Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-101-2025
https://doi.org/10.5194/gmd-18-101-2025
Model description paper
 | 
14 Jan 2025
Model description paper |  | 14 Jan 2025

Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data

Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer

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Cited articles

Antonescu, B., Seifert, P., O'Connor, E., and Fomba, K.: Custom collection of categorize, and model data from Mindelo on 15 Jul 2022, ACTRIS Cloud remote sensing data centre unit (CLU) [data set], https://doi.org/10.60656/c5e09106ba0246bc, 2024. a
Battaglia, A. and Kollias, P.: Using Ice Clouds for Mitigating the EarthCARE Doppler Radar Mispointing, IEEE T. Geosci. Remote, 53, 2079–2085, https://doi.org/10.1109/TGRS.2014.2353219, 2015. a
Battaglia, A., Haynes, J. M., L'Ecuyer, T., and Simmer, C.: Identifying multiple-scattering-affected profiles in CloudSat observations over the oceans, J. Geophys. Res.-Atmos., 113, D00A17, https://doi.org/10.1029/2008JD009960, 2008. a
Battaglia, A., Kollias, P., Dhillon, R., Lamer, K., Khairoutdinov, M., and Watters, D.: Mind the gap – Part 2: Improving quantitative estimates of cloud and rain water path in oceanic warm rain using spaceborne radars, Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, 2020a. a
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K., Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and Furukawa, K.: Spaceborne Cloud and Precipitation Radars: Status, Challenges, and Ways Forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020b. a
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Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
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