Targeted Observations for Improving Tropical Cyclone Predictability – DOTSTAR, TH08, TCS-08, and T-PARC Chun-Chieh Wu Acknowledging collaborators in DOTSTAR and T-PARC: Po-Hsiung Lin, Jan-Huey Chen, Shin-Guan Chen, TDRC, COOK (NTU), Chi, Yeh, Wu, Cheng, Chen (CWB), PL Lin (NCU), CH Liu, K.-H. Chou (CCU), Chia Chou (RCEC), AIDC, Sim Aberson (HRD), T. Nakazawa, M. Yamaguchi (JMA/MRI), Sharan Majumdar (U. of Miami), Melinda Peng, Simon Chang, C. Reynolds (NRL), Martin Weissmann (DLR), R. Buizza (ECMWF), Tim Li (IPRC), Mu Mu (LASG, CAS), S. Park (Ewha Univ.), H. M. Kim (Yonsei Univ.), S. Yoden (Kyoto Univ.), D. Parsons, C. Davis, W.C. Lee (NCAR) Grants: NSC, CWB, RCEC/Academia Sinica, ONR

Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR, 2003 – 2008) Up to present, 38 missions have been conducted in DOTSTAR for 31 typhoons, with 630 dropwindsondes deployed during the 200 flight hours. 27 typhoons affecting Taiwan 20 typhoons affecting (mainland) China 6 typhoons affecting Japan 3 typhoons affecting Korea 7 typhoons affecting Philippines Useful real-time data available to major operational forecast centers Positive impact to the track forecasts to models in major operation centers (NCEP/GFS, FNMOC/NOGAPS, JMA/GSM) Targeted observation Wu et al. (2005 BAMS, 2007a JAS, 2007b WF, 2009a,b,c MWR), Chou and Wu (2008 MWR), Chen et al. (2009 MWR), Yamaguchi et al. (2009 MWR), Chou et al. (2009 JGR)

(36) (34)(33) (27)(19)(14) NCEP GFS Impact from 2003 to 2008 (All 36 cases) Paired t-test statistical examination * : statistically significant at the 90% confidence level ** : statistically significant at the 95% confidence level Wu et al. 2009d ** * *

Since 2003, several objective methods, have been proposed and tested for operational/research surveillance missions in the environment of Atlantic hurricanes conducted by HRD/NOAA (Aberson 2003) and NW Pacific typhoons by DOTSTAR ( Wu et al ). –NCEP/GFS ensemble variance (collaborating with Aberson) –ETKF (collaborating with Majumdar) –NOGAPS Singular Vector (collaborating with Reynolds and Peng) –Adjoint-Derived Sensitivity Steering Vector (ADSSV) –JMA moist Singular Vector (collaborating with Yamaguchi) -- ECMWF Singular Vector (Wu et al. 2007b) (Aberson 2003) (Peng and Reynolds 2006) (Majumdar et al. 2006) (Yamaguchi et al. 2007) Targeted observations in DOTSTAR and T-PARC (Buizza et al. 2006)

Red: (I) all dropsonde obs Blue: (II) no dropsonde obs Green: (III) Three dropsonde obs within the sensitive region Light blue: (IV) Six dropsonde obs outside the sensitive region (Yamaguchi et al. 2009, MWR) × CONSON’s center position Sensitive analysis result Sensitive region shows vertically accumulated total energy by the 1st moist singular vector. Targeted area for the SV calculation is 25N- 30N, 120E-130E. Impact of DOTSTAR data : OSE result on CONSON’s (2004) track forecast Wind & Z at 500hPa ×

