1. Adaptive targeting in OSSE Outline Adaptive observing / data processing techniques in OSSE Addition to OSSE Link with THORPEX Link with T-PARC Yucheng Song and Zoltan Toth

2. (1)Adaptive data observing/processing techniques in OSSE Test methods/platforms/application in OSSE framework  Develop software into OSSE Ensemble (T126 or T170) product generation in OSSE ETKF targeting strategy (certain instruments)  Evaluate data impact by certain instruments like UAS, Doppler Wind Lidar

3. NCEP Operational GEFS NAEFS (NCEP/GEFS) 80 perturbations in cycling (see next slide) Replaced previous 56 perturbations in ensemble transform (ET) cycling 20 perturbed long forecasts (16-d) in each cycle Replaced previous14 long forecasts in each cycle

4. T00Z 80m 6hrs T06Z 80m T12Z 80m T18Z 80m Up to 16-d Re-scaling 6 hours ET cycle NCEP ensemble (ET) Next T00Z

5. Concept of ET KF ET KF Ensemble Transform Kalman Filter for short ET KF provides a framework for estimating the effect of observations on forecast error covariance ET KF uses ensemble transformation and a normalization to obtain the prediction error covariance matrix associated with a particular deployment of observational resources Linearity is assumed for ensemble transformation

6. ET KF formulation

8. The ETKF spotted the target area Expected error reduction propagation Targeting methods - ETKF Storm Dropsondes to be made by G-IV

9. Study the lifecycle of perturbations as they originate from the tropics, Asia, and/or the polar front, travel through the Pacific waveguide, and affect high impact wintertime weather events over North America and the Arctic MAIN THEME Influence of tropical Flare-ups in western Pacific (IR) on deep cyclogenesis in northeast Pacific captured by Ensemble Transform targeting method

10. Better adaptive strategy if implemented (examples) The optimal sampling region located in the jet core

11. (2)Additions to OSSE Assess threat of high impact events based on ensemble – automatically pick high impact events at 3-day leading time Run ET/ET KF targeting for each high impact case Dispatch observing systems/data processing resources (before and inside DA) Wind Lidar, UAV etc. Assimilate targeted data (carry out adaptive data processing) Evaluation (EXAMPLES NEXT FROM WSR)

12. Impact of Data 500mb height 250mb height Precipitation Surface pressure Contours are 1000mb geopotential height, shades are differences in the fields between two experiments

13. Forecast verification Red contours show forecast improvement due to WSR dropsondes, blue contours show forecast degradation 500mb height 250mb height Sea Level Pressure

14. Forecast Verification for Temperature (Measure by root-mean-square errors) RMS error reduction vs. forecast lead time 10-20% RMS error reduction in Temperature 60 hr forecast is equivalent to 48hr forecast

15. (3)Link with THOPREX THORPEX – A World Weather Research Program (WWRP): Accelerate improvements in skill/utility of 1-14 day weather forecasts Long-term (10-yrs) global research program in areas of: Observing system, data assimilation, numerical modeling/ensemble, socioec. appl. Strong link with operational Numerical Weather Prediction (NWP) centers International program under WMO

16. THORPEX evaluation metrics (1) Possible new probabilistic guidance products for high impact events  Hydrometeorology Extreme hydro-meteorological events, incl. dry and wet spells (CONUS) Quantitative extreme river flow forecasting (OCONUS)  Tropical / winter storm prediction Extreme surface wind speed Extreme precipitation (related to wet spells) Storm surges  Aviation forecasting Flight restriction Icing, visibility, fog, clear air turbulence  Health and public safety Hot and cold spells

17. THORPEX evaluation metrics (2) “Legacy” NCEP internal probabilistic scores to assess long-term progress  General circulation Probabilistic 1000 & 500mb height forecasts  Storm Strike probability for track Probability of intensity (central pressure or wind-based)

18. (4) T-PARC interests Global optimal positioning of “observing” systems in OSSE Improve forecast accuracy

19. Day 3-4 Radiosondes Russia Day 3-4 GEMS Driftsonde s Aerosonde s D 2-3 G-IV D 1-2 C-130 UAS D-1 UAS P-3 CONUS VR NA VR Day 5-6 Radiosonde s Tibet Extensive observational platforms during T-PARC winter phase allow us to track the potential storms and take additional observations as the perturbation propagate downstream into Arctic and US continents T-PARC PROPOSED OBSERVING PLATFORMS

20. Before and after field campaign “Nature” is defined as a series of states corresponding to the real atmosphere Generated by very high resolution model runs nudged by operational analysis (GDAS) Advantages:  Use T-PARC type OSE to calibrate OSSE system – much easier to calibrate, community will be convinced if we can reproduce their OSE work  Retrospective work after T-PARC: T-PARC represent only one configuration of global observing system, with OSSE such defined, many other configuration can be tested  This is an alternative

21. Advantages (more) Ease of calibration (one-to-one comparison, can quantitatively evaluate osse system based on a SINGLE (or few) case(s), instead of requiring a large sample of cases Close to realistic representation of model related uncertainty No need to painstakingly evaluate or amend osse nature run Can use humidity (cloud, moisture) observations from real world to decide if certain observations can be made or not in osse world - potentially a big contribution to making osse real life-like Same nature can be redone with higher resolution or other type of model (using operational analysis as forcing) - direct comparison of different OSSE systems possible Estimate how proposed new observing systems would help analysis/forecast for real life significant events (Katrina, etc) Post field campaign analysis: Add significant value by osse testing of alternative deployments (after calibration in which actual and simulated field phase observations are assimilated and their impacts are compared in both OSE and OSSE framework

22. Concern: Improved analysis might not mean improved forecast for individual cases  We think statistically it will improve forecasts

23. OSSE strategy 1. Implement ET similarly as NCEP operational Ensemble forecast system Coding development Initial conditions (Data analysis from conventional data + radiance data assimilation) 2. Targeting strategy similarly as WSR – Identify typical storm cases in the Nature run use targeting strategy to find sensitive areas to target 1. Increase data resolution in sensitive areas (adaptive grid) 2. Direct observation

24. T-PARC interests ( Ideas can be tested in OSSE) Rossby-wave plays a major role in the development of high impact weather events over North America and the Arctic on the 3-5 days forecast time scale Additional remotely sensed and in situ data can complement the standard observational network in capturing critical processes in Rossby-wave initiation and propagation Adaptive configuration of the observing network and data processing can significantly improve the quality of data assimilation and forecast products Regime dependent planning/targeting Case dependent targeting New DA, modeling and ensemble methods can better capture and predict the initiation and propagation of Rossby-waves leading to high impact events Forecast products, including those developed as part of the TPARC research, will have significant social and/or economic value

25. Sequence of analysis fields  Dynamically consistent – NOT COMPLETELY Lack of consistency interferes with forecast evaluation  Only analysis quality can be evaluated directly  NATURE MODEL CAN BE RUN ALONG WITH OSSE FCST  Dynamics/physics different from assimilating model – MOST REALISTIC REPRESENTATION OF MODEL ERRORS?  PERFECT MODEL SCENARIO NOT POSSIBLE Differences should correspond to difference between nature & our models No difference means perfect model assumption, THORPEX interest  “Realistic” - YES Climate stats matching reality - YES  Moisture variables realistic so obs locations can be chosen realistically  YES Same weather as in nature - YES  Allows direct comparison between OSSE & OSE results for reliable calibration using small amount of data - YES