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Appendix A\
Summary of Satellite Data Used in NCEP's Operational Data Assimilation
Systems

**Note**: Refer to Appendix Q for the acronyms used in this appendix.

Polar orbiting platforms used in NCEP's operational data assimilation
systems include high-quality data from functioning instruments on the
following platforms:

-   **NOAA polar orbiting satellites** (e.g., HIRS, AMSU-A, AMSU-B,
    AVHRR)

-   **Defense Meteorological Satellite Program (DMSP);** for example:
    SSM/I

-   **NASA;** for example:

    -   **TRMM** (e.g., TMI)

    -   **QuikSCAT** (e.g., SeaWinds)

    -   **Aqua** (e.g., AMSU-A, AIRS, MODIS)

    -   **Terra** (e.g., MODIS)

-   **European Remote Sensing Satellite (ERS)**---for example:

    -   **ERS-2 (**e.g., WS \[Wind Scatterometer\])

**[NOTES]{.underline}**

-   Atmospheric observations (all instruments in the above table except
    altimeter observations) are used by:

    -   NCEP Global Data Assimilation System (GDAS)

    -   NCEP Regional Data Assimilation System (RDAS)

> (Provided they meet the data cutoff times of 1:15 for the RDAS and
> 2:45 for the GDAS)

-   Sea Surface Temperature (SST) retrievals are used by all atmospheric
    weather models for a daily lower boundary condition.

-   Ocean observations (altimeter observations in table above) are used
    in NCEP's:

    -   Global Ocean Data Assimilation System (GODAS) for climate
        forecasting

    -   Real-Time Ocean Forecast System (RTOFS) for daily ocean
        forecasting

    -   Global Wave Forecast System

# Appendix B Important Upgrades to Global Models and Operational Use of High Resolution Regional Models

Improvement in hurricane track forecasts has been well documented over
the past three decades. Figure B-1 shows the reduction of the 72 hour
official track error of the TPC/NHC over this period from 400 nm to less
than 200 nm. The improved skill closely follows the continuous
advancement of operational numerical models and their enhanced forecast
capabilities.

![](media/image1.jpeg){width="3.6251968503937007in"
height="2.7188976377952754in"}The documentation of model track skill
originated with CLIPER in the 1970s, a statistical model based on
climatology and persistence, which became the benchmark for track skill
for all future model track forecasts (Neuman 1972). An improvement of
track skill continued in the 1980s with the use of a hybrid model that
combined statistical techniques with background fields from NOAA's
global model (e.g., the NHC-83, NHC-90 \[Neuman and McAdie 1991\]), the
development of a barotropic model (VICBAR) for operations, a one level
advection model (BAMS), and a quasi-lagrangian dynamical model (QLM)
that ran at NCEP in the late 1980s and early 1990s. These early models
all helped define the downward linear trend into the early-mid 1990s to
reduce the forecast errors at all forecast times. The description and
performance characteristics of these early track models are described in
DeMaria (1997); McAdie and Lawrence (2000).

As shown in Figure B-1, a pronounced acceleration in track forecast
skill occurred in the mid-1990s with increased use of global models
(e.g., the Global Forecast System (GFS; formerly the MRF/AVN) run at the
National Centers for Environmental Prediction (NCEP); the Navy
Operational Global Atmospheric Prediction System (NOGAPS) run at Fleet
Numerical Meteorology and Oceanography Center (FNMOC), and the United
Kingdom Meteorological Office global model (UKMO)). The increase in
forecast skill is tied to the availability of global observations (e.g.,
satellites), an advancement of global modeling numerical techniques that
could maximize the usefulness of these data, and increased
sophistication in representing model physics to provide routine high
quality global analyses.

Since the early 1990s, the horizontal resolution of the NOGAPS global
spectral model has tripled while its vertical resolution has nearly
doubled. For example, from 1989 to1994, the NOGAPS model resolution was
T79L18 (\~165 km horizontal resolution, 18 vertical levels), using the
Arakawa-Schubert convective parameterization scheme (Arakawa-Schubert
1974) ; from 1994 to 2000, the NOGAPS model resolution was either
T159L18 or T159L24 (by 1998), using the Arakawa-Schubert convective
parameterization scheme. From 2000 to 2002, the NOGAPS model resolution
was T159L24, using the Emanuel convective parameterization scheme
(Emanuel 1991, Emanuel and Zivkovic-Rothman 1999). Finally, from 2002 to
the present, the resolution of the NOGAPS model has been T239L30, using
the Emanuel convective parameterization scheme. Similar upgrades to
other global models also occurred.

There were other important upgrades to the global models, and a few
examples follow. To represent the hurricane scale, a bogus vortex was
developed by Lord (1993) and incorporated in the NCEP global model. The
direct use of satellite radiances replaced the use of retrievals in the
GFS data assimilation system at NCEP in 1995 (Derber and Wu 1998). The
assimilation of high density multispectral GOES-8 winds (Velden et al.
1997) into NOGAPS was initiated at FNMOC in 1996 (Goerss et al. 1998).

Although global model forecasts were advancing to provide better track
forecasts, after the devastation of Hurricane Andrew in 1992 in South
Florida and a clear inability of the global models to forecast the high
regime catastrophic landfall winds, a resurgence in developing high
resolution dynamical hurricane models became a focus not only for
improving track forecasts but also offered promise for providing the
higher resolution forecast models needed to address hurricane intensity
forecasts. Research for more than two decades since the late 1970s, led
by the pioneering effort of Yoshio Kurihara at NOAA's Geophysical a
Dynamics Laboratory (GFDL), led to the development of a movable nested
grid hurricane model (Kurihara and Bender 1980). The TPC/NHC requested
that the high resolution, nested, movable GFDL model be evaluated to
assess its performance in a semi-operational mode at the TPC/NHC in
1993. The seminal GFDL forecasts of Hurricane Emily and the recurvature
of this storm off the outer banks of North Carolina in 1993 led to a
pioneering collaboration between NOAA research and NOAA operations.

For the 1994 hurricane season, the GFDL model was monitored for
operational performance. Due to its promising performance in providing
higher track skill for the Atlantic and East Pacific basins than all
other operational models, the GFDL model was transitioned into NCEP
operations for the 1995 hurricane season. A version of the GFDL that is
run at FNMOC, the GFDN, became operational in May 1996 (Rennick 1999).
Although many transitional modeling and code obstacles existed at the
time to transition a research model into operations, the joint efforts
of the NCEP Environmental Modeling Center (EMC) and GFDL became a
defining collaboration that has endured to the present.

With continuous yearly upgrades to the GFDL model, which were aligned
with the upgrades to the NCEP global model, the GFDL model became the
top track performance model and the mainstay for hurricane forecast
guidance at TPC/NHC (Kurihara et al, 1998). In carrying out the joint
vision for operational performance standards, the close collaboration
between EMC and GFDL is considered one the most successful
collaborations within NOAA and perhaps within the U.S. modeling
community between research and operations.

As skillful track forecasts became more consistently deliverable to
TPC/NHC, CPHC, and JTWC forecasters during the 1990s, particularly with
the operational implementation of the GFDL and GFDN models, more
attention became focused on improving intensity forecasts from dynamical
models within the hurricane community. An aspect of addressing the
intensity issue required the coupling of the atmosphere with the ocean.
To meet this requirement, the University of Rhode Island (URI) offered
an ocean model that could be readily coupled to the GFDL model (Bender
et al. 2001). The coupled GFDL model was run on 163 forecasts during the
1995--98 seasons (Bender and Ginnis 2000). The coupling of the
atmosphere with the ocean improved intensity forecasts, with the mean
absolute error in the forecast of central pressure reduced by about 26
percent compared to the operational (non-coupled) GFDL model.

The coupled GFDL model became operational in 2001. It provided an
upgrade to the GFDL system for hurricane scenarios where changes in sea
surface temperatures (SSTs) were important to intensity changes. This
effort formed unique three-way collaborations between operational
hurricane modeling at EMC, NOAA research, and academia. With support
through the USWRP, this close working collaboration has continued to the
present.

To date, the coupled GFDL model has become the benchmark for performance
against which the future operational NCEP hurricane model---the
Hurricane Weather and Research Forecast system (HWRF)---will be measured
for track forecast skill and forecast consistency.

# Appendix C Research Models

### The NASA GEOS-5 Atmospheric Model and Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at the NASA Goddard
Space Flight Center is developing a new atmospheric data assimilation
system (DAS) to synthesize the large volume of observations from Earth
Observing System (EOS) satellites and other satellites. This system will
be used for a global atmospheric reanalysis of the satellite era as well
as to generate products in support of NASA instrument teams. The
reanalysis, referred to as the Modern Era Retrospective-analysis for
Research and Applications (MERRA),[^1] supports NASA's Earth science
interests by placing the current suite of research satellite
observations in a climate context and by providing the science and
applications communities with state-of-the-art global analyses.

The DAS consists of the Goddard Earth Observing System version 5
(GEOS-5) atmospheric model coupled to the Grid-point Statistical
Interpolation (GSI) analysis scheme being developed by NCEP/EMC and
GMAO.

The GEOS-5 atmospheric model is a weather-and-climate-capable model
using the finite-volume dynamical core (Lin, 2004). In developing
GEOS-5, attention has focused on the representation of moist processes
(see <http://gmao.gsfc.nasa.gov/systems/geos5/>). The moist physics
package uses a single phase prognostic condensate and a prognostic cloud
fraction. Two separate cloud types are distinguished by their source:
*"anvil" cloud* originates from detraining convection, and *large-scale
cloud* originates from a PDF-based condensation calculation. Ice and
liquid phases for each cloud type are considered. Once created,
condensate and fraction from the anvil and statistical cloud types
experience the same loss processes: evaporation of condensate and
fraction, auto-conversion of liquid or mixed phase condensate,
sedimentation of frozen condensate, and accretion of condensate by
falling precipitation. Development of GEOS-5 was guided by a realistic
representation of tracer transports and stratospheric dynamics. The
ozone analysis of the DAS is input to the radiation package along with
an aerosol climatology. GEOS-5 is coupled to a catchment-based
hydrologic model (Koster et al. 2000) and a sophisticated multi-layer
snow model (Stieglitz et al. 2001).

The GSI analysis solver was developed at NCEP to support inhomogeneous
and anisotropic 3D background error covariances (e.g., Wu et al., 2002;
Derber et al. 2003; Purser et al. 2003). The data streams currently
assimilated by the DAS are listed in table C-1. The DAS is currently
being used to test the impact of data selection strategies for AIRS
radiance data and the impact of MODIS derived motion vector winds on
weather prediction skill. A clear advantage of NASA's use of the GSI
solver is the relative ease of transition of new techniques to
operational models.

For MERRA and for regular products, the system will use a 0.5°
resolution model and analysis, with 72 levels to 0.01 hPa. The GEOS-5
model is being run globally at 0.25° horizontal resolution to generate
5-day forecasts of tropical cyclone activity as a contribution to the
MAP06 project
([http://map06.gsfc.nasa.gov](http://map06.gsfc.nasa.gov/)). The model
is being initialized with the 0.5° DAS. This project will provide a
critical test of the weather capabilities of the model and DAS.

**Table C-1.** Observation Data Sources and Parameters Used as Input to
the NCEP DAS

**[Conventional Data]{.underline}**

Radiosondes

Pibal winds

Wind profiles

Conventional aircraft reports, ASDAR, MDCARS

NEXRAD radar winds

Dropsondes

GMS, METEOSAT, cloud drift IR and visible winds

MODIS clear sky and water vapor winds

GOES cloud drift IR winds

GOES water vapor cloud top winds

Surface land observation

Surface ship and buoy observations

SSM/I rain rate and wind speed

TMI rain rate

QuikSCAT wind speed and direction

**[Satellite Data]{.underline}**

TOVS 1b radiances

DMSP SSM/I radiances

GOES sounder T~B~

Aqua/AIRS radiances (150 channels)

Aqua/AMSU-A radiances

SBUV2 ozone (Version 8 retrievals)

### The Florida State University Global Model and Multimodel Superensemble

The Florida State University (FSU) global model (Krishnamurti et al.
1991) uses a spectral transform method with semi-implicit time
differencing to solve the dynamic equations. The model has a horizontal
grid resolution of T126 (\~80 km) and uses 14 layers in the vertical
between roughly 50 and 1000 hPa. An array of physical parameterization
schemes is employed for shallow and deep convection, dry convective
adjustment, surface fluxes, planetary boundary layer mixing, short and
longwave radiation, interaction of clouds with radiation, and surface
energy balance. The model is initialized from large-scale analyses from
the European Center for Medium Range Weather Forecasting (ECMWF) with
0.5 ° horizontal resolution and 28 vertical levels. Precipitation
estimates from NASA's Tropical Rainfall Measuring Mission (TRMM) and
Defense Meteorological Satellites Program Special Sensor Microwave
Imager (SSMI) satellites are used as input for physical initialization
to improve the initial representation of precipitation processes in the
model. The FSU model has shown good success in predicting hurricane
tracks (Williford et al. 1998).

A significant advance in hurricane prediction research came with the
development of the FSU multimodel superensemble forecast system
(Krishnamurti et al. 1999; 2000a, 2000b; 2001). This system utilizes
track and intensity forecasts from several global and regional forecast
models including NCEP's Aviation global model, the U.S. Navy's
Operational Global Atmospheric Prediction System (NOGAPS), the ECMWF
global model, the FSU global model, and the GFDL hurricane forecast
model, in addition to several simpler dynamical and statistical models
used by NHC. A key part of the multimodel superensemble is the training
phase, in which prior forecasts and observations are used to derive
linear regression--based statistical coefficients. During the forecast
period, the superensemble forecasts are constructed using these
statistical coefficients and current multimodel forecasts. Williford et
al. (2003) showed that the superensemble method performed well in 1999
and therefore offers a promising new approach to forecasting, but
further testing over several seasons is needed.

