A concept for the assimilation of satellite cloud information in an Ensemble Kalman Filter: single‐observation experiments

A new approach is introduced to assimilate cloud information into a convection-permitting numerical weather prediction model with an ensemble Kalman filter. Cloud information is obtained from a satellite cloud-top height product based on Meteosat SEVIRI data. To assure quality and specify the observation error based on data reliability, the satellite cloud-top height is merged with cloud-top height derived from radiosonde humidity soundings. For that purpose, the radiosonde cloud-top information is assumed to be representative not only for the location of the ascent itself but also in spatially and temporally nearby areas which have the same cloud type according to a satellite cloud-type classification. From the cloud-top height information, different variables are derived to be used as ‘pseudo observations’ within the ensemble Kalman filter. At cloudy locations these are cloud-top height and 100% relative humidity at that cloud-top height. Also zero cloud cover amounts at up to three heights are assimilated at cloud-free observation locations and above low- and mid-level cloud observations. The approach has been applied in a number of single-observation experiments to assess the effect in detail of assimilating these variables on the atmospheric column. Three examples are shown of single-observation experiments for a stable wintertime high-pressure synoptic situation with low stratus clouds, where model background and observation have large discrepancies. The ensemble Kalman filter is able to draw the ensemble closer to the observations. The analysis shows improved cloud variables in both cloudy and cloud-free scenes. A reasonable effect on the temperature profiles through cross-correlations in the background ensemble is obtained. The approach represents a method to exploit high-resolution remote-sensing data for short-range meso-γ-scale weather forecast models with a view to applying this method in a rapid-update cycle, particularly useful for local-scale weather phenomena.

[1]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[2]  Laurent Bertino,et al.  Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment , 2009 .

[3]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[4]  Jason A. Otkin,et al.  Clear and cloudy sky infrared brightness temperature assimilation using an ensemble Kalman filter , 2010 .

[5]  D. Blake,et al.  The compatibility between aircraft and ground-based air quality measurements , 1994 .

[6]  A. Benedetti,et al.  Assimilation of MODIS Cloud Optical Depths in the ECMWF Model , 2008 .

[7]  M. Tiedtke.  A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models , 1989 .

[8]  M. Bocquet,et al.  Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation , 2010 .

[9]  P. Bauer,et al.  Experimental 1D + 4D‐Var assimilation of CloudSat observations , 2012 .

[10]  Massimo Bonavita,et al.  Ensemble data assimilation with the CNMCA regional forecasting system , 2010 .

[11]  Nils Gustafsson,et al.  The impact of cloud‐affected IR radiances on forecast accuracy of a limited‐area NWP model , 2013 .

[12]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[13]  F. Ament,et al.  Evaluation of MAP D-PHASE heavy precipitation alerts in Switzerland during summer 2007 , 2011 .

[14]  P. Dahlgren,et al.  Assimilation of SEVIRI infrared radiances with HIRLAM 4D‐Var , 2009 .

[15]  Louis J. Wicker,et al.  Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses , 2011 .

[16]  A. McNally.  The direct assimilation of cloud‐affected satellite infrared radiances in the ECMWF 4D‐Var , 2009 .

[17]  J. Schmetz,et al.  Operational Cloud-Motion Winds from Meteosat Infrared Images , 1993 .

[18]  K. D. Beheng,et al.  A double-moment parameterization for simulating autoconversion, accretion and selfcollection , 2001 .

[19]  B. Macpherson,et al.  The Impact of MOPS Moisture Data in the U.K. Meteorological Office Mesoscale Data Assimilation Scheme , 1996 .

[20]  J. Schmetz,et al.  AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG) , 2002 .

[21]  B. Ritter,et al.  A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations , 1992 .

[22]  Ming Hu,et al.  A Regional GSI-Based Ensemble Kalman Filter Data Assimilation System for the Rapid Refresh Configuration: Testing at Reduced Resolution , 2013 .

[23]  E. Kalnay,et al.  Effective assimilation of global precipitation: simulation experiments , 2013 .

[24]  Philippe Lopez,et al.  The capability of 4D‐Var systems to assimilate cloud‐affected satellite infrared radiances , 2004 .

[25]  Jason A. Otkin,et al.  Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event , 2012 .

[26]  M. Baldauf,et al.  Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities , 2011 .

[27]  Jason A. Otkin,et al.  Assessing the Impact of the Covariance Localization Radius when Assimilating Infrared Brightness Temperature Observations Using an Ensemble Kalman Filter , 2012 .

[28]  K. Okamoto.  Assimilation of overcast cloudy infrared radiances of the geostationary MTSAT‐1R imager , 2013 .

[29]  Peter N. Francis,et al.  Variational assimilation of cloud fraction in the operational Met Office Unified Model , 2011 .

[30]  Wei Huang,et al.  NCAR Command Language (NCL) , 2012 .

[31]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[32]  William L. Smith,et al.  Improved Cloud Motion Wind Vector and Altitude Assignment Using VAS. , 1983 .

[33]  David D. Parrish,et al.  GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments , 2013 .

[34]  Tami C. Bond,et al.  Critical assessment of the current state of scientific knowledge, terminology, and research needs concerning the role of organic aerosols in the atmosphere, climate, and global change , 2005 .

[35]  C. Schraff,et al.  Mesoscale data assimilation and prediction of low stratus in the Alpine region , 1997 .

[36]  M. Derrien,et al.  MSG/SEVIRI cloud mask and type from SAFNWC , 2005 .

[37]  B. Fay,et al.  Evaluation of high-resolution forecasts with the non-hydrostaticnumerical weather prediction model Lokalmodell for urban air pollutionepisodes in Helsinki, Oslo and Valencia , 2006 .

[38]  C. Snyder,et al.  A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses , 2009 .

[39]  Vincent Guidard,et al.  Towards the use of microphysical variables for the assimilation of cloud‐affected infrared radiances , 2013 .

[40]  Liangping Li,et al.  An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering , 2011 .

[41]  Bernhard Mayer,et al.  Observation Operator for Visible and Near-Infrared Satellite Reflectances , 2014 .

[42]  Martin Verlaan,et al.  Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms , 2014 .

[43]  Peter Bauer,et al.  Assimilating Satellite Observations of Clouds and Precipitation into NWP Models , 2011 .