# 4 Future Capabilities to Meet Operational Needs

Chapter 3 summarized the current capabilities of the Nation's tropical
cyclone forecast and warning program. These capabilities reflect the
vast improvements in track forecasting and modest improvements in
intensity forecasting over the past two decades. These improvements
resulted primarily through major advances in observations, global and
regional operational NWP modeling systems, development of sophisticated
data assimilation techniques and improved representation of model
physics, and through major investments in supercomputing at operational
NWP centers. While global and regional-scale NWP models have proven
highly successful at forecasting tropical cyclone tracks, coupled models
with much higher resolution will be necessary to make further strides in
forecasting tropical cyclone intensity, structure (wind radii), sea
state and storm surge, and precipitation. Increased skill in forecasting
intensity and structure, sea state and storm surge, and precipitation is
now on the horizon, much as improving track forecast skill was a decade
ago.

To continue to advance operational tropical cyclone forecasting
capability, the Nation must be committed to supporting---through
research, development, and transition to operations---the following key
areas vital to the tropical cyclone forecast and warning program:

-   Advanced observations

-   Advanced data assimilation technologies

-   Advanced NWP models

-   Investment in human and infrastructure[^1] resources

Continued exploitation of observations via advanced data assimilation
systems and improved NWP models will enhance the forecast guidance
provided to forecasters on tropical cyclone track, intensity, structure
(wind radii), sea state and storm surge, and precipitation. Viable
processes and sufficient funding are integral to this effort to transfer
new technology and research results into improved operational analysis
and prediction at the tropical cyclone forecast and warning centers.
Only a community-wide effort can meet the operational needs of the
tropical cyclone forecast and warning centers, introduced previously in
this plan and summarized in the next section.

Following a review of the operational needs of the tropical cyclone
forecast and warning centers, this chapter summarizes current and
planned activities regarding data collection (observations), advanced
NWP modeling systems, and data assimilation technologies. It concludes
with a discussion on transitioning research results to operational
capabilities.

## 4.1 Operational Needs of the Tropical Cyclone Forecast and Warning Centers

As explained in section 2.8.1, a list of operational needs of the three
U.S. tropical forecast and warning centers was validated at the 59^th^
Interdepartmental Hurricane Conference (IHC) (held in March 2005) and
updated in a NOAA/OAR Announcement of Federal Funding Opportunity (AFFO)
in June 2006. Table 4-1 lists these operational needs, in the priority
order given in the 2006 AFFO, along with related needs statements
stressed by the DOD participants at the 59^th^ IHC. This listing thus
represents the best available compilation and prioritization of
operational needs across the three U.S. centers: the TPC/NHC, JTWC, and
CPHC.

The operational needs in table 4-1 can be characterized by seven
tropical cyclone--related, day-to-day operational forecast and warning
categories identified with the bullets below. This plan addresses all of
these categories.

+--------------------------+-------------------------------------------+
| -   Intensity            | -   Storm surge                           |
+--------------------------+-------------------------------------------+
| -   Structure            | -   Precipitation                         |
+--------------------------+-------------------------------------------+
| -   Track                | -   Observations                          |
+--------------------------+-------------------------------------------+
| -   Sea state            |                                           |
+--------------------------+-------------------------------------------+

The June 2006 AFFO also noted four high-priority areas identified by the
NCEP/EMC for advancing NWP modeling and forecasting capabilities. These
research priorities, summarized below, have been included in the
research priorities outlined in chapter 5.

-   General model improvements to advance track and intensity forecasts
    \[EMC-1\]

-   Improved boundary layer representation for coupled air/sea/land
    models by, for example, exploiting results from field projects such
    as C-BLAST (for improved parameterization of surface fluxes in high
    wind regimes, and effects of sea spray on transfer coefficients)
    \[EMC-2\].

-   Model validation techniques suitable for three dimensional, high
    resolution verification for all phases of the tropical cyclone life
    cycle \[EMC-3\]

-   Diagnostic techniques to further increase the utility of global
    models (e.g., NCEP, UKMO, NOGAPS) in forecasting tropical cyclone
    genesis \[EMC-4\]

**To meet the operational needs summarized in Table 4-1, focused efforts
and sufficient investment in the four areas of advanced observations,
advanced data assimilation technologies, advanced NWP models, and
investment in human and infrastructure resources are critical to the
future success of the Nation's tropical cyclone forecast and warning
program.**

**Table 4-1. Operational Needs of the Tropical Cyclone Forecast and
Warning Centers (TPC/NHC, JTWC, and CPHC)**

+-------+--------------------------------------------------+-----------+
| **    | **Operational Need**                             | **Ca      |
| Prior |                                                  | tegorya** |
| ity** |                                                  |           |
+-------+--------------------------------------------------+-----------+
| **1** | Guidance for tropical cyclone intensity change,  | I         |
|       | with highest priority on the onset, duration,    |           |
|       | and magnitude of rapid intensification events.   |           |
|       | Similar guidance is also needed on when rapid    |           |
|       | over-water weakening (such as had been observed  |           |
|       | in recent Gulf of Mexico hurricanes) will occur. |           |
+-------+--------------------------------------------------+-----------+
| **2** | Improved observational systems in the storm and  | O         |
|       | its environment that provide data for forecaster |           |
|       | analysis and model initialization.               |           |
+-------+--------------------------------------------------+-----------+
| **3** | Statistically-based real-time guidance on        | I, T, P   |
|       | guidance for track, intensity and precipitation  |           |
|       | (e.g., multi-model consensus approaches),        |           |
|       | provided to forecasters in probabilistic and     |           |
|       | other formats.                                   |           |
+-------+--------------------------------------------------+-----------+
| **4** | Enhancements to the operational environment to   | A         |
|       | increase forecaster efficiency, by expediting    |           |
|       | analysis, forecast, coordination, and/or         |           |
|       | communication activities.                        |           |
+-------+--------------------------------------------------+-----------+
| **5** | Improved storm surge guidance models, including  | SG        |
|       | guidance on breaking waves and featuring         |           |
|       | high-resolution input and output (including      |           |
|       | probabilistic formats).                          |           |
+-------+--------------------------------------------------+-----------+
| **6** | Operational analysis of the surface wind field   | O, I, S,  |
|       | (including maximum sustained winds) in tropical  | A         |
|       | cyclones. This also includes methods for         |           |
|       | forecasting the wind field over elevated terrain |           |
|       | and high-rise buildings.                         |           |
+-------+--------------------------------------------------+-----------+
| **7** | Guidance for changes in tropical cyclone         | S, SS     |
|       | size/wind structure and related parameters,      |           |
|       | including combined sea heights.                  |           |
+-------+--------------------------------------------------+-----------+
| **8** | Guidance for tropical cyclone precipitation      | P         |
|       | amount and distribution.                         |           |
+-------+--------------------------------------------------+-----------+
| **9** | Improved utility of microwave satellite and      | O, I, S   |
|       | radar data in tropical cyclone analysis,         |           |
|       | particularly to determine structure and          |           |
|       | intensity.                                       |           |
+-------+--------------------------------------------------+-----------+
| *     | Probabilistic forecast guidance for tropical     | I, S      |
| *10** | cyclone surface wind speed.                      |           |
+-------+--------------------------------------------------+-----------+
| *     | Guidance for tropical cyclone genesis that       | I, S, T   |
| *11** | exhibits a high probability of detection and a   |           |
|       | low false alarm rate, and/or provides            |           |
|       | probability of genesis.                          |           |
+-------+--------------------------------------------------+-----------+
| *     | Identification, and then reduction of, the       | T, I      |
| *12** | occurrence of guidance and official track        |           |
|       | outliers, focusing on both large speed errors    |           |
|       | (e.g., accelerating recurvers and stalling       |           |
|       | storms) and large direction errors (e.g.,        |           |
|       | loops), and on specific forecast problems,       |           |
|       | including interactions between upper-level       |           |
|       | troughs and tropical cyclones, track forecasts   |           |
|       | near mountainous areas, and extratropical        |           |
|       | transition.                                      |           |
+-------+--------------------------------------------------+-----------+
| *     | Improved techniques for estimating the intensity | I         |
| *13** | of tropical cyclones passing over and north of   |           |
|       | sea-surface temperature gradients (e.g., in the  |           |
|       | eastern North Pacific Ocean and the Atlantic     |           |
|       | Gulf Stream).                                    |           |
+-------+--------------------------------------------------+-----------+
| *     | Quantitative guidance tools for seasonal         | CL        |
| *14** | tropical cyclone forecasts for the Atlantic and  |           |
|       | North Pacific basins, using statistical and/or   |           |
|       | dynamical methodologies.                         |           |
+-------+--------------------------------------------------+-----------+
| **    | **DOD Operational Need b**                       |           |
| DOD** |                                                  |           |
|       |                                                  |           |
| **Pri |                                                  |           |
| ority |                                                  |           |
| ^b^** |                                                  |           |
+-------+--------------------------------------------------+-----------+
| **1** | Improved track forecasts out to 5 days.          | T         |
+-------+--------------------------------------------------+-----------+
| **2** | Improved structure forecasts: radius of 50-kt    | S         |
|       | and 35-kt wind radii.                            |           |
+-------+--------------------------------------------------+-----------+
| **3** | More accurate forecasts of wave heights and      | SS        |
|       | radius of 12 foot seas.                          |           |
+-------+--------------------------------------------------+-----------+

a Category abbreviations: A = Automation; CL = Intraseasonal/Interannual
Variability (Climate); I = Intensity; O = Observations; P =
Precipitation; S = Structure; SG= Storm Surge; SS = Sea State; T =
Track.

b As described in section 2.8.1, attendees at the 59th Interdepartmental
Hurricane Conference validated the top 14 operational needs. The DOD
emphasized their top three priorities, which are indicated at the bottom
portion of the table. DOD's remaining needs were the same as the top
portion of the table.

