WINTERTIME COMPONENT OF THE THORPEX PACIFIC - ASIAN REGIONAL CAMPAIGN
AND THE INTERNATIONAL POLAR YEAR

January 2009 -- March 2009

Compiled by Yucheng Song and Zoltan Toth

With contributions from:

Yoshio Asuma, Craig Bishop, Edmand Chang, Juan Caballero, Chris Doyle,
Brian Etherton, Pierre Gauthier, Ron Gelaro, Mary Hart, Steve Koch, Rolf
Langland, Chungu Lu, Tim Machok, John Manobianco, Lynn McMurdie, Mitch
Moncrieff, Rebacca Morss, Dave Parsons, Simon Pellerin, Mel Shapiro,
Istvan Szunyogh, Chris Velden,Gary Wick, Milija Zupanski

**SUMMARY**

Over the past decade, there has been steady progress in the numerical
model forecast and data assimilation systems. However, failures in
wintertime weather forecasts in the middle latitudes and the Arctic
happen on a regular basis, especially at a longer lead time, say 3-5
days. Inaccuracies in the numerical forecasts can be traced back to two
sources: imperfect model and imperfect initial conditions due to data
assimilation (DA) systems with insufficient observations. With an
improving global observational network and advanced observing systems,
the challenges to the research community and operational centers are how
to effectively design and utilize these voluminous data, especially the
satellite data over cloudy areas that need calibration and validation
and how to identify and fill any remaining data gaps. The plan listed
the main science hypotheses surrounding the role of Rossby-wave
initiation and propagation in the development of high impact weather
events. We hypothesize that adaptively configuring the observing network
and data processing can improve the quality of assimilation and forecast
products which have significant social and/or economic values.

Adaptive targeting techniques such as ETKF, SV and other adaptive
techniques will be developed, tested and inter-compared in the current
plan. Issues related to the application of these techniques for a 3-5
day lead time will be studied. There are potential application of these
adaptive techniques for newer observing systems such as Lidar, UAS, and
driftsondes. The plan lays out research activities related to a variety
of scientific issues such as eddy dynamics, cloud parameterization,
multi-scale interaction, air-sea interaction and storm genesis.

**1. Theme and Context**

The science theme of this plan is to understand how perturbations from
the tropics, Asia and polar front travel through waveguide and turn into
high impact weather events affecting forecast in Arctic and North
America during winter time. This plan is in line with THORPEX's
objective to improve 1-14 day high impact weather forecasts

Pacific winter storms affect not only the western states directly hit,
but may also affect weather patterns throughout the North America. High
winds, heavy rain, and extreme flooding can occur in a very short period
of time as the storm comes ashore. Storms that form far out over the
tropical western pacific, Asia or Siberia may affect weather patterns
across the entire North America as the storms develop downstream. The
direct and indirect human and economic damages of high impact winter
storms are comparable to other natural disasters such as earthquakes,
tornados and hurricanes. For example, in the March 1993 Superstorm,
newspaper \"reports\" showed damage from \$1 billion to as much as \$6
billion and some 200 to 300 deaths. As coastal populations continue to
grow, the impact of major weather events could be more significant than
before. Accurate warnings based on accurate predictions of these storms
are as timely as needed to emergy managers, private industry and the
general public for appropriate safety preparations..

However, the prediction of these storms is hindered because they
initiate and develop over the areas where data is sparse. Winter storm
data over these areas are limited because most meteorological
technology, such as the Doppler radar, used by the National Weather
Service is land-based. Satellite data over these areas haven't been
widely used due to the lack of research on calibration, validation with
in-situ observations issues. In addition, gathering research data for
climate and weather computer models is essential to the oceanic and
atmospheric research in expanding its understanding of the air-sea
interactions that cause much of the high impact weather.

One major goal of this plan is to improve the accuracy of predictions
for polar and North America high impact winter weather for 3 to 5 days
and beyond. This provides major opportunities to accelerate observing
system design work -- this has strong implication to the GEOSS (see
Appendix) which aims at the design and utilization of an optimal global
observing network. The plan will provide the research communities with
collaborative opportunities with operational centers on a variety of
scientific issues such as eddy dynamics, cloud parameterization, storm
genesis, satellite data assimilation, multi-scale interaction.

Through this plan, we expect to achieve the following outcome:

1\) Better scientific understanding of the atmospheric processes
relevant for 3-5 days Arctic and NA high impact weather forecasts and
how they are represented through numerical data assimilation, modeling,
and ensemble forecasts;

2\) Improved adaptive observational, DA, numerical modeling and ensemble
methods

3\) Real-time test of the effect of an improved observing system on high
impact weather forecasts during IPY;

4\) Higher quality and enhanced utility of high impact weather forecast
products

**2. Scientific Hypotheses**

The main hypotheses of the current plan are:

(1) **Rossby-wave propagation plays a major role in the development of
    high impact weather events over North America and the Arctic on the
    3-5 days forecast time scale**

> Downstream development by eddies in the mid-latitudes often triggers
> severe weather events in Arctic and North America. There have been
> tremendous amount of publications related to the Rossby wave dynamics.
> Yet their practical application in real world cases needs further
> investigation.
>
> **Issues / questions:** How are the Rossby waves initiated? What will
> be their paths? Is classical ray-path theory relevant in highly
> nonlinear situation? What atmospheric physical processes are involved
> in the conversion of a tropical convection into a Rossby wave? What is
> the role of baroclinic processes in Rossby Wave propagation? How
> kinetic energy is cascaded up-scale and down-scale? Under what
> condition can the Rossby waves be blocked and affect the predictive
> skill downstream? What is missing from NWP models to properly capture
> Rossby-wave initiation, growth and propagation?
>
> **Proposed activities**:

a.  Mutliscale model assimilation and forecast

b.  Diagnostics (Conventional Rossby wave dynamics, new theories)

c.  Analysis, station observations in Arctic and North America, radar
    reflectivity and satellite images to be used for verification

> **Evaluation**

a.  Evaluate initiation processes in the models

b.  Evaluate storm structure, storm tracks in the models.

c.  Study microphysics features associated with the storms during their
    different propagating stages.

d.  Investigate the interaction among synoptic scale, meso-scale and
    microphysics processes with improved analysis and multi-scale model
    forecast during this winter phase of T-PARC.

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(2) **Additional remotely sensed and in situ data can complement the
    standard observational network in capturing critical processes in
    Rossby-wave initiation and propagation**

> **Issues/Questions:** What phenomena and features need special
> attention for capturing Rossby wave propagation? Are there any weak
> points (areas, variables, etc.) that need observational enhancements?
> What areas influence the most high impact events over NA and the
> Arctic in the 3-5 day forecast range? What features need to be
> resolved in order to improve the forecast in Arctic and NA?
>
> The link with tropical convection stage of T-PARC and IPY could
> provide some guidance on the deployment of observing platforms for
> winter phase. This regime dependent feature of targeting could decide
> what areas need more observation. For example, the El Nino and La Nina
> years could bring in different winter weather systems.
>
> **Proposed activities:**

a.  Work closely with the early phases of T-PARC and IPY

b.  Study climatologically sensitive areas for targeting based on
    different climate regimes

c.  Experiments with OSSE, OSE with existing satellite and simulated
    data generated from the current new Nature run. The goal is to
    design the experimental setup for field phase for certain
    instruments.

d.  Modeling and observational studies performed with existing data and
    models to address the overall scientific issues of winter-time
    forecast accuracy that can be performed before the actual field
    phases of T-PARC.

