Numerical weather forecast models have biases caused by insufficient grid resolution and incomplete physical processes, especially near the land surface. Therefore, the Japan Meteorological Agency (JMA) has been operationally post-processing the forecast model outputs to correct biases. The operational post-processing method uses a Kalman filter (KF) algorithm for surface temperature prediction. Recent reports have shown that deep convolutional neural networks (CNNs) outperform the JMA operational method in correcting temperature forecast biases. This study combined the CNN-based bias correction scheme with the JMA operational KF algorithm. We expected that the combination of CNNs and a KF would improve the post-processing performance, as the CNNs modify large horizontal structures, and then, the KF corrects minor spatiotemporal deviations. As expected, we confirmed that the combination outperformed both CNNs and the KF alone. This study demonstrated the advantages of the new method in correcting coastal fronts, heat waves, and radiative cooling biases.

 A historical atmospheric reanalysis from 1850 to 2015 was performed using an atmospheric general circulation model assimilating surface pressure observations archived in international databases, with perturbed observational sea surface temperatures as a lower boundary condition. Posterior spread during data assimilation provides quantitative information on the uncertainty in the historical reanalysis. The reanalysis reproduces the evolution of the three-dimensional atmosphere close to those of the operational centers. Newly archived surface pressure observations greatly reduced the uncertainties in the present reanalysis over East Asia in the early 20th century. A scheme for assimilating tropical cyclone tracks and intensities was developed. The scheme was superior to the present several reanalyses in reproducing the intensity close to the observations and the positions. The reanalysis provides possible images of atmospheric circulations before reanalyses with full-scale observations become available, and opportunities for investigating extreme events that occurred before World War II. Incorporating dynamical downscaling with a regional model that includes detailed topography and sophisticated physics is an application of historical reanalysis to reveal the details of past extreme events. Some examples of past heavy rainfall events in Japan are shown using a downscaling experiment, together with dense rainfall observations over the Japanese islands.

 The Japan Meteorological Agency (JMA) has developed the third Japanese global atmospheric reanalysis, the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q). The objective of JRA-3Q is to improve quality in terms of issues identified in the previous Japanese 55-year Reanalysis (JRA-55) and to extend the reanalysis period further into the past. JRA-3Q is based on the TL479 version of the JMA global Numerical Weather Prediction (NWP) system as of December 2018 and uses results of developments in the operational NWP system, boundary conditions, and forcing fields achieved at JMA since JRA-55. It covers the period from September 1947, when Typhoon Kathleen brought severe flood damage to Japan, and uses rescued historical observations to extend its analyses backwards in time about 10 years earlier than JRA-55. This paper describes the data assimilation system, forecast model, observations, boundary conditions, and forcing fields used to produce JRA-3Q as well as the basic characteristics of the JRA-3Q product. The initial quality evaluation revealed major improvements from JRA-55 in the global energy budget and representation of tropical cyclones (TCs). One of the major problems in JRA-55—global energy imbalance with excess upward net energy flux at the top of the atmosphere and at the surface—has been significantly reduced in JRA-3Q. Another problem—a trend of artificial weakening of TCs—has been resolved through the use of a method that generates TC bogus based on the JMA operational system. There remain several problems such that volcanic-induced stratospheric warming is smaller than expected. This paper discusses the causes of such problems and possible solutions in future reanalyses.

 It is well-known in rainfall ensemble forecasts that ensemble means suffer substantially from the diffusion effect resulting from the averaging operator. Therefore, ensemble means are rarely used in practice. The use of the arithmetic average to compute ensemble means is equivalent to the definition of ensemble means as centers of mass or barycenters of all ensemble members where each ensemble member is considered as a point in a high-dimensional Euclidean space. This study uses the limitation of ensemble means as evidence to support the viewpoint that the geometry of rainfall distributions is not the familiar Euclidean space, but a different space. The rigorously mathematical theory underlying this space has already been developed in the theory of optimal transport (OT) with various applications in data science.

