observation system experiments for typhoon jangmi (200815...

Asia-Pacific J. Atmos. Sci, 46(3), 305-316, 2010

DOI:10.1007/s13143-010-1007-y

Observation System Experiments for Typhoon Jangmi (200815) Observed During

T-PARC

Byoung-Joo Jung1, Hyun Mee Kim

1, Yeon-Hee Kim

2, Eun-Hee Jeon

2 and Ki-Hoon Kim

2

1Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University, Seoul, Korea2Forecast Research Laboratory, National Institute of Meteorological research/KMA, Seoul, Korea

(Manuscript received 4 Jan 2010; revised 18 May 2010; accepted 25 May 2010)© The Korean Meteorological Society and Springer 2010

Abstract: In this study, the impact of various types of observations

on the track forecast of Tropical Cyclone (TC) Jangmi (200815) is

examined by using the Weather Research and Forecasting (WRF)

model and the corresponding three-dimensional variational (3DVAR)

data assimilation system. TC Jangmi is a recurving typhoon that is

observed as part of the THORPEX Pacific Asian Regional Campaign

(T-PARC). Conventional observations from the Korea Meteor-

ological Administration (KMA) and targeted dropsonde observations

from the Dropwindsonde Observations for Typhoon Surveillance

near the Taiwan Region (DOTSTAR) were used for a series of obser-

vation system experiments (OSEs). We found that the assimilation of

observations in oceanic areas is important to analyze environmental

flows (such as the North Pacific high) and to predict the recurvature

of TC Jangmi. The assimilation of targeted dropsonde observations

(DROP) results in a significant impact on the track forecast. Ob-

servations of ocean surface winds (QSCAT) and satellite temperature

soundings (SATEM) also contribute positively to the track forecast,

especially two- to three-day forecasts. The impact of sensitivity

guidance such as real-time singular vectors (SVs) was evaluated in

additional experiments.

Key words: OSE, THORPEX, T-PARC, adaptive observation guidance,

singular vectors, tropical cyclone

1. Introduction

Tropical cyclones (TCs) are characterized by strong vortices

(winds) and severe precipitation. When TCs approach coastal

regions or make landfall, they cause a great deal of socioeco-

nomic damage. However, it is difficult to predict the movement

and development of TCs, because during their lifetimes these

cyclones are mostly located in oceanic regions where obser-

vational networks are sparse.

Nevertheless, there have been steady improvements in the

performance of TC track forecasts achieved primarily by using

bogus techniques and new observational systems. Bogus tech-

niques insert a vortex structure into analysis fields based on the

TC report and the empirical formula (Kurihara et al., 1995;

Kwon et al., 2002; Chou and Wu, 2008). Recently, the bogus

data assimilation (BDA) techniques were developed that use

bogus observations in the framework of variational data assimi-

lation systems (Zou and Xiao, 2000; Pu and Braun, 2001; Wu

et al., 2006; Xiao et al., 2006, 2008). More balanced analysis

fields can be obtained in model space by constraints of vari-

ational data assimilation systems in BDA. The use of new

observational systems also increases the forecast skill of TC

track. Due to the sparse observational networks in ocean areas,

the use of satellite-derived observations can improve forecast

skill of TCs. The atmospheric motion vectors (AMVs; Velden

et al., 2005; Pu et al., 2008) that are retrieved from tracking

clouds and moisture by various satellite channels provide dense

wind observations for whole levels of the atmosphere. Langland

et al. (2009) showed that rapid scan AMV from Geostationary

Operational Environmental Satellites (GOES) improved the track

forecast for hurricane Katrina (200512), especially for the 84-

to 120-hour forecast time. Global Posi-tioning System (GPS)

Radio-Occultation (RO) observations are also applied to various

meteorological applications (e.g., weather, climate, and space

weather; Anthes et al., 2008). Huang et al. (2005) showed that

the assimilation of GPS refractivity sounding improved the

prediction of track and precipitation for Typhoon Nari (200116).

Since the early 1980s, dropsonde observations have been

used to enhance the core and environment prediction of TCs

(Burpee et al., 1996). Atmospheric soundings inside TCs can

be observed by GPS dropsondes that are released directly into

TCs from airplanes. Dropsonde observations from synoptic flow

experiments produced significant improvements (16% - 30%

error reduction for 12-60-hour forecasts) in 15 cases observed

from 1982 to 1995 (Burpee et al., 1996). For the five TC cases

observed during 1997, dropsonde observations improved the

mean track forecast by 32% and the intensity forecast by 20%

within 48 hours before landfall by using the Geophysical Fluid

Dynamics Laboratory (GFDL) hurricane model (Aberson and

Franklin, 1999). Aberson (2002) summarized the impact of

dropsonde observations for 24 missions during 1997 and 1998.

