Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-6929-2021
https://doi.org/10.5194/amt-14-6929-2021
Research article
 | 
01 Nov 2021
Research article |  | 01 Nov 2021

Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements

Stefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian J. Butterworth, Christopher Florian, Matthias Mauder, David M. Plummer, Luise Wanner, Ke Xu, and Ankur R. Desai

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Cited articles

Atlas, R., Hoffman, R. N., Ma, Z., Emmitt, G. D., Wood, S. A., Jr., Greco, S., Tucker, S., Bucci, L., Annane, B., Hardesty, R. M., and Murillo, S.: Observing System Simulation Experiments (OSSEs) to evaluate the potential impact of an Optical Autocovariance Wind Lidar (OAWL) on numerical weather prediction, J. Atmos. Ocean. Tech., 32, 1593–1613, https://doi.org/10.1175/jtech-d-15-0038.1, 2015. 
Avissar, R. and Schmidt, T.: An evaluation of the scale at which ground-surface heat flux patchiness affects the convective boundary layer using large-eddy simulations, J. Atmos. Sci., 55, 2666–2689, https://doi.org/10.1175/1520-0469(1998)055<2666:aeotsa>2.0.co;2, 1998. 
Bertoldi, G., Kustas, W., and Albertson, J.: Evaluating source area contributions from aircraft flux measurements over heterogeneous land using large-eddy simulation, Bound.-Lay. Meteorol., 147, 261–279, https://doi.org/10.1007/s10546-012-9781-y, 2013. 
Beyrich, F. and Mengelkamp, H.-T.: Evaporation over a heterogeneous land surface: EVA_GRIPS and the LITFASS-2003 experiment – an overview, Bound.-Lay. Meteorol., 121, 5–32, https://doi.org/10.1007/s10546-006-9079-z, 2006. 
Beyrich, F., Herzog, H. J., and Neisser, J.: The LITFASS project of DWD and the LITFASS-98 experiment: The project strategy and the experimental setup, Theor. Appl. Climatol., 73, 3–18, https://doi.org/10.1007/s00704-002-0690-8, 2002. 
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Short summary
The key points are the following. (i) Integrative observing system design can multiply the information gain of surface–atmosphere field measurements. (ii) Catalyzing numerical simulations and first-principles machine learning open up observing system simulation experiments to novel applications. (iii) Use cases include natural climate solutions, emission inventory validation, urban air quality, and industry leak detection.