 In the theory of OT, all distributions are required to have the same total mass. This requirement is rarely satisfied in rainfall ensemble forecasts. We, therefore, develop the geometry of rainfall distributions from an extension of OT called unbalanced OT. This geometry is associated with the Gaussian-Hellinger (GH) distance, defined as the optimal cost to push a source distribution to a destination distribution with penalties on the mass discrepancy between mass transportation and original mass distributions. Applications of the new geometry of rainfall distributions in practice are enabled by the fast and scalable Sinkhorn-Knopp algorithms, in which GH distances or GH barycenters can be approximated in real-time. In the new geometry, ensemble means are identified with GH barycenters, and the diffusion effect, as in the case of arithmetic means, is avoided. New ensemble means being placed side-by-side with deterministic forecasts provide useful information for forecasters in decision-making.

 The trend of strong typhoons over the recent 30 years was analyzed using Dvorak reanalysis data from 1987 to 2016 produced by Japan Meteorological Agency. The strong typhoons were defined in this study as tropical cyclones equivalent to category 4 and 5 on the Saffir-Simpson scale. The temporal homogeneity of the Dvorak reanalysis data is expected to be much better than that of best track data. Results showed no statistically significant increasing trend in strong typhoons with large inter-annual and multi-year scale variations. Meanwhile, the spatial distribution of the genesis locations of tropical cyclones, which could influence whether or not they develop into strong typhoons, varied locally during the analysis period. The changes in genesis locations may have influenced the overall trend of strong typhoons during the analysis period. The results with the new Dvorak reanalysis data highlight the need for the accumulation of high quality data over time as well as for careful interpretation of trend analysis results seen in previous studies.

 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.

In the discretization of the primitive equations for numerical calculations, a formulation of a three-dimensional spectral model that uses the spectral method not only in the horizontal direction but also in the vertical direction is proposed. In this formulation, the Legendre polynomial expansion is used for the vertical discretization. It is shown that semi-implicit time integration can be efficiently done under this formulation. Then, a numerical model based on this formulation is developed and several benchmark numerical calculations proposed in previous studies are performed to show that this implementation of the primitive equations can give accurate numerical solutions with a relatively small degrees of freedom in the vertical discretization. It is also shown that, by performing several calculations with different vertical degrees of freedom, a characteristic property of the spectral method is observed in which the error of the numerical solution decreases rapidly when the number of vertical degrees of freedom is increased. It is also noted that an alternative to the sponge layer can be devised to suppress the reflected waves under this formulation, and that a “toy” model can be derived as an application of this formulation, in which the vertical degrees of freedom are reduced to the minimum.

The near-real-time merged satellite and in-situ data global daily sea surface temperature (SST) of the Japan Meteorological Agency (hereinafter abbreviated as R-MGD) is subjected to filtering out short-time-scale fluctuations from observations prior to the analysis time. Therefore, the rapid SST change due to the passage of tropical cyclones (TCs) is thought to cause biases. Here, the biases in the R-MGD with respect to in-situ observations were quantified along the passage of TCs in the western North Pacific. First, we examined a case study on the approach of three successive TCs in August–September 2020. The R-MGD had positive biases of > 2°C just after the passage of three TCs, and negative biases were observed after one week of the last TC's passage. The comparison of the R-MGD with a moored buoy indicates that the biases can be explained by short-term fluctuations filtered out and the SST prior to the analysis time in R-MGD analysis. Second, the composite analysis from May 2015–October 2020 indicates that the statistically significant biases at the observation points ranged between −1 days and 4 days for positive biases and between 7 days and 14 days for negative biases relative to the time of the closest approach of a TC within 500 km. The positive SST bias is largely associated with cold subsurface water and intense TCs, being pronounced in the mid-latitude, except around the Kuroshio and Kuroshio extension regions. The assimilation of in-situ observations recorded within 72 h prior to the R-MGD analysis time through additional optimal interpolation alleviates these biases because this process redeems short-time-scale fluctuations. The impact on TC forecasts and the validity of the optimal interpolation experiment against the independent observations were also investigated.