It was found that the improvements seen in two-year samples

are not promising. The additional dropsonde data provided

statistically significant improvements with the GFDL model

only at the 12 hour forecasts. It was demonstrated that accurate

synthetic data and more widespread coverage of dropsonde data

are necessary to improve track forecasts. Wu et al. (2007)

evaluated 10 missions of Dropwindsonde Observations for

Corresponding Author: Hyun Mee Kim, Department of AtmosphericSciences, Yonsei University, Shinchon-dong 134, Seodaemun-ku, Seoul,120-749, KoreaE-mail: [email protected]

306 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES

Typhoon Surveillance near the Taiwan Region (DOTSTAR)

2004 using four operational models and one research model.

They showed that the 72-hour average track error is reduced by

22%, which is consistent with the forecast improvement in

Atlantic tropical cyclones studies. Aberson (2008) found that

problems with the quality control, issues with the complex data

assimilation itself, and inner-core assimilation can lead to the

large forecast degradations in several cases.

Recently, sensitivity guidance (Majumdar et al., 2006; Wu et

al., 2009) has been incorporated in the assimilation of dropsonde

observations. Assimilation of only a subset of data from subject-

ively sampled target regions produced a statistically significant

reduction of track error by 25% (Aberson, 2003). Aberson and

Etherton (2006) found that additional improvement of track

forecasts can be achieved by the use of Ensemble Transform

Kalman Filter (ETKF) for assimilating dropsonde data. Yamaguchi

et al. (2009) also used sensitivity guidance in the assimilation of

dropsonde observations with the Japan Meteorological Agency

(JMA) global model for the track forecast of TC Conson

(200404).

In this study, the impacts of various types of observations on

the track forecast of TC Jangmi (200815) were examined by the

use of the Weather Research and Forecasting (WRF) model and

corresponding three-dimensional variational (3DVAR) data as-

similation system. The impacts of observations in regions indi-

cated by sensitivity guidance are also examined in an additional

experiment.

A short review of THe Observing System Research and Pre-

dictability EXperiment (THORPEX) Pacific Asian Regional

Campaign (T-PARC) and case descriptions for TC Jangmi are

given in section 2. The model configuration and experimental

design are summarized in section 3. The observations and sen-

sitivity guidance used in this study are also presented in section

3. Results of a series of observation system experiments are pres-

ented in section 4, and the summary and discussion are presented

in section 5.

2. T-PARC 2008 and case description

a. T-PARC 2008

THORPEX is an international research program designed to

accelerate improvements in the accuracy of one-day to two-week

high-impact weather forecasts (WMO, 2002). Its objectives

include: i) global-to-regional influences on the evolution and

predictability of weather systems, ii) global observing-system

design and demonstration, iii) targeting and assimilation of

observations, and iv) societal, economic, and environmental

benefits of improved forecasts (Shapiro and Thorpe, 2004).

These objectives are established by international collaborations

of academic institutions, operational forecast centers, and users

of forecast products. The core initiatives include international

field campaigns focused on regional forecast problems facing

Africa, Asia, Europe, North America, and the Southern Hemi-

sphere. During August and September 2008, the T-PARC was

implemented in the Western North Pacific to investigate forma-

tion, structures, targeting, extratropical transition, and downstream

impacts of tropical cyclones. Four aircraft were used and many

new observations (buoy drop, driftsonde, and airborne radar) were

examined for the first time in western Pacific regions. Additional

scientific background that also related to the Tropical Cyclone

Structure (TCS081), can be found in the Elsberry and Harr (2008).

b. TC JANGMI (200815)

Figure 1 shows the best track and minimum sea level pressures

for TC Jangmi from the Regional Specialized Meteorological

Center (RSMC) Tokyo. After its formation on 1200 UTC 24

September 2008, TC Jangmi intensified, moving northwestward

(Fig. 1).

Figure 2a shows the mean sea level pressure and geopotential

height on 500 hPa at 1200 UTC 26 September 2008. TC Jangmi

was moving northwestward along the side of the North Pacific

High (denoted by 5880 gpm contour line and shading). It

reached its lowest central pressure of 905 hPa at 1200 UTC 27

September 2008 (Fig. 2b). It made landfall on Taiwan about

0900 UTC 28 September 2008 (Fig. 2c). After its landfall, its

intensity weakened and it recurved to the northeast (Fig. 2d).

