1. NOAA THORPEX Highlights John Cortinas Tom Hamill NOAA/OAR 1

2. Implementation of EnKF concepts into operations NOAA/THORPEX, in its early years, provided seed money to many organizations (e.g., ESRL/PSD, U. Maryland, and NRL) for the development and inter-comparison of ensemble-based data assimilation methods. In the second round of funding (after a budget cut), ESRL/PSD received funding for more concerted development. This funding was supplemented with PSD base funds and HIWPP funds. ESRL/PSD demonstrated significant improvements, for example in hurricane track forecasts (see next page) In 2010-2012 timeframe, ESRL/PSD worked closely with EMC (supported by OST funds) to develop and implement a hybrid ensemble Kalman filter/3D-Var global data assimilation system. This system was implemented in May 2012, resulting in a significant jump in the performance of NCEP’s global models. 2

3. NOAA Support ~$1.3M annually for research and international program office support – $588,000 to international program office from 2009-2011 (sole supporter from US agencies) – NOAA budget discontinued funding in 2012 Research funding supported numerous projects that involved NOAA investigators working with cooperative institute partners 3

4. Some articles on EnKF supported by NOAA THORPEX research Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 3078- 3089. Hamill, T. M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. J. Benjamin, 2011: Predictions of 2010's tropical cyclones using the GFS and ensemble- based data assimilation methods. Mon. Wea. Rev., 139, 3243-3247. Hamill, T. M., and J. S. Whitaker, 2011: What Constrains Spread Growth in Forecasts Initialized from Ensemble Kalman Filters? Mon. Wea. Rev., 139, 117-131. (50) Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. J. Benjamin, 2011: Global ensemble predictions of 2009's tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668-688. Hamill, T. M., J. S. Whitaker, J. L. Anderson, and C. Snyder, 2009: Comment on "Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems. J. Atmos. Sci., 66, 3498-3500. Bougeault, P., Z. Toth, many others, T. M. Hamill, and many others, 2009: The THORPEX Interactive Grand Global Ensemble (TIGGE). Bull Amer. Meteor. Soc., 91, 1059-1072. Wang, X., T. M. Hamill, J. S. Whitaker, C. H. Bishop, 2009: A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales. Mon. Wea. Rev., 137, 3219-3232. Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463-482. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: real observation experiments. Mon. Wea. Rev., 136, 5132-5147. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: observing system simulation experiments. Mon. Wea. Rev., 136, 5116-5131. Wang, X., T. M. Hamill, and C. Snyder, 2007: On the theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes Mon. Wea. Rev., 135, 222-227. Wang, X., T. M. Hamill, C. Snyder, and C. H. Bishop, 2006: A Comparison of Hybrid Ensemble Transform Kalman Filter-OI and Ensemble Square-Root filter Analysis Schemes. Mon. Wea. Rev., 135, 1055-1076. Hamill, T. M., 2006: Ensemble-based atmospheric data assimilation Chapter 6 of Predictability of Weather and Climate, Cambridge Press, 124-156. Hamill, T. M., and J. S. Whitaker, 2005. Accounting for the error due to unresolved scales in ensemble data assimilation: a comparison of different approaches. Mon. Wea. Rev., 133, 3132-3147.Evaluating methods to account for system errors in ensemble data assimilation. Predictions of 2010's tropical cyclones using the GFS and ensemble- based data assimilation methods.What Constrains Spread Growth in Forecasts Initialized from Ensemble Kalman Filters?Global ensemble predictions of 2009's tropical cyclones initialized with an ensemble Kalman filter. Comment on "Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems. The THORPEX Interactive Grand Global Ensemble (TIGGE).A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales.Ensemble data assimilation with the NCEP Global Forecast System.A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: real observation experiments. A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: observing system simulation experiments.On the theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes A Comparison of Hybrid Ensemble Transform Kalman Filter-OI and Ensemble Square-Root filter Analysis SchemesEnsemble-based atmospheric data assimilation Tippett, M., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square-root filters. Mon. Wea. Rev., 131,1485-1490.Ensemble square-root filters. 4

5. Skill of tropical cyclone track forecasts from EnKF compared to then-operational NCEP GEFS ensemble (2009) Mean error and spread of 2009 Northern Hemisphere track forecasts initialized from EnKF vs. other operational and experimental (FIM) forecast systems. from Hamill et al., 2011 MWRHamill et al., 2011 MWR 5

6. Targeted observation research Winter Storms Reconnaissance (WSR) ~2002-2011 was supported by NOAA THORPEX funds. Dropsonde data from reconnaissance aircraft was dropped into areas deemed important, largely in the Pacific basin, aiming for reducing forecast error for significant land-falling storms that had potential for rapid error growth. WMO/DAOS recommended a careful evaluation of the impact of WSR data with a modern data assimilation system. A NOAA-THORPEX funded study observing system experiment was conducted using 2011 WSR data, examining skill of forecasts with and without the extra data. Result: no significant impact (too few observations). Consequence: WSR was cancelled for 2014 winter storms season. Recommendation to concentrate targeting concepts on other ideas, such as targeted assimilation of denser cloud-drift wind data. 6

7. Comparison of forecasts with and without assimilation of WSR data From Hamill et al., 2013 MWR (http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-12-00309.1) Error of forecasts with assimilation of dropsonde data (CONTROL) vs. without (NODROP) measured in pre-computed downstream verification region. 7

8. TIGGE (THORPEX Interactive Grand Global Ensemble) NOAA THORPEX contributed to the establishment of multi-model ensemble archives that could be used for a variety of forecast purposes, including studies of the effect of multi-model ensembles. See Bougeault et al. overview article in Bulletin of the American Meteorological Society, Aug 2010.overview article 8

9. Example of TIGGE-facilitated research: multi-model vs. reforecast-calibrated Here, a comparison of multi-model ensemble skill of 2-meter temperature forecasts vs. the various individual systems. Also plotted is the skill of ECMWF’s forecasts, post-processed using their internal reforecast database. from Hagedorn et al. 2012.Hagedorn et al. 9

10. Forecastability of atmospheric rivers Gary Wick (ESRL/PSD) received funds for studying how well atmospheric rivers were forecast in various global models. Study published in WAF Dec 2013.WAF Dec 2013 10

11. Issues NOAA THORPEX funding was eliminated in 2011. – Several of the research projects were cancelled, and/or had to find funding from other sources. – Support of WMO’s International Program Office for THORPEX was terminated earlier than originally planned. Hindered ability of NOAA to leverage other international THORPEX research. Many activities such as planned workshops had to be cancelled due to lack of funding. No shared contribution to international program office by US agencies, only NOAA. US agencies lacked a strong coordinated effort to collaborate and communicate results. No overall performance metrics to evaluate progress toward goals. 11