensification. This means that improving the forecasting of these storms is a major priority as was specified in the 2017 Weather Research and Forecasting Innovation Act and the Hurricane Forecast Improvement Program.

A suite of models has been developed with support from NOAA’s Joint Hurricane Testbed to aid forecasters at the National Hurricane Center in predicting rapid intensification in both the Atlantic and Pacific basins. These models use the National Center for Environmental Prediction’s global model output and satellite data to estimate the probability that a tropical cyclone will undergo rapid intensification at a given lead time (defined as the 95th percentile of over-water tropical cyclone intensity change).

One such model that was developed jointly by researchers from AOML, NHC, and NOAA/NESDIS is commonly referred to as the SHIPS rapid intensification index (SHIPS-RII). It uses linear discriminant analysis to estimate the probability of rapid intensification. Although initially developed for the single forecast lead time of 24-h, in recent years the model was developed for the added lead times of 12-h, 36-h, 48-h and 72-h. See the figures to the right to learn more about the outputs for the SHIPS-RII tool.

To read more about the SHIPS-RII model, click on each subject below, or download the most recent publication:

Evaluating Environmental Impacts on Tropical Cyclone Rapid Intensification Predictability Utilizing Statistical Models.

Forecast Skill

Figure 1 shows the skill of the SHIPS-RII  for the 4-year (2016-2019) period during which the multi-lead time forecasts were first made available to the NHC.  The figure indicates that the SHIPS-RII forecasts were skillful relative to climatology (see Kaplan et al. 2015 for more information about the verification methodology) in both the Atlantic and eastern/Central Pacific basins with significantly more skill displayed by the SHIPS-RII in the latter basin.

Fig. 1. The skill of the 2016-2019 operational SHIPS-RII forecasts for the 4-year (2016-2019) period relative to that of climatology for the 20-kt/12-h, 30-kt/24-h, 45-kt/36-h, 55-kt/48-h and 65-kt/72-h rapid intensification thresholds for the Atlantic (blue bar) and Central and eastern North Pacific (yellow bar) basins.

Fig. 1. The skill of the 2016-2019 operational SHIPS-RII forecasts for the 4-year (2016-2019) period relative to that of climatology for the 20-kt/12-h, 30-kt/24-h, 45-kt/36-h, 55-kt/48-h and 65-kt/72-h rapid intensification thresholds for the Atlantic (blue bar) and Central and eastern North Pacific (yellow bar) basins.

Reliability Diagram

Figure 2 provides reliability diagrams depicting the forecasted versus observed probability of rapid intensification for that same 4-year operational sample in both the Atlantic and eastern/Central Pacific basins. The diagrams indicate that the SHIPS-RII forecasts exhibited reasonably good reliability in both basins (i.e. each curve lies fairly close to the black line indicating perfect agreement between the forecasted versus observed probability of rapid) with higher forecasted probabilities observed for the eastern/Central Pacific basin.

Fig. 2. Reliability diagrams depicting the forecasted versus observed probability of RI of the 2016-2019 operational SHIPS-RII forecasts for the 20-kt/12-h (yellow line), 30-kt/24-h (blue line) , 45-kt/36-h (green line), 55-kt/48-h (purple line) and 65-kt/72-h (red line) rapid intensification thresholds. Separate reliability diagrams are shown for the Atlantic (left panel) and central/Eastern North Pacific basin (right panel. The diagonal black line depicted on both panels indicates perfect reliability. Image Credit: NOAA AOML.

Michael (2018) Forecasts

Figure 3 provides an example of the operational SHIPS-RII forecasts for the 30-kt/24h rapid intensification threshold for Hurricane Michael (2018). It can be seen that the SHIPS-RII forecasted probabilities were elevated relative to those of climatology for the entire forecast period but were highest just prior to Michael’s initial period of rapid intensification. However, the SHIPS-RII probabilities were only modestly  higher than climatology during the latter period of rapid intensification that Michael underwent just prior to making landfall along the Florida panhandle underscoring the limitations of the model.

Fig. 3. SHIPS-RII operational probability forecasts for the 30-kt/24-h RI threshold (solid blue curve) for Hurricane Michael (2018). The best track intensity is indicated by the black curve and each 24-h time period during which RI was observed is depicted in red. The climatological probability of RI (dashed blue line) and time of landfall (vertical brown line) are also presented.

