T-PARC (Summer Phase)

Sharanya J. Majumdar (RSMAS/U. Miami)Christopher S. Velden (CIMSS / U. Wisconsin)

Section 4.7, THORPEX/DAOS WG Fourth Meeting27-28 June 2011.

• Objective: To improve 1-3 day forecasts by obtaining targeted observations in regions with high sensitivity.

• During the field phase, a team identified potential opportunities to collect targeted observations:– Cases selected 2-3 days prior to observation time.– Common verification regions, Guam, Taiwan, and Japan– Individually selected verification regions: calculations

performed through ECMWF/Met Office PREVIEW DTS

• Final flight paths chosen one day prior, based on targeted observation guidance and team consensus.

• Post field phase: Data denial experiments; observation impact experiments; different events considered.

MWR Special Collection http://journals.ametsoc.org/page/Cyclone_Predictability

IWTC-VII, La Réunion, France15-20 November 2010 Special Focus 1a: Targeted observations for TC track forecasting. C.-C. Wu and Sharan Majumdar

Outline

• Tropical Cyclone Track– Aircraft: Dropwindsonde and Wind Lidar data– Satellite: AMVs and radiances

• Other forecasts– Mid-latitudes– Downstream impacts

Tropical Cyclone Track

DOTSTAR Astra jet

DLR Falcon 20 US Air ForceWC-130

US NRLP-3

F. Harnisch

M. WeissmannPeriod: 2008090900-2008091812 and 2008092412-2008092900

• Harnisch and Weissmann (MWR 2010)Separation of dropwindsonde observations into 3 subsets:

→ typhoon vicinity: largest improvements of ECMWF track→ remote sensitive regions: small positive to neutral

influence → typhoon center and core: overall neutral influence

• Weissmann et al. (MWR 2011)NCEP and WRF/3dVar: Improvement from 20-40% Comparably low influence in ECMWF and JMA.

• Lower forecast errors without dropsondes in ECMWF & JMA • More extensive use of satellite data and 4d-Var?

• Chou et al. (MWR 2011)Mean 1–5 day NCEP track forecast error is reduced by 10–

20% for DOTSTAR and T-PARC cases (not as beneficial in ECMWF)

The different behaviour of the models emphasizes that the benefit depends strongly on the quality of the first-guess field and the assimilation system

• YH Kim et al. (APJAS, 2010): 17-22% improvement to short-range track forecasts. Mid-tropospheric data most effective (WRF/3dVar).

• Jung et al. (APJAS, 2010): observations over ocean more important than over land. Dropwindsondes most important at times they were launched. Otherwise, QuikSCAT and SATEM data were most important. Observations in sensitive areas improved the forecast (WRF/3dVar).

• HM Kim et al. (2011): Positive impact of dropwindsondes can be found in ensemble forecasts (WRF/EnKF).

• NOTE: Radiances not assimilated in these studies

Airborne Doppler Wind Lidar

Sinlaku

16W

IR Satellite Image09/11/08 1830 LT

Okinawa Tokyo

M. Weissmann

Weissmann et al. (QJRMS, in review)

• 2500 high-density, high-accuracy wind profiles measured from DLR Falcon during Typhoon Sinlaku.

• Data denial– ECMWF track forecasts improved by ~50 km for 1-5 days.– NOGAPS track forecasts did not improve (due to bogus?) – Improvement in 500 hPa and 1000 hPa Z.

• Adjoint method– Total relative DWL contribution 2x as large in NOGAPS as ECMWF. – Impact per ob is comparable to other platforms (higher in NOGAPS)

• Atmospherics Dynamics Mission Aeolus (ADM-Aeolus) lidar instrument planned for launch by ESA (2013?)