### MM5

The Pennsylvania State University---National Center for Atmospheric
Research mesoscale model is a limited-area, nonhydrostatic,
terrain-following sigma-coordinate model designed to simulate or predict
mesoscale and regional-scale atmospheric circulations. It was developed
as a community mesoscale model and the Fifth-Generation model (MM5) is
the latest in a series developed from a mesoscale model used by Richard
Anthes at Pennsylvania State University in the early 1970\'s, later
documented by Anthes and Warner (1978). Since that time, it has
undergone many changes designed to broaden its use. These include (i) a
multiple-nest capability; (ii) nonhydrostatic dynamics, which allows the
model to be used at a few-kilometer scale; (iii) multitasking capability
on shared- and distributed-memory machines; (iv) four-dimensional
data-assimilation capability; and (v) expanded physics options. This
model has been used extensively by the research community to conduct
both idealized and real-case simulations in order to study the dynamics
and physics of hurricanes, often at very high horizontal grid resolution
(\~1-6 km), as well as to examine the impacts of various observations on
hurricane simulations via data assimilation. Such studies have examined
(a) the genesis of hurricanes; (b) the influence of shear on storm
intensity and precipitation distribution; (c) the organization of upward
motion in the hurricane eyewall and the role of buoyancy; (d) the
sensitivity of hurricane intensity and precipitation to boundary layer,
cumulus, and microphysical parameterizations; (e) vortex Rossby wave
dynamics; (f) the impact of atmosphere-ocean coupling; (f) techniques
for inserting bogus vortices for model initialization; (g) and satellite
data assimilation. While use of this model has led to significant
advances in our understanding of hurricanes, its relevance to
operational forecasting has been limited because of the large
differences between the MM5 model and operational models and the lack of
a pathway for transition of research results to operations. With the
advent of the WRF model, use of the MM5 model is expected to
significantly decline.

### WRF

The Weather Research and Forecasting (WRF) Model is the next-generation
mesocale numerical weather prediction system designed to serve both
operational forecasting and atmospheric research needs. The effort to
develop WRF has been a collaborative partnership, principally among the
National Center for Atmospheric Research (NCAR), NOAA/NCEP, the NOAA
Global Systems Division of the Earth System Research Laboratory (ESRL)
(formerly the Forecast Systems Laboratory), the Air Force Weather Agency
(AFWA), the Naval Research Laboratory, the University of Oklahoma, and
the Federal Aviation Administration.

WRF features two dynamic cores, the Advanced Research WRF (ARW) core
developed at NCAR and the Nonhydrostatic Mesoscale Model (NMM) core
developed by NCEP. The NMM is being implemented operationally as the
core of the HWRF and is described in section 4.4.2. The ARW core is
based upon equations that are fully compressible and nonhydrostatic. The
horizontal grid has Arakawa C-grid staggering with a vertical coordinate
based on terrain-following hydrostatic pressure. Time integration uses a
3^rd^ order Runge-Kutta scheme with smaller time steps for acoustic and
gravity-wave modes. Current data assimilation capabilities are
experimental and are based upon a 3-dimensional variational (3D-VAR)
data assimilation system (Barker et al. 2004). Four-dimensional
variational data assimilation (4D-VAR) is also under development.

Application of the ARW model generally follows that of MM5: It is used
to study the dynamical and physical processes related to hurricane
genesis, intensification, rainfall, landfall, and extratropical
transition. In addition to basic research, NCAR has implemented the ARW
model as an experimental hurricane prediction system run in real time in
2004 and 2005. Forecasts in 2004 and 2005 used the same grid spacing and
physics options. A 2-way nested configuration was used that features a
12 km outer fixed domain with an inner 4 km mesh. During 2004, the 4 km
nest was fixed in space and contained 450x500 points in the north-south
and east-west directions, respectively. The location of the 4 km domain
was chosen to contain the storm throughout the 48 h forecast period. In
2005, a feature-following capability was added that positions the nest
at the location of the minimum 500 hPa geopotential height within a
radius of the last position of the vortex center (or within a radius of
the first guess, when first starting). The repositioning occurs every 15
simulation minutes, and the width of the search radius is based on the
maximum distance the vortex can move at 40 m s^-1^.

On the 12 km domain, the Kain-Fritsch cumulus parameterization was used,
while the inner domain used no parameterization. Both domains used an
explicit microphysics scheme that predicts only one cloud variable
(water for temperatures greater than 0ºC and ice for temperatures less
than 0ºC) and one precipitation variable, either rain or snow (again
thresholded on 0ºC). Both domains use the Yonsei University (YSU) scheme
for the planetary boundary layer (Noh et al. 2001). This is a
first-order closure scheme that is similar in concept to the scheme of
Hong and Pan (1996), but in comparison tests it appears less biased
toward excessive vertical mixing.

The forecasts were integrated from 00 UTC and occasionally at 12 UTC
during the time when a hurricane threatened landfall within either 48 h
(2004) or 72 h (2005). During 2004, both domains were initialized
directly from the NCEP Global Forecast System (GFS) model with no
additional data assimilation or balancing. In 2005, forecasts were
initialized using the GFDL model, with the GFS used only when the GFDL
was unavailable.

Evaluation of the skill of the forecast system is ongoing, but several
seasons of forecasts with a stable model configuration and
initialization technique will likely be required to assess forecast
skill effectively. An advantage of the ARW over MM5 is that, because
both the ARW and NMM WRF use a similar modeling framework, transitioning
research results to operations is easier. However, any techniques or
model physics developed for the ARW must be implemented within and fully
tested with the NMM core.

# Appendix D 2005 Forecasts and Models Used at the TPC/NHC and CPHC

  -------- -------------------------------- ------------------------ ---------------- ---------------
  **ID**   **Name/Description**1            **Type**                 **Timeliness\    **Parameters\
                                                                     (Early/Late)**   Forecast**

  OFCL     Official NHC or CPHC forecast                                              Trk, Int

  CLP5     CLIPER5 (Climatology and         Statistical baseline     E                Trk
           Persistence model)                                                         

  SHF5     SHIFOR5 (Climatology and         Statistical baseline     E                Int
           Persistence model)                                                         

  A98E     NHC98 (Atlantic)2                Statistical-dynamical    E                Trk

  P91E     NHC91 (Pacific)                  Statistical-dynamical    E                Trk

  BAMS     Beta and advection model         Single-layer trajectory  E                Trk
           (shallow layer)                                                            

  BAMM     Beta and advection model (medium Single-layer trajectory  E                Trk
           layer)                                                                     

  BAMD     Beta and advection model (deep   Single-layer trajectory  E                Trk
           layer)                                                                     

  LBAR     Limited area barotropic model    Single-layer regional    E                Trk
                                            dynamical                                 

  GFDL     NWS/Geophysical Fluid Dynamics   Multi-layer regional     L                Trk, Int
           Laboratory model                 dynamical                                 

  GFSO     NWS/Global Forecast System       Multi-layer global       L                Trk, Int
           (formerly Aviation)              dynamical                                 

  UKM      United Kingdom Met Service model Multi-layer global       L                Trk, Int
                                            dynamical                                 

  NGPS     Navy Operational Global          Multi-layer global       L                Trk, Int
           Prediction System                dynamical                                 

  GFDN     Navy version of GFDL             Multi-layer regional     L                Trk, Int
                                            dynamical                                 

  CMC      Environment Canada global model  Multi-level global       L                Trk, Int
                                            dynamical                                 

  ETA      NWS/Eta                          Multi-level regional     L                Trk, Int
                                            dynamical                                 

  AFW1     Air Force MM52                   Multi-layer regional     L                Trk, Int
                                            dynamical                                 

  OFCI     Previous cycle OFCL, adjusted    Interpolated             E                Trk, Int

  GFDI     Previous cycle GFDL, adjusted    Interpolated-dynamical   E                Trk, Int

  GFSI     Previous cycle GFS, adjusted     Interpolated-dynamical   E                Trk, Int

  UKMI     Previous cycle UKM, adjusted     Interpolated-dynamical   E                Trk, Int

  NGPI     Previous cycle NGPS, adjusted    Interpolated-dynamical   E                Trk, Int

  GFNI     Previous cycle GFDN, adjusted    Interpolated-dynamical   E                Trk, Int

  AEMI     Previous cycle AEMN, adjusted    Consensus                E                Trk, Int

  SHIP     Statistical Hurricane Intensity  Statistical-dynamical    E                Int
           Prediction Scheme (SHIPS)                                                  

  DSHP     SHIPS with inland decay          Statistical-dynamical    E                Int

  AEMN     GFS ensemble mean                Consensus                L                Trk, Int

  GUNA     Average of GFDI, UKMI, NGPI, and Consensus                E                Trk
           GFSI                                                                       

  CONU     Average of at least 2 of GFDI,   Consensus                E                Trk
           UKMI, NGPI, GFSI, and GFNI                                                 

  ICON     Average of GFDI and DSHP         Consensus                E                Int

  FSSE     FSU Super-ensemble2              Weighted consensus       E                Trk, Int

  GUNS     Average of GFDI, UKMI, NGPI      Consensus                E                Trk
  -------- -------------------------------- ------------------------ ---------------- ---------------

1\. Items are used by both TPC/NHC and CPHC unless otherwise footnoted
(and highlighted in blue).

2\. Item is used only at TPC/NHC.

# Appendix E 2005 Forecasts and Models Used at the JTWC

+----+----------------------------------+-------------+-------+-------+
| ** | **Name/Description**             | **Type**    | **T   | **P   |
| ID |                                  |             | imeli | arame |
| ** |                                  |             | ness\ | ters\ |
|    |                                  |             | (Ear  | Forec |
|    |                                  |             | ly/La | ast** |
|    |                                  |             | te)** |       |
+====+==================================+=============+=======+=======+
| JT | Official JTWC forecast           |             |       | Trk,  |
| WC |                                  |             |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| GF | NWS/Global Forecast System       | Multi-layer | L     | Trk,  |
| SO | (formerly Aviation)              | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| GF | Previous cycle GFS, adjusted     | Interpolate | E     | Trk,  |
| SI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JA | CDR Mike Fiorino (NHC) vortex    | Multi-layer | E     | Trk,  |
| VN | tracker applied to GFSO \*       | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JA | Previous cycle JAVN, adjusted \* | Interpolate | E     | Trk,  |
| VI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| U  | United Kingdom Met Service model | Multi-layer | L     | Trk,  |
| KM |                                  | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| UK | Previous cycle UKM, adjusted     | Interpolate | E     | Trk,  |
| MI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JU | CDR Mike Fiorino (NHC) vortex    | Multi-layer | E     | Trk,  |
| KM | tracker applied to UKM \*        | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JU | Previous cycle JUKM, adjusted \* | Interpolate | E     | Trk,  |
| KI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| NG | Navy Operational Global          | Multi-layer | L     | Trk,  |
| PS | Prediction System                | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| NG | Previous cycle NGPS, adjusted    | Interpolate | E     | Trk,  |
| PI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JN | CDR Mike Fiorino (NHC) vortex    | Multi-layer | E     | Trk,  |
| GP | tracker applied to NGPS \*       | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JN | Previous cycle JNGP, adjusted \* | Interpolate | E     | Trk,  |
| GI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| GF | Navy version of GFDL             | Multi-layer | L     | Trk,  |
| DN |                                  | regional    |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| GF | Previous cycle GFDN, adjusted    | Interpolate | E     | Trk,  |
| NI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| AF | Air Force MM5                    | Multi-layer | L     | Trk,  |
| W1 |                                  | regional    |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| AF | Previous cycle AFW1, adjusted \* | Interpolate | E     | Trk,  |
| WI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| CO | Coupled Ocean Atmosphere         | Multi-layer | E     | Trk,  |
| WP | Prediction System \*             | regional    |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| CO | Previous cycle COWP, adjusted \* | Interpolate | E     | Trk,  |
| WI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| EG | United Kingdom Met Service model | Multi-layer | E     | Trk,  |
| RR | \*                               | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| EG | Previous cycle EGRR, adjusted \* | Interpolate | E     | Trk,  |
| RI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JG | Japan Meteorological Agency      | Multi-layer | E     | Trk,  |
| SM | Global Spectral Model \*         | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JG | Previous cycle JGSM, adjusted \* | Interpolate | E     | Trk,  |
| SI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JJ | CDR Mike Fiorino (NHC) vortex    | Multi-layer | E     | Trk,  |
| GS | tracker applied to JGSM \*       | global      |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JJ | Previous cycle JJGS \*           | Interpolate | E     | Trk,  |
| GI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| JT | Japan Meteorological Agency      | Multi-layer | E     | Trk,  |
| YM | Typhoon Model \*                 | regional    |       | Int   |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| JT | Previous cycle JTYM, adjusted \* | Interpolate | E     | Trk,  |
| YI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| TC | Australia Bureau of Met Tropical | Multi-layer | E     | Trk,  |
| LP | Cyclone                          | regional    |       | Int   |
|    |                                  | dynamical   |       |       |
|    | Limited Area Prediction System   |             |       |       |
|    | \*                               |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| TC | Previous cycle TCLP, adjusted \* | Interpolate | E     | Trk,  |
| LI |                                  | d-dynamical |       | Int   |
+----+----------------------------------+-------------+-------+-------+
| WB | University of Munich (Harry      | S           | E     | Trk   |
| AR | Weber) Barotropic Model \*       | ingle-layer |       |       |
|    |                                  | global      |       |       |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| WB | Previous cycle WBAR, adjusted \* | Interpolate | E     | Trk   |
| AI |                                  | d-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| CO | Average of at least 2 of AFWI,   | Consensus   | E     | Trk,  |
| NW | AVNI, COWI, EGRI, GFNI, JGSI,    |             |       | Int   |
|    | JTYI, NGPI, TCLI, WBAI \*        |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| SB | Beta and advection model         | S           | E     | Trk   |
| AM | (shallow layer)                  | ingle-layer |       |       |
|    |                                  | trajectory  |       |       |
|    | initialized from NOGAPS \*       |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| MB | Beta and advection model (medium | S           | E     | Trk   |
| AM | layer)                           | ingle-layer |       |       |
|    |                                  | trajectory  |       |       |
|    | initialized from NOGAPS \*       |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| FB | Beta and advection model (deep   | S           | E     | Trk   |
| AM | layer) \*                        | ingle-layer |       |       |
|    |                                  | trajectory  |       |       |
|    | initialized from NOGAPS          |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| ST | CIRA/NESDIS Statistical Typhoon  | Statistica  | E     | Int   |
| IP | Intensity Prediction System \*   | l-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| ST | CIRA/NESDIS Statistical Typhoon  | Statistica  | E     | Int   |
| ID | Intensity Prediction System      | l-dynamical |       |       |
|    | (Decay Model) \*                 |             |       |       |
+----+----------------------------------+-------------+-------+-------+
| ST | 5 Day Statistical Intensity      | Statistica  | E     | Int   |
| 5D | Forecast (STIFOR) \*             | l-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| ST | CONW Statistical Intensity       | Statistica  | E     | Int   |
| 10 | Prediction Scheme \*             | l-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| PT | Météo France Model \*            | Multi-layer | E     | Trk   |
| RO |                                  | regional    |       |       |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| KB | Korea Meteorological Agency      | S           | E     | Trk   |
| AR | Barotropic Model \*              | ingle-layer |       |       |
|    |                                  | global      |       |       |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| KB | Previous cycle KBAR, adjusted \* | Interpolate | E     | Trk   |
| AI |                                  | d-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| KR | Korea Meteorological Agency      | S           | E     | Trk   |
| EG | Regional Model \*                | ingle-layer |       |       |
|    |                                  | regional    |       |       |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| KR | Previous cycle KREG, adjusted \* | Interpolate | E     | Trk   |
| EI |                                  | d-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| K4 | Korea Meteorological Agency      | Multi-layer | E     | Trk   |
| 26 | Global Model (low resolution) \* | global      |       |       |
|    |                                  | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| K4 | Previous cycle K426, adjusted \* | Interpolate | E     | Trk   |
| 2I |                                  | d-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+
| K2 | Korea Meteorological Agency      | Multi-layer | E     | Trk   |
| 13 | Global Model (high resolution)   | global      |       |       |
|    | \*                               | dynamical   |       |       |
+----+----------------------------------+-------------+-------+-------+
| K2 | Previous cycle K213, adjusted \* | Interpolate | E     | Trk   |
| 1I |                                  | d-dynamical |       |       |
+----+----------------------------------+-------------+-------+-------+

