An Observing System Experiment for Tropical Cyclone Targeting TechniquesUsing the Global Forecast System

SIM D. ABERSON

NOAA/AOML/Hurricane Research Division, Miami, Florida

SHARANYA J. MAJUMDAR

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

CAROLYN A. REYNOLDS

Naval Research Laboratory, Monterey, California

BRIAN J. ETHERTON

Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

(Manuscript received 22 February 2010, in final form 30 July 2010)

ABSTRACT

In 1997, the National Oceanic and Atmospheric Administration’s National Hurricane Center and the

Hurricane Research Division began operational synoptic surveillance missions with the Gulfstream IV-SP jet

aircraft to improve the numerical guidance for hurricanes that threaten the continental United States, Puerto

Rico, the U.S. Virgin Islands, and Hawaii. The dropwindsonde observations from these missions were pro-

cessed and formatted aboard the aircraft and sent to the National Centers for Environmental Prediction and

the Global Telecommunications System to be ingested into the Global Forecasting System, which serves as

initial and boundary conditions for regional numerical models that also forecast tropical cyclone track and

intensity. As a result of limited aircraft resources, optimal observing strategies for these missions are in-

vestigated. An Observing System Experiment in which different configurations of the dropwindsonde data

based on three targeting techniques (ensemble variance, ensemble transform Kalman filter, and total energy

singular vectors) are assimilated into the model system was conducted. All three techniques show some

promise in obtaining maximal forecast improvements while limiting flight time and expendables. The data

taken within and around the regions specified by the total energy singular vectors provide the largest forecast

improvements, though the sample size is too small to make any operational recommendations. Case studies

show that the impact of dropwindsonde data obtained either outside of fully sampled, or within nonfully

sampled target regions is generally, though not always, small; this suggests that the techniques are able to

discern in which regions extra observations will impact the particular forecast.

1. Introduction

From 1982 to 1996, the National Oceanic and Atmo-

spheric Administration (NOAA) P-3 aircraft released

dropwindsondes in data-sparse tropical cyclone (TC) en-

vironments to improve numerical track forecasts. These

experiments yielded significant improvements in the

primary numerical guidance for National Hurricane

Center (NHC) track forecasts (Burpee et al. 1996) and

led to the procurement of a Gulfstream-IV-SP jet aircraft

(G-IV) in 1996 for use in operational daily or, since 2002,

twice-daily ‘‘synoptic surveillance’’ missions. The G-IV is

deployed during the critical watch and warning period

24–60 h prior to anticipated landfall in the continental

United States, Puerto Rico, the U.S. Virgin Islands, or

Hawaii. Between 25 and 30 dropwindsondes are de-

ployed during each mission, sometimes supplemented

Corresponding author address: Sim D. Aberson, NOAA/AOML/

Hurricane Research Division, 4301 Rickenbacker Cswy., Miami,

FL 33149.

E-mail: sim.aberson@noaa.gov

MARCH 2011 A B E R S O N E T A L . 895

DOI: 10.1175/2010MWR3397.1

� 2011 American Meteorological Society

with additional dropwindsondes from missions conducted

with the U.S. Air Force C-130 or NOAA P-3 aircraft,

thus providing broader environmental sampling than the

G-IV accomplishes alone. Similar missions have been

regularly conducted in the western Pacific since 2003 as

part of Dropwindsonde Observations for Typhoon Sur-

veillance near the Taiwan Region (DOTSTAR), with

additional participation of other international agencies

during 2008 as part of The Observing System Research

and Predictability Experiment (THORPEX) Pacific Area

Regional Campaign (T-PARC; Wu et al. 2005, 2007;

Weissmann et al. 2011; Harnisch and Weissmann 2010).

During the first 10 yr of surveillance (1997–2006), 175

missions were conducted (Aberson 2010), leading to a

10%–15% improvement in 12–60-h track forecasts from

the NOAA Global Forecast System (GFS). Since a com-

plete mission costs about $40,000, far less than the esti-

mated $1 million average needed to evacuate just one

mile of coastline for landfall (Aberson et al. 2006), such

missions, regularly conducted, are very cost effective.

Questions remain as to whether the dropwindsondes

are deployed optimally and as to whether larger error

reductions are possible. If the TC environment is sam-

pled symmetrically in all directions from the storm cen-

ter, for example, the various sensitive areas in which

additional observations would most improve the fore-

cast may not be sampled. One strategy to find these

sensitive ‘‘targets’’ was based on the hypothesis that

regularly spaced observations collected in areas of high

deep-layer-mean (DLM; 850–200 hPa) wind variance

from the National Centers for Environmental Predic-

tion (NCEP) Global Ensemble Forecasting System valid

at the observing time would reduce growing analysis

errors in the steering flow, thereby reducing track fore-

casts errors (Aberson 2003). The advantage of targeting

observations based on this ensemble variance (EV) ver-

sus uniform sampling has been demonstrated in simple

models (Lorenz and Emanuel 1998; Morss et al. 2001).