Improved targeting methods for TCs

Majumdar et al. (2011, QJRMS)

Ensemble sensitivityMesoscale SVs

Var-ETKF

Doyle et al. (2011, CISE)

Kim et al. (2011, WAF, in press)

Moist Adjoint

Mahajan and Hakim (2011)

TC track: impact of satellite data

JAMC 2011,

in press

JAMC 2011,

in press

JAMC 2011,

in press

JAMC 2011,

in press

Rapid-scan: further reduces the 3-5 day NOGAPS track forecast errors

Hourly AMVs: reduce mean 3-5 day track forecast errors by 6-10%

• Framework: NCAR Data Assimilation Research Testbed (DART)• Data assimilation: Ensemble Kalman Filter (EnKF)• Model: Advanced Research WRF (WRF-ARW)

• Ensemble members: 32; Case: Typhoon Sinlaku (2008)• Assimilation cycle started Sep. 1st, 2008. (one week before genesis)• 9km moving nest grid with feedback to 27km grid in the forecasts when TC is

present.• Deterministic: ECMWF 1.125°x1.125° (Baseline)

CIMSS: Cooperative Institute for Meteorological Satellite Studies ; JMA: Japan Meteorological Agency

Assimilation of AMVs on the mesoscale

Analysis Track and Intensity

CIMSS

JMA Best Track

CIMSS

Structure

CTL

CIMSS

CTL CIMSS

09/09:00Z

09/10:00Z

09/11:00ZUpper-lev Div (left)Azi-mean Vort (Right)

Analysis increment – Theta

Prior

Post

Targeting Typhoon season with extra-satellite data

Selective data thinning experiments

• Cntrl : 1.25o Global • SV-Sat: 1.25o Global and 0.625o in SV areas.• Drop : 1.25o Global +Targeted Dropsondes• SV-Sat-Drop: Targeted Dropsondes+ SV areas 0.625o

Additional information

• All experiments are run at T799TL95/159/255 L91 (12-hour 4D-Var) • 06-30 September 2008• Verification and SV-target region 10-50N, 110-180E• 20 Leading T95L62 SV• SVs area cover 20% of the target region

C. Cardinali

09 + 1011 Sept

SV

-Sat

+ D

rop

cntrl

Sinlaku 09-19 September: mean track error km

Dro

p

cntr

cntrl

SV

-S

at

C. Cardinali

Forecast Sensitivity to Obs: SV-Sat+Drop

Sinlaku

0

5

10

15

20

25

30

35

40

45

50

900 912 1000 1012 1100 1112 1200 1212 1300 1312 1400 1412 1500 1512 1600 1612 1700 1712

MS

LP

Err

or

(hP

a)

AN T+12 T+24Forecast error andVerifying analysis

• Extra-satellite data gave a more consistent impact due to homogeneous coverage and data diversity (moist, temperature, cloud, precipitation and surface wind)

C. Cardinali

only 2 Sinlaku flights

Influence on ECMWF midlatitude forecasts

910 913 916 919 922 925 928-150

-100

-50

0

50

100Pacific; lead time:96 h

diff

. fo

reca

st e

rro

r (m

2 /s2 )

date

improved track forecast --> improved first-guess for subsequent days --> improved mid-latitude forecast

overall neutral influence of observations during ET, although these were partly guided by SV calculations optimized for the Pacific

deterioration im

provement

M. Weissmann

Downstream Impacts• Aberson (MWR 2011, in press)• Dropwindsonde data provide global improvements to

NCEP GFS TC track forecasts of about 10% through 72 h, but decreasing at longer forecast lead times.

T-PARC Accomplishments

• Demonstrated utility of coordinated aircraft missions, and dropwindsonde and DWL data

• Benefits of higher spatial and temporal density of satellite winds and radiances

• Improvements to forecasts downstream, although targeting strategy not essential here

• Accelerated use of TIGGE: full fields and CXML database

• Large number of peer-reviewed publications

Recommendations (from IWTC-VII)• Aircraft observations are limited (particularly in NW Pacific):

make improved use of existing observations. Satellite radiance data, and AMVs. Special rawinsonde launches?

• Given that observations / models / DA evolve, need to frequently review targeted observing programs.

• Explore new strategies, in basic research, OSEs and OSSEs.

• Consider new observing platforms e.g. UAS, wind lidars.

• Coordinate use of observations (e.g. EURORISK PREVIEW)

• Explore tropical cyclone formation, structure and intensity.