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Highlights of studies at DLR and LMU based on aircraft observations during T‑PARC

Picture taken by Yomiuri Shimbun on 11 Sept. 2008

Martin Weissmann1,2, Florian Harnisch1,2 and Kathrin Folger1

Hans-Ertel-Centre for Weather Research, DA Branch, LMU München, Germany

Formerly: DLR Oberpfaffenhofen

Falcon fundingCollaborators:C. Cardinali, ECMWF, Reading, United KingdomR. Langland and P. Pauley, NRL, Monterey, USAT. Nakazawa, WMO, Geneva, SwitzerlandM. Wirth, S. Rahm, DLR Oberpfaffenhofen, GermanyY. Ohta and K. Yamashita, JMA, JapanC.-C. Wu, K.-H. Chou and P.-H. Lin, NTU, TaiwanY. Kim and E.-H. Yeon, NIMR, KoreaS. Aberson, NOAA/AOML/HRD, USA

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Taiwan:DOTSTARAstra Jet

Atsugi:DLR Falcon

Guam:US AirForce WC-130NRL P-3

THORPEX Pacific Asian Regional Campaign (T-PARC, Aug – Oct 2008)

DLR Falcon:•25 flights•Dropsondes•Wind lidar•Water vapour lidar

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Dropsonde observations

Campaign objectives:•TC genesis and structure•Targeted observations for NWP•ET transition

Evaluation of dropsonde impact

Period: Three weeks in September 2008

Typhoons: Sinlaku and Jangmi

Models:•ECMWF (global)•JMA GSM (global)•NCEP GFS (global)•WRF-ARW (regional)

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 ECMWF IFS JMA GSM

JapanKMA WRF NIMR KOREA

NCEP GFS

Resolution TL799L91 (~25 km)

TL959L60(~20 km) 30 km

T382L64(~38 km)

DA-method 12h 4D-VAR 6h 4D-VAR 6h 3D-Var 6h 3D-Var

Domain Globe Globe 190*190 grid points

Globe

Bogus NO NO (YES in oper. version)

NO vortex relocation, bogus if no vortex in first guess (rare)

Use of TCcore and eyewall observations

YES YES YES NO

Denied observations

Pacific dropsondesdriftsondesJMA ship SYNOPJMA ship TEMP

Pacific dropsondesJMA ship TEMPJMA special TEMP

Atlantic dropsondes

Atlantic and Pacific dropsondesdriftsondes

Model set-up used for the evaluation of dropsonde impact

(Weissmann, Harnisch, Wu, Lin, Ohta, Yamashita, Kim, Jeon, Nakazawa and Aberson, 2011, MWR)

ECMWF, JMA and NCEP systems from 2008/2009/2010 (no hybrid DA)

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Influence of T-PARC dropsondes on typhoon track forecasts

24 36 48 60 72 84 96 108 1200

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GFS typhoon forecasts, lead time: 24-120 h

GFS NoDrop

GFS Drop

NCEP GFS3D-Var

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WRF, lead time: 0-72 h

WRF NoDrop

WRF Drop

KMA WRF3D-VAR

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JMA, lead time: 0-84 h

JMA NoDrop

JMA Drop

JMA GSM4D-Var

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ECMWF and GFS typhoon forecasts, lead time: 24-120 h

EC NoDrop

EC Drop

ECMWF4D-VAR

All models show some reduction of typhoon track forecast errors with dropsondes, but impact strongly depends on DA system

Different scales!

(Weissmann, Harnisch, Wu, Lin, Ohta, Yamashita, Kim, Jeon, Nakazawa and Aberson, 2011, MWR)

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• Large improvement in NCEP-GFS and WRF (models with 3D-Var, less other obs., larger errors)

• Lower impact in JMA and ECMWF (4D-Var, more satellite observations, lower errors)

• Best forecast both with and without dropsondes by ECMWF

• GFS with dropsondes comparable to ECMWF despite 3D-Var and less satellite observations

Influence of T-PARC dropsondes on typhoon track forecasts

(Weissmann, Harnisch, Wu, Lin, Ohta, Yamashita, Kim, Jeon, Nakazawa and Aberson, 2011, MWR)