\* Denotes models used exclusively at JTWC

# Appendix F Track Guidance Model Errors for 2005

The number of cases ("# Cases" at bottom of each table) indicates the
number of JTWC forecasts represented in the error computation.

**Table F-1**. Homogeneous Comparison of Selected Subset of **Atlantic
Basin** Early Track Guidance Model Errors (n mi) **for 2005**

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **OFCL**   31.0       54.2     77.3     100.2    146.1    195.6    248.4

  **CLP5**   46.9       100.3    162.3    211.9    271.4    341.9    457.9

  **GFSI**   35.6       60.2     85.3     116.3    198.3    275.7    359.6

  **GFDI**   33.9       57.7     78.9     103.9    163.3    253.6    337.4

  **GFNI**   37.1       66.1     96.8     129.6    207.9    299.4    405.4

  **UKMI**   38.1       65.9     93.9     118.5    169.5    216.2    263.1

  **NGPI**   34.7       61.6     90.3     118.8    178.0    236.3    324.5

  **GUNA**   29.1       50.1     72.5     96.1     148.0    194.7    249.9

  **CONU**   29.4       50.6     74.0     97.7     150.2    197.3    257.1

  **FSSE**   29.3       49.5     72.1     96.1     156.6    219.8    261.9

  **AEMI**   35.7       60.7     85.9     113.4    181.8    240.6    264.0

  \# Cases   398        358      319      268      183      110      71
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table F-2. Homogeneous Comparison of Selected Subset of Eastern
Pacific Ocean Basin Early Track Guidance Model Errors (n mi)** for 2005

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **OFCL**   28.0       48.0     65.9     79.4     103.6    119.0    132.8

  **CLP5**   34.6       71.0     109.8    146.9    215.0    262.4    319.9

  **GFSI**   34.2       60.4     87.4     113.6    176.0    238.2    256.8

  **GFDI**   32.0       57.1     78.4     97.6     153.1    218.0    288.9

  **GFNI**   41.5       74.9     104.5    125.5    161.2    206.4    244.0

  **UKMI**   36.9       63.3     89.8     113.0    173.2    221.6    297.6

  **NGPI**   38.4       71.2     97.9     123.3    174.6    220.6    267.1

  **GUNA**   27.5       45.9     63.6     77.6     108.3    128.1    147.5

  **CONU**   28.7       48.9     67.3     80.6     108.9    131.7    152.1

  **AEMI**   34.0       61.2     88.8     117.8    176.8    236.0    265.8

  **BAMS**   35.6       63.5     94.9     125.7    182.4    235.8    293.4

  **BAMM**   34.4       58.0     82.2     107.5    148.8    181.5    227.7

  **BAMD**   36.9       65.3     94.1     120.8    169.0    225.2    256.0

  **LBAR**   32.6       69.3     116.2    164.4    255.7    337.9    437.1

  **P91E**   34.6       66.6     100.1    136.3    227.5    318.8    456.4

  **\#       172        149      135      119      93       73       53
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table F-3. Heterogeneous Comparison of Selected Subset of Northwest
Pacific Ocean Basin Early Track Guidance Model Errors (n mi) against
Official (JTWC) Forecast for 2005**

+--------+-------+-------+-------+-------+-------+-------+-------+
| Model  | For   |       |       |       |       |       |       |
| ID     | ecast |       |       |       |       |       |       |
|        | P     |       |       |       |       |       |       |
|        | eriod |       |       |       |       |       |       |
|        | (h)   |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
|        | *     | *     | *     | *     | *     | *     | **    |
|        | *12** | *24** | *36** | *48** | *72** | *96** | 120** |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 40  | > 61  | > 81  | > 102 | > 156 | > 231 | > 284 |
| JTWC** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 36  | > 55  | > 73  | > 94  | > 142 | > 207 | > 272 |
| CONW** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 45  | > 78  | > 115 | > 156 | > 279 | > \-- | > \-- |
| AFWI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 37  | > 61  | > 85  | > 115 | > 191 | > 296 | > 393 |
| AVNI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 44  | > 78  | > 118 | > 161 | > 255 | > \-- | > \-- |
| COWI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 46  | > 76  | > 108 | > 139 | > 211 | > 252 | > 306 |
| EGRI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 44  | > 74  | > 103 | > 130 | > 204 | > 287 | > 381 |
| GFNI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 38  | > 61  | > 80  | > 99  | > 141 | > 234 | > \-- |
| JGSI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 38  | > 61  | > 84  | > 113 | > 171 | > \-- | > \-- |
| JTYI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 43  | > 70  | > 96  | > 119 | > 182 | > 278 | > 360 |
| NGPI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 45  | > 78  | > 117 | > 158 | > 246 | > 253 | > 275 |
| TCLI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **   | > 49  | > 83  | > 117 | > 154 | > 228 | > \-- | > \-- |
| WBAI** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+
| > **\# | > 543 | > 503 | > 452 | > 403 | > 312 | > 176 | > 119 |
| > C    |       |       |       |       |       |       |       |
| ases** |       |       |       |       |       |       |       |
+--------+-------+-------+-------+-------+-------+-------+-------+

**Table F-4. Heterogeneous Comparison of Selected Subset of North Indian
Ocean Early Track Guidance Model Errors (n mi) against Official (JTWC)
Forecast for 2005**

+--------+--------+--------+--------+--------+--------+--------+--------+
| Model  | Fo     |        |        |        |        |        |        |
| ID     | recast |        |        |        |        |        |        |
|        | Period |        |        |        |        |        |        |
|        | (h)    |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
|        | **12** | **24** | **36** | **48** | **72** | **96** | *      |
|        |        |        |        |        |        |        | *120** |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 42   | > 62   | > 84   | > 116  | > 118  | > 133  | > 134  |
| JTWC** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 41   | > 65   | > 91   | > 116  | > 123  | > 154  | > 185  |
| CONW** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 46   | > 77   | > 107  | > 138  | > 112  | > \--  | > \--  |
| AFWI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 44   | > 60   | > 77   | > 94   | > 91   | > 84   | > 69   |
| AVNI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 40   | > 57   | > 66   | > 83   | > 112  | > \--  | > \--  |
| COWI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 45   | > 70   | > 98   | > 122  | > 229  | > 272  | > 79   |
| EGRI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 51   | > 67   | > 88   | > 114  | > 166  | > 279  | > 405  |
| GFNI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 34   | > 52   | > 77   | > 113  | > 213  | > \--  | > \--  |
| JGSI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 45   | > 76   | > 121  | > 187  | > 317  | > \--  | > \--  |
| JTYI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 46   | > 71   | > 93   | > 121  | > 78   | > 95   | > 107  |
| NGPI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 57   | > 96   | > 141  | > 179  | > 203  | > \--  | > \--  |
| TCLI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **     | > 58   | > 113  | > 174  | > 237  | > 197  | > \--  | > \--  |
| WBAI** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+
| **\#   | > 77   | > 67   | > 56   | > 49   | > 18   | > 10   | > 8    |
| C      |        |        |        |        |        |        |        |
| ases** |        |        |        |        |        |        |        |
+--------+--------+--------+--------+--------+--------+--------+--------+

**Table F-5.** Heterogeneous Comparison of Selected Subset of **Southern
Hemisphere** Early Track Guidance Model Errors (n mi) against Official
(JTWC) Forecast **for 2005**

**(Comparison at 96 and 120 h are against CONW.)**

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **JTWC**   41         69       91       114      199      \--      \--

  **CONW**   40         67       94       117      207      248      292

  **AFWI**   58         103      136      176      274      \--      \--

  **AVNI**   44         75       111      146      223      310      394

  **EGRI**   45         74       95       137      259      325      366

  **GFNI**   51         89       122      147      210      282      373

  **NGPI**   45         80       118      148      242      333      409

  **TCLI**   52         91       134      149      338      \--      \--

  **WBAI**   55         108      165      211      321      \--      \--

  **\#       239        213      192      169      41       \--      \--
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

# Appendix G Intensity Guidance Model Errors for 2005

Although not computed operationally, included for reference in tables
G-1 and G-2 is a simple intensity consensus model (ICON) that is an
average of GFDI and DSHP. In each table, The number of cases ("# Cases"
at bottom of each table) indicates the number of JTWC forecasts
represented in the error computation.

**Table G-1. Homogeneous Comparison of Selected Subset of Atlantic Basin
Early Intensity Guidance Model Errors (kt)** for 2005

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **OFCL**   7.8        11.9     14.4     16.1     19.3     17.8     20.1

  **SHF5**   10.8       17.2     20.6     21.9     24.9     26.4     23.8

  **GFDI**   9.7        14.1     16.8     18.0     21.1     23.6     24.1

  **SHIP**   10.2       15.9     19.0     19.8     21.6     22.3     23.6

  **DSHP**   9.0        12.9     15.5     17.7     20.8     20.2     23.8

  **FSSE**   8.6        12.7     15.4     17.4     21.2     23.1     23.0

  **ICON**   8.8        12.4     14.7     16.3     19.5     20.2     21.9

  **\#       430        401      356      312      231      161      112
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table G-2. Homogeneous Comparison of Selected Subset of East Pacific
Ocean Basin Early Intensity Guidance Model Errors (kt)** for 2005

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **OFCL**   6.0        10.2     13.9     16.5     18.5     19.0     19.9

  **SHF5**   7.0        11.7     15.4     18.5     20.1     20.1     19.0

  **GFDI**   7.4        11.5     14.7     17.0     20.4     20.0     17.2

  **SHIP**   6.6        10.6     13.9     16.6     19.4     20.5     22.1

  **DSHP**   6.3        10.1     13.4     16.0     19.3     20.5     22.1

  **ICON**   6.3        9.8      12.5     14.5     17.7     17.3     16.5

  **\#       247        220      190      165      129      102      85
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table G-3. Heterogeneous Comparison of Western North Pacific Ocean
Early Intensity Guidance Models (kts) against Official (JTWC) Forecast
for 2005**

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **JTWC**   7.2        11.6     14.8     17.6     23.4     25.7     26.6

  **CONW**   9.4        16.4     22.1     25.8     29.7     30.8     28.2

  **AFWI**   11.2       19.0     24.4     27.3     30.0     \--      \--

  **AVNI**   10.6       18.5     24.8     29.5     35.9     39.5     38.3

  **GFNI**   9.3        15.1     20.4     23.5     24.5     24.3     23.8

  **JGSI**   10.6       18.3     23.8     27.3     33.2     47.0     \--

  **JTYI**   9.2        15.2     20.0     23.6     27.0     \--      \--

  **NGPI**   11.3       18.6     24.1     28.4     33.8     34.4     32.8

  **TCLI**   9.6        16.6     21.6     24.6     27.3     44.5     59.0

  **ST5D**   7.8        13.1     17.8     20.7     25.5     26.3     24.9

  **STIP**   8.2        14.0     17.8     20.8     25.5     28.4     26.6

  **\#       543        503      452      403      312      176      119
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table G-4. Heterogeneous Comparison of North Indian Ocean Early
Intensity Guidance Models (kts) against Official (JTWC) Forecast for
2005**

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **JTWC**   4.8        8.1      11.4     13.0     21.4     11.0     11.3

