In a global numerical weather prediction (NWP) modeling framework we study the implementation of Gaussian uncertainty of individual particles into the assimilation step of a localized adaptive particle filter (LAPF). We obtain a local representation of the prior distribution as a mixture of basis functions. In the assimilation step, the filter calculates the individual weight coefficients and new particle locations. It can be viewed as a combination of the LAPF and a localized version of a Gaussian mixture filter, i.e., a Localized Mixture Coefficients Particle Filter (LMCPF).

 Here, we investigate the feasibility of the LMCPF within a global operational framework and evaluate the relationship between prior and posterior distributions and observations. Our simulations are carried out in a standard pre-operational experimental set-up with the full global observing system, 52 km global resolution and 10⁶ model variables. Statistics of particle movement in the assimilation step are calculated. The mixture approach is able to deal with the discrepancy between prior distributions and observation location in a real-world framework and to pull the particles towards the observations in a much better way than the pure LAPF. This shows that using Gaussian uncertainty can be an important tool to improve the analysis and forecast quality in a particle filter framework.

 A new operational seasonal forecast system, Japan Meteorological Agency (JMA)/Meteorological Research Institute (MRI) Coupled Prediction System (CPS) version 3 (JMA/MRI–CPS3), has been developed. This system represents a major upgrade of the former system, CPS2. CPS3 comprises atmosphere, land, ocean, and sea ice forecast models and the necessary initialization systems for these models. For historical reforecasts, the atmospheric reanalysis dataset JRA-3Q provides initial conditions for the atmosphere and the external forcings for land, ocean, and sea ice analysis. In the operational forecast, JMA's operational atmospheric analysis is used in conjunction with JRA-3Q to initialize the system in near-real time. The land surface model is initialized using an uncoupled free simulation, forced by the atmospheric analysis. The ocean and sea ice models are initialized with the global ocean data assimilation system MOVE-G3, which incorporates a newly developed four-dimensional variational method for temperature, salinity, and sea surface height and a three-dimensional method for sea ice concentration. Compared with the previous system, the CPS3 forecast model components have approximately 2-4 times higher resolution: the atmosphere and land models are configured with ∼ 55 km horizontal resolution, with 100 vertical atmosphere layers; and the ocean and sea ice models have a resolution of 0.25° × 0.25°, with 60 vertical ocean layers. The physical processes of the atmosphere are greatly refined in CPS3 relative to CPS2, resulting in improved representation of sub-seasonal to seasonal scale variability, including the eastward propagation of the Madden–Julian Oscillation, winter blocking highs in the North Atlantic, and coupled atmosphere–ocean variability during El Niño–Southern Oscillation events. Our historical reforecast experiment for 1991-2020 suggests that CPS3 has greater forecast skill than CPS2. The usability of the model output has been improved in CPS3 by reorganizing the operation schedule to provide daily updates of five-member ensemble forecasts.

 In drylands, the dry vegetation coverage affects dust occurrence by modulating threshold friction velocity (or wind speed) for dust emission. However, there has been little research into quantifying the effect of dry vegetation coverage on dust occurrence. This study investigated spatial and temporal variations of dust occurrence and three definitions of strong wind frequency over the Gobi Desert and surrounding regions in March and April, months when dust occurrence is frequent, during 2001-2021. We evaluated the effects of variations in dry vegetation on dust occurrence by using the threat scores of forecasted dust occurrences for each strong wind definition. Our results indicate that dry vegetation, which was derived from the MODIS Soil Tillage Index, affects dust occurrence more remarkably in April than in March. In March, land surface parameters such as soil freeze-thaw and snow cover, in addition to dry vegetation coverage, should be considered to explain dust variations in that month. However, use of the threshold wind speed estimated from dry vegetation coverage improved the prediction accuracy of dust occurrence in April. Therefore, we propose that the dry vegetation coverage is a key factor controlling dust occurrence variations in April. The findings imply that estimation of dry vegetation coverage should be applied to dust models.

 The observation operator (OO) is essential in data assimilation (DA) to derive the model equivalent of observations from the model variables. In the satellite DA, the OO for satellite microwave brightness temperature (BT) is usually based on the radiative transfer model (RTM) with a bias correction procedure. To explore the possibility to obtain OO without using physically based RTM, this study applied machine learning (ML) as OO (ML-OO) to assimilate BT from Advanced Microwave Sounding Unit-A (AMSU-A) channels 6 and 7 over oceans and channel 8 over both land and oceans under clear-sky conditions. We used a reference system, consisting of the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF). The radiative transfer for TOVS (RTTOV) was implemented in the system as OO, combined with a separate bias correction procedure (RTTOV-OO). The DA experiment was performed for one month to assimilate conventional observations and BT using the reference system. Model forecasts from the experiment were paired with observations for training the ML models to obtain ML-OO. In addition, three DA experiments were conducted, which revealed that DA of the conventional observations and BT using ML-OO was slightly inferior, compared to that of RTTOV-OO, but it was better than the assimilation based on only conventional observations. Moreover, ML-OO treated bias internally, thereby simplifying the overall system framework. The proposed ML-OO has limitations due to (1) the inability to treat bias realistically when a significant change is present in the satellite characteristics, (2) inapplicability for many channels, (3) deteriorated performance, compared with that of RTTOV-OO in terms of accuracy and computational speed, and (4) physically based RTM is still used to train the ML-OO. Future studies can alleviate these drawbacks, thereby improving the proposed ML-OO.

　プリミティブ方程式系を数値計算するための離散化について、水平方向だけでなく鉛直方向にもスペクトル法を用いる3次元スペクトルモデルの定式化を提案する。この定式化において、鉛直方向の離散化にはルジャンドル多項式展開を用いている。この定式化のもとでは、セミインプリシット数値積分が効率的に行えることが示される。この定式化に基づいて数値モデルを開発し、先行研究で提案されているいくつかのベンチマーク数値計算を行う。その結果、プリミティブ方程式系のこの実装により、比較的少ない鉛直方向の自由度でも高精度の数値解が得られることが示される。また、いくつかの異なる鉛直自由度の計算を行うことにより、鉛直方向の自由度を増やすと数値解の誤差が急速に減少するというスペクトル法特有の性質が見られることも示される。さらに、この定式化のもとで、反射波を抑制するためのスポンジ層に代わる工夫が示されるとともに、この定式化の応用として、鉛直自由度を極限まで減らした「トイ」モデルも導かれる。