Afterward, TC Jangmi lost its symmetric structure and dis-

appeared at 0000 UTC 1 October 2008 in the southern oceanic

area of Kyushu Island.

3. Model configuration and experimental framework

a. Model configuration

This study uses the Weather Research and Forecasting (WRF)

model version 2.2 (Skamarock et al., 2005) and the WRF 3DVAR

version 2.2 beta (Barker et al., 2003; 2004). The model domain

for this study is 200 × 200 horizontal grids (centered at 33oN in

latitude and 133oE in longitude), with a 30 km horizontal reso-

lution focused on the East Asia region and 31 vertical levels.

The model domain is shown in Fig. 2a. The model's initial and

lateral boundary condition is the National Centers for Environ-

mental Prediction (NCEP) final analysis (FNL; 1o× 1

o global

grid). Physical parameterizations used in the simulation include

New Kain-Fritsch scheme (Kain, 2004) for cumulus parameteri-

zation, WRF Single Momentum 6 class scheme (Hong and

Lim, 2006) for microphysics parameterization, Dudhia scheme

(Dudhia, 1989) for shortwave radiation parameterization, Rapid

Radiative Transfer Model (RRTM) scheme (Mlawer et al., 1997)

for longwave radiation parameterization, Yonsei University

(YSU) scheme (Hong et al., 2006) for planetary boundary layer

parameterization, and Noah Land Surface model (Chen and

Dudhia, 2001) for land surface parameterization.

The Background Error Statistics (BES) provide error infor-

1The TCS08 is one of the cooperating field program of T-PARC.

31 August 2010 Byoung-Joo Jung et al. 307

mation of background fields and work as a transformation

operator to convert model space to analysis space, and vice

versa. The most typical method used to calculate the BES is the

National Meteorological Center (NMC) method (Parrish and

Derber, 1992). The NMC method provides the climatological

estimate of BES by longtime average of forecast differences

verified at the same time:

=

. (1)

Xt, X

b, and εb represent the true state, background state, and

the true background error, respectively. Xf represents the forecast

state at T + 12 or T + 24 hours verified at the same time. In this

study, four-month statistics from July to October 2007 are cal-

culated and used to construct the BES using gen_be utilities in

the WRF 3DVAR system described in Barker et al. (2003, 2004).

b. Observations

As observations for data assimilation, conventional observa-

tions through the Global Telecommunication System (GTS)

from the Korea Meteorological Administration (KMA) were

used. These include SYNOP (surface observation from weather

station), AWS (Automatic weather system), SHIP (surface obser-

vation from ship), BUOY (surface observation from buoy),

TEMP (upper-air observation from radiosonde), AMDAR (air-

craft meteorological data relay), AIREP (aircraft report), PILOT

(wind observations from tracking balloons), SATEM, QSCAT,

and PROFL (wind profiler). The targeted dropsonde observa-

tions were made at 0000 UTC 27 and 0000 UTC 28 September

2008 by Astra aircraft of the DOTSTAR project (Wu et al.,

2005) as parts of T-PARC 2008.

Figure 3 shows the observational distributions at 0000 UTC 27

September 2008. The targeted dropsonde observation (DROP) is

around TC Jangmi (Fig. 3a). The ocean surface wind measured

by scatterometer (QSCAT2) is distributed along the path of a

polar orbit satellite (Fig. 3b). QSCAT in the center of TC Jangmi

is missing on account of strong convective rains. SATEM is a

temperature sounding retrieved from the radiances measured by

polar orbit satellites. SATEM is distributed in both land and

oceanic area and is relatively sparse (Fig. 3c). The distributions

of the other observations are shown in Fig. 3d. Most of the

observations are located in land areas, especially in Korea and

Japan. There are very dense observing networks in those areas.

There are additional observations in the oceanic area, mostly

from aircraft. However, the distribution of these observations is

limited by the predetermined routes of the aircraft, and the

availability of these observations is not good, compared to the

North American region.

c. Experimental design

Three sets of experiments were conducted according to the

distributions and types of observations that were assimilated

into the 3DVAR system. All experiments were initiated at 0600

UTC 26 September 2008 with National Centers for Environ-

mental Prediction (NCEP) Final analysis (FNL) data as an initial

field. WRF 3DVAR data assimilation is applied in cycling mode

with six-hour windows to the 0000 UTC 28 September 2008.

The first set of experiments (OSE set-1) is composed of ALL,

LAND, and SEA (Figs. 4a, b, and c). The experiment called

“ALL” assimilated all available observations. The experiments

called “LAND” and “SEA” only assimilated observations on

the land and oceanic areas, respectively. The relative importance

B Xb

Xt

–( ) Xb

Xt

–( )T

=

εbεbT X

fT 24+( ) X

fT 12+( )–[ ] X

fT 24+( ) X

fT 12+( )–[ ]

T

∼

2The validation of this observation type in the typhoon environment is studied in Chou et al. (2010).