  **CONW**   7.1        11.6     12.9     13.5     14.5     15.3     5.7

  **AFWI**   7.7        14.5     18.5     21.1     25.6     \--      \--

  **AVNI**   7.0        11.5     14.5     16.7     14.9     12.4     9.0

  **GFNI**   8.2        13.3     16.0     16.3     13.2     16.2     14.0

  **JGSI**   8.9        12.9     17.5     19.7     26.0     \--      \--

  **JTYI**   8.0        15.3     19.5     22.8     23.5     \--      \--

  **NGPI**   7.9        12.7     13.6     15.3     15.6     17.4     7.8

  **TCLI**   9.0        13.8     16.6     19.0     18.5     \--      \--

  **ST5D**   6.8        11.6     12.2     14.5     13.2     16.6     9.5

  **STIP**   9.6        17.8     25.1     28.2     28.6     \--      \--

  **\#       77         67       56       49       18       10       8
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

**Table G-5.** Heterogeneous Comparison of **Southern Hemisphere** Early
Intensity Guidance Models (kts) against Official (JTWC) Forecast **for
2005**

**(Comparison at 96 and 120 h are against CONW)**

  ---------- ---------- -------- -------- -------- -------- -------- ---------
  Model ID   Forecast                                                
             Period (h)                                              

             **12**     **24**   **36**   **48**   **72**   **96**   **120**

  **JTWC**   9.4        15.7     21.7     25.5     32.9     \--      \--

  **CONW**   10.5       19.0     25.4     29.5     34.7     32.2     35.7

  **AFWI**   11.1       20.1     28.0     32.8     16.8     \--      \--

  **AVNI**   11.8       20.7     27.3     32.7     44.8     36.4     40.3

  **GFNI**   10.0       17.7     23.5     26.8     25.3     27.7     38.6

  **NGPI**   12.4       21.6     28.5     32.4     39.5     38.2     41.3

  **TCLI**   13.7       19.5     22.2     23.7     23.0     \--      \--

  **ST5D**   10.3       17.5     22.4     24.6     29.6     25.9     24.5

  **\#       239        213      192      169      41       140      106
  Cases**                                                            
  ---------- ---------- -------- -------- -------- -------- -------- ---------

# Appendix H Article from *NOAA Magazine*

**NOAA IS ENCOURAGING EVERYONE TO PREPARE FOR HURRICANE SEASON**

July 30, 2006 --- [NOAA](http://www.noaa.gov/) is ready as we enter the
peak of the [North Atlantic Hurricane
Season](http://www.cpc.ncep.noaa.gov/products/outlooks/hurricane2002/hurricane2002_background.html)
and we want to make sure you are as well. While NOAA will again provide
the best possible
[forecasts](http://www.nhc.noaa.gov/HAW2/english/forecast_process.shtml),
it is vital that everyone living in
[hurricane](http://www.nhc.noaa.gov/HAW2/english/basics.shtml) prone
areas be
[prepared](http://www.nhc.noaa.gov/HAW2/english/disaster_prevention.shtml).
[Max
Mayfield](http://www.whitehouse.gov/government/m_mayfield-bio.html),
director of the [NOAA National Hurricane
Center](http://www.nhc.noaa.gov/) says, "The message for everyone is the
same, whether we have an active season or a below-normal season, you've
got to have a plan in place and you've got to be ready to implement that
plan. Remember one hurricane hitting where you live is enough to make it
a bad season." ![](media/image2.wmf){width="2.5375in"
height="2.6881944444444446in"}

**How is NOAA Prepared for Hurricane Season**

This year, NOAA committed more than \$300 million dollars to track and
forecast hurricanes. In FY 2007, NOAA requested an additional \$109
million dollars for hurricane-related investments. Currently, NOAA is
focusing on further improving hurricane
[track](http://hurricane.csc.noaa.gov/hurricanes/index.htm) and
[intensity](http://www.nhc.noaa.gov/aboutsshs.shtml) forecasting through
better
[observations](http://www.nhc.noaa.gov/HAW2/english/forecast/observations.shtml),
enhancing its
[modeling](http://www.nhc.noaa.gov/HAW2/english/basics/models.shtml)
efforts (including those related to storm surge and inland flooding) and
the continuation of [Joint Hurricane
Testbed](http://nhc.boulder.noaa.gov/jht/index.shtml) to advance the
transfer of new [research](http://www.aoml.noaa.gov/hrd/) and technology
into operational hurricane prediction.

Improving NOAA equipment is also critical. [NOAA
aircraft](http://www.aoc.noaa.gov/), the [W-P3
Orions](http://www.aoc.noaa.gov/aircraft.htm) and the [Gulf Stream
IV](http://www.aoc.noaa.gov/aircraft.htm), provide essential
observations and data critical to the NOAA National Hurricane Center
forecasters and supplement the U.S. Air Force Reserve reconnaissance
flights. The \$14.2 million dollars NOAA received in FY 2006
supplemental appropriations to improve future aircraft service will add
an additional W-P3 in 2007, and upgrade the radar and instrumentation on
all of NOAA's aircraft.![](media/image3.wmf){width="2.625in"
height="1.8256944444444445in"}

NOAA also works year-round with federal, state and local emergency
managers; educating them about weather effects from hurricanes, while
they educate NOAA about response issues and their challenges. It is a
constant learning process and the key is working together to ensure that
the public takes appropriate action this hurricane season.
![](media/image4.wmf){width="2.790277777777778in"
height="1.8229166666666667in"}

Most preparedness activity and outreach takes place outside hurricane
season. In May of 2006, as part of NOAA's ongoing mission to enhance
economic security and national safety, the NOAA National Weather Service
again led its annual [Hurricane Awareness
Tour](http://www.noaanews.noaa.gov/stories2006/s2626.htm) --- this year
focusing on Gulf Coast states. The tour helped raise awareness about the
potential effects from a hurricane landfall with FEMA, local
governments, emergency managers, schools, the public and the media
working as a team to increase [hurricane
awareness](http://www.nhc.noaa.gov/HAW2/english/intro.shtml) and
encourage preparedness in this vulnerable area of the nation.

# Appendix I NPOESS Satellite Data Pertinent to Tropical Cyclone Analysis and Forecasting

This appendix reviews the NPOESS sensors and data that are pertinent to
tropical cyclone analysis and forecasting. The expectations of use are
derived from extrapolations of current practices for both analysis and
NWP models. As noted in the footnotes associated with the discussions
regarding the Conical Microwave Imager/Sounder and Radar Altimeter, the
exact specifications and future acquisition of both of these sensors are
in doubt/jeopardy.

**Visible/Infrared Imager/Radiometer Suite (VIIRS)**

-   Polar-Orbiting VIS/IR Imagery

    -   Not currently used directly in NWP systems except for sea
        surface temperature (SST) estimates (see next bullet). In the
        future, it may provide some useful information after substantial
        development effort.

-   IR SSTs

    -   VIIRS provides retrievals (currently) and radiances (in the
        future) for SST estimates. Technology will evolve rapidly over
        next 5 years so that in the NPOESS era, direct use of radiances
        will provide the best SST information.

**Crosstrack Infrared Sounder (CrIS) and Advanced Technology Microwave
Sounder (ATMS)**

-   Temperature and moisture retrievals

    -   Temperature and moisture retrievals are not used either in
        analysis or in NWP models.

-   Radiances

    -   IR (CrIS) and microwave (ATMS) radiances are used in data
        assimilation to provide essential temperature and moisture
        information for initializing hurricane forecast models.

**Conical Microwave Imager/Sounder (CMIS)**[^2]

-   For analysis, a microwave imager/sounder can view tropical cyclone
    inner-core structure often obscured by upper-level clouds and thus
    masked in visible and infrared imagery.

-   For analysis, a microwave imager/sounder can derive column
    integrated atmospheric water vapor over the oceans, also known as
    total precipitable water (TPW). TPW measurements are useful for the
    analysis of tropical cyclone intensity trends. TPW can also be
    derived using IR and microwave radiances through data assimilation.

-   A microwave imager/sounder can provide surface wind information as
    well as integrated moisture information for analysis in
    non-precipitating regions. For analysis, surface wind and rain rate
    information are the only quantities currently used from microwave
    imager. Rain contamination is an important quality control issue for
    both analysis and NWP.

    -   The radius of 50 kt winds is a critical parameter for ship
        routing and the radius of 34 kt winds are important for coastal
        evacuations because these storm-response activities must be
        completed before the arrival of gale force winds.

```{=html}
<!-- -->
```
-   Microwave estimates of surface winds suffer in measuring high winds
    and are contaminated by heavy rain

-   Microwave radiances may contribute to statistical intensity
    prediction models.

-   For NWP, current practice is to derive wind speeds from imagery.
    However, in the future, microwave radiance information will be used
    directly to provide a cleaner signal for data assimilation.

    -   Integrated moisture estimates are useful for the analysis of
        tropical cyclone intensity trends.

-   Integrated moisture can also be derived using IR and microwave
    sounder radiances through data assimilation.

    -   A microwave imager/sounder can provide intermittent analysis of
        rainfall rate and some cloud properties such as liquid water.

        -   However, it does not provide time-continuous information in
            general. Impact on analysis depends critically on time
            continuity of coverage (i.e., number of satellites, time
            between overpasses).

        -   This information may be useful in future for NWP models.

        -   Similar information can be provided by ATMS.

    -   A microwave imager/sounder may provide intermittent analysis of
        tropospheric warm core structure, rain rate, some cloud
        properties, and approximate wind structure (using a diagnostic
        model) due to direct overpasses.

-   It does not provide time-continuous information in general. Impact
    on analysis depends critically on time continuity of coverage (i.e.,
    number of satellites, time between overpasses).

    -   Rain rate and cloud properties may be useful in future for NWP
        models.

**Radar Altimeter (ALT)**[^3]

-   Radar altimetry measures sea surface height and wave heights.

-   Many studies have shown that the ocean's subsurface thermal
    structure plays an important role in tropical cyclone
    intensification. The subsurface structure can often be deduced from
    satellite altimetry data.

    -   The modeling of the oceanic heat content (OHC) shows that the
        ocean energy available to the storm can vary considerably,
        depending on the subsurface ocean structure. The OHC can be
        estimated using a combination of sea surface temperature and
        ocean altimeter measurements.

-   For NWP, altimeter measurements are critical to providing
    information (through the ocean and wave data assimilation process)
    to coupled atmosphere-ocean-wave hurricane NWP models.

# Appendix J Metop Satellite Data Pertinent to Tropical Cyclone Analysis and Forecasting

This appendix reviews the MetOp sensors and data that are potentially
pertinent to tropical cyclone analysis and forecasting. The expectations
of use are derived from extrapolations of current practices for both
analysis and NWP models.

### Infrared Atmospheric Sounding Interferometer (IASI) 

IASI is one of the most advanced onboard instruments measuring infrared
(IR) radiation emitted from the surface of the Earth to derive data of
unprecedented accuracy and resolution on humidity and atmospheric
temperature profiles in the troposphere and lower stratosphere. It also
can measure some of the chemical components playing a key role in
climate monitoring, global change, and atmospheric chemistry.

### The Microwave Humidity Sounder (MHS) 

MHS acquires measurements at various altitudes of atmospheric humidity,
including rain, snow, hail and sleet, and temperature by measuring
microwave radiation emitted from the surface of the Earth.

### Advanced Scatterometer (ASCAT) 

ASCAT, an enhanced follow-on instrument to the highly successful
scatterometers flown on ESA\'s ERS-1 and ERS-2 satellites, measures wind
speed and direction over the ocean. Its six antennas allow for
simultaneous coverage of two swaths on either side of the satellite
ground track, providing twice the information of the earlier
instruments. ASCAT also contributes to activities in areas as diverse as
land and sea ice monitoring, soil moisture, snow properties, and soil
thawing.

### Advanced Microwave Sounding Units (AMSU-A1 and AMSU-A2) 

The AMSU instruments measure scene radiance in the microwave spectrum.
The data from these instruments are used in conjunction with the
High-resolution Infrared Sounder (HIRS) instrument to calculate the
global atmospheric temperature and humidity profiles from the Earth\'s
surface to the upper stratosphere. The data are also used to provide
precipitation and surface measurements including snow cover, sea ice
concentration, and soil moisture.

### High-resolution Infrared Radiation Sounder (HIRS/4) 

HIRS/4 is a 20-channel radiometric sounder measuring radiance in the IR
spectrum. Data from HIRS/4 are used in conjunction with data from the
AMSU instruments to calculate the atmosphere\'s vertical temperature
profile and pressure from the Earth\'s surface to about 40 km altitude.
HIRS/4 data are also used to determine ocean surface temperatures, total
atmospheric ozone levels, precipitable water, cloud height and coverage,
and surface radiance.

# Appendix K NCEP Data Assimilation Development

## 1. Advanced Data Assimilation Techniques

Recently, new techniques have been developed to improve data
assimilation. Broadly speaking, these techniques may be classified in
three categories: 4D-VAR, Ensemble Data Assimilation (EDA), and
Situation-Dependent Background Errors (SDBE). A short description of
these three techniques follows.

### 4D-VAR

The 4D-VAR technique has the following advantages:

-   All observation increments over the data window are considered at
    their observing time.

-   The impacts of all observations on the model solution are realized
    at the observing time in the model.

-   4D-VAR allows for some time and space variability of the background
    error, although efforts to implement this degree of freedom have
    been rudimentary so far, even at ECMWF.

-   In principle, the resulting analysis is a model solution so that it
    is a balanced, model-adjusted state. In practice, this ideal balance
    is not achieved because of inconsistencies introduced by
    simplifications and approximations.

The disadvantages of 4D-VAR are the following:

-   In addition to needing a 3D-VAR framework, 4D-VAR requires
    approximately three times more software, including a tangent linear
    and adjoint versions of the forecast model. Every change to the
    model (e.g., physics, dynamics) will impact the 4D-VAR system
    directly. Any inconsistencies in the entire 4D-VAR system will cause
    it to perform suboptimally. These interrelationships may slow
    development of the entire forecast system.

