Precise Measurement of the Neutrino Mixing Parameter θ₂₃ from Muon Neutrino Disappearance in an Off-Axis Beam

New data from the T2K neutrino oscillation experiment produce the most precise measurement of the neutrino mixing parameter θ 23 . Using an off-axis neutrino beam with a peak energy of 0.6 GeV and a data set corresponding to 6.57×10 20 protons on target, T2K has fit the energy-dependent ν μ oscillation probability to determine oscillation parameters. The 68% confidence limit on sin 2 (θ 23 ) is 0.514 +0.055 −0.056 (0.511±0.055 ), assuming normal (inverted) mass hierarchy. The best-fit mass-squared splitting for normal hierarchy is Δm 2 32 =(2.51±0.10)×10 −3   eV 2 /c 4 (inverted hierarchy: Δm 2 13 =(2.48±0.10)×10 −3   eV 2 /c 4 ). Adding a model of multinucleon interactions that affect neutrino energy reconstruction is found to produce only small biases in neutrino oscillation parameter extraction at current levels of statistical uncertainty.

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Accurate rainfall data are the key input parameter for modelling river discharge and soil loss. Remote areas of Ethiopia often lack adequate precipitation data and where these data are available, there might be substantial temporal or spatial gaps. To counter this challenge, the Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) readily provides weather data for any geographic location on earth between 1979 and 2014. This study assesses the applicability of CFSR weather data to three watersheds in the Blue Nile Basin in Ethiopia. To this end, the Soil and Water Assessment Tool (SWAT) was set up to simulate discharge and soil loss, using CFSR and conventional weather data, in three small-scale watersheds ranging from 112 to 477 ha. Calibrated simulation results were compared to observed river discharge and observed soil loss over a period of 32 years. The conventional weather data resulted in very good discharge outputs for all three watersheds, while the CFSR weather data resulted in unsatisfactory discharge outputs for all of the three gauging stations. Soil loss simulation with conventional weather inputs yielded satisfactory outputs for two of three watersheds, while the CFSR weather input resulted in three unsatisfactory results. Overall, the simulations with the conventional data resulted in far better results for discharge and soil loss than simulations with CFSR data. The simulations with CFSR data were unable to adequately represent the specific regional climate for the three watersheds, performing even worse in climatic areas with two rainy seasons. Hence, CFSR data should not be used lightly in remote areas with no conventional weather data where no prior analysis is possible.