JPEG2000 Image Compression on Solar EUV Images

For future solar missions as well as ground-based telescopes, efficient ways to return and process data have become increasingly important. Solar Orbiter, which is the next ESA/NASA mission to explore the Sun and the heliosphere, is a deep-space mission, which implies a limited telemetry rate that makes efficient onboard data compression a necessity to achieve the mission science goals. Missions like the Solar Dynamics Observatory (SDO) and future ground-based telescopes such as the Daniel K. Inouye Solar Telescope, on the other hand, face the challenge of making petabyte-sized solar data archives accessible to the solar community. New image compression standards address these challenges by implementing efficient and flexible compression algorithms that can be tailored to user requirements. We analyse solar images from the Atmospheric Imaging Assembly (AIA) instrument onboard SDO to study the effect of lossy JPEG2000 (from the Joint Photographic Experts Group 2000) image compression at different bitrates. To assess the quality of compressed images, we use the mean structural similarity (MSSIM) index as well as the widely used peak signal-to-noise ratio (PSNR) as metrics and compare the two in the context of solar EUV images. In addition, we perform tests to validate the scientific use of the lossily compressed images by analysing examples of an on-disc and off-limb coronal-loop oscillation time-series observed by AIA/SDO.     

Sensitivity of hydrological models to uncertainty in rainfall input

Abstract

Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km². The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall–runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning.

Citation Arnaud, P., Lavabre, J., Fouchier, C., Diss, S. & Javelle, P. (2011) Sensitivity of hydrological models to uncertainty of rainfall input. Hydrol. Sci. J. 56(3), 397–410.     

Cooling history of lunar Mg-suite gabbronorite 76255, troctolite 76535 and Stillwater pyroxenite SC-936: The record in exsolution and ordering in pyroxenes

We have determined cooling rates of orthopyroxene crystals from two Mg-suite lunar samples (gabbronorite 76255 and troctolite 76535) and one terrestrial sample (orthopyroxenite SC-936 from the Stillwater Complex), on the basis of their Fe–Mg ordering states. In addition, a cooling rate of 76255 was determined by modeling the formation of exsolution lamellae in pyroxenes. The M1–M2 site occupancies of the orthopyroxene crystals were determined by single crystal X-ray diffraction and the rate constant for the ordering reaction was used along with calibrations of the equilibrium intracrystalline fractionation of Fe and Mg as a function of temperature to calculate cooling rates. The closure temperatures (T_C) of cation ordering are 525 °C for 76255, 500 °C for 76535 and 350 °C for SC-936 corresponding to cooling rates of 4 × 10⁻² °C/year at the closure temperature for the lunar samples and 10⁻⁶ °C/year for the Stillwater sample. A cooling rate for 76255, determined by simulating the exsolution process, is 1.7 × 10⁻² °C/year at a closure temperature for exsolution of 700 °C. The Fe–Mg ordering cooling rate determined for 76535 reflects a complex thermal history superimposed on the initial plutonic provenance established for this sample [McCallum, I.S., Schwartz, J.M., 2001. Lunar Mg suite: thermobarometry and petrogenesis of parental magmas. J. Geophys. Res. 106, 27969–27983]. The preservation of a crystallization age of 4.51 Ga and a metamorphic age of 4.25 Ga for 76535 is consistent with a model in which excavation of this sample from the lower lunar crust took place while the sample was at a temperature above the closure temperatures for the Sm–Nd, U–Pb and Ar–Ar isotopic systems. Temperatures in excess of the isotopic closure temperatures (i.e., >600 °C) in the lower lunar crust were maintained by heat diffusing from concentrations of U- and Th-rich KREEP material at the base of the crust. On the other hand, 76255 formed at a much shallower depth in the lunar crust (2 km) and was well below its isotopic closure temperatures at the time of excavation, most likely during the Serenitatis basin-forming impact event. Both lunar samples were reheated during transport to the surface and deposition in hot ejecta blankets. The reheating was short lived but apparently sufficient to redistribute Fe and Mg in M sites in orthopyroxenes. For the lunar samples, the cooling rates based on Fe–Mg ordering represent final stage cooling within an ejecta blanket.     

Finite Prandtl number 2-D convection at high Rayleigh numbers

Finite Prandtl number thermal convection is important to the dynamics of planetary bodies in the solar system. For example, the complex geology on the surface of the Jovian moon Europa is caused by a convecting, brine-rich global ocean that deforms the overlying icy “lithosphere”. We have conducted a systematic study on the variations of the convection style, as Prandtl numbers are varied from 7 to 100 at Rayleigh numbers 106 and 108. Numerical simulations show that changes in the Prandtl number could exert significant effects on the shear flow, the number of convection cells, the degree of layering in the convection, and the number and size of the plumes in the convecting fluid. We found that for a given Rayleigh number, the convection style can change from single cell to layered convection, for increasing Prandtl number from 7 to 100. These results are important for determining the surface deformation on the Jovian moon Europa. They also have important implications for surface heat flow on Europa, and for the interior heat transfer of the early Earth during its magma ocean phase. Electronic Supplementary Material is available if you access this article at . On that page (frame on the left side), a link takes you directly to the supplementary material. Electronic Supplementary Material is available if you access this article at . On that page (frame on the left side), a link takes you directly to the supplementary material.     

Interpreting the clustering of radio sources

We develop the formalism required to interpret, within a CDM framework, the angular clustering of sources in a deep radio survey. The effect of non-linear evolution of density perturbations is discussed, as is the effect of the assumed redshift distribution of sources. We also investigate what redshift ranges contribute to the clustering signal at different angular scales. Application of the formalism is focused on the clustering detected in the FIRST survey, but measurements made for other radio surveys are also investigated. We comment on the implications for the evolution of clustering.