A simulation study was conducted to investigate the retrieval of meso-γ scale precipitable water vapor (PWV) distribution with the Quasi-Zenith Satellite System (QZSS) using output from a non-hydrostatic model (JMA NHM). The evaluation was performed on PWV values obtained by simulating three different methods: using all GPS satellites above an elevation angle higher than 10° (PWVG) (conventional Global Navigation Satellite System (GNSS) meteorology method), using only the QZSS satellite at the highest elevation (PWVQ), and using only the GPS satellite at the highest elevation (PWVHG). The three methods were compared by assuming the vertically integrated water vapor amounts of the model as true PWV. As a result, the root mean square errors of PWVG, PWVQ, and PWVHG were 2.78, 0.13, and 0.59 mm, respectively, 5 min before the rainfall. The time series of PWVHG had a large discontinuity (˜ 2 mm) when the GPS satellite with the highest elevation changed, while that of PWVQ was small because the elevation at which the highest QZSS satellites change was much higher. The standard deviation of PWVQ was smaller than those of PWVG and PWVHG, which vary significantly depending on GPS satellite geometry. When the spatial distributions of PWVG and PWVQ were compared to the meso-γ scale distribution of the reference PWV, PWVG smoothed out the PWV fluctuations, whereas PWVQ captured them well, due to the higher spatial resolution achievable using only high-elevation slant paths. These results suggest that meso-γ scale water vapor fluctuations associated with a thunderstorm can be retrieved using a dense GNSS receiver network and analyzing PWV from a single high-elevation GNSS satellite. In this study, we focus on QZSS, since this constellation would be especially promising in this context, and it would provide nearly continuous PWV observations as its highest satellite changes, contrary to using the highest satellites from multiple GNSS constellations.
Evaluations of the summer/winter Asian monsoon through the late 20th century (1981-2000) were conducted on the basis of model simulations using 20 Coupled Model Intercomparison Project Phase 3 (CMIP3) and 24 Phase 5 (CMIP5) multi-model datasets, and comparisons of the results with many types of observational data. Skill metrics have been calculated in terms of reproducibility of seasonal mean structures. The projected thermal structure of the mid to upper troposphere, which is an important driving force of the Asian monsoon, was also evaluated. Overall, the skills of the CMIP5 multi-model ensemble (MME) mean results have been improved, as compared with those of the CMIP3 MME. Considering these evaluations, we examined projected future (2081-2100) changes in the summer/winter Asian monsoon, including those of the tropical Hadley-Walker circulation, for mid-range emission scenarios (SRES-A1B for CMIP3 and RCP4.5 for CMIP5). The CMIP3 MME shows projected increases in precipitation and attenuation of circulation over broad regions of Asia. This so-called “wind-precipitation paradox” is a characteristic property of the Asian monsoon under a CO2-rich atmosphere. The CMIP5 MME, on the other hand, shows a projected acceleration of climatological low-level monsoon westerlies, particularly in subtropical regions (10°-20°N), which therefore requires a partial revision of the wind-precipitation paradox. In terms of meridional temperature gradients (MTGs), the CMIP5 MME datasets project marked mid to upper tropospheric warming over the western Indian Ocean, as compared with other regions of the Indian and western Pacific oceans. At higher latitudes, the projected warming rate is relatively small to the northwest of the Tibetan Plateau, and projected MTGs are reduced in this region. In the summer Asian monsoon, the different circulation change between CMIP3 and CMIP5 MME despite the common MTG weakening is a notable feature.
Eight different interpolation methods (inverse distance weighting, spline with tension, thin-plate spline, completely regularized spline, ordinary kriging, simple kriging, universal kriging, and linear regression (LR) model) were comparatively analyzed to determine the spatial distribution of monthly reference evapotranspiration (ET0) values calculated using the Hargreaves method (ET0-HG). To compare the different methods, observation stations were divided into two data groups (58 stations for analysis and 14 stations for validation). The correlation coefficients for all months ranged between 0.68 and 0.90. Of all the methods evaluated, the LR model was found to give the optimum results. The highest correlation coefficient was observed with the LR model for all months except March, April, June, and September. UK showed lower correlation coefficients than the LR model for all months except June and September and was found to be the second-best method. Secondary data contributed positively to ET0-HG interpolation; therefore, certain spatial data was incorporated into the LR model. The relationship between the monthly ET0-HG values produced using the most efficient interpolation method, and the values calculated with the Penman-Monteith method (ET0-PM) were also investigated in this study. Correlation coefficients between the methods varied between 0.65 and 0.86 for all months. The largest difference between the two methods was observed in June (-2.27 mm day-1), and the smallest difference was seen in February (0.02 mm day-1). In conclusion, Hargreaves method was found to be easier to use in calculating ET0 for the creation of ET0 maps, in which the use of LR was found to be more reliable than other interpolation methods.