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909 910 911 912 913 914 915 916 917 918-500

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0

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500Difference track error Exp-Control; fctime: 72h

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date

ECMWF

JMA

GFS

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Low mean impact of JMA due to two deteriorating forecasts, whereas majority of forecasts improves(both deteriorating forecasts contain observations in typhoon core and eyewall region)

SINLAKU JANGMI

deterioration

improvem

ent

Dropsonde impact for individual forecasts

All models show more improving than deteriorating forecasts

(Weissmann, Harnisch, Wu, Lin, Ohta, Yamashita, Kim, Jeon, Nakazawa and Aberson, 2011, MWR)

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Comparison of dropsonde targeting strategies

Concept for ideal mission and sensitivity experiments: Joint mission on 11 September •Falcon obs. in sensitive area highlighted by e.g. SV, ETKF (red)•DOTSTAR observations in typhoon surrounding (blue)•WC-130 observations in typhoon center (green)

Evaluation of targeting strategies in ECMWF system

Japan

China

ETKF

SV

(Harnisch and Weissmann, 2010, MWR)

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Typhoon center and core:overall neutral impact

Typhoon vicinity:improvement of the track

forecast

Remote sensitive regions:small positive to neutral

impact

Which dropsonde observations are most beneficial?

ECMWF Integrated Forecast System (Harnisch and Weissmann, 2010, MWR)

Possible reasons:•SV resolution•Insufficient sampling of region•Low analysis error

Possible reasons:•Model resolution•Static B-matrix•Observation error, QC

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Doppler wind lidar (DWL) assimilation in ECMWF and NOGAPS

Airborne scanning DWL:• Coherent 2 µm Doppler lidar• Profiles of horizontal wind • Horiz. resolution ~5 km• Vert. resolution 100 m• Accuracy: 0.5 - 1 m/s• Representative observations

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Mean ECMWF track forecast error

ECMWF

EC CONTROL

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EC DROP

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ECCTR AN

ECDWL ANECCTR FC48

ECDWL FC48

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Mean NRL NOGAPS track forecast error

NOGAPS

NRL CONTROL

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NRLCTR AN

NRLDWL ANNRLCTR FC48

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Mean ECMWF track forecast error

ECMWF

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ECDWL ANECCTR FC48

ECDWL FC48

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Mean NRL NOGAPS track forecast error

NOGAPS

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NRL DWL

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NRLCTR AN

NRLDWL ANNRLCTR FC48

NRLDWL FC48

Impact of 1 DWL similar to dropsondes from 4 aircraft

Low impact on TC forecasts, likely due to bogus that “competes“ with other observations near TC(Weissmann, Langland, Pauley, Rahm and Cardinali,

2012, QJRMS)

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Relative reduction of global 24-h forecast error by different observations:•Overall, significant impact of DWL in both systems•NOGAPS: DWL impact largest after TPW, bogus and scatterometer•ECMWF: DWL impact similar to aircraft observations•Larger total (accumulated) impact of DWL in NOGAPS although small impact on TC tracks (likely due to bogus near TC, but less other satellite observations elsewhere)

d) Rel. contr. per obs., area 20-50 N, 120-160 W

-0.0010 0.0000 0.0010 0.0020 0.0030

TEMPAMV GEO

AircraftDWL

Land SYNOPShip SYNOP

SSMI SFC WINDSSMI-TPW

SCAT SFC WINDASCAT SFC WIND

WINDSAT SFC WINDWINDSAT-TPW

TC SynthAMSU-A

IASISSMISAQUA

NOGAPS FEC per obs. (%) in area 20-50 N, 120-160 W

DWL

TPW

TPW

SCAT

bogus

b) Mean relative contribution per observation

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SYNOPAIRCRAFT

DWLDRIBU

TEMP/PILOTAMV GEO

SCATHIRS

AMSU-AAIRSIASI

GPS-ROSSM/I

AMSR-EMHS

AMSU-BMTSAT-Rad

O3

ECMWF FEC per observation (%) in area 20-50 N, 120-160 W

DWLaircraft buoys

AMVs

SYNOP

radiosonde

SCAT

ECMWFNOGAPS

FSO impact per observation (area 20-50N, 120-160W)