Fig. 1. (a) Best track and (b) minimum sea level pressure of TC Jangmi(200815) from Regional Specialized Meteorological Center (RSMC)Tokyo. The corresponding date (month/day) of best track is denoted in(a), and the times (0000 UTC 27 and 0000 UTC 28 September 2008)of targeted dropsonde observation are denoted by dashed line in (b).


of observations in each area on the typhoon track forecast was

tested in these experiments. The second set of experiments

(OSE set-2) was a data denial experiment. As mentioned in

section 2b, DROP, QSCAT, and SATEM were the main obser-

vational types over the oceanic area. To identify the relative

importance of those oceanic observations, experiments called

“ALL-DROP”, “ALL-QSCAT”, and “ALL-SATEM” were con-

ducted. Each experiment assimilated all available observations

except DROP, QSCAT, and SATEM observations, respectively.

The third set of experiment was configured to identify the

impact of observations located in the regions indicated by a

sensitivity guidance3. Here, observations that distributed in both

land and the regions denoted by the sensitivity guidance were

assimilated, and this experiment was denoted as LAND + SV

(Fig. 4d) similar to the experiment of Buizza et al. (2007). More

detailed information about the sensitivity guidance used is given

3In this study, total energy singular vectors based on MM5 adjoint modeling system (Kim et al., 2008) calculated in real-time are used as thesensitivity guidance.

Fig. 2. Mean sea level pressure (thin lines with 4 hPa interval) and geopotential height (thick lines with 60 gpm interval) on 500 hPa of the analysisat (a) 1200 UTC 26, (b) 1200 UTC 27, and (d) 0000 UTC 29 September 2008. The radar reflectivity (shaded with 5 dBZ interval) from CentralWeather Bureau (CWB) of Taiwan at 0900 UTC 28 September 2008 is shown in (c). The subtropical ridge is denoted by shaded area (over 5880gpm) and the translational velocity of TC Jangmi is denoted by thick arrows.


in section 3d.

For all experiments, three-day forecasts were performed

initiated at 0000 UTC 27, 1200 UTC 27, and 0000 UTC 28. All

forecast typhoon tracks were verified with RSMC Tokyo best

track.

d. Singular vectors (SVs)

As sensitivity guidance in the LAND + SV experiment, real-

time fifth-generation mesoscale model (MM5) singular vectors

(SVs) were used4. The MM5 SV were provided in real time to

the European Centre for Medium-Range Weather Forecasts

(ECMWF) data targeting system (DTS) and the Japan Meteor-

ological Agency (JMA) T-PARC website during the period of

T-PARC field experiment (1 August - 4 October 2008). The SVs

are the fastest growing perturbations given basic state and norms

(Kim and Morgan, 2002). The MM5 SVs are calculated by

MM5 Adjoint Modeling System (Zou et al., 1997) and Lanczos

algorithm. The domain for MM5 SVs is 50 × 50 horizontal grids

(centered at 25°N in latitude and 125°E in longitude), with a

120 km horizontal resolution (Fig. 5f). In the calculation of real-

time MM5 SVs, the dry total energy (TE) norms were used at

Fig. 3. Distributions of observations for (a) targeted dropsonde (DROP), (b) ocean surface wind (QSCAT), (c) satellite temperature sounding(SATEM), and (d) the others at 0000 UTC 27 September 2008. The mean sea level pressure (solid lines with 4 hPa intervals) of the analysis issuperimposed.

4A different model (i.e. MM5) is used for sensitivity guidance because it is not feasible to calculate the SVs in the WRF system. The MM5 SVs areonly used to sort the observations in the LAND + SV experiment.


both initial and final times (Zou et al., 1997; Palmer et al., 1998;

Kim et al., 2008; Kim and Jung, 2009a, b). Because the sen-

sitivity guidance for fixed target regions should be provided in

advance of a targeting time, the two- to four-day forecasts of

NCEP Global Forecast System (GFS) were used as the initial

and boundary conditions for the calculation of MM5 SVs. For

moisture processes, both Grell convective parameterization

(Grell, 1993) and explicit moisture schemes (Dudhia, 1989)

were used for the nonlinear model integrations, but only large-

scale condensation parameterization was used for the tangent

linear and adjoint model integrations.

