Tropical Cyclone

BACKGROUND

RAL’s Joint Numerical Testbed Program (JNTP) has a number of efforts related to tropical cyclone (TC) forecasting.  These will be described below as they relate to the JNTP’s Developmental Testbed Center (DTC); the Tropical Cyclone Modeling Team (TCMT); the Tropical Cyclone Guidance Project (TCGP); and the Tropical Cyclone Data Project (TCDP).

The Developmental Testbed Center works closely with NCEP’s Environmental Modeling Center (EMC) to support the Hurricane Weather Research and Forecasting (HWRF) system to the research community. The team also tests new capabilities coming from the research community to determine their potential for improving the forecast skill of HWRF.  The goals of this work are to accelerate the improvement in TC forecasts by providing a mechanism for efficiently transitioning research into operations, and through extensive testing of new capabilities to determine their impacts on operational predictions.

The focus of RAL’s Tropical Cyclone Modeling Team is testing and evaluation of experimental models for tropical cyclone forecasting. Currently, the primary sponsor of the TCMT is NOAA’s Hurricane Forecast Improvement Project (HFIP).  In coordination with the HFIP teams, the TCMT collects, evaluates, and provides results of tropical cyclone track forecasts to the broader HFIP community. Statistical approaches and new graphical displays are developed by the TCMT. Current efforts are focused on methods of evaluation for ensemble tropical cyclone forecasts of rapid intensity change and development of a specialized display and diagnostic evaluation system for use at the National Hurricane Center (NHC).

The TCGP provides real-time visualizations of TC track and intensity guidance through an outward-facing web page that receives millions of hits each year. TCGP also collects real-time tropical cyclone guidance data from numerical prediction centers around the world and collates these data into a publicly-available global repository of TC forecast aids. The site is widely used by forecasters, emergency managers, government agencies (e.g., NOAA, FEMA, DHS), private-sector firms (e.g., ship-routing, transportation and logistics, energy producers, energy and risk trading, media), weather enthusiasts, and the general public. The overarching goal of TCGP is to foster increased development of forecast aids for global basins by engaging the wider community of operational centers, academic researchers, and commercial interests, ultimately moving the community’s focus beyond track and intensity to a more expansive focus on TC structure.

The goal of TCDP is to provide and maintain a new historical database of TC wind structure parameters that is based on high quality observational sources such as high-resolution flight level data. This new database uses objective methods to optimally estimate the various database parameters, as well as to provide time-dependent error bounds on the estimated parameters. It is intended to provide the highest quality database possible for parametric wind modeling applications and model evaluation activities (e.g., verification), and to support basic and applied research on TC intensity and structure change.

FY2018 ACCOMPLISHMENTS

Developmental Testbed Center

Advancing HWRF physics

During FY2018, the DTC’s Hurricane team partnered with DTC Visitor Program Principal Investigators and subject area experts to help coordinate and test the performance of alternate physics schemes and innovations relative to the current parameterizations within the HWRF physics suite. Physics advancements considered for testing covered radiation, planetary boundary layer (PBL) and cumulus parameterizations. In addition to coordination and support for HWRF physics developers, the DTC evaluated the code readiness of candidate physics advancements and consulted with the EMC hurricane team on top priorities for HWRF 2018 pre-implementation testing. Radiation and cumulus parameterization scheme testing were undertaken by DTC.

Retrospective cases were run for select storms in the Atlantic (AL) basin that occurred during the 2015-2017 hurricane seasons. Storms were selected to establish a homogeneous set of cases covering a variety of conditions. Parallel experiments were run to test the sensitivity of the experimental physics configurations. The control (H18C) was run using the 2017 operational HWRF default settings. A new cloud overlap technique for the RRTMG radiation parameterization, exponential cloud overlap, was tested as a replacement for the default maximum-random assumption (H18R).  The exponential cloud overlap technique alters the overlap of continuous cloud layers to allow for an exponential transition from maximum to random. Additionally, a cumulus parameterization replacement test (H18G) was run to investigate the impact of the Grell-Freitas (GF) scheme compared to the operational scale-aware simplified Arakawa-Schubert (SAS). The GF scheme employs an ensemble approach to represent convection, using a collection of parameters and algorithms to represent convective triggers, vertical mass flux, and closures. The scheme is scale-aware, making it suitable for HWRF’s nested grid configuration.