## 4.2 Data Collection: Plans for Observation Platforms and Instruments

![](media/image2.jpeg){width="5.5in"
height="4.063385826771653in"}Continuing to advance observational
capabilities for tropical cyclone analysis and numerical weather
prediction is a vital component of the Nation's tropical cyclone
program. There are many planned observation platforms and instruments to
enhance observational capabilities critical to both tropical cyclone
forecasters and tropical cyclone NWP systems to help meet the
operational needs identified in Table 4-1. Some of the most promising
platforms/instruments are described in sections 4.2.1 through 4.2.10
below. With numerous new observational platforms and sensors potentially
available in the next several years, a coordinated approach is needed to
improving tropical cyclone reconnaissance and surveillance systems
(manned, unmanned, spaced-based, etc.).

### 4.2.1 The GOES-N and GOES-R Series of Geostationary Satellites

On January 28, 1998, NOAA and NASA awarded a contract for the next
generation of spacecraft for the Geostationary Operational Environmental
Satellite (GOES) system (current GOES capability is discussed in section
3.1.2). GOES-N (figure 4-1), the first in this next generation of three
GOES satellites, was launched in May 2006 and was renamed GOES-13 after
reaching geostationary orbit and becoming an operational satellite. The
GOES-N series of satellites will be followed by the GOES-R series, which
is currently in the program definition and risk reduction phase. NASA\'s
Goddard Space Flight Center is responsible for procuring, developing,
and testing the spacecraft, instruments, and unique ground equipment for
GOES spacecraft. NOAA is responsible for the overall program and its
funding, for in-orbit system operation, and for determining GOES
satellite replacement needs.

![](media/image3.jpeg){width="3.6251968503937007in"
height="3.149212598425197in"}

The instruments pertinent to tropical cyclone analysis and forecasting
in the GOES-N series will continue to be an imager and a sounder. The
imager is similar to the GOES-M imager, The GOES-N Sounder satisfies
mission requirements for multispectral sensing in the visible and
infrared regions.

### 4.2.2 NPOESS and NPP

The National Polar-orbiting Operational Environmental Satellite System
(NPOESS) program converges the existing polar-orbiting systems of the
DOC and the DOD. It is managed by a tri-agency integrated program office
(the NPOESS IPO), which includes personnel from the DOC, the DOD, and
NASA. Appendix I reviews the NPOESS sensors and data that are pertinent
to tropical cyclone analysis and forecasting.

The NPOESS Preparatory Project (NPP) is an instrument risk-reduction
project being conducted jointly by the NPOESS IPO and NASA. It is
designed to bridge between the NASA Earth Observing System (EOS) program
and NPOESS for developing the following sensors: Advanced Technology
Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Ozone
Mapping and Profiler Suite (OMPS), and Visible/Infrared Imager
Radiometer Suite (VIIRS). The NPP spacecraft is scheduled for launch in
2010. The launch of the first NPOESS satellite is tentatively scheduled
for 2013.

Although profiling buoys provide information to that from
satellite-based altimeters, the latter are essential for observing the
ocean over large regions where in situ data are unavailable. The NPOESS
altimeter, which previously was part of the baseline instrument package,
has been placed into a Deferred/Government Furnished Equipment (GFE)
category. The loss of this instrument without a suitable replacement
will create a capability gap in determining the energy available in the
oceans---a capability that is needed to help monitor and understand
tropical cyclone intensity changes. **Since satellite altimetry is vital
to addressing the needs of the tropical cyclone forecast and warning
centers summarized in table 4-1, the JAG/TCR strongly endorses the
acquisition of an altimeter instrument for NPOESS as an alternative to
the cancelled NPOESS ALT instrument.**

### 4.2.3 MetOp Satellites

MetOp-A, launched in October 2006 and renamed MetOp-2 when it became
operational, is Europe's first operational polar-orbiting weather
satellite. The MetOp program is a joint project of the European Space
Agency (ESA) and the European Organization for the Exploitation of
Meteorological Satellites (EUMETSAT). ESA cofunds the MetOp satellites
while EUMETSAT has overall system authority, develops the ground
segment, cofunds the satellites, develops and procures the instruments,
procures the launchers, and operates the end-to-end system. NOAA funds
the U. S. instruments for MetOp. The present plan is to launch three
MetOp satellites sequentially to maintain the service for at least 14
years.

MetOp-2 carries a set of seven "heritage" instruments provided by NOAA
and the French Space Agency (Centre National d\'Etudes Spatiales; CNES)
and a new generation of five European instruments that provide improved
sensing capabilities of value to both meteorologists and climatologists.
The new instruments will augment the accuracy of temperature, humidity,
wind speed, and wind direction measurements, especially over the ocean,
and will provide a more accurate profile of ozone in the atmosphere. The
MetOp sensors and data that are potentially pertinent to tropical
cyclone analysis and forecasting are described in appendix J, which also
links these instruments to the operational priorities and capability
limitations in section 4.1.

### 4.2.4 Jason-2 and Oceansat 3 Satellites

Jason-2, a cooperative effort of CNES, EUMETSAT, NASA, and NOAA, will
carry an instrument payload including the next generation of the
Poseidon altimeter (Poseidon-3). One of its objectives is to provide a
minimum of 3 years of measurement of global ocean surface topography.
This mission, which will take over and continue the Topex/Poseidon and
Jason-1 missions, is scheduled to launch in June 2008.

The CNES-built AltiKa altimeter, which will work in the Ka-band at 35
GHz) on board the Indian Space Research Organization (ISRO) Oceansat 3
satellite, will complement the Jason-2 capability. Oceansat 3 is a
cooperative effort of CNES and ISRO. It is planned to launch in the
first half of 2009, with a design life of 3 years.

### 4.2.5 Global Precipitation Measurement Mission

The Global Precipitation Measurement (GPM) mission builds on the success
of the Tropical Rainfall Measuring Mission (TRMM), a joint project of
NASA and the National Space Development Agency of Japan. TRMM, which was
launched in November 1997, is the first satellite mission dedicated to
measuring tropical and subtropical rainfall. TRMM's contributions to
understanding and prediction of tropical cyclone behavior have been
substantial, as indicated by the following examples (Shepherd 2005):

-   TRMM data have established the association of energy-releasing deep
    convective clouds in the eyewall of tropical cyclones with storm
    intensifications. For example, TRMM data documented, in near-real
    time, the development of deep convective clouds, extending to 16 km)
    in the eyewall of Hurricane Katrina on August 28, when it
    intensified to category 5.

-   TRMM is currently the only satellite that can provide rain structure
    information over open oceans, where most tropical cyclones breed and
    intensify. TRMM data provided a rain history of Katrina from her
    genesis over the Bahamas until her transition to an extratropical
    storm over the Ohio Valley.

Although TRMM has exceeded expectations and has provided a wealth of new
knowledge about tropical cyclones and other phenomena (such as shifts in
the El Nino Southern Oscillation), the mission has intrinsic limitations
that the GPM mission is designed to overcome.

The GPM mission will be a constellation of satellites, consisting of one
core spacecraft, similar to the TRMM satellite, and a fleet of smaller
satellites carrying only microwave radiometers. The major sensors on the
GPM core satellite are the Dual-frequency Precipitation Radar (DPR) and
the GPM Microwave Imager (GMI), both of which represent advances in
capability over the corresponding TRMM instruments. The GPM
constellation will provide coverage that is both global and more
frequent than TRMM coverage. It will be capable of measuring rain rates
from as little as a hundredth of an inch per hour to as much as 4 inches
per hour. NASA and the Japanese Aerospace Exploration Agency (JAXA) are
working together to build and launch GPM. The core spacecraft is
scheduled to launch in 2013; the constellation satellites are scheduled
for launch late in 2014.

### 4.2.6 Constellation Observing System for Meteorology, Ionosphere, and Climate

![](media/image4.jpeg){width="3.585433070866142in"
height="3.1681102362204725in"}![](media/image5.jpeg){width="2.410236220472441in"
height="3.177165354330709in"}The Constellation Observing System for
Meteorology, Ionosphere, and Climate (COSMIC) consists of a
constellation of six satellites (figure 4-2) that were launched in April
2006 (figure 4-3). This satellite network will probe the atmosphere
using radio occultation. Each satellite intercepts GPS signals that have
passed through (have been occulted by) the atmosphere close to the
horizon. This path brings the signal through a deep cross-section of the
atmosphere. Variations in electron density, air density, temperature,
and moisture bend the signal and change its speed. By measuring these
signal shifts, scientists can determine the atmospheric conditions that
produced them to produce temperature and water vapor profiles along
thousands of angled, pencil-like segments of atmosphere. The horizontal
scale of a single occultation sounding is between 200 and 600 km, with a
typical value being 300 km. The vertical resolution of the receivers
will vary from about 100 meters in the lower part of the troposphere to
about 1 km in the upper troposphere and stratosphere. From COSMIC data,
scientists will be able to infer the state of the atmosphere above some
2,500 locations every 24 hours, including vast stretches of ocean
inadequately profiled by current satellites and other tools The high
vertical resolution of COSMIC data will complement the high horizontal
resolution of present satellite sounding systems. Together, these
observing system will provide accurate, high-resolution global
observations with excellent and consistent representation of the
important horizontal and vertical scales of motion in the atmosphere.
Work is ongoing to assimilate GPS radio occultation data, such as that
from COSMIC, into NWP models.