**Evaluation:**

a.  Evaluate the effectiveness of targeting based on regimes on 3-5 day
    forecast

b.  Evaluate models' capabilities in capturing the essential features
    for Rossby wave propagation

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(3) **Adaptive configuration of the observing network and data
    processing can significantly improve the quality of data
    assimilation and forecast products**

> **Issues/Questions:** Multiscale approach to targeting (LAM model
> use). At the 3-5 day time range, is it possible to apply adaptive
> observation on a regular basis to improve general forecasts
> downstream, in addition to targeting to improve specific forecasts?
> How can satellite and in-situ data be used to improve forecast based
> on the different regimes? Are there any other new adaptive techniques
> that could deal with nonlinearities and non-gaussian PDF issues in
> current methods?
>
> The advent of new observing platforms onboard with state-of-art
> instruments/sensors has promising applications in meteorology. These
> new platforms have limited operating duration; they include Unmanned
> Aircraft Systems (UAS), driftsondes, and direct detection Doppler wind
> Lidar onboard Satellites, GEMS. In addition, some additional
> non-routine radiosondes could be deployed in data sparse areas (Such
> as radiosondes from Siberia and Tibet). Adaptive techniques could
> optimize their application for numerical weather prediction by smart
> deployment to areas of interest. In light of this, two kinds of
> targeting activities are considered:
>
> **A. Regime dependent planning/targeting**
>
> The regime dependent planning stage is closed related to the work
> related to hypothesis (2). This is an important step as effective case
> dependent targeting will have a high probability capturing crucial
> winter weather systems if adaptive observation platforms are well
> positioned.
>
> **B. Case dependent targeting**
>
> Based on the past and current (e.g. WSR) project verification over US
> continents and Arctic (Alaska), we know that adaptive methods (such as
> ETKF) work in general for the 1-3 day forecast range, but can it work
> for 3-5 days? What needs to be changed in order to extend the forecast
> range? Recent work by Majumdar et al. (personal communication)
> suggests that under certain conditions the ETKF can be extended beyond
> 3 days, given a sufficiently large ensemble size. With the recent
> progress in the North American Ensemble Forecast System (NAEFS), more
> ensemble member products are now provided to weather forecasters in
> both US, Canada and Mexico for a forecast period that runs out to 2
> weeks. In addition, ensemble products from ECMWF and CMC are
> accessible at NCEP and have been used by the WSR program. The
> preliminary results show the signal is more coherent than the ETKF
> from 2006, which had less independent members. NRL is currently
> setting up an ensemble sampling study to develop guidance for
> medium-range targeting. These ongoing improvements in ensemble
> products provide rich opportunities to compare and develop/enhance
> several adaptive techniques for longer lead time severe weather
> forecasts. .
>
> **Proposed activities**:

a.  Diagnostic studies -- climate studies of contingency plans/ regime
    dependent targeting research in collaboration with ET phase (ENSO,
    MJO, AO, NPO phases), link with Tropical year phase, WCRP/THORPEX
    subseasonal prediction and IPY.

b.  Development of new adaptive techniques (refer to section 4).

c.  Study differences in the guidance by different methods to provide
    possible guidelines on the limitations of applying each method

d.  Identify the \"regional target areas\" for the special satellite
    (and some in-situ) observations based on sensitivity climatology.
    These areas will depend on the conventional observing network and
    prevailing flow regimes at the 2-3 week time scale.

e.  Develop new adaptive observational techniques for longer lead time
    (3-5 day).

**Evaluation:**

a.  Evaluate current adaptive techniques such as ETKF and Singular
    Vector (SV)

b.  Evaluate the relevance of nonlinearities in adaptive observational
    techniques

c.  Evaluate the relevance of non-Gaussian PDF assumption in adaptive
    observational techniques

d.  Evaluate methods for increasing the degrees of freedom in
    ensemble-based adaptive observational techniques. This research
    should address the four-dimensional (spatio-temporal) nature of the
    adaptive observation problem.

> **(4) Hypothesis: New DA, modeling and ensemble methods can better
> capture and predict the initiation and propagation of Rossby-waves
> leading to high impact events**
>
> **Issues/questions:** Case-dependent forecast error covariance info
> (ens-DA, 4-DVAR); model-related uncertainty in ensemble forecasting;
> multiscale modeling to follow Rossby-wave propagation. Can improved
> DA, multi-scale numerical models, ensemble forecast approach reduces
> forecast uncertainties?
>
> The extensive observations during T-PARC and the IPY will provide more
> cases to study the model uncertainties during periods of high impact
> wintertime events. Research questions could be addressed in the
> current plan such as how well the data assimilation system and
> numerical models are for 3-5 day forecast to high impact events? How
> accurate are storm's track, intensity and structure predicted?
>
> Satellite data comprises 99% of the total data received by operational
> weather and climate prediction centers but much is not utilized due to
> the lack of scientific development. The voluminous amount of data also
> poses challenges to the current computational resources. Numerical
> weather prediction will benefit from methods like adaptive
> assimilation or optimal thinning of these data. Adaptive use of
> satellites and new observing systems such as Doppler Wind Lidar,
> driftsondes, dropsondes, radiosondes, and special IPY observations can
> enhance the prediction of high impact weather events in the Arctic and
> North America. The in-situ observations can improve the calibration
> and validation of space-based observations such as validation of
> instrument bias and observation error in satellite temperature, wind,
> and moisture observations. Both adaptive collection and use (in DA) of
> these observations will be pursued. The observations obtained in the
> field campaign can also be used to validate model moist physical
> parameterizations.
>
> **Proposed activities:**

a.  Satellite data assimiliation with in-situ data calibration,
    validation

b.  Embeded model following downstream development

c.  Develop methods to assimilate observations adaptively for improved
    high impact weather forecasts

d.  Improve the assimilation of current and future atmospheric, sea ice,
    and ocean and land surface data, e.g., radiance and other satellite
    (AIRS, MODIS) and in-situ data, especially over ice/snow.

e.  Contribute to the design of the next generation global atmospheric,
    ice, and land surface observing system for the two polar and the
    adjacent regions of the Earth within the framework of the GEOSS
    program.

f.  Develop methods for optimal thinning or super-obing of satellite
    data.

g.  Develop new data assimilation system capable of retaining
    information that spans the analysis cycles of prediction models.

**Evaluation:**

a.  Evaluate forecasted Rossby wave patterns based on satellite data,
    station data and collected in-situ data from T-PARC and IPY

b.  Evaluate forecast improvement due to new DA, forecast modeling and
    ensemble modeling

c.  Evaluate the effectiveness of multiscale modeling in improving the
    forecast

> **(5) Forecast products, including those developed as part of the
> TPARC research, will have significant social and/or economic value.**

**Issues/Questions:** How success for TPARC research can be assessed
form a social or economic point of view? What new products may have the
most significant social and/or economic impact? How are 3-5 day
forecasts currently used in planning to mitigate damage from severe
winter storms? How can improved 3-5 day forecasts of the type that
T-PARC can help provide, including information about forecast
uncertainty, help in such decisions? What would have been the effects of
the storms if there were no forewarning? What impact does a relatively
accurate forecast several days in advance have over a forecast of say 1
day in advance? What would be the impact if there were no forecasts?

**Proposed activities:**

a.  Perform winter storm damage decision-making case studies associated
    with IPY in Alaska, and case studies in the western U.S. focusing on
    an issue like water resource management instead of or in addition to
    damage-related decisions during T-PARC winter phase.

b.  Develop New ocean wave, sea ice, river flow, freezing ice, etc
    ensemble products; outreach in Arctic region (IPY applications);
    interface with HMT over western US are the new products?

**Evaluation:**

a.  Evaluate/verify the real value of improved forecasts on society

b.  Understand how society responds to forecast information related to
    winter weather disasters, especially in the probabilistic
    forecasting world.

**3. Concept of Operations**

(1). Identify potential high impact weather forecast as verification
events

Position over Arctic and North America

Lead time 6-8 days

Use NAEFS ensemble forecast products

> Possible US NWS, Mexican Service ([**Servicio Meteorológico
> Nacional**](http://smn.cna.gob.mx/)) forecasters involvement

(2). Determine sensitive areas affecting verification events at
different times

Use ETKF or ET or other techniques

Inter-compare results from different methods

Select consensus

(3). Observe conditions in sensitive areas

Use various observing platforms as

Sensitive areas move through their domain as lead time shortens

(4). Assimilate all standard and adaptive observations

Use operational DA and forecast systems

Improved NAEFS forecasts

(5). Generate new experimental products based on improved NAEFS system

These include sea ice, freezing spray, river flow forecast etc.

Solicit direct feedback from user community

Enhance products in post-TPARC developments

(6). Evaluate impact of adaptive observations and other NWP methods

Use either operational or enhanced DA/Modeling/ensemble system

Consider differences between DA/forecast systems with/without adaptive
data

Inter-compare operational and experimental NWP systems

**4. Infrastructure, procedures**

**4.1 Observing platforms**

As shown in the schematic diagram in Figure 1, the planned winter time
observational platforms cover a wide area of Asia and Pacific. The
extensive observational platforms during T-PARC winter phase allow us to
track the potential storms and take additional observations as the
perturbations propagate downstream into Arctic and North America
continents. The following listed the proposed and other possible
platforms and contact information.

