Japan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.

Abstract. This article presents new software for the analysis of global dynamical fields in (re)analyses, weather forecasts and climate models. A new diagnostic tool, developed within the MODES project, allows one to diagnose properties of balanced and inertio-gravity (IG) circulations across many scales. In particular, the IG spectrum, which has only recently become observable, can be studied simultaneously in the mass and wind fields while considering the whole model depth in contrast to the majority of studies.The paper includes the theory of normal-mode function (NMF) expansion, technical details of the Fortran 90 code, examples of namelists which control the software execution and outputs of the software application on the ERA Interim reanalysis data set. The applied libraries and default compiler are from the open-source domain. A limited understanding of Fortran suffices for the successful implementation of the software.The presented application of the software to the ERA Interim data set reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 10 % of the total wave energy. However, on sub-synoptic scales, IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally averaged and equatorial circulation provide a reference for the validation of climate models.

This study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite‐derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non‐Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non‐Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation‐error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast.

The Local Ensemble Transform Kalman Filter (LETKF) is implemented with the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) to assimilate the real-world observation data. First, the NICAM-LETKF system was developed using grid conversions between the NICAM's icosahedral grid and LETKF's uniform longitude-latitude grid to take advantage of the existing codes of Miyoshi. The grid conversions require additional computations and may cause additional interpolation error. Therefore, the LETKF code is modified, so that the LETKF reads and writes the NICAM's icosahedral grid data directly. We call this new version ICO-LETKF. In this study, the two systems are tested and compared using real conventional observations. The results show that the ICO-LETKF successfully accelerates the computations and improves the analyses.(Citation: Terasaki, K., M. Sawada, and T. Miyoshi, 2015: Local ensemble transform Kalman filter experiments with the non hydrostatic icosahedral atmospheric model NICAM. SOLA, 11, 23−26,

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