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EISCAT_3D is a project to build a multi-site phased-array incoherent scatter radar system in northern Fenno-Scandinavia. We demonstrate via numerical simulation how useful monochromatic images taken by a multi-point imager network are for auroral research in the EISCAT_3D project. We apply the generalized aurora computed tomography (G-ACT) method to modelled observational data from real instruments, such as the Auroral Large Imaging System (ALIS) and the EISCAT_3D radar. G-ACT is a method for reconstructing the three-dimensional (3D) distribution of auroral emissions and ionospheric electron density (corresponding to the horizontal two-dimensional (2D) distribution of energy spectra of precipitating electrons) from multi-instrument data. It is assumed that the EISCAT_3D radar scans an area of 0.8° in geographic latitude and 3° in longitude at an altitude of 130 km with 10 × 10 beams from the radar core site at Skibotn (69.35° N, 20.37° E). Two neighboring discrete arcs are assumed to appear in the observation region of the EISCAT_3D radar. The reconstruction results from G-ACT are compared with those from the normal ACT as well as the ionospheric electron density from the radar. It is found that G-ACT can interpolate the ionospheric electron density at a much higher spatial resolution than that observed by the EISCAT_3D radar. Furthermore, the multiple arcs reconstructed by G-ACT are more precise than those by ACT. In particular, underestimation of the ionospheric electron density and precipitating electrons' energy fluxes inside the arcs is significantly improved by G-ACT including the EISCAT_3D data. Even when the ACT reconstruction is difficult due to the unsuitable locations of the imager sites relative to the discrete arcs and/or a small number of available images, G-ACT allows us to obtain better reconstruction results. [ABSTRACT FROM AUTHOR]

Non-Gaussian probability density functions (PDFs) in convection initiation (CI) and development were investigated using a particle filter with a storm-scale numerical prediction model and an adaptive observation error estimator (NHM-RPF). An observing system simulation experiment (OSSE) was conducted with a 90-min assimilation period and 1000 particles at a 2-km grid spacing. Pseudosurface observations of potential temperature (PT), winds, water vapor (QV), and pseudoradar observations of rainwater (QR) in the lower troposphere were created in a nature run that simulated a well-developed cumulonimbus. The results of the OSSE (PF) show a significant improvement in comparison to ensemble simulations without any observations. The Gaussianity of the PDFs for PF in the CI area was evaluated using the Bayesian information criterion to compare goodness-of-fit of Gaussian, two-Gaussian mixture, and histogram models. The PDFs are strongly non-Gaussian when NHM-RPF produces diverse particles over the CI period. The non-Gaussian PDF of the updraft is followed by the upper-bounded PDF of the relative humidity, which produces non- Gaussian PDFs of QV and PT. The PDFs of the cloud water and QR are strongly non-Gaussian throughout the experimental period. We conclude that the non-Gaussianity of the CI originated from the non- Gaussianity of the updraft. In addition, we show that the adaptive observation error estimator significantly contributes to the stability of PF and the robustness to many observations. [ABSTRACT FROM AUTHOR]

We have developed a statistical downscaling method for generating probabilistic climate projections using multiple general circulation models (GCMs). A regression model was established so that the combination of coefficients of the GCMs reflects the characteristics of the variation of observations at each grid point. We adopted the elastic net penalty to estimate the regression model, considering model projection similarities. Using an observation system with a high spatial resolution, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Mean precipitation is generally projected to increase associated with higher temperatures and consequently increased atmospheric moisture content. Weakening of the winter monsoon may cause precipitation decreases in some areas. There is a high probability of a temperature increase in excess of 4 K in most areas of Japan by the end of the 21st century under the CMIP5 RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm increases along the Pacific coast of Japan during the summer season and along the coast of the Japan Sea during the winter season. Our probabilistic climate projection using statistical methods is expected to provide useful information to stakeholders involved in impact studies and risk assessments. [ABSTRACT FROM AUTHOR]