4.1.1 Proposed winter time observational platforms

1)  NOAA and NASA satellites

> In orbit and under planning
>
> a\. Can be used for adaptive collection and processing
>
> 2\) G-lV out of Japan
>
> Under planning
>
> a\. Can reach 45,000 feet high, centered on 00Z UTC
>
> b\. Contribution from NWS WSR program
>
> 3\) C-130 out of Anchorage (USAF)
>
> Under planning

a.  Can reach 35,000 feet high, centered on 00Z UTC

b.  Part of WSR program

> 4\) P3 out of West Coast of US ONR
>
> To be coordinated with SMA

a.  planned contribution by HMT/NOAA

> Field activities from Sierra Hydrometeorology Atmospheric River
> Experiment (SHARE)

b.  Lidar installation possibility

> 5\) Enhanced Siberian network
>
> Being planned
>
> a\. Collaboration with Roshydromet with NOAA and/or NRL involvement
>
> b\. 4 new stations will be added by 2008, one by 2009
>
> c\. Enhanced 6 week observational period
>
> d\. 20 of 40 stations might be called to take extra observations (4
> times a day)
>
> 4.1.2 Other possible platforms
>
> 6\) Tibetan Plateau network enhancement
>
> Asian THORPEX community for contribution
>
> 7\) Global Hawk
>
> Under planning

a.  In collaboration with NOAA/UAS program

b.  Possible Doppler Wind Lidar installation

> 8\) Driftsonde deployment out of east coast of Asia
>
> No funding source identified
>
> To be explored

a.  Possible contribution from Asian THORPEX community

b.  NCAR and/or French contribution

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9)  DLR Falcon out of Japan or Hawaii

> No funding source identified
>
> Request DLR contribution

10) GEMS (Global Environmental Micro Sensors)

> Could cover large volumes of target zones.
>
> The impacts of GEM observations could be tested by OSSE.

11) G-V (NCAR)

> Part of contingency plans
>
> a\. Possible collaboration with NCAR scientists
>
> ![](media/image1.png){width="5.8929133858267715in"
> height="3.7216535433070868in"}
>
> Figure 1 A schematic diagram showing the likely wintertime components
> of T-PARC experiment.

**4.2 Laboratory studies (early stage)**

> 1\) OSSE (Observing System Simulation Experiment)
>
> UAS (FSL/NOAA)
>
> GEMS **(**ENSCO)
>
> 2\) OSE (Observing System Experiment)
>
> OSE (NCEP)
>
> 3\) Climate data diagnostics
>
> Modeling and observational studies performed with existing data and
> models to address the overall scientific issues of winter-time
> forecast accuracy that can be performed before the actual field phases
> of T-Parc. .

**4.3 Intercomparison of different adaptive techniques**

ETKF (NCEP/U of Miami, NRL)

SV (NRL)

Sensitivity climatology work (NRL and GSFC/NASA)

New techniques considering nonlinearities and non-Gaussian PDF
(CIRA/CSU)

**4.3 Data impact intercomparison of various assimilation techniques and
models**

GSI (NCEP)

4D-VAR (ECMWF)

NOGAPS NRL

GSI (GSFC/NASA)

Ensemble based data assimilation

4DVAR/3DVAR (MSC)

**4.4 Ensemble Product: NAEFS, TIGGE**

The NAEFS (North America Ensemble Forecast System) is run jointly by MSC
(Canada) and US NWS. It combines NCEP and CMC global ensemble systems
and provides products for up to 2 weeks in advance. TIGGE (THORPEX
Interactive Grand Global Ensemble) has a strong tie with the NAEFS.
TIGGE provides research archive of global operational ensemble forecasts
generated by \~10 centers.

**4.5 Product generation and applications**

> The current proposal will bring new product development such as
> downscaling, post processing, ocean waves, sea ice, frozen spray,
> storm surge, stream-flow probabilistic forecasts based on ensemble
> products (TIGGE). These provide opportunities for testing the
> applicability of ensemble products in longer lead time forecast.
>
> Improved forecasts translate into enhanced guidance for users,
> development of new products (sea ice, freezing spray, storm surge),
> and other applications. Better weather forecasts enable the public to
> better cope with climate changes as well. The probabilistic approach
> to model uncertainties and downscaling/post-processing provides better
> representation of high impact weather events in models. Assessing the
> economic effects of high impact winter storms is more difficult than
> those direct damage-driven weather phenomena such as hurricanes,
> flooding, tornadoes and hail. Death tolls from other phenomena tend to
> be concentrated and obvious (for example, from physical trauma or
> drowning), such as in hurricanes and tornado outbreaks. Traffic
> accidents and deaths and injuries that result from snow storms can
> occur in different areas, unlike the concentrated nature of other
> weather events.

**4.6 Evaluation**

> Evaluation is a very important step of wintertime T-PARC. These will
> involve the participation from research community on Rossby wave
> diagnostics, storm structure evaluation (different scales, including
> microphysical), tracks, and forecast verification.
>
> Develop methods to study the data impact (OSE) with numerical models
> and data assimilation systems and enhanced OSSE studies to generalize
> field findings. Satellite data should also be applied in the
> evaluation stage.

**4.8 Coordination, web pages**

> Groups work in parallel, post results on web, daily conference calls,
> including process scientists with their input and interest; remote
> sensors input also for calibration/validation purpuses; Cloud
> properties needed for forward operators in cludy regions (Airborne
> radar might be helpful)

**5. Links with other phases of T-PARC and IPY**

> As mentioned in the regime dependent targeting/planning stage (Section
> 2), the winter component of T-PARC is strongly tied with the two
> earlier phases. In the study of the lifecycle of perturbations that
> originate either in the tropics or over Asia, travel along Pacific jet
> and transition into polar lows over the Arctic or winter storms over
> the North America, the current plan is conceptually connected to the
> early phase of T-PARC. However, due to the differing emphasis
> regarding high impact events for Asia versus the North America and
> polar regions, the times of the observing periods are different (The
> time period for the early phase of T-PARC is Aug-Sept for Asia because
> of special interest in the Beijing Olympics and our current plan is
> Jan-Mar because of its interest in winter weather).
>
> In the meantime, this plan has a significant link to the IPY. It
> involves studying Mid-latitude -- Polar interactions and it will
> improve new weather products for Polar Regions and other IPY
> activities. Through link with IPY, this plan also aims at advancing
> our understanding of polar-global interaction by studying
> teleconnections on all scales.
>
> This T-PARC winter phase will carry forward some of the scientific
> goals of the early phase of T-PARC (refer to
> <http://www.ucar.edu/na-thorpex/>), such as advancing the study of ET
> and its impact on forecast skills, upper-tropospheric wave trains and
> the genesis and evolution of tropical cyclones. The current plan
> focuses mainly on the weather systems that could have high societal
> impacts during the winter phase.
>
> The winter phase is the most active period for mid-latitude baroclinic
> eddies. Winter storms associated with these eddies are the key
> challenges in winter weather forecasting. Due to the initial condition
> and model errors, forecasts of the tracks, intensity and structures of
> these storms are often uncertain. Strategies such as ensemble
> forecasts, improved use of satellite data in cloudy areas, and new
> data assimilation methods can be pursued to tackle these
> uncertainties.

