# Presentation: 411581

## Preliminary ReportNOAA Science Advisory BoardHurricane Intensity Research Working Group

- John T. Snow
- University of Oklahoma
- Revised 16 March 2006
- Originally delivered to the NOAA SAB on 9 March 2006

## Outline

- Motivation
- Charge
- Working Group Membership
- Process
- Hurricane Intensity
- Preliminary Findings
- Six Preliminary Recommendations
- Summary

## Motivation

- Continual improvement over the last two decades in forecasting of hurricane track
**Simultaneously, little improvement in forecasting of intensity *****– why*****?**
- Recent “surprises”: rapid changes in hurricane intensity, especially just before landfall
- Charley – 2004 – rapid strengthening just before landfall
- Katrina – 2005 – rapid weakening just before landfall
- Wilma – 2005 – rapid strengthening to record central low pressure

## From www.nhc.noaa.gov; provided by Mark DeMaria

- From _[www.nhc.noaa.gov](http://www.nhc.noaa.gov/)_; provided by Mark DeMaria

**Notes:**
This figure from www.nhc.noaa.gov, provided by Mark DeMaria.

This plot shows the improvement over four decades of the error in location (track) at three (in recent year, five) key forecast times, with the most improvement coming at 48- and 72-hour forecast times. This improvement has come about due to 1) enhanced (in both quality and quantity) observations better characterizing the synoptic scale environment in which the hurricane is embedded, and 2) improved numerical models (especially the GFDL model) which better replicate the steering of the storm by that larger scale environment.

## Charge

- Independently assess current “state-of-science” and R&D activities in NOAA and elsewhere with respect to hurricane intensity, and then
- Recommend an agenda of R&D activities that will lead to an improved understanding of the processes determining hurricane intensity and the timely transfer of that understanding to operations.

**Notes:**
The Hurricane Intensity Research Review Working Group should conduct an independent review of NOAA’s hurricane intensity research, development, and transition to operations. The Working Group should develop findings and recommendations to ensure that this work results in improved operational forecasts. This review is to address NOAA’s approach to its research and development efforts in support of improved observations, numerical modeling and operational warnings and forecasts. Rather than continue with incremental improvements in understanding and in intensity forecasts, NOAA seeks advice to help answer fundamental questions on the dynamics and behavior of hurricanes that will lead to significant improvements in forecasting and service to the Nation. This review is to include NOAA’s working arrangements with other Federal agencies and the academic community, and the level of effort and resources devoted to this work currently and planned.

## Working Group Membership

**Dr. John Snow**University of Oklahoma
**Dr. Howard Baum**National Institute of Standards and Technology
**Dr. Shu-Hua Chen **University of California, Davis
**Dr. Russell L. Elsberry **Naval Postgraduate School
**Dr. Francis Edward Fendell**Northrop Grumman Space Technology

**Dr. Greg Holland**National Center for Atmospheric Research
**Dr. Tiruvalam N. Krishnamurti **Florida State University
**Dr. Michael T. Montgomery**Colorado State University
**Dr. Richard Rotunno**National Center for Atmospheric Research
**Dr. Peter Webster**Georgia Institute of Technology

## Process

- Meetings
- Washington, D.C.
- Miami, FL
- Boulder, CO
- Teleconferences
- ftp site
- Preliminary Report
- Feedback, and additional teleconferences and meetings
- Coordination with NSB study
- Final Report

**Notes:**
N.B.: Need detailed plan covering steps in period from now through submission of final report in late July or Augst.

## Hurricane Intensity

- NHC: Intensity = peak sustained surface winds anywhere in the storm
**Occurs through a set of poorly understood, complex processes involving both external environmental influences and internal vortex dynamics, e.g.,**
- Environmental influences, atmosphere and ocean
- Internal variability (e.g., eyewall replacement cycle)
- Rapid intensification and weakening
**Wide range of time and space scales involved**

## Preliminary Findings 1: Key National Hurricane Center Guidance Needs

- Formation phase to become named tropical storm
**Timing and magnitude of rapid intensification**
**Decay and re-intensification cycles**
**Timing and magnitude of rapid decay**

**Notes:**
The accurate forecasting of hurricane intensity was identified by the USWRP Hurricane Landfall program as a “Grand Challenge”.  It remains so today.

