Hurricane Weather Research Forecast Model (2024)

  • 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. InState 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. InState 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. InState 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. InState 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. InState of the Climate in 2007,D.H. Levinson and J.H. Lawrimore (eds.). Bulletin of the American Meteorological Society,89(7):S68-S71.

  • Hurricane Weather Research Forecast Model (2024)

    FAQs

    What is the best forecast model for a hurricane? ›

    This makes HWRF the highest resolution hurricane model ever implemented for operations in the National Weather Service. Overall, HWRF has improved its intensity prediction skills by a minimum of 40% to 60%.

    What is the most reliable weather forecast model? ›

    Global models with worldwide weather forecasts

    The ECMWF is generally considered to be the most accurate global model, with the US's GFS slightly behind.

    What forecast model does NOAA use? ›

    The Global Forecast System (GFS) is a National Centers for Environmental Prediction (NCEP) weather forecast model that generates data for dozens of atmospheric and land-soil variables, including temperatures, winds, precipitation, soil moisture, and atmospheric ozone concentration.

    What are the two major models we use to track and predict hurricanes? ›

    The NHC and other official tropical cyclone forecast centers use two different forms of dynamical model guidance during the forecast process: "early" and "late" models.

    Is the GFS or Euro model more accurate? ›

    Most of the time, the European model is the most accurate. For example, the Euro was the first model that showed the southward shift of the storm on Monday. Eventually, the other models followed. The GFS is a weather forecast model that collects data for land-soil and atmospheric variables.

    What forecast model should I use? ›

    Time Series Model – best for continuous data with clear trends. A time series model focuses on historical data and patterns to predict future trends. This is arguably the most straightforward type of forecasting model and is commonly used in stock market predictions, sales forecasting, and even weather forecasts.

    Why do weather forecasters use spaghetti models? ›

    Spaghetti models show where a tropical system may go. When clustered together, forecast confidence is high. But spaghetti plots do not show where impacts will occur.

    What is the highest accuracy weather forecast? ›

    IBM's The Weather Company Continues to Be the World's Most Accurate Forecaster Overall, Despite Growing Competition and Amid Weather's Increased Impact. - The Weather Company was over 3 times more likely to be the most accurate forecaster than other weather providers studied.

    Which two models would be most useful in predicting weather? ›

    First and most importantly, ECMWF and GFS are the two most common global weather models. This means that they provide weather forecasts for the entire world due to their spatial resolution or grid with a series of points where the weather is predicted, which cover the entire globe.

    Which model is better, ECMWF or GFS? ›

    Let's summarize the main differences between both leading weather forecasting models: Resolution: GFS runs at a lower resolution than the ECMWF model. The grid points in the GFS model are located farther apart (every 13 kilometers) than the ECMWF model (every 9 kilometers).

    Is Hrrr or Nam more accurate? ›

    NAM vs HRRR: accuracy

    In general, yes. The former surpasses the latter in the main parameter of the accuracy of weather models — the resolution, as much as four times (3 km vs. 12 km).

    What is the difference between GEFS and GFS? ›

    The fundamental difference between the GFS and GEFS is that the GFS is made of 1 deterministic solution while the GEFS is made of 30 ensemble solutions. Likewise, the ECMWF is 1 solution while the EPS is 50 solutions, and the CMC is 1 solution while the CMCE is 20 solutions.

    What is the most trusted hurricane model? ›

    In 2021, the GFS was the most accurate model followed by the European. Overall, the official forecast was the most accurate in terms of forecast track accuracy. The NHC also relies heavily on consensus models. The TVCN is a popular choice for tropical track forecasting.

    Which model is best for hurricanes? ›

    The model with the highest success ratio (rewarding correct genesis forecasts combined with fewest false alarms) was the European (ECMWF), followed by the UKMET, the GFS, and Canadian models.

    What is the newest hurricane model? ›

    HAFS is NOAA's newest operational hurricane model. Version 1.0 went into operations in June 2023, and version 2.0 is planned to go into operations this summer in early July with upgrades to model initialization and model physics to continue to improve the accuracy of track and intensity forecasts.

    What is the best map projection for hurricanes? ›

    The limited distortion in the low latitudes is one reason why the Mercator projection is the map of choice for tropical forecasters. Another reason for its favored-map status is the relative ease in plotting and interpreting the tracks of tropical cyclones.

    What type of forecast method would hurricane forecasters most likely use? ›

    Forecasters use satellite data to estimate characteristics of a storm, including the location of its center, its past motion (within 6-12 hours), and its intensity (maximum wind speed). Atlantic and Pacific Geostationary (GOES) satellites can continuously observe hurricanes from their formation to dissipation.

    What is the best website for hurricane prediction? ›

    The National Hurricane Center (NHC) is the trusted source for hurricane information. Resources include predicted storm tracks and wind speed probabilities.

    What is the difference between GFS and Ecmwf hurricanes? ›

    Let's summarize the main differences between both leading weather forecasting models: Resolution: GFS runs at a lower resolution than the ECMWF model. The grid points in the GFS model are located farther apart (every 13 kilometers) than the ECMWF model (every 9 kilometers).

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