1. The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback.
- Author
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Spath, Florian, Rajtschan, Verena, Weber, Tobias K. D., Morandage, Shehan, Lange, Diego, Abbas, Syed Saqlain, Behrendt, Andreas, Ingwersen, Joachim, Streck, Thilo, and Wulfmeyer, Volker
- Subjects
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OBSERVATORIES , *THROUGHFALL , *CONVECTIVE boundary layer (Meteorology) , *PHYSICAL sciences , *REMOTE sensing , *ATMOSPHERIC boundary layer , *DIFFERENTIAL absorption lidar - Abstract
Important topics in Land-Atmosphere (L-A) feedback research are water and energy balances and heterogeneities of fluxes at the land-surface and in the ABL. To target these questions, the Land-Atmosphere Feedback Observatory (LAFO) has been installed in Southwest Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped in three components: atmosphere, soil and land-surface and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For that the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables humidity, temperature and wind. At the land-surface eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. With a water and temperature sensor network the soil water content and temperature is monitored in the agricultural investigation area. The observations in LAFO are organized in operational measurements and intensive observation periods (IOPs). Operational measurements aim for long timeseries dataset to investigate statistics as we present as example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 hour case study with dynamic and thermodynamic profiles with lidar as well as a surface layer observation with the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both long-term observations and IOPs are important for improving the representation of L-A feedbacks in climate and numerical weather prediction models. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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