**APPENDIX A Related Ongoing Work and Potential Collaborators**

NOAA-THORPEX funded projects:

1)  Rolf Langland (NRL-Monterey) and Ronald Gelaro (NASA-GMAO)

2)  NCAR driftsonde work

3)  Chrisopher Velden, University of Wisconsin/CIMSS

4)  Emmitt / Atlas, NASA/GSFC

5)  Sharanya Majumdar, U of Miami

6)  Ens-DA effort, collaborators funded under NOAA-THORPEX

    For a list of NOAA THORPEX research grants, visit

> <http://www.emc.ncep.noaa.gov/gmb/ens/THORPEX/THORPEX-grants.html>

7)  NWS WSR program

http://www.emc.ncep.noaa.gov/gmb/targobs/index.htm

8)  NOAA UAS work

9)  NOAA OSSE work

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a)  NCEP -- web reference

> http://www.emc.ncep.noaa.gov/research/osse/THORPEX/index.html

b)  ESRL

11\) NESDIS satellite work (Jaime Daniels)

12\) NASA satellite

13\) NRL observing system evaluation and design studies

14\) JCSDA DA work (Jim Yoe)

15\) CIMSS (Jeff Key)

**Sources for Related Future Research**

1)  Internal NOAA efforts, including NOAA THORPEX work

2)  Possible funding for external PIs (FY10 and beyond, contingent on
    NOAA THORPEX budget)

3)  NRL

4)  NASA

5)  NSF sponsored investigators

    **Organization / Logistics**

Form a WG composed of interested PIs; closely coordinate with
international TPARC and IPY activities; agencies and PIs to pursue
additional funding opportunities

> **APPENDIX B Personal research interests**
>
> **Yoshio Asuma (Division of Earth and Planetary Sciences, Graduate
> School of Sciences, Hokkaido University, Sapporo, Japan):**
>
> Cyclogenesis, multiscale problem, up and downscale effects, mesoscale
> structure affecting larger scales amd vice versa
>
> **Craig Bishop (Naval Research Laboratory/NOAA, Monterey CA):**
>
> 4DVAR error covariance; how it relates to targeting, DA (clouds/moist
> processes included)
>
> **Dave H. Bromwich (Polar Meteorology Group, Byrd Polar Research
> Center The Ohio State University ):**
>
> Arctic (45N -- pole) regional reanalysis with IPY funding from NSF
> (2000-2010) -Arctic System Reanalysis. Interested in hypotheis (4):
> new Data assimilation system and how to improve the assimilation of
> current and future atmospheric, sea ice, and ocean and land surface
> data, e.g., radiance and other satellite (AIRS, MODIS) and in-situ
> data, especially over ice/snow.
>
> **Edmund Chang** (**Stony Brook University, SUNY, Stony Brook, NY):**
>
> Process studies related to multiscale processes, identifying cases
> when propagation of fine scale info upscale to larger scales is or is
> not critical
>
> **Juan Caballero (Department of Marine Meterology, Mexico):**
>
> Cold fronts affecting Mexico, ensemble forecasting using different
> initialization methods (DA); Addition of Mexican observations
> (surface, by Navy, NMSM, other agencies) to GTS distribution
>
> **Chris Doyle (Meteorological Service of Canada, Vancouver, British
> Columbia, Canada):**
>
> Verification related to forecast applications, evaluation of effect of
> data in this respect
>
> **Pierre Gauthier (Data Assimilation and Satellite Meteorology
> Section\
> Meteorological Research Division, Atmospheric Science and Technology
> Directorate Environment Canada):**
>
> Better use of satellite data, biases of satellite data, observational
> error estimation (related to thinning), sensitivity to observations,
> related intercomparison among different groups
>
> **Ron Gelaro (NASA GSFC):**
>
> Adaptive use of satellite observations
>
> **Greg Hakim (Department of Atmospheric Sciences, University of
> Washington):**
>
> Interested in the winter phase of TPARC and motivated by the need for
> better understanding of the detailed sources of wave activity upstream
> of the main extratropical Rossby wave guides over Asia and the North
> Pacific, and the dynamics of forecast errors downstream of these
> regions. Using TPARC observations, plan to use ensemble data
> assimilation as a tool to better understand the predictability and
> dynamics of weather systems in the wave guides.
>
> **Steve Koch (NOAA Research -- Forecast Systems Laboratory, Boulder,
> CO):**
>
> \(a\) Forecast bias reduction; (b) OSSE studies for finding best
> adaptive use of UAS resources; (c) Diabatic initialization, link with
> Rossby wave dispersion processes
>
> **Rolf Langland (Global Modeling Section, Naval Research Laboratory,
> Monterey, CA)**
>
> Targeting beyond short range (medium-range); How these targets differ
> from short range targets, spatial distribution of these vs. available
> observing resources
>
> **Chungu Lu (CIRA, Colorado State University, Fort Collins, CO):**
>
> Forecast verification of high-impact weather events using targeted
> obs; verification coupling T-PARC & HMT activities; Rossby wave ray
> tracing/NPO;
>
> **John Manobiunco (ENSCO, inc.):**
>
> How aggregate impact of more observations in sensitive areas increase,
> at linear or slower rate?
>
> **Mitch Moncrieff (UCAR, Boulder, CO):**
>
> Multiscale modeling of organized convection, up-scale effects,
> generation mechanism for Rossby waves, + advanced DA and modeling to
> retain observed info in forecast (cycling in DA)
>
> **Simon Pellerin(Meteorological Research Division, Environment Canada.
> Dorval, Québec, Canada):**
>
> Intercomparison of various DA methods (4DVAR vs 3DVAR, etc)
>
> **Mel Shapiro (Environmental Technology Laboratory, Boulder, Colo):**
>
> Easy access to data sets: a) Global ensemble; b) Mesoscale downscaled
> info; a) Obs data, especially satellite info overlaid on dynamical
> forecast info
>
> **Chris Velden (University of Wisconsin, Madison, WI):**
>
> Improving the Impact of Satellite Data in NWP Using THORPEX
> Opportunities
>
> **Gary Wick (Earch System Research Laboratory, NOAA, Boulder, CO) :**
>
> Evaluation of new ensemble techniques with satellite data
>
> **Milija Zupanski (Colo. State Univ./CIRA):**
>
> Dynamical structure of relevant structures, what features matter in
> DA/modeling; improve targeting, ensemble methods in space/time
> continuum of scales

**APPENDIX C Related Projects**

**THORPEX**

THORPEX is a long-term research program organized under the World
Meteorological Organization\'s World Weather Research Program. THORPEX
research seeks to accelerate improvements in the accuracy of
high-impact, 1-14 day weather forecasts for the benefit of society, the
economy, and environmental stewardship. THORPEX seeks to reduce or
mitigate the effects of natural disasters on society by transforming
timely and accurate weather forecasts into specific and definite
information in support of decisions that produce the desired benefits.

**T-PARC**

The THORPEX Pacific Asian Regional Campaign (T-PARC) is a multi-national
field campaign planned by the Asian and North American Regional
Committees and their associated national science committees that
addresses the shorter-range dynamics and forecast skill of one region
(East Asia and the western North Pacific) and its impact on the
medium-range dynamics and forecast skill of downstream regions (eastern
North Pacific, North America and perhaps stretching to Europe). By
allowing increased collaboration between the academic community and
operational centers, the research outcome is expected to benefit
society. The first two components of T-PARC, tropical storm and
Extra-tropical Transition (ET), will take place between May and December
2008 with a concentrated US measurement campaign during August and
September. The wintertime component is the third part of the proposed
T-PARC (January- March 09), which is the major focus of this proposal.
In addition to being a proposed THORPEX program, T-PARC has received the
enthusiastic endorsement of the international Polar Year (IPY) Joint
(ICSU and WMO) Committee as part of a THORPEX research cluster for IPY.

**IPY**

The International Polar Year is a large scientific program focused on
the Arctic and the Antarctic running from March 2007 to February 2009.
It is organized through the International Council for Science (ICSU) and
the World Meteorological Organization (WMO), and is actually the fourth
polar year, following those in 1882-3, 1932-3, and 1957-8. In order to
have full and equal coverage of both the Arctic and the Antarctic, IPY
2007-8 covers two full annual cycles from March 2007 to March 2009 and
will involve over 200 projects, with thousands of scientists from over
60 nations examining a wide range of physical, biological, and social
research topics. It is also an unprecedented opportunity to demonstrate,
follow, and get involved with cutting edge science in real-time.

**STAP**

Short-Term Arctic Predictability (STAP) is a series of activiites
proposed by NOAA within the IPY project. Its main focus is to study the
variability and predictability of Arctic atmospheric, sea ice, ocean
wave, and land surface events, and their interaction with global
processes on the 3-to-90-day time scale. The plan calls for the
deployment of remotely operated vehicles (aerosondes), manned aircraft
(using dropsondes and other instruments), and driftsondes over regions
inside and outside the Arctic. The STAP covers the period between March
2007 to Feburary 2009. The experimental data will be complemented by
dropsonde data collected within the operational Winter Storm
Reconnaissance program over the northeastern Pacific, between Hawaii and
Alaska. The STAP planes provide the backbone of the winter component of
T-PARC.

