Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-1975-2020
https://doi.org/10.5194/gmd-13-1975-2020
Model description paper
 | 
21 Apr 2020
Model description paper |  | 21 Apr 2020

The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory

Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann

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Cited articles

Albrecht, B. A.: Parameterization of trade-cumulus cloud amounts, J. Atmos. Sci., 38, 97–105, https://doi.org/10.1175/1520-0469(1981)038<0097:POTCCA>2.0.CO;2, 1981. 
Angevine, W, Olson, J., Kenyon, J., Gustafson, W., Endo, S., Suselj, K., and Turner, D.: Shallow cumulus in WRF parameterizations evaluated against LASSO large-eddy simulations, Mon. Weather Rev., 146, 4303–4322, https://doi.org/10.1175/MWR-D-18-0115.1, 2018. 
Andsager, K., Beard, K. V., and Laird, N. F.: Laboratory measurements of axis ratios for large drops, J. Atmos. Sci., 56, 2673–2683, https://doi.org/10.1175/1520-0469(1999)056<2673:LMOARF>2.0.CO;2, 1999. 
Atmospheric Radiation Measurement (ARM) Research Facility. LASSO Data Bundles, 363618.0′′ N, 97296.0′′ W: Southern Great Plains Central Facility (C1), compiled by: Gustafson, W. I., Vogelmann, A. M., Cheng, X., Endo, S., Johnson, K. L., Krishna, B., Li, Z., Toto, T., and Xiao, H., ARM Data Archive: Oak Ridge, TN, USA, https://doi.org/10.5439/1342961, data set accessed at: September 2017. 
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, Wiley, New York, 530 p., ISBN 0-471-29340-7, ISBN 978-0-471-29340-8 (second edition), 1998. 
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Short summary
We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.