Kei Yoshimura 교수

In this paper we present a multidecadal and global three‐dimensional stable water isotope data set. This is accomplished by incorporating processes of the stable water isotopes into an atmospheric general circulation model and by applying a spectral nudging technique toward Reanalysis dynamical fields. Unlike the global model simulations forced only by sea surface temperature (SST), the dynamical fields used in the simulation are never far from observation because the spectral nudging technique

Abstract With the aim of producing higher-resolution global reanalysis datasets from coarse-resolution reanalysis, a global version of the dynamical downscaling using a global spectral model is developed. A variant of spectral nudging, the modified form of scale-selective bias correction developed for regional models is adopted. The method includes 1) nudging of temperature in addition to the zonal and meridional components of winds, 2) nudging to the perturbation field rather than to the pertur

Stable water isotopes (D and 18 O) in precipitation have large spatial and temporal variability and are used widely to trace the global hydrologic cycle. The two models that have been used in the past to examine the variability of precipitation isotopes are Rayleigh‐type models and isotope‐atmospheric general circulation models. The causes of short‐term (1–10 day) variability in precipitation isotopes, however, remain unclear. This study seeks to explain isotope variability quantitatively at suc

In this paper an isotope‐incorporated regional model is developed and utilized for simulations of an atmospheric river event that occurred in March 2005. A set of sensitivity experiments and comparisons with observations confirm that the kinetic isotopic exchange between falling droplets and ambient water vapor below the cloud base was mostly responsible for the initial enrichment and subsequent rapid drop of the deuterium abundance in precipitation observed during the event even under humid con

Abstract. Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remot

Recent advancements in the measurement and modeling of heavy stable water isotopes (i.e., H218O and HDO), especially in situ and remote sensing spectroscopic vapor isotope measurements and isotope-incorporated general circulation and regional models, have rapidly improved our understanding of the behavior of water isotopes on Earth. These advancements have significantly increased the recognition of the usefulness of information on stable water isotopes in the geoscience community. This study rev

We performed an intensive comparison of an isotope-incorporated atmospheric general circulation model with vapor isotopologue ratio observation data by two quasi-global satellite sensors in preparation for data assimilation of water isotope ratios. A global Isotope-incorporated Global Spectral Model simulation nudged toward the reanalysis wind field, atmospheric total column data from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) on Envisat, and midtropospheric

A statistical approach that considers the bias and uncertainty of models is proposed for interpreting the simulated river discharge as a flood risk. A 29-year simulation was performed to estimate parameters of the Gumbel distribution for the probability of extreme discharge. The estimated discharge probability index (DPI) showed clear agreement with observed values. Even more strikingly, high DPI in the simulation corresponded to actual flood damage records. This indicates that the real-time sim

Real-time precipitation forecast facilitates water management and water-associated disaster early warning. However, numerical weather prediction (NWP) models provide precipitation forecasts with bias. This study proposed to combine support vector machine (SVM) regression with quantile-based bias correction method to improve real-time 39-hour precipitation forecasts in Japan. Five methods were compared and evaluated against observations, which include SVM regression, quantile mapping (QM), cumula