Radiosondes (weather sensors launched by balloon) have obtained vertical
profiles since the 1930s. However, they are launched only twice a day in
most spots, and few are deployed over the ocean. In contrast, the COSMIC
data will be collected continuously across the globe (figure 4-4). The
GPS radio signals can be picked up by the low-orbiting COSMIC receivers
even through clouds, which are an obstacle for satellite-borne
infrared-sensing instruments.

![](media/image6.jpeg){width="5.25in" height="3.805511811023622in"}The
COSMIC satellite network, which was developed through a U.S.-Taiwan
partnership, was built to a system design provided by the University
Corporation for Atmospheric Research (UCAR), where the COSMIC Project
Office is based. Orbital Sciences Corporation designed the COSMIC
spacecraft. Taiwan\'s National Science Council and National Space
Organization and the NSF in the United States are providing primary
support for COSMIC. Other collaborators include NASA and NASA's Jet
Propulsion Laboratory, NOAA, the Air Force, and NRL-Monterey.

### 4.2.7 Unmanned Aircraft Systems

Unmanned aircraft systems (UASs) represent a new type of observing
platform that potentially can benefit tropical cyclone analysis and
forecasting. UASs are being evaluated for their potential to obtain
critical observations in and around tropical cyclones. As an example, in
September 2005, a UAS flew into Hurricane Ophelia. The aircraft provided
the first-ever detailed observations of the near-surface and high-wind
tropical cyclone environment, an area often too dangerous for manned
aircraft to observe directly. A unanimous recommendation from the
interagency participants in a workshop on UASs, sponsored by NOAA, NASA,
and the DOE and held in Las Vegas, Nevada, on February 28 to March 1,
2006, was that a demonstration should be conducted for low-level
observations by a UAS in a hurricane. The objective of the demonstration
should be to obtain detailed observations of the near-surface tropical
cyclone boundary layer environment. Some key questions this proposed
demonstration project could address include the following:

-   Will UAS platforms/instruments provide data that will improve
    tropical cyclone intensity forecasts?

-   Will UAS platforms/instruments help improve our understanding of the
    rarely observed tropical cyclone boundary layer environment?

-   Will the data improve the models and provide a better analysis for
    forecasters, particularly in the short-term?

-   Will the data provide more information than already provided by
    satellites and manned aircraft?

-   Where are the current data gaps and what combination of UAS and
    sensors would serve to fill those gaps?

-   Could the UAS test platform(s) be used to develop and test new
    instruments?

At the same UAS workshop, the Air Force provided an update on the
Weather Scout Unmanned Aerial Vehicle (WSUAV) initiative. The overall
goal of the WSUAV is to improve situational and predictive battlespace
awareness for commanders and mission planners in all the military
services, in order to mitigate weather impacts on operations. A WSUAV
would carry a flexible, directed weather sensor suite capable of
operating in data-sparse and data-denied areas. The Pacific Air Forces
(headquartered at Hickam Air Force Base, Hawaii) has been the lead
agency working the WSUAV concept since Super-typhoon Pongsana served as
a wake-up call to senior leaders to improve typhoon forecasting
procedures. With manned aircraft missions for weather reconnaissance no
longer operating in the western Pacific, the Pacific Air Forces began
developing the WSUAV capability to augment satellite-based weather
reconnaissance capability. Rather than buy or build a UAS focused on
this mission, the initial decision was to lease a UAS as a commercial
off-the-shelf data service, with the data to be available to the
operational commander in near real time.

### 4.2.8 Multifunction Phased Array Radar

Multiple Federal agencies currently rely on radar networks to provide
essential services to the Nation. The principal current uses are for
weather surveillance, other atmospheric observations, and aircraft
surveillance. A multifunction phased array radar (MPAR) network with the
capabilities described in a recently published report (OFCM 2006) could
perform all of the existing civilian radar functions. In addition, other
existing and emerging needs not being adequately met by current systems
could be met with this same MPAR network. Agencies whose current
capabilities in essential mission areas could be improved by the
enhanced weather surveillance capabilities of MPAR include the
Department of Commerce (NOAA and NOAA/NWS), the Department of
Transportation (Federal Aviation Administration and Federal Highway
Administration), NASA, the Department of Agriculture (including the U.S.
Forest Service), the Department of the Interior (National Park Service,
Bureau of Land Management, and U.S. Geological Survey), the Department
of Homeland Security (Federal Emergency Management Agency, U.S. Fire
Administration, and U.S. Coast Guard), the DOD (Air Force, Navy, and
Army for domestic and homeland defense operations), and the U.S.
Environmental Protection Agency.

Two features of MPAR that would particularly benefit tropical cyclone
forecast and warning services are its adaptive scanning and dwell
capabilities. Because MPAR can produce multiple radar beams, each of
which can be independently shaped and targeted, high-interest weather
features can be targeted adaptively without interfering with full-volume
scanning essential during a major cyclonic event. MPAR can target a beam
to dwell on features such as convective cells spun off by a tropical
cyclone over land or bands of heavy precipitation. Thus, two principal
advantages of MPAR for tropical cyclone forecast and warning are longer
lead times for tornado warnings and more accurate, higher-resolution
precipitation rates to feed into QPF and hydrologic models.

At low scanning elevations, an MPAR beam can avoid beam blockage and
ground clutter. At high scanning elevations, a larger angular volume can
be covered, decreasing the full-volume scan time without decreasing
resolution. These and other features of MPAR will contribute generally
to improved weather surveillance, including tropical cyclone forecast
and warning. In the future, weather radar system will continue to be
essential to the tropical cyclone program as coastal radars provide a
seamless transition from airborne to land-based observations, as a
hurricane approaches the U.S. mainland.

When MPAR capabilities are compared with those of conventional radar
technology, the technical advantages of MPAR are substantial. However,
before a decision is made between continuing with single-function radars
or an MPAR network, some specific technical issues need further testing
and demonstration to ensure that the necessary MPAR technology is mature
enough to proceed with this major shift in strategy. This work is
ongoing.

### 4.2.9 Airborne Scanning Radar Altimeter

As stated in table 3-3, the NASA-developed airborne scanning radar
altimeter (SRA), which has been tested during hurricane field
experiments, was designed primarily to measure the energetic portion of
the directional wave spectrum by generating a topographic map of the sea
surface. The SRA uses the radar return to measure the significant wave
height and can resolve low-frequency surface waves. It sweeps a radar
beam of l^o^ half-power width (two-way) across the aircraft ground track
within [+]{.underline}22^o^ of nadir, simultaneously measuring the
backscattered power at its 36 GHz (8.3 mm) operating frequency and the
range to the sea surface at 64 points spaced across the swath at 0.7^o^
incidence angle intervals (Walsh et al. 2002). The range readings
produce raster lines of sea surface topography at a 10 Hz rate. The SRA
was designed primarily to produce sea surface directional wave spectra,
but the backscattered power measurements can also be used to determine
path-integrated rain rate below the aircraft.

This instrument has potential for transition into operations within the
next several years. It will be particularly helpful to TPC/NHC for
real-time wave forecasting and to NCEP/EMC for assimilation into
HWRF/HYCOM in addition to providing critical verification of WAVEWATCH
III results. Airborne SRA will contribute to operational priorities 1a,
2a, 2b, 7, and 8 in table 4-1.

### 4.2.10 Improvements to IWRAP

The Imaging Wind and Rain Airborne Profiler (IWRAP) is a dual-frequency,
conically-scanning Doppler radar operating at C- and Ku-bands. The
instrument measures rain reflectivity *Z*e and Doppler velocity with a
range resolution of 30 m. It also measures surface wind vectors via
scatterometry (Contreras et al. 2006). As mentioned in table 3-3, IWRAP
is the first high-resolution dual-band airborne Doppler radar designed
to study the inner core of tropical cyclones. It has flown on research
missions aboard a NOAA WP-3D aircraft during the 2002, 2003, 2004, and
2005 hurricane seasons as part of ONR's Coupled Boundary Layers Air-Sea
Transfer (CBLAST) experiment, NASA's Ocean Vector Winds research, and
the NOAA/NESDIS Ocean Winds and Rain experiments.

One of the lessons learned during the above experiments was that IWRAP,
as then configured, was constrained in retrieving the wind field at the
lowest part of the ocean-atmosphere boundary layer. Because of the
instrument's off-nadir observing geometry, the radar return from the
ocean surface interferes with the precipitation measurements from which
the wind field is derived (Fernandez et al. 2006). To overcome this
limitation, IWRAP was recently equipped with a new data acquisition
system that allows acquisition of the raw radar return data, enabling
post-capture spectral processing to separate both ocean and rain
contributions. With this improvement, the wind field can be derived from
the radar data virtually down to the ocean surface, creating a unique
opportunity to estimate the drag coefficient in very high wind
conditions. Moreover, the unique ability of the improved IWRAP to
estimate the rain spectrum could lead to better understanding and
characterization of the rain processes within the inner core of tropical
cyclones. Further improvements that could aid in meeting these
operational priorities include: (1) more efficient antenna design, (2) a
unique single RF channel design, and (3) frequency diversity Doppler
technique to decouple the range signal from Doppler ambiguities
(Carswell and McMillan 2006).