**Adaptive Observing**

Adaptive observing, which has been a crucial part of THORPEX project,
involves using a sophisticated adaptive techniques such as Ensemble
Transformed Kalman Filter (ETKF) and adjoint-based methods to give
guidance on where to take extra observations to improve forecasts of
high-impact weather events. In recent years, there has been growing
interest in improving NWP forecast accuracy by employing adaptive
strategies using observational systems in areas where analysis
uncertainties are large. Dropsondes from airplanes, space-based and
ground-based sensors have been used over data sparse sensitive regions
such as the Northeastern Pacific.

The adaptive observation concepts have been tested in previous field
experiments including FASTEX, NORPEX, and ATREC and have shown promising
results. Based on the positive results from these programs, the National
Weather Service established the Winter Storm Reconnaissance (WSR)
program in 1999. This is an annual wintertime program where the NOAA
G-lV and USAF Reserve C-130 planes deploy dropsonde observations for the
purpose of improving winter storm forecasts. After streamlining its
procedures, the WSR program became operational in 2001. In the
operational WSR program, threatening winter weather events are
identified by NWS WFOs and NCEP Service Centers (primarily HPC). The
Senior Duty Meteorologist (SDM) at NCEP collects the requests for extra
observations, and through the use of ETKF techniques based on NCEP and
ECMWF ensemble products, determines the optimal location for the
adaptive observations to be taken. Over the past few years the NCEP
Winter Storm Reconnaissance has been able to improve the forecast in
verification areas on average over 70% of the time. The average error
reduction within the verification regions that were preselected based
upon large expected  societal impact/threat (spanning the continental US
and Alaska) is on the order of 10-20%.

![](media/image3.png){width="3.9229166666666666in"
height="3.0256944444444445in"}

> Figure 2 Expected forecast error reduction in verification region (red
> circle) by ETKF method based on 111-member combined ensemble

**GEOSS**

On February 16, 2005, 61 countries agreed to a plan that over the next
10 years will revolutionize our understanding of the Earth and how it
works. Agreement on a 10-year implementation plan for a Global Earth
Observation System of Systems (GEOSS) was reached by member countries of
the Group on Earth Observations at the Third Observation Summit held in
Brussels.

GEOSS is envisioned as a large national and international cooperative
effort to bring together existing and new hardware and software, making
it all compatible in order to supply data and information at no cost.
The U.S. and other developed nations have a unique role in developing
and maintaining the system, collecting data, enhancing data
distribution, and providing models to help all of the world\'s nations.
EPA has a strong commitment to the GEOSS initiative. THORPEX has a
strong connection to GEOSS. One of the major goals of THORPEX is to
advance our knowledge of global-to-regional influences on the
initiation, evolution, and predictability of high-impact weather events
and to design the strategy for interactive forecasting and targeted
observations, thus contributing to the process of evolving the WMO
Global Observing System (GOS) which is recognized as a core component of
the future Global Earth Observation System of Systems (GEOSS).

**Review of past THORPEX field projects**

(*a*) *Fronts and Atlantic Storm-Track Experiment (FASTEX)*

The first significant field test of objective targeting observing was
performed during January and February 1997 as part of FASTEX (Joly *et
al.* 1999). The primary targeting goal in FASTEX was to improve
short-range forecasts of cyclones making landfall, and of frontal waves
over Ireland and Britain. Approximately 400 targeted dropsondes were
deployed over the North Atlantic in 20 forecast cases during FASTEX,
using the NOAA Gulfstream-IV jet and a Learjet.

Objective targeting methods developed for use in FASTEX included: total
energy SVs (TESVs, Palmer *et al.* 1998; Bergot 1999; Buizza and Montani
1999), adjoint sensitivity gradients (Langland and Rohaly 1996; Bergot
1999), ensemble transform (Bishop and Toth 1999), and an inverse tangent
linear technique (Pu and Kalnay 1999). In addition, some target areas in
FASTEX were identified subjectively on the basis of ensemble forecast
spread and using forecasts of upper-tropospheric potential-vorticity
features. Snyder (1996) summarizes the early discussion of targeting
concepts developed for use in FASTEX. In this era of targeting, no
specific procedure was used to optimize the sampling patterns used to
deploy the targeted dropsonde profiles, or to consider in any detail the
distribution of regular satellite and *in situ* observation data.

In FASTEX it was demonstrated that targeting guidance could be produced
on a reliable schedule, and that observing system resources (e.g.
reconnaissance aircraft) could be deployed to provide observation data
in designated target areas for assimilation in real time. The impact on
forecasts of targeted observing in FASTEX is described in papers by
Bergot (1999), Gelaro *et al.* (1999), Langland *et al.* (1999a),
Montani *et al.* (1999), Szunyogh *et al.* (1999), Doerenbecher and
Bergot (2001), Fourrie *et al.* (2002) and others.

Bergot (2001) evaluated the impact of all targeted dropsonde data in 20
FASTEX cases using both four-dimensional-variational data assimilation
(4D-Var) and 3D-Var. Using 4D-Var, there was an average 24 h forecast
error reduction of about 10 percent, measured with an error norm
representing 850mb kinetic energy. A maximum error reduction of 51 per
cent was found in Intensive Observation Period 18. Some situations where
targeted data did not improve forecasts were found to be associated with
inadequate sampling of target areas. This study also notes that the use
of 4D-Var increased the percentage of forecast cases improved by
targeted data, and also increased the average magnitude of the
improvement of forecasts.

(*b*) *North Pacific Experiment (NORPEX)*

A field program called the North Pacific Experiment (NORPEX, Langland
*et al.* 1999b) dedicated entirely to targeted observing, was conducted
in the north-east Pacific during January and February 1998. NORPEX was a
collaboration between the Naval Research Laboratory (NRL), the National
Centers for Environmental Prediction (NCEP), the NOAA Aircraft
Operations Center and the 53rd Weather Reconnaissance Squadron of the
United States Air Force Reserve. Approximately 700 dropsondes were
deployed during NORPEX during 40 aircraft targeting missions from Hawaii
and Alaska.

The targeting guidance in NORPEX was based primarily on TESVs provided
by NRL and ensemble transform provided by NCEP, using logistical
procedures similar to those in FASTEX. In a comparison of TESV and ETKF
targeting guidance, Majumdar *et al.* (2002b) found significant overlap
between target areas identified by the two methods in a majority of
NORPEX cases. However, TESV and ETKF target areas during NORPEX differed
substantially in some cases, particularly for smaller-scale target
features.

In a general sense, SV targeting emphasizes the rapid growth of forecast
errors from small initial-condition uncertainties, while ensemble-based
targeting places more importance on the reduction of large, but not
necessarily rapidly growing, initial condition errors. SV target areas
in midlatitude winter forecast cases are often in the middle and lower
troposphere, and in the early stages of baroclinic wave developments. By
contrast, ETKF targets are often in the upper troposphere and in
more-developed stages of baroclinic waves, which tend to have larger
ensemble variance.

Langland *et al.* (1999b) found that the dropsonde data obtained in
NORPEX produced an average 10 per cent reduction in 2-day forecast error
over western North America. Cardinali and Buizza (2003) suggest that the
dropsonde data in NORPEX were not closely collocated with maxima of the
European Centre for Medium-Range Weather Forecasts (ECMWF) 'analysis
SVs', and that this could explain why the targeted dropsonde data did
not have larger impact on forecast error in the ECMWF forecast system.

In addition to targeted dropsonde data, a set of high-resolution wind
observations derived from geostationary satellite imagery (Velden *et
al.* 1997) were provided over the entire North Pacific during NORPEX.
Thesewind data were used by Gelaro *et al.* (2000) to demonstrate two
key results for predictability and targeting: that analysis corrections
in the middle and lower troposphere produced most of the impact on 2-day
forecasts in NORPEX; and that a significant fraction of error growth in
forecasts is explained by a small number of SVs with the largest growth
rates.

