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.

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SUMMARYThe progress and status of a new atmospheric re-analysis, JRA-25 the Japanese 25-year Re-analysis which covers the 26 years from 1979 to 2004, are introduced. Observational data include some newly produced for JRA-25 and advantages and drawbacks of performance are briefly described. JRA-25 has many advantages such as its handling of precipitation amounts, tropical cyclone analysis, and the extent of low-level cloud along western continents that are among the best compared to other re-analyses. The snow analysis is also good and stable. JRA-25 outputs analysis products every 6 hours.

21The high temporal and spatial resolutions of geostationary satellite observations achieved by

The impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.