Addressed here are the three NHC Guidance Needs shown in red.

## Preliminary Findings 2: Current Situation

- Significant number of activities – observational, R&D, tech transfer, operational -- underway inside (and outside) of NOAA, but no single point of leadership
- Interconnections, coordination, collaboration between activities range from poor to good
- Given complexity of problem to be addressed, “critical mass” is lacking – limited human resources, financial wherewithal, no national focus  progress relatively slow
**Numerous bright spots on which to build, leverage**

## Six Preliminary Recommendations

- Attaining Critical Mass
- Observations of Hurricane Structure
- Data Assimilation
- High Resolution, Coupled Air-Sea-Land Mesoscale Modeling and Analysis
- Operations Research and Socio-Economic Impacts
- Accelerating Transfer from Research to Operations
**Point: Recommendations interdependent**

**Notes:**
Very much applied science; there is basic science but it is implicit. Much of what is proposed is development work. Potential partners on the basic science side include NSF, ONR, NASA  coordinate with NSB report

Introduce a consistent set of language:

Time Horizons
Short term: 1 to 2 years – potential for impact in 2007 hurricane season if activity started in Fall 2006; little impact on budget process
Medium term: 2-5 years – potential for impact in 2009 hurricane season; does impact future budgets
Long term: >5 years – potential for impact but time frame is clearly long

R&D
Research: seeking new knowledge and understanding about nature, to include the development of specialized tools and techniques for such purposes. 
Development: converting existing scientific knowledge, tools, and techniques into operational tools and techniques that produce improved products and services

## Slide 12

**Notes:**
The loop process shown is for research and development purposes. However, a similar loop applies to operations; the OSSE would then become the control system for an adaptive observing system.

## 1. Attaining Critical Mass

**Mesoscale Modeling is a key activity** – with redirection of GFDL, limited development/implementation staff at NCEP, and lack of modeling capability at HRD, NOAA currently has very limited resources for both in-house mesoscale model development and interfacing with wider research community
**Short-term:** Ensure H-WRF is implemented in a timely manner; requires an enhanced effort, better connections to external research community
**Short-term:** Increase modeling capability at HRD (numerous immediate paybacks) and enhance exchanges between NCEP and broader community
**Medium-term:** Establish hurricane R&D *center of specialized expertise* in proximity to NHC, drawing on HRD, NCEP, DTC, JHT others  restore separate lab status?
**Medium-term:** Cooperative efforts with other government agencies, universities, private sector

## 2. Observations of Hurricane Structure

- Numerous observing systems provide data on hurricanes (satellites – critical for monitoring, aircraft, buoys, radar) and promising new technology is in-hand (SFMR) or on horizon (UAS, radar on G-4)
**Near-term:** Airborne and surface-based radars offer best opportunity to observe mesoscale fields in core region  real time assimilation into models
**Near-term:** “Low and Slow” Unmanned Aircraft Systems (UAS) demonstrations to assess ability to provide low altitude *in situ* observations, complementing manned aircraft observations
**Medium-term:** Observing System Simulation Experiments (OSSEs) needed to determine optimal configurations of observing systems for improved forecasts

**Notes:**
Both OSSE and research/operational adaptive sensing networks require hi-res meso-scale model results for decision making.

## 3. Data Assimilation (DA)

- Crucial to obtaining value from nontraditional observations (radar, satellite, dropsonde, etc...) to initialize, update models
**Near-term:** Explore developing first-guess fields using mesoscale model output as well as global model output
**Near-term:** 3DVAR DA
**Medium-term:** 4DDA* *

**Notes:**
Can gain real value from observed data only if they are properly assimilated.

After Chris Snyder and Dusanka Zupanski (2006) 

Data Assimilation is Crucial

* Initial conditions very important for intensity prediction (vortex size and intensity, moisture field, environment).  
* DA must be done properly to avoid spurious transients in forecasts.
* More obs shown to improve track forecasts  likely same for intensity
* Cycling of forecast-analysis system allows unobserved, small-scale information from dynamics (e.g., vortex structure) to improve analysis and subsequent forecasts.