(*c*) *Winter Storm Reconnaissance Program (WSRP)*

Starting in 2001, NOAA has maintained a 2-month annual dropsonde
targeting program in the north-east Pacific called the Winter Storm
Reconnaissance Program (WSRP). The primary goal of the WSRP is 'to
reduce uncertainty in 24 to 96 h forecasts for synoptic-scale weather
events associated with potentially large societal impact over the
continental United States and Alaska'. The introduction of WSRP adaptive
observations into an operational framework at the National Weather
Service is described by Toth *et al.* (2002).

During 7 years of the WSRP (2001--2007), approximately 400 targeted
dropsondes per year have been deployed from aircraft based in Hawaii and
Alaska. In a typical year about 35 targeting missions are tasked. Flight
tracks for dropsonde deployment in WSRP are selected from a set of 54
predesigned patterns; the targeting guidance used to make these
selections is provided by the ETKF (Bishop *et al.* 2001; Majumdar *et
al.* 2001; Majumdar *et al.* 2002a). In Szunyogh *et al.* (2000, 2002)
and Toth *et al.* (2002) it is reported that about 70 per cent of WSRP
forecasts are improved by the dropsonde data, including beneficial
impacts on predicted surface pressure, tropospheric wind, and
precipitation. The greatest forecast improvements are generally produced
in cases which have the largest forecast errors (Szunyogh *et al.*
2002). The average short-range forecast improvement of about 10 to 20
percent (Toth *et al.* 2002) represents approximately a 12 h gain in
forecast skill from the addition of WSRP dropsondes.

Szunyogh *et al.* (2002) note that the forecast impact of targeted
dropsonde data propagates eastward via the downstream development
mechanism through the Pacific storm track at an average speed of 30◦
longitude per day, which is faster than the average speed of
synoptic-scale troughs, ridges, and surface frontal features. In many
cases the largest forecast improvement is found along the leading edge
of the signal produced by the dropsondes as it propagates downstream.

(*d*) *North Atlantic THORPEX Regional Campaign (NA-TReC)*

A targeting field program called the North Atlantic THORPEX Regional
Campaign (NA-TReC) was conducted in the North Atlantic from October to
December 2003 (Mansfield *et al.* 2005). Forecast cases of interest
included European wind storms, heavy Mediterranean rainfall, and winter
storms affecting the east coast of North America. A primary focus in
NA-TReC was a test of new objective targeting methods: SVs with norms
based on estimates of analysis error (Gelaro *et al.* 2002; Leutbecher
*et al.* 2002); adjoint models with moist physical processes (Coutinho
*et al.* 2004); observation sensitivity (Langland and Baker 2004a); and
updated versions of an ensemble transform Kalman filter (Majumdar *et
al.* 2002a). Leutbecher *et al.* (2004) reports that the SV and ETKF
targeting guidance differed substantially in about 50 per cent of the
NA-TReC cases, in contrast to the more general agreement between
targeting methods found in NORPEX by Majumdar *et al.* (2002b).

Targeted observations in NA-TReC included dropsondes from four
reconnaissance aircraft (primarily in a region of the western Atlantic
basin), buoy and ship data, selected upper-air profiles from radiosonde
stations in North America and Europe, and observations from selected
trans-Atlantic commercial aircraft. Most of these data were available in
real time and used in operational forecast models. The experiment is
considered a success, in that it was shown possible to adaptively
control a complex set of observational resources to provide measurements
in identified target areas. The impact of NA-TReC targeted data has been
evaluated in the operational forecast systems of ECMWF, Meteo France,
the UK Met Office (UKMO), the Japan Meteorological Agency (JMA), NCEP
and NRL-FNMOC, and is found to provide somewhat smaller forecast
improvement than from observation data obtained in FASTEX, NORPEX, and
WSRP. The impact of targeted data is somewhat different in each forecast
system, because of variations in the types of regular observations that
are assimilated, and differences in forecast models and data
assimilation procedures.

The data impact study by Fourrie *et al.* (2006) at Meteo France reports
that the addition of targeted data improved forecasts of 1000 hPa height
by at least 10 per cent in nine out of 22 NA-TReC cases over a European
verification domain. The same study shows improvements of a few per cent
in the RMS errors of 500 and 850 hPa height forecasts to 96 h. Larger
improvements are noted in some locations, such as the lower
stratosphere, due to the inclusion of targeted radiosonde data. The
study by Iriguchi (2005) at JMA shows improvements of a few per cent in
terms of 500mb anomaly correlation in forecasts to 72 h, and slightly
larger beneficial impacts in longer forecasts to 216 h. Several studies
have shown that targeted observations generally have larger impact on
forecasts when the data are cycled in the assimilation procedure, so
that targeted data at previous times can influence the background for
the assimilation of current targeted data.

It can be noted that considerably fewer targeted dropsondes were
deployed in NATReC in comparison to previous targeting field programs,
which may partially account for the lesser impact reported in this field
program. In FASTEX, for example, the NOAA Gulfstream-IV made four
round-trip Atlantic crossings between Ireland and Canada, but only one
such mission was performed in NA-TReC. In addition, the average level of
forecast uncertainty was not especially large during NA-TReC (Mansfield
*et al.* 2005), which made forecast improvement from the addition of
targeted observations more difficult.

Langland (2005) reports that the data from dropsondes in NA-TReC have
about three times more impact *per observation* than data from
radiosondes, which indicates that targeted dropsondes were deployed in
areas of enhanced sensitivity. However, the total impact of NA-TreC
dropsondes on a measure of short-range forecast error is relatively
small, because the amount of dropsonde data is much less than that
provided by radiosondes and other *in situ* and satellite observing
systems. The same study also reports a large beneficial impact from
observation data provided by commercial aircraft in the NA-TReC domain,
including some aircraft data that were provided on request for
targeting.

These results from the NA-TReC suggest that the potential impact of
targeted dropsonde data in the North Atlantic has reduced since the time
of FASTEX, probably because of substantial increases in the amounts of
regular observations from aircraft and satellites during the 6-year
interval between these field programs. Requirements for targeted
observing are continually changing due to upgrades of the regular *in
situ* and satellite observation network, and because of improvements in
data assimilation techniques and forecast models.

During the Atlantic THORPEX Regional Campaign (A-TReC) in autumn 2003,
the airborne Doppler Lidar (DLR) was used to observe wind in predicted
sensitive regions. It was shown that Lidar observations have a
significant impact on the analyses as well as on forecasts due to their
high accuracy and spatial resolution (Martin Weissmann and Carla
Cardinali 2007). These measurements reduced the errors in the 1-4 day
forecasts of geopotential height, wind, and humidity over Europe
throughout the troposphere. On average, Doppler Lidar measurements
reduced the 2-4 day forecast error of geopotential height over Europe by
3%. This is a promising result, considering that observations were
gathered over only 28.5 flight hours. Dropsondes released in the same
area where the Doppler Lidar was operating show good agreement in terms
of measured winds, but had a smaller analysis impact and less of a
reduction in forecast error.

Both the Lidar and dropsonde data reduced the forecast error, with the
Lidar data having more of an impact than the dropsonde data. This was
the first time Lidar observations were assimilated into a global model.
Airborne Lidar had the lowest observation error of all the operational
wind observations, due to higher representativity and lower instrument
error. Data impact on the analysis was about 40% higher for lidar than
dropsondes and total information content was about three times higher.
Lidar observations over the North Atlantic reduced forecast error by
2% - 6%, with a mean of 3%, while dropsondes had a mean reduction of 1%.
DWL data exhibited a clear positive impact on forecast skill for 2-4 day
lead times.

(*e*) *Tropical cyclone targeting*

Objective targeting methods have not been extensively applied in the
context of hurricane and tropical cyclone forecasting, although
dropsondes have for many years been deployed to measure the general
environment of North Atlantic hurricanes. Since 1998sample areas with
large ensemble forecast variance, which has improved short-range
forecasts of some tropical cyclone tracks by as much as 25 per cent, as
reported by Aberson (2003). This study also notes that sampling the
target extremum (maximum ensemble variance in that study) *and*
surrounding sensitive regions is more effective than sampling *only* the
extremum. The importance of correcting certain aspects of the analysis
of the large-scale environment for tropical cyclone prediction has been
noted in earlier studies (e.g. Tuleya and Kurihara 1981) prior to the
development of currently used objective targeting methods.