Aberson (2003) showed that the assimilation of the data

subset from fully sampled1 targets produced a statisti-

cally significant GFS track forecast error reduction of up

to 25%, thereby yielding larger improvements than were

possible by assimilating all available surveillance data.

The deficiencies of symmetric sampling were attributed

to suboptimal data assimilation schemes and their im-

pact near targets that were bisected or otherwise not fully

sampled.

Advanced techniques, such as the ensemble transform

Kalman filter (ETKF; Bishop et al. 2001) and singular

vectors (SVs; Palmer et al. 1998), have been evaluated

in idealized and operational models. These techniques

have now been extended for use in tropical cyclone pre-

diction. Their respective guidance has been compared

in the Atlantic and western Pacific basins (Majumdar

et al. 2006; Reynolds et al. 2007; Wu et al. 2009). Unlike

the EV, they focus on specific forecasts and seek optimal

sampling locations in which the assimilation of new data

would modify that particular forecast. Yamaguchi et al.

(2009) showed, in one case with one targeting technique,

that the assimilation of a small subset of dropwindsonde

data in a well-chosen target area may provide significant

track forecast improvements.

The applicability of these three techniques for im-

proving TC forecasts is presented in the framework of

an Observing System Experiment. Either all the drop-

windsonde data or only those data within the fully sam-

pled targets are included in the model runs described

below. Given that dropwindsonde data obtained in trop-

ical cyclone environments lead to an average 10%–15%

reduction2 in track forecast error during the first 60 h

(Aberson 2010), the resulting track forecasts from these

model runs are compared to those obtained from model

runs in which all the dropwindsonde data have been as-

similated. The focus of this study is to examine whether,

for each targeting technique, using the subsample of

dropwindsonde data obtained only in fully sampled tar-

gets provides the same average results as using all the

dropwindsonde data (suggesting that the particular tech-

nique is able to accurately define sensitive regions). The

model and the three targeting techniques are briefly

described in the next section. General results from the

three targeting techniques and individual case studies

are provided in section 3, followed by a discussion and

the conclusions.

2. Overview and procedures

a. Background

The G-IV released 25–30 dropwindsondes at 150–

200-km intervals during each mission. The dropwind-

sondes sample the atmosphere below the aircraft flight

level (near 150 hPa). In those cases in which additional

aircraft supplemented the G-IV, 20–25 dropwindsondes

1 Aberson (2003) considers a target to be ‘‘fully sampled’’ if

dropwindsondes, with rawinsondes released from land stations,

sample the extremum and the edges of the feature in an approxi-

mately regularly spaced grid at about the resolution of the North

American Rawinsonde Network (about 250 km).

2 Each of the targeting subsamples shows similar improvements,

though they are slightly larger (18%–29%) during the first 24 h of

the forecasts. This could be because many of these cases include

sampling by multiple aircraft; Aberson (2002) showed that the

number of aircraft sampling the tropical cyclone environment was

related to the amount of forecast improvement in each case.

896 M O N T H L Y W E A T H E R R E V I E W VOLUME 139

were released at the same horizontal resolution from

around 400 (P-3) or 300 hPa (C-130). The G-IV did not

penetrate the TC core during surveillance missions; when

other aircraft participated in the missions, at least one

usually gathered data near the center. Hurricane Research

Division (HRD) meteorologists aboard the NOAA air-

craft validated the wind and thermodynamic data and

generated standard (TEMPDROP) messages for trans-

mission to NCEP and the Global Telecommunications

System in preparation for assimilation into numerical

models until 2006. This task on the G-IV has been car-

ried out by NOAA/Aircraft Operations Center meteo-

rologists in most cases since then.

b. Model

The version of the GFS operational at the time of

each mission was used to assess the impact of the drop-

windsonde data. Several upgrades to the quality control

algorithm, vortex initialization, data assimilation, and

global spectral model were made during the 3 yr covered

in this study. The quality control algorithm involves op-

timal interpolation and hierarchical decision making to

evaluate observations before they are input to the anal-

ysis (Woollen 1991). Vortex relocation (Liu et al. 2000)

places TCs in the first guess (background) field to their

operationally analyzed positions (as in Kurihara et al.

1995) ensuring that they are initialized in the proper lo-

cations. The data assimilation scheme was the spectral

statistical interpolation (Parrish and Derber 1992): the

background field (the previous 6-h forecast) is combined

with observations with a three-dimensional variational

multivariate formalism. The global spectral model hori-

zontal resolution was spectral triangular 254 (T254), and

the vertical coordinate extended from the surface to

about 2.7 hPa with 64 (L64) unequally spaced sigma

levels on a Lorenz grid (Caplan et al. 1997; Surgi et al.