Inter-comparison of Targeted Observation Guidance for Tropical Cyclones in the Western North Pacific Chun-Chieh Wu 1, Jan-Huey Chen 1, Melinda Peng 2, Sharan Majumdar 3, Carolyn Reynolds 2, Sim Aberson 4, Munehiko Yamaguchi 5, Roberto Buizza 6, Shin-Gan Chen 1, Tetsuo Nakazawa 7 and, Kun-Husan Chou 1 To highlight the unique dynamic features in affecting the TC tracks, we compare six different targeted techniques based on 84 cases of two-day forecasts of the Northwest Pacific tropical cyclones in The six targeted methods: TESVs form ECMWF, NOGAPS, and EPS of JMA ETKF DLM wind variance ADSSV Wu et al. (2009b, MWR)

Inter-comparison of targeted guidance Common target locations –Modified Equitable Threat Scores (METS) (Majumdar et al. 2006) Provides the quantitative measure of how the leading targets of two sets of guidance are similar to each other. Leading targets = 2 % of total grid points in the domain # 10 WP04 Ewiniar Ti= To= expected number of common grid points between all feasible realizations of guidance number of common grid points between each pairs of guidance number of leading targets Wu et al. (2009b, MWR)

Common features: sub-tropical high Wu et al. (2009b, MWR)

Common features: mid-latitude trough Wu et al. (2009b, MWR)

DOTSTAR P3 11 September, 2009, Typhoon Sinlaku DOTSTAR + Falcon + P3 + C130 Flight tracks P3 C130 Falcon First time with four aircrafts observing typhoons over NW Pacific ocean T-PARC

00 UTC Sept. 10, 2008; 00 UTC Sept. 11, 2008 (Wu et al. 2009e) Impact of dropwindsondes to NCEP GFS forecasts of Sinlaku 12 UTC Sept. 11, 2008 (JMA/GSM, from Nakazawa) Degradation due to the inner-core dropsonde data (Aberson 2008)

3 hour besttrack data, interpolated to 30 minutes interval by cubic-spline method. TC radius (34, 50 kts) data from JTWC. DOTSTAR (Wu et al. 2005, 2007) surface wind data (MBL150, Franklin 2003) on 26 July UTC (final time of the initialization period). EnKF data assimilation Observations: position, motion vector, axisymmetric structure 50 kts 34 kts After Willoughby et al. (2006) DOTSTAR flights (Wu et al. 2009f)

Experiment “ TK-MS ” 3. Experiments on initialization Lowest sigma level (Wu et al. 2009f)

Experiment “ TK-MS-TP-ALL ” 4. Experiments on update cycle analysis Inner eyewall Outer eyewall Inner eyewall Outer eyewall (Wu et al. 2009f) time

Validation and OSE studies: added value and data assimilation (cost-effective?) Understanding and physical interpretation of the structure of the targeted guidance products (ADSSV, SV, ETKF), along with the PV dynamics Targeted observations of other data (especially the satellite data: satellite thinning) EnKF data assimilation and dynamical analyses Intercomparison of targeted schemes - to gain more insights into the physics of targeted observations Intercomparison of the data impact to different model systems in T-PARC Future perspectives

Q: the small reduction in the forecast errors do not justify the cost of the targeted observations. A: I believe this is not the case, at least for the targeting of tropical cyclones. Q: Based on observation sensitivity calculations, although the positive analysis and forecast impact per observation is larger for the targeted observations than for the other types of observations, because of the relatively low number of targeted observations, their overall impact on analysis and forecast quality is very small. A: That may well be the case for several reasons. The volume of lower-quality satellite observations that are assimilated into models has been increasing over the past decade, and they may overwhelm the small number of higher-quality dropwindsonde observations. Also, some data assimilation schemes such as NCEP's GSI produce conservative analysis increments, i.e. they don't let the observation have a significant large-scale influence on the model atmospheric flow. Overall -Sure that we need to better assess the added value of targeted observation for TCs. For TCs, may need to assess the added values of track improvements due to G-IV observations, as well as the statistics for DOTSTAR missions. Given that the benefit of even a small improvement in TC track forecasts is so high, the targeted observations are very cost-effective. For example, in the USA, it is informally reported that it costs $1M to evacuate a mile of coastal population.