[1] The Sea of Japan side of Central Japan is one of the heaviest snowfall areas in the world. We investigate near-future snow cover changes on the Sea of Japan side using a regional climate model. We perform the pseudo global warming (PGW) downscaling based on the five global climate models (GCMs). The changes in snow cover strongly depend on the elevation; decrease in the ratios of snow cover is larger in the lower elevations. The decrease ratios of the maximum accumulated snowfall in the short term, such as 1 day, are smaller than those in the long term, such as 1 week. We conduct the PGW experiments focusing on specific periods when a 2 K warming at 850 hPa is projected by the individual GCMs (PGW-2K85). The PGW-2K85 experiments show different changes in precipitation, resulting in snow cover changes in spite of similar warming conditions. Simplified sensitivity experiments that assume homogenous warming of the atmosphere (2 K) and the sea surface show that the altitude dependency of snow cover changes is similar to that in the PGW-2K85 experiments, while the uncertainty of changes in the sea surface temperature influences the snow cover changes both in the lower and higher elevations. The decrease in snowfall is, however, underestimated in the simplified sensitivity experiments as compared with the PGW experiments. Most GCMs project an increase in dry static stability and some GCMs project an anticyclonic anomaly over Central Japan, indicating the inhibition of precipitation, including snowfall, in the PGW experiments.Citation: Kawase, H., M. Hara, T. Yoshikane, N. N. Ishizaki, F. Uno, H. Hatsushika, and F. Kimura (2013), Altitude dependency of future snow cover changes over Central Japan evaluated by a regional climate model,

This study assessed the sensitivity of the simulated future impact on forage yield over Japan to the precipitation change in growing season derived from the multiple downscaling models, taking the regional climate projection ensemble dataset for Japan as an example. Three regional climate models (RCMs: NHRCM, NRAMS, and TWRF), and one statistical model (CDFDM) provided the fine-resolution (20-km) climate data over Japan from the climate projection performed by a global climate model (GCM: MIROCHI) under A1B scenario. With the common boundaries for the RCMs (and predictor for the statistical model), there is a consistency for the increased summer precipitation. However, discrepancies were found for the degree of precipitation increase and change in the mean summer maximum number of consecutive dry days. These discrepancies caused the spread of the simulated future change in forage yield over Japan by 3.3-11.4% (2081-2100), relative to the present-day one (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). These results showed that the direction (increase or decrease) and amplitude of the simulated future impact di¤er between the climate scenarios from the downscaling models and those from the parent GCM, indicating that climate downscaling is a source of uncertainty in simulating future impact.

This study evaluated the performance of a regional weather prediction model. The horizontal resolution is increased to the sub-kilometer scale in a series of experiments over areas of Japan through the summer or winter seasons of 2015−2016. The performance improves less when increasing the horizontal resolution from 2 km to 1 km or 500 m than it does from 5 km to 2 km, especially when topography and ice microphysics are less relevant. Although the velocity and magnitude of updrafts, cloud size, and convection in the boundary layer indeed change with the horizontal resolution, these differences turn out to have little impact on the model performance.(Citation: Ito, J., S. Hayashi, A. Hashimoto, H. Ohtake, F. Uno, H. Yoshimura, T. Kato, and Y. Yamada, 2017: Stalled improvement in a numerical weather prediction model as horizontal resolution increases to the sub-kilometer scale. SOLA, 13, 151− 156,

A large number of photovoltaic (PV) power systems have been adopted in Japan after a feed‐in tariff was introduced in 2012. However, PV power generation data from residential rooftop and/or ground‐mounted PV systems, and larger MW‐size PV plants have not been measured accurately in real‐time. This is because PV power monitoring instruments (eg, smart meters) have not collected a sufficient amount of power generation data. In order to realize adequate safety control of electric power systems under high PV‐penetration conditions, it is important to fully understand the temporal and spatial variations associated with PV power generation. In this study, we estimated the PV power generation for a regional area (ie, prefecture or municipality) in terms of PV power installation capacity and satellite‐estimated solar irradiance using a Japanese geostationary satellite, Himawari‐8. The satellite‐derived regionally integrated PV power estimations were validated with reference data provided by electric power companies. The validation results showed that these estimations were comparable to the reference data, provided by the Kyushu Electric Power Company Inc. (Kyushu) and the Tokyo Electric Power Company Inc. (TEPCO). However, the results also identified slight overestimations of PV power in the spring and summer seasons. An advantage of the proposed method is that it does not require land‐based monitoring instruments, which can lead to increased operational cost savings for PV power systems. Furthermore, in consideration of future PV power penetration scenarios, it is suggested that PV power in excess of regional power demands could be generated under the same weather conditions.

