[1] Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 Â 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 Â 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002AMSR-E period ( -2006 between ASI and NASA-Team 2 are below À2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.

Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

Frost flowers grow on newly‐formed sea ice from a saturated water vapour layer. They provide a large effective surface area and a reservoir of sea salt ions in the liquid phase with triple the ion concentration of sea water. Recently, frost flowers have been recognised as the dominant source of sea salt aerosol in the Antarctic, and it has been speculated that they could be involved in processes causing severe tropospheric ozone depletion events during the polar sunrise. These events can be explained by heterogeneous autocatalytic reactions taking place on salt‐laden ice surfaces which exponentially increase the reactive gas phase bromine (“bromine explosion”). We analyzed tropospheric bromine monoxide (BrO) and the sea ice coverage both measured from satellite sensors. Our model based interpretation shows that young ice regions potentially covered with frost flowers seem to be the source of bromine found in bromine explosion events.

[1] Methods to detect tropical deep convective clouds and convective overshooting from measurements at the three water vapor channels (183.3 ± 1, 183.3 ± 3, and 183.3 ± 7 GHz) of the Advanced Microwave Sounding Unit-B (AMSU-B) are presented. Thresholds for the brightness temperature differences between the three channels are suggested as criterion to detect deep convective clouds, and an order relation between the differences is used to detect convective overshooting. The procedure is based on an investigation of the influence of deep convective cloud systems on the microwave brightness temperatures at frequencies from 89 to 220 GHz using simultaneous aircraft microwave and radar measurements over two tropical deep convective cloud systems, taken during the Tropical Rainfall Measuring Mission (TRMM) Large Scale Biosphere-Atmosphere Experiment (LBA) campaign. Two other aircraft cases with deep convective cloud systems observed during the Third Convection and Moisture Experiment (CAMEX-3) are used to validate the criteria. Furthermore, a microwave radiative transfer model and simulated mature tropical squall line data derived from the Goddard Cumulus Ensemble (GCE) model are used to validate the procedures and to adapt the criteria to the varying viewing angle of AMSU-B. These methods are employed to investigate the distributions of deep convective clouds and convective overshooting in the tropics (30°S to 30°N) for the four 3-month seasons from March 2003 to February 2004 using the AMSU-B data from NOAA-15, -16, and -17. The distributions show a seasonal variability of shifting from the winter hemisphere to the summer hemisphere. The distributions of deep convective clouds follow the seasonal patterns of the surface rainfall rates. The deep convective clouds over land penetrate more frequently into the tropical tropopause layer than those over ocean. The averaged deep convective cloud fraction is about 0.3% in the tropics, and convective overshooting contributes about 26% to this.Citation: Hong, G., G. Heygster, J. Miao, and K. Kunzi (2005), Detection of tropical deep convective clouds from AMSU-B water vapor channels measurements,

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