-   Operational maintenance and change-management of a 4D-VAR system is
    much more difficult, due to its complexity and larger volume of code
    (see above). Code management costs will increase as will
    coordination time between scientists working on different parts of
    the system.

-   A full (no approximations) 4D-VAR system is 10-30 times more
    expensive computationally than 3D-VAR. 4D-VAR systems with
    approximations or simplifications are generally 2--5 times more
    expensive than 3D-VAR. Examples of simplifying approximations
    currently used at operational NWP centers include performing the
    analysis at lower horizontal resolution and using a simplified
    assimilating model (e.g., no physics or simplified physics).

In addition to the examples noted above, there are many ways of
simplifying a 4D-VAR system. One possible simplification involves the
"model" used in the 4D-VAR. It has been customary to use the same
forecast model as in the free forecast. Therefore, simplifications have
been made in the model physics or in horizontal/vertical resolution
relative to the forecast model. However, a fresh look at the 4D-VAR
problem may be in order. It may be feasible to construct a simple model
for observation increments that can become part of the 4D-VAR technique.
This model would remove the need for using the full free forecast model
and its accompanying tangent linear and adjoint models.

### Ensemble Data Assimilation

In EDA, the most likely atmospheric state is produced by finding the
linear combination of ensemble forecast realizations that best matches
the available observations. With EDA, background errors can be estimated
directly from the ensemble at every analysis time and throughout the
forecast domain. In a fully interactive EDA system, the ensemble
perturbations are derived from the analysis error covariance. In this
way, information from both the analysis and ensemble are used in a
consistent manner. Although EDA is a relatively new technology, it is
being vigorously pursued by about half a dozen groups in the research
community, including a one-person effort at NCEP/EMC. The consistent use
of information by the analysis and ensemble generation techniques is the
major goal of an EDA-based system. However, it is yet to be demonstrated
that this can be done reliably in an operational setting. A comparison
of various EDA schemes is currently being sponsored by the THORPEX
program.

EDA has the following advantages:

-   No ancillary model components such as tangent linear and adjoint
    models are required; therefore, the code infrastructure is reduced
    considerably.

-   The analysis code can be simpler, although in practice this may not
    necessarily be the case.

-   There is a natural information feedback between the ensemble and
    data assimilation systems, which has not been fully explored in the
    4D-VAR context. Unfortunately, some preliminary investigations by
    ECMWF in this area have been disappointing, so a lot more work needs
    to be done.

-   Ensemble forecasts scale very well on massively parallel computers
    and, therefore, are very efficient to run operationally.

EDA has the following disadvantages:

-   It is much less mature in practical applications than 3D-VAR. There
    are still many unknowns regarding ensemble construction, stability
    of background error formulation, and the impact of model
    error---particularly, any model bias. Many of the studies showing
    extremely optimistic results have been done with simulated data or
    without any large data source (e.g., satellite data).

-   The technique appears to be very sensitive to the characteristics of
    the background (model) error, even more than 3D-VAR and 4D-VAR.

-   Costs are proportional to the ensemble size and resolution. An
    ensemble run at full horizontal and vertical resolution would be
    highly desirable, although some cost reduction can be achieved by
    running the ensembles at lower resolution.

-   The ensemble generation technique is critical; short term (3--6
    hour) ensemble characteristics have not been well characterized.

-   It is critical that the ensembles span the entire possible range of
    analysis states. If observations lie outside the ensemble envelope,
    extrapolation errors will be potentially fatal (i.e., could cause a
    major bust).

### Situation-Dependent Background Errors

It is widely recognized that the major outstanding analysis problem is
improved formulation of the background error part of the analysis
equation. Many improvements over the past 10 years have been in this
area, including a major upgrade to the ECMWF system. Nonetheless, 3D-VAR
systems have background error formulations that are constant in time and
geographically varying in a very limited way (e.g., latitudinal and
vertically varying only, derived empirically from the model forecast
climatology). The SDBE approach attacks the fundamental analysis problem
directly and is particularly relevant to the hurricane problem. Some
early work on this was done at ECMWF, the Met Office (METO), but was
abandoned in favor of a simplified 4D-VAR.

One of the most significant modeling challenges to improve numerical
forecasts of hurricane structure and intensity in high-resolution models
is the initialization of the hurricane vortex. To advance this effort, a
local 3D-VAR using SDBE covariances is being developed at EMC to
initialize the hurricane core circulation in the HWRF using real-time
airborne Doppler radar from NOAA's WP-3D aircraft and the newly funded
instrument upgrade package on the NOAA Gulfstream IV aircraft (see
section 3.1.1)**.** For storms approaching landfall, the data
assimilation will also make use of the coastal WSR-88D high resolution
radar data. The NCEP Gridpoint Statistical Interpolation (GSI) now
contains coding structures intended for admission of SDBE and will be
exploited in the HWRF to initialize the hurricane core through
development of flow-dependent algorithms. Developing SDBE using
extensions to the GSI has the following advantages:

-   It addresses directly the most fundamental part of the analysis
    problem.

-   There would be direct continuity with previous work, including
    diagnostics, performance statistics, and other infrastructure
    software, and ease of comparison and diagnosis that comes with
    incremental change.

-   The methodology is affordable now in a development and testing mode,
    while resources can be garnered for final testing and operational
    implementation in 1--3 years.

-   The methodology is innovative and has a good chance of succeeding.

-   It can be applied most advantageously in a 4D-VAR context.

-   It can incorporate information from ensemble forecast runs.

The preferred development strategy for an NCEP Global and Regional
Advanced Data Assimilation System (GRADAS) is, first and foremost, to
develop SDBE within the GSI. 4D-VAR extensions to the GSI, using a
simple model for observation increments, will also be developed for
improved use of high time-resolution observations such as surface and
radar data and satellite imagery. This approach will result in
systematic and incremental augmentations of the current NCEP global and
regional analysis code and produce a simplified 4D-VAR that can also use
ensemble-based information.

## 2. EMC's Data Assimilation Priorities

The following data assimilation priorities at EMC are associated with
development of the above strategy:

-   Improving the background error covariances and their evolution for
    the atmosphere, ocean, and land

-   Assessing the impact of atmosphere, ocean, and land model errors and
    biases

-   Identifying the key variables to be measured for NWP, including the
    requirements of accuracy and resolution in time and space and the
    tradeoffs between resolution and areal coverage

-   Development of strategies to extract maximum meteorological
    information from the data (e.g., adaptive thinning, "super-obbing,"
    recursive filters, etc.)

-   Specifying the observation errors, especially in sensitive regions
    such as the inner core, and for surface observations in steep
    topography

-   Development of techniques for optimal use of spatially dense
    correlated observations

-   Development of adaptive quality-control techniques

-   Development of assimilation techniques for available quantities
    (e.g., Doppler line-of-sight winds, air-sea fluxes, trace gases,
    aerosols).

-   Modeling of radiative interactions with microphysics and aerosols

## 3. Data Assimilation Challenges for the Tropics and Hurricanes

Data assimilation for the tropics and hurricanes includes the following
specific challenges:

-   **Balance equations**: In the tropics (and for mesoscale in
    general), balance is dominated by moist processes and is much more
    complex than for the larger scales. Failure to properly treat the
    balance issues will result in a rapid loss of useful information at
    the beginning of the forecast. The increase in nonlinearity due to
    moist processes makes the tropical/hurricane problem more difficult
    to solve.

-   **Analysis variables:** To accurately analyze variables in the
    tropics such as cloud liquid water and cloud ice, a balance has to
    be achieved and all the fields involved need to be initialized. This
    means that the surface and ocean fields must be correctly specified.
    The ability to achieve a realistic balance is not as straightforward
    as for the larger scales.

-   **Background error covariance:** For the tropics, it is essential to
    have circulation-dependent error covariances, but they are difficult
    to determine. For example, the structure of the background error
    covariances for cloud and surface fields are almost certainly
    dependent on small-scale dynamics that are not well known.
    Furthermore, it is critical to include in the background error
    covariances the relationships between the variables (e.g., water
    vapor and clouds).

## 4. Focused Data Assimilation Efforts Dealing with the Coupled Ocean Model

The coupled ocean model data assimilation efforts will focus on these
items:

-   Upper ocean and mixed layer as being of primary importance

-   Skin temperature, which is a primary measurement from satellites

-   Bulk water temperatures obtained from ship observations (the
    satellite retrievals are calibrated to the bulk temperature)

-   Profiles of the thermal (and salinity) structure and mixed layer
    depth that are provided by floats and expendable conductivity
    temperature and depth probes

# Appendix L NCEP Global Model Development

## 1. NCEP Global Model Development

This appendix describes an evolutionary plan for the NCEP global model.
A number of external considerations are described, since they must be
included in any long-range planning. These considerations include the
emerging Earth System Modeling Framework (ESMF), the separate evolution
of the model adiabatic dynamics and physics components, a short review
on the basics of forecast model techniques, the concept of primary and
secondary models, forecast system diversity, and interaction with other
NOAA modeling groups for both the weather and climate applications.

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:

1.  Global weather (1--16 days) with many applications such as Aviation,
    medium-range\
    (3--8 days) precipitation and severe weather, **hurricane tracks**

2.  **Initial and boundary conditions for hurricane regional model
    (i.e., HWRF)**

3.  Boundary conditions for North American run

4.  Boundary and initial conditions and background field for the
    Regional Spectral Model

5.  Driver for ocean wave models and, in the future, other ocean models

6.  Ozone distribution and transport and, in the future, other
    atmospheric constituents

7.  **Background field for global data assimilation system**

8.  **Ensemble system model (to include hurricane tracks)**

9.  Coupled Climate Forecast System (CFS) model

The predecessor to the current NCEP GFS was developed in the late 1970's
and was first implemented in August 1980. This model was based on the
spectral representation for all forecast variables. In response to
increased computing resources and changing computer architecture at
NCEP, the GFS has evolved to higher resolution, both horizontally and
vertically, and a more modular code structure. The current horizontal
resolution is T382, or approximately 35 km; vertically there are 64
layers in a domain from the surface to 0.2 hPa (approximately 55 km).
The GFS adiabatic dynamics and physics require application of Fourier
and Legendre transforms to convert between spectral and gridpoint
spaces. Advective processes are computed on the transform grid from
spectral coefficients. A sigma (normalized pressure) vertical coordinate
in current used (September 2004). A hybrid sigma-pressure coordinate
option is included in the operational code and will be fully tested for
operations in FY2005. The time integration scheme is a three-time-level
leap-frog scheme with semi-implicit integration. Physical
parameterizations and nonlinear dynamics computations are applied on a
reduced Gaussian grid for computational economy. Changes to the physical
parameterizations occur on the average of twice per year, with changes
to the adiabatic dynamics much less frequently.

### Ensembles and Forecast System Diversity

When initial and model related errors are well captured, ensemble
forecasts can convey case-dependent variations in forecast uncertainty.
Currently no other methods can provide such information. Variations in
forecast uncertainty can have a significant impact on users. Small
expected errors in the track of a hurricane (figure L-1a), for example,
call for a different emergency response from a case when the possible
tracks cover a larger area of the coast (figure L-1b). Therefore, all
uncertain forecast information must be presented in a probabilistic or
other format that conveys the associated forecast uncertainty.

Ensembles can be formed in a number of ways. One can collect single
forecasts generated by different NWP centers. Methods have also been
devised to simulate initial and model-related errors. Today, in addition
to a single higher resolution forecast, most NWP centers, including
NCEP, also generate their own set of global ensemble forecasts. The NCEP
Global Ensemble Forecast System (GEFS) recently underwent two major
changes that are significant for hurricane forecasting. First, with an
implementation in 2005, the initial perturbations related to tropical
storms were revised. With the use of the hurricane relocation algorithm,
the position of the tropical storms is no longer perturbed, and the
perturbations in the magnitude and shape of the storms are better
controlled (figure L-1). These changes further improved the track
prediction performance of the GEFS system. As figure L-2 shows, there
was a significant reduction in the error of the ensemble mean track.
Importantly, the spread in the ensemble also was reduced to a level that
now closely matches that of the error. This is an indication of a
well-calibrated track forecasting system that is statistically reliable
and can generate probabilistic forecasts that are consistent with
observations. With these changes, the performance of the ensemble mean
track exceeds that of the higher resolution Global Forecast System (GFS)
averaged over the 2005 Atlantic hurricane season (figure L-3, courtesy
of Jim Goerss, U. S. Navy) for all lead times, beginning with 12 hours
(not shown in figure L-2). Beyond 72 hours, the ensemble mean forecasts
typically also have lower error than the much higher resolution GFDL
forecast.

The second change is related to the implementation of a multi-center
ensemble approach that is aimed at optimally combining ensembles
generated first in North America (North American Ensemble Forecast
System, NAEFS, currently NCEP and Meteorological Service of Canada
ensembles are available, FNMOC and possibly UK MetOffice ensembles to be
added later). The NAEFS effort includes the exchange of all ensemble
members generated by the participating centers for a large number of
variables; the optimal combination of information from the different
ensembles; the statistical bias correction of many of the variables; and
the expression of the forecasts in terms of climatological percentiles,
based on the NCAR-NCEP reanalysis data, allowing for a simple
downscaling of the forecasts.

NCEP is interested in working with the hurricane user community in
developing new and improved products based on the NAEFS and other
ensemble data. Bias-corrected and downscaled probabilistic high wind,
precipitation and other products are examples of the opportunities for
providing more diverse and informative products generated automatically
for the user community. Plans are also being considered for using the
ensemble approach in limited area (WRF) hurricane ensemble forecasting.