The outcome of the pre-implementation testing was to adopt the RRTMG exponential cloud overlap method into the 2018 operational HWRF configuration due to reduced intensity errors, particularly out to 2 days. Because the configuration with the G-F cumulus parameterization demonstrated track errors at the longest lead times, it was not adopted for 2018 operations. A description of the full evaluation can be found in the final report on the DTC website located at: http://www.dtcenter.org/eval/hwrf_GF_CO_2017/.

A comparison of HWRF-simulated brightness temperatures (BT) with Geostationary Operational Environmental Satellite (GOES-13, channel 4) BTs is one approach the Hurricane team utilized to further analyze the experimental HWRF configurations. The exponential cloud overlap assumption used in H18R is more sophisticated than the random overlap assumption used in H18C.  Because of these differences, the model simulated BTs were compared to observed BTs from GOES-13 to identify any improvements in the H18R configuration.

Fig. 1. Fractions skill score (FSS) of the model-simulated brightness temperatures from the H18C (black lines) and H18R (red lines) experiments on domain d03 at the 72-hr forecast lead time. The FSS is plotted as a function of neighborhood size and brightness temperature interval: a) BT < 230 K, b) 230 K < BT < 250 K, c) 250 K < BT < 270 K, and d) 270 K < BT < 290 K.
Fig. 1. Fractions skill score (FSS) of the model-simulated brightness temperatures from the H18C (black lines) and H18R (red lines) experiments on domain d03 at the 72-hr forecast lead time. The FSS is plotted as a function of neighborhood size and brightness temperature interval: a) BT < 230 K, b) 230 K < BT < 250 K, c) 250 K < BT < 270 K, and d) 270 K < BT < 290 K.R

Probability density functions comparing the observed BTs from GOES-13 to each of the model configurations for the 24- and 72-hour forecast lead time for both the parent domain (d01) and inner-most nest (d03) exhibit functionally similar behavior between the two configurations relative to the observations (not shown). On both domains and forecast lead times, the fraction of simulated clouds with BTs between 230-270 K is too small relative to the observed clouds, while there are too many simulated clouds with BTs of 285-295 K. These biases are particularly apparent on d03, which is storm-centered and consists of mainly clouds associated with the tropical cyclone.

Fractions skill score (FSS), a neighborhood-based spatial-verification metric that ranges in value from zero (no skill) to one (perfect skill), was also computed to investigate the skill of each configuration. The FSS for the 72-hour forecasts on d03 are shown in Figure 1, where H18R is 1–3% less skillful than H18C in depicting the location of the coldest clouds (Fig. 1a). However, H18R improves the placement of clouds with moderately cold BTs of 230–270 K (Fig. 1b,c), with up to 3% more skill in depicting these clouds. Both H18R and H18C perform similarly for the warmest clouds (Fig. 1d).

Tropical Cyclone Modeling Team

Development of a Tropical Cyclone Display and Diagnostic System

A next-generation display and diagnostic system has been developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and the broader tropical cyclone (TC) research community.  The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models.  The system is built upon modern and flexible technology, including OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database.  The system provides an easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity.  Consensus forecasts can be computed and displayed interactively.  The system is designed to display observed (best track) and forecasted hurricane track information for both real-time and historical TC cyclones.  Display configurations are easily adaptable to meet the needs of the end-user preferences.

Fig. 2: Examples of the NHC-Display tool showing the F-Deck editor (top panel), B-deck editor (middle panel), and wind radii display (bottom panel).
Fig. 2: Examples of the NHC-Display tool showing the F-Deck editor (top panel), B-deck editor (middle panel), and wind radii display (bottom panel).

New technologies developed this year include an advanced tool for editing the hurricane fix-position database (F-deck) and the best-track database (B-deck).  The F-deck editing tool allows NHC staff to add or edit the estimated location of hurricanes using fixed-position information from aircraft analysis, radar, satellite, microwave, and scatterometer observations.  This information is used to improve the location of the hurricane in the B-deck database during post-hurricane season analysis.  A wind radii tool has been added to the available features this past year.  Examples of the display system capabilities are shown in Fig. 2.  Other ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks and the development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts.  The display system was used by NHC during the 2018 Hurricane season as supporting tool to the official forecast system.   