Fig. 3. SHIPS-RII operational probability forecasts for the 30-kt/24-h RI threshold (solid blue curve) for Hurricane Michael (2018). The best track intensity is indicated by the black curve and each 24-h time period during which RI was observed is depicted in red. The climatological probability of RI (dashed blue line) and time of landfall (vertical brown line) are also presented.

stormpagedata

Our Data

Below is a link to our data page, which offers a wealth of storm-centered and global model data. We provide global and storm centered “nest” plots, which include the HWRF and HWRF-B models. A combination of the model guidance is essential to have a holistic view of active storms and weather patterns. Each model delivers a suite of data around six-hour increments, which covers winds at different levels of the atmosphere and various types of precipitation. Our data page also contains archives for model runs of different storms in the Northeast Pacific and Atlantic basins.

Steering Flow in the Atlantic from HWRF B
Steering Flow in the Atlantic from HWRF B

Supporting the Hurricane Field Program

The numerical modeling group supports the NOAA Hurricane Field Program by delivering experimental hurricane forecasts to HRD scientists, who play a pivotal role in planning aircraft missions. For example, the Hurricane Analysis and Forecast System (HAFS) provides planners with high resolution forecasts across the entire North Atlantic basin and the Basin-scale HWRF (HWRF-B) provides an alternative option to the operational HWRF. These experimental models can also help determine what data types or regions to target in missions through research and data sensitivity studies.

Monitoring the in-flight Coyote UAS from the piloting station on the P3. Image credit: NOAA

Key Accomplishments

  • Transitions start from HWRF to FV3-based HAFS system.

    2019

  • HWRF versions incorporated into NCEP Operations.

    Incorporated a further increment of the horizontal resolution, from 18/6/2 km to 13.5/4.5/1.5 km

    2018

  • Implementation of the improved HWRF Data Assimilation system

    2017

  • Five-Year Improvements Mark: New SAS and GFS-EDMF physics suites were implemented

    2016

  • Sundararaman Gopalakrishnan, Thiago Quirino, and Frank Marks awarded the Department of Commerce Gold Medal and for Xuejin Zhang, a CIMAS Gold Medal for scientific excellence for developing and implementing the high-resolution HWRF model.

    Increased horizontal resolution from 27/9/3 km to 18/6/2 km across all domains.

    2015

  • South Florida Federal Executive Board- Scientific Employee of the Year.

    HWRF was run in real-time in all global basins beyond the North Atlantic.

    2014

  • Operational assimilation of Doppler Radar data from NOAA P-3s into HWRF model.

    Double-nested, cloud-resolving version of HWRF was run for the first time at 3 km horizontal resolution with improved physics based on observations.

    OAR Scientific Employee of the Year.

    Upgraded physics and vortex initialization were adopted.

    2013

  • Nominated for the South Florida Federal Executive Board’s Employee of the Year Award in the Scientific Category.

    Certificate of Appreciation for contributions in developing the Advanced HWRF High-Resolution model.

    Received NOAA Administrator’s Award.

    2012

  • Multi-nested, High-Resolution HWRF becomes an official operational model.

    Increased horizontal resolution from double-nested from 27/9 to 27/9/3, making HWRF the first-ever highest-resolution model running in operation for hurricane predictions.

    Received Verner E. Suomi Award from the American Meteorological Society.

    2011

  • Received NOAA/OAR Outstanding Scientific Paper Award

    2010

  • NOAA’s HWRF model is officially adopted by the National Hurricane Center as one of its primary numerical guidance models

    2007

  • NOAA’s HWRF modeling system is developed by the National Centers for Environmental Prediction specifically for hurricane forecasting.

    2006

Acknowledgements

With much gratitude, AOML’s Hurricane Modeling Group acknowledges the vital scientific and executive support that it has received from NOAA and AOML’s upper-management since its inception. The support of Dr. Frank Marks, director of the Hurricane Research Division at AOML, and Dr. Robert Atlas, former director of AOML,  has been crucial in the group’s quest to fulfill one of NOAA’s axiomatic goals: to improve hurricane track, structure, and intensity predictions by advancing NOAA’s HWRF modeling system.