+-----------------------------------------------------------------------+
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|                                                                       |
| **(a)**                                                               |
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| **(b)**                                                               |
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**Figure L-1.** Two forecast examples for Hurricane Ivan generated with
the 2005 version of the NCEP Global Ensemble Forecast System. The
ensemble in figure L-1a indicates a case with relatively small track
uncertainty while that in figure L-1b shows a case with large
uncertainty. Such information can be critical in emergency management
applications. Overprotection can be avoided in the first, while
increased vulnerability can be indicated in the second case.

![](media/image7.wmf)**Figure L-2.** Track error of (solid lines) and
spread around the ensemble mean forecast (dashed lines) for 8/23-10/1
2004 Atlantic storms with the then operational (blue) and since
implemented (red) versions of the NCEP Global Ensemble Forecast System.
The closely matching error and spread curves indicate an ensemble
forecast system that is statistically reliable for tropical storm
prediction applications.

![](media/image8.wmf)

**Figure L-3.** NCEP global ensemble mean (green) and Global Forecast
System (GFS, red) tropical storm track forecast errors (nm) averaged
over the 2005 Atlantic Hurricane season. The error in the ensemble mean
track is lower than that in the high resolution single forecast at all
lead times. Of interest, the computational cost of generating either the
lower resolution ensemble or the higher resolution single forecast is
similar. (Courtesy of Dr. James Goerss, U. S. Navy.)

### ESMF and the Common Modeling Structure

The ESMF is a multi-agency project to develop both a model
superstructure and infrastructure. The superstructure is defined as a
set of standards that allow new components to be coupled together with
minimal impact on remaining components. Components may be defined as
complete models (e.g. ocean model) or parts of a complete model (e.g.
dynamics, physics, or parts of each). The infrastructure is a set of
portable, reusable utility routines that can be used across different
models.

Both superstructure and infrastructure must be flexible enough to allow
evolution of NCEP's models and general enough to accommodate both global
and regional models and data assimilation modules for each application.
It must also accommodate both primary and secondary models, some of
which could originate from other parts of NOAA or from outside NOAA.
ESMF-compatible code should be easily transferable to NCEP operations,
given the high degree of modularity and portability standards inherent
with ESMF.

## 2. Model Requirements

The next-generation NCEP global model must provide skillful guidance for
all applications listed in the introduction to Appendix L, demonstrate
at least the computational efficiency of NCEP's current global model and
provide the flexibility to meet future demands. The following set of
highly desirable global numerical model properties should be considered
in developing NCEP's next-generation model.

### Requirements for Dynamics

The next-generation NCEP model will evolve to one with the following
properties:

#### "Same" Model for Global and Regional Applications

The model dynamics should be capable of representing both the global and
mesoscale circulations in the atmosphere. The current resolution of the
global model is \~35 km, which is very similar to the resolution of
NCEP's regional model in September 2000. Other international NWP
centers, such as the UK Met Office and the Canadian Meteorological
Center (CMC), use the same dynamics for both global and regional models.
This strategy facilitates the data assimilation applications since the
model background properties are more consistent across various
applications. It reduces code maintenance for complex communications
strategies and dynamics algorithms, and it facilitates the inevitable
march to improved forecasts through higher horizontal and vertical
resolution.

#### Non-hydrostatic Option through a "Switch"

If a single dynamics is required for both global and mesoscale
applications, then the ability to switch efficiently between hydrostatic
and nonhydrostatic dynamics is critical. Although non-hydrostatic
dynamics for global applications appears necessary at horizontal
resolutions of below 10-12 km, this resolution will not be feasible
operationally for many years to come. However, the experience gained at
regional forecasting, where full non-hydrostatic dynamics is now
operational, can be useful provided the same dynamics structure is used.
Moreover, some improvements to the vertical velocity calculation may
provide an improved dynamical response to convective heating, even at
coarser resolution. This is an area of active research.

#### ESMF standards

As stated above, ESMF standards will enhance portability and reuse of
software, provide a common modeling structure, and thereby decrease the
time needed to bring in model components from outside NCEP (provided
they are ESMF-compatible). This common modeling structure can be used to
provide a more unified system for running NCEP's operational global and
regional forecast systems.

#### Two-Way Nesting

A well-designed, two-way nesting capability is required if a single
model structure is going to be used for both global and regional models.
One-way nesting is used to drive NCEP's North American run. However, at
this time, the global and regional models are separate modules with very
different dynamics and physics. An earlier forecast from the global
model supplies the boundary conditions for the regional model, which is
run before the global model. In the future, when NPOESS satellite data
is available with shorter latency than current data, it may be possible
to drive the North American model (and others) with concurrent boundary
conditions from NCEP's global model. At an even further stage of
development, a two-way nesting capability may be possible if the global
and regional models have a common dynamics and physics. The full,
two-way nested configuration for global forecasting is an area of active
research at this time.

#### Conservative Scheme for Adiabatic Dynamics

The dynamics should be formulated to conserve, in a domain-integrated
sense, as many of the quantities in the continuous formulation of the
equations as possible. First order quantities, such as mass, should be
conserved as demonstrated by the continuous forecast equations.
Moreover, the continuous equations can also be combined to demonstrate
the conservations of quadratic quantities such as potential vorticity,
total energy, and enstrophy under well-defined conditions (e.g.,
adiabatic, nondivergent flow). It appears desirable to have a
discretization of the continuous equations that preserves these
relationships, although there is not unanimous agreement on this issue.
A scheme that does not formally conserve these quantities should still
be tested and evaluated for its conservation properties. Such
conservation may become increasingly important with the length of the
forecast application.

#### Hybrid Vertical Coordinate and Vertical Discretization

A hybrid coordinate, with a terrain-following coordinate near the lower
boundary but approaching an isentropic coordinate in the upper
troposphere and stratosphere, may provide a better discretization of the
dynamics, due to reduced vertical discretization errors. In addition,
careful vertical discretization may be helpful in providing improved
conservative properties for the dynamics. These are leading edge
research problems, however, and considerable work is needed in this
area.

#### Formal Accuracy

The formal accuracy of a particular numerical scheme is an important
factor, although less formally accurate schemes with good conservation
properties have been shown to be excellent schemes. In addition, as
horizontal resolution increases, the influence of formal accuracy
becomes less. One area of research concerns the discretization of
physical processes, which can contribute positively to a more accurate
solution.

#### Consistent Treatment of All Forms of Water Substance

The heat content and density of air and all forms of moisture needs to
be accounted for in a consistent manner. Also, the impacts of water
vapor and other gases on the gas constant and specific heat must be
addressed consistently.

#### Conservation of Tracers, Including Moisture

The total quantities of tracers should be conserved in the absence of
sources and sinks. The numerical schemes should not produce unrealistic
quantities such as negative values.

### Requirements for Physics

The GFS physics has been applied to daily, global weather forecasting,
to Seasonal to Interannual (S/I) forecasting with the coupled Climate
Forecast System (CFS), to regional mesoscale forecasting with the
Regional Spectral Model (RSM), and to hurricane forecasting in the GFDL
model. In the case of the CFS, the GFS physics produced coupled
atmosphere-ocean simulations with very small (\< 0.5 K) climate drift in
the tropic. Furthermore, the GFS physics appears to have considerable
skill in forecasting seasonal tropical wind shear, which is a major
predictor of tropical cyclogenesis and convective activity in general.

The application of physical parameterizations for high horizontal and
vertical resolution is an area of active research. Use of ultra-high
resolution (1-2 km) nested models to capture the cloud-scale physics for
a global model (also called "superparameterization") may prove fruitful
for directing new development in the future. Assumptions common to
current physical parameterizations, e.g., hydrostatic and isobaric
processes, may prove limiting at resolutions below 10 km, in much the
same way as the hydrostatic assumption is unsatisfactory for the
adiabatic dynamics. The general application of physical
parameterizations with general ("non-sigma") vertical coordinates also
needs to be explored.

To facilitate and encourage more advanced research on global modeling,
the NCEP GFS can be transformed into a more general system, while
maintaining a strong heritage and connection to operations. In this way,
incremental and controlled evolution can be achieved with reduced risk.

### Requirements for Computational Efficiency

NCEP's job suite is defined by a time window for data assimilation,
model integration and product generation; by the number of available
computing processors; and by the speed of the computing interconnect
fabric. The object is to produce the most accurate forecast within the
allowable time window, provided the forecast system code is maintainable
and upgradeable within resources available to EMC and its partners.

## 3. Model Development Strategies

At present, there are five principal strategic options for development
of NCEP's next-generation dynamics:

1.  Upgrade the current operational spectral model (sigma-pressure
    hybrid version)

2.  Upscale the Non-hydrostatic Mesoscale Model (NMM) to global domain

3.  Apply Semi-Implicit (SI) and Fully-Implicit (FI) Semi-Lagrangian
    (SL) formulation (Kar dynamics)

4.  Adopt the Finite-Volume (FV) dynamics

5.  Adopt the University of Wisconsin sigma-theta dynamics

To minimize future code rewriting and reorganization, these development
efforts should take place within an ESMF-compatible structure.
Preliminary work on such a structure has begun and is still evolving.
Although it is currently unclear to what degree an ESMF-compatible
structure can be suitable for operations, it should be able to house
each of the above strategies, which are discussed in more detail below.
To give a common beginning to all development efforts, the codes need to
be placed into this structure as a first step. This step will make it
easier for all participants to share code as soon as possible, will
allow results to be compared more readily, and will save development
time because the ESMF infrastructure codes will provide standard
techniques for implementing message passing, other communications
chores, standardized gridding, etc.

### Evolve the current operational spectral model (sigma-pressure hybrid).

After placing the current operational, sigma-pressure hybrid spectral
model into an ESMF-compatible structure, the model could be developed
further by taking the following steps:

-   Improve the accuracy of the vertical discretization

-   Generalize the vertical coordinate, which can allow a sigma-theta
    option

-   Add FISL and/or SISL capabilities

-   Improve mass and thermodynamic consistency for all forms of water

-   Continue to experiment with high resolution downscaling using the
    RSM

This development strategy has the following advantages:

-   Strong continuity with operations will allow evolutionary progress.

-   An ESMF-compatible structure is being constructed and tested.

-   Spectral method potentially has the most accurate horizontal
    dynamics formulation.

-   Tracers are already included, although they are not in the most
    economical or even the most desirable form.

-   A regional model (the RSM) has already been constructed and tested;
    it is part of the Short-Range Ensemble Forecast (SREF) system.

This strategy has the following disadvantages:

-   The computational efficiency on higher resolution, limited area
    domains may decrease for spectral models, due to the overhead in
    converting from grid to spectral space with a smaller number of grid
    points.

-   A nesting technique will require additional code support since
    different spectral functions must be used for the global and
    regional applications.

-   Adding fully implicit time differencing and SL advection to increase
    the time step will involve major changes to code structure and
    require considerable resources.

-   In all likelihood, introduction of FISL and/or SISL techniques will
    reduce conservation properties.

### Upscale NMM to Global Domain

The NMM became the primary regional model at NCEP on June 13, 2006, when
the Gridpoint Statistical Interpolation (GSI) system was coupled with
the NMM in the Weather Research and Forecasting (WRF) system structure.
The NMM can be converted into a global model on a latitude-longitude
grid by filtering the smallest waves near the poles to ensure
computational stability (as in many Eulerian gridpoint models on a
sphere). A second strategy is to integrate the model on two separate
domains using mercator grids, with a coupling mechanism between the
domains for information transfer. Using the ESMF-compatible structure,
coupling may be facilitated. For participation in this global model
development project, the following steps must be taken:

-   Place the NMM into the ESMF-compatible structure

-   Either (a) add low pass filters in the polar regions to allow longer
    time steps or (b) couple domain components on two mercator grids
    (bi-mercator \[BM\] technique)

-   Consider the possibility of a stretched grid (as already done
    by CMC) for global and regional applications

-   Continue to develop dynamics in response to requirements stated in
    Section II

This development strategy has the following advantages:

-   The NMM dynamics is very scalable and has been shown to run
    efficiently and to give good quality forecasts on a regional domain.

-   The NMM uses a hybrid sigma-pressure vertical coordinate already.

-   The NMM currently allows a non-hydrostatic option via a switch.

-   Global upscaling through low pass filtering in polar regions is a
    known technology, but may not be without risk.

This strategy has the following disadvantages:

-   Upscaling the NMM dynamics from regional to global introduces some
    risk, but this may be mitigated by introducing global model physics
    into the NMM. Nevertheless, thorough testing will be required
    because of the large number of new weather regimes that must be
    forecast skillfully, such as the tropics and the Arctic.

-   Resources will need to be expended to move the NMM into the NCEP
    ESMF-compatible modeling structure.

-   The BM strategy is innovative, but it is also high risk because of
    the possible inconsistencies of the evolving model solutions on
    separate grids and the problem of extracting regional boundary
    conditions where the grids are stitched together.

### Apply Semi-Implicit (SI) and Fully-Implicit (FI) Semi-Lagrangian (SL) Formulation (Kar)

This formulation uses a FI or SI formulation of the non-advective,
nonlinear terms in the full dynamics equations. In addition, SL
advection can be used consistently with the SI or FI formulation. Using
both of these techniques together can allow increased efficiency through
longer time steps, but not without additional of significant
computational overhead. Initial results using a shallow water model have
been very encouraging, and development of a 3D hydrostatic FISL model is
well underway. Application of these techniques to operational-grade
models, such as the GFS and the NMM, can be done more efficiently by
using these models in an ESMF-compatible framework which other members
of the group are using. For participation in this global model
development project, the following strategic options could be taken:

1.  Formulate, apply and restructure the NMM and/or GFS to use SI, FI
    and SL techniques

2.  Continue to develop the FISL and SISL shallow water formulations
    into a full, 3D, operational grade model

Option 1 is preferred since the work required to develop, test, and
implement a new, operational grade model is estimated to be less.
Application of FISL or SISL techniques could be directed at either the
NMM or GFS. The result of this application will be a totally different
type of model, which may have a good combination of accuracy and
computational efficiency. SL techniques have, however, no formal
conservation guarantee of mass or dynamical quantities, although the
impact of exact conservation alone may not be of critical importance.
Considerable research, using isentropic diagnostics, needs to be done.
It should be noted that ECMWF uses a spectral-SL scheme for forecasts in
daily and seasonal time domains.