### 4.2.11 The Ocean Surface Vector Winds Data Requirement

As noted in section 3.1.2, operational tropical cyclone forecasters at
TPC/NHC, CPHC, and JTWC rely heavily on ocean surface vector winds
(OSVW) data in their forecast and warning activities. The first
operational use of OSVW data began in 2000. The primary resource for
this analysis has been data from the SeaWinds sensor on the NASA
QuikSCAT satellite. Forecasters also use OSVW data from the WindSat
instrument, when available, aboard the Coriolis satellite. Also noted in
tables 3-4 and 3-5, QuikSCAT data have been assimilated routinely for
NWP modeling at NCEP and NRL (FNMOC) since 2002 and 2004, respectively.
However, *seven years after the first operational use of OSVW data, the
Nation still has no plans for an operational OSVW data stream that
addresses the present and future requirements for satellite-based OSVW
observations, including the platforms and instruments described above
(sections 4.2.1--4.2.10).* The OSVW requirements, defined at a
NOAA-sponsored OSVW Workshop (Chang et al. 2006) are as follows:

-   Provide accurate measurements in the presence of extreme wind
    conditions such as those found in intense storms and cyclones by
    extending the upper wind speed limit to 165 kt, (in the Category 5
    hurricane range); provide accurate measurements in the presence of
    rain

-   Increase spatial resolution (decrease the characteristic dimensions)
    of individual measurements to allow definition of small-scale
    features in synoptic and mesoscale systems

-   Provide accurate vector wind measurements closer to the coast

-   Allow estimation of the required 1-minute sustained wind speed from
    the instantaneous spatially averaged wind measured by the
    space-based instruments

-   Emphasize the overall operational requirement for an observing
    system (likely multiplatform) that satisfies revisit frequency
    requirements for measurements at every open-ocean location

As encapsulated in the workshop summary report, "Establishing an
operational satellite OSVW data stream and closing the OSVW capability
gaps will result in more accurate warnings, watches, and short-term
forecasts; improved analyses, model initializations, and atmospheric
forcing of ocean models; and a better understanding of coastal and
oceanic phenomena." The bottom line: meeting this operational
requirement will significantly improve the Nation's operational tropical
cyclone forecast and warning capabilities. ***The development of new
observational technologies is a research priority identified in chapter
5 of this report. Due to the importance of OSVW data---for use by
tropical cyclone forecasters and in tropical cyclone NWP systems---the
JAG/TCR strongly endorses the development and acquisition of a
capability to meet the OSVW observation requirements. This capability is
absolutely critical to meeting the operational needs of the tropical
cyclone forecast and warning centers summarized in table 4-1.***

### 4.2.12 Summary

These new and improved observation systems, which are under development
or being planned, hold substantial promise to provide important
information for tropical cyclone analysis, NWP modeling, and fundamental
understanding of the tropical cyclone atmosphere and ocean environment,
the tropical cyclone inner and outer cores, and the mutual interaction
between these components. These systems include in situ measurements of
winds over oceans in areas with tropical cyclones, as well as
remote-sensing methods to measure temperatures, humidity, winds, sea
surface heights, and ocean wave heights and swell motion. The
remote-sensing data will be provided from a combination of sensors
located on aircraft, on polar-orbiting and geostationary satellites, and
on land (e.g., weather surveillance radar). **However, there are
significant observation gaps that must be addressed, particularly
satellite altimetry in the wake of the loss of the NPOESS ALT instrument
(section 4.2.2) and the requirement for operational OSVW data (section
4.2.11). Researchers and system developers must work together to seek
viable solutions for these observation gaps.**

## 4.3 Statistical Analysis and Prediction Techniques 

As mentioned in section 3.2, statistical modeling and analysis tools
play substantial roles in tropical cyclone monitoring and prediction.
While numerical modeling will take an increasingly larger role in
tropical cyclone forecasting in the future, the need for statistical
approaches will continue. Statistical models will continue to provide
benchmarks for assessing the forecast skill of dynamic NWP models. They
are also valuable for estimating uncertainties in forecasts of storm
intensity and precipitation.

Track prediction is conducted most skillfully today with NWP models,
although statistical models provide the benchmark against which that
skill is measured. Even so, the use of optimal combinations of
statistical and dynamic model predictions for track guidance, whether
combined through simple consensus, corrected consensus, or
super-ensemble techniques, will continue to be the state of practice for
the foreseeable future. For intensity and structure forecasting,
statistical methods likely will be competitive with numerical models for
at least several more years. Continued improvement of sophisticated
statistical intensity approaches---such as the Statistical Hurricane
Intensity Prediction System---is needed and should be encouraged.

### 4.4 Advanced NWP Modeling Systems

Progress is being made to meet the operational needs of the tropical
cyclone forecast and warning centers identified in table 4-1. Recent
improvements in the GFDL operational coupled hurricane model (see
section 3.3.2), for example, have led to improved intensity forecasts,
which are now competitive with the operational statistical intensity
guidance made available to tropical cyclone forecasters (see figures 3-8
and 3-9). As mentioned at the opening of this chapter, increased skill
in forecasting intensity and structure, sea state and storm surge, and
precipitation is now on the horizon, much as improving track forecast
skill was a decade ago. These gains stem from the continuing
improvements in observational capabilities described in section 3.1, as
well as the advances in NWP model physics and data assimilation systems
reviewed in section 3.3.

Nevertheless, substantial challenges to tropical cyclone forecasting
remain. For example, current numerical guidance products show little
skill in forecasting *rapid changes* in hurricane intensity or hurricane
structure. None of the current numerical model guidance captured: (1)
the rapid intensification, or the rapid decay before landfall, of
Hurricane Lili (2002) in the Gulf of Mexico; (2) the rapid
intensification of Hurricane Charley four and a half hours before
landfall along the southwest coast of Florida in 2004; or (3) the
unprecedented rapid intensification of Hurricane Wilma in the northwest
Caribbean in 2005. For Lili and Charley, preparations probably occurred
over a broader area than would have been necessary if more accurate
forecasts of intensity and structure had been available.

The difficulties in forecasting intensity and structure, along with
other hurricane forecast challenges, are far more complex than simply
running a higher resolution hurricane model on a more powerful computer.
The extreme environment created by a tropical cyclone, coupled with the
ability to describe features at resolutions not previously
computationally feasible, necessitates the development of innovative
coupled-model approaches with advanced subgrid-scale physical
parameterizations. Improving NWP guidance for tropical cyclone
forecasting requires (1) sufficient computing resources, (2) a coupled
air-sea-land prediction system with advanced physics, (3) a
state-of-the-art data assimilation capability that can take advantage of
current and next generation observations for initializing the hurricane
core circulation and the tropical environment in NWP models, and (4)
sufficient investment in human and infrastructure[^2] resources.

Improving tropical cyclone forecast guidance for TPC/NHC, CPHC, and JTWC
forecasters regarding intensity, structure, track, sea state and storm
surge, and precipitation is the overall goal guiding the ongoing
development of next generation hurricane forecast systems. The work led
by NOAA/EMC to develop the HWRF Air-Sea-Land Hurricane Prediction System
is described in section 4.4.2. The parallel, and complementary, effort,
led by NRL-Monterey and FNMOC, to continue improving the COAMPS Tropical
Cyclone System is described in section 4.4.3. These complementary
development efforts should be a national priority. They should form the
basis for projects supporting hurricane research and collaboration among
experts from the university community, international researchers, the
private sector, and other Federal agencies.

**Additional human and infrastructure resources (e.g., items such as
computational power, network bandwidth, architectural/engineering
requirements, and maintenance of applicable systems) will be necessary
to support development of advanced data assimilation [and]{.underline}
NWP modeling systems.** In any development of advanced techniques, it is
critical to have a balance of human and computing resources not only for
the development and deployment of the initial implementation but also
for subsequent maintenance and future code enhancement activities. The
additional computational expense and code complexity of any advanced
technique permeates the entire development and operational activities at
an operational center. On the development side, all experiments must use
a parallel version of an operational system. Experimental changes are
therefore carefully controlled, and interpretation of results is
facilitated. Due to the great complexity of modern NWP forecast systems,
diagnosing results requires extreme care and scientific discipline, as
well as the resources (infrastructure, computational, personnel, and
budgetary) to run adequate samples of cases (often requiring several
months of data assimilation).

### 4.4.1 The Earth System Modeling Framework

The ESMF collaboration is building standards-based, open-source software
that aims to increase software reuse, component interoperability,
performance portability, and ease of use in Earth science applications
(Balaji et al. 2004). The project is a multi-institutional,
cross-disciplinary collaboration that includes many of the largest Earth
science modeling centers in the United States. The ESMF defines an
architecture for composing complex, coupled systems and includes data
structures and utilities for developing individual models. The aim is to
create a framework usable by individual researchers as well as major
operational and research centers. ESMF is funded by the NASA Science
Mission Directorate, the DOD High Performance Computing Modernization
Program (HPCMP), and NSF.

The basic idea behind ESMF, as explained on the ESMF website
([http://www.esmf.ucar.edu](http://www.esmf.ucar.edu/)), is that
complicated applications should be broken up into smaller pieces, or
components. A component is a unit of software composition that has a
coherent function, well-delineated behavior, and a standard calling
interface. Components can be assembled to create multiple applications,
and different implementations of a component may be available. In ESMF,
a component may be a physical domain or a function such as a coupler or
input-output (I/O) system.

Figure 4-5 is an ESMF example, showing the structure of the NASA GEOS-5
Atmospheric General Circulation Model (AGCM) (Collins 2005). (For more
information on the GEOS-5 AGCM, see appendix C.) Each box in the
diagram, including the couplers, is an ESMF component. Every component
has a standard interface so that it is swappable with other versions of
that component. In addition, new components can easily be added to the
hierarchical system. Coupling tools include regridding and
redistribution methods.