Other studies reporting beneficial impact of dropsondes on tropical
cyclone forecasts include those by Burpee *et al.* (1996), Tuleya and
Lord (1997) and Aberson and Franklin (1999). In some cases, the
observation target area for a tropical cyclone forecast may be
associated with a midlatitude weather feature far upstream that
interacts with the cyclone at a later time. For example, in a 48 h
forecast for Hurricane Katrina, the ETKF and SV target guidance both
indicate that the forecast during landfall is sensitive to initial
conditions in a region of the central United States, as well as an area
surrounding Katrina at the initial time of the forecast.

The tropical targeting field program called Dropwindsonde Observations
for Typhoon Surveillance near the Taiwan Region (DOTSTAR) is described
by Wu *et al.* (2005). On 1 September 2003 the first DOTSTAR mission was
successfully completed around Typhoon Dujuan. Seven DOTSTAR typhoon
missions were conducted during 2004 and the program is expected to
continue on an annual basis. A new targeting method called the
Adjoint-Derived Sensitivity Steering Vector (Wu *et al.* 2006) will be
tested in DOTSTAR. As noted by Majumdar *et al.* (2006) 'the utility of
objective strategies to improve tropical cyclone forecasts remains
unexplored'. Given the large societal impact of the landfall stage of
Atlantic hurricanes and Pacific typhoons, there is significant interest
in determining whether new approaches to targeted observing can improve
the forecast skill of tropical cyclones' tracks and intensities.

**REFERENCES**

THORPEX International science plan
(http://www.wmo.ch/thorpex/plans.html)

THORPEX International Research implementation plan
(http://www.wmo.ch/thorpex/plans.html)

NA -- THORPEX science plan (http://www.ucar.edu/na-thorpex/)

US -- Thorpex science plan

GEOSS (Global Earth Observation System of Systems)
(http://www.epa.gov/geoss/index.html)

Aberson, S. D. 2003: Targeted observations to improve operational
tropical cyclone track forecast guidance. *Mon. Weather Rev.*, **131,**
1613--1628

Aberson, S. D. and Franklin, J. L. 1999: Impact on hurricane track and
intensity forecasts of GPS dropwindsonde observations from the
first-season flights of the NOAA Gulfstream-IV jet aircraft. *Bull. Am.
Meteorol. Soc*., **80,** 421--427

Andersson, et al. 2005: Assimilation and modeling of the atmospheric
hydrological cycle in the ECMWF forecasting system. *Bull. Am.Meteorol.
Soc.*, **86,** 387--402

Baker, N. L. and Daley, R. 2000: Observation and background adjoint
sensitivity in the adaptive

observation-targeting problem. *Q. J. R. Meteorol. Soc*., **126,**
1431--1454

Asuma, Y. et al., 2000: Wintertime precipitation behavior in the western
Canadian Arctic region, *J. Geophys. Res*., **105**, 14,927-14939

Bergot, T. 1999: Adaptive observations during FASTEX: A systematic
survey of upstream flights. *Q. J. R. Meteorol. Soc*., **125,**
3271--3298

Bergot, T. 2001: Influence of the assimilation scheme on the efficiency
of adaptive observations. *Q. J. R. Meteorol. Soc*., **127,** 635--660

Bergot, T. and Doerenbecher, A. 2002: A study of the optimization of
targeted observations using adjoint-based methods. *Q. J. R. Meteorol.
Soc*., **128,** 1689--1712

Bishop, C. H. and Toth, Z. 1999: Ensemble transformation and adaptive
observations. *J. Atmos.*

*Sci*., **56,** 1748--1765

Bishop, C. H., Etherton, B. J. and Majumdar, S. J.2001: Adaptive
sampling with the ensemble transform Kalman filter. I:Theoretical
aspects. *Mon. Weather Rev*., **129,** 420--436

Buizza, R. and Montani, A. 1999: Targeting observations using singular
vectors. *J. Atmos. Sci*., **56,**2965--2985 ISSUES IN TARGETED
OBSERVING 3423

Burpee, R. W., Franklin, J. L.,Lord, S. J., Tuleya, R. E. and Aberson,
S. D.1996: The impact of Omega dropwindsondes on operational hurricane
track forecast models. *Bull. Am. Meteorol. Soc*.,**77,** 925--933

Cardinali, C. and Buizza, R. 2003: Forecast skill of targeted
observations: A singular-vector-based diagnostic. *J. Atmos. Sci*.,
**60,** 1927--1940

Cardinali, C., Pezzulli, S. and Andersson, E.2004: Influence-matrix
diagnostic of a data assimilation system. *Q. J. R.* *Meteorol. Soc*.,
**130,** 2767--2786

Coutinho, M. M., Hoskins, B. J. and Buizza, R. 2004 The influence of
physical processes on extratropical singular vectors. *J. Atmos. Sci.*,
**61,** 195--209

Doerenbecher, A. and Bergot, T. 2001: Sensitivity to observations
applied to FASTEX cases. *Nonlinear Processes Geophys*., **8,** 467--481

Doerenbecher, A., Bergot, T. and Bouttier, F. 2000: 'Sensitivity to
observations and targeted observations'. In Proceedings of the fourth
symposium on integrated observing systems, 9--14 January 2000, Long
Beach, California. American Meteorological Society, Boston, USA

Fourrie, N. and Rabier, F. 2004: Cloud characteristics and channel
selection for IASI radiances

in meteorologically sensitive areas. *Q. J. R. Meteorol. Soc*., **130,**
1839--1856

Fourrie, N., Doerenbecher, A., Bergot, T. and Joly, A. 2002: Adjoint
sensitivity of the forecast to TOVS observations. *Q. J. R.* *Meteorol.
Soc*., **128,** 2759--2777

Fourrie, N., Marchal, D., Rabier, F., Chapnik, B. and Desroziers, G.
2006: Impact study of the 2003 North Atlantic THORPEX regional campaign.
*Q. J. R. Meteorol. Soc*., **132,** 275--295

Gelaro, R., Buizza, R., Palmer, T. N. and Klinker, E. 1998: Sensitivity
analysis of forecast error and the construction of optimal perturbations
using singular vectors. *J. Atmos. Sci.*, **55,** 1012--1037

Gelaro, R., Langland, R. H., Rohaly, G. D. and Rosmond, T. E. 1999: An
assessment of the singular vector approach to targeted observing using
the FASTEX dataset. *Q. J. R. Meteorol. Soc.*, **125,** 3299--3327

Gelaro, R., Reynolds, C. A., Langland, R. H. and Rohaly, G. D. 2000: A
predictability study using geostationary satellite wind observations
during NORPEX. *Mon. Weather Rev*., **128,** 3789--3807

Gelaro, R., Rosmond, T. and Daley, R. 2002: Singular vector calculations
with an analysis error variance metric. *Mon. Weather Rev*., **130,**
1166--1186

Hello, G., Lalaurette, F. and Thepaut, J.-N. 2000: Combined use of
sensitivity information and observations to improve meteorological
forecasts: A feasibility study applied to the 'Christmas Storm' case.
*Q. J. R. Meteorol. Soc.*, **126,** 621--647

Hoskins, B. J. and Coutinho, M. M. 2005: Moist singular vectors and the
predictability of some high impact European cyclones. *Q. J. R.
Meteorol. Soc.*, **131,** 581--601

Iriguchi, T. 2005: 'Impact of NA-TreC extra observations on JMA global
forecasts'. In Proceedings of the THORPEX science symposium, 6--10
December 2004, Montreal, Canada. World Meteorological Organization,
Geneva, Switzerland

Joly, A., et al. 1999: Overview of the field phase of the Fronts and
Atlantic storm-track

Experiment (FASTEX). *Q. J. R. Meteorol. Soc*., **125,** 3131--3163

Klinker, E., Rabier, F. and Gelaro, R.1998: Estimation of key analysis
errors using the adjoint technique. *Q. J. R. Meteorol. Soc.*, **124,**
1909--1933

Langland, R. 2005: Observation impact during the North Atlantic
TReC-2003. *Mon.Weather Rev.*, **133,** 2297--2309

Langland, R. H. and Baker, N. L. 2004a: 'A technical description of the
NAVDAS adjoint system'.NRL/MR/7530-04-8746. Available from the Naval
Research Laboratory, Monterey, CA 93943-5502, USA

Langland, R. H. and Baker, N. L. 2004b: Estimation of observation impact
using the NRL atmospheric variational data assimilation adjoint system.
*Tellus*, **56A,** 189--201

Langland, R. H. and Rohaly, G. D. 1996: 'Adjoint-based targeting of
observations for FASTEX cyclones'.Pp. 369--371 in Proceedings of the
seventh conference on mesoscale processes, 9--13 September 1996,
Reading, UK. American Meteorological Society, Boston, USA

Langland, R. H., Gelaro, R., Rohaly, G. D. and Shapiro, M. A. 1999a:
Targeted observations in FASTEX: Adjoint-based targeting procedures and
data impact experiments in IOP-17 and IOP-18.

