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Interseismic tectonic motion manifests itself as a long (10’s to 100’s km) wavelength signal. The magnitude and the extent of the signal is crucial to understand kinematics of the crustal motion. For two decades, GPS measurements have been the main source of information for observing such a signal. In this study we use Persistent Scatterer Interferometry (PSI) observations, which provides better spatial resolution, to monitor tectonic signal overWest Anatolia. The region is characterized by horstgraben morphology which is controlled by oblique-slip normal faults. The faults cause an extension circa 25-30 mm/yr in NE-SW direction as observed by sparse GPS network measurements. In our analysis, we have used 42 ERS images acquired between 1992 and 2001 years. We have identified coherent interferograms which would reduce the noise level in the rural areas leading to increased point density. Finally we compare our PSI results with two other GPS studies within the region. The modeled interseismic signal from a recent GPS study ([1]) agrees with the one modeled from that of PSI observations in trend direction.

Earth observation satellites are a valuable source of data that can be used to better understand the Earth system dynamics. However, analysis of satellite image time series requires an accurate spatial co-registration so that the multi-temporal pixel entities offer a true temporal view of the study area. This implies that all the observations must be mapped to a common system of grid cells (gridding). Two common grids can be defined as a reference: (1) a grid defined by an external dataset in a given coordinate system or (2) a grid defined by one of the images of the time series. The aim of this paper is to study the impact that gridding has on the quality of ENVISAT/MERIS image time series, which is quantified using a time series of images acquired over The Netherlands. First, the impact of the reference map grid selection is evaluated in terms of geolocation errors and pixel overlap. Then, the effect of observation geometry (due to the fact that images are acquired from slightly different orbits) is studied. Results show that it is of paramount importance to identify areas with small pixel overlap in order to further analyze the reliability of the products derived from these areas.

The Helheim glacier in south-east Greenland faces a retreat and thinning like many other Greenland outlet glaciers, but showed two particular huge and fast retreats between 2000 and 2005, coinciding with an acceleration of glacier flow [1]. The estimation of glacier retreat and thinning are established procedures based on a variety of remote sensing techniques, e.g. SAR interferometry (InSAR) [1]. However, due to densification processes, e.g. refreezing of meltwater, changes in the mass balance cannot be calculated solely from changes in the volume of a glacier derived from interferometric elevation models [2]. A near surface densification related to refreezing of meltwater leads to a reduction in surface elevation, but does not contribute to a mass loss and thus cannot be attributed to a potential sea level rise. Likewise, the measurement of snow accumulation and its spatial distribution is error prone due to the penetration bias of InSAR measurements. While local data of accumulation or densification give precise information [2], satellite based polarimetric and interferometric SAR (Pol-InSAR) data has the potential to assess these changes in the near surface snow and firn layers with a better spatial and temporal coverage. This study investigates X-band (TanDEM-X) Pol-InSAR data in order to retrieve information about the temporal and spatial variations of surface and near- surface parameters of the Helheim glacier. [1] I.M. Howat, I. Joughin, S. Tulaczyk, and S. Gogineni, “Rapid retreat and acceleration of Helheim Glacier, east Greenland”, Geophysical Research Letters, vol. 32, 2005 [2] V. Parry, P. Nienow, D. Mair, J. Scott, B. Hubbard, K. Steffen and D. Wingham, “Investigations of meltwater refreezing and density variations in the snowpack and firn within the percolation zone of the Greenland ice sheet”, Annals of Glaciology, vol 46, no. A252, pp. 61-68, 2007

Long-wavelength (e.g. P- and L- band) SAR systems can penetrate tens of meters deep into ice bodies. Hence, they are sensitive to the ice surface as well as to sub-surface (volume) ice structures. Both contributions are present in the SAR signature that has to be interpreted accordingly. For this, significant attention has been given to model-based decomposition techniques of polarimetric SAR (PolSAR) data. In this sense, this paper extends and investigates in detail the modeling of potential scattering contributions with the attempt to explain long-wavelength PolSAR signatures of subpolar glacier ice. The main effort is the development of an extended volume scattering component; different kinds of inclusions typically present in glacier ice (e.g. air bubbles, oriented crystals fabrics, etc.) are modeled by particle clouds with variable particle shape and orientation in a three dimensional space. For the case of oriented particles, the volume anisotropy induces differential propagation effects. The associated differential propagation velocities (phase differences) and losses are accounted for the different polarimetric channels. Second order mechanisms generated from the interaction between adjacent particles or internal ice layers might play a relevant role. For instance, double reflections can cause significant co-polarization phase difference as well as increase in entropy (in the far range), despite their very low scattering amplitude. Finally, a surface scattering contribution from the air/ice interface at the glacier surface must be considered. Covariance matrices of the above mentioned contributions are modeled and incoherently combined. Different possible scenarios are then simulated and analyzed (particle shape and orientation, inclusions nature and volume fraction, distribution of power contributions, etc...). A performance assessment is conducted by comparing modeled PolSAR signature to fully polarimetric SAR data at L- and P-band acquired by DLR’s E-SAR system, over the Austfonna ice-cap, in the Svalbard archipelago, Norway.

