, Karina Apodaca, and Man Zhang Warn-on-Forecast and High-Impact Weather Workshop, February 6-7, 2013, National Weather Center, Norman, OK Utility of GOES-R geostationary lightning mapper (GLM) using hybrid variational- ensemble data assimilation in regional applications Milija Zupanski, Karina Apodaca, and Man Zhang Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins, Colorado, U. S. A. [ ] Acknowledgements: -Louis Grasso, John Knaff, Mark DeMaria, Steve Lord -JCSDA -NOAA NESDIS/GOES-R

Goals of the project (1)Develop capability to use GOES-R Geostationary Lightning Mapper (GLM) observations in prototype hybrid variational-ensemble data assimilation system (HVEDAS) (2)Evaluate its impact in regional data assimilation (DA) applications to severe weather (3)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 system incorporates - 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 system additionally 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)

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

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 Surface weather map Valid 04/27/2011 at 00UTC SPC storm reports Valid 04/27/2011 Focus on 9 km inner domain Tornado outbreak of April 27-28, 2011, southeastern U.S.

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

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

Assimilation of WWLLN lightning observations: single observation experiment Relevant for data assimilation: lightning observations impact initial conditions of model dynamical variables 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

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

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

Assimilation of WWLLN lightning observations: Local impact on storm environment Analysis increments: Lightning data assimilation increases the advection of low-level vorticity into the region of large CAPE 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

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. Related publications

 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 Summary 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