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The Assessment of the DAOS WG on Observation Targeting

Talk presented by

Rolf Langland (NRL-Monterey)

DAOS Working Group

THIRD THORPEX International Science Symposium

Monterey, CA, 16 Sept 2009

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Data Assimilation and Observing Strategies THORPEX Working Group

Members: Pierre Gauthier (Univ. of Quebec, co-chair)

Florence Rabier (Metéo France, co-chair)

Carla Cardinali (ECMWF)

Ron Gelaro (NASA-GMAO)

Ko Koizumi (JMA)

Rolf Langland (NRL-Monterey)

Andrew Lorenc (UK Met Office)

Peter Steinle (Australian Bureau of Met)

Michael Tsyroulnikov (Hydromet, Russia)

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THORPEX DAOS Working Group OBJECTIVES

• Promote research activities that lead to more- optimal use of observations and understanding the sources of errors in analyses and forecasts

• Contribute to development of a strategy for evolution of the global observing system to support NWP

• Provide guidance and evaluation for observation deployment (including targeting) in THORPEX regional campaigns

DAOS-WG Overview Talk - by Pierre Gauthier

17:30 Thurs Sept 17

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Targeted Observing Goals

Deploy sets of additional observations to improve the forecast skill of weather events anticipated to have large societal impact

• Strong extratropical cyclones and frontal waves

• Tropical cyclones and typhoons

Identification of target areas: adjoint and ensemble-based methods

Observational resources: dropsondes, radiosondes, ship and land-surface observations, aircraft-based lidar, satellite observations

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Field Programs for Targeted Observing

Winter storm targeting• North Atlantic (FASTEX-1997, ATREC-2003)

• Eastern North Pacific (NORPEX-1998, WSR-1999-2009)

• Entire North Pacific (Winter T-PARC 2009)

Hurricane / tropical cyclone targeting• North Atlantic (NOAA-HRD, 2000-2009)

• Western Pacific (DOTSTAR, 2003-2009)

T-PARC (TCS-08) 2008

Other Programs: AMMA, IPY

Participants: Meteo France, ECMWF, UKMO, NRL, NCEP, NCAR, JMA, Taiwan, NOAA-AOC, NOAA-HRD, USAF Hurricane Hunters, NASA, CIMSS, MIT, Univ. of Miami, Penn State Univ., others

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Does “dropsonde targeting work” ?

Yes - however ……

• Dropsonde deployments provide only partial and intermittent surveys of target areas, so “full impact” of targeting has not been realized

”Targeted observing” may be more effective with methods other than dropsonde deployment (e.g., use of satellite observations)

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Targeting to improve 42-hour forecast of intense cyclone over Ireland and Great Britain

Forecast Verification Region

Singular Vector Target in FASTEX IOP-17

Dropsondes from NOAA G-IV

NOGAPS

SV-based Target Area

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Song,Toth & Majumdar 2008

70% of WSRP cases improved by up to 12 hours

ETKF Target in NOAA Winter Storm Reconnaissance Program

Dropsondes from NOAA G-IV – only in localized area of maximum sensitivity

ETKF Target Area Predicted Signal Propagation from Dropsonde Assimilation

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Impact of NORPEX targeted dropsondes16 January – 27 February 1998 (NRL-NCEP)

RMSE 500mb ht of 2-day forecasts

Error (m) with targeted dropsondes

45 forecast cases, ~ 10% mean error reduction over western North America, using NOGAPS forecast model

700 dropsondes IMPROVED FORECASTS (n=35)

DEGRADED FORECASTS (n=10)

Langland et al. 1999 (BAMS)

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THE LAW OF LARGE (and small) NUMBERS OF OBSERVATIONS

Degraded Forecast targeting deployment #1

Improved Forecast targeting deployment #2

Assimilating a very large number of observations is (almost) guaranteed to improve forecast skill. Not true for smaller sub-sets of observations ….

