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Adaptive targeting in OSSE

Explore adaptive data observing and processing techniques in OSSE, including the use of ensemble generation, ETKF targeting strategy, and evaluation of data impact. Link with THORPEX and T-PARC for improved weather forecasts.

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Adaptive targeting in OSSE

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

  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

  7. ET KF formulation

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

  9. MAIN THEME 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 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 Surface pressure Precipitation Contours are 1000mb geopotential height, shades are differences in the fields between two experiments 500mb height 250mb height

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

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

  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 interestsGlobal optimal positioning of “observing” systems in OSSEImprove forecast accuracy

  19. T-PARC PROPOSED OBSERVING PLATFORMS Day 3-4 Radiosondes Russia NA VR Day 5-6 Radiosondes Tibet CONUS VR D 2-3 G-IV D 1-2 C-130 UAS D-1 UAS P-3 Day 3-4 GEMS Driftsondes Aerosondes 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

  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

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