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Peter N. Blossey, Christopher S. Bretherton, and Johannes Mohrmann

evaluating the simulations against a range of observations, including in situ measurements, aircraft-borne radar and lidar, and satellite-based remote sensing, the model cannot be tuned to match a particular observation. In addition to the initial exploration of these cases in the present paper, we hope that these Lagrangian case studies will be used by other researchers to illuminate the processes that control real cloudiness transitions. The CSET field campaign ( Albrecht et al. 2019 ) took place over

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Christopher S. Bretherton, Isabel L. McCoy, Johannes Mohrmann, Robert Wood, Virendra Ghate, Andrew Gettelman, Charles G. Bardeen, Bruce A. Albrecht, and Paquita Zuidema

potential cloud-controlling factors, inversion stability and cloud droplet number concentration. Section 6 compares observations from an illustrative CSET flight with reanalysis and a weather-nudged climate model, followed by a summary in section 7 . 2. CSET observations and analysis methods a. Measurements used in this study The G-V instrumentation used for CSET was described in detail by A19 . It included a 94-GHz cloud radar, a high spectral resolution lidar, dropsondes, and in situ probes for

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Mampi Sarkar, Paquita Zuidema, Bruce Albrecht, Virendra Ghate, Jorgen Jensen, Johannes Mohrmann, and Robert Wood

⁡ ( RWC i υ i L υ , i ) − ⁡ ( RWC i − 1 υ i − 1 L υ , i − 1 ) . The rainwater content at cloud base is set equal to that from the in-cloud leg. Although collision–coalescence will increase the rainwater content between in-cloud leg and cloud base, the assumption that the raindrop size distribution measured during in-cloud leg scales well with cloud-base precipitation is supported by observations ( Wood 2005a ). The mean cloud base height is derived from lidar measurements from the nearest 150-m level

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Johannes Mohrmann, Christopher S. Bretherton, Isabel L. McCoy, Jeremy McGibbon, Robert Wood, Virendra Ghate, Bruce Albrecht, Mampi Sarkar, Paquita Zuidema, and Rabindra Palikonda

was empirically chosen to balance the competing interests in reducing noise in box-averaged quantities while avoiding including observations from regions subject to significantly different large-scale forcings; a comparison of the GOES cloud fraction estimate to that derived from a radar-lidar cloud mask can be found in Bretherton et al. (2019) . Supplemental data are drawn from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, version 5 (ERA5) [ Copernicus Climate Change

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