Group photo of AOML's hurricane model scientists. Image Credit: NOAA AOML.
From left: Xuejin Zhang, Russell St. Fleur, Jun Zhang, Robert Black, Gus Alaka, Lew Gramer, Andy Hazelton, William Barry, Sundararaman (Gopal) Gopalakrishnan, Mu-Chieh (Laura) Ko, Hua Leighton, William Ramstrom.

A Community Model

Over the past decade, we have collaborated closely with the Environmental Modeling Center, Developmental Testbed Center, and the National Hurricane Center to develop and improve the operational HWRF model. This partnership allows HWRF to be used by both forecasters and researchers for operational and experimental purposes.

Featured Publication

Azimuthal Distribution of Deep Convection, Environmental Factors, and Tropical Cyclone Rapid Intensification: A Perspective from HWRF Ensemble Forecasts of Hurricane Edouard (2014)

Abstract: Forecasts from the operational Hurricane Weather Research and Forecasting (HWRF)-based ensemble prediction system for Hurricane Edouard (2014) are analyzed to study the differences in both the tropical cyclone inner-core structure and large-scale environment between rapidly intensifying (RI) and non intensifying (NI) ensemble members. An analysis of the inner-core structure reveals that as deep convection wraps around from the downshear side of the storm to the upshear-left quadrant for RI members, vortex tilt and asymmetry reduce rapidly, and rapid intensification occurs. For NI members, deep convection stays trapped in the downshear/downshear-right quadrant, and storms do not intensify. The budget calculation of tangential wind tendency reveals that the positive radial eddy vorticity flux for RI members contributes significantly to spinning up the tangential wind in the middle and upper levels and reduces vortex tilt. The negative eddy vorticity flux for NI members spins down the tangential wind in the middle and upper levels and does not help the vortex become vertically aligned. An analysis of the environmental flow shows that the cyclonic component of the storm-relative upper-level environmental flow in the left-of-shear quadrants aids the cyclonic propagation of deep convection and helps establish the configuration that leads to the positive radial vorticity flux for RI members. In contrast, the anticyclonic component of the storm relative mid- and upper-level environmental flow in the left-of-shear quadrants inhibits the cyclonic propagation of deep convection and suppresses the positive radial eddy vorticity flux for NI members. Environmental moisture in the downshear-right quadrant is also shown to be important for the formation of deep convection for RI members.

Azimuthal distribution HWRF Paper

Publications & References

  • 2018

    Kieu, C., K. Keshavamurthy, V. Tallapragada, S. Gopalakrishnan, and S. Trahan (2018): On the growth of intensity forecast errors in the operational hurricane weather research and forecasting (HWRF) model. Quarterly Journal of the Royal Meteorological Soc.144:1803–1819. https://doi.org/10.1002/qj.3344.

    Leighton, H., S. Gopalakrishnan, J.A. Zhang, R.F. Rogers, Z. Zhang, and V. Tallapragada (2018): Azimuthal distribution of deep convection, environmental factors and tropical cyclone rapid intensification: A perspective from HWRF ensemble forecasts of Hurricane Edouard (2014). Journal of the Atmospheric Sciences, 75(1):275-295. https://doi.org/10.1175/JAS-D-17-0171.1.

    Zhang, J.A., F.D. Marks, J.A. Sippel, R.F. Rogers, X. Zhang, S.G. Gopalakrishnan, Z. Zhang, and V. Tallapragada (2018): Evaluating the Impact of Improvement in the Horizontal Diffusion Parameterization on Hurricane Prediction in the Operational Hurricane Weather Research and Forecast (HWRF) Model. Weather and Forecasting, 33, 317–329. https://doi.org/10.1175/WAF-D-17-0097.1.

  • 2017

    Alaka, G. J., X. Zhang, S. G. Gopalakrishnan, S. B. Goldenberg, and F. D. Marks, 2017: Performance of basin-scale HWRF tropical cyclone track forecasts. Wea. Forecast., 32(3):1253-1271, doi:10.1175/WAF-D-16-0150.1.

    Goldenberg, S., S. Gopalakrishnan, V. Tallapragada, T. Quirino, F. Marks, S. Trahan, X. Zhang, and R. Atlas, 2015: The 2012 triply-nested, high-resolution operational version of the hurricane weather research and forecasting system (HWRF): Track and intensity forecast verifications. Wea. Forecast., doi: 10.1175/WAF-D-14-00098.1.