To identify the sensitive regions objectively, the composite

SV was constructed by singular value–weighted summation of

first to third SVs in energy units as in Kim and Jung (2009a, b).

The composite SV was interpolated to 0.5 degree by 0.5 degree

grid, and then 10% of grids sorted from the largest values were

selected as the “sensitive” regions. The objectively selected sen-

sitivity guidance and the distribution of observations for LAND +

SV experiment are shown in Fig. 5.

4. Results

a. OSE set-1

Figure 6 shows the forecast track and track error of the first

set of experiments, verified with the RSMC best track. For

three-day track forecasts, the SEA experiment showed similar

performance to the ALL experiment, and even better perfor-

mance than the ALL experiment in the forecast initiated at

0000 UTC 28 September 2008. The 12-hour forecast difference

of 500 hPa geopotential height between the ALL and the

LAND experiments initiated at 0000 UTC 27 September 2008

shows a dipole structure near the center of TC Jangmi (Fig. 7a).

Fig. 4. Schematics for the (a) ALL, (b) LAND, (c) SEA, and (d)LAND + SV experiments. The observations in the shaded regions areassimilated for each experiment.

Fig. 5. Distribution of real-time sensitivity guidance (J kg−1, shaded) and observations for LAND + SV experiment at (a) 0000 UTC 27, (b) 0600UTC 27, (c) 1200 UTC 27, (d) 1800 UTC 27, and (e) 0000 UTC 28 September 2008. The model domain for MM5 SV calculation is show as ashaded area in (f).


At this time, the LAND experiment predicts the geopotential

height more southwestward than the ALL experiment. There-

fore, the LAND experiment that did not assimilate the obser-

vations in the oceanic area predicts the typhoon track toward

the southern part of China, and the recurvature of TC Jangmi

occurred closer to China for the LAND experiment compared

Fig. 6. The left panel shows the tracks of three-day forecast for OSE set-1 and LAND + SV experiment initiated at (a) 0000 UTC 27, (c) 1200 UTC27, and (e) 0000 UTC 28 September 2008. The RSMC best track is denoted by black thick line. The right panels show the track error (km) of three-day forecasts initiated at (b) 0000 UTC 27, (d) 1200 UTC 27, and (f) 0000 UTC 28 September 2008, verified with RSMC best track.


to other experiments (Figs. 6a and 7b).

b. OSE set-2

In the results of the first set of experiments (Fig. 6 and section

4a), it was found that the observations in the oceanic area are

more important to the track forecast of TC Jangmi. To in-

vestigate the effects of DROP, QSCAT, and SATEM, which are

the main observation types in the oceanic area, data denial

experiments were conducted. The impact of each observation

type was evaluated by the degree of relative degradations in

track forecasts. In the analysis time, the difference between the

ALL and other data denial experiments was co-located with the

distribution of examined observations (compare Figs. 3a-c and

8a-c). The largest analysis differences were observed at the right

half-circle near the center of TC Jangmi by DROP (Figs. 8a and

8d). SATEM makes smaller but broader analysis differences

than DROP (Fig. 8c). In the forecast initiated at 0000 UTC 27

September 2008, DROP is the most important. QSCAT and

SATEM also have positive impacts to the track forecast. In the

forecast initiated at 1200 UTC 27 September 2008, DROP,

QSCAT, and SATEM have positive impacts on the track forecast

(not shown). While DROP has a positive impact in one- to

three-day forecasts initiated at 0000 UTC 27 (Fig. 9b), the drop-

sonde observations have the biggest impact in one-day forecast

initiated at 1200 UTC 27 (Fig. 9d). In the two- to three-day

forecasts initiated at 1200 UTC 27, QSCAT and SATEM are

more important than DROP (Fig. 9d). This is caused by the fact

that there are no deopsonde observations at 1200 UTC 27

September 2008. Therefore, for one-day forecasts, DROP has

more impact due to the dropsonde observations assimilated

earlier (i.e., 0000 UTC 27 September) and QSCAT and SATEM

have more impact for two- to three-day forecasts due to the

assimilation of the latest information (1200 UTC 27 September).