Fcst Error Increase

Fcst Error Reduction

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Forecast Impact of Targeted Data – (adding 10-50 dropsondes at single assimilation times)

• Targeted data improves the average skill of short-range forecasts*, by ~ 10–20% over localized verification regions – maximum improvements up to 50% forecast error reduction in localized areas

• In all analysis / forecast systems*, and for all targeting methodologies, it is found that ~ 20-30% of forecast cases are neutral or degraded by the addition of targeted data

• Impact “per-observation” of targeted (dropsonde) data is about 3x larger than random observations, but total impact is generally limited by the relatively small number of targeted data

Mid-latitudeTargeting Program Results FASTEX, NORPEX, ATREC, WSRP, Winter-TPARC

* Based on published forecast impact studies performed at NCEP, ECMWF, Meteo France, UKMO, NRL

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NRL SVs

U. Miami/NCEP ETKFUKMO ETKF

ECMWF SVs

• Results from up to 6 different centres displayed in common format• > 500 individual cases during Aug- Sept 2008

U. Yonsei SVs

JMA SVs

Data Targeting System

Super Typhoon Jangmi: Targeting Time 28 Sept. 2008

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TCS-08: Typhoon Sinlaku NCEP GFS initialized 00 UTC 10th Sept

WITH DROPS

JMA BEST TRACK

Effect of drops:Strengthened vortex and subtropical ridge, inducing northwestward flow

WITHOUT DROPS

500 hPa ASYMMETRIC WIND DIFF

+18 h

Provided by Sharan Majumdar (U. Miami)

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• Targeted observing produces significant improvements to some TC track forecasts – some cases not improved

• Different methods (ETKF, adjoint are in general agreement on identification of target areas

• Satellite observations (including rapid-scan winds) can provide more-complete and more-frequent coverage of target areas and may produce larger improvements in TC track forecasts

Tropical Cyclone Targeting Program Results NOAA-HRD, DOTSTAR, TCS-08 (summer-TPARC)

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Improvement of Katrina track forecasts with assimilation of Rapid-Scan wind observations

Track Error

(n mi)

NOGAPS forecast length (hr)

Control forecasts – no rapid-scan winds

Track forecast error significantly reduced

Forecasts with rapid-scan winds

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DAOS-WG recommendations for Targeted Observing Field Programs

1. Expensive observation campaigns should not be justified based only on previous methods of targeting

2. Develop and test new targeting approaches – consider use of targeted satellite observations

3. Carefully consider data assimilation issues (impacts of small vs. large sets of observations, frequency of special observations, etc. )

4. Consider pre-campaign tests with OSEs or OSSEs

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Targeted Observing Research Issues

1. Impact of targeted observations from previous field programs (esp. WSRP, TPARC)

2. OSSEs and predictability experiments with synthetic observations

3. Adaptive selection and assimilation of satellite observations (less than 10% of available data currently used)

4. Potential for targeted observations to improve medium-range forecasts

What is the potential benefit from observing larger sections of the targeting subspace, instead of attempting to survey the smaller-scale areas of maximum sensitivity which have been the primary focus of previous field programs?

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How much benefit can we obtain by “tuning” the network of existing regular satellite and in-situ observations in a targeted sense?

- On-request rapid-scan wind data

- Targeted satellite channel selection and data-thinning

- Increase observations from commercial aircraft

- Request radiosondes at non-standard times

Targeting Strategies –

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1x10-3 J kg-1 (24h Moist Total Energy Norm)

Error Reduction Error Increase

Circled stations provided ten or more profiles

Siberian Raobs – Winter TPARC

Petropavlosk (32540)- very large impact

Adaptive tuning of the regular observing system

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Other Data Assimilation Priorities – DAOS WG

1. Diagnosis, understanding and reduction of model errors

2. Improved characterization of observation and background error, esp. for satellite observations and oceanic regions

3. Development of advanced, computationally efficient data assimilation systems, including 4D-Var and the Ensemble Kalman Filter

Advances in these areas are likely to improve forecasts as much or more than dropsonde-based

targeted observing, by itself

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Uncertainty in Atmospheric Analyses highly correlated with observing resource patterns

UKMET-GFS (200-500 hPa thickness – RMSDiff – 6 months)

Thickness (m)

Based on analyses at 0000 UTC and 1200 UTC from 1 Jan to 30 June 2007 Langland et al. (Tellus, 2008)

SMALL UNCERTAINTY

RAOBS AIRCRAFT

LARGE UNCERTAINTY SATELLITE OBS

What implications

does this have for future targeting

programs?

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Impact of removing ALL winter Pacific obs in ECMWF 4D-Var: Normalized 500hPa

geopotential height rmse differences between SEAIN

forecast and SEAOUT. Blue-purple show the negative impact and yellow-black

positive impact of SEAOUT. Panels (a)–(d) show forecast errors for days 1, 2, 5 and 7.

Kelly et al. 2007

Day 1

Day 2

Day 5

Day 7