  • 2016

    Gopalakrishnan, S., C.V. Srinavas, and K. Bhatia. The hurricane boundary layer. In Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Predictions, U.C. Mohanty and S.G. Gopalakrishnan (eds.). Springer Netherlands, 589-626, doi:10.1007/978-94-024-0896-6 2016

    Gopalakrishnan, F. Toepfer, R. Gall, F. Marks, E. N. Rappaport, V. Tallapragada, S. Forsythe-Newell, A. Aksoy, J. W. Bao, M. Bender, L. Bernardet, J. Cione, M. Biswas, J. Cangialosi, M. DeMaria, M.Morin,J. Doyle, J. L. Franklin, S. Goldenberg, George Halliwell, C. Holt, S. Jason, H. S. Kim, P. Kucera, N. Lett, P. McCaslin, A. Mehra, M. Mills, J. Moskaitis, A. Sergio, J. Sippel, S. Trahan, H. Tolman, R. Torn, X. Wang, J. Whitaker, D. A. Zelinsky, F. Zhang, X. Zhang, Z. Zhang, 2015 HFIP R&D Activities Summary: Recent Results and Operational Implementation, 2016 (http://www.hfip.org/documents/HFIP_AnnualReport_FY2015.pdf)

    Quirino, T., and S.G. Gopalakrishnan. Advanced diagnostics for the HWRF hurricane modeling system. In Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Predictions, U.C. Mohanty and S.G. Gopalakrishnan (eds.). Springer Netherlands, 517-534, doi:10.1007/978-94-024-0896-6 2016

    Mohanty, U.C., and S.G. Gopalakrishnan (eds.). Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Predictions. Springer Netherlands, 746 pp., doi:10.1007/978-94-024-0896-6 2016

    Zhang, X., S.G. Gopalakrishnan, S. Trahan, T.S. Quirino, Q. Liu, Z. Zhang, G. Alaka, and and V. Tallapragada. Representing multiple scales in the Hurricane Weather Research and Forecasting modeling system: Design of multiple sets of movable multilevel nesting and the basin-scale HWRF forecast verification. Weather and Forecasting, 31(6):2019-2034, doi:10.1175/WAF-D-16-0087.1 2016

  • 2015

    Atlas, R., V. Tallapragada, and S. Gopalakrishnan. Advances in tropical cyclone intensity forecasts. Marine Technology Society Journal, 49(6):149-160, doi:10.4031/MTSJ.49.6.2 2015

    Bernardet, L., V. Tallapragada, S. Bao, S. Trahan, Y. Kwon, Q. Liu, M. Tong, M. Biswas, T. Brown, D. Stark, L. Carson, R. Yablonsky, E. Uhlhorn, S. Gopalakrishnan, X. Zhang, T. Marchok, B. Kuo, and R. Gall. Community support and transition of research to operations for the Hurricane Weather Research and Forecasting model. Bulletin of the American Meteorological Society, 96(6):953-960, doi:10.1175/BAMS-D-13-00093.1 2015

    Chen, H., and S.G. Gopalakrishnan. A study on the asymmetric rapid intensification of Hurricane Earl (2010) using the HWRF system. Journal of the Atmospheric Sciences, 72(2):531-550, doi:10.1175/JAS-D-14-0097.1 2015

    Goldenberg, S.B., S.G. Gopalakrishnan, V. Tallapragada, T. Quirino, F. Marks, S. Trahan, X. Zhang, and R. Atlas. The 2012 triply-nested, high-resolution operational version of the Hurricane Weather Research and Forecasting System (HWRF): Track and intensity forecast verifications. Weather and Forecasting, 30(3):710-729, doi:10.1175/WAF-D-14-00098.1 2015

    Halliwell, G.R., S. Gopalakrishnan, F. Marks, and D. Willey. Idealized study of ocean impacts on tropical cyclone intensity forecasts. Monthly Weather Review, 143(4):1142-1165, doi:10.1175/MWR-D-14-00022.1 2015

