Ensemble Transform Sensitivity and Targeted Observations: An OSSE Case Study

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

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

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, …)

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

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

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

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

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

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

ENSEMBLE TRANSFORM SENSITIVITY

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

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

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)

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

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

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

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

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

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

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

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

Ensemble Transform Sensitivity and Targeted Observations: An OSSE Case Study