## Slide 16

**Notes:**
After Chris Snyder and Dusanka Zupanski (2006); image courtesy Q. Xiao

3DVar Assimilation of Doppler Radar Obs
Typhoon Rusa, 30 Aug 2002
Upper Left – observed radar reflectivity (Doppler winds also available)

WRF 3DVar with MM5, using dx = 10 km
48 h assimilation period

Observed reflectivity vs 3h forecasts for 3 experiments: 
Upper Right: no radar data 
Lower Left: radar radial velocity only
Lower Right: radial velocity plus reflectivity
Points:
* International connection  Korean research on typhoons
* Importance of ingesting radar-observed mesoscale fields (reflectivity, Doppler winds) as input to model
* Argues for lowering minimum beam elevation angle on WSR-88D to 0.25o above local horizontal

## Plot from Peter Black 2005, provided by Greg Holland, 2006

**Notes:**
Ophelia, 16 August 2006, off east coast of the U.S.

1st image:

Satellite image at time of second closest approach of Aerosonde to wind center and just after WP-3D SFMR penetration across the eye

2nd image:

Dark blue: Buoy winds
Dark Red: NOAA WP-3D 700 mb winds
Black: Aerosonde winds (flew downwind along major band to east and across major band to north)

Peak 700 mb winds of 67 kt were 10 nm southwest of center, as opposed ot 55 kt SFMR surface winds northeast of center (near small inner eye).

## Plot from Peter Black 2005, provided by Greg Holland, 2006

**Notes:**
Ophelia, 16 August 2006, off east coast of the U.S.

Dark blue: Buoy winds
Light blue: NOAA WP-3D SFMR surface winds
Black: Aerosonde winds (SFMR winds southwest of center were within 10 min of Aerosonde measurements)

Aerosonde closest approach to wind center was 30 nm southwest and 25 nm northeast. Peak winds at 2500 ft were 65 kt southeast of center and 75 kt north of center. Excellent agreement was found between buoy, SFMR, and aerosonde winds adjusted to surface values.

POINT: The types of nontraditional data shown here and on the previous slide  (satellite; asynchronous, moving platform) illustrate what must be assimilated into mesoscale models.

## 4. High Resolution, Coupled Air-Sea-Land Mesoscale Modeling and Analysis

- Many indications that models must have resolution approaching 1 km to capture phenomena in the core region important to accurate prediction of intensity; coupling between ocean surface and atmosphere is also critical
**Near-term:** Exploit recent field experiments (e.g., CBLAST, RAINEX) for improving models
**Medium-term:** Additional field experiments to validate high-resolution coupled models
**Medium-term:** NOAA acquire capability to produce 1 km resolution forecasts

**Notes:**
Notes from a presentation by Christopher Davis, Wei Wang, Greg Holland, Kristen Corbosiero, and Mark DeMaria

* Fully explicit model configuration offers
Lack of spurious development
Realistic fine-scale features, e.g. rainbands
Improvements in track forecasting
* Sub-2-km grid spacing important for capturing inner core dynamics
Rapid intensification (e.g., work by S. Chen - U. Miami)
Eye-wall replacement cycles
* Need: Verification metrics appropriate for high-resolution mesoscale models  high-resolution data sets needed for both development and verification
* High-resolution model must be initialized with advanced data assimilation schemes that ensure mesoscale information is retained, used in the model
First-order ocean feedback effects very important (surface waves, drag coefficient), but full two-way interactive 3-D ocean model need not top priority at this time
-----------
Improvements in intensity forecasting would likely lead to improvements in track forecasting as well.

