Evolution of tropical deep-convective clouds derived from ground-based imaging spectroradiometer measurements
Final Report Abstract
The project aimed to analyse the impact of aerosol particles and thermodynamic conditions on the evolution of convective clouds in the Amazon rain forest. Two intensive field campaigns were performed at the Amazon Tall Tower Observatory (ATTO) at the end of the dry season, when large variabilities of aerosol conditions can be observed. Contrary to the planning (due to serious customs issues), that included the usage of active and passive remote sensing, only a reduced setup of instrumentation could be installed at the tower. The original objectives had to be adjusted accordingly, based on the available data set (IR camera, visual cameras). Instead of documenting the cloud evolution with respect to microstructure, the focus of the study was set on macrophysical cloud properties depending on aerosol particle conditions. The structure of clouds that approached ATTO was derived from 2D fields of the brightness temperature (Tb) emitted from cloud sides. The retrieval assumes that the cloud side can be considered as cloud base for the same sensor viewing geometry, such that the 3D problem can be transferred into a 1D model scenario. That approach was tested against synthetic cloud side Tb-data from simulations achieved with the new backward-propagating Monte Carlo radiative transfer model called LEIPSIC. Within this project, this model has been extensively validated and improved for cloud and aerosol scenarios as expected at ATTO. The comparison of the retrieved and true distances between observer and cloud elements has shown differences of up to 200 m, which increases for higher viewing angles due to the increasing atmospheric contribution. It was furthermore found that the retrieval does not require a priori information of the cloud microphysical properties for optically thick clouds. At cloud edges, where the cloud is optically thin, the retrieval reveals limitations because the emission of the background imprints the measured Tb. The cloud type analysis has identified a regular daily pattern for the one-month period of observations. Shallow cumulus clouds developed on all days but deep convection started later than 4 pm when the manual observations has to be stopped. Therefore, the evolution of only shallow liquid cumulus clouds was observed by tracking the same cloud over a period of about 10 minutes. Cloud base height (CBH) and cloud top height (CTH) were estimated for 186 individual clouds. Each day of observation was classified according to the aerosol conditions into four classes from clean to very polluted. Some indications were found that the CBH and CTH were lower on less polluted days. However, cloud geometric thickness was not affected. The cloud evolution rate, defined as the change of CTH and CBH with time, was analyzed and did not show any dependence on the aerosol conditions. In fact, the evolution rate for these shallow cumuli seems to be more sensitive to the stage of cloud development itself. First attempts were made to match the solar radiance fields of the reconstructed clouds with measurements, taken by the fully calibrated digital camera, by adjusting the cloud microphysical properties. As a first guess adiabatic cloud profiles were assumed with additional information from satellite observations. It was shown, that the sensitivity of the red-to-green ratio to the cloud microphysics can be used to constrain the model input of the microphysical vertical profiles.
Publications
- Evolution of deep convective clouds derived from ground-based measurements Wissenschaftliche Mitteilungen aus dem Institut für Meteorologie der Universität Leipzig. 2018. pp. 53-62
Mendes de Barros, K.; Jäkel, E.; Schäfer, M.; Stapf, J.; Wendisch, M.
- A biased sampling approach to accelerate backward Monte Carlo atmospheric radiative transfer simulations and its application to Arctic heterogeneous cloud and surface conditions, JQSRT, 240, 106690
Sun, B., Jäkel, E., Schäfer, M., Wendisch, M.
(See online at https://doi.org/10.1016/j.jqsrt.2019.106690)
