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Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University

JCSDA Science Workshop on Satellite Data Assimilation June 5-7, 2013, NCWCP, College Park, MD Utility of GOES -R ABI and GLM instruments in regional data assimilation for high- impact weather. Milija Zupanski Cooperative Institute for Research in the Atmosphere

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Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University

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  1. JCSDA Science Workshop on Satellite Data AssimilationJune 5-7, 2013, NCWCP, College Park, MDUtility of GOES-R ABI and GLM instruments in regional data assimilation for high-impact weather Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University

  2. Acknowledgements • Programs: • GOES-R Risk Reduction • JCSDA • NSF Collaboration in Mathematical Geosciences (NSF-CMG) • People: • Karina Apodaca (CIRA), Man Zhang (CIRA), Lous Grasso (CIRA) • Mark DeMaria (NOAA/STAR), John Knaff (NOAA/STAR) • Min-Jeong Kim (CIRA-NOAA/EMC) • Jun Li (CIMSS, Univ. Wisconsin) • High-End Computing: • NOAA S4 (SSEC, Univ. Wisconsin) • NCAR CISL (Yellowstone)

  3. Research • Objectives: • - Assess the benefit of future GOES-R observations (ABI and GLM) • - Use realistic NOAA systems • - Conduct necessary development to maximize the benefit of GOES-R • The research encompasses: • - assimilation of cloudy radiances (infrared and microwave) • - cloud-resolving data assimilation applications to high-impact weather • - regional hybrid variational-ensemble data assimilation • The following components are incorporated: • Weather Research and Forecasting Model (WRF): • - WRF-NMM, WRF-ARW, hurricane WRF (HWRF) • Observation operator: • - NCEP Gridpoint Statistical Interpolation (GSI) • - Community Radiative Transfer Model (CRTM) • Data assimilation: • - Maximum Likelihood Ensemble Filter (MLEF) • - Use of regional hybrid GSI in the future

  4. High-impact weather and clouds GOES-R • Severe weather • Thunderstorms • Tornadoes • Rainfall • Hail • Flash floods • Moore tornado (05/20/2013) • Wide-spread supercell thunderstorms – indicated by clouds

  5. High-impact weather and clouds • Hurricane Sandy (2012) • Tropical cyclones • High winds • Storm surge • Rainfall • Floods • Tornadoes • Rip currents • Improving assimilation and prediction of clouds is critical for reducing uncertainty of high-impact weather

  6. Assimilation of clouds for high-impact weather GOES-R • Clouds are observed by satellites • Microwave (MW), Infrared (IR) • Lightning (future GLM): Signature of high-impact weather and clouds Cloudy satellite radiances are generally not assimilated in weather operations: • Nonlinearity (and non-differentiability) • Microphysical processes • Radiative Transfer (RT) model • Forecast error covariance • Flow-dependent, cross-variable correlations, microphysics, dynamics • Computational limitations • High-dimensional state vector for cloud-resolving data assimilation • Required additional RT model calculations • Potentially useful information about clouds is mostly discarded. Can we do better?

  7. Research scope and tools Assimilate observations that can add new information about clouds and the storm environment • MW: precipitation-affected radiances (microphysics/precipitation) • IR: cloud-affected radiances (cloud top/location) • Lightning flash rate (cloud ice, storm environment) • AIRS SFOV vertical profiles of q and T (storm environment) Regional data assimilation of tropical cyclone core area and severe weather • hurricane WRF (HWRF) • WRF-NMM Data assimilation components • Maximum Likelihood Ensemble Filter (MLEF) • Forward GSI • Forward CRTM

  8. Data assimilation algorithm: Maximum Likelihood Ensemble Filter (MLEF) • A hybrid between EnKF and variational methods • iterative minimization (variational) • multiple realizations of model and observation operators for uncertainty (ensemble) • Deterministic control forecast • Nonlinear analysis solution by an iterative minimization • Improved minimization efficiency by an implicit Hessian preconditioning Fast convergence (1-2 iterations) from an arbitrary initial state

  9. MLEF is modular • Observation module: • Transform/interpolate from model to observations • Read observations • Output observation info: increments, errors, … • Forecast module: • Import the forecasting system with pre- and post-processing • Make namelist.input on-the-fly • Data assimilation module: • Controls the processing of the model and observation info • Hessian preconditioning, gradient, minimization iterations • State vector and uncertainty estimates

