Topic 3.3: Targeted observations and data assimilation in track prediction Rapporteur: Chun-Chieh Wu PSA Working Group: Sim Aberson, Brian Etherton, Sharanya.

Topic 3.3: Targeted observations and data assimilation in track prediction

Rapporteur: Chun-Chieh Wu PSA

Working Group: Sim Aberson, Brian Etherton, Sharanya J. Majumdar, Seon Park, Melinda S. Peng, Zhaoxia Pu, Michael Morgan, Steve Tracton, Samuel Westrelin, and Munehiko Yamaguchi

Sixth International Workshop on Tropical Cyclones

Outline

Introduction Surveillance programs using the dropwindso

ndes Targeted observations for tropical cyclones Comparison of targeted observing methods Other data to be targeted and assimilated Issues and concerns Recommendations

Improving the understanding and forecasting of TCs

Dynamics of the typhoon system

Initial conditionDynamics of

the model

Data assimilation and/or Initialization

Multi-scale interaction

Air-sea interaction

Terrain/PBL effect

New Observation

Cost effective?

Introduction

• Accurate forecast of tropical cyclones– Realistic numerical models– Accurate representation of meteorological fields

• Observation data– Surface observations, soundings, and ships– Dropwindsonde data– Satellite data – Radar data

• Data assimilation

Surveillance programs using the Dropwindsondes

• The impact of dropwindsonde data– Between 1982 and 1996, the HRD conducted 20 “syn

optic flow” experiments.• Burpee et al. (1996)

– The average error reductions in the consensus forecasts from three dynamical models varied from 16% to 30%.

Dropwindsondes

• Aberson and Franklin (1999)– The dropwindsonde o

bservations improved the mean track forecasts of the GFDL model by as much as 32%.

• In 1997, the HRD began operational synoptic surveillance mission with the G-IV jet aircraft.

No drop

with drop

• Aberson (2002)– The additional dropwindsonde data from the syn

optic surveillance missions provided statistically significant improvements in the GFDL forecast only at 12 h.

• TC vortex initialization schemes • The amount of data coverage

• Aberson (2003) and ongoing…– The improvement through targeted observations

Dropwindsondes

JMA,UKMO,

….

DOTSTAR (Dropwindsonde Observations of Typhoon Surveillance near t

he Taiwan Region)

Astra jet of AIDC69

(Wu et al. 2005a, BAMS)

69

DOTSTAR observationsUp to 2006, 24 missions have been conducted in DOTSTAR for 20 typhoons, with 386 dropsondes deployed during the 129 flight hours.18 typhoons affecting Taiwan

8 typhoons affecting mainland China

4 typhoons affecting Japan

2 typhoons affecting Korea

5 typhoons affecting Philippines

23. Saomai22. Bopha21. Kaemi20. Bilis 24. Shanshan

NCEP GFS : 14%

JMA GSM : 19%

NOGAPS : 14%

ENSEMBLE : 22%

The impact of DOTSTAR data on global models in 2004

(Wu et al. 2006a, WF)

Sim

Aberson

Tetsuo

Nakazawa

Melinda Peng

Background on targeted observations

• Adaptive observations : observations targeted in sensitive regions can reduce the initial condition’s uncertainties, and thus decrease forecast error.

• Targeted observation is an active research topic in NWP, with plans for field programsplans for field programs, tests of new observing systetests of new observing systemsms, and application of new concepts in predictability and dpredictability and data assimilationata assimilation. (Langland 2005)

• Factors associated with adaptive observations - Observation density, variables and errors - Magnitude of uncertainty - Data assimilation system - Growth of uncertainty

Adaptive observation strategies• Dynamics-based strategy

SV, adjoint sensitivity, and PV.

• Uncertainty-based strategy.

Ensemble variance

• Joint dynamics-uncertainty based strategy.

The ideal one would be the strategy that use both of dynamics and uncertainty information (e.g., ETKF, VARSV).

(Since 1997, developed for mid-lat, FASTEX)

• Since 2003, several objective methods, have been proposed and tested for operational surveillance missions in the environment of Atlantic hurricanes conducted by HRD/NOAA (Aberson 2003) and NW Pacific typhoons by DOTSTAR (Wu et al. 2005).

– NCEP/GFS ensemble variance (collaborating with Aberson)– ETKF (collaborating with Majumdar)– NOGAPS Singular Vector (collaborating with Reynolds and Peng)– Adjoint-Derived Sensitivity Steering Vector (ADSSV)Adjoint-Derived Sensitivity Steering Vector (ADSSV)– JMA moist Singular VectorJMA moist Singular Vector (collaborating with Yamaguchi)

(Wu et al. 2006b, JAS)

(Aberson 2003, MWR)

(Peng and Reynolds 2006, JAS)

(Majumdar et al. 2006, MWR)

Comparison of targeted observations in DOTSTAREnsemble Variances, Toth and Kalnay (1993)

ETKF, Bishop and Majumdar (2001)

FNMOC SV, Palmer et al. (1998) ADSSV, Wu et al. (2006)• DOTSTAR(Wu et al. 2006b)

•G-IV surveillance Comparison of targeted techniques(Etherton et al. 2006)Maumdar et al. 2006Reynolds et al. 2006 More comprehensive comparisons are

ongoing.

