Assimilation of Meteosat Second Generation (MSG) satellite data in a regional numerical weather prediction model using a one-dimensional variational approach

The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.

Elf magnetic field influence on ION Channels studied by Patch Clamp Technique: exposure set up and "Whole Cell" measurements on Potassium currents

The aim of this thesis was to study the effects of extremely low frequency (ELF) electromagnetic magnetic fields on potassium currents in neural cell lines ( Neuroblastoma SK-N-BE ), using the whole-cell Patch Clamp technique. Such technique is a sophisticated tool capable to investigate the electrophysiological activity at a single cell, and even at single channel level. The total potassium ion currents through the cell membrane was measured while exposing the cells to a combination of static (DC) and alternate (AC) magnetic fields according to the prediction of the so-called ‘ Ion Resonance Hypothesis ’. For this purpose we have designed and fabricated a magnetic field exposure system reaching a good compromise between magnetic field homogeneity and accessibility to the biological sample under the microscope. The magnetic field exposure system consists of three large orthogonal pairs of square coils surrounding the patch clamp set up and connected to the signal generation unit, able to generate different combinations of static and/or alternate magnetic fields. Such system was characterized in term of field distribution and uniformity through computation and direct field measurements. No statistically significant changes in the potassium ion currents through cell membrane were reveled when the cells were exposed to AC/DC magnetic field combination according to the afore mentioned ‘Ion Resonance Hypothesis’.