Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. (2011) Experimental 1D + 4D-Var assimilation of CloudSat observations M. Janiskov´ a,* P. Lopez and P. Bauer ECMWF, Reading, UK *Correspondence to: M. Janiskov ´ a, ECMWF, Shinfield Park, Reading, RG2 9AX, UK. E-mail: marta.janiskova@ecmwf.int Observations providing three-dimensional information on clouds from space-borne active instruments on board CloudSat and CALIPSO are already available and new satellites, such as EarthCARE, should appear in the near future. This opens new possibilities for exploring the usefulness of this kind of observation, not only for improving model parametrizations but also for investigating their usage in data assimilation to extract information from the data so as to improve the initial atmospheric state. In this study, a 1D + 4D-Var technique has been selected to study the impact of observations related to clouds on 4D-Var analyses and subsequent forecasts. Using this two-step approach, temperature and specific humidity profiles retrieved from 1D-Var assimilation of CloudSat observations have been included in the 4D-Var system. Several experiments have been run for a couple of selected meteorological situations. Copyright c  2011 Royal Meteorological Society Key Words: cloud radar; observation operator; variational technique Received 29 July 2011; Revised 2 November 2011; Accepted 10 November 2011; Published online in Wiley Online Library Citation: Janiskov´ a M, Lopez P, Bauer P. 2011. Experimental 1D + 4D-Var assimilation of CloudSat observations. Q. J. R. Meteorol. Soc. DOI:10.1002/qj.988 1. Introduction Numerical weather prediction (NWP) models have improved considerably over the past few years in the forecast of clouds, thanks to progress in parametrizations. However, there is still a need to explore the options for model improvement further, and therefore options for the assimilation of data related to clouds from active and pas- sive sensors. This article is about the latter. Observations providing three-dimensional information on clouds from space-borne active instruments as CloudSat and the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are already available and new missions, such as the Earth, Clouds, Aerosols and Radiation Explorer (Earth- CARE) should appear in the near future. The challenge is to identify information that can be extracted from such data sources and transformed through the model into better knowledge about the atmospheric state. Despite the major influence of clouds and precipitation on the atmospheric water and energy balance, there is still no explicit analysis of clouds in global data- assimilation systems. The cloud contributions to the satellite radiances are mostly removed from the assimilation systems. In mesoscale models, cloud analyses based on nudging techniques have been introduced (e.g. Macpherson et al., 1996; Lipton and Modica, 1999; Bayler et al., 2000). Several experimental assimilation studies using cloudy observations have also been performed. An attempt at exploiting visible and infrared cloudy satellite radiances in four-dimensional variational assimilation (4D-Var) has been made by Vuki´ cevi´ c et al. (2004) using a mesoscale model. On the global scale, the capability of 4D-Var assimilation systems to assimilate cloud-affected satellite infrared radiances using observations from the narrow-band Advanced Infrared Sounder (AIRS) has been investigated by Chevallier et al. (2004). Benedetti and Janiskov´ a (2008) performed some experiments for assimilation of cloud optical depths retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the European Centre for Medium-Range Weather Forecasts (ECMWF) 4D-Var system. The use of cloud retrievals from radar data has been explored in one-dimensional variational (1D- Var) assimilation studies (Janiskov´ a et al., 2002b; Benedetti et al., 2003a,b; Benedetti and Janiskov ´ a, 2004). Experimental assimilation of the Atmospheric Radiation Measurement programme (ARM) cloud radar observations combined Copyright c  2011 Royal Meteorological Society