2. 未来观测系统的优化设计： 观测系统模拟实验和目标观测方法 謝元富, 王宏利 張宇， Zoltan Toth Global Systems Division Earth System Research Lab

3. Outline NOAA observation systems How to optimally design an observation system What is OSSE A NOAA Joint OSSE system and collaborations Targeting observation techniques Observation data impact of UAS by an OSSE Some applications of UAS OSSE and targeting observation techniques Future plans

4. 3 National Oceanic and Atmospheric Administration (NOAA) NOAA’s Earth Observing Systems Land- Based Air-Based Ocean- Based Space-Based

5. 4 National Oceanic and Atmospheric Administration (NOAA) NOAA’s Current Earth Observing Systems Ships —18 NOAA owned and operated vessels Aircraft — 14 NOAA owned and operated planes Buoys —more than 19 separate systems worldwide (exceeding 3400 buoys) Radars —121 weather radars Surface Weather and Climate Systems –NWS Automated Surface Observing System (312) –Surface-based Climate Networks (>1000) –U.S. Climate Reference Network (114) –U.S. Historical Climate Network (1221) Upper Air Systems –Weather balloons (92 sites) and 35 wind profilers –Dropped sensors from aircraft (tracking hurricanes and other marine storms) Research Systems –Autonomous Underwater Vehicles –Unmanned Aircraft Systems Satellites — 16 meteorological satellites in 3 separate constellations –Polar-orbiting Operational Environmental Satellites - Last POES (NOAA-19) was launched February 6, 2009 –Geostationary Operational Environmental Satellites - GOES-O scheduled to be launched in April 2009 –Defense Meteorological Satellite Program –Jason 2 satellite altimetry NOAA’s diverse workforce provides crucial value-added interpretation and analysis of data collected from these observing systems

6. 5 National Oceanic and Atmospheric Administration (NOAA) NOAA cannot achieve its Mission to Understand and Predict without a sustainable, robust Earth observation system Image description: Sea surface temperature (SST) during El Nino in 1997

7. How to Optimally Design an Weather Observation System For given budget, it is challenge to optimally deploy observation systems, stationary and mobile: Weather is both climatological and situational; Length scale differences in space and time; Uniform observations are not optimal; Observation Systems Simulation Experiments (OSSE) and targeting observation techniques are useful for an optimal design.

8. True Atmosphere Old Observation instrument Analysis and Forecast system “ ” Nature Run What is an OSSE New instrument?

9. What is an OSSE? A long free model run is used as the “truth” - the Nature Run The Nature Run fields are used to back out “synthetic observations” from all current and new observing systems. The synthetic observations are assimilated into a different operational model Forecasts are made with the second model and compared with the Nature Run to quantify improvements due to the new observing system Early OSSE works confirmed data impact when observation systems have actually launched (ERS, NSCAT and AIRS, Atlas 1985,1997 and so on). An OSSE is a modeling experiment used to evaluate the impact of new observing systems on operational forecasts when actual observational data is not available.

10. Forecast Model Simulated Observations Validation of End-to-End OSSE Set-up Nature Runs DAS OSSE Metrics OSSE TESTBED COMPONENTS Current ECMWFGLA.25° T213GLA.5° T511(MM5) T799(WRF) Validation and Augmentation Valid. Cyclones Mean fields Clouds Precip Aug. Clouds Aerosols Turbulence Current Scatteromet er Rawinsonde Surface Aircraft TOVS AIRS AMV Future DWL CMIS Molnya UAS CL Balloon GPS Sounding Buoy Rocket - Realism of simulated observations and input - Impact model not too close to NR Global GFS FIM Regional WRF HWRF NAM Cyclones, Fronts, Jets Anomaly Correlation Air Traffic Routing Precip.Utility Load Mgmt. Current GSI 4DVAR EnKF H*Wind Targeting Observation schemes

11. NOAA Joint OSSE System Nature Run: – ECMWF operational model, T511/91L resolution 13 month free integration, 01 May 2005 - 31 May 2006 13 Atlantic basin tropical cyclones which show sufficiently realistic track behavior – NASA GMAO GEOS-5: 7km resolution with only 72 vertical levels 13 month free integration the same as ECMWF NR NR validation is underway Forecast Model: GSI/GFS – Newly upgraded NCEP operational version to 2014 – T384/64L resolution – 120 hour forecasts at 00Z and 12Z Calibration done for ECMWF NR: – GSD has invested 4 year effort on the calibration working with NASA GMAO, NCEP/EMC, NESDIS, AOML and JCSDA; – Calibration done for 5 major data sources: RAOB, AMSU-A, ACAR, AIRS and GOES

12. Synthetic Observations Goal: replicate the data set used operationally in 2005- 2006 with observations taken from the Nature Run – Conventional, OSBUV (ozone), GOES radiance observations developed by NCEP/NOAA – AIRS, AMSU-A, AMSU-B, HIRS2, HIRS3, MSU developed by GMAO/NASA Random errors added to “perfect” observations – Most conventional observations, GOES radiance: uncorrelated errors – Conventional sounding observations: vertically correlated errors – Radiance observations: horizontally correlated errors

13. Targeting Observation Techniques 张宇（气科院） and 刘德强（大气所） contributions to our GSD OSSE targeting observation techniques An improved Ensemble Transformation (ET) method is proposed (submitted to AAS) Lyapunov exponent Other improvement and methods are under investigation.