## The following sequence of four slides makes the case for
-- high resolution modeling to critical capture features in the core
-- coupling between atmosphere and ocean surface to properly represent surface drag effects

- -- high resolution modeling to critical capture features in the core
- -- coupling between atmosphere and ocean surface to properly represent surface drag effects

**Notes:**
Comments from Shuyi Chen (U. Miami): “... The two key results from RAINEX and CBLAST are “high resolution” and “full atmosphere-wave-ocean-land” coupling. Storm intensity in theoretical and numerical models are often simplified into two numbers, MSLP [Minimum Sea Level Pressure (anywhere in the system)] and MWS [Maximum Wind Speed (anywhere in the system)]. Operations use the latter for practical reasons (i.e., storm impact). MSLP is controlled by the horizontal and vertical structure of the storm. However, in addition to the structure, MWS is also determined by the surface properties such as the sfc roughness, which is very sensitive to how well a model can resolve/represent both land surface and wave surface waves. I include two slides here to demonstrate this point. “

## Provided by G. Holland, NCAR

**Notes:**
This is the first of two plots showing impact of high resolution. These curves are from WRF forecasts run during RAINEX at NCAR by Greg Holland in collaboration with Shuyi Chen et al. at RSMAS/UM)

Ust28 and ust25 refer to two different model runs using different parametric representation of drag (u*) in the uncoupled WRF model
3kcd = 3 km resolution WRF model with coupling to sea surface (waves) and 1.33 km nested grid to improve resolution in the core region
Wrfrt = real time forecast run, 4 km resolution

## Provided by S, Chen, U. Miami

**Notes:**
This is the second of two plots showing impact of high resolution. This shows results from four forecasts were made during RAINEX in real-time using MM5 and WRF with various large-scale operational model forecast fields (GFS, NOGAPS, CMC, GFDL) as initial and lateral boundary conditions (referred to as high-res mini ensemble). This plot shows four MM5&WRF forecasts using the GFDL large-scale fields as initial and lateral boundary conditions. Both MM5 and WRF are configured as triple-nested domains with 15, 5, and 1.67km resolutions, respectively. The two inner nests (5 and 1.67km) are vortex-following moving grids. 

MM5-gfdl 5km (MM5 model, two domain nest – 15 and 5km)
MM5-gfdl 1.6km (MM5 model, three domain nest – 15, 5, and 1.67km)
WRF-gfdl 5km (WRF model, two domain nest – 15 and 5km)
WRF-gfdl 1.6km (WRF model, three domain nest – 15, 5, and 1.67km)

gfdl = GFDL model forecast fields as initial and lateral boundary conditions for the MM5 and WRF forecasts.

## Slide 23

**Notes:**
The preceding two plots show that high resolution (grid spacing approaching 1 km, desirably slightly less) is essential to capture the fine structure and high-gradient zones of the inner core (e.g., consider that the eye and eyewall were only 5-10km in diameter in Wilma and Charley, and ~15-40 km in Katrina and Rita), and phenomena such as eye wall cycling. Course grid models (4 km and larger) seem to never catch up with storm evolution; they apparently do not capture essential fine scale detail. 

## Provided by S. Chen, U. Miami

**Notes:**
This is the first of two plots showing  results of modeling efforts by S. Chen et al., U. Miami, for Hurricane Frances. This and the following slide from Chen et al. 2006, BAMS.
These are numerical simulations of Hurricane Frances made during CBLAST using uncoupled and coupled air-sea models.

Maximum Wind Speed (MWS, solid)
Minimum Sea-Level Pressure (MSLP, dashed lines) 

Uncoupled: Atmosphere (MM5 nested grids with 1.67km in the inner-most domain)
A-O: Coupled Atmosphere-Ocean models (same resolution as in uncoupled)
A-W-O: Coupled Atmosphere-Wave-Ocean models (same resolution as in uncoupled)

The uncoupled high-res model overestimates MSLP (over-intensifying), but underestimates MWS (wrong pressure-wind relationship) (blue lines). Coupling to ocean circulation model alone corrects MSLP (through improved heat fluxes), but still underestimates MWS (green lines). Full coupling to surface waves and ocean seems to produce the best MSLP and MWS (red lines). These results are consistent model simulations of other hurricanes such as Floyd (1999) and Bonnie (1998).

## Provided by S. Chen, U. Miami

**Notes:**
This is the second of two plots showing results of modeling efforts by S. Chen et al., U. Miami, for Hurricane Frances. This and the preceding slide from Chen et al. 2006, BAMS.

A comparison of the uncoupled and coupled simulations described on previous slide to the observed pressure-wind relationship based on HURDAT data for the Atlantic (dashed, from Chris Landsea).  Green and red points represent coupled models.

