T-PARC (Summer Phase) Sharanya J. Majumdar (RSMAS/U. Miami) Christopher S. Velden (CIMSS / U. Wisconsin) Section 4.7, THORPEX/DAOS WG Fourth Meeting 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 IWTC-VII, La Réunion, France 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 Force WC-130 US NRL P-3 F. Harnisch

M. Weissmann Period: and

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 Image 09/11/ LT OkinawaTokyo 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 sensitivity Mesoscale 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

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%

JAMC 2011, in press

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. 1 st, (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 09/11:00Z Upper-lev Div (left) Azi-mean Vort (Right)

Analysis increment – Theta Prior Post

Targeting Typhoon season with extra-satellite data Selective data thinning experiments Cntrl: 1.25 o Global SV-Sat: 1.25 o Global and o in SV areas. Drop: 1.25 o Global +Targeted Dropsondes SV-Sat-Drop: Targeted Dropsondes+ SV areas o Additional information All experiments are run at T799TL95/159/255 L91 (12-hour 4D-Var) September 2008 Verification and SV-target region 10-50N, E 20 Leading T95L62 SV SVs area cover 20% of the target region C. Cardinali

Sept SV-Sat + Drop cntrl Sinlaku September: mean track error km Drop cntr cntrl SV -Sat C. Cardinali

Forecast Sensitivity to Obs: SV-Sat+Drop Forecast error and Verifying 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 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 improvement 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.