  10. Assessment: Quantifying satellite information using Shannon information measures Entropy Change of entropy due to observations Gaussian pdf greatly reduces the complexity since entropy is related to covariance Change of entropy Degrees of freedom for signal (DFS) In ensemble DA methods DH and DFS can be computed exactly in ensemble subspace: Since the singular values s are by-product of assimilation, the flow-dependent DH and DFScan be computed

  11. All-sky microwave radiance assimilation in Tropical Cyclone core area • Model: NOAA HWRF (operational in 2011, 27km/9km) • Results for TC core area (inner nest) at 9 km resolution • Observations: AMSU-A all-sky radiances, Channels 1-9 and 15 assimilated • Data assimilation interval: 6 hours • Number of ensembles: 32 • Hurricane Daniele (2010) • Control variable: pd, T, q, u, v, cwm • Bias correction from clear-sky GSI output • From M. Zhang et al. (2013a, Mon. Wea. Rev.)

  12. Hurricane Danielle (2010): All-sky AMSU-A information content (DFS) Cycle 1 Cycle 3 Cycle 5 Cycle 7 ASR CSR Cloudy radiance observations add new information throughout the storm development

  13. Hurricane Danielle (2010) (b) (a) 6-hour forecast of total cloud condensate Data assimilation cycle 8 – valid1200 UTC 26 Aug 2010 IR imagery AMSU-A retrieved precipitation rate CTL – control experiment ASR – all-sky experiment (c) (d) All-sky MW radiances improve short-range forecast of clouds in hurricane core area

  14. Hurricane Danielle (2010): Intensity Hurricane Danielle (2010): Time series of the minimum sea level pressure (hPa) for NHC best track data (thick grey line) and MLEF-HWRF experiments ASR (solid) and CSR (dashed) between 1800 UTC 24 Aug and 1800 UTC 26 Aug 2010.

  15. All-sky infrared radiance assimilation for Tropical Cyclone core area • Model: NOAA HWRF (operational in 2011, 27km/9km) • Results for TC core area (inner nest) at 9 km resolution • Observations: SEVIRI all-sky radiances [10.8 mm - proxy for GOES-R ABI) • Data assimilation interval: 1 hour • Number of ensembles: 32 • Hurricane Fred (2009) • No bias correction (advantage of clear-sky GSI correction not obvious) • From M. Zhang et al. (2013b)

  16. Hurricane Fred (2009): Analysis Control experiment All-sky radiance assimilation Verification: AMSU-A NOAA-16 retrieved cloud liquid water Total cloud condensate (cwm) Valid 0600 UTC 9 Sep 2009 Assimilation of all-sky infrared radiance is able to improve clouds in TC core (a) (a) (b) (b) (c) (c)

  17. Hurricane Fred (2009): 21-hour forecast Total cloud condensate (cwm) Valid 0300 UTC 10 Sep 2009 Verification: Seviri radiance observations Control experiment All-sky radiance assimilation Assimilation of all-sky infrared radiance improves the forecast of clouds in TC core area (a) (a) (b) (b) (c) (c)

  18. Hurricane Fred (2009): Assimilation of all-sky SEVIRI and AIRS SFOV (q,T) in HWRF outer domain Degrees of freedom for signal- DFS MSG SEVIRI and AIRS q profile MSG SEVIRI and AIRS T profile MSG SEVIRI only • Hurricane Fred (2010) • No data thinning for SFOV profiles In outer domain (with less clouds) DFS shows a benefit from AIRS SFOV specific humidity and temperature data (temperature has a stronger impact)

  19. Lightning data assimilation: Severe weather applications • Model: NOAA WRF-NMM (27km/9km) • Results for the inner nest at 9 km resolution • Observations: WWLLN [proxy for GOES-R GLM) • Data assimilation interval: 6 hours • Number of ensembles: 32 • Tornado outbreak over Southeast US in April 2011 • From Apodaca et al. (2013)

  20. Severe weather outbreak over the southeastern US on April 25-18, 2011 Model domain and tornado reports for April 27, 2011 D01 D02 20

