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Ensemble Transform Sensitivity and Targeted Observations: An OSSE Case Study

This study examines the use of Ensemble Transform Sensitivity (ETS) methodologies within Observing System Simulation Experiments (OSSEs) to improve numerical weather prediction (NWP) accuracy. By evaluating adaptive observations and their impact on forecast quality, we present new insights into the optimal deployment of Unmanned Aerial Systems (UAS) and innovative observation strategies. Our findings underscore the importance of selecting suitable observation platforms and illustrate how adaptive observations can significantly enhance forecast performance, utilizing advanced NOAA OSSE systems and real-world case studies.

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Ensemble Transform Sensitivity and Targeted Observations: An OSSE Case Study

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  1. Ensemble Transform Sensitivity and Targeted Observations: An OSSE Case Study YuanfuXie, Hongli Wang, and Zoltan Toth Acknowledgements: R. Atlas, R. Hood, G. Wick Global Systems Division NOAA ESRL/GSD/FAB

  2. OUTLINE • Introduction • Observing System Simulation Experiments (OSSEs) • Unmanned Areal Systems (UAS) • Adaptive observations • New Ensemble Transform Sensitivity (ETS) method • NOAA Joint OSSE System • TC OSSE case studies

  3. OSSE 101 OSSEs are NWP experiments used to evaluate the impact of new observing systems on numerical forecasts when actual observational data are not yet available • Long model integration used as “truth” - Nature Run (NR) • “Synthetic observations” generated - current & new observing systems • Synthetic observations assimilated into NWP analyses • With & w/o new type(s) of observations • Forecasts made from both analyses • Forecast model different from NR model (“imperfect” model) • Two forecasts compared with NR • Quantify improvements due to new observing system • Earlier OSSE results confirmed after launch of observing systems • ERS, NSCAT, AIRS, etc - Atlas 1985,1997, …)

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

  5. WHY WE NEED UAS OSSEs? • Assist in optimal selection of UAS platforms • Cost / benefit analysis - Are UAS a good investment? • Combined use with manned aircraft & other observing systems • Design of UAS missions • Flight paths • Instrumentation

  6. ADAPTIVE OBSERVATIONS • Purpose:Improve forecasts by deployment of adaptive observations • Questions: • When & where to deploy? • Techniques: • ADJ (Adjoint sensitivity) • SV (Singular Vectors) • ET (Ensemble Transform) • ETKF (Ensemble Transform Kalman Filter) A B C Estimate Strategy: Sensitive Areas Verification Areas Improve Fcst tvVerification time tiTargeting time

  7. ENSEMBLE TRANSFORM (ET) Bishop & Toth 1999 Xe XeC Transform Matrix C Ensemble perturbations Perturbations transformed to represent effect of adaptive observations Forecast error covariance Analysis error covariance • Error variance is sum of the diagonal elements of the forecast error covariance matrix • Total Energy norm used

  8. ENSEMBLE TRANSFORM - 2 • Advantages • Determines expected forecast error reduction • Much faster than adjoint-based methods • Limitations • Works in subspace of ensemble perturvbations • Spurious correlations due to limited ensemble size • Must carry out separate calculation for each possible observational deployment

  9. PROPOSED METHOD - ENSEMBLE TRANSFORM SENSITIVITY (ETS) • Calculates the gradientof the total forecast error variance to analysis error variance • First order approximation of ET • Needs to calculate only a single transformation matrix • Much increased computational efficiency • Helpful in high resolution / global applications

  10. ENSEMBLE TRANSFORM SENSITIVITY

  11. ET vs. ETS TRANSFORM MATRICES ET ETS • ETS advantages: • More efficient as no separate transfer matrices needed for various adaptive observational configurations • Sensitivity proportional to analysis variance - areas with large analysis variance will show more sensitivity

  12. NOAA JOINT OSSE SYSTEM • Nature Run - ECMWF operational model (2005) • T511/91L resolution • 13-month integration forced w May 2005 - May 2006 analyzed SST • 13 Atlantic basin tropical cyclones w realistic track behavior • A new global NR at 7km at GMAO is under validation • Analysis - Forecast system- NCEP operational GSI/GFS • T382/64L resolution • Hybrid GSI (2013) • 120-hour forecasts at 00Z and 12Z • Calibration– to ensure simulated & real impacts similar • Calibration for RAOB, AMSU-A, ACAR, AIRS & GOES observations • GSD/ESRL, jointly with NASA GMAO, NCEP/EMC, NESDIS, AOML & JCSDA

  13. HYBRID GSI • Cost function • Bf: (fixed) background error covariance • Bens: Background error covariance from ensemble • β: weighting factor (0.25 for Bf) • High resolution GDAS (T382) and low resolution ensemble forecasts (T254)

  14. ASSIMILATED OBSERVATIONS Amsua-n15 • PrepBufr data • Amsu-A: n15,16, aqua • Amsu-B: n17 • Airs: aqua • Hirs2: n14 • Hirs3: n17 • Sndr: g10,g12 Airs-Aqua

  15. EXPERIMENTAL SPECIFICS • Targeting • ETS • 30-member EnKF ensemble • Initial conditions • AL01 – “00Z Aug 4 2005” • AL02 – “00Z Aug 23 2005”

  16. HYBRID GSI PERFORMANCE – AL01 Hybrid GDAS produced very good track predictions compared to old GSI HYB Fixed B

  17. ETS RESULTS - AL01 Surface pressure (ens. mean) ETS (shades) T=0d T=-1d T=-2d T=-3d

  18. ETS RESULTS – AL02 Surface pressure (ens. mean) ETS (shades) T=0d T=-2d T=-3d

  19. DATA IMPACT STUDIES - AL02 Targeting time: 00Z Aug 22Verification time: 00Z Aug 25 Target ETS (shaded) Maximum

  20. DATA IMPACT RESULTS – AL02 Impact of initial time Impact of targeted obs

  21. SUMMARY • NOAA Joint global OSSE system updated with Hybrid GSI & TC relocation • Both schemes contribute to more accurate TC forecast tracks • New targeting method (ETS) developed & tested • Computationally more efficient • Sensitivity proportional to analysis error variance • ETS sensitivity appears around TC location • Impact of targeted observations need further analysis • Choice of norm to be studied

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