How the dropsonde data improve the forecast? How the dropsonde data improve the forecast? Typhoon Conson (2004) as an exampleTyphoon Conson (2004) as an example

JMA-GSM

(Nakazawa 2004, THORPEX meting)

Typhoon Conson (2004) 8 June 1200UTC

Evaluate a SV method as a strategy for Targeting ObservationJMA has executed Observing System Experiments (OSEs) to investigate the usefulness of the singular vector method as a strategy for sensitive analysis.

For the initial time of 12UTC 08 June 2004 when totally 16 dropsondes were dropped into typhoon CONSON by the DOTSTAR (Dropsonde Observation for Typhoon Surveillance near the Taiwan Region) project, 4 predictions with JMA Global Spectral Model (TL319L40) about the use of the dropsondes in the global 4D-Var analysis are executed.

(I) all dropsonde observations are used for making the initial condition

(II) dropsondes are not used at all

(III) only 3 data within a sensitive region are used (4, 9, 12)

(IV) only data outside of a sensitive region are used (6, 8, 10, 13, 15, 16)

The distribution means vertically accumulated total energy by the 1st moist singular vector.

Targeted area for the SV calculation is N25-N30, E120-E130.

Optimization time interval is 24 hours.

Sensitive analysis result

x

CONSON’s center position

(From Yamaguchi)

OSEs result on CONSON’s track forecast

(III) (I) (IV)

(II) is almost same with (IV)similar

Red: (I) all dropsonde observations are used for making the initial condition

Blue: (II) dropsondes are not used at all

Green: (III) only 3 data within a sensitive region are used (4, 9, 12)

Water: (IV) only data outside of a sensitive region are used (6, 8, 10, 13, 15, 16)

(From Yamaguchi)

• Observations for data assimilation– To date, “Targeted” observations for TCs are mainly dr

opwindsondes deployed from the aircraft.– There is considerable scope for extending targeted ob

serving strategies to include other types of data, most prominently from satellites.

• GOES (Zou et al. 2001) and TRMM (Pu et al. 2004) • Microwave radiances (Bauer et al. 2006a, b).

– The collection of satellite and in-situ data from field programs (e.g. CAMEX-4, Kamineni et al. 2006) with different spatial and temporal resolutions and error characteristics (Fisher 2003; Berre et al 2006, Westrelin et al 2006) will continue to play a very important role in improving tropical cyclone track forecasts.

Other data to be targeted and assimilated

– Questions more specific to targeted observations can be addressed over the next decade:

• Given the abundance of satellite data that will be available for assimilation, what subsets of the data are the most necessary for assimilation to improve the tropical cyclone forecast? (satellite data thinning)

• What are the optimal variables, three-dimensional structures, and spatial and temporal density that are necessary for observation?

Other data o be targeted and assimialted

Issues of concerns (Langland 2005 and THORPEX) • Although the impact of observations is greater when selec

ted in a sensitive area, the few observations deployed may not make a substantial impact on the forecasts.

• The statistical evaluation of the significance of the measured impact requires a large number of cases.

• Current diagnostics used to evaluate forecasts provides a good assessment of the validity of forecasts (skill), but it may not be sufficient to reveal whether these improvements are relevant to applications (value).

• The use of climatological sensitivities may lead to improvements on average and be more cost effective than targeted observations on demand.

• Overall, there was a considerable question as to the value of targeting, especially when isolated from the more general issues of observing system sensitivities in design of an “optimal” mix of available observing platforms.

Recommendation• Need to assess the influence of the data assimilation scheme

on the effectiveness of targeted observations.• More studies of varying definitions, interpretations, and signifi

cance of sensitive regions (e.g., different methods, metrics) • More work on sampling strategies in sensitive areas, e.g., im

mediate storm environment for shorter range prediction versus remote areas relevant to longer range forecasts – including the impact of large scales in meso-scales models.

• More work on metrics to assess the impact of targeting – or more generally on any changes in the observation network.

• Emphasis of the potential value of OSEs and OSSEs in assessing potential observing system impacts prior to actual field programs.

• Stronger efforts to develop alternative observing platforms (other than the dropwindsondes) for targeting, especially adaptively selecting satellite observations by revising the data thinning algorithms currently used.

• Improvement and continuous refinement of targeted observing strategies.

THORPEX-PARC Experiments and Collaborating Efforts (from Dave Parsons)

NRL P-3 and NRL P-3 and HIAPER with theHIAPER with theDLR Wind LidarDLR Wind Lidar

NRL P-3 and NRL P-3 and HIAPER with theHIAPER with theDLR Wind LidarDLR Wind Lidar

Upgraded Russian Upgraded Russian Radiosonde Network for IPYRadiosonde Network for IPY

Winter storms Winter storms reconnaissancereconnaissanceand driftsondeand driftsonde

JAMSTEC/IORGGJAMSTEC/IORGG