## Slide 26

**Notes:**
The preceding two plots show that 
*The uncoupled atmosphere model tends to overestimate MSLP and underestimate MWS, mostly due to the lack of coupling to the ocean (i.e., wrong pressure-wind relationship). 
*The fully coupled atmosphere-wave-ocean model produces the best estimates of MSLP and MWS, largely due to a better representation of surface roughness in high winds with coupling to a wave model.

## In talking about the coupling between atmosphere and ocean, we need to discriminate between 1) coupling to ocean surface via a wave model to get exchange coefficients correct, and 2) coupling to ocean circulation to resolve features such storm-induced ocean surface cooling, the Gulf Stream, the Gulf loop current, etc... We  need to emphasize those elements most important to the intensity problem.

**Technical issues w/r/t coupling to ocean**
- * Vortex-following nested grids have already been implemented in WRF, so running a 1km-resolution model is primarily a computational issue.
- * Since wave field beneath hurricane is for the most part locally generated, wind-driven waves, running a coupled atmosphere-ocean surface-wave model in real-time is straightforward since the wave model can be initialized from  the wind field.
- *At present, initializing in real-time a dynamic ocean model – required to get ocean circulations responding to overlying wind field -- may be a difficult problem in the near-term due to lack of appropriate observations and accurate data assimilation in ocean models at high-resolution
**Where additional research is needed:**
- High-resolution mesoscale  models (ones with the eye and eyewall resolved at 1km or less) are much more sensitive to the air-sea interface than models running at low resolution, especially in regard to exchange coefficients for momentum, heat, and moisture fluxes. Uncoupled parameterizations based on the Charnock relationship give  wrong magnitudes for surface drag, and more importantly, miss the asymmetry and complex features of the surface drag field. The asymmetry in surface drag induced by waves beneath a hurricane is probably impossible to parameterize without waves.  need additional research on this wind wave/ocean surface atmosphere coupling problem to better understand fluxes. The importance of getting these exchanges properly represented is substantiated in the figures shown here.
- * In the near term, it appears to be sufficient to use a non-dynamic representation of the upper ocean updated from observations every few days, provided 1) the storm moves fast, and 2) the model includes a good wave model to capture the exchanges. This should bring sufficient information on ocean features such as the Gulf Stream and the Loop Current to include the impact of their presence along the track on storm intensity.
- *In the mid-term, for storm-induced ocean cooling, we can suggest use of a simple 1-D upper-ocean mode parameterization as the surface layer cooling will then be proportional to translation speed. One of the problems with 1-D parameterization is that in nature the cold wake is asymmetric (with a strong right-side bias) due to the inertial motion in the upper-ocean. Long term,  a fully 3-D ocean model may be the only way to resolve the asymmetric distribution of surface layer cooling and bringing explicit evolution of ocean features such as the Gulf Stream or loop current.

**Notes:**
The above is my summary and restatement of notes provided by S. Chen, U. Miami.

## 5. Operations Research and Socio-Economic Impacts

**Near-term:** Understand how forecasters, other users will apply improved intensity predictions to decision making, use to inform design of intensity products and services
- explore results from improved track forecasting
**Medium-term:** Explore possibilities, options for providing “impact” products as opposed to simple warnings about intensity

## NHC Hurricane Warning Lengths

**Notes:**
This slide courtesy of Mark DeMaria. It shows total length (nmi) of coastline warned for 883 individual hurricane Warnings 1963-2005 (with a quadratic trend line). 
Consider that for this same period, Slide 4 shows continual improvement in track forecasts (and hence in predicted points of landfall). Thus the increase in total length of coast warned from the early 1960s to the early 1990s indicated by the quadratic fit is counter-intuitive. However, beginning in the mid-’90s, the length of coastline warned began to decrease as one would expect with improved track forecasts.
See next slide for an possible explanation of this behavior.