  21. Lightning observation operator • WWLLN can observe only cloud-to-ground (C-G) flashes • GLM will observe both C-G and intra-cloud flashes • Regression between lightning flash rate and model variables • - Best regression suggests cloud ice and vertical graupel flux • (McCaul et al. 2009) • WRF-NMM microphysics (Ferrier) does not predict cloud ice: • - Need to rely on less accurate regression: maximum vertical updraft • Present: • - Use max vertical updraft and WWLLN • Future: • - Include more complex microphysics to improve obs operator • - Use better GLM proxy observations (C-G and intra-cloud) • - Increase the resolution to 1-3 km

  22. Information content of lightning observations: DFS 2011_04_27-00:00:00 Cycle 1 2011_04_27-12:00:00 Cycle 3 2011_04_28-00:00:00 Cycle 5 WWLLN Observations WWLLN Observations WWLLN Observations Flow dependent information added by assimilating lightning data • Time-dependent covariance Pf shows direct relationship to obs.

  23. Lightning data assimilation with MLEF: Single observation experiment Analysis response to a single observation of flash rate in a 6-hour interval Psfc increment Temp increment Wind increment Assimilation of lightning observations impacts all model variables and improves storm environment conditions: important for maintaining impact of assimilation

  24. Possible new research directions (1) Combine all observations in applications to TC/severe weather - All-sky infrared radiances(GOES-R ABI) - Lightning (GOES-R GLM) - All-sky microwave radiances - AIRS/IASI (sounder) - NOAA operational observations (GSI) (2) Extend the utility of GOES-R instruments to air quality - Impact of lightning on NOx and O3 (air quality) - Use ABI and GLM for high-impact weather and AQ (3) Examine the impact of WV channels for TC genesis - channel difference contains important information (J. Knaff) (4) Further development of hybrid variational-ensemble systems - hybrid forecast error covariance with optimal Hessian preconditioning

  25. Hybrid data assimilation • Extend MLEF to include static/variational and ensemble error covariances • Hybrid ensemble-variational error covariance with optimal Hessian preconditioning • Single DA system (no separate variational and ensemble algorithms) • Potential improvements over the existing hybrid DA systems: • Optimal Hessian preconditioning (e.g., observation term included) • Considerable computational savings: adaptive ensemble forecasting • Single DA system is easier to maintain than two systems (Var and EnKF) static ens

  26. Potential relevance to operations (1) Utilize the development of the MLEF regional hybrid data assimilation for all-sky radiances and GLM to improve hybrid GSI - observation processing and QC is same (GSI) - evaluated in realistic situations using NOAA systems - reduced code transfer/development - explore the utility of MLEF ensembles in regional hybrid GSI (2) Add GLM assimilation capability to operational DA system - New observations accessed through GSI and CRTM - BUFR format (3) Regional assimilation of combined GLM and all-sky ABI observations for warn-on-forecast and hurricane prediction - Results show relevance of these observations for prediction of clouds - Could be used in combination with hybrid GSI or as a standalone system for high-resolution nest (e.g., TC core, WRF-NMM inner nest)

  27. Future work • Combine all observations in applications to TC/severe weather • - All-sky infrared radiances(GOES-R ABI) • - Lightning (GOES-R GLM) • - All-sky microwave radiances • - AIRS/IASI (sounder) • - NOAA operational observations (GSI) • Examine the impact of WV channels for TCgenesis • Impact of lightning on NOx and O3 (air quality) • - Use ABI and GLM for high-impact weather and AQ • Coupled models (atmosphere-chemistry-land-ocean) • Further development of hybrid variational-ensemble systems • - hybrid forecast error covariance with optimal Hessian preconditioning

  28. Thank you! References: Apodaca, K., M. Zupanski, M. DeMaria, J. Knaff, and L. Grasso, 2013: Evaluating the potential impact of assimilating satellite lightning data utilizing hybrid (variational-ensemble) methods. Submitted to Tellus. Zhang, M., M. Zupanski, M.-J. Kim, and J. Knaff, 2013: Assimilating AMSU-A Radiances in TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010). Mon. Wea. Rev., in print. Zupanski M., 2013: All-sky satellite radiance data assimilation: Methodology and Challenges. Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications (Vol. II), S.-K. Park and L. Xu, Eds, Springer-VerlagBerlin Heidelberg, 730 pp.

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