1. Assimilating Cloudy Infrared Brightness Temperatures in High-Resolution Numerical Models Using Ensemble Data Assimilation Jason A. Otkin and Rebecca Cintineo University of Wisconsin-Madison, CIMSS/SSEC Africa Perianez, Annika Schomburg, Robin Faulwetter, Andreas Rhodin, Hendrik Reich, and Roland Potthast German Deutscher Wetterdienst Thomas Jones University of Oklahoma, CIMMS Funding: NOAA GOES-R Risk Reduction Program and the U.S. Weather Research Program via Cooperative Agreement NA104400013.

2. Infrared Data Assimilation Studies Each study used ensemble data assimilation systems Kilometer-scale Ensemble Data Assimilation (KENDA) Data Assimilation Research Testbed (DART) All data assimilation experiments were run over regional-scale domains at convection-permitting resolutions (3-4 km) Focus on infrared water vapor bands on geostationary sensors, such as SEVIRI and the GOES-R Advanced Baseline Imager Provide valuable information about clouds and water vapor Both clear and cloudy sky infrared brightness temperatures were assimilated

3. SEVIRI Infrared Brightness Temperature Assimilation Regional-scale assimilation experiments were performed using the COSMO model and KENDA ensemble data assimilation system Most of this work was accomplished during a 2-month visiting scientist trip to the German DWD last year Assimilated clear and cloudy sky infrared brightness temperatures from the MSG SEVIRI sensor on the COSMO-DE domain that covers central Europe with 2.8 km resolution Experiments focused on the assimilation of 7.3  m brightness temperatures sensitive to clouds and water vapor in the middle and upper troposphere Simulated brightness temperatures were computed using version 10.2 of RTTOV Wyser (1998) method used to compute ice effective diameters

4. SEVIRI Infrared Brightness Temperature Assimilation Developed a cloud dependent bias correction scheme that removes the bias based on the cloud top height Bias statistics were computed separately for clear sky grid points and for low (CTH 6 km) clouds Statistics were only computed when the observed and simulated cloud top heights were within 600 m of each other Observed cloud height obtained from the Nowcasting SAF Observation-minus-background (OMB) errors evaluated during a 3.5 day passive monitoring period period prior to start of the assimilation experiments Average bias values for each cloud type during this period were subsequently used during the assimilation experiments

5. Bias Monitoring of SEVIRI Observations Statistics shown for clear (black), low (red), mid (blue), and high clouds (green) Errors vary with cloud regime High clouds have largest errors Largest errors occur when thin cirrus is present Need cloud- dependent bias correction RMSE Bias Mean Tb

6. SEVIRI Infrared Brightness Temperature Assimilation Two assimilation experiments were performed: Control – conventional observations only SEVIRI– conventional + SEVIRI 7.3  m T b (mid-level WV) Clear and cloudy sky brightness temperatures assimilated once per hour during a 12-hour assimilation period with a 40 member ensemble Bias correction values were set to -2.9 K for clear grid points and low- level clouds, -3.1 K for mid-level clouds, and -4.4 K for high clouds Applied to all observations, not just those that cloud match Bias corrections were applied to the individual ensemble members rather than to the observations to account for differences in the cloud field in each ensemble member

7. Assimilation Results with Cloud Dependent Bias Correction Error time series for Control (black line) and SEVIRI (red line) assimilation cases Statistics are shown for cloud matched grid points Errors are reduced when SEVIRI 7.3  m observations are assimilated using the cloud-dependent bias correction scheme

8. Analysis Accuracy After Last Assimilation Time Large improvements made to the relative humidity field, especially in the middle and upper troposphere where observations are most sensitive Smaller RMSE when infrared observations are assimilated Neutral impact on temperature and wind fields These results are similar to those found during prior OSSE studies with GOES-R ABI brightness temperatures

9. Brightness Temperature Assimilation at Convective Scales A regional-scale OSSE was used to evaluate the potential impact of assimilating GOES-R ABI brightness temperatures and Doppler radar observations at convection-resolving resolutions Assimilation experiments performed using the WRF model, the DART ensemble data assimilation system, and the SOI forward radiative transfer model Synthetic satellite and radar observations created using output from a 2-km resolution truth simulation of a severe thunderstorm event Assimilation experiments were performed using a 50-member ensemble containing 4-km horizontal resolution and 52 vertical levels Clear and cloudy sky ABI Band 9 (6.95  m) brightness temperatures assimilated every 5 minutes during a 2-hr period 1-hour ensemble forecasts performed after assimilation period

10. Simulated Radar Reflectivity During 1-Hour Forecast Period Truth simulation had a long line of thunderstorms Control without satellite and radar assimilation is the least accurate Initial thunderstorm structure more accurate when satellite and radar observations were assimilated Best structure was obtained when both satellite and radar observations were assimilated Thunderstorms maintained organization longer during the Combo case Truth Control Satellite Radar Combo 2300 UTC2330 UTC0000 UTC

11. Simulated ABI 6.95  m T b During 1-Hour Forecast Period Truth simulation had a long line of thunderstorms Initial thunderstorm structure more accurate when satellite and radar observations were assimilated separately Best structure was obtained when both satellite and radar observations were assimilated Satellites can fill in data gaps even within data rich locations such as the central United States Results show that radar and satellite observations provide complementary information about the atmospheric state Truth Control Satellite Radar Combo 2300 UTC2330 UTC0000 UTC

12. References Cintineo, R., J. A. Otkin, T. A. Jones, S. Koch, L. Wicker, and D. Stensrud, 2015: Assimilation of GOES-R ABI satellite and WSR-88D radar observations during a convection-resolving OSSE. Submitted to Mon. Wea. Rev. Jones, T. A., J. A. Otkin, D. J. Stensrud, and K. Knopfmeier, 2014: Forecast evaluation of an Observing System Simulation Experiment assimilating both radar and satellite data. Mon. Wea. Rev., 142, 107-124. Jones, T. A., J. A. Otkin, D. J. Stensrud, and K. Knopfmeier, 2013: Assimilation of simulated GOES-R satellite radiances and WSR-88D Doppler radar reflectivity and velocity using an Observing System Simulation Experiment. Mon. Wea. Rev., 141, 3273-3299. Otkin, J. A., 2012: Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event. J. Geophys. Res., 117, D19203, doi:10.1029/2012JD017568. Otkin, J. A., 2012: Assessing the impact of the covariance localization radius when assimilating infrared brightness temperature observations using an ensemble Kalman filter. Mon. Wea. Rev., 140, 543-561. Otkin, J. A., 2010: Clear and cloudy-sky infrared brightness temperature assimilation using an ensemble Kalman filter. J. Geophys Res., 115, D19207, doi:10.1029/2009JD013759.