1998) in 2004; the resolution was increased to T382L64

in 2005. Minor changes to model physics were made

during the 3 yr encompassed in the study.

c. Choice of data to assimilate

Multiple GFS runs were made for each mission. The

first run has all dropwindsonde observations assimilated

(hereafter operational). To demonstrate the effective-

ness of each of the three targeting techniques (described

below) to define sensitive regions, additional GFS runs

have been performed when less than two-thirds of the

dropwindsonde observations fully sample a particular

target, as in Aberson (2003). All dropwindsonde data

outside target regions (for any technique), or within tar-

gets that are not fully sampled, are removed from the

data assimilation system. The numerical values provided

by each technique are ignored, as in Aberson (2003) since

any maximum is expected to be an area of relative sen-

sitivity. The choice of which dropwindsondes to include

in each test run is therefore subjective, but consistent

from case to case and between techniques. The key is to

sample targets so as to limit potentially spurious incre-

ment spread outside the observed region into locations

where they are likely to grow. In this way, the growth

of errors introduced into the model by the suboptimal

data assimilation scheme is minimized. This agrees with

Bergot et al. (1999), who state that with then-current data

assimilation techniques, the entire target must be sam-

pled, not only the extremum. All other observations from

the NCEP ‘‘final’’ archive were ingested into the assimi-

lation system for both sets of runs.

d. Targeting techniques

1) ENSEMBLE VARIANCE

The EV from the NCEP Global Ensemble Forecast

System (GEFS) provides a general approach to TC tar-

geting (Aberson 2003). Random perturbations are evolved

and rescaled using a low-resolution (T126) version of the

GFS (Toth and Kalnay 1993; Lorenz and Emanuel 1998).

Since a forecast is the first guess for a subsequent GFS

cycle, locations in which perturbations are large are

those in which initial condition errors have recently

grown. The TC tracks depend on the environmental

DLM flow, so the DLM wind variance at the targeting

(observing) time is used. Aberson (2003) showed that

GFS DLM increments grow where the EV is large, and

decay where it is small, and that the growth is inde-

pendent of the increment size.

Numerous upgrades to the GEFS have been imple-

mented since the Aberson (2003) study. Most involve

perturbation rescaling, masking, and sizing. On 16 August

2005, the breeding cycle was reduced from 24 to 6 h, and

a TC vortex relocation technique was instituted; the lat-

ter change increased the GEFS effectiveness by requir-

ing that the TC existed near the diagnosed location. On

30 May 2006, the ensemble size was increased from 10

to 14 every 6 h, and the ensemble transform technique

replaced breeding (Wei et al. 2008). However, the es-

sence of the original breeding technique remains within

the GEFS.

2) ETKF

The ETKF uses ensemble-based data assimilation the-

ory (Bishop et al. 2001) to predict the reduction in fore-

cast error variance within a verification area for feasible

deployments of targeted observations. To mimic opera-

tions, the NCEP GEFS output available when flight track

planning must be completed that initialized 48 h prior

to the observing time ta is used. The ETKF computation

MARCH 2011 A B E R S O N E T A L . 897

is a two-stage process. First, the analysis error covariance

matrix at ta pertaining to the routine observational net-

work of rawinsonde and satellite-based temperature ob-

servations Pr(ta) is found by solving the Kalman filter

error statistics equations. Next, the analysis error covar-

iance matrix for the observational network augmented

by the qth hypothetical targeted observation Pq(ta) is

computed. The associated ‘‘signal covariance’’ matrix, or

the reduction in forecast error covariance valid at the

verification time (here, ty 5 ta 1 48 h), is deduced. The

trace of this matrix localized within a verification region,

defined as a 500-km radius circle centered at the Official

NHC forecast location at ty, is referred to as the ‘‘signal

variance.’’ The ETKF guidance represents this as a func-

tion of the central location of adjacent targeted obser-

vations. The area with the highest signal variance within

the verification region (the target) is deemed optimal for

sampling. Since TC tracks depend on the environmental

DLM flow, that flow is used as the metric in this study.

Further details are provided in Majumdar et al. (2006).

