ALBERT

Description: The MIPAS instrument onboard the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements represent a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. Cloud detection sensitivity is comparable to spaceborne lidars, and it is possible to classify different cloud types from the spectral measurements in different atmospheric windows regions. Here we present a new PSC classification scheme based on the combination of a well-established two-colour ratio method and multiple 2D brightness temperature difference probability density functions. The method is a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption. The method has been tested in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition. The Bayesian classifier distinguishes between solid particles of ice and nitric acid trihydrate (NAT), as well as liquid droplets of super-cooled ternary solution (STS). The classification results are compared to coincident measurements from the space borne lidar CALIOP instrument over the temporal overlap of both satellite missions (June 2006 to March 2012). Both datasets show a good agreement for the specific PSC classes, although the viewing geometries, vertical and horizontal resolution are quite different. Discrepancies are observed for the MIPAS ice class. The Bayesian classifier for MIPAS identifies substantially more ice clouds in the southern hemisphere polar vortex than CALIOP. This disagreement is attributed in parts to the difference in the sensitivity on mixed-type clouds. Ice seems to dominate the spectral behaviour in the limb infrared spectra and may cause an overestimation in ice occurrence compared to the real fraction of ice within the PSC area in the polar vortex. The entire MIPAS measurement period was processed with the new classification approach. Examples like the detection of the Antarctic NAT belt during early winter, and its possible link to mountain wave events over the Antarctic Peninsula, which are observed by the AIRS instrument, are highlighting the importance of a climatology of in total 9 southern and 10 northern hemisphere winters. The new dataset is valuable both for detailed process studies, and for comparisons with and improvements of the PSC parameterisations used in chemistry transport and climate models.

Description: Altitude resolved aerosol detection in the upper troposphere and lower stratosphere (UTLS) is a challenging task for remote sensing instruments. Here, we introduce a new method for detecting aerosol in the UTLS based on infrared limb emission measurements. The method applies an improved aerosol-cloud-index that indicates infrared limb spectra affected by aerosol and ice clouds. For the discrimination between aerosol and ice clouds we developed a new method based on brightness temperature difference correlations. The discrimination thresholds for the new method were derived from radiative transfer simulations (including scattering) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)/Envisat measurements obtained in 2011. The method not only reliably separates aerosol from ice clouds, but also provides characteristic yet overlapping correlation patterns for volcanic ash and sulfate aerosol. We demonstrate the value of the new approach for volcanic ash and sulfate aerosol originating from the Grímsvötn (Iceland), Puyehue-Cordón Caulle (Chile) and Nabro (Eritrea) eruptions by comparing with Atmospheric Infrared Sounder (AIRS) volcanic ash and SO2 measurements.

Description: Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against the globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with the ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2(−1%) with a standard deviation of 101 W m−2 (20%). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2%) for every month in 2006 and the standard deviation is 62 W m−2 (12%). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.