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ISOBAR

Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer

Operation of a quadcopter system during the ISOBAR campaign

 The purpose of the basic research project ISOBAR is to increase our understanding of the Atmospheric Boundary Layer (ABL) in the Arctic. In particular, we aim to study the physical processes governing the turbulent exchange under stable conditions, which are not well represented in current Numerical Weather Prediction (NWP) and climate models, due to insufficient parameterization schemes for the Stable Boundary Layer(SBL). Applying new innovative observation strategies, which include meteorological Remotely Piloted Aircraft Systems (RPAS) in addition to well-established ground based and profiling systems, we will provide data sets on the turbulent structure of the SBL, with unique spatial and temporal resolution. The project includes the test and characterization of the RPAS based turbulence sensors through laboratory experiments and a validation campaign at Andøya Space Center. Three different RPAS systems, the Multipurpose Atmospheric Sensor Carrier(MASC, for long-range horizontal turbulence measurements), the Small Unmanned Meteorological Observer (SUMO, for turbulence measurements and vertical profiles) and the Advanced Mission and Operation Research (AMOR) multicopter system (for vertical profiles of the Surface Layer and fixed-location turbulence measurements) will be applied during two four-week long campaigns. These campaigns will focus on the SBL over homogeneous sea-ice (Arctic Ocean around Svalbard, winter/spring 2017) and surface heterogeneities due to partially open water (western fjords of Svalbard,winter/spring 2018). Collocated and coordinated measurements by a large number of RPAS (2 MASC, 7 SUMO, 2-4 AMOR) will provide a unique opportunity to sample the relevant data with so far unreached resolution. Supported by Single Column Model and Large-Eddy Simulation experiments we will use the collected data sets to develop new SBL parameterization schemes and implement them in the state-of-the-art Weather and Research Forecasting model (WRF).