Tropical Cyclones and Related Extreme Weather

Weather phenomena that are extreme from a meteorological point of view can also be extreme in how they affect society.  Among the examples that come readily to mind are hurricanes and other tropical cyclones, which are subjects of several projects in RAL.


Tropical cyclones (TCs) present numerous societal and environmental risks to coastal and marine regions around the world.  Significant progress in understanding and predicting TCs has been achieved in recent decades because of advancements in fundamental science and engineering, high-resolution numerical weather prediction (NWP) tools, and computers. Yet despite such advancements the prediction of a TC’s genesis, intensity, and overall wind field still can be quite inaccurate, especially in cities and suburbs in a storm’s path.  One reason for inaccuracy is that TCs are nonlinear and multi-scale.  The large-scale environment surrounding a storm; its inner-core dynamics, convective processes, microphysics, and turbulence; and its interactions with the ocean, land, and build-up structures shape the storm’s evolution, especially its wind near the ground, which challenges NWP models in numerous ways.

RAL conducts scientific research on a wide variety of TC problems in the realms of theory, observations, models, and operational forecast applications.  In the last year, RAL has conducted research in the following areas:

  • Study of TC wind impacts on urban and built-up areas for landfalling TCs through the use of high-resolution numerical models, large-eddy simulations, and analysis of laboratory and observational data
  • Real-time implementation of NWP model post-processing techniques to improve forecasts of TC track, intensity, and wind
  • Statistical and machine learning methods for predicting TC intensity change

Accomplishments in FY2019

Multi-scale modeling of extreme winds in the urban canopy

The project team added to Cloud Model 1 (CM1) new code for representing vertical faces, which is required for modeling wind in the urban canopy of coastal cities.  The approach, called the internal boundary method (IBM), is based on work by Briscolini and Santangelo (1989).  The project team is working with the Center for Severe Weather Research to validate the IBM in CM1 with a dataset of observations from mobile Doppler radar that captured high-velocity wind around and over buildings on a barrier island as Hurricane Frances (2004) made landfall at Fort Pierce, FL.  We are also working with Forrest Masters at the NSF/NHERI wind tunnel facility at the University of Florida to model flows around and over simple arrangements of city buildings to generate another source of validation for CM1.  In addition, NSAP modified the Yonsei University (YSU) atmospheric boundary-layer (ABL) parameterization scheme to work with the building effects parameterization (BEP) and building environment model (BEM) in the Weather Research and Forecasting (WRF) Model (Hendricks et al 2019a).  It is now possible to include the YSU scheme for idealized and real-case hurricane simulations, an important step forward for the larger research community.  Before moving on to simulating with the new YSU scheme the high winds of hurricanes, the project team validated the modified YSU scheme against observations from Houston during the passage of a cold front (Hendricks et al 2019b).

Real-Time Implementation of the Analog Ensemble for Tropical Cyclone Intensity Change

Using NOAA’s Jet supercomputer, we conducted a second year of real-time testing with an analog ensemble (AnEn) tailored for TC prediction (e.g., Alessandrini et al. 2018) for both the Atlantic and Eastern Pacific hurricane seasons.  We specifically focused on an AnEn designed to improve the prediction of the Hurricane Weather Research and Forecasting (HWRF) model’s of intensity change with the specific goal of improving the prediction of rapid intensification (RI).  RI is a challenge for NWP models since RI is defined as the 95th percentile of intensity change over a given time period.  Earlier in 2019, we also submitted a manuscript demonstrating the AnEn’s real-time performance in the 2018 hurricane season (Lewis et al. 2020).  Figure 1 shows an example of the forecast skill of the HWRF-based AnEn from the 2018 Atlantic and Eastern Pacific hurricane seasons.

Real-Time Implementation of the Analog Ensemble for Tropical Cyclone Intensity Change
Figure 1. A homogeneous comparison of Brier skill scores (BSS) for RI forecasts generated by the deterministic HWRF, operational “DTOPS” probabilistic model, the operational “SHIPS-RII” model, and the AnEn for the RI defined over 24-, 48-, and 72-h lead times for the 2018 hurricane seasons in the Atlantic (left) and Eastern Pacific (right).

Statistical and machine learning methods for predicting TC intensity change

In FY2019, we also developed and evaluated statistical and machine-learning-based methods for TC intensity-change prediction.  So far, all methods have been based on HWRF output and have been dedicated to the improvement of TC intensity and RI prediction.  Both a simple logistic-regression technique and deep-learning-based feed-forward neural network (Cloud et al. 2019) have been derived to use one-dimensional predictors derived from HWRF forecast fields.  These predictors describe properties of the large-scale conditions impacting the TC and also the inner-core aspects of a TC according to the forecast model.  Both methods have shown promising skill in the prediction of RI.  In addition, we have developed a first-generation convolutional neural network that uses the entire forecast fields from HWRF.  Efforts are underway to improve its predictive skill and ability to interpret important physical processes and error tendencies in the HWRF model.

Plans for FY2020

Operational scale simulations of historical TCs in the US and Asia

Using knowledge gained from high-resolution simulations with CM1, RAL and collaborators will use the WRF Model to simulate several historical hurricanes, including Wilma (2005) and Irma (2017) in the US, Faxai (2019) in Japan, and Hato (2017) in China.  These simulations will enable us to compare methods of approximating the aggregate effects of city buildings before and after the WRF Model has been improved to produce better results when winds are hurricane strength.  In addition, idealized simulations with the WRF Model will be used to systematically explore the effects of storm speed, angle of approach, and land surface properties on the surface wind field in the urban canopy.

NWP Post-processing methods

In the realm of post-processing, FY2020 will include the submission of a manuscript highlighting the AnEn use in predicting TC track and wind structure.  We will also continue to communicate with our NOAA sponsor the results of real-time AnEn testing for the possibility of operational implementation.  In addition, work will continue in advancing machine learning methods for TC intensity prediction, which includes producing a skillful and interpretable convolutional neural network and testing this algorithm in a real-time environment for the 2020 hurricane season.  Finally, we are developing an experimental version of the AnEn for the HWRF that uses a convolutional neural network to recognize analogs in the two-dimensional forecast fields.


Alessandrini, S., L. Delle Monache, C. M. Rozoff and W. E. Lewis, 2018: Probabilistic prediction of tropical cyclone intensity with an analog ensemble. Mon. Wea. Rev., 146, 1723-1744.

Briscolini, M., and P. Santangelo, 1989: Development of the mask method for incompressible unsteady flows. J. Comput. Phys.84, 57–75, doi:10.1016/0021-9991(89)90181-2.

Cloud, K. A., B. J. Reich, C. M. Rozoff, S. Alessandrini, W. E. Lewis, and L. Delle Monache, 2019: A feed forward neural network based on model output statistics for short-term hurricane intensity prediction. Wea. Forecasting, 34, 985-997.

Hendricks, E. A., J. C. Knievel, and Y. Wang, 2019a: Addition of multiple-layer urban canopy models to a nonlocal planetary boundary layer parameterization and evaluation in ideal and real scenarios.  J. Appl. Meteor. Climatol., submitted.

Hendricks, E. A., J. C. Knievel, and Y. Wang, 2019b: Evaluation of a hierarchy of urban canopy parameterizations in mesoscale model simulations of the passage of a cold front in Houston.  J. Appl. Meteor. Climatol., submitted.

Lewis, W. E., C. Rozoff, S. Alessandrini, and L. Delle Monache, 2020: Performance of the HWRF Rapid Intensification Analog Ensemble (HWRF RI-AnEn) during the 2017 and 2018 HFIP Real-Time Demonstrations. Wea. Forecasting, in revision.