3) NAVY OPERATIONAL GLOBAL ATMOSPHERIC

PREDICTION SYSTEM SVS

Localized SVs are a type of targeted analysis error

covariance (AEC) optimals that have been used to iden-

tify observation target areas for both midlatitude storms

and TCs (Palmer et al. 1998; Buizza and Montani 1999;

Majumdar et al. 2006; Reynolds et al. 2007). The optimals

of the tangent forward propagator L(ta; ty) and its adjoint

LT sample the directions of maximum growth during the

optimization time interval between ta and ty, evolving

into the leading eigenvectors of the forecast error co-

variance matrix P(ty). For an analysis error covariance

metric kvk2a 5 hv(ta); [Pa(ta)]�1v(ta)i and verification-time

metric kvk2y 5 hv(t

y); [Py(t

y)]�1v(t

y)i, where h�;�i indicates

an inner product, the analysis-time optimals vi(ta), where

i indicates an index number, are computed via the eigen-

value problem:

(GL)T[Py(ty)]�1(GL)v

i(t

a) 5 s2

i [Pa(ta)]�1v

i(t

a), (1)

where G localizes perturbations within the verification re-

gion (Buizza 1994). The square roots of the eigenvalues

si are the singular values, and the eigenvectors vi(ta) are

the right SVs, of GL with respect to the metrics (Noble

and Daniel 1977).

Targeted AEC optimals in which Pa and Py are identi-

cal, diagonal, and elements equal to the fixed total energy

weights, such that (Pa)21 5 (Py)21 5 E and kvkE2 5

hv; Evi, are known as total energy SVs (TESVs). The

sensitivity pattern s is a composite of the vertically in-

tegrated total energy of the leading SV weighted by the

singular values:

s 5 �3

j51

s2j

s21

ej(x, t), (2)

where ej(x, t) is the vertically integrated total energy

of the jth SV at location x, and the first three SVs are

used. The TESVs are calculated using the Navy Opera-

tional Global Atmospheric Prediction System (NOGAPS;

Hogan and Rosmond 1991; Peng et al. 2004) tangent and

adjoint models. The SVs are calculated at a reduced

T79L30 resolution, although the linearization is based

on the trajectory from the full physics, high-resolution

(T239L30) operational NOGAPS forecast. The models

include surface drag and horizontal and vertical diffusion,

but not moist processes (Rosmond 1997). The optimization

time and lead time are both 48 h. A local projection op-

erator is used to define the verification region, here a large

fixed area covering the Gulf of Mexico eastward (i.e., the

box in Fig. 4c). Examination of the final-time SVs confirms

that the targets are relevant to the forecast of interest.

Although the SVs used here are from the NOGAPS

forecasting system, the similarity noted in previous stud-

ies of SVs calculated using different forecasting systems

TABLE 1. Track forecast errors (km) for homogeneous samples of synoptic surveillance missions for each of the three targeting

techniques. Times at which one version of the GFS provides statistically significantly smaller errors than another at the 85% level are

shown in boldface; those at the 95% level are shown in boldface italics.

Model 12 h 24 h 36 h 48 h 60 h 72 h 84 h 96 h 108 h 120 h

Operational 43.2 67.5 104.8 141.7 200.1 280.7 328.7 413.4 489.5 607.6

EV 46.6 76.4 108.6 152.9 215.7 284.8 314.4 398.4 446.0 594.1

No. of cases 27 27 27 27 27 27 25 23 21 19

Operational 47.6 68.7 100.2 132.6 193.3 258.4 294.5 375.2 427.5 500.7

ETKF 51.9 79.9 112.7 137.4 176.1 233.1 267.9 349.0 375.2 468.5

No. of cases 35 35 35 35 34 33 31 29 26 24

Operational 30.3 51.9 86.2 146.4 179.2 235.8 295.7 305.2 292.6 495.7

SV 32.2 36.1 71.1 128.6 154.1 165.8 265.5 310.8 340.5 540.0

No. of cases 6 6 6 6 5 5 4 3 3 2

898 M O N T H L Y W E A T H E R R E V I E W VOLUME 139

supports their relevance for NCEP forecasts. For exam-

ple, Majumdar et al. (2006) find that for 78 guidance

products for 2-day forecasts for the 2004 Atlantic hurri-

cane season, the European Centre for Medium-Range

Weather Forecasts (ECMWF) and NOGAPS SV guid-

ance identifies similar synoptic-scale target regions in

90% of the cases. Wu et al. (2009) likewise find strong

similarities between SVs calculated using the ECMWF,

NOGAPS, and the Japan Meteorological Agency (JMA)

systems for typhoons in the western Pacific for the 2006

season (e.g., the products on the large domain were sim-

ilar to each other in about 80% of the 84 cases consid-

ered). Although a comparison with NCEP SVs is not

possible, the strong similarity between the ECMWF,

NOGAPS, and JMA SV guidance suggests that the

sensitive regions identified by the NOGAPS system will

be relevant to other large-scale (i.e., global) forecast

systems.

