SPITZER observations of the λ Orionis cluster. II. Disks around solar-type and low-mass stars

We present IRAC/MIPS Spitzer Space Telescope observations of the solar-type and the low-mass stellar population of the young (~5Myr) λ Orionis cluster. Combining optical and Two Micron All Sky Survey photometry, we identify 436 stars as probable members of the cluster. Given the distance (450 pc) and the age of the cluster, our sample ranges in mass from 2 M_⊙ to objects below the substellar limit. With the addition of the Spitzer mid-infrared data, we have identified 49 stars bearing disks in the stellar cluster. Using spectral energy distribution slopes, we place objects in several classes: non-excess stars (diskless), stars with optically thick disks, stars with “evolved disks” (with smaller excesses than optically thick disk systems), and “transitional disk” candidates (in which the inner disk is partially or fully cleared). The disk fraction depends on the stellar mass, ranging from ~6% for K-type stars (R_C − J 4). We confirm the dependence of disk fraction on stellar mass in this age range found in other studies. Regarding clustering levels, the overall fraction of disks in the λ Orionis cluster is similar to those reported in other stellar groups with ages normally quoted as ~5Myr

Quantitative features of multifractal subtleties in time series

Based on the Multifractal Detrended Fluctuation Analysis (MFDFA) and on the Wavelet Transform Modulus Maxima (WTMM) methods we investigate the origin of multifractality in the time series. Series fluctuating according to a qGaussian distribution, both uncorrelated and correlated in time, are used. For the uncorrelated series at the border (q=5/3) between the Gaussian and the Levy basins of attraction asymptotically we find a phase-like transition between monofractal and bifractal characteristics. This indicates that these may solely be the specific nonlinear temporal correlations that organize the series into a genuine multifractal hierarchy. For analyzing various features of multifractality due to such correlations, we use the model series generated from the binomial cascade as well as empirical series. Then, within the temporal ranges of well developed power-law correlations we find a fast convergence in all multifractal measures. Besides of its practical significance this fact may reflect another manifestation of a conjectured q-generalized Central Limit Theorem

Wavelet versus Detrended Fluctuation Analysis of multifractal structures

We perform a comparative study of applicability of the Multifractal Detrended Fluctuation Analysis (MFDFA) and the Wavelet Transform Modulus Maxima (WTMM) method in proper detecting of mono- and multifractal character of data. We quantify the performance of both methods by using different sorts of artificial signals generated according to a few well-known exactly soluble mathematical models: monofractal fractional Brownian motion, bifractal Levy flights, and different sorts of multifractal binomial cascades. Our results show that in majority of situations in which one does not know a priori the fractal properties of a process, choosing MFDFA should be recommended. In particular, WTMM gives biased outcomes for the fractional Brownian motion with different values of Hurst exponent, indicating spurious multifractality. In some cases WTMM can also give different results if one applies different wavelets. We do not exclude using WTMM in real data analysis, but it occurs that while one may apply MFDFA in a more automatic fashion, WTMM has to be applied with care. In the second part of our work, we perform an analogous analysis on empirical data coming from the American and from the German stock market. For this data both methods detect rich multifractality in terms of broad f(alpha), but MFDFA suggests that this multifractality is poorer than in the case of WTMM.Comment: substantially extended version, to appear in Phys.Rev.

Spitzer observations of the Orion OB1 association: disk census in the low mass stars

We present new Spitzer Space Telescope observations of two fields in the Orion OB1 association. We report here IRAC/MIPS observations for 115 confirmed members and 41 photometric candidates of the ~10 Myr 25 Orionis aggregate in the OB1a subassociation, and 106 confirmed members and 65 photometric candidates of the 5 Myr region located in the OB1b subassociation. The 25 Orionis aggregate shows a disk frequency of 6% while the field in the OB1b subassociation shows a disk frequency of 13%. Combining IRAC, MIPS and 2MASS photometry we place stars bearing disks in several classes: stars with optically thick disks (class II systems), stars with an inner transitional disks (transitional disk candidates) and stars with "evolved disks"; the last exhibit smaller IRAC/MIPS excesses than class II systems. In all, we identify 1 transitional disk candidate in the 25 Orionis aggregate and 3 in the OB1b field; this represents ~10% of the disk bearing stars, indicating that the transitional disk phase can be relatively fast. We find that the frequency of disks is a function of the stellar mass, suggesting a maximum around stars with spectral type M0. Comparing the infrared excess in the IRAC bands among several stellar groups we find that inner disk emission decays with stellar age, showing a correlation with the respective disk frequencies. The disk emission at the IRAC and MIPS bands in several stellar groups indicates that disk dissipation takes place faster in the inner region of the disks. Comparison with models of irradiated accretion disks, computed with several degrees of settling, suggests that the decrease in the overall accretion rate observed in young stellar groups is not sufficient to explain the weak disk emission observed in the IRAC bands for disk bearing stars with ages 5 Myr or older.Comment: Accepted in the Astrophysical Journa

Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. <br><br> In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations