Accurate and rapid source term estimation is critical for consequence assessment and
emergency decision-making in nuclear accidents. Neural network methods provide a …

In March 2011, severe nuclear accident happened at the Fukushima Dai-ichi Nuclear Power
Plant (FDNPP) after the gigantic earthquake and following huge tsunami wave. A lot of …

Temporal absences in observation records lead to release losses during the source term
inversions of atmospheric radionuclide emissions. Consequently, objectively-estimated …

Understanding the transport of 137Cs emitted during the Fukushima accident is challenging
because the critical emissions that produced the high-deposition area are not adequately …

Eleven years after the Fukushima accident, independent objective estimates of the
atmospheric 137 Cs release still suffer from discontinuities such as negative release terms …

Wet deposition, including both in-and below-cloud scavenging, is critical for the atmospheric
transport modeling of 137 Cs following the Fukushima Daiichi Nuclear power plant (FDNPP) …

We have analyzed near-global stratospheric data (and mesospheric data as well for H 2 O)
in terms of absolute abundances, variability, and trends for O 3, H 2 O, HCl, N 2 O, and HNO …

The severity of wildfires is increasing globally. In this study, we used data from the Global
Change Observation Mission-Climate/Second-generation Global Imager (GCOM-C/SGLI) to …

Surrogate modeling is one of the most promising applications of deep learning techniques in
meteorology. The purpose of this study was to downscale surface wind fields in a gridded …

Source term estimation based on environmental monitoring data is a key method for
obtaining source information, which is required by the emergency response system. This …