The positive impact of dropsonde observations on typhoon

track forecasts in four operational models during T-PARC is

also reported in Weissmann et al. (2010). Even though Aberson

(2008) reported that the inner-core dropsonde assimilation may

degrade the forecast due to the representation error and assimi-

lation limitation, the forecast degradation is not occurred in this

study because the dropsonde observations used in this study are

circularly distributed at least 120 km distant from the typhoon

center.

c. LAND + SV experiment

Finally, the results of the LAND + SV experiment are shown

in Fig. 6. In this experiment, the impact of assimilating obser-

vations in the area denoted by the sensitivity guidance was

investigated. The sensitive regions are defined as described in the

section 3d. The impact of observations in the sensitive regions

was evaluated by comparing the performance of the LAND +

SV experiment to that of the LAND experiment. As the result,

the additional use of observations denoted by the sensitivity

guidance has a positive impact on the typhoon track forecast,

compared to the LAND experiment, especially at 0000 UTC 27

and 0000 UTC 28 September 2008. These are the times when

the targeted dropsonde observations were available. At 1200

UTC 27, the impact of observations in the sensitive regions is

small for a one-day forecast, but the impact increased for two-

to three-day forecasts (Fig. 6d), which is related to the fact that

most of additional observations denoted by the sensitivity guid-

Fig. 7. (a) The 12 hour forecast difference (shaded, gpm) of 500 hPa geopotential height between the ALL and the LAND experiments and (b) the48 hour forecast (shaded, gpm) of 500 hPa geopotential height for the LAND experiment initiated at 0000 UTC 27 September 2008. The solid linesdenote the 500 hPa geopotential height (60 gpm interval) for the ALL experiment at the corresponding forecast time.


ance were composed of QSCAT and SATEM at this time.

QSCAT and SATEM at 1200 UTC 27 had more impact for two-

to three-day forecasts in the data denial experiment (Fig. 9d and

section 4b).

5. Summary and discussion

To investigate the impacts of observations on the track

forecast of TC Jangmi (200815), a series of observation system

experiments were conducted with WRF model and corres-

ponding 3DVAR data assimilation system. TC Jangmi was a

recurving typhoon occurred during the T-PARC period that

made landfall on Taiwan. The conventional observations from

KMA GTS system and the targeted dropsonde observations

from DOTSTAR are used.

In the first set of experiments (ALL, LAND, and SEA), the

ALL and SEA experiments show similar performance on the

track forecasts. The LAND experiment was unable to forecast

the recurving feature at the proper time. From this, it was found

that the assimilation of observations in the oceanic area was

important on the track forecast of TC Jangmi. To investigate the

relative importance of observation types in the oceanic area,

data denial experiments were also conducted (i.e., ALL, ALL-

DROP, ALL-QSCAT, and ALL-SATEM). DROP, QSCAT, and

Fig. 8. The analysis difference (shaded, gpm) of 850 hPa geopotential height between the ALL and (a) ALL-DROP, (b) ALL-QSCAT, and (c) ALL-SATEM experiments at 0000 UTC 27 and (d) ALL-DROP at 0000 UTC 28 September 2009. The 850 hPa geopotential height (30 gpm interval) ofthe ALL experiment at the corresponding time is superposed with solid lines, and the thick solid line denotes the 1500 gpm lines.


SATEM had positive impacts in the track forecast of TC

Jangmi. DROP was the most important at 0000 UTC 27 and

0000 UTC 28 September 2008 when the dropsonde observations

were available. However, at 1200 UTC 27 September 2008

QSCAT and SATEM were more important for two- to three-

day forecasts because the impact of dropsonde observations

assimilated 12-hour earlier decreased as the forecast time be-

comes longer. In addition to these two sets of experiments, the

Fig. 9. The left panel shows the tracks of three-day forecast for OSE-2 initiated at (a) 0000 UTC 27, (c) 1200 UTC 27, and (e) 0000 UTC 28September 2008. The RSMC best track is denoted by black thick line. The right panels show the track error (km) of three-day forecasts initiated at(b) 0000 UTC 27, (d) 1200 UTC 27, and (f) 0000 UTC 28 September 2008, verified with RSMC best track.


LAND + SV experiment was performed to investigate the im-

pact of observations in the regions indicated by the sensitivity

guidance. The objectively selected composite total energy SVs

that were provided to ECMWF DTS and JMA during T-PARC

were used as the sensitivity guidance. The assimilation of add-

itional observations located at the area denoted by the sensitivity

guidance reduced the track error in LAND + SV experiment,

and also improved the recurving features of TC Jangmi.

From a series of observation system experiments in this

study, it was found that the assimilation of observations near the

center of TCs (e.g., dropsonde observations) as well as the envir-

onmental large-scale regions is important on the track forecast.

Acknowledgements. The authors wish to thank two anonym-

ous reviewers for their valuable comments, and Professor

Chun-Chieh Wu of National Taiwan University for providing

DOTSTAR observational data used in this study. This study

was supported by the principal project, “Development and appli-

cation of technology for weather forecast (NIMR-2009-B-1)”

of the National Institute of Meteorological Research of Korea

Meteorological Administration, and by the BK21 program.

