The AD/VI-Aeo/us mission will provide global wind profile observationswith the aim to demonstrate improvement in atmospheric wind analyses for the benefit of numerical weather prediction and climate studies.
[1] The retrieval of sea ice motion with the Maximum Cross-Correlation (MCC) method from low-resolution (10-15 km) spaceborne imaging sensors is challenged by a dominating quantization noise as the time span of displacement vectors is shortened. To allow investigating shorter displacements from these instruments, we introduce an alternative sea ice motion tracking algorithm that builds on the MCC method but relies on a continuous optimization step for computing the motion vector. The prime effect of this method is to effectively dampen the quantization noise, an artifact of the MCC. It allows for retrieving spatially smooth 48 h sea ice motion vector fields in the Arctic. Strategies to detect and correct erroneous vectors as well as to optimally merge several polarization channels of a given instrument are also described. A test processing chain is implemented and run with several active and passive microwave imagers (Advanced Microwave Scanning Radiometer-EOS (AMSR-E), Special Sensor Microwave Imager, and Advanced Scatterometer) during three Arctic autumn, winter, and spring seasons. Ice motion vectors are collocated to and compared with GPS positions of in situ drifters. Error statistics are shown to be ranging from 2.5 to 4.5 km (standard deviation for components of the vectors) depending on the sensor, without significant bias. We discuss the relative contribution of measurement and representativeness errors by analyzing monthly validation statistics. The 37 GHz channels of the AMSR-E instrument allow for the best validation statistics. The operational low-resolution sea ice drift product of the EUMETSAT OSI SAF (European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility) is based on the algorithms presented in this paper.
Polar lows are intense mesoscale cyclones that mainly occur during the winter over the sea in polar regions. Owing to their small spatial scale with a diameter between 200 and 1000 km, simulating polar lows is a challenging task. In this study we investigated how increased resolution of a numerical weather prediction model impacts its ability to simulate polar lows. We focused on a polar low that was successfully captured by three different flights during the IPY-THORPEX field campaign in 2008. Verifying model results against campaign data showed that decreasing the horizontal grid spacing from 12 to 4 km significantly improved the simulation of the developing polar low, and a further decrease to 1 km gave further improvement. A model run with latent heating reduced to 10% indicated an extensive influence of diabatic heating in this polar low case, and we suggest that the improved model performance at higher resolution could be connected to the model's handling of convection. Additional simulations starting 24 h later showed that the initial conditions were important for the model performance, and when simulating another polar low case we found that the model failed to produce the polar low independent of the resolution. This shows that while higher resolution indeed may give improved predictions of polar lows, other factors like synoptic situation, lateral boundaries and the initial condition may also be important.
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The manuscript addresses the need for tropospheric and stratospheric wind profiles and discusses capabilities to fulfil such need. To follow up the Aeolus mission an international operational UV Doppler Wind Lidar constellation is suggested.
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