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Acknowledgements: Louis Grasso, John Knaff , Mark DeMaria , Steve Lord JCSDA NOAA NESDIS/GOES-R

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Acknowledgements: Louis Grasso, John Knaff , Mark DeMaria , Steve Lord JCSDA NOAA NESDIS/GOES-R

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  1. Warn-on-Forecast and High-Impact Weather Workshop,February 6-7, 2013, National Weather Center, Norman, OKUtility of GOES-R geostationary lightning mapper (GLM) using hybrid variational-ensemble data assimilation in regional applicationsMilija Zupanski, Karina Apodaca, and Man ZhangCooperative Institute for Research in the AtmosphereColorado State UniversityFort Collins, Colorado, U. S. A.[ http://www.cira.colostate.edu/projects/ensemble/ ] • Acknowledgements: • Louis Grasso, John Knaff, Mark DeMaria, Steve Lord • JCSDA • NOAA NESDIS/GOES-R

  2. Goals of the project • Develop capability to use GOES-R Geostationary Lightning Mapper (GLM) observations in prototype hybrid variational-ensemble data assimilation system (HVEDAS) • Evaluate its impact in regional data assimilation (DA) applications to severe weather • If there is a NOAA interest in further investigation/implementation of GLM observations in data assimilation, support such an effort in collaboration with EMC. • Benchmark systemincorporates • - WRF-NMM • - Vertical updraft lightning observation operator • - WWLLN lightning flash rate (proxy for GOES-R GLM) • - Prototype hybrid variational-ensemble DA system (Maximum Likelihood Ensemble Filter) • Enhanced systemadditionally incorporates • - GSI+CRTM forward (nonlinear) operators • - All-sky SEVIRI IR radiances (proxy for GOES-R ABI) • - All-sky MW radiances (AMSU-A) • - Vertical profiles of T and Q (AIRS, IASI) • - NOAA HWRF • - Hydrometeor-based lightning observation operator (e.g., McCaul et al. 2009)

  3. Enhanced system with MLEF-HWRF: Assimilation of cloudy radiances (M. Zhang et al. 2013a,b) MW: AMSU-A cloudy radiance assimilation (Total cloud condensate) All-sky radiance assimilation Radar obs Clear-sky radiance assimilation IR: SEVIRI cloudy radiance assimilation (Total cloud condensate) AMSU-A NOAA-16 retrieved cloud liquid water Control run All-sky radiance assimilation obs • Assimilation is able to improve clouds in TC • Improved TC intensity • Marginal (but positive) impact on TC track

  4. Benchmark system: experimental setup • NOAA WRF-NMM model at 27km / 9km resolution • Use MLEF as a prototype HVEDAS • 32 ensembles • 6-hour assimilation interval • World Wide Lightning Location Network (WWLLN) observations • Control variables: PD, T, Q, U, V, CWM Tornado outbreak of April 27-28, 2011, southeastern U.S. Surface weather map Valid 04/27/2011 at 00UTC SPC storm reports Valid 04/27/2011 Focus on 9 km inner domain

  5. Lightning flash rate observation operator Current version: - maximum vertical velocity - works with any microphysics, but less accurate Next version: - cloud hydrometeor based (graupel flux, cloud ice – McCaul et al. 2009) - requires more advanced microphysics, but more realistic Evaluation steps for new observation type: 1. Observation bias/pdf - check skewness of probability density function 2. Single observation experiment - analysis response, impact on model initial conditions 3. Observation information measure - quantify impact of observations in assimilation 4. Physical interpretation of data assimilation - check whether analysis correction appears physically consistent

  6. Lightning observation bias / pdf Normalized innovation vector: histogram/pdf Original formulation • Since the original pdf is skewed, need to correct observation operator • Introduce multiplication parameter a and minimize cost function Corrected formulation histogram/pdf

  7. Assimilation of WWLLN lightning observations: single observation experiment Impact of a single lightning observation on the analysis: Q increment at 700 hPa Valid 04/27 at 12UTC T increment at 700 hPa Valid 04/27 at 12UTC Wind increment at 700 hPa Valid 04/27 at 12UTC Relevant for data assimilation: lightning observations impact initial conditions of model dynamical variables

  8. Observation information content using Shannon information measures Use Shannon information (e.g. entropy) as an objective, pdf-based quantification of information (Rodgers 2000; Zupanski et al. 2007) Entropy Change of entropy due to observations • Gaussian pdf greatly reduce the complexity since entropy is related to covariance Change of entropy / degrees of freedom for signal (DFS) In ensemble DA methods DFS can be computed exactlyin ensemble subspace: Since eigenvalues of the matrix ZTZ are known and the matrix inversion is defined in ensemble space, the flow-dependent DFS can be computed

  9. Assimilation of WWLLN lightning observations:Degrees of Freedom for Signal Cycle 1 04/27/11 at 00UTC Cycle 3 04/27/11 at 12UTC Cycle 5 04/28/11 at 00UTC • Time-dependent information content • Shows the actual use of observations in each data assimilation cycle • Pixels correspond to error covariance localization used in DA

  10. Assimilation of WWLLN lightning observations: Local impact on storm environment Analysis increments: Wind increment at 850 hPa Valid 04/28 at 00UTC Vorticity increment at 850 hPa Valid 04/28 at 00UTC Background CAPE Valid 04/28 at 00UTC Lightning data assimilation increases the advection of low-level vorticity into the region of large CAPE

  11. Related publications Apodaca, K., M. Zupanski, M. Zhang, M. DeMaria, L. D. Grasso, J. A. Knaff, and G. DeMaria, 2013:Evaluating the potential impact of assimilating GOES-R GLM satellite lightning observations. To be submitted to Tellus. (Feb 2013) Zhang, M., M. Zupanski, M.-J. Kim, and J. Knaff, 2013a: Direct Assimilation of all-sky AMSU-A Radiances in TC inner core: Hurricane Danielle (2010). Mon. Wea. Rev., accepted with minor revisions. Zhang, M., M. Zupanski, and J. Knaff, 2013b: Impact assessment of SEVIRI data assimilation for Hurricane model initialization. To be submitted to Q. J. Roy. Meteorol. Soc. (April 2013) Zupanski M., 2013: All-sky satellite radiance data assimilation: Methodology and Challenges. Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, S.-K. Park and L. Xu, Eds, Springer-Verlag Berlin, in print.

  12. Summary • WRF-NMM, MLEF, and WWLLN observations combined in a prototype regional HVEDAS • Maximum updraft-based lightning observation operator requires on-line correction • Preliminary results encouraging Future Work • Use of more advanced lightning observation operator (McCaul et al. 2009) • Combined assimilation of WWLLN and NCEP observations (e.g., GSI+CRTM) • Combined assimilation of all-sky MW, IR (ABI) radiances and lightning (GLM) • Conduct a thorough evaluation of the value-added impact of lightning data in regional data assimilation applications to: • - Tropical cyclones • - Severe weather • - Focus on forecast evaluation

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