t-parc (summer phase) sharanya j. majumdar (rsmas/u. miami) christopher s. velden (cimss / u....
TRANSCRIPT
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.