This development strategy has the following advantages:

-   The code and scripting surrounding the GFS or NMM models can be used
    to house the FISL and SISL dynamics so that it need not be developed
    for a new model.

-   The new techniques can be compared cleanly within the operational
    GFS or NMM frameworks.

-   If successful, the new techniques will result in an evolved model
    with increased efficiency and most, if not all, of the same good
    characteristics of NCEP's current operational model.

This strategy has the following disadvantages:

-   The work necessary to restructure the GFS and/or NMM for efficient
    operation on parallel computing architectures, is large. It will
    involve new strategies and code for defining haloes in the GFS.

-   The formal non-conservation of mass may be troublesome for NCEP's
    Seasonal-to-Interannual climate forecast mission.

### Adopt the Finite-Volume (FV) Dynamics

The FV dynamics has been tested at NASA/GSFC, GFDL, and NCAR for climate
applications and, more recently, for data assimilation using NCEP's GSI
code. The FV dynamics may have better conservative properties than other
SL formulations. While NASA/GSFC has had some experience using the FV
model in a cycled data assimilation system, this system has not yet
achieved the same maturity in testing as NCEP's system.

This development strategy has the following advantages:

-   Substantial community testing has been done for climate
    applications.

-   Community support should be available from GMAO and possibly GFDL.

-   Conservation properties may be improved over traditional SL schemes.

This strategy has the following disadvantages:

-   The FV model has not yet been fully demonstrated for NWP and data
    assimilations.

-   The work to downscale this model to a regional application is
    underway but not complete.

-   A non-hydrostatic formulation of this model is under development but
    not currently available.

-   A generalized coordinate version is not yet available.

### Adopt the University of Wisconsin Dynamics

The University of Wisconsin (UW) dynamics is a specific implementation
of a sigma-theta hybrid coordinate, which has potentially very nice
conservation properties. Detailed score comparisons with NCEP's GFS,
which have been made for the past two years, show that the UW model has
comparable 500 hPa height scores and improved moisture verification
scores when NCEP's physics are used. More detailed comparisons,
including tropical forecast skill, will be useful to demonstrate the
potential advantages of the hybrid coordinate. Thus, the UW model will
be run by UW personnel on NCEP's computer for comparisons using NCEP's
verification suite. This activity should produce improved understanding
of the impacts of dynamics formulations on global forecasts at different
time and space scales.

This development strategy has the following advantages:

-   The model has some potentially nice conservation properties.

-   Side-by-side testing can be useful in understanding the behavior of
    NCEP's model.

This strategy has the following disadvantages:

-   This model is not supported.

-   The model's computational efficiency and program structure are
    unknown.

-   The model has not been fully tested for the broad variety of NCEP's
    applications.

-   The formal accuracy of this model may be less than second order.

### 4. The Chosen Strategy

It is currently unclear whether either the spectral or gridpoint
discretizations will be ultimately superior or whether neither will
demonstrate a clear advantage. Among international weather centers, both
spectral (ECMWF, Japan Meteorological Agency) and gridpoint (Met Office,
Canada) methods are used. However, a spectral method has only been used
at the Japan Meteorological Agency for both global and regional
applications, where both methods are required. It appears that the most
popular choice is a gridpoint method, either through direct nesting or a
stretched grid technique. It should be recalled that spectral models
still evaluate advective processes on a grid, so that the choice of
discretizing the advection boils down to representing horizontal
gradients from spectral coefficients, from horizontal interpolations in
a SL technique, or from finite difference approximations from grid
values.

The chosen strategy is to consolidate EMC model development efforts into
three projects as follows:

1.  Develop an ESMF-compatible Prototype Framework (PF), which will run
    the latest version of the NCEP Global Forecast System (GFS)

2.  Upscale the NMM to a global domain and incorporate SISL and FISL
    techniques

3.  Generalize the PF to incorporate the NMM as both a global and
    regional model

The outcome of the above strategy will determine the longer term work
(2007--2011). If preliminary projects for producing operational,
ESMF-compatible systems are successful, this 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., CO2, aerosols), marine ecosystem monitoring and
prediction, hydrological prediction, water and air quality monitoring
and prediction, and space weather forecasting.

# Appendix M Some Future Work Planned for the HWRF Air-Sea-Land-Hurricane Prediction System

1.  The following additional tasks are associated with WAVEWATCH III:

    -   Include new stress and flux parameterizations in the wave model
        for use in coupling with the HWRF model as necessary and
        feasible.

    -   Include shallow water (surf zone) physics parameterizations in
        the WAVEWATCH III model, utilizing established parameterizations
        from models such as Simulating WAves Nearshore (SWAN) and STeady
        State spectral WAVE (STWAVE). Note that the multi-grid version
        of WAVEWATCH III that is presently under development at NCEP
        already includes the capability of drying (movement over land)
        and wetting (back over water) of grid points.

    -   Expand WAVEWATCH III to include irregular and/or unstructured
        grid approaches for the use in coastal areas. This approach will
        provide wave forcing for inundation models at the local
        resolution of such models. In the first approach, a full
        time-resolving model will be considered. Such a model may be
        excessively expensive for operational use and is intended mainly
        to demonstrate the physical feasibility of coupled modeling of
        waves and surges

    -   Economical modeling of waves on irregular and/or unstructured
        grids may require implicit propagation schemes and/or the use of
        a quasi-steady approach. Implementation of such approaches in
        WAVEWATCH III can build upon established techniques for coastal
        wave models.

2.  The work underway on HYCOM modeling with interaction with the ADCIRC
    team at NOS includes the following activities:

    -   Body tides and the complicated problem of including tidal
        boundary conditions at the open boundaries of the domain have
        been implemented in HYCOM. Calibration of the tides is underway.

    -   Simulations during hurricane events from HYCOM alone. These
        simulations show adequate skill in storm surge predictions while
        using operational GDAS winds. The surge estimates from the Real
        Time Ocean Forecast System (RTOFS) can be used as a first guess
        of the surge and serve to guide the deployment of the ADCIRC
        model in areas for which detailed advance knowledge of
        inundation is useful.

    -   In December 2005, the HYCOM-based RTOFS for the Atlantic became
        operational. The work done on the tides in HYCOM was essential
        to this development. For hurricane events, the model has a
        resolution of 4-7 km. Daily fields of nowcast and forecasts of
        sea surface elevations and transports are now available to
        provide open boundary conditions to ADCIRC. Air-sea fluxes and
        wave fields from real-time and historical storms generated by
        this system are used for testing and validation of the new
        hurricane system components.

    -   Strategies for coupling of HYCOM fields to the high resolution
        NOS coastal models and the representation of coast line
        configuration and coastal bathymetry are currently underway.
        They include NOS requirements for wave-related fluxes in their
        coastal models.

    -   Work continues on including turbulent boundary layer effects in
        HYCOM due to the waves. A series of simulations will be carried
        out jointly with NOS to deal with problems related to open
        boundary nesting of HYCOM and ADCIRC for selected cases. In
        addition, work on improving the representation of tides in HYCOM
        will continue.

3.  The additional planned work dealing with the HWRF, the Noah LSM, the
    prediction of the distribution of low level surface winds and
    rainfall amounts, and forecasting of stream and river flow and flood
    levels includes the following activities:

    -   Couple the LSM with the movable, nested grid of the HWRF;
        investigate the impact of the Noah LSM on the prediction of the
        distribution of low level surface winds and rainfall amounts and
        the overall decay rate upon landfall. This will be contrasted
        with the simple one-layer slab model currently in the HWRF and
        the GFDL operational hurricane models.

    -   Compare the predicted wind and rainfall amounts from HWRF with
        observations, including the proposed meso-network in Alabama by
        the U.S. Army. This will include present hurricanes as well as
        significant landfalling cases that have occurred over the past
        few years. Standard verification techniques for rainfall
        verification will be used, as well as new techniques designed
        especially for landfalling hurricanes (see section 3.4.5).

    -   Initiate a project that will use the runoff output of the Noah
        LSM as input to various objective techniques to forecast river
        flow and flooding. Successful precipitation prediction by itself
        may be attractive, but the true importance of precipitation lies
        as an input to provide accurate forecasting of stream and river
        flow and flood levels. Traditionally, river and flood forecasts
        have not used hurricane model predictions of precipitation as
        input to predict river and flood forecasts. Evaluation of
        model-predicted wind and precipitation fields will continue.

    -   Upgrade and change (as required) the HWRF model physics packages
        to improve skill of precipitation and wind fields, especially
        the distribution of low-level surface winds and precipitation.
        The upgrades and changes will be based on the aforementioned
        verification and evaluation of predicted precipitation and wind
        fields and their deviations from the observed fields determined
        from historical observations and the proposed meso-net data in
        Alabama. The predictability of intense destructive features will
        be evaluated through the use of ensemble and high resolution
        forecasts.

    -   Continue the evaluation of the effect of utilizing
        HWRF-determined runoff in the forecast of river flow and
        flooding. The evaluation will be contrasted with more basic
        forecasts utilizing precipitation without regard to moisture
        conditions of the underlying soil. The basic forecasts may
        include coarse resolution rainfall forecasts from simple models
        of climatological hurricane rainfall and forecaster-subjective
        methods of supplying QPF. In addition, further refinements will
        be made to other physics packages of HWRF to improve predictive
        skill.

# Appendix N Questions from the *Air-Sea Interactions in Tropical Cyclones Workshop*

## (Shay et al. 2005)

Focused questions arose from the *Air-Sea Interactions in Tropical
Cyclones Workshop* involving the collaboration of the hurricane air-sea
community in addressing fundamental issues needed to advance the HWRF
and other air-sea coupled hurricane models.

1.  Where is the air-sea community on observing and modeling the oceanic
    and coupled response to tropical cyclones? What is the
    state-of-the-art in areas of air-sea interaction/boundary layer
    processes and upper ocean physics? What promising technologies are
    on the horizon? Will they be available over the next 2 to 5 years?

2.  How can we maximize recently acquired data sets such as ONR-CBLAST,
    NSF/ NOAA Isidore/Lili, HFP, and MMS Georges data sets?

3.  What are the relevant time/space scales at which models need to be
    resolved relative to intensity change?

4.  What is the impact of oceanic coupling on forecasting the
    atmospheric structure and intensity?

5.  How do we improve initialization schemes? How important are positive
    feedback regimes such as the Gulf Stream and the Loop Current on
    storm intensity and structure?

6.  Can we use some of the work from GODAE for assimilation of
    satellite, drifter, and float data?

7.  What observations are needed to improve mixing parameterizations?
    What about wave coupling to the OML and ABL?

8.  What is the appropriate mix of observations needed to improve the
    ocean and air-sea boundary layer processes in oceanic or coupled
    models?

9.  What metric(s) need to be implemented for consistent assessment of
    model(s) performance? For example, is showing intensity changes from
    models enough for a validation? How do we implement data and metrics
    in near-real time for forecasting needs?

10. What new real-time experimental plans need to be developed to
    support model forecasts? For example, sampling scenarios may differ
    over the Loop Current than the subtropical front in the North
    Atlantic.

11. Do we follow the life cycle of one storm, or observe two storms
    under differing oceanic conditions each year? Will this approach
    provide enough statistics to really improve the models?

12. How do we maximize use of GOOS float and ship-of-opportunity data?
    Will NDBC upgrades be useful? What about Coastal Ocean Observing
    Systems?

13. Do we rely on moored instrumentation or do we integrate time series
    from floats/drifters with snapshots from expendable sensors from
    aircraft?

14. Where do we see satellite remote sensing support going? What type of
    data will be useful in supporting experimental plans and data
    assimilation in models?

# Appendix P Social Science Research

  -----------------------------------------------------------------------
  **Representative Research Questions**
  -----------------------------------------------------------------------
  **1. Warning Process**

  How does information flow from forecasters to various types of decision
  makers?

  How should probabilistic forecasts be structured to promote public
  understanding?

  Do people respond better to consistent forecasts with lower
  probabilities of accuracy, or should forecasters sacrifice consistency
  for reasonable accuracy?

  Are terms like watch, warning, and surge well understood or should new
  terminologies be developed and tested?

  Are current watch/warning lead times the most useful to responders?

  What graphics and visualization techniques promote appropriate
  reactions?

  Is the Saffir-Simpson scale adequate, or would a different or
  additional scale be more useful?

  How can the level of danger from surge, rainfall, and inland flooding
  be conveyed effectively?

  How do risk perceptions vary in heterogeneous populations?

  How can warning messages target high-risk groups?

  What are the consequences of broadcast media consolidation to the
  warning process?

  Can local WFOs be used more effectively in the warning process?

  **2. Decision Making**

  What are the processes by which various user groups receive, interpret,
  and use forecasts and warnings?

  How do end users handle conflicting messages?

  How can NWS products and processes be improved to promote more
  effective decisions and responses?

  How do forecast and warning messages influence timing in decision
  making?

  What products and timing best meet the needs of various categories of
  businesses and organizations?

  How do social vulnerability issues (gender, race, class) play out in
  risk perception and response?

  How do formal and informal social networks affect response to warning
  messages?

  What are the best methods for educating various user groups in the
  effective use of forecasts and warnings in their decision-making
  processes?

  How do the cultures of various organizations involved in responding to
  forecasts and warnings encourage or impede change?

  How can the forecast community understand and navigate the political
  processes involved in hurricane-related decisions?

  **3. Behavioral Response**

  How can response and evacuation behavior best be modeled?

  How do context and resources affect the timing of hurricane response
  among various user groups?

  What are the lags between various warning messages and protective
  actions?