    Quirino, T.S., J. Delgado, and X. Zhang. Improving the scalability of a hurricane forecast system in mixed-parallel environments. Proceedings, 16th IEEE International Conference on High Performance Computing and Communications, Paris, France, August 20-22, 2014. IEEE Computer Society, 276-281, 2015

    Zhang, D.-L., L. Zhu, X. Zhang, and V. Tallapragada. Sensitivity of idealized hurricane intensity and structures under varying background flows and initial vortex intensities to different vertical resolutions in HWRF. Monthly Weather Review, 143(3):914-932, doi:10.1175/MWR-D-14-00102.1 2015

    Zhu, P., Z. Zhu, S. Gopalakrishnan, R. Black, F.D. Marks, V. Tallapragada, J.A. Zhang, X. Zhang, and C. Gao. Impact of sub-grid scale processes on eyewall replacement cycle of tropical cyclones in HWRF system. Geophysical Research Letters, 42(22):10027-10036, doi:10.1002/2015GL066436 2015

  • 2014

    Gall, R., F. Toepfer, F. Marks, E.N. Rappaport, A. Aksoy, S. Aberson, J.W. Bao, M. Bender, S. Benjamin, L. Bernardet, M. Biswas, B. Brown, J. Cangialosi, C. Davis, M. DeMaria, J. Doyle, M. Fiorino, J. Franklin, I. Ginis, S. Gopalakrishnan, T. Hamill, R. Hodur, H.S. Kim, J. Knaff, T. Krishnamurti, P. Kucera, Y. Kwon, W. Lapenta, N. Lett, S. Lord, T. Marchok, E. Mifflin, M. Morin, K. Musgrave, L. Nance, C. Reynolds, V. Tallapragada, H. Tolman, R. Torn, G. Vandenberghe, T. Vukicevic, X. Wang, Y. Weng, J. Whittaker, R. Yablonsky, D.-L. Zhang, F. Zhang, J. Zhang, X. Zhang, and D.A. Delinsky. Hurricane Forecast Improvement Project: 2013 HFIP R&D activities summary—Recent results and operational implementation. HFIP Technical Report, HFIP2014-2, 50 pp., 2014

    Pattanayak, S., U.C. Mohanty, and S.G. Gopalakrishnan. Improvement in track and intensity prediction of Indian seas tropical cyclones with vortex assimilation. In Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change, U.C. Mohanty, M. Mohapatra, O.P. Singh, B.K. Bandyopadhyay, and L.S. Rathore (eds.). Springer Publishing, 219-229, doi:10.1007/978-94-007-7720-0 2014

  • 2013

    Gopalakrishnan, S.G., F. Marks, J.A. Zhang, X. Zhang, J.-W. Bao, and V. Tallapragada. A study of the impacts of vertical diffusion on the structure and intensity of tropical cyclones using the high resolution HWRF system. Journal of the Atmospheric Sciences, 70(2):524-541, doi:10.1175/JAS-D-11-0340.1 2013

    Rogers, R.F., S.D. Aberson, A. Aksoy, B. Annane, M. Black, J.J. Cione, N. Dorst, J. Dunion, J.F. Gamache, S.B. Goldenberg, S.G. Gopalakrishnan, J. Kaplan, B.W. Klotz, S. Lorsolo, F.D. Marks, S.T. Murillo, M.D. Powell, P.D. Reasor, K.J. Sellwood, E.W. Uhlhorn, T. Vukicevic, J.A. Zhang, and X. Zhang. NOAA’s Hurricane Intensity Forecasting Experiment (IFEX): A progress report. Bulletin of the American Meteorological Society, 94(6):859-882, doi:10.1175/BAMS-D-12-00089 2013

  • 2012

    Bao, J.-W., S.G. GOPALAKRISHNAN, S.A. Michelson, F.D. Marks, and M.T. Montgomery, 2012: Impact of physics representations in the HWRF model on simulated hurricane structure and wind-pressure relationships. Monthly Weather Review, 140(10):3278-3299 (doi:10.1175/MWR-D-11-00332.1).

    Bell, G.D., E.S. Blake, C.W. Landsea, T.B. Kimberlain, S.B. GOLDENBERG, J. Schemm, and R.J. Pasch, 2012:  The tropics: Atlantic basin.  In State of the Climate in 2011, J. Blunden and D.S. Arndt (eds.).  Bulletin of the American Meteorological Society, 93(7):S99-S105.

