Improving an optimal estimation algorithm for surface and atmospheric parameter retrieval using passive microwave data in the Arctic

In this study we present improvements on an integrated retrieval method for atmospheric and surface parameters in the Arctic. The instrument used is the Advanced Microwave Scanning Radiometer - Earth Observing System (EOS) (AMSR-E) radiometer on board NASAa s Aqua satellite. The core of the method is a forward model which can ingest bulk data for seven geophysical parameters to reproduce the brightness temperatures observed by a passive microwave radiometer. The method inverts the forward model and produces ensembles of the seven parameters: wind speed, integrated water vapor, liquid water path, sea and ice temperature, sea ice concentration and multi-year ice fraction. The method is constrained using numerical weather prediction data in order to retrieve a set of geophysical parameters that best fit the measurements. An iterative method minimizes the cost function and finds the optimal ensemble of the seven parameters that best match the observed brightness temperatures

A systematic assessment of water vapor products in the Arctic: from instantaneous measurements to monthly means

Water vapor is an important component in the water and energy cycle of the Arctic. Especially in light of Arctic amplification, changes in water vapor are of high interest but are difficult to observe due to the data sparsity of the region. The ACLOUD/PASCAL campaigns performed in May/June 2017 in the Arctic North Atlantic sector offers the opportunity to investigate the quality of various satellite and reanalysis products. Compared to reference measurements at R/V Polarstern frozen into the ice (around 82∘ N, 10∘ E) and at Ny-Ålesund, the integrated water vapor (IWV) from Infrared Atmospheric Sounding Interferometer (IASI) L2PPFv6 shows the best performance among all satellite products. Using all radiosonde stations within the region indicates some differences that might relate to different radiosonde types used. Atmospheric river events can cause rapid IWV changes by more than a factor of 2 in the Arctic. Despite the relatively dense sampling by polar-orbiting satellites, daily means can deviate by up to 50 % due to strong spatio-temporal IWV variability. For monthly mean values, this weather-induced variability cancels out, but systematic differences dominate, which particularly appear over different surface types, e.g., ocean and sea ice. In the data-sparse central Arctic north of 84∘ N, strong differences of 30 % in IWV monthly means between satellite products occur in the month of June, which likely result from the difficulties in considering the complex and changing surface characteristics of the melting ice within the retrieval algorithms. There is hope that the detailed surface characterization performed as part of the recently finished Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) will foster the improvement of future retrieval algorithms

Total water vapor retrieval data in the Arctic between 2007-2009 using microwave humidity sounders

Quantitative retrievals of atmospheric water vapour in the Arctic present numerous challenges because of the particular climate characteristics of this area. Here, we attempt to build upon the work of Melsheimer and Heygster (2008) to retrieve total atmospheric water vapour (TWV) in the Arctic from satellite microwave radiometers. While the above-mentioned algorithm deals primarily with the ice-covered central Arctic, with this work we aim to extend the coverage to partially ice covered and ice-free areas. By using modelled values for the microwave emissivity of the ice-free sea surface, we develop two sub-algorithms using different sets of channels that deal solely with open-ocean areas. The new algorithm extends the spatial coverage of the retrieval throughout the year but especially in the warmer months when higher TWV values are frequent. The high TWV measurements over both sea-ice and open-water surfaces are, however, connected to larger uncertainties as the retrieval values are close to the instrument saturation limits. This approach allows us to apply the algorithm to regions where previously no data were available and ensures a more consistent physical analysis of the satellite measurements by taking into account the contribution of the surface emissivity to the measured signal. The retrieved fields of TWV using two separate versions of the AMSU-B retrieval algorithm, an original version with higher accuracy but low coverage and the new version with greatly increased Arctic coverage but higher retrieval uncertainty over ice free areas. The data covers 4 representative months, March, June, September and December in 2007, 2008 and 2009. This data collection is split by year, month and retrieval method