Abstract: Invited talk at CMB2013, held at Okinawa, Japan, 1-14 June 2013

Abstract: Contributed talk at AP-RASC'13 held in Taiwan, september 3-7 2013

Abstract: Invited talk at SCAR-AAA workshop (Scientific Committee on Antarctic Research – Astronomy and Astrophysics from Antarctica), held in Siena, Italy, 24-26 july 2013

Abstract: The impact of observations on analysis uncertainty and forecast performance was investigated for austral spring 2010 over the southern polar area for four different systems (NRL, GMAO, ECMWF and Météo-France) at the time of the Concordiasi field experiment. The largest multi-model variance in 500 hPa height analyses is found in the southern sub-Antarctic oceanic region, where there are rapidly evolving weather systems, rapid forecast-error growth, and fewer upper-air wind observation data to constrain the analyses. The total impact of all observations on the model forecast was computed using the 24 h forecast sensitivity-to-observations diagnostic. Observation types that contribute most to the reduction of the forecast error are shown to be AMSU, IASI, AIRS, GPS-RO, radiosonde, surface and atmospheric motion vector observations. For sounding data, radiosondes and dropsondes, one can note a large impact on the analysis and forecasts of temperature at low levels and a large impact of wind at high levels. Observing system experiments using the Concordiasi dropsondes show a large impact of the observations over the Antarctic plateau extending to lower latitudes with the forecast range, with the largest impact around 50–70°S. These experiments indicate there is a potential benefit from using radiance data better over land and sea-ice and from innovative atmospheric motion vectors obtained from a combination of various satellites to fill the current data gaps and improve numerical weather prediction analyses in this region.

Abstract: Identifying and tracking the projection of magnetospheric regions on the high-latitude ionosphere is of primary importance for studying the Solar Wind-Magnetosphere-Ionosphere system and for space weather applications. By its unique spatial coverage and temporal resolution, the Super Dual Auroral Radar Network (SuperDARN) provides key parameters, such as the Doppler spectral width, which allows the monitoring of the ionospheric footprint of some magnetospheric boundaries in near real-time. In this study, we present the first results of a statistical approach for monitoring these magnetospheric boundaries. The singular value decomposition is used as a data reduction tool to describe the backscattered echoes with a small set of parameters. One of these is strongly correlated with the Doppler spectral width, and can thus be used as a proxy for it. Based on this, we propose a Bayesian classifier for identifying the spectral width boundary, which is classically associated with the Polar Cap boundary. The results are in good agreement with previous studies. Two advantages of the method are: the possibility to apply it in near real-time, and its capacity to select the appropriate threshold level for the boundary detection.