![](media/image7.jpeg){width="5.625196850393701in"
height="3.41259842519685in"}

One ESMF-based initiative to create an interoperable modeling
environment within a particular domain is DOD's Battlespace Environments
Institute (BEI). The BEI is a DOD-wide effort to develop integrated
forecasts by achieving a full coupling of environmental models to
analyze and predict the total battlespace environment. The connection
with the ESMF community enables broad civilian-DOD, cross-service, and
cross-agency collaboration. The BEI is a High Performance Computing
(HPC) Software Applications Institute (HSAI) sponsored by the DOD HPCMP.
The BEI lead is at NRL Stennis Space Center, Mississippi. Other
participants include NRL-Monterey, the U.S. Army Engineer Research and
Development Center (ERDC) in Vicksburg, Mississippi, NCAR, and AFWA.[^3]

It is possible to "wrap" an entire existing model with ESMF-compliant
interfaces without changing the model's internal data structures. In
effect, the model with interfaces becomes one component box in the
framework, enabling that model's software to be coupled more easily with
other ESMF components. Some current models that can run as ESMF
components include the Navy's COAMPS and NOGAPS models, NCEP's GFS and
WRF-NMM modeling systems, and NCAR's WRF-ARW, as well as the GEOS-5
system and other models at NASA/GMAO. As explained below, NOAA and DOD
both plan to pursue development of ESMF-compatible systems for their
global and regional models used in preparing guidance for tropical
cyclone forecasting.

In summary, participation in the ESMF collaboration of all these
modeling system development activities---including the NOAA and DOD
tropical cyclone modeling systems discussed below---will enhance the
connections between the operational centers and the research modeling
communities. With time, ESMF will increasingly become an infrastructure
for more efficient transition of results from research modeling
activities to operational forecasting, as discussed in section 4.5.
Plans are currently underway to advance the timeline to transition to
ESMF.

### 4.4.2 NOAA's Plans for Hurricane Prediction Systems

The NCEP/EMC plan for addressing the operational priorities listed in
table 4-1 focuses on development of the HWRF Air-Sea-Land Hurricane
Prediction System. Model development for this system will be based on
the EMC HWRF model and will draw on the community modeling
infrastructure paradigm. This approach facilitates comparison of
results, while promoting research and technology that can, after
testing, be transitioned efficiently to EMC's operational systems.
Subsections below describe NCEP's development plans for, respectively,
the next generation of GFS as the global model and the HWRF Air-Sea-Land
Hurricane Prediction System as the high-resolution tropical cyclone
forecasting system nested under GFS. These component systems will
provide the modeling system backbone for EMC's forecast guidance for
tropical cyclones. The first subsection provides an overview of NCEP's
plans for an improved data assimilation capability to support both
components of this planned forecasting system while enhancing the
utility of existing and newly emerging observational data.

#### Data Assimilation Development for GFS and HWRF

The development of new techniques for assimilating high-resolution data
sets is a fundamental activity required for advancing numerical
prediction of hurricane intensity and structure, both of which are
important to improved forecasts of intensity, structure, sea state,
storm surge, and precipitation. New data sources are critical to
initializing the forecast system in two key domains: (1) the large-scale
environment; and (2) the vortex core. By 2010, the GFS will run as a
global atmosphere/ocean coupled system with an advanced four-dimensional
data assimilation system, called A4DDA. One of the most significant
challenges to be met by NCEP and other operational NWP modeling centers
over the next two decades is the assimilation of satellite data (Surgi
2006 and 2004). As stated in sections 1.5.3 and 2.4.6, this challenge is
being addressed through the JCSDA. Additionally, initializing the
hurricane core in the HWRF with real-time airborne radar data, as
discussed in section 3.1.6, poses a new challenge in mesoscale data
assimilation.

**NCEP proposes to develop a next generation data assimilation
capability for both global and regional applications. The approach is
multi-faceted, is directed toward the most fundamental problems in data
assimilation, and requires major new investments in development
personnel, development computing resources, and operational computing
resources.**

##### Advanced Data Assimilation Techniques

The new techniques developed recently to improve data assimilation can
be classified broadly into three categories: four-dimensional
variational data assimilation (4D-VAR), ensemble data assimilation
(EDA), and situation-dependent background errors (SDBE). The overall
thrust of these advanced techniques is to improve the use of
observations with high time resolution (e.g., hourly) and to improve the
projection of observed information onto the proper spatial scales and
patterns in the analysis. General descriptions for each category,
including advantages and disadvantages of the techniques in each, are
included in appendix K.

It is widely recognized that the major outstanding analysis problem is
improved formulation of the background error part of the analysis
equation. For a robust enhancement to operational analyses, items such
as improved background errors must be developed very carefully;
otherwise, erratic results (namely, major busts) will occur. The SDBE
approach attacks the fundamental analysis problem directly and is
particularly relevant to the tropical cyclone prediction problem.
However, there has been little explicit development of the SDBE
technique. Some early work on SDBE was done at the Met Office (METO) of
ECMWF, but that approach was abandoned in favor of a simplified 4D-VAR.

One of the most significant challenges to improving the prediction of
hurricane structure and intensity using high-resolution models is the
initialization of the hurricane vortex. To advance this effort, NCEP/EMC
is developing a local 3D-VAR technique for HWRF in which SDBE
covariances are used to initialize the hurricane core circulation with
real-time airborne Doppler radar data. These data come from NOAA's WP-3D
aircraft or the newly funded instrument upgrade package on the NOAA
Gulfstream IV (see sections 3.1.1 and 3.1.6)**.** For storms approaching
landfall, the data assimilation system will also use the high-resolution
data from coastal WSR-88D radars. The NCEP Gridpoint Statistical
Interpolation (GSI) analysis system now contains coding structures
intended for inclusion of SDBE in the analysis. Through the development
of flow-dependent algorithms, GSI will be exploited in the HWRF
Air-Sea-Land Hurricane Prediction System to initialize the hurricane
core. Appendix K further details the advantages of incorporating SDBE
corrections using extensions to the GSI.

##### GRADAS: NCEP's Global and Regional Advanced Data Assimilation System

For increased efficiency of code development and sustained maintenance,
a data assimilation system should be adaptable to both the global model
and regional applications that are most advantageous to the hurricane
problem. Although different scientific and computational considerations
are involved for each application, having one basic code system will
increase focus, lower code maintenance costs, and afford users much
increased flexibility. To prepare for a more unified global and regional
numerical forecast system at NCEP, ongoing consolidation efforts are
occurring in many areas, including handling of the observations,
verification, ensemble products, and model physics. An ESMF-compatible
code superstructure and infrastructure are also being developed.

The preferred development strategy for the planned NCEP Global and
Regional Advanced Data Assimilation System (GRADAS) is, first and
foremost, to develop SDBE within the GSI. To improve use of observations
with a high time resolution, such as surface and airborne radar data and
satellite imagery, 4D-VAR extensions to the GSI will be developed, using
a simple model for observation increments. These improvements will occur
as systematic and incremental augmentations to the current NCEP global
and regional analysis code. The simplified 4D-VAR approach will also
enable use of ensemble-based information. Appendix K contains a listing
of the NCEP data assimilation priorities associated with this
development strategy, a brief review of the data assimilation challenges
for NWP modeling of weather in the tropics and tropical cyclones in
particular, and the data assimilation efforts focused on dealing with a
coupled ocean-atmosphere modeling system.

##### Essential Observational data to be Assimilated into the HWRF Prediction System

Table 4-2 summarizes essential observations to be assimilated into the
HWRF Air-Sea-Land Hurricane Prediction System to aid in meeting the
operational needs listed in table 4-1. As previously discussed in
section 3.1.3, a coordinated effort to improve ocean observations and
develop a coherent ocean data assimilation system will increase the
accuracy and resolution of the thermal structure of the upper ocean
layer. The observations of interest include data from both in situ
instruments (e.g., AXBT, XBT, and drifting buoys) and data from
altimeters---for example, from satellites such as JASON-1, ERS-2, and
GFO.

#### NCEP Global Model Development

The Global Forecast System (GFS) has many critical applications and
functions in the NCEP operational job suite and is the cornerstone of
NCEP's suite. Some of these forecast applications are noted below, with
explicit relevance to hurricanes highlighted in bold type:

-   Global weather (1 to16 days) with applications such as aviation,
    medium-range (3 to8 days) precipitation and severe weather, and
    **hurricane tracks**

**Table 4-2.** Essential Observations to be Assimilated into HWRF

+-------------+--------------------------------------------------------+
| **En        | -   **"Routine:"** operational atmospheric             |
| vironmental |     observations, including satellite data (see        |
| Flow**      |     Appendix A)                                        |
|             |                                                        |
|             | -   **Additional:**                                    |
|             |                                                        |
|             | ```{=html}                                             |
|             | <!-- -->                                               |
|             | ```                                                    |
|             | -   Data obtained from NOAA G-IV surveillance missions |
|             |                                                        |
|             | -   Data obtained from next generation (e.g., NPOESS,  |
|             |     MetOp) satellite instruments (see Appendices I and |
|             |     J)                                                 |
+-------------+--------------------------------------------------------+
| **Ocean**   | -   **"Routine:"** operational oceanic observations,   |
|             |     including satellite altimeter data                 |
|             |                                                        |
|             | -   **Additional:** XBTs and AXBTs (other              |
|             |     air-deployable expendable instruments---reference  |
|             |     Table 3-3), drifting profiler floats               |
+-------------+--------------------------------------------------------+
| **Hurricane | -   **Additional:** Airborne "trifecta"                |
| Core**      |                                                        |
|             | ```{=html}                                             |
|             | <!-- -->                                               |
|             | ```                                                    |
|             | -   SFMR (surface winds) from NOAA WP-3D (when tasked) |
|             |     or AFRES WC-130J (when installed), or NOAA G-IV    |
|             |     (new initiative)                                   |
|             |                                                        |
|             | -   Airborne tail Doppler radar (3D structure) from    |
|             |     WP-3D (when tasked) or NOAA G-IV (new initiative)  |
+-------------+--------------------------------------------------------+

-   **Initial and boundary conditions for hurricane regional model
    (i.e., GFDL currently, HWRF in the future)**

-   Boundary conditions for NCEP's North American run (WRF-based
    regional model)

-   Boundary and initial conditions and background field for the
    Regional Spectral Model

-   Driver for ocean wave models and, in the future, other ocean models

-   Ozone distribution and transport and, in the future, other
    atmospheric constituents

-   **Background field for global data assimilation system**

-   **Ensemble system model (to include hurricane tracks)**

-   Coupled Climate Forecast System (CFS) model

In developing its next generation global model, NCEP considered the
following specific items:

-   Ensembles and forecast system diversity

-   ESMF and the common modeling infrastructure/framework.