Atmospheric transport of aerosols such as black carbon (BC) affects the absorption/scattering of solar radiation, precipitation, and snow/ice cover, especially in areas of low human activity such as the Arctic. The resolution dependency of simulated BC transport from Siberia to the Arctic, related to the well-developed low-pressure systems in September, was evaluated using the Nonhydrostatic Icosahedral Atmospheric Model–Spectral Radiation Transport Model for Aerosol Species (NICAM-SPRINTARS) with fine (∼ 56 km) and coarse (∼ 220 km) horizontal resolutions. These low-pressure systems have a large horizontal scale (∼ 2000 km) with the well-developed central pressure located on the transport pathway from East Asia to the Arctic through Siberia. In recent years, the events analysis of the most developed low-pressure system indicated that the high-BC area in the Bering Sea observed by the Japanese research vessel Mirai on September 26–27, 2016, moved to the Arctic with a filamental structure from the low's center to the behind of the cold front and ahead of the warm front in relation to its ascending motion on September 27–28, 2016. The composite analysis for the developed low-pressure events in September from 2015 to 2018 indicated that the high-BC area was located eastward of the low's center in relation to the ascending motion over the low's center and northward/eastward area. Since the area of the maximum ascending motion has a small horizontal scale, this was not well simulated by the 220-km experiment. The study identified that the BC transport to the Arctic in September is enhanced by the well-developed low-pressure systems. The results of the transport model indicate that the material transport processes to the Arctic by the well-developed low-pressure systems are enhanced in the fine horizontal resolution (∼ 56 km) models relative to the coarse horizontal resolution (∼ 220 km) models.

Ultra-high-resolution numerical weather prediction (NWP) experiments over a large domain have been conducted to investigate the impacts of different factors of an NWP model in simulating the Hiroshima heavy rain event in August 2014. This is a continuation of the study in Part 1 in which similar experiments were carried out for the Izu Oshima heavy rain event in October 2013. We have demonstrated the benefit of using a high-resolution model (500-m grid spacing or less) with a large domain in simulating torrential rain events.

The simulated location and intensity of the rain band in the Hiroshima case has been shown to be sensitive to the model resolution. The simulation at the 2-km grid spacing could reproduce the rain band, but with a shift to the northeast. This displacement error was reduced when the grid spacing was further reduced to 500 m and 250 m. The best simulation both in the location and intensity was obtained at the 250-m grid spacing. The planetary boundary layer schemes had a smaller impact in this case, which is different from the Izu Oshima case.

This study also investigated the dependency of simulated convective cores (CCs) on model resolutions. The local rate of change of the number of CCs with respect to the model resolution was found to start decreasing at very high resolutions that are around the 500-m grid spacing. This implies the number of CCs tends to converge when the resolution goes beyond 500 m.

Methane (CH₄) is an important greenhouse gas and plays a significant role in tropospheric and stratospheric chemistry. Despite the relevance of methane (CH₄) in human-induced climate change and air pollution chemistry, there is no scientific consensus on the causes of changes in its growth rates and variability over the past three decades. We use a well-validated chemistry–transport model for simulating CH₄ concentration and estimation of regional CH₄ emissions by inverse modeling during 1988–2016. The control simulations are conducted using seasonally varying hydroxyl (OH) concentrations and assumed no interannual variability. Using inverse modeling of atmospheric observations, emission inventories, a wetland model, and a δ¹³C-CH₄ box model, we show that reductions in emissions from Europe and Russia since 1988, particularly from oil–gas exploitation and enteric fermentation, led to decreased CH₄ growth rates in the 1990s. This period was followed by a quasi-stationary state of CH₄ in the atmosphere during the early 2000s. CH₄ resumed growth from 2007, which we attribute to increases in emissions from coal mining mainly in China and the intensification of ruminant farming in tropical regions. A sensitivity simulation using interannually varying OH shows that regional emission estimates by inversion are unaffected for the mid- and high latitude areas. We show that meridional shift in CH₄ emissions toward the lower latitudes and the increase in CH₄ loss by hydroxyl (OH) over the tropics finely balance out, keeping the CH₄ gradients between the southern hemispheric tropical and polar sites relatively unchanged during 1988–2016. The latitudinal emissions shift is confirmed using the global distributions of the total column CH₄ observations via satellite remote sensing. During our analysis period, there is no evidence of emission enhancement due to climate warming, including the boreal regions. These findings highlight key sectors for effective emission reduction strategies toward climate change mitigation.