3. Results

Table 1 shows homogeneous comparisons of GFS

track forecast errors for runs in which all dropwindsonde

observations (operational), or subsets of observations

that fully sample each of the three sets of targets, are

assimilated. The two sets of forecast errors are statisti-

cally significantly different at the 85% level at 12, 24, 48,

60, 84, and 108 h in the EV cases, though only at the last

two times does the subset improve the forecasts. Minor

improvements to the forecasts during the critical watch

and warning period (24–60 h before landfall when pre-

parations must be completed) are produced when all

data are assimilated.3 The two sets of forecast errors are

statistically significantly different at the 85% level at

12, 24, 84, and 108 h in the ETKF sample; the first two

forecast times are degradations, and the last two are

improvements, so these results are slightly better than

those for the EV cases. Fewer cases are available for the

SV targets than for the others because the relatively

large SV target size prohibits full sampling of the entire

target regions with the usual one-aircraft missions. The

two sets of forecast errors are statistically significantly

different at the 85% level from 24 to 72 h, and the dif-

ferences are improvements at all these forecast times.

As a result of the small sample size, any conclusive

statement that SVs are a superior targeting technique

is premature. The important result is that, for the ETKF

and SV techniques, the data outside the target regions

have no, or very little, positive impact on the ultimate

track forecasts; for the EV, the data outside the target

regions has some positive impact early in the forecast. A

discussion of two examples in which all three targeting

FIG. 1. GFS forecast tracks for Hurricane Emily initialized at

0000 UTC 18 Jul 2005 for the operational run and for those runs

in which only a subset of targeted observations were assimilated.

The best track is shown with the tropical storm symbols. Positions

are plotted every 12 h through 120 h. Emily dissipated 84 h into the

forecast. The operational GFS did not forecast dissipation. The

ETKF and EV runs forecast dissipation at 90 h; the SV run forecasts

dissipation at 96 h.

FIG. 2. Initial DLM wind analysis of the operational GFS at

0000 UTC 18 Jul 2005. The hurricane symbol marks the best-track

location of Emily. The dotted line represents the subtropical ridge

axis.

3 Contrary to these results, Aberson (2003) reported that the

assimilation of those dropwindsonde data within and around target

regions as defined by the EV provided statistically better forecasts

than those using all the data. Since 1999, the sampling strategy

employed during missions has largely followed recommendations

reported in Aberson (2003). The cases reported here are generally

those in which additional aircraft increased the data coverage,

improving forecasts (Aberson 2002), or in which targets could

not be fully sampled with one aircraft, leading to degradations

(Aberson 2003). These results should therefore not be construed as

meaning that the EV technique is no longer valid, nor that it is less

capable than the other techniques to define sensitive regions.

MARCH 2011 A B E R S O N E T A L . 899

techniques are available, and of another illuminating

example, follows.

a. Hurricane Emily: 0000 UTC 18 July 2005

Figure 1 shows the operational GFS forecast track, as

well as those from runs in which data subsets, depending

upon the targeting techniques, were assimilated. The

removal of the nontargeted data had a small positive

impact during the first 2 days of the forecast, though by

the approximate landfall time, the forecast spread from

the various model runs increased to about 250 km. This

suggests that all three targeting techniques are effective

in discerning the regions in which additional data will

have a large impact on the subsequent forecast.

Emily was located in the northwestern Caribbean Sea

moving westward under the influence of a strong sub-

tropical high centered over the southeastern United States

at the targeting time (Fig. 2). Some of the guidance sug-

gested a northward turn before a possible Texas landfall

since the ridge extended only to the western edge of the

Gulf of Mexico. NHC tasked a two-aircraft mission, with

the G-IV sampling the region around Emily and the

southeastern Gulf of Mexico, and a C-130 sampling the

remainder of the Gulf of Mexico (Fig. 3). The two-plane

mission allowed for adequate sampling of the target re-

gions from all three techniques (Fig. 4).

Runs using only those data within fully sampled tar-

gets provided better forecasts than the operational run.

The assimilation of data outside target regions that were

not fully sampled caused a southward forecast bias (Fig. 1).

The EV target was nearly symmetric around Emily

(Fig. 4a), so that most of the data in the Gulf of Mexico,

except those closest to the Yucatan peninsula, those from

two dropwindsondes released near 258N, and from three

in the Caribbean Sea, were removed from the EV as-

similation. The ETKF and SV targets were both located

on the eastern side of Emily in the region of anticyclonic

curvature of the DLM flow (Figs. 4b,c, and 2). Data from

three dropwindsondes along the northern and western

FIG. 3. Flight path and dropwindsonde release locations (green)

for the G-IV (Tampa to Tampa) and C-130 (Keesler Air Force

Base to Keesler Air Force Base) for the surveillance mission at

nominal time 0000 UTC 18 Aug 2005. The dots represent rawin-

sonde locations.

