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This paper presents a validation and intercomparison of the non-linear Partition Rescaling and Shift Algorithm (PARSA) for deriving full two-dimensional ocean wave spectra from ENIVSAT Advanced Synthetic Aperture Radar (ASAR) Wave Mode (WM) data. ASAR WM data acquired globally are used for the validation exercise by comparing the retrieved significant wave height (SWH), zero up-crossing wave period (Tm02), swell SWH (H12, for waves with a period longer than 12 s), mean wave frequency and mean wave direction to in situ buoy measurements and results from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis wave model and the Deutscher Wetterdienst (DWD) forecast wave model. An intercomparison of results from the PARSA algorithm with those from the quasi-linear retrieval algorithm adopted by the European Space Agency (ESA) to generate the ASAR WM level-2 product (ASA_WVW_2P, termed WVW hereafter) is also presented in this paper. In addition to the intercomparison with the existing ASAR WM WVW product, sea state parameters (SWH and Tm02), integrated from the PARSA spectra are also compared to the results derived using the empirical algorithm CWAVE_ENV. Validation results indicate that the PARSA inversion can yield full two-dimensional ocean wave spectra. The retrieved SWH corresponds well to buoy measurements with a scatter index of 21%, as demonstrated by 1247 collocated data pairs. By comparing SWH to the ECMWF reanalysis wave model and the DWD forecast wave model, better agreement is achieved, with scatter indices of 9% and 16%, respectively. In addition to comparing conventional integral wave parameters normally used to assess the quality of inverted spectra, a comparison of individual PARSA spectra chosen in different sea state with the nearest numerical wave model spectra and the WVW spectra is performed to illustrate two-dimensional spectral differences. [ABSTRACT FROM AUTHOR]

The detection of ground fog from satellite data is of interest in operational nowcasting applications, as well as in studies of the climate system. A discrimination between fog at the ground and other low-stratus situations from satellite data requires information on cloud vertical geometry to establish whether the cloud touches the ground. This article introduces a technique that allows for the discrimination between low stratus and (ground) fog on the basis of geostationary satellite imagery. The cloud-base height is derived using a subadiabatic model of cloud microphysics. In this model, the cloud base is varied until model liquid-water path matches that retrieved from satellite data. The performance of this technique is shown to be good in a comparison with METeorological Aerodrome Report data comprising 1030 satellite scenes. With a hit rate of 81% and a threat score of 0.62, the skill is satisfactory. [ABSTRACT FROM AUTHOR]

Severe and sustained episodes of hot weather during the summer season are associated with marked short-term increases in morbidity and mortality in the United States and Europe. The death toll in an unprepared region can be substantial, as was evidenced in the 2003 heat event in Western Europe. There is growing interest in developing early warning systems to advise the public when weather conditions pose risks to health. These systems link meteorological forecasts of dangerous weather with public health actions. The principal components of an early warning system include identification and forecasting of the event (including consistent, standardised weather criteria for when warnings are activated and deactivated), prediction of possible health outcomes that could occur, an effective and timely response plan that targets high-risk populations, and an ongoing evaluation and revision of the system and its components. A particular challenge is the development of effective communication of the behavioural changes needed to prevent adverse health impacts. [ABSTRACT FROM AUTHOR]