In this paper, precipitation rate retrieval algorithms for the Global Precipitation Measurement mission's Dual-frequency Precipitation Radar (DPR) are developed. The DPR consists of a Ku-band radar (KuPR; 13.6 GHz) and a Ka-band radar (KaPR; 35.5 GHz). For the KuPR, an algorithm similar to the Tropical Rainfall Measuring Mission's Precipitation Radar algorithm is developed, with the relation between precipitation rate R and massweighted mean diameter D_m (R−D_m relation) replacing the relation between the specific attenuation k and effective radar reflectivity factor Z_e. The R−D_m relation can also be applied to the KaPR and dual-frequency algorithms. In both the single-frequency and dual-frequency algorithms, the forward retrieval method is applied with an assumed adjustment factor for the R−D_m relation (ε) and the results are evaluated to select the best value of ε. The advantages of the dual-frequency algorithm are the availability of the dual-frequency surface reference technique and the ZfKa method, which is a method to use the attenuation-corrected radar reflectivity factor Z_f of KaPR, to select ε as well as the possibility to selectively use measurements from KuPR or KaPR. This paper also describes the derivation of the scattering table and the R−D_m relation as well as the procedure for non-uniform beam filling correction in detail. The outputs are then statistically analyzed to demonstrate algorithm performance.

This study investigates the global drop size distribution (DSD) of rainfall and its relationship to large-scale precipitation characteristics using the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory. This study focuses on seasonal variations in the dominant precipitation systems regarding variations in DSD. A mass-weighted mean diameter (D_m), which is estimated based on the dualfrequency information derived from the GPM/DPR, is statistically analyzed as a typical parameter of the DSD. Values of the annual mean D_m, in general, are larger over land than over the oceans, and the relationship between D_m and precipitation rate (R) is not a simple one-to-one relationship. Furthermore, D_m exhibits statistically significant seasonal variations, specifically over the northwest Pacific Ocean, whereas R shows insignificant variations, indicating the variations in R cannot explain the distinct seasonal changes in D_m. Focusing on the seasonal variation in D_m over the northwest Pacific Ocean, the results indicate that the variation in D_m is related to the seasonal change in the dominant precipitation systems. In the summer over the northwest Pacific Ocean, D_m is related to the organized precipitation systems associated with the Baiu front over the mid-latitudes and tropical disturbances over the subtropical region, with relatively higher precipitation top heights, composed of both stratiform and convective precipitations. Contrary to the summer, larger D_m over the mid-latitudes in winter is related to extratropical frontal systems with ice particles in the upper layers, which consists of more stratiform precipitation in the storm track region. The smaller D_m over the subtropical northwest Pacific Ocean in winter is associated with shallow convective precipitation systems with trade-wind cumulus clouds and cumulus congestus under the subtropical high.

Based on observations, the number of tropical cyclones (TCs) approaching the southern coast of Japan, including Tokyo, has increased over the last 40 years, and these TCs are increasing in strength when they approach land. The environmental conditions for TC development have become more favorable, with warmer sea surface temperature, less vertical wind shear, and more moisture in the atmosphere. In addition, the translation speed of TCs has decreased, which indicates a longer influence time. Comparison of the synoptic environment during July–October between the first (1980–1999, P1) and second (2000–2019, P2) 20 years shows that the sub-tropical high is strengthened in P2, where the western and northern edge of the high extends further into the west and north, respectively. Also, the westerly jet is weakened in P2 over and south of Japan in the middle-to-upper troposphere. These changes in the synoptic environment are considered to play a role in the increase in the number of TCs approaching Tokyo and also in the creation of more favorable conditions for TC development. The relationship between the changes in TC characteristics over the last 40 years and global warming is unclear. As the Pacific Decadal Oscillation is in a positive phase in P1 and in a negative phase in many years of P2, decadal oscillations may have played some role in the increase in the number of approaching TCs and in the changes in the synoptic environment.