REFERENCES

Aberson, S. D., 2002: Two years of operational hurricane synoptic surveil-

lance. Wea. Forecasting, 17, 1101-1110.

______, 2003: Targeted observations to improve operational tropical

cyclone track forecast guidance. Mon. Wea. Rev., 131, 1613-1628.

______, 2008: Large forecast degradations due to synoptic surveillance

during the 2004 and 2005 hurricane seasons. Mon. Wea. Rev., 136,

3138-3150.

______, and J. L. Franklin, 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. Amer. Meteor.

Soc., 80, 421-427.

______, and B. J. Etherton, 2006: Targeting and data assimilation studies

during Hurricane Humberto (2001). J. Atmos. Sci., 63, 175-186.

Anthes, R. A., and Coauthors, 2008: The COSMIC/FORMOSAT-3

Mission: Early results. Bull. Amer. Meteor. Soc., 89, 313-333.

Burpee, R. W., S. D. Aberson, J. L. Franklin, S. J. Lord, and R. E. Tuleya,

1996: The impact of omega dropwindsondes on operational hurricane

track forecast models. Bull. Amer. Meteor. Soc., 77, 925-933.

Barker, D., W. Huang, T.-R. Guo, and A. Bourgeois, 2003: A three-

dimensional variational (3DVAR) data assimilation system for use with

MM5. NCAR Tech. Note. NCAR/TN-453 + STR, 68 pp.

______, ______, ______, ______, and Q. N. Xiao, 2004: A three-

dimensional variational data assimilation system for MM5: Imple-

mentation and initial results. Mon. Wea. Rev., 132, 897-914.

Buizza, R., C. Cardinali, G. Kelly, and J.-N. Thepaut, 2007: The value of

observations. II: the value of observations located in singular-vector-

based target areas. Quart. J. Roy. Meteor. Soc., 133, 1817-1832.

Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-

hydrology model with the Penn State-NCAR MM5 modeling system.

Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569-

585.

Chou, K.-H., and C.-C. Wu, 2008: Development of the typhoon

initialization in a mesoscale model - Combination of the bogused vortex

with the dropwindsonde data in DOTSTAR. Mon. Wea. Rev., 136, 865-

879.

______, ______, P.-H. Lin, and S. Majumdar, 2010: Validation of

QuikSCAT wind vectors by dropwindsonde data from Dropwindsonde

Observations for Typhoon Surveillance Near the Taiwan Region

(DOTSTAR). J. Geophys. Res., 115, D02109, doi:10.1029/2009-

JD012131.

Dudhia, J., 1989: Numerical study of convection observed during the

winter monsoon experiment using a mesoscale two-dimensional model.

J. Atmos. Sci., 46, 3077-3107.

Elsberry, R. L., and P. A. Harr, 2008: Tropical cyclone structure (TCS08)

field experiment science basis, observational platforms, and strategy.

Asia-Pacific J. Atmos. Sci., 44, 209-231.

Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus

parameterizations. Mon. Wea. Rev., 121, 764-787.

Hong, S.-Y., and J.-O. Lim, 2006: The WRF single-moment 6-class

microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129-151.

______, Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package

with an explicit treatment of entrainment processes. Mon. Wea. Rev.,

134, 2318-2341.

Huang, C.-Y., Y.-H. Kuo, S.-H. Chen, and F. Vandenberghe, 2005: Im-

provements in typhoon forecast with assimilated GPS occultation

refractivity. Wea. Forecasting., 20, 931-953.

Kain, J. S., 2004: The Kain–Fritsch convective parameterization scheme:

An update. J. Appl. Meteorol., 43, 170-181.

Kim, H. M., and M. Morgan, 2002: Dependence of singular vector

structure and evolution on the choice of norm. J. Atmos. Sci., 59, 3099-

3116.

______, and B.-J. Jung, 2009a: Singular vector structure and evolution of a

recurving tropical cyclone. Mon. Wea. Rev., 137, 505-524.

______, and ______, 2009b: Influence of moist physics and norms on

singular vectors for a tropical cyclone. Mon. Wea. Rev., 137, 525-543.

______, ______, Y.-H. Kim, and H.-S. Lee, 2008: Adaptive observation

quidance applied to Typhoon Rusa: Implications for THORPEX-PARC

2008. Asia-Pacific J. Atmos. Sci., 44, 297-312.

Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995:

Improvements in the GFDL hurricane prediction system. Mon. Wea.

Rev., 121, 2030-2045.