(Weissmann, Langland, Pauley, Rahm and Cardinali, 2012, QJRMS)

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Water vapour lidar (DIAL) assimilation at ECMWF

• Observations from 8 flights assimilated in ECMWF system• Verification with independent dropsondes shows analysis improvement• Weak forecast impact in most cases, but improvement in two events with modified downstream development

S-Korea Japan

Okinawa

(Harnisch, Weissmann, Cardinali and Wirth, 2011, QJRMS)

Forecast impact

Improvement

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Height correction of AMVs with lidar cloud top observations

Atmospheric motion vectors (AMVs) are the only available wind information in many regions of the globe, but:•Height assignment errors lead to significant wind errors•These errors are correlated over several hundred km only a small fraction of AMVs is used for NWP

T-PARC offered a unique data set to test the potential of correcting AMVs with lidar information•Availablility of collocated lidar observations and independent dropsonde winds for verification

Best results were achieved for assigning AMVs to a 100 hPa layer beneath lidar cloud tops

On average, AMV wind errors were reduced by 14% with lidar correction

(Weissmann, Folger and Lange, 2013, JAMC)

Layer beneath lidar cloud top

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Application of the correction with CALIPSO lidar observations

~1200 collocated MSG-AMVs/CALIPSO observations per day

error for AMV level

error for layer 25% above, 75% beneatherror for lidar layers

Results:•12-17% lower AMV wind error•Lower bias•Lower error correlation

(further details on poster)

(Folger and Weissmann , 2014, JAMC)

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Dropsonde impact in different models:•Some benefit of dropsonde observations in all systems •Large impact in NCEP GFS and KMA WRF (models with 3D-Var, less satellite observations and larger errors)

Overall, targeted dropsondes are beneficial for TC forecasts, whereas results for mid-latitude targeting are neutral or small (latter not shown)

Specific ECMWF results:•Largest impact from dropsondes in typhoon vicinity•Low impact from dropsondes in remote “sensitive areas” indicated by singular vectors or ETKF•Neutral impact from core/eyewall dropsondes

Doppler wind lidar (DWL)•Overall significant and comparably large impact•Emphasizes high expectations for ADM-Aeolus satellite (despite some differences of the systems)

DIAL assimilation:•Improved humidity analysis, but weak forecast impact in most cases•Forecast impact when humidity is transported in mid-latitudes and downstream development is modified

AMV height correction with lidar cloud top observations:•Significant reduction of AMV wind errors (12-17%)•Lower bias and lower error correlation

Conclusions

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Thanks to the T-PARC Falcon team!

Organisation: Martin Weissmann (DLR), Ulrich Schumann (DLR), Sarah Jones (KIT, DWD), Pat Harr (NPS), Tetsuo Nakazawa (JMA MRI, WMO), Dave Parsons (WMO, U. Oklahoma), Jim Moore (NCAR)

US Naval Air Facility Atsugi

Atsugi support team: Stephan Rahm (DLR), Rudolf Simmet (DLR), Oliver Reitebuch (DLR), Christian Lemmerz (DLR), Benjamin Witschas (DLR), Martin Wirth (DLR), Andreas Fix (DLR), Axel Amediek (DLR), Michael Esselborn (DLR), Gerhard Ehret (DLR), Felix Steinebach (DLR), Peter Mahnke (DLR), Reinhold Busen (DLR), Florian Harnisch (DLR), Johannes Dahl (DLR), Andreas Schäfler (DLR), Lisa Klanner (DLR), Kotaro Bessho (JMA), Sarah Jones (KIT, DWD), Doris Anwender (KIT), Simon Lang (KIT)

Aircraft team: Andrea Hausold (DLR), Frank Probst (DLR), Stefan Grillenbeck (DLR), Philipp Weber (DLR), Roland Welser (DLR), Michael Grossrubatscher (DLR), Christian Hinz (DLR), Wolfgang Meier (DLR), Josef Wiesmiller (DLR), Alexander Wolf (DLR), Christian Mallaun (DLR), Martin Zöger (DLR)

Falcon funding