  What changes in the warning process are likely to promote evacuation
  among those who should leave, while deterring unnecessary evacuation?

  What are the effects of "false alarms" on future hurricane response
  among different user groups?

  What are the effects of various warning methods and processes on
  traffic patterns?

  How do media accounts of a hurricane event affect future behavior?

  How can behavioral studies from various events be structured to allow
  for comparative research?

  How can behavioral data bases be made available to researchers while
  protecting the identify of respondents?

  **4. Social Impacts and Valuation**

  How can relevant social costs be included in the economic analyses of
  hurricane impacts?

  How can the methods and tools of social science be used to document
  long-term social and economic costs?

  What are the costs and benefits of either shrinking the warning area or
  increasing lead times?

  Can meaningful metrics be developed to measure the economic value of
  hurricane forecasts and warnings that take into account the quality of
  the forecast, the value of communication process variables, and the
  value of responsiveness?

  Is it even relevant to put a value on improved forecasts separate from
  the entire communication and response process?

  How can spatial data analysis most effectively result in a clearer
  understanding of hurricane impacts?

  How can impact measurements take into account the relative value of
  losses to various segments of the affected population?
  -----------------------------------------------------------------------

# Appendix Q List of Acronyms

3D-VAR Three-Dimensional Variational Data Assimilation

4DDA Four-Dimensional Data Assimilation

4D-VAR Four-Dimensional Variational Data Assimilation

ADCIRC Advanced Circulation \[Model\]

ADOS Autonomous Drifting Ocean Station

ADT Advanced Dvorak Technique

AFFO Announcement of Federal Funding Opportunity

AFRES U.S. Air Force Reserve

AFWA Air Force Weather Agency

AMOP Administrative Model Oversight Panel

AMS American Meteorological Society

AMSR Advanced Microwave Scanning Radiometer

AMSU Advanced Microwave Sounding Unit

AOML Atlantic Oceanographic and Meteorological Laboratory (of NOAA/OAR)

AOR Area of Responsibility

APP American Meteorological Society Policy Program

ASCAT Advanced Scatterometer \[MetOp-A satellite instrument\]

ASOS Automated Surface Observing Systems

ATCF Automated Tropical Cyclone Forecasting System

ATMS Advanced Technology Microwave Sounder

AVAPS Airborne Vertical Atmospheric Profiling

AVN Aviation Model \[NOAA/NCEP predecessor to GFS\]

AXBT Airborne Expendable Bathythermographs

AXCP Airborne Expendable Current Profilers

AXCTD Airborne Expendable Conductivity Temperature and Depth

BASC Board on Atmospheric Sciences and Climate (of NAS/NRC)

BAT Best Available Turbulence

BEI Battlespace Environments Institute

BFRL Building and Fire Research Laboratory (of NIST)

BOM Australia Bureau of Meteorology

CAMEX Convection and Moisture Experiment

CARCAH Chief, Aerial Reconnaissance Coordination, All Hurricanes

CBLAST-DRI Coupled Boundary Layers Air-Sea Transfer Departmental
Research Initiative

CENR Committee on Environment and Natural Resources (of NSTC)

CHL Coastal and Hydraulics Laboratory (of USACE/ERDC)

CICS Cooperative Institute for Climate Studies \[University of
Maryland\]

CIMSS Cooperative Institute for Meteorological Satellite Studies

CIOSS Cooperative Institute for Oceanographic Satellite Studies \[Oregon
State University\]

CIRA Cooperative Institute for Research in the Atmosphere

CLIPER Climatology and Persistence \[Model\]

CMA Chinese Meteorological Administration

CMIS Conical Microwave Imager/Sounder

CNES Centre National d\'Etudes Spatiales

CNMOC Commander, Naval Oceanographic and Meteorological Command

COAMPS Coupled Ocean/Atmosphere Mesoscale Prediction System

CoP community of practice

COSMIC Constellation Observing System for Meteorology, Ionosphere and
Climate

CPHC Central Pacific Hurricane Center

CrIS Cross-track Infrared Sounder

CWB Taiwan Central Weather Bureau

DAC Drifter Data Assembly Center \[of GDP\]

DHS U.S. Department of Homeland Security

DMSP Defense Meteorological Satellite Program

DOC U.S. Department of Commerce; Drifter Operations Center \[of GDP\]

DOD U.S. Department of Defense

DPR Dual-frequency Precipitation Radar

DR \[NAS/NRC\] Disasters Roundtable

EAS Emergency Alert System

ECMWF European Center for Medium-Range Weather Forecasting

EDA ensemble data assimilation

EIR enhanced infrared

EMC \[NOAA/NCEP\] Environmental Modeling Center

EOS Earth Observing System

ERDC U.S. Army Engineer Research and Development Center

ESA European Space Agency

ESMF Earth System Modeling Framework

EUMETSAT European Organization for the Exploitation of Meteorological
Satellites

FAA Federal Aviation Administration

FCMSSR Federal Committee for Meteorological Services and Supporting
Research

FEMA Federal Emergency Management Agency

FNMOC Navy Fleet Numerical Meteorology and Oceanography Center

GDAS \[NOAA/NCEP\] Global Data Assimilation System

GDP Global Drifter Program

GEOSS Global Earth Observation System of Systems

GFDL Geophysical Fluid Dynamics Laboratory \[NOAA/OAR\]

GFDN Geophysical Fluid Dynamics Laboratory Hurricane Prediction
System---Navy version

GFO \[NOAA\] Geostat Follow-On \[mission\]

GFS \[NCEP\] Global Forecast System

GMAO Global Modeling and Assimilation Office (of NASA/GSFC)

GMI GPM Microwave Imager

GODAE Global Ocean Data Assimilation Experiment

GOES Geostationary Operational Environmental Satellite

GOOS \[NOAA\] Global Ocean Observing System

GOOS \[NOAA\] Global Ocean Observing System

GPM Global Precipitation Measurement

GPS Global Positioning System

GRADAS Global and Regional Advanced Data Assimilation System

GSFC NASA Goddard Space Flight Center

GSI \[NCEP\] Gridpoint Statistical Interpolation \[System\]

HFSEWG Hurricane Forecast Social and Economic Working Group

HHS U.S. Department of Health and Human Services

HIAPER High-performance Instrumented Airborne Platform for Environmental
Research

HIRS High Resolution Infrared Radiation Sounder

HIRWG Hurricane Intensity Research Working Group \[of NOAA/SAB\]

HLT Hurricane Liaison Team

HPC Hydrological Prediction Center (of NOAA/NCEP)

HPCMP High Performance Computing Modernization Program \[DOD\]

HRD Hurricane Research Division (of NOAA/OAR/AOML)

HSAI HPC \[High Performance Computing\] Software Applications Institute
\[institutes are sponsored by HPCMP\]

HSE \[NSF\] Task Force on Hurricane Science and Engineering

HUD U.S. Department of Housing and Urban Development

HWRF Hurricane Weather Research and Forecast \[Model\] (see WRF)

HYCOM Hybrid-Coordinate Ocean Model \[NCEP\]

IASI Infrared Atmospheric Sounding Interferometer

ICMSSR Interdepartmental Committee for Meteorological Services and
Supporting Research

IDEA Integrated Dynamics through Earth's Atmosphere (joint NASA-NOAA
initiative)

IEOS Integrated Earth Observation System

IFEX Intensity Forecast Experiment

IHC Interdepartmental Hurricane Conference

IPO Integrated Program Office

ISRO Indian Space Research Organization

IWGEO Interagency Working Group on Earth Observations \[replaced by US
GEO\]

IWRAP Imaging Wind and Rain Profiling System

JAG/TCR Joint Action Group for Tropical Cyclone Research

JAG/TCR Joint Action Group for Tropical Cyclone Research

JAXA Japanese Aerospace Exploration Agency

JCSDA Joint Center for Satellite Data Assimilation

JHT Joint Hurricane Testbed

JMA Japan Meteorological Agency

JTWC Joint Typhoon Warning Center

KMA Korean Meteorological Administration

LSM land surface model

MAP Modeling, Analysis, and Prediction \[Program\]

MHS Microwave Humidity Sounder

MODIS Moderate Resolution Imaging Spectroradiometer

MRF Medium Range Forecast model \[NOAA/NCEP predecessor to GFS\]

MTSAT Multifunctional Transport Satellite (Japanese geostationary
satellite)

MURI Multidisciplinary Research Program of the University Research
Initiative

MVOI multivariate optimum interpolation

MWW3 Mulit-grid WAVEWATCH III \[ocean wave model\]

NAS National Academy of Sciences

NASA National Aeronautics and Space Administration

NAVDAS NRL Atmospheric Variational Data Assimilation System

NAVDAS-AR NAVDAS Accelerated Representer

NCAR National Center for Atmospheric Research

NCEP National Center for Atmospheric Research

NCO NCEP Central Operations

NCOM NRL Coastal Ocean Model

NDBC National Data Buoy Center

NESDIS \[NOAA} National Environmental Satellite, Data, and Information
Service

NHC National Hurricane Center

NHOP National Hurricane Operations Plan

NHP National Hurricane Program (of DHS/FEMA)

NIST National Institute of Standards and Technology

NLDAS North American Land Data Assimilation System

NLETS National Law Enforcement Telecommunications System

NMM Nonhydrostatic Mesoscale Model

NOGAPS Navy Operational Global Atmospheric Prediction System

NOPP National Ocean Partnership Program

NOS \[NOAA\] National Ocean Service

NPOESS National Polar-orbiting Operational Environmental Satellite
System

NPP NPOESS Preparatory Project

NRC National Research Council

NRL Naval Research Laboratory

NRL-MRY NRL-Monterey

NSB National Science Board (of NSF)

NSF National Science Foundation

NSSL \[NOAA\] National Severe Storm Laboratory

NSTC National Science and Technology Council

NWP Numeric Weather Prediction

NWS National Weather Service

OAR \[NOAA\] Office of Oceanic and Atmospheric Research

OFCM Office of the Federal Coordinator for Meteorological Services and
Supporting Research

OHC ocean heat content

OML oceanic mixed layer

OMPS Ozone Mapping and Profiler Suite

ONR Office of Naval Research

OPC Ocean Prediction Center \[of NOAA/NCEP\]

OSTP Office of Science and Technology Policy

OSU Oregon State University

QPF quantitative precipitation forecasting

QuikSCAT Quick Scatterometer

R&D research and development

RAINEX Rainband and Intensity Change Experiment

R-CLIPER Rainfall Climatology and Persistence model

recco reconnaissance code

RSMC Regional Specialized Meteorological Center

RTOFC Real Time Ocean Forecast System

RTP Rapid Transition Project \[in U.S. Navy/ONR R&D process\]

S&T Science and Technology

SAB \[NOAA\] Science Advisory Board

SAMHSA Substance Abuse and Mental Health Services Administration (of
HHS)

SATCOM satellite communications

SATCON satellite consensus

SDBE situation-dependent background errors

SFMR Stepped Frequency Microwave Radiometer

SHIFOR Statistical Hurricane Intensity Forecast \[Model\]

SHIPS Statistical Hurricane Intensity Prediction Scheme

SLOSH Sea, Lake, and Overland Surge \[Model\]

SPC Storm Prediction Center (of NOAA/NCEP)

SRA Scanning Radar Altimeter

SSI spectral statistical interpolation

SSM/I Special Sensor Microwave Imager

SSMI/S Special Sensor Microwave Imager/Sounder

SST sea surface temperature

STAR Center for Satellite Applications and Research (of NOAA/NESDIS)

STI Shanghai Typhoon Institute

STIP Science and Technology Infusion Plan (NOAA)

STIPS Statistical Typhoon Intensity Prediction Scheme

SWMF Space Weather Modeling Framework

TAFB Tropical Analysis and Forecast Branch (of NOAA/NCEP/TPC)

TCHP tropical cyclone heat potential

TCSP Tropical Cloud Systems and Processes

THORPEX The Observing-system Research and Predictability Experiment

TMI Tropical Microwave Imager

TPC Tropical Prediction Center (of NOAA/NCEP)

TRaP Tropical Rainfall Potential \[method for estimating rainfall\]

TRMM Tropical Rainfall Measuring Mission

TSB Tropical Support Branch (of NOAA/NCEP/TPC)

TUTT Tropical Upper Tropospheric Trough

UAS unmanned aircraft system

UAS unmanned aircraft system

UKMP United Kingdom Meteorological Office; also the UKMO global model

UKMP United Kingdom Meteorological Office; also the UKMO global model

US GEO United States Group on Earth Observations \[of CENR, replaces
IWGEO\]

US GEO United States Group on Earth Observations \[of CENR, replaces
IWGEO\]

USACE U.S. Army Corps of Engineers

USACE U.S. Army Corps of Engineers

USDOT U.S. Department of Transportation

USDOT U.S. Department of Transportation

USGS U.S. Geological Survey

USGS U.S. Geological Survey

USWRP U.S. Weather Research Program

USWRP U.S. Weather Research Program

VIIRS Visible/Infrared Imager Radiometer Suite

VOS Volunteer Observing Ship \[program\]

WCM Warning Coordination Meteorologist \[at a NWS WFO\]

WCR warm core ring

WFO National Weather Service Forecast Office

WMO World Meteorological Organization

WRF Weather Research and Forecasting \[Model\]

WSUAV Weather Scout Unmanned Aerial Vehicle

[^1]: MERRA website is <http://gmao.gsfc.nasa.gov/merra/>.

[^2]: CMIS has been terminated; a new Microwave Imager/Sounder will be
    competed---not available until C2 (i.e., 2016 at the earliest).

[^3]: The NPOESS ALT, a previously baselined sensor, has been placed
    into a Deferred/Government Furnished Equipment (GFE) category. The
    ALT sensor will not be on NPOESS unless an external government
    agency agrees to sponsor the acquisition of the sensor and provides
    it to the NPOESS IPO.