Kwon, H. J., S.-H. Won, M.-H. Ahn, A.-S. Suh, and H.-S. Chung, 2002:

GFDL-Type Typhoon Initialization in MM5. Mon. Wea. Rev., 130,

2966-2974.

Langland, R. H., C. Velden, P. M. Pauley, and H. Berger, 2009: Impact of

Satellite-Derived Rapid-Scan Wind Observations on Numerical Model

Forecasts of Hurricane Katrina. Mon. Wea. Rev., 137, 1615-1622.

Majumdar, S. J., S. D. Aberson, C. H. Bishop, R. Buizza, M. S. Peng, and

C. A. Reynolds, 2006: A comparison of adaptive observing guidance

for Atlantic tropical cyclones. Mon. Wea. Rev., 134, 2354-2372.

Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough,

1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a

validated correlated-k model for the longwave. J. Geophys. Res., 102,

16663-16682.

Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular

vectors, metrics, and adaptive observations. J. Atmos. Sci., 55, 633-653.

Parrish, D. F., and J. C. Derber, 1992: The National Meteorological

Center’s Spectral Statistical Interpolation analysis system. Mon. Wea.

Rev., 120, 1747-1763.

Pu, Z.-X., and S. A. Braun, 2001: Evaluation of bogus vortex techniques

with four-dimensional variational data assimilation. Mon. Wea. Rev.,

129, 2023-2039.

______, X. Li, C. S. Velden, S. D. Aberson, and W. T. Lui, 2008: The

impact of aircraft dropsonde and satellite wind data on numerical

simulations of two landfalling tropical storms during the Tropical Cloud

Systems and Processes Experiment. Wea. Forecasting., 23, 62-79.

Shapiro, M., and A. Thorpe, 2004: THORPEX International science plan,

Version 3, 2 November 2004, WMO/TD No. 1246, WWRP/THORPEX

No.2. [Available online at http://www.wmo.int/thorpex.]


Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W.

Wang, and J. G. Powers, 2005: A description of the Advanced Research

WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp.

Velden, C., and Coauthors, 2005: Recent innovations in deriving trop-

ospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc.,

86, 205-223.

Weissmann M., F. Harnisch, C.-C. Wu, P.-H. Lin, Y. Ohta, K. Yamashita,

Y.-K. Kim, E.-H. Jeon, T. Nakazawa, and S. Aberson, 2010: The

influence of dropsondes on typhoon track and mid-latitude forecasts.

Mon. Wea. Rev., in press.

WMO, 2002: International core steering committee for THORPEX First

session (ICSC-1) Final report, 15-16 October 2002. [Available online at

http://www.wmo.int/thorpex.]

Wu, C.-C., and Coauthors, 2005: Dropwindsonde Observations for Typhoon

Surveillance near the Taiwan Region (DOTSTAR): An Overview. Bull.

Amer. Meteor. Soc., 86, 787-790.

______, K.-H. Chou, Y. Wang, and Y.-H. Kuo, 2006: Tropical cyclone

initialization and prediction based on four-dimensional variational data

assimilation. J. Atmos. Sci., 63, 2383-2395.

______, ______, P.-H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa,

2007: The impact of dropwindsonde data on typhoon track forecasts in

DOTSTAR. Wea. Forecasting., 22, 1157-1176.

______, and Coauthors, 2009: Inter-comparison of Targeted Observation

Guidance for Tropical Cyclones in the North western Pacific. Mon.

Wea. Rev., 137, 2471-2492.

Xiao, Q., Y.-H. Kuo, Y. Zhang, D. M. Barker, and D.-J. Won, 2006: A

tropical cyclone bogus data assimilation scheme in the MM5 3D-Var

system and numerical experiments with Typhoon Rusa (2002) near

landfall. J. Meteor. Soc. Japan., 84, 671-689.

______, L. Chen, and X. Zhang, 2008: Evaluations of BDA Scheme using

the Advanced Research WRF (ARW) Model. J. Appl. Meteor.

Climatol., 48, 680-689.

Yamaguchi, M., T. Iriguchi, T. Nakazawa, and C.-C. Wu, 2009: An ob-

serving system experiment for Typhoon Conson (2004) using a singular

vector method and DOTSTAR data. Mon. Wea. Rev., 137, 2801-2816.

Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of

a mature hurricane using a variational bogus data assimilation scheme.

J. Atmos. Sci., 57, 836-860.

______, F. Vandenberghe, M. Pondeca, and Y.-H. Kuo, 1997: Introduction

to adjoint techniques and the MM5 adjoint modeling system. NCAR

Tech. Note NCAR/TN-435STR, 110 pp.

observation system experiments for typhoon jangmi (200815...

Documents