Website: http://www.hfip.org/nhc-display

Ensemble Rapid Intensification Products

Fig 3. HWRF ensemble mean (HWMN) frequency distributions for the RI/RW bins during 0-24 (left), 24-48 (center), and 48-72 (right) time periods.
Fig 3. HWRF ensemble mean (HWMN) frequency distributions for the RI/RW bins during 0-24 (left), 24-48 (center), and 48-72 (right) time periods.

Building on last year’s HFIP Demo Rapid Intensification (RI) activities, the RI probability for all available model configurations were computed and displayed for each initialization time in 2018. Ensemble RI products were generated to show the ensemble frequency distributions of each model configuration for RI/RW (rapid weakening) during the three time periods (Figure 3) for a single initialization of Hurricane Florence. 

Ensemble RI products were generated and posted online for distribution to the community. Website: https://www.ral.ucar.edu/projects/hfip/d2018/ensRI/

Tropical Cyclone Guidance Project (TCGP)

New TCGP Data and Visualization

Fig 4. An example curve boxplot visualization for Hurricane Harvey, showing the median track (yellow), the central envelope (dark purple shading), the outer envelope (light purple shading), and the outlier tracks (blue).
Fig 4. An example curve boxplot visualization for Hurricane Harvey, showing the median track (yellow), the central envelope (dark purple shading), the outer envelope (light purple shading), and the outlier tracks (blue).

During FY2017, TCGP continued to provide reliable visualizations of the publicly available TC guidance product. Many companies and other sites rely on TCGP’s open data repository; web logs show on order of 15-25 GB of automatic data downloads per day. Through collaboration with Mahsa Mirzargar (University of Miami), a new visualization, called the curve boxplot, has been implemented in TCGP to analyze and visualize ensemble tracks. Analogous to the conventional boxplot, the curve boxplot provides the statistical summarization of an ensemble in terms of its main features: the most representative member (i.e., median), quantile information, and potential outliers. Figure 4 shows an example visualization of some early track forecasts from what the disturbance that eventually became Hurricane Harvey.

Tropical Cyclone Data Project (TCDP)

Research Use of TC Datasets of Aircraft and Satellite Observations

To support the goal of providing a new database of historical database of TC wind structure parameters, TCDP released three major source datasets to the public in FY2016. Each dataset is a high quality research-grade dataset of TC-focused wind data, from aircraft or satellite observations. The datasets use modern standards and formats, and serve as source data input for the new historical database. These include the:

  • Enhanced Vortex Data Message Dataset (VDM+, released 25 Nov 2015),
  • QuikSCAT Tropical Cyclone Radial Structure Dataset (QSCAT-R, released 23 Dec 2015), and
  • Extended Flight Level Dataset for Tropical Cyclones (FLIGHT+, released 20 April 2016).

Research use of these datasets has continued to increase. VDM+ now has 29 registered users and FLIGHT+ has 18 registered users. Many of the users are graduate students who are using the datasets in their thesis or doctoral research. By the end of FY2017, the VDM+ Dataset had been used in three published papers, a Ph.D. dissertation, and a Master’s thesis. The FLIGHT+ Dataset had been used in one Master’s thesis and three published papers.

FY2019 PLANS

Developmental Testbed Center

For FY2019, the DTC will continue its work aimed at improving the HWRF physics through partnerships with physics developers. The performance of alternate physics schemes and innovations to the current parameterizations within the HWRF physics suite will be investigated. Retrospective forecasts using the most recent HWRF model version will be conducted to evaluate the performance of each innovation. Upgrades to the atmospheric component of HWRF will be passed to EMC to be included in its pre-implementation testing for the HWRF 2019 implementation.

Tropical Cyclone Modeling Team

For FY2019, the TCMT will continue to enhance graphical display diagnostic tools with additional analysis features and inclusion of additional gridded fields (satellite, forecast products, SST observations). New diagnostic tools are being developed using input from the National Hurricane Center (NHC) staff.  Additionally, ensemble rapid intensification (RI) forecasts for the 2019 hurricane season will be evaluated and made available through the TCMT website.  The TCMT is also developing new techniques to evaluate ensemble RI forecasts.