-   Model dynamics

-   Model physics

-   Computational efficiency

During 2006, NCEP developed and analyzed five strategic options for the
development of the next generation model's dynamics. After weighing the
advantages and disadvantages, and in light of the above considerations,
NCEP adopted a strategy of consolidating EMC model development efforts
into three projects:

-   Develop an ESMF-compatible Prototype Framework, which will run the
    latest version of GFS

-   Scale up the Nonhydrostatic Mesoscale Model (NMM) to a global domain
    and incorporate semi-implicit and fully implicit semi-Lagrangian
    techniques

-   Generalize the Prototype Framework to incorporate the NMM as both a
    global and regional model

The outcome of this strategy will determine EMC's longer-term
development work (for the period 2007--2011). If these preliminary
projects for producing operational, ESMF-compatible systems are
successful, the results will enable efficient testing of "multi-model"
strategies as well as expanding the suite of operational products to
include ocean prediction, environmental monitoring (e.g., carbon dioxide
and aerosols), marine ecosystem monitoring and prediction, hydrologic
prediction, water and air quality monitoring and prediction, and space
weather forecasting. Appendix L contains a more detailed discussion of
these plans for global model development.

#### The HWRF Air-Sea-Land Hurricane Prediction System

![](media/image8.jpeg){width="4.5in" height="3.3854330708661418in"}The
HWRF Air-Sea-Land Hurricane Prediction System, which will be available
for use by TPC/NHC and CPHC forecasters, is NOAA's next generation,
high-resolution hurricane prediction system. Figure 4-6 and the
following bullets describe this system, which is designed to address the
tropical cyclone forecasting challenges of intensity, structure, track,
sea state/storm surge, and precipitation.

-   NOAA's next generation hurricane prediction system

-   A coupled air-sea-land prediction system

-   Advanced data assimilation capability to initialize the hurricane
    core circulation with real-time airborne Doppler radar and other
    data assimilation advancements for the coupled air-sea hurricane
    environment

-   Advanced physics suitable for high-resolution and coupled
    air-sea-wave-land modeling processes

-   Land-surface component coupled to hydrology/inundation models

-   Moving nests with two-way feedback in the atmospheric, ocean, and
    wave models

-   Coupling to dynamic storm surge model

HWRF is scheduled for operational implementation at NCEP in 2007, at
which time it will replace the current GFDL operational hurricane model.
In this initial operational implementation, HWRF will have an updated
movable, two-way nested grid. The inner grid horizontal resolution will
be 9 km. The outer grid will have a horizontal resolution of 27 km,
extending over a 75x75 degree domain, and a vertical resolution of 42
levels. Over the next several years after this initial implementation,
HWRF will be enhanced with incremental increases in horizontal and
vertical resolution. However, as noted in chapter 5, the relative merits
of very high resolution deterministic model guidance compared with
advanced probabilistic guidance will be a research priority.

The next five subsections describe planned NCEP work on other model
systems that will serve as components, shown schematically in figure
4-6, of the HWRF Air-Sea-Land Hurricane Prediction System. Several of
these (e.g., NMM, HYCOM, and WAVEWATCH III, and the Noah LSM) have wider
applications in the NCEP operational suite beyond tropical cyclone
forecasting; their roles in the HWRF Prediction System are emphasized
here.

##### NMM in the HWRF Air-Sea-Land Prediction System

The regional NMM will be used as the core model of the HWRF system. The
NMM has been developed at NCEP within the larger WRF community modeling
effort (Janjic et al. 2001; Janjic 2003). In NMM, the favorable features
of hydrostatic model formulation are preserved within the valid range of
the hydrostatic approximation.

-   The basic idea for NMM was to split the system of nonhydrostatic
    equations into two parts: (a) the part that corresponds to the
    hydrostatic system, but including corrections due to the vertical
    acceleration; and (b) the part that is used to compute the
    corrections appearing in the first system. Thus, the nonhydrostatic
    effects are introduced as an add--on module that can be turned on or
    off. In this way, the hydrostatic and nonhydrostatic solutions can
    be compared, or the model can be run in the hydrostatic mode at
    lower resolutions to reduce the computational cost.

-   The numerical schemes were designed following the principles set out
    by Janjic (1977, 1979, 1984). The approach employs "isotropic"
    horizontal finite differencing that conserves a variety of basic and
    derived dynamical and quadratic quantities. Among these are
    conservation of energy and entropy, which improves the accuracy of
    the nonlinear dynamics (Arakawa 1966).

-   In the vertical dimension, the hybrid pressure-sigma coordinate
    (Arakawa and Lamb 1977) was chosen as the primary option. Since the
    hydrostatic pressure is used as the vertical coordinate above 400
    hPa, the possible inaccuracies due to the sloping coordinate
    surfaces are restricted only to about the lower half of the
    atmosphere. Note that the largest errors in the sigma coordinate
    generally occur in the stratosphere. Thus, the most serious problems
    associated with the sloping sigma surfaces are eliminated.

##### Hybrid Coordinate Ocean Model and WAVEWATCH III

As figure 4-6 indicates, the HWRF component will be coupled to the
Hybrid Coordinate Model (HYCOM), a three-dimensional ocean circulation
model. Thus, HWRF-HYCOM will replace the GFDL-POM modeling system
currently used in NCEP operational forecasting (see section 3.3.3).
HYCOM will have an ocean data assimilation system to take advantage of
remote and in situ hurricane-related ocean observations. A hybrid
coordinate model is isopycnal in the open, stratified ocean but smoothly
reverts to a terrain-following coordinate system in shallow coastal
regions and to *z*-level coordinates in the mixed layer or in
unstratified seas. The theoretical foundation for implementing a hybrid
coordinate system in an ocean circulation model was set forth by Bleck
and Boudra (1981) and Bleck and Benjamin (1993).

HYCOM has been developed by the multi-institutional HYCOM Consortium for
Data-Assimilative Ocean Modeling, which continues to improve the model
and promotes its use by the community.[^4] The consortium is funded by
the National Ocean Partnership Program (NOPP), which was established by
Congress in 1997 and now includes partners from academia (U.S. and
French), U.S. government entities, industry, and a French government
institute. Principal collaborators in HYCOM's development have been the
University of Miami, the Naval Research Laboratory (NRL Stennis Space
Center and NRL-Monterey), and the Los Alamos National Laboratory. NOAA
participants in NOPP include the National Ocean Service (NOS), the
Marine Modeling and Analysis Branch of NCEP, NOAA/AOML, TPC/NHC, and the
Ocean Prediction Service.

As discussed in section 3.3, including the dynamic feedback of surface
waves on the air-sea processes and the ocean thermal structure has
produced significant improvements in forecasting hurricane structure. To
incorporate this feedback in the larger prediction system, HWRF will
also be coupled to an advanced wave model from the NCEP operational
WAVEWATCH III suite of models. This advanced multi-grid (multiscale)
wave model will include a movable, nested telescoping grid around the
hurricane that is physically and computationally consistent with the
coupled HWRF-HYCOM system. This Multi-grid WAVEWATCH III (MWW3) will
simulate several grid configurations with different resolutions into a
single wave model, with full two-way information flow between all grids
for higher resolution near coastlines and lower resolutions in the deep
ocean. In the deep ocean and in shelf areas, the initial benefit from
the MWW3 in the HWRF context is parameterization of fluxes from the
ocean to the atmosphere. In shelf and coastal waters, the initial
benefit will be for coupling with a storm surge model, as discussed
below.

In addition to a movable, nested telescoping grid around the hurricane,
the new MWW3 will allow for a more directed application of
high-resolution wave modeling at the coast. This application will render
the present large regional wave models obsolete. As the MWW3 system is
incrementally implemented through 2009, its multi-grid approach is
expected to make a 5 km coastal resolution feasible for operational
modeling of the entire U.S. coastline. With this dramatic increase in
coastal resolution, the need for surf-zone physics in the wave model
will become even more urgent. Appendix M contains further information on
other roles for WAVEWATCH III and MWW3 beyond their role as components
in the HWRF Hurricane Prediction System.

##### Coupling ADCIRC to the HWRF System for Dynamic Storm Surge Modeling

As noted in section 3.4.4, ocean waves drive near-shore circulation
patterns and can produce storm surges even when local winds are
negligible. The waves produced by a tropical cyclone couple with the
local water depth and near-shore topography to force the local
circulation and other characteristics of a storm surge. This physical
coupling underlies the requirement to incorporate coupled wave-surge
modeling to improve prediction of hurricane-induced storm surges.

