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Understanding clouds and precipitation at the sub kilometer scale using HAMP (UCP-HAMP)

Subject Area Atmospheric Science
Term from 2016 to 2020
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 316835045
 
Final Report Year 2020

Final Report Abstract

Incomplete understanding of clouds, in particular concerning their macrophysically characteristics and their microphysical decomposition, is a main reason for uncertain climate predictions. Our knowledge bases mainly on either high-resolution ground-based observations at single sites or global satellite observation with poor spatial and temporal resolution. The research aircraft HALO was equipped as flying super-site with the so-called NARVAL payload to close the gap between these two observing systems. This long-term research approach requires a series of measurement campaigns, the generation of scientific data products and targeted scientific data analysis. The project “Understanding clouds and precipitation at the sub kilometer scale using HAMP (UCP-HAMP)” has provided the following contribution to these three essential aspects: The project team has operated jointly with the Max-Planck-Institute for Meteorology and DLR the “HALO Microwave Package (HAMP)” during the campaigns NARVAL-II und NAWDEX and supported flight planning. The calibration of the microwave radiometers has been investigated in detail and a correction procedure using radiative transfer calculation for cloud free dropsonde profiles has been established. Unified measurement data of in total 295 flight hours during 37 flights are made available by a data publication. Both a regression scheme and neural network have been developed to derive vertically integrated water vapor and liquid water (LWP) contents. The latter also allowing the retrieval of rain water path. The comparison of retrievals by sensors at different wavelength reveals the advantage of sensor synergy: thin clouds with LWP less than 20 gm^-2 can only be detected correctly using additionally LIDAR information. At the same time, the LWP of thick clouds (LWP larger than 100 gm^-2) can be determined with uncertainties smaller than 10%. Observations by the cloud radar of HAMP were used to generate a data set of single cloud entities called the “Segmented Cloud Archive” (SCA). According to the characteristics of these single clouds, the flight parts are assigned to different cloud regimes allowing for a statistical data analysis. Using the SCA data set, we analyzed which influencing factor determine whether a cloud starts to precipitate. Cloud depth is identified as best indicator. The threshold determining the onset of precipitation, which is highly relevant for modelling, was quantified by roughly 1 km depth. As part of climatological comparison of the campaigns NARVAL-I South and NARVAL-II, we investigated the impact of dry and wet seasons on cloud statics. As expected, the integrated vapor content increases by almost a factor of two during the wet season and reaches as maximum of even 60 kg m-2. Remarkably, the cloud cover reduces in parallel down to only 25% and likewise the mean LWP decreases. Higher LWP during dry season results to more frequent light rain, whereas the few clouds during wet season generate more cloud ice and produce consequently more intense precipitation. All methods developed during NARVAL-II, ranging from microwave radiometer calibration, retrieval algorithms to the segmentation of cloud entities, form the essential basis to perform the follow-up campaign EUREC4A in early 2020.

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