FIG. 4. (a) EV, (b) ETKF, and (c) SV targets for the Hurricane

Emily case. In each, the locations of dropwindsondes are repre-

sented by green (assimilated) and black (nonassimilated) circles.

The best-track location of Hurricane Emily is represented by the

red hurricane symbol. The boxes in (b) and (c) represent the ver-

ification region for the respective targeting technique. In (b) the

yellow hurricane symbol represents the forecast location of Emily

at the targeting time from the forecast initiated at the initial time

48 h earlier, and the red hurricane symbol represents the forecast

location at the verification time 48 h after the targeting time.

900 M O N T H L Y W E A T H E R R E V I E W VOLUME 139

coasts of the Yucatan peninsula and from the eastern-

most two dropwindsondes were removed from the SV

assimilation, in addition to the Gulf of Mexico drop-

windsonde data removed in the EV run, resulting in the

northernmost forecast. The ETKF Gulf of Mexico data

were the same as for the EV run, but the data from the

two easternmost and one southernmost dropwindsondes

were removed. The EV and ETKF forecast tracks were

closest to each other because their targets were the most

similar to each other, though they slowly diverged through

the forecast. The removal of the dropwindsonde data in

the Gulf of Mexico appears to cause subtle differences

in the initial analyses leading to the northward motion

in the targeted runs. The EV dropwindsonde analysis

had a region of high relative vertical vorticity in the

upper troposphere over the north-central Gulf of Mexico

(Fig. 5a); the operational analysis with all the dropwind-

sonde data had a weaker feature than the EV analysis

(Fig. 5b). The circulation moved slowly northwestward

in the targeted runs (EV being representative; Fig. 5c), and

its resultant inverted trough was located over Louisiana

by 36 h into the forecast; the circulation moved slowly

northward and weakened in the operational run (Fig. 5d).

The inverted trough in the targeted runs allowed for the

enhanced northward component of motion resulting in

improved forecasts versus the operational run.

The nontarget dropwindsonde data in the Gulf of

Mexico were obtained from the C-130 flying near 350 hPa,

below the normal G-IV flight altitude. The relatively

strong upper-level feature that was stronger in the op-

erational run than in the targeted runs resulted from the

data assimilation system spreading the good dropwind-

sonde information from below 350 hPa upward into a

region with no data. The vertical spread of information

into this data-sparse region is similar to the horizontal

spread that can lead to forecast failures (Aberson 2002,

FIG. 5. (a),(b) Initial 200-hPa and (c),(d) 36-h DLM vertical vorticity and streamline analyses from the (a),(c) EV and (b),(d) operational

GFS initialized at 0000 UTC 18 Jul 2005. The circle marks the vorticity maximum discussed in the text.

MARCH 2011 A B E R S O N E T A L . 901

2003, 2008). Though sampling the entire troposphere in

this region may not have led to a forecast track improve-

ment, this case illustrates that the data assimilation can

spread information vertically into data-sparse regions.

Obtaining soundings through the depth of the troposphere

for improving hurricane track forecasting may be impor-

tant since the DLM flow usually steers tropical cyclones.

This case confirms that the removal of data outside fully

sampled target regions can lead to the larger forecast

track improvements than the inclusion of all the data

(Aberson 2003).

b. Hurricane Ophelia: 0000 UTC 11 September 2005

Figure 6 shows the model forecast tracks for Hurri-

cane Ophelia initialized at 0000 UTC 11 September

2005. Ophelia was meandering slowly off the south-

eastern United States coast between two anticyclones:

one over the central Mississippi River Valley and the

other in the central Atlantic Ocean (Fig. 7). Other fea-

tures complicating the forecast were a trough extending

southwestward from Ophelia across central Florida, and

three shortwave troughs moving through the longwave

trough off the East Coast (one moving southward over

New England, one over New Brunswick, and the other

at the base of the trough south of Newfoundland). Two

saddle points were between Ophelia and nearby troughs:

one northeast and one southwest of the storm. Finally,

a wind speed maximum and associated relative vorticity

maximum are seen rotating around the western edge of

the subtropical ridge southeast of Ophelia.

A one-plane mission was tasked for the G-IV because

of the proximity and potential threat to the coastline.

The targeted data in all three cases caused the model to

accelerate Ophelia northward more than in the opera-

tional run, degrading the forecasts. The main difference

is that the targeted data projected a landfall in New

England, whereas the run with all the dropwindsonde

data did not forecast landfall there.

The EV found three targets near Ophelia (Fig. 8a): 1)

Ophelia itself, 2) the saddle point between Ophelia and

the midlatitude trough to the northeast, and 3) the wind

speed maximum along the western edge of the mid-

Atlantic subtropical high; the third feature was not fully

sampled. The ETKF identified three targets (Fig. 8b),

the first and third of the EV targets and the base of the

trough northeast of Ophelia; the last of these was not

sampled as it was outside aircraft range. Three SV target

FIG. 6. GFS forecast tracks for Hurricane Ophelia initialized at

0000 UTC 11 Sep 2005 for the operational run and for those runs

in which only a subset of targeted observations were assimilated.

