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We analyse Indian summer monsoon (ISM) seasonal reforecasts by CFSv2 model, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), to examine the cause for highest all-India ISM rainfall (ISMR) forecast skill with February (L3) ICs. The reported highest L3 skill is based on correlation between observed and predicted interannual variation (IAV) of ISMR. Other scores such as mean error, bias, RMSE, mean, standard deviation and coefficient of variation, indicate higher or comparable skill for April(L1)/May(L0) ICs. Though theory suggests that forecast skill degrades with increase in lead-time, CFSv2 shows highest skill with L3 ICs, due to predicting 1983 ISMR excess for which other ICs fail. But this correct prediction is caused by wrong forecast of La Nina or cooling of equatorial central Pacific (NINO3.4) during ISM season. In observation, normal sea surface temperatures (SSTs) prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean or EQUINOO, which CFSv2 failed to capture with all ICs. Major results are reaffirmed by analysing an optimum number of 5 experimental reforecasts by current version of CFSv2 with late-April/early-May ICs having short yet useful lead-time. These reforecasts showed least seasonal biases and highest ISMR correlation skill if 1983 is excluded. Model deficiencies such as over-sensitivity of ISMR to SST variation over NINO3.4 (ENSO) and unrealistic influence of ENSO on EQUINOO, contribute to errors in ISMR forecasting. Whereas, in observation, ISMR is influenced by both ENSO and EQUINOO. Forecast skill for Boreal summer ENSO is found to be deficient with lowest skill for L3/L4 ICs, hinting the possible influence of long lead-time induced dynamical drift. The results warrant the need for minimisation of bias in SST boundary forcing to achieve improved ISMR forecasts., Comment: 33 pages, 4 tables, 9 figures and 1 appendix

This paper outlines image domain material decomposition algorithms that have been routinely used in MARS spectral CT systems. These algorithms (known collectively as MARS-MD) are based on a pragmatic heuristic for solving the under-determined problem where there are more materials than energy bins. This heuristic contains three parts: (1) splitting the problem into a number of possible sub-problems, each containing fewer materials; (2) solving each sub-problem; and (3) applying rejection criteria to eliminate all but one sub-problem's solution. An advantage of this process is that different constraints can be applied to each sub-problem if necessary. In addition, the result of this process is that solutions will be sparse in the material domain, which reduces crossover of signal between material images. Two algorithms based on this process are presented: the Segmentation variant, which uses segmented material classes to define each sub-problem; and the Angular Rejection variant, which defines the rejection criteria using the angle between reconstructed attenuation vectors.

This paper discusses methods for reducing beam hardening effects using spectral data for biomaterial applications. A small-animal spectral scanner operating in the diagnostic energy range was used. We investigate the use of photon-processing features of the Medipix3RX ASIC in reducing beam hardening and associated artefacts. A fully operational charge summing mode was used during the imaging routine. We present spectral data collected for metal alloy samples, its analysis using algebraic 3D reconstruction software and volume visualisation using a custom volume rendering software. Narrow high energy acquisition using the photon-processing detector revealed substantial reduction in beam hardening effects and metal artefacts.

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531]., Comment: 11 pages, 6 figs

Undoped and Mn doped ZnO samples with different percentage of Mn content (1 mol%, 2 mol% and 3mol%) were synthesized by a simple solvo-thermal method. We have studied the structural, chemical and optical properties of the samples by using x-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive x-ray (EDX) analysis, Fourier transform infrared (FTIR) spectroscopy and UV-VIS spectroscopy. The XRD spectra show that all the samples are hexagonal wurtzite structures. The lattice parameters calculated for the Mn doped ZnO from the XRD pattern were found to be slightly larger than those of the undoped ZnO, which indicates substitution of Mn in ZnO lattice. SEM photograph shows the grain size of undoped ZnO is bigger than the Mn doped ZnO indicating hindrance of grain growth upon Mn doping. As the Mn doping increases the optical band gap decreases for the range of Mn doping reported here.

We present in this paper the changes in the room temperature magnetic property of ZnO on Mn doping prepared using solvo-thermal process. The zero field cooled (ZFC) and field cooled (FC) magnetisation of undoped ZnO showed bifurcation and magnetic hysteresis at room temperature. Upon Mn doping the magnetic hysteresis at room temperature and the bifurcation in ZFC-FC magnetization vanishes. The results seem to indicate that undoped ZnO is ferromagnetic while on the other hand the Mn doped ZnO is not a ferromagnetic system. We observe that on addition of Mn atoms the system shows antiferromagnetism with very giant magnetic moments., Comment: 5 figures

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.