## NHC Hurricane Warning Lengths and Lead Times 

**NHC storm total hurricane**
**warning lengths 1963-2005**

**NHC storm average hurricane**
**warning lead times 1963-2005**

**Notes:**
This two-panel slide courtesy of Mark DeMaria. The left panel is the same as the previous slide, but where each point represents the total coastline warned for the entire storm. The right panel shows the corresponding warning lead times for each storm, again with a quadratic best fit. The lead time is calculated for each breakpoint included in the warning that also had hurricane winds, and is the time from when the warning was first issued to the time hurricane winds arrived. Each point is the time averaged over all points for each storm. 

Explanatory comment from DeMaria: ... “In coordination with TPC, I updated the data through 2005, which shows that the trend for warning longer coastline lengths has been reversed since the late 1990's ... The attached slide also shows the time series of the lead times of the hurricane warnings for the period 1963-2005. The lead time has been increasing over the past 10 to 15 years or so. 
The interpretation of the new data is that as track errors were reduced in the 70's and 80's, the improved skill primarily went into the increased lead time. Also, the warned areas in the 1960's were probably too small (the forecasters really tried to pin-point the landfall areas, sometimes incorrectly), and were adjusted upwards in the 1970's and 80's. As the track errors continued to improve, the forecasters were able to both reduce the size of the warned area and increase the lead time, from the late 1990's to the present. “ ... 
The complex non-intuitive ways that improvements in forecasting tools impact prediction products and services point to the need for operations research and formulation of applications in parallel with the development of the improved forecasting tools.

## Likelihood of Wind Damage – Bush, 2006

## Damage to EP Infrastructure – Bush, 2006

**Notes:**
This work represents a fusion of data bases describing the built environment in detail (in this case down to the substation level) and historical experiences with different types of construction at various locations, carried out on a geographic information system (GIS) with the meteorological input as the driver.

## 6. Accelerating Transfer from Research to Operations

- Many players; requires higher level coordination and support
**Near-term:** Establish a “NOAA Hurricane Team” to provide better in-house focus (NCEP/NHC, OAR/AOML/HRD) and appoint standing SAB working group on hurricane research to advise this Team
**Near-term:** Evaluation, intercomparison of in-house models with community models; visitor, post-doc programs, enhanced Joint Hurricane Testbed to link in-house efforts with wider community; establish explicit hurricane theme for a cooperative institute
**Medium-term:** Hurricane R&D *center of specialized expertise* in proximity to NHC with enhanced modeling development expertise

**Notes:**

Joint Hurricane Testbed is a demonstrated success for operational testing and evaluation, leading to implementation of certain types of new tools and technology, e.g. improved statistical techniques.

DTC is better suited for evaluating whole modeling systems.

## Attaining Critical Mass  co-locate leadership, resources, organization in Miami to achieve goal
Research and Development 

Operations Research and Socio-Economic Impacts  optimize utilization of enhanced intensity forecasts
Accelerating Transfer from Research to Operations  expedite implementation of research results to operations through programs such as JHT and DTC

**Research and Development ******
**Operations Research and Socio-Economic Impacts**  optimize utilization of enhanced intensity forecasts
**Accelerating Transfer from Research to Operations**  expedite implementation of research results to operations through programs such as JHT and DTC

- Summary

## Slide 35

## Slide 36

## Slide 37

**Notes:**
These two figures courtesy of Roger Pielke, Jr. (2006). 

First image: TPC/NHC – This shows the improvement over three decades of the error in location (track) at three key forecast times, with the most improvement coming at the longer forecast times. This improvement has come about due to 1) enhanced observations better characterizing the synoptic scale environment in which the hurricane is embedded, and 2) improved numerical models (especially the GFDL model) which better replicate the steering of the storm by that larger scale environment.
-------
Second image (buried behind the first): Jarrell and Demaria (1999) – This shows that the length of coastline warned has increased over time. When interpreted in light of improvement shown in the first image, this leads to the counter-intuitive result that as predictions of track (hence landfall) have improved, the length of coast warned has increased.
Comment received later from Mark DeMaria: “The second ...,  which shows the length of coastline warned, is outdated and misleading [when viewed from 2006]. That slide shows the data through 1999 from a study I did with Jerry Jarrell, the former TPC director.”  See later analysis in Slides 22 and 23 which shows a change in trend concerning the length of coast warned.