The best track is shown with tropical storm symbols. Positions are

plotted every 12 h through 120 h.

FIG. 7. Initial DLM streamline, isotach (speeds given in gray-

shade bar), and vorticity (shaded) analysis of the operational GFS

at 0000 UTC 11 Sep 2005. Ophelia is located just south of Cape

Hatteras. Troughs are marked with dotted lines.

902 M O N T H L Y W E A T H E R R E V I E W VOLUME 139

regions were seen (Fig. 8c): 1) the eastern half of Ophelia,

2) the trough extending southwestward from Ophelia to

central Florida, and 3) the midlatitude shortwave trough

over New England; the first two (with concurrent rawin-

sonde releases in Florida and the Bahamas) were fully

sampled, but the lack of data off the New England coast

prevented full sampling of the third feature. In the dis-

cussion of this case, the impacts in the SV run are rep-

resentative of the others, so only the SV impact results are

shown.

A large amount of the dropwindsonde data within

target regions were removed in the SV and EV runs

because the respective targets did not meet the criterion

of being fully sampled. Though the data outside the tar-

get regions had only a small impact on the track forecasts

in the Emily case, the data removed in the current case

were within partially sampled target regions, and their

removal led to large short-range track forecast im-

provements. The cause of these improvements was that

the runs in which the targeted subsets of data were as-

similated initialized the midlatitude vorticity maximum/

confluence region northeast of Ophelia (identified as an

ETKF and EV target) and the southerly flow between

Ophelia and the subtropical ridge to its east (identified

FIG. 8. (a) EV, (b) ETKF, and (c) SV targets for the Hurricane

Ophelia case. In each, the locations of dropwindsondes are repre-

sented by green (assimilated) and black (nonassimilated) circles.

The best-track location of Hurricane Ophelia is represented by

the red hurricane symbol. The circle and box in (b) and (c), re-

spectively, represent the verification region for the respective tar-

geting technique.

FIG. 9. DLM wind (a) initial and (b) 12-h differences between the

operational and SV targeted GFS runs initialized at 0000 UTC

11 Sep 2005. Contour intervals are shown every 1 m s21; streamlines

are shown only in regions with differences .1 m s21.

MARCH 2011 A B E R S O N E T A L . 903

as an ETKF and EV target) to be stronger than in the

operational run initial condition. The vorticity maximum/

confluence northeast of Ophelia (the cyclonic difference

streamlines in Fig. 9a) was advected westward, and the

enhanced southerly flow to the east of Ophelia moved

northward. The two features combined and acted to ad-

vect Ophelia northward more rapidly in the targeted runs

than in the operational one during the early part of the

forecast. These increments are expected to grow be-

cause each of these regions was identified as targets;

the actual growth might be erroneous since the targets

were not fully sampled during the mission. This case

shows that the targeting techniques can be effective in

discerning which regions will have error growth that

affect the TC track forecast; it also shows the importance

of fully sampling the target regions, because removing

data that only partially sampled targets improved the

track forecasts.

c. Hurricane Ivan: 0000 UTC 11 September 2004

Relatively small track forecast differences between

the targeted and symmetrically sampled data are shown

in the above two cases. The Hurricane Ivan case ini-

tialized at 0000 UTC 11 September 2004 shows that

large differences can occur. Figure 10 shows the ETKF

and operational forecast tracks for this case. Neither SV

nor EV cases were run because few or no data outside

their targets were gathered. Ivan was moving toward the

west-northwest just south of Jamaica. A ridge extended

east-northeastward from central Florida, and a large

mid- and upper-level cyclonic circulation was centered

about 2500 km northeast of Ivan (Fig. 11). The opera-

tional forecast track called for Ivan to recurve and make

landfall near Tampa Bay.

A one-plane mission was tasked for the G-IV because

of the potential threat to Florida and the Gulf Coast.

The ETKF target was centered on Hurricane Ivan (Fig. 12)

with weak secondary maxima located outside the sam-

pled region south of Guatemala and on the western side

of the cyclonic circulation northeast of Ivan. The data

obtained between 678 and 708W were removed from the

ETKF run; the impact of these data was spread north-

eastward by the data assimilation into the cyclonic cir-

culation northeast of Ivan, where rapid error growth is

expected by the ETKF. This led to a westward extension

of the low, resulting in a northward component of mo-

tion to Ivan (Fig. 13). This case again shows the effec-

tiveness of the ETKF in finding where errors affecting the

particular forecast are expected to be large and grow and

suggests that data should be obtained mainly close to

targets since the then-current data assimilation techniques

may erroneously spread the data impact into other regions

degrading the forecast (Aberson 2003).