Satellite-based remote sensing measurements of atmospheric sulphur dioxide (SO2) provide valuable information on anthropogenic pollution and volcanic activity. Sensors like GOME-2 on MetOp-A make it possible to monitor SO2 emissions on a global scale and daily basis. SO2 total column amounts are retrieved in near-real time using the UV range of backscattered sunlight making it possible to detect and track volcanic eruption plumes as a valuable tool for aviation warning. For aviation safety the correct determination of the plume height is a central issue. Therefore a novel method has been developed for the determination of the plume height in near-real time based on the operational DOAS retrieval combined with an iterative look-up table (LUT) approach. The method has been applied to the eruption of Eyjafjöll volcano, April - May 2010, and to the eruption of Kilauea, July 2008.

Nadir infrared (IR) sounding can be used to derive information on trace gases relevant for climate and air quality. For vertical column density retrievals using SCIAMACHY near IR nadir observations, the BIRRA (Beer InfraRed Retrieval Algorithm) code has recently been implemented in the operational level 1 – 2 processor. For analysis of thermal IR nadir observations of AIRS, GOSAT, IASI, or TES, a closely related code CERVISA (Column EstimatoR Vertical Infrared Sounding of the Atmosphere) has been developed. Both codes share a large portion of modules, e.g., for line-by-line absorption and the nonlinear least squares solver. The essential difference is the part of the forward model devoted to radiative transfer through the atmosphere, i.e., Beer’s law for the near IR versus Schwarzschild’s equation for the thermal IR. For the ongoing validation of the BIRRA carbon monoxide CO and methane CH4 products intercomparisons with thermal IR sounding data are performed. CERVISA retrieval results are compared both to the operational products of the IR sounder considered and to SCIAMACHY products retrieved with BIRRA.

Bora – a gusty, cold, downslope, north-easterly air flow blowing over the sea from the lee side of the Dinaric Alps – is among better known Adriatic winter winds. Its onset, longevity and severity are determined by larger atmospheric flows, but ultimately controlled by local orographic features. Bora outbreaks exhibit lateral shearing and jets of higher wind speed that provoke vigorous response. Measurements and modelling studies concur that in the Adriatic Sea an episode of Bora wind provokes a transient multiple-gyre response (e.g. Kuzmić and Orlić, Annales Geophysicae, 5B, 103-112, 1987 ; Kuzmić et al., Journal of Geophysical Research, 112 (C3), 2007) imbedded in cyclonic general circulation. Powerful bora wind events leave surface imprints detectable throughout the electromagnetic spectrum (e.g. Kuzmić and Tomažić, EUMETSAT P.50, 2007). In this study we gauge our ability to discern the bora wind over the Adriatic Sea exploiting the synergy of SAR data, scatterometer data, and numerical model simulations. More specifically, we explore how well the location and shape of bora jets and wakes are captured in the Envisat ASAR scenes, Quikscat SeaWinds scatterometer wind-vector fields, and WRF atmospheric model simulations, focused on an eight-day period in December 2007 when several bora events of varying intensity took place. The Weather Research and Forecasting (WRF) model is used employing two-way coupling and three- or four-stage nested grids, with the lowest nest ensuring very-high resolution (≈1km) simulations. The data from the three sources are interpolated in space and time as necessary, to conform particular comparison grid. Looking at sensor-registered and model-derived sigma-nought and wind-vector fields we sought to cross-validate different sensor- and model-based responses, paying particular attention to the spatial extent and variability of the bora jets and wakes, spatial resolution of detected and modeled fields, as well as to the wind direction issue in SAR-derived wind vector fields.