*Q. J. R. Meteorol. Soc.*, **125,** 3241--3270

Langland, R. H. et al. 1999b: The North Pacific Experiment (NORPEX-98):
Targeted observations for improved North American weather forecasts.
*Bull.* *Am. Meteorol. Soc*., **80,** 1363--1384

Langland, R. H., Shapiro, M. A. and Gelaro, R. 2002: Initial condition
sensitivity and error growth in forecasts of the 25 January 2000 East
coast snowstorm. *Mon. Weather Rev*., **130,** 957--974

Langland, R.H. 2005: Issues in targeted observing, *Q. J. R. Meteorol.
Soc.* (2005), **131**, 3409--3425

Leutbecher, M. 2003: A reduced rank estimate of forecast error variance
changes due to intermittent modifications of the observing network. *J.
Atmos. Sci.*, **60,** 729--742

Leutbecher, M., Barkmeijer, J.,Palmer, T. N. and Thorpe, A. J. 2002:
Potential improvement of forecasts of two severe storms using targeted
observations. *Q. J. R. Meteorol. Soc*., **128,** 1641-- 1670

Leutbecher, M., Doerenbecher, A., Grazzini, F. and Cardinali, C. 2004:
'Planning of adaptive observations during the Atlantic THORPEX regional
campaign 2003'. 16--25 in ECMWF Newsletter No. 102. Available from the
European Centre for Medium-Range Weather Forecasts, Reading, UK

McNally, A. P. 2002: A note on the occurrence of cloud in
meteorologically sensitive areas and the implications for advanced
infrared sensors. *Q. J. R. Meteorol. Soc.*, **128,** 2551--2556

Majumdar, S. J., Bishop, C. H., Etherton, B. J., Szunyogh, I. and Toth,
Z. 2001: Can an ensemble transform Kalman filter predict the reduction
in forecast-error variance produced by targeted observations? *Q. J. R.
Meteorol. Soc.*, **127,** 2803--2820

Majumdar, S. J., Bishop, C. H., Etherton, B. J. and Toth, Z. 2002a:
Adaptive sampling with the ensemble transform Kalman filter. II: Field
program implementation. *Mon. Weather Rev.*, **130,**

1356--1369

Majumdar, S. J., Bishop, C. H., Buizza, R. and Gelaro, R. 2002b: A
comparison of ensemble-transform Kalman-filter targeting guidance with
ECMWF and NRL total-energy singular

vector guidance. *Q. J. R. Meteorol. Soc.*, **128,** 2527--2549

Majumdar, S. J., et al. 2006 A comparison of adaptive observing guidance
for Atlantic tropical

cyclones. *Mon. Weather Rev*., in press

Mansfield, D., Richardson, D. and Truscott, B. 2005: An overview of the
Atlantic THORPEX Regional Campaign, A-TRec. In Proceedings of the
THORPEX Science Symposium, 6--10 December 2004, Montreal, Canada. World
Meteorological Organization, Geneva, Switzerland.

Montani, A., Thorpe, A. J., Buizza, R. and Unden, P. 1999: Forecast
skill of the ECMWF model using targeted observations during FASTEX. *Q.
J. R. Meteorol. Soc.*, **125,** 3219--3240

Morss, R. E., K. A. Emanuel, and C. Snyder, 2001: [Idealized adaptive
observation strategies for improving numerical weather
prediction](http://www.isse.ucar.edu/rebeccam/adaptiveobs_jas2001_abs.html)[.]{.underline}
*Journal of the Atmospheric Sciences*, **58**, 210-232.

Morss, R. E. and Emanuel, K. A 2002: Influence of added observations on
analysis and forecast errors: Results from idealized systems. *Q. J. R.
Meteorol. Soc.*, **128,** 285--321

Palmer, T. N., Gelaro, R., Barkmeijer, J. and Buizza, R. 1998: Singular
vectors, metrics, and adaptive observations. *J. Atmos.* *Sci*., **55,**
633--653

Pu, Z.-X. and Kalnay, E. 1999: Targeting observations with the
quasi-inverse linear and adjoint NCEP global models: Performance during
FASTEX. *Q. J. R. Meteorol. Soc.*, **125,** 3329--3337

Rabier, F., Klinker, E., Courtier, P. and Hollingsworth, A. 1996
Sensitivity of forecast errors to initial conditions. *Q. J. R.*
*Meteorol. Soc.*, **122,** 121--150

Rabier, F., Fourri´e, N., Chafai, D. and Prunet, P. 2002: Channel
selection methods for infrared atmospheric sounding interferometer
radiances. *Q. J. R. Meteorol. Soc.*, **128,** 1011--1028

Reynolds, C. A. and Rosmond, T. E. 2003: Nonlinear growth of
singular-vector-based perturbations, *Q. J. R. Meteorol. Soc*., **129,**
3059--3078

Snyder, C. 1996: Summary of an informal workshop on adaptive
observations and FASTEX. *Bull. Am. Meteorol. Soc*., **77,** 953--961
ISSUES IN TARGETED OBSERVING 3425

Szunyogh, I., et al. 1999: Ensemble-based targeting experiments during
FASTEX: The effect of dropsonde data from the Lear Jet. *Q. J. R.
Meteorol.* *Soc.*, **125,** 3189--3217

Szunyogh, I., Toth, Z., Morss, R. E., Majumdar, S. J., Etherton, B. J.
and Bishop, C. H. 2000: The effect of targeted dropsonde observations
during the 1999 Winter Storm Reconnaissance Program. *Mon. Weather
Rev.*, **128,** 3520--3537

Szunyogh, I., Toth, Z., Zimin, A. V., Majumdar, S. J. and Persson, A.
2002: Propagation of the effect of targeted observations: The 2000
Winter Storm Reconnaissance Program. *Mon. Weather Rev.*, **130,**
1144--1165

Toth, Z., et al. 2002: 'Adaptive observations at NCEP: Past, present,
and future'. In Proceedings of the symposium on observations, data
assimilation, and probabilistic prediction, 13--17 January

2002, Orlando, Fla. American Meteorological Society, Boston, USA

Tuleya, R. E. and Kurihara, Y. 1981: A numerical study on the effects of
environmental flow on tropical storm genesis. *Mon. Weather Rev.*,
**109,** 2487--2506

Tuleya, R. E. and Lord, S. J. 1997: The impact of dropwindsonde data on
GFDL hurricane model forecasts using global analyses. *Weather and
Forecasting*, **12,** 307--323

Velden, C. S., et al.1997: Upper-tropospheric winds derived from
geostationary satellite water vapor observations. *Bull. Am. Meteorol.
Soc.*, **78,** 173--195

Velden, C.S., et al. 2005: Recent innovations in deriving tropospheric
winds from meteorological

satellites. *Bull. Am. Meteorol. Soc.,* **86,** 205--223

Wu, C.-C., et al 2005: Dropwindsonde observations for typhoon
surveillance near the Taiwan Region (DOTSTAR). *Bull. Am. Meteorol.
Soc*., **86,** 787--790

Wu, C.-C., Lin, P.-H., Chen, J.-H. and Chou, K.-H. 2006: Targeted
observations of tropical cyclones based on the adjointderived
sensitivity steering vector. *Geophys. Res. Lett.*, in Press

Martin Weissmann and Carla Cardinali 2007: The impact of airborne
Doppler lidar observations on ECMWF forecasts, *Q. J. R. Meteorol. Soc.,
submitted.*