We investigated the seasonal variation of snow cover at different altitudes using station data, satellite data, and a high-resolution numerical model around the Japan's Northern Alps during three winters (2011/12, 2012/13, and 2013/14). The satellite data showed that the snow cover fraction was largest in 2012/13 before late December, which indicates that much of snowfall occurs at higher elevations in the early winter. In midwinter, the snow cover fraction was over 90% in 2011/12, while it was approximately 70% and 60% in 2012/13 and 2013/14, respectively. The station data also showed the greatest snow depth at lower elevations in 2011/12 among the three winters. The numerical model well simulates the year-to-year and monthly variations of the snow cover fraction, although the threshold of snow depth are larger than that of the satellite data. The numerical simulations indicate that the total amount of snowfall is controlled by spring snowfall as much as by winter snowfall at higher elevations. The year-toyear variations of spring snowfall are relatively larger than those of winter snowfall, resulting in different year-to-year variations of snow cover at lower and higher elevations.

A notable declining trend in the eating quality of rice panicles has been observed in Kyushu, Western Japan, since the 1990's. As solar radiation is one of the factors determining eating quality, this study investigated the recent time trends and variations in the mean and accumulated radiation for the ripening period of paddy rice in Kyushu during the period 1979-2007. The 3-year running mean radiation data, which are dynamically downscaled reanalysis data using the non-hydrostatic regional climate model (NHRCM) with a grid interval of 20 km, were used for analysis after the validation. From a meta-analysis of governmental crop statistics, it was found that the ripening period (i.e., the period between heading and harvesting) in Kyushu was shortened by 10 days in a 29-year period and generally occurred between the end of August and early October in the 2000's but between early September and the end of October in the 1980's. The change in the length and timing of the ripening period resulted in decreased accumulated radiation for the period, although the mean radiation during the period increased as a result of the earlier timing of the ripening period. The empirical orthogonal function (EOF) analysis results showed that the earlier timing and shorter ripening period are the most dominant factors explaining the radiation change during the ripening period in Kyushu in the past three decades. In addition, a more westward extension in Pacific anticyclones and an associated change in the locations of precipitation that decreased the mean and accumulated radiation for the ripening period in the area were frequently observed in the 2000's. These results indicate a more adverse radiation condition for paddy rice production in the 2000's compared to the 1980's and 1990's.

This study focuses on the main factor of regional difference in Altitudinal Dependency of Snow Depth (ADSD) and discusses the applicable range of snow cover estimation method with ADSD. We use the high-density surface observational data and a regional climate model in Niigata Prefecture. The estimation method with ADSD produces significant estimation error if the method is applied to broad areas. The high-density observational data show the regional difference of ADSD between windward and leeward areas with a coastal mountain. Numerical simulation reproduces these observational results. We evaluate mountain effects on the regional difference of ADSD using sensitivity experiments. In the sensitivity experiment, the altitude is changed from a mountain to a flat plain. The sensitivity experiment shows that the regional differences of ADSD are not simulated. The results indicate that the applicable range of the estimation method with ADSD is limited to a single slope and mountain. It should be noted that this method has a high probability of increasing the estimation error in

The objective of this study is to propose and evaluate a set of modifications to enhance a machine-learning-based method for forecasting day-ahead solar irradiation. To assess the proposed modifications, they were implemented in an initial forecast method, and their effectiveness was analyzed using two years of data on a national scale in Japan. In addition, the accuracy of the modified method was compared with one of the forecast methods for solar irradiation used by the Japan Meteorological Agency (JMA), namely, the mesoscale model (MSM). Such forecasts were made publicly available only recently, which makes this study one of the first ones to compare them with machine-learning-based forecasts. The annual root-mean-square error (RMSE) of local forecasts of the JMA-MSM varied from 0.1 to 0.14 kW h m−2; the regional equivalent varied from 0.062 to 0.091 kW h m−2. In comparison with these results, the modified model achieved an average RMSE reduction of 7.5% on the local scale and 16% on the regional scale. The modified model also had a skill score that was 23% higher than that of the JMA model. Furthermore, the performance of the JMA model had strong spatial and seasonal dependencies, which were reduced in the machine-learning-based forecasts. The results show that the proposed modifications are effective in reducing large forecasts errors, but they cannot compensate for situations in which the input data used to make the forecasts are highly inaccurate.