FIG. 10. GFS forecast tracks for Hurricane Ivan initialized at

0000 UTC 11 Sep 2004 for the operational run and for the run in

which only a subset of ETKF targeted observations were assimi-

lated. The best track is shown with tropical storm symbols. Posi-

tions are plotted every 12 h through 120 h.

FIG. 11. Initial DLM wind analysis of the operational GFS at

0000 UTC 11 Sep 2004.

904 M O N T H L Y W E A T H E R R E V I E W VOLUME 139

4. Discussion

Using oversampled datasets obtained during synoptic

surveillance during 2004–2006, three targeting techniques

to find regions in which extra dropwindsonde observa-

tions should improve TC track forecasts are investigated

with a series of Observing System Experiments. Since all

the dropwindsonde data obtained in these cases led to

substantial average forecast track improvements, in the

current study, either all the dropwindsonde data or only

those data within the fully sampled targets are included.

This study was performed to examine whether using the

particular subsample of data obtained only in fully

sampled target regions provides the same average results

as using all the dropwindsonde data for each of the three

targeting techniques. An affirmative result would suggest

that the particular technique tested is able to accurately

define sensitive regions. This information could be used

to optimize the sampling strategy for tropical cyclone

targeting. Because all three techniques show that the re-

moval of at least 33% of the dropwindsonde data, those

data outside fully sampled target regions, rarely leads to

forecast degradations and may lead to improvements,

they all show some promise in obtaining maximal forecast

improvements while limiting flight time and dropwind-

sonde expendables. The SVs provided the best forecasts,

though the sample size is too small to make any opera-

tional recommendations.4

Individual cases are examined to deduce the impacts

of the removal of at least one-third of the data, those

residing outside fully sampled target regions, from the

model data assimilation cycle. The increments within

(without) target regions amplify (decay) in all cases, sug-

gesting that each of the techniques studied has promise

in identifying regions in which the biggest forecast im-

pact will be obtained. Negative impacts of data removal

are generally due to imperfect data assimilation systems

(Aberson 2008), and they can be mitigated with improved

sampling strategies. Spread of the data vertically and

horizontally into relatively data-sparse regions, in some

cases into secondary target regions, caused the data

FIG. 12. ETKF targets for the Hurricane Ivan case. The locations

of dropwindsondes are represented by green (assimilated) and

black (nonassimilated) circles. The best-track location of Hurri-

cane Ivan is represented by the red hurricane symbol. The circle

represents the verification region.

FIG. 13. DLM wind (top) initial and (bottom) 6-h differences be-

tween the operational and ETKF GFS runs initialized at 0000 UTC

11 Sep 2004. Contour intervals are shown every 1 m s21; stream-

lines are shown only in regions with differences .1 m s21.

4 Yamaguchi et al. (2009) found that data from as few as three

dropwindsondes can have a huge impact on track forecasts. Using

their criterion could increase the sample sizes for all the techniques

but is beyond the scope of this current work.

MARCH 2011 A B E R S O N E T A L . 905

outside target regions to degrade the forecasts. The re-

sults continue to suggest that data should be obtained

mainly within and immediately around target regions so

as to adequately sample them and to limit spread of data,

both horizontally and vertically, into regions with rela-

tively sparse data, and which the targeting techniques

suggest will have amplifying errors.

These results can be extended to other, similar observing

systems, such as rawinsondes and satellite wind and radi-

ance data. During threatening situations, NHC requests

that regular observing sites in the southeastern United

States release off-time (0600 and 1800 UTC) rawinsondes.

Since rawinsonde data are obtained through the tropo-

sphere in regularly spaced grids like dropwindsonde data,

the techniques reported here may be used to decide

whether to request off-time releases, to maximize the data

impact if such a request is made, and to include additional

regions outside the southeastern United States.

Acknowledgments. The first author thanks NCEP/

EMC for help in running the models and in providing the

computer resources to make this study possible. Bob

Kohler and Bill Barry provided computer support at

HRD. Altug Aksoy, Sundararaman Gopalakrishnan,

Mike Jankulak, Tomislava Vukicevic, and an anonymous

reviewer improved previous versions of this manuscript.

The authors thank the NOAA/Aircraft Operations

Center (AOC) flight crews, AOC G-IV project manager

Jack Parrish, and HRD personnel who participated in the

flights, in addition to Air Force C-130 crews that also

provided surveillance data over the years. CAR grate-

fully acknowledges the support from the sponsor, ONR

PE-0601153N, and computer resources provided by the

Department of Defense High-performance Computing

program. The Joint Hurricane Testbed funded SJM and

BJE for some of this work.

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