The effectiveness of the probability ellipse for tropical cyclone (TC) track forecasts is investigated with multiple ensembles from the Japan Meteorological Agency (JMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and the Met Office in the United Kingdom (UKMO). All TCs during the 3 years from 2016 to 2018 are included in the verification. We show that the multiple ensembles composed of these four global ensembles are capable of predicting the situation-dependent uncertainties of TC track forecasts appropriately in both the along-track (AT) and cross-track (CT) directions. The use of a probability circle involves the implicit assumption of an isotropic error distribution, whereas the introduction of the probability ellipse makes it possible to provide information as to which is more uncertain; the direction or speed of TC movement. Compared to the probability circle adopted operationally at JMA, the probability ellipse can potentially reduce the area by 16, 15, and 24 %, on average, at forecast times (FT) of 3, 4, and 5 days, respectively, indicating that narrowing warning areas of TC track forecasts by the probability ellipse enables us to enhance disaster prevention/mitigation measures.

More than one hundred days were simulated over very large domains with fine (0.156 km to 2.5 km) grid spacing for realistic conditions to test the hypothesis that storm (kilometer) and large-eddy (hectometer) resolving simulations would provide an improved representation of clouds and precipitation in atmospheric simulations. At scales that resolve convective storms (storm-resolving for short), the vertical velocity variance becomes resolved and a better physical basis is achieved for representing clouds and precipitation. Similarly to past studies we found an improved representation of precipitation at kilometer scales, as compared to models with parameterized convection. The main precipitation features (location, diurnal cycle and spatial propagation) are well captured already at kilometer scales, and refining resolution to hectometer scales does not substantially change the simulations in these respects. It does, however, lead to a reduction in the precipitation on the time-scales considered – most notably over the ocean in the tropics. Changes in the distribution of precipitation, with less frequent extremes are also found in simulations incorporating hectometer scales. Hectometer scales appear to be more important for the representation of clouds, and make it possible to capture many important aspects of the cloud field, from the vertical distribution of cloud cover, to the distribution of cloud sizes, and to the diel (daily) cycle. Qualitative improvements, particularly in the ability to differentiate cumulus from stratiform clouds, are seen when one reduces the grid spacing from kilometer to hectometer scales. At the hectometer scale new challenges arise, but the similarity of observed and simulated scales, and the more direct connection between the circulation and the unconstrained degrees of freedom make these challenges less daunting. This quality, combined with already improved simulation as compared to more parameterized models, underpins our conviction that the use and further development of storm-resolving models offers exciting opportunities for advancing understanding of climate and climate change.

This study investigates a new possible process linking the quasi-stationary Rossby wave propagation (SWP) over Eurasia along the Asian jet and the Pacific-Japan (PJ) pattern through the Rossby wave breaking (RWB) near the jet exit region during boreal summer using a reanalysis dataset. To assess the statistical significance of the process, we conduct a lag composite analysis of the past 44 RWB events east of Japan. The result of the lag composite analysis shows that the SWP along the Asian jet induces the RWB accompanied by an amplified anomalous anticyclone east of Japan. The associated “inverse-S” shaped overturning of the upper-level potential vorticity (PV) distribution causes the southwestward intrusion of the high PV toward the subtropical western North Pacific (WNP). The Q-vector diagnosis and vorticity budget analysis indicate that the upper-level positive vorticity advection associated with the RWB is an important factor dynamically inducing ascent and reinforcing convection over the subtropical WNP, which in turn excites the subsequent PJ pattern. Classification of the cases by RWB strength indicates that the stronger RWB is significantly related to the stronger preceding SWP and subsequent enhanced PJ pattern, and vice versa. A partial correlation analysis of all the cases quantitatively shows the greater contribution of the upper-level positive vorticity advection over the subtropical WNP to the enhanced convection in this area and the formation of the PJ pattern, compared to that of the anomalous warm sea surface temperature condition. These results show that the SWP along the Asian jet can excite the PJ pattern, through the RWB east of Japan and the consequent intrusion of the high PV toward the subtropical WNP.