    BLACK, R.A., and J. Hallett, 2012: Rain rate and water content in hurricanes compared with summer rain in Miami, Florida.  Journal of Applied Meteorology and Climatology, 51(12):2218-2235 (doi:10.1175/JAMC-D-11-0144.1).

    GOPALAKRISHNAN, S.G., S. GOLDENBERG, T. QUIRINO, F. Marks, X. ZHANG, K.-S. Yeh, R. Atlas, and V. Tallapragada, 2012: Towards improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics.  Weather and Forecasting, 27(3):647-666 (doi:10.1175/WAF-D-11-00055.1).

    Laureano-Bozeman, M., D. Niyogi, S. GOPALAKRISHNAN, F.D. Marks, X. ZHANG, and V. Tallapragada, 2012: An HWRF-based ensemble assessment of the land surface feedback on the post-landfall intensification of Tropical Storm Fay (2008). Natural Hazards, 63(3):1543-1571 (doi:10.1007/s11069-011-9841-5).

    Pattanayak, S., U.C. Mohanty, and S.G. GOPALAKRISHNAN, 2012: Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system.  Natural Hazards, 63(3):1413-1437 (doi:10.1007/s11069-011-9863-z).

    Yeh, K.-S., X. ZHANG, S.G. GOPALAKRISHNAN, S. Aberson, R. Rogers, F.D. Marks, and R. Atlas, 2012: Performance of the experimental HWRF in the 2008 hurricane season. Natural Hazards, 63(3):1439-1449 (doi:10.1007/s11069-011-9787-7).

  • Prior to 2012

    2011

    Bell, G.D., E.S. Blake, T.B. Kimberlain, C.W. Landsea, J. Schemm, R.J. Pasch, and S.B. GOLDENBERG, 2011: The tropics: Atlantic basin.  In State of the Climate in 2010, J. Blunden, D.S. Arndt, and M.O. Baringer (eds.). Bulletin of the American Meteorological Society, 92(6):S115-S121 (doi:10.1175/1520-0477-92.6.S1).

    GOPALAKRISHNAN, S.G., F. Marks, X. ZHANG, J.-W. Bao, K.-S. Yeh, and R. Atlas, 2011:  The Experimental HWRF system:  A study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Monthly Weather Review, 139(6):1762-1784 (doi:10.1175/ 2010MWR3535.1).

    ZHANG, X., T.S. QUIRINO, K.-S. Yeh, S.G. GOPALAKRISHNAN, F.D. Marks, S.B. GOLDENBERG, and S. Aberson, 2011:  HWRFx: Improving hurricane forecasts with high-resolution modeling. Computing in Science and Engineering, 13(1):13-21 (doi:10.1109/MCSE.2010.121).

    2010

    Bell, G.D., E.S. Blake, T.B. Kimberlain, C.W. Landsea, R.J. Pasch, J. Schemm, and S.B. GOLDENBERG, 2010:  Atlantic basin. In State of the Climate in 2009, D.S. Arndt. M.O. Baringer, and M.R. Johnson (eds.).  Bulletin of the American Meteorological Society, 91(7):84-88.

    2009

    Bell, G.D., E. Blake, S.B. GOLDENBERG, T. Kimberlain, C.W. Landsea, R. Pasch, and J. Schemm, 2009:  Tropical cyclones: Atlantic basin.  In State of the Climate in 2008, T.C. Peterson and M.O. Baringer (eds.).  Bulletin of the American Meteorological Society, 90(8):S79-S83.

    Panda, J., M. Sharan, and S.G. GOPALAKRISHNAN, 2009:  Study of regional-scale boundary layer characteristics over northern India with a special reference to the role of the Thar Desert in regional-scale transport. Journal of Applied Meteorology and Climatology, 48(11):2377-2402.

    2008

    Bell, G.D., E. Blake, C.W. Landsea, S.B. GOLDENBERG, R. Pasch, and T. Kimberlain, 2008:  The tropics: Atlantic basin. In State of the Climate in 2007, D.H. Levinson and J.H. Lawrimore (eds.).  Bulletin of the American Meteorological Society, 89(7):S68-S71.

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