A long-term global atmospheric reanalysis, named ''Japanese 25-year Reanalysis (JRA-25)'' was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. The analysis covers the period from 1979 to 2004. This is the first long-term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and specially collected observational data, were used to generate a consistent and high-quality reanalysis dataset designed for climate research and operational monitoring and forecasts. One of the many purposes of JRA-25 is to enhance the analysis to a high quality in the Asian region.Six-hourly data assimilation cycles were performed, producing 6-hourly atmospheric analysis and forecast fields of various physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120 km) and 40 vertical layers with the top level at 0.4 hPa. In addition to conventional surface and upper air observations, atmospheric motion vector (AMV) wind retrieved from geostationary satellites, brightness temperature from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from orbital satellite microwave radiometer radiance and other satellite data are assimilated with three-dimensional variational method (3D-Var). JMA produced daily sea surface temperature (SST), sea ice and three-dimensional ozone profiles for JRA-25. A new quality control method for TOVS data was developed and applied in advance.Many advantages have been found in the JRA-25 reanalysis. Predicted 6-hour global total precipitation distribution and amount are well reproduced both in space and time. The performance of the long time series of the global precipitation is the best among the other reanalyses, with few unrealistic variations from degraded satellite data contaminated by volcanic eruptions. Secondly, JRA-25 is the first reanalysis to assimilate wind profiles around tropical cyclones reconstructed from historical best track information; tropical cyclones were analyzed properly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is well simulated and snow depth analysis is also of a good quality. The article also covers material which requires attention when using JRA-25.

Variability in tropical cyclone (TC) days in the western North Pacific (WNP) since the late 1970s is investigated based on two datasets. As an overall behavior, the intense TC days have increased for the last 30 years from both the Japan Meteorological Agency (JMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. Both datasets show that TC days with an intensity of Saffir-Simpson category 2 or higher have increased by 15 30% over the past 30 years. In terms of the detailed behavior of this increase in intense TC days, the contribution obtained from the JMA dataset is different from that of the JTWC. The JMA dataset reveals that the increase in moderately intense TC days contributes to the overall increase in intense TC days, while the JTWC dataset shows that the increase in extremely intense TC days has the dominant contribution. The difference between the two datasets becomes significant after 1987 when aircraft reconnaissance by the US Air Force was deactivated. The difference between the assessed contributions is due to different implementations of the Dvorak technique, the basis for TC intensity estimation at the JMA and the JTWC after the deactivation of aircraft reconnaissance.

Precipitable water vapor (PWV), which was obtained from the nation wide GPS (Global Positioning System) network over Japan, operated by the Geographical Survey Institute (GSI), was assimilated into the meso data assimilation (DA) system of the Japan Meteorological Agency (JMA). Two different methods were examined; one is an optimum interpolation (OI) method, and the other is a 4-dimensional variational (4D-Var) method. Using the analysis data derived from both systems, a number of forecast experiments for rainfall events in Baiu and summer seasons were carried out using the JMA meso scale numerical weather prediction model (MSM). Remarkable improvements in rainfall forecast were seen in several cases, both for the OI and 4D-Var experiments. A statistical score of the 4D-Var experiments, however, showed that the impact of GPS PWV was almost neutral for rainfall forecast, and no substantial improvements were obtained. One of the reasons might be that GPS sites used for the experiments were too few, and sparsely distributed compared to the rainfall systems. Another reason specific for the 4D-Var is that, although the 4D-Var improved PWV analyses, it sometimes modified vertical profiles of water vapor significantly, which brought about different static stability from the first guess of the model (MSM) or from the observations. These results suggested the importance of correct assimilation of vertical profiles of water vapor.

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The Global Positioning System/Meteorology Project (GPS/MET), a 5‐year project begun in April 1997, will closely monitor atmospheric water vapor over Japan. Using this information, scientists can improve forecasting of catastrophic weather in Japan and also correct transient drifts of a few centimeters per week that occur in estimates of crustal deformation derived from GPS data. Though satellite‐based GPS has become popular for monitoring crustal deformation with mm to cm accuracy, transient drifts, which change seasonally, are probably caused by changing amounts of water vapor in the atmosphere that delay GPS microwave signal propagation. Determining the amount of water vapor will enable scientists to improve the orecasting of catastrophic weather events and also correct GPS data. For example, water vapor data with high space‐time resolution is urgently needed to predict local torrential rain events that cause serious damage in Japan during the rainy season.