14. 研究回顾 —— 目标观测研究的方法 目前敏感区识别方法主要可以归结为两类： 基于伴随模式的方法 奇异向量方法(Palmer et al., 1998; Gelaro et al., 1999; Buizza and Montani, 1999; Bogert et al., 1999) 伴随敏感性方法(Baker and Daley, 2000; Kim et al., 2004) 条件非线性最优扰动方法(Mu et al., 2009) 基于集合的方法 集合转换方法(Bishop and Toth, 1999)， 集合转换卡曼滤波方法(Bishop et al., 2001) 此外还包括繁殖向量方法(Lorenz and Emanuel, 1998; Toth and Kalnay, 1997) 和准求逆线性方法(Pu et al., 1997; Pu and Kalnay, 1999; Reynolds et al., 2000) 等。 Courtesy slide from Deqiang Liu

15. 目标观测 “ 四步走 ” 资料同化 敏感区识别 数值预报 在敏感区 增加观测 敏感区识别是目标观测 研究的核心问题 Courtesy slide from Deqiang Liu

16. ENSEMBLE TRANSFORM (Bishop and Toth 1999) Ensemble based perturbations perturbations associated with adaptive observations Transform Matrix C XeXe XeXe XeCXeC XeCXeC Forecast error covariance: Assumption: Ensemble number is ideally large; Ensemble members statistically independent. The transformed ensemble error covariance A e can denote the error covariance A g associated with the adaptive observations Assumption: Forecast model M are linear Analysis error covariance:

17. ET Adjoint (ETA)

18. TRANSFORM MATRIX ET VET

19. TRANSFORM MATRIX Inversely proportional Proportional a l is the analysis error variance at the l th grid point. It should varies in different areas. The sensitive areas might be associated with the higher a l region.

20. ET and VET VET can get the optimal sensitive areas without calculate all the possible targeting networks; VET is faster than ET; – Only need to calculate the transform matrix once. VET’s sensitive areas are more close to reality. – The derivation of forecast error covariance is proportional to analysis error variance

21. COMPARE OF ETA AND ET

22. ETA’s results are dominated by the signal of u at 200hPa ET’s signal dismissed the signal of u at 200hPa SIGNAL AFTER THE INCREASE OF AG

23. UAS OSSEs Assist with selection of optimal choice of UAS platforms – Are UAS a good investment? – Combine with manned aircraft and other observing systems Design of UAS missions – Flight paths – Instrumentation – UAS platforms UAS data needs

24. NOAA is looking at a broad range of UAS platforms to fill data gaps…….. Slide courtesy of Sara Summers

25. UAS Experiments Impact of HALE UAS dropsondes on hurricane track forecasts Initial case study focused on track forecasts for 5 Aug 12Z for AL01 UAS simulations include realistic aircraft flight, dropsonde advection and significant levels Tested a variety of circumnavigational flight paths

26. Optimal design: UAS Flight Paths

27. Continuous Observing by UAS Repeat Trajectory F serially from Aug 1 12Z to Aug 7 00Z –Assume multiple UAS trading off Look at all 120 hour forecasts during the cycling time period Evaluate the UAS data impact

28. Aug 1 12Z - Aug 7 00Z

30. Identify UAS data needed for hurricane track forecasts

32. Significance of OSSEs for future observations OSSE is a very powerful tool, –Everything can be simulated, existing or future with cost/benefit consideration –Experiments can be repeated with any configuration and designs –DA schemes and forecast systems can also be evaluated –Experience and techniques can be gained for future observation systems before their installation –Identification of observation needs –Optimal design of future observation systems Limitation needs to be minimized –Calibration –Model error representation

33. AL02: Sensitive region Verification time: 00Z Aug 25 2005 Targeted time: -3 day ETS (shaded) DA-Maximum DA-Target

34. AL02 Track Forecast

35. Targeting Observation Techniques: Improvement needed Current targeting observation techniques are helpful with careful interpretation of sensitive regions; Some positive results have been achieved; Most applications are large scales; Challenges for nonlinear events; Some ideas to improve these techniques; – E.g., Lyapunov exponent

36. Future Work Improving targeting observation techniques, particularly on small scales OSE (data denial experiment) for UAS data campaign 2015 Validating new Nature Runs, GMAO and ECMWF high resolution runs Hyperspectral data impact Building NOAA OSSE testbed Evaluating different data assimilation systems