Hurricane storm surge height and coastal inundation are strongly
influenced by both meteorological conditions and the geometry of the
shelf and coast (especially features that act as hydrodynamic controls).
Therefore, NCEP is partnering with the Coast Survey Development
Laboratory in NOAA/NOS to provide predictions of storm surge height by
combining the coupled HWRF system with a high-resolution coastal
hydrodynamic model: the ADvanced CIRCulation (ADCIRC) model (Luettich et
al. 1992; Luettich and Westerink 2004). (Appendix M, paragraph 2, has
details on this planned collaborative work.) ADCIRC has several features
beneficial for storm surge application and has been demonstrated to be
effective at predicting water levels in complex coastal systems (Blain
et al. 1994, Blain et al. 1998). The ADCIRC code solves the fully
nonlinear governing equations for shallow water using the generalized
wave continuity equation formulation. This formulation minimizes
spurious oscillations without excessive nonphysical dissipation by
propagating the shortest so-called 2Δ*x* waves. For efficient
computation of water levels developing over a short time scale, the
two-dimensional depth-integrated form of the model is applied. To
exploit the advantages of an unstructured grid technique, ADCIRC uses a
second-order Galerkin finite element method to solve the governing
equations.

To model the propagation of the hurricane-generated storm surge wave
from offshore across the shelf and inland, increasingly higher
resolution is required as the wave approaches shore. The unstructured
grid methodology not only provides this but can readily and accurately
represent irregular shoreline and coastal features including barrier
islands, rivers and waterways, and topography. ADCIRC domains with
resolution exceeding 100 m are routinely used to determine storm surge
propagation in riverine and estuarine systems while providing
orders-of-magnitude larger elements at locations with deep water
boundary conditions. Furthermore, ADCIRC has a wetting and drying scheme
that predicts flood wave propagation over initially dry topography and
incorporates weir formulae to model overtopping of vertical barriers to
flow, such as levees.

To summarize, the HYCOM basin-scale ocean circulation model will provide
the boundary conditions to the ADCIRC high-resolution coastal model. The
meteorological conditions, including description of the hurricane wind
and pressure fields will be specified by the HWRF model. Coupling of
MWW3 with HWRF and HYCOM will allow more realistic modeling of wind-wave
interactions and storm-driven wave propagation in the deep ocean and as
a storm approaches the coast. This coupled HWRF-HYCOM-MWW3 system will
provide an operational product with which coastal inundation can be
predicted via the inclusion of the high-resolution ADCIRC coastal model.

**Advanced, high resolution, regional model-based probabilistic guidance
(e.g., HWRF ensembles) are necessary to adequately address forecast
uncertainties in critical storm attributes and related impacts such as
intensity, structure, track, precipitation, and *storm surge*.
Therefore, the use of a dynamic storm surge model (i.e., coupling of
ADCIRC to the HWRF system) is critically dependent on having sufficient
computing power to run HWRF ensembles. This is discussed further
below.**

##### The Atmosphere-Ocean Boundary Layer in the HWRF Prediction System

As discussed in section 2.8.6, an important workshop on air-sea
interactions was held at NCEP/EMC in May 2005. The workshop's objective
was to bring the research and operational communities together to
discuss and identify fundamental issues related to the physics of
air-sea interactions under high-wind regimes in hurricanes and the
representation of those interactions in NCEP's next generation coupled
hurricane models (Shay et al. 2005). A series of focused questions from
this workshop, listed in appendix N, has fostered collaboration across
the entire community in hurricane air-sea R&D, with the goal of
resolving fundamental issues in order to advance the HWRF and other
air-sea coupled hurricane models. Many of the problem areas covered by
these questions are represented in the JAG/TCR research priorities
listed in chapter 5.

Some progress is being made with results from CBLAST-DRI and other
important data sets in advancing flux parameterizations in the GFDL
model, as noted in section 3.3.2. However, progress needs to
aggressively continue in refining the momentum fluxes and in the
formulation of the enthalpy fluxes. Preliminary experiments in the GFDL
model have shown extreme sensitivity of the hurricane track (and
presumably the hurricane structure) to the formulation of the heat
fluxes. The investigation on the impact on hurricane structure to the
momentum fluxes with coupling to the waves is also under investigation
at URI and will continue in collaboration with EMC for HWRF.

##### The Noah Land-Surface Model as an HWRF Prediction System Component

A comprehensive land surface model (LSM) known as Noah was developed
collaboratively by **N**CEP, the University of **O**regon, the U.S.
**A**ir Force, and the NOAA/NWS Office of **H**ydrology (figure 4-7).
More information on the origins, development, and physical references
for the Noah LSM is provided by Ek et al. (2003). The far-reaching
physical improvements added to the Noah LSM by NCEP/EMC over the past 15
years reflect numerous collaborations between EMC\'s Land/Hydrology Team
and many external partners in Federal laboratories and the university
research community.

The plans for coupling of HWRF to the Noah LSM are key to addressing the
challenge of forecasting inland flooding by improving QPF for
landfalling tropical cyclones. HWRF implementation plans call for the
Noah LSM to be coupled with the HWRF atmospheric model to provide
rainfall input to hydrology and inundation models and improve
predictions of inland flooding. Early experiments directed toward this
objective involved replacing the GFDL's current "slab" LSM with the Noah
LSM to determine the latter's impact on improving hurricane intensity,
structure, and precipitation forecasts for landfalling hurricanes (Shen
et al. 2004). In these preliminary experiments, relatively small impact
was demonstrated for hurricane intensity and structure, but *very
promising results were found for improved precipitation forecasts.* This
testing is now being extended to the fully coupled high-resolution HWRF
system, an effort that will be aided by a proposed collaboration with
the University of South Alabama. In particular, land-wind interaction
effects need to be modeled better, to forecast more accurately the decay
of wind intensity over land and the effects on wind and precipitation
distributions.

![](media/image9.jpeg){width="5.935433070866142in"
height="4.475196850393701in"}The Noah LSM simulates land surface
temperature, the components of the surface energy balance (eight
surface-energy flux components such as latent, sensible, and ground heat
flux), the components of the surface water balance (infiltration of
precipitation, runoff, surface evaporation, snow pack evolution, and
changes in soil water storage), and the evolution of soil temperature
and soil moisture (both liquid and frozen) in four soil layers (with
layer thicknesses of 10, 30, 60, 100 cm). The surface infiltration
scheme in the Noah LSM accounts for subgrid spatial variability in soil
moisture and precipitation. The surface evaporation treatment includes
direct evaporation from soil, transpiration from vegetation, evaporation
of precipitation intercepted by the foliage of the vegetation, and snow
sublimation.

For mid-latitude applications, advancement and assessment of the Noah
LSM includes testing with NCEP\'s coupled land-atmosphere models (e.g.,
GFS, Eta, and the mid-latitude WRF) and with the uncoupled North
American Land Data Assimilation System (NLDAS). A Noah version that is
essentially a stand-alone, uncoupled, one-dimensional column model is
frequently configured to execute at locations where validating
ground-based surface-flux stations are available. The NLDAS is a
comprehensive continental-scale LSM testbed developed by EMC and its LSM
partners for the purpose of testing and evaluating Noah and several
other LSMs(Mitchell et al. 2004). It includes a stream-flow submodel for
predicting river stage. This stream-flow model can execute as a
standalone post-processing step that is driven by the runoff fields
forecast by the Noah LSM component of NCEP NWP models (e.g., Eta, GFS,
or the mid-latitude WRF).

Appendix M, paragraph 3, describes the plans for HWRF coupling with the
Noah LSM and for testing the coupled system's forecast skill with
respect to the distribution of low-level surface winds and rainfall
amounts, stream and river flows, and flood levels.

#### Development of HWRF Ensembles

Another future task at NCEP, beyond developing and testing the above
components of the HWRF Air-Sea-Land Prediction System, is the
development of HWRF ensembles. Ensemble forecasting techniques are
important to the overall plan to improve tropical cyclone forecast
guidance. In the integrated HWRF Prediction System depicted in figure
4-6, improvements in one component directly influence other system
components. For instance, ensemble simulations to reduce and estimate
the uncertainty in the strength and path of a hurricane will be useful
for all system components, including the storm surge and inundation
component. Probabilistic guidance based on advanced high-resolution
regional models (e.g., HWRF and COAMPS ensembles) is necessary to
estimate adequately the forecast uncertainties in critical storm
attributes and in related impacts such as intensity, structure, track,
precipitation, and storm surge. However, the capability to run HWRF and
COAMPS ensembles and multi-model ensembles within operational
forecasting time constraints depends directly on the available computing
power. As stated above, the utility of coupling the HWRF system with a
dynamic storm surge model (see figure 4-5) is also critically dependent
on having sufficient computing power. The feasibility of coupling the
HWRF system to ADCIRC for operational forecasting depends entirely on
having sufficient computing power.

In the development of advanced, high-resolution probabilistic guidance
(e.g., HWRF ensembles), construction and configuration of optimal
ensembles is a research priority identified in chapter 5. Another
research priority noted there is to investigate the relative value,
within the operational forecasting context, of *very* high-resolution
deterministic forecasts versus ensembles.

### 4.4.3 DOD Plans for Hurricane Prediction Systems

The U. S. Navy and U. S. Air Force contribute substantially to the
Nation's tropical cyclone forecast and warning capability. Their plans
for future NWP and ocean modeling are reviewed below.

#### Planned Data Assimilation Development

NRL has developed a prototype 4D-VAR data assimilation system, the
NAVDAS Advanced Representer (NAVDAS-AR), which is planned for
operational implementation at FNMOC in 2008. NRL will be collaborating
with NASA/GMAO and NCEP/EMC on further development of NAVDAS-AR.

The benefits of 4D-VAR have been demonstrated at other global forecast
centers, most notably at the ECMWF. One such benefit is the ability of
4D-VAR assimilation to make better use of same-level observations that
are observed at multiple times, such as ship and buoy observations,
QuikScat ocean wind vectors, and SSM/I ocean surface wind speeds. These
types of observations are especially abundant in the tropics, and
current 3D-VAR data assimilation systems do not take full advantage of
them. Furthermore, the unique design of NAVDAS-AR makes it much more
computationally efficient than current 3D-VAR data assimilation systems
for the order of magnitude (or more) increase in observational data
expected with future satellite systems.

