The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple regression model for forecasting tropical cyclone (TC) intensity [both central pressure (Pmin) and maximum wind speed (Vmax)]. To further improve the accuracy of the Japan Meteorological Agency version of SHIPS, five new predictors associated with TC rainfall and structural features were incorporated into the scheme. Four of the five predictors were primarily derived from the hourly Global Satellite Mapping of Precipitation (GSMaP) reanalysis product, which is a microwave satellite-derived rainfall dataset. The predictors include the axisymmetry of rainfall distribution around a TC multiplied by ocean heat content (OHC), rainfall areal coverage, the radius of maximum azimuthal mean rainfall, and total volumetric rain multiplied by OHC. The fifth predictor is the Rossby number. Among these predictors, the axisymmetry multiplied by OHC had the greatest impact on intensity change, particularly, at forecast times up to 42 h. The forecast results up to 5 days showed that the mean absolute error (MAE) of the Pmin forecast in SHIPS with the new predictors was improved by over 6% in the first half of the forecast period. The MAE of the Vmax forecast was also improved by nearly 4%. Regarding the Pmin forecast, the improvement was greatest (up to 13%) for steady-state TCs, including those initialized as tropical depressions, with slight improvement (2%–5%) for intensifying TCs. Finally, a real-time forecast experiment utilizing the hourly near-real-time GSMaP product demonstrated the improvement of the SHIPS forecasts, confirming feasibility for operational use.

A scheme for the assimilation of radiance data from the Advanced TIROS Operational Vertical Sounder (ATOVS) into the global three-dimensional variational (3DVar) analysis system at the Japan Meteorological Agency (JMA) is described. It makes better use of ATOVS observations than the previous ATOVS retrieval assimilation scheme. Several procedures have been developed: advanced thinning, identification of cloud/rain-affected radiances, removal of radiance data which are erroneous or not well simulated with a current fast radiative transfer model and/or numerical weather prediction (NWP) model, selection of adequate channels and assignment of observation errors for different observation conditions, such as clear/cloud/rain and surface type, and correction of radiance biases in which the effects of NWP model biases are minimized.It was found from parallel experiments that the radiance assimilation shows significant improvement over the retrieval assimilation in many respects. Analyzed temperature and water vapor are improved when verified against radiosonde and the Special Sensor Microwave/Imager (SSM/I) observations. Impacts on forecasts are positive on the globe, especially for short-range forecasts. The reduction in the root mean square errors by the radiance assimilation reaches up to 0.5 K for the analyzed temperature against RAOBs, and about 5-10% for the 500 hPa geopotential height at day 1 to day 4 forecast. As a result of these findings, the ATOVS radiance assimilation was operationally implemented in the JMA global analysis system on

To discuss the feasibility of the Himawari follow-on program, impacts of a hyperspectral sounder on a geostationary satellite (GeoHSS) is assessed using an observing system simulation experiment. Hypothetical GeoHSS observations are simulated by using an accurate reanalysis dataset for a heavy rainfall event in western Japan in 2018. The global data assimilation experiment demonstrates that the assimilation of clear-sky radiances of the GeoHSS improves the forecasts of the representative meteorological field and slightly reduces the typhoon position error. The regional data assimilation experiment shows that assimilating temperature and relative humidity profiles derived from the GeoHSS improves the heavy rainfall in the Chugoku region of western Japan as a result of enhanced southwesterly moisture flow off the northwestern coast of the Kyushu Island. These results suggest that the GeoHSS provides valuable information on frequently available vertically resolved temperature and humidity and thus improves the forecasts of severe events.

This study investigates prediction of TC intensity in the western North Pacific basin using a statistical-dynamical model called the Statistical Hurricane Intensity Prediction Scheme (SHIPS), with data sources in operations at the Japan Meteorological Agency (JMA) such as the JMA/Global Spectral Model forecast fields. In addition to predicting the change in the maximum wind (Vmax) as in the original SHIPS technique, another version of SHIPS for predicting the change in the minimum sea-level pressure (Pmin) has been developed. With 13 years of training samples, a total of 26 predictors were selected from among 52 through stepwise regression. Based on three years of independent samples, the root mean square errors of both Vmax and Pmin by the 26-predictor SHIPS model were found to be much smaller than those of the JMA/GSM and a simple climatology and persistence intensity model, which JMA official intensity forecasts are currently mainly based on. The prediction accuracy was not sensitive to the number of predictors as long as the leading predictors were included. Benefits of operationalizing SHIPS include a reduction in the errors of the JMA official intensity forecasts and an extension of their forecast length beyond the current 3 days (e.g., 5 days).

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