The following is a list of observation systems that are targeted for
assimilation by NAVDAS-AR in the future and that are expected to have
the greatest impact on tropical forecasting:

-   AMSU-B water vapor retrievals (1D-VAR approach)

-   High-resolution Infrared Radiation Sounder--3 (HIRS/3) radiances

-   COSMIC GPS refractivities

-   Military aircraft soundings (ACARS) of winds and temperatures

-   SSMI/S radiances

-   AIRS and AMSU radiances from Aqua

-   HIRS/4 radiances

-   Microwave Humidity Sounder (MHS) retrievals (1D-VAR approach)

-   MetOp instruments: Infrared Atmospheric Sounding Interferometer
    (IASI), HIRS, AMSU, MHS, and ASCAT

-   NPOESS CrIS/ATMS radiances (with NPP)

-   Geostationary radiances

-   WindSat radiances

-   SSMI radiances

By providing more effective incorporation of this information from
satellite systems and permitting the assimilation of more satellite
observations, NAVDAS-AR will further enhance the forecast skill of
NOGAPS for tropical cyclone tracks.

#### Planned Enhancement of the NOGAPS Global Model

A new land surface parameterization was transitioned into NOGAPS in
August 2006. This transition provided NOGAPS with improved lower
atmospheric forecasts of temperature and moisture and resulted in
improved track forecasts for tropical cyclones. While a number of
upgrades are planned for the NOGAPS global spectral model in the future,
the following upgrades are expected to have the most impact on tropical
forecasting:

-   Upgrade to the physics of the land surface parameterization in
    NOGAPS (FY 2007). This transition is planned to improve the
    lower-atmospheric forecasts over tropical regions.

-   Transition the semi-Lagrangian advection of moisture (FY 2007). This
    transition will improve NOGAPS forecasts of moisture and clouds and
    will especially affect forecasts for the tropics.

-   Transition new flux algorithms for the planetary boundary layer
    parameterization of NOGAPS (FY 2008). The improved surface fluxes
    are expected to have a large impact on forecasts for the tropics,
    especially in the vicinity of tropical cyclones.

-   Transition the semi-Lagrangian/semi-implicit advection of
    temperature, vorticity, and divergence; increase horizontal
    resolution and the number of vertical levels to 48 (FY 2008). The
    semi-Lagrangian scheme will permit use of a substantially longer
    model time-step, which will allow a higher-resolution model to be
    run within the same operational constraints on run time and computer
    resources.

#### Plans for High Resolution Regional Models

For the U. S. Navy, regional high-resolution model development is based
on enhancements to COAMPS. The future COAMPS Tropical Cyclone System
will make use of ESMF compliance to couple the NRL Coastal Ocean Model
(NCOM) and WAVEWATCH III to the COAMPS nonhydrostatic atmospheric
dynamical core. The resulting coupled system will use moving nests with
two-way feedback in the atmospheric model and will eventually include
nested grids in the ocean models. The target horizontal resolution for
the atmospheric and ocean models is 5 km or less on the finest grid
mesh. The finest grid mesh will be capable of explicitly resolving deep
convection. In order to model the fluxes more accurately, the
atmospheric and ocean wave models will interact with a sea spray
submodel to represent the air-sea exchanges in the wave boundary layer.
An analysis system that is capable of initializing a tropical cyclone
with the proper intensity will be used.

Development of this COAMPS Tropical Cyclone System will occur in
parallel with NCEP's development of the HWRF Air-Sea-Land Prediction
System. Statistics on tropical cyclone track guidance clearly
demonstrate that superior guidance is produced using a multi-model
consensus. In the future, a multi-model capability for consensus
forecasting of tropical cyclone intensity will be equally valuable.

As discussed in section 3.3.2, AFWA currently runs the MM5 model to
produce bulletins for use by tropical cyclone forecasters at JTWC.
AFWA's ability to transition new tropical cyclone prediction
capabilities based on a high-resolution regional model arises from its
community modeling partnerships. The transition from MM5 to NCAR's
Advanced Research WRF (WRF-ARW) as the NWP engine is one example.
WRF-ARW also will have an improved tropical cyclone-following capability
that will maintain the tropical cyclone in the center of the nest.
Specific capabilities and schedule are currently under review.

## 4.5 Transitioning Research Results to Operational Capability

Section 3.5 reviewed current processes in place for transitioning
hurricane research results into operational capability at an.
operational center. This section describes current plans for improving
the efficiency of the transition process, often through enhanced
collaboration involving entities in the tropical cyclone R&D community
(see chapter 2) in partnership with one or more of the operational
centers for tropical cyclone forecasts and warnings (described in
section 1.4).

### 4.5.1 Plans for Major Transition Programs at JCSDA

The JCSDA (see sections 2.4.6 and 3.5.5 for background and current
capabilities, respectively) has plans for major transition programs in
two areas of particular relevance to tropical cyclone forecast and
warning:

-   A major near-term goal for the JCSDA is to lay the groundwork for
    and establish a common data assimilation infrastructure for
    assessing new satellite data and optimizing the utilization of these
    data in operational models. A step toward this goal will be to
    establish on JCSDA computer systems parallel versions of the global
    and regional data assimilation systems used at NCEP/EMC, NASA/GMAO,
    and the DOD partners in the Center (Le Marshall et al. 2005).

-   As part of NOAA's involvement in The Observing-system Research and
    Predictability Experiment (THORPEX), JCSDA will be working with
    NOAA's THORPEX Science and Implementation Team to prepare in advance
    for new satellite-based observations before the spacecraft are
    launched. Thus, data assimilation systems will be ready for use when
    data are first acquired. In addition, JCSDA will work with the
    THORPEX team on evaluating new data assimilation strategies during
    the THORPEX Observing System Tests

By 2007, JCSDA deliverables will include developing a community forecast
and data assimilation system for both global- and regional-scale
applications. This system will be accessible to the research community
through the U.S. Weather Research Program and will serve as the primary
mechanism for infusing data from both research and operational
satellites into NCEP, GMAO, and DOD operations.

### 4.5.2 Developmental Testbed Center

The HWRF, a special hurricane-oriented version of WRF, is scheduled for
operational implementation in 2007. The Developmental Testbed Center
(DTC) is a facility where the NWP research and operational communities
interact to accelerate testing and evaluation of new models and
techniques for research applications and operational implementation,
without interfering with current operations. Having the DTC support the
HWRF Air-Sea-Land Hurricane Prediction System could increase
opportunities in the NWP hurricane research community to collaborate
with NCEP and have a direct influence on improving operational hurricane
forecasts. More information on the DTC is at the following Web site:
<http://www.dtcenter.org/index.php>.

### 4.5.3 Essential Elements of an Efficient Transition Process in a Community Context

The programs/processes described above and in section 3.5 play an
integral part in implementing valuable research results in operations.
Essential elements of an effective transition of research to operations
program include the following:

-   Research and model development priorities, such as those presented
    in chapter 5 of this plan, must be clearly articulated to the
    tropical cyclone community.

-   There needs to be a steady flow of *relevant* research.

-   The tropical cyclone R&D community must be aware of the transition
    of research to operations processes.

-   The transition process needs to be supported by sufficient human and
    infrastructure resources for transition of research to operations
    activities, including the resources to support collaborative
    ventures.

-   The program needs a long-term commitment of adequate resources to
    improve and maintain the sufficient infrastructure.

Funding for the transition of research to operations remains deficient.
Within an era of constant or diminishing dollars, R&D and transition
needs will likely be competing with each other. ***A mechanism is needed
to enhance development and transition of research to operations
activities throughout the tropical cyclone operations and research
community to further improve operational, high-resolution tropical
cyclone NWP models, thereby maximizing benefits for the Nation.*** This
need is discussed in more detail in chapter 6.

In summary, having a viable program for highly capable transition of
research to operations is essential to achieving marked improvements in
the Nation's tropical cyclone forecast and warning capability in a
timely and efficient manner. Increasing human and infrastructure
resources at NRL/FNMOC and NCEP/EMC and increasing the flow of relevant
research focused on improvements to the operational NWP systems (i.e.,
focused on the NWP research priorities outlined in chapter 5) will
greatly aid in improving these systems, thus increasing the protection
of human lives, enhancing personnel safety, and improving economic
benefits.

## 4.6 Summary

This chapter introduced the operational needs of the tropical cyclone
forecast and warning centers, and indicated to meet the needs would take
continued advancements in observations, data assimilation technologies,
and tropical cyclone NWP models. Key to these advancements is the
Nations commitment to provide sufficient human and infrastructure
resources for tropical cyclone research, development, and transition of
research to operations activities, along with sufficient resources for
the operational NWP model environment. Through these mechanisms and
commitments, operational tropical cyclone forecasting skill will
increase, which will lead to reduced loss of life, injuries, and
vulnerability to tropical cyclones. The next chapter discusses the
tropical cyclone research priorities.

[^1]: Infrastructure resources are related to items such as
    computational power, network bandwidth,\
    architectural/engineering requirements, and maintenance of
    applicable systems.

[^2]: Infrastructure resources are related to items such as
    computational power, network bandwidth,\
    architectural/engineering requirements, and maintenance of
    applicable systems.

[^3]: NRL Press Release, May 31, 2005.
    <http://www.nrl.navy.mil/pressRelease.php?Y=2005&R=32-05r>

[^4]: The consortium's website, which provides download access to the
    HYCOM code, documentation, and other support for new users, is
    <http://hycom.rsmas.miami.edu/index.shtml>.