We developed 55 models for predicting the number of ambulance transport due to heatstroke (hereafter, referred to as the number of patients with heatstroke) on the next day in Tokyo, using different combinations of 11 explanatory variables sets and five methods (three statistical models and two machine learning) for ten years (2010–2019). The root mean square error (RMSE) for the number of heatstroke patients was minimal when the best model was developed by combining six explanatory variables (temperature, relative humidity, wind speed, solar radiation, number of days since June 1, and the number of patients with heatstroke on the previous day) and the generalized additive model. The best model remarkably improved prediction by 52.1 % compared to a widely used model, which primarily utilizes temperature as an explanatory variable and the generalized linear model as a method. Further analysis investigating the contribution of the explanatory variables and prediction method showed that RMSE was reduced by 49.7 % using the above six explanatory variables compared to using the only temperature and by 14.6 % using the generalized additive model compared to using the generalized linear model.

Translation speed is an important factor determining locally accumulated disasters induced by tropical cyclones (TCs). We found that the basin wide TC translation speed over the western North Pacific (WNP) in the late season (October–December) experienced an abrupt decrease in the early 1980s. However, this slowdown cannot be explained by the previously proposed deceleration in large-scale steering. In this study, we demonstrated that this slowdown results from the decreased proportion of subtropical TC track frequency in the early 1980s. Because late-season large-scale steering flow in the subtropical WNP is much greater than that in the tropical WNP, TCs influencing the subtropical WNP generally hold greater translation speed than that of TCs staying in the tropical WNP. Thus, a decrease in the ratio of subtropical TC track frequency can lead to a notable decrease in the basin wide TC translation speed. The decreased ratio of subtropical TC track frequency results from the strengthened southwestward steering and the reduced ratio of TC genesis in the tropical eastern WNP, which is linked to a WNP anticyclonic circulation that appears to be driven by the Atlantic Multi-decadal Oscillation. The result introduces the crucial role of TC track shift in the basin wide TC translation speed and has important implications for understanding the effects of climate change on TC translation speed.

The cross-validation of radars in a network is important in making consistent retrievals across the domain and assuring the product quality. During the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign, two C-band radars, namely the Colorado State University C-band Hydrological Instrument for Volumetric Observations (CSU-CHIVO) and the C-band Scanning ARM Precipitation Radar (CSAPR-2), were deployed near the Sierras de Cordoba in Argentina, a region known for having some of the most intense severe weather in the world. In addition to these two radars, the operational radar of the Cordoba City, the Radar Meteorologico Argentino 1 (RMA-1), adds another instrument to the RELAMPAGO network. This study presents an intercomparison between the RELAMPAGO C-band radars using the GPM spaceborne radar as a common reference. A method to bring ground-based radars into better agreement is also proposed. Moreover, the attenuation correction for the C-band radar is studied in the context of intercomparing two radars. The attenuation coefficients are computed for the RELAMPAGO domain using the local disdrometers deployed during the campaign. After the attenuation correction, CSU-CHIVO, CSAPR-2, and RMA-1 compare well with GPM-DPR with a high correlation and bias less than 1 dB.

We intercompared the cloud properties of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) simulation output over the Atlantic Ocean. The domain averaged outgoing longwave radiation (OLR) is relatively similar across the models, but the net shortwave radiation at the top of the atmosphere (NSR) shows large differences among the models. The models capture the triple modes of cloud systems corresponding to shallow, congestus, and high clouds, although their partition in these three categories is strongly model dependent. The simulated height of the shallow and congestus peaks is more robust than the peak of high clouds, whereas cloud water content exhibits larger intermodel differences than does cloud ice content.

Furthermore, we investigated the resolution dependency of the vertical profiles of clouds for NICAM (Nonhydrostatic ICosahedral Atmospheric Model), ICON (Icosahedral Nonhydrostatic), and IFS (Integrated Forecasting System). We found that the averaged mixing ratio of ice clouds consistently increased with finer grid spacing. Such a consistent signal is not apparent for the mixing ratio of liquid clouds for shallow and congestus clouds. The impact of the grid spacing on OLR is smaller than on NSR and also much smaller than the intermodel differences.

In 2018, Typhoon Trami made landfall in Japan and maintained its intensity for a few days, then rapidly weakened after its recurvature. Subsequently, Typhoon Kong-Rey passed through the waters cooled by Trami while rapidly weakening. The region where both typhoons rapidly weakened is a region rich in oceanic mesoscale eddies overlying the Subtropical Countercurrent. To understand the role of a cold-core eddy, in the intensity change of these two typhoons, we examined the similarity and differences between the two typhoons, utilizing numerical simulations with a 2-km-mesh nonhydrostatic atmosphere model and an atmospheric-wave-ocean coupled model. Sensitivity experiments were performed by assuming a significant magnitude on the weakening of Trami during the mature phase; for example, we embedded an artificial cold-core eddy with a magnitude not based on in situ observations to gauge initial oceanic conditions. In contrast for Kong-Rey, nine ensemble simulations for initial atmospheric conditions were conducted instead of different-day initial oceanic conditions. The simulated rapid weakening of two typhoons was related to the low upper-ocean heat content caused by typhoon-induced sea surface cooling (SSC). Most simulations for Trami and Kong-Rey show a tendency of overdevelopment during the mature or weakening phase; the overdevelopment of Trami is caused by insufficiently simulated SSC and the embedded artificial cold eddy, which promoted the SSC; whereas, the overdevelopment of Kong-Rey is related to the failure of track simulation. A reasonable simulated track of Kong-Rey required greater time traveling over the Trami-induced SSC area to enhance weakening by reduction in inner-core moisture transport toward the center near the surface and in the inflow boundary layer on the upshear side. The reductions in downward motion in the center and the associated adiabatic heating were closely related to weakening in both typhoons.

Understanding of the tropical atmosphere is elaborated around two elementary ideas, one being that density is homogenized on isobars, which is referred to as the weak temperature gradient (WTG), and the other being that the vertical thermal structure follows a moist-adiabatic lapse rate. This study uses simulations from global storm-resolving models to investigate the accuracy of these ideas. Our results show that horizontally, the density temperature appears to be homogeneous, but only in the mid- and lower troposphere (between 400 hPa and 800 hPa). To achieve a homogeneous density temperature, the horizontal absolute temperature structure adjusts to balance the horizontal moisture difference. Thus, water vapor plays an important role in the horizontal temperature distribution. Density temperature patterns in the mid- and lower troposphere vary by about 0.3 K on the scale of individual ocean basins but differ by 1 K among basins. We use equivalent potential temperature to explore the vertical structure of the tropical atmosphere, and we compare the results assuming the temperature following pseudo-adiabat and reversible-adiabat (isentropic) with the effect of condensate loading. Our results suggest that the tropical atmosphere in saturated convective regions tends to adopt a thermal structure that is isentropic below the zero-degree isotherm and pseudo-adiabatic above it. However, the tropical mean temperature is substantially colder and is set by the bulk of convection, which is affected by entrainment in the lower troposphere.

This study investigates the future changes in East Asian summer monsoon (EASM) precipitation and the associated atmospheric circulation changes based on ensemble projections with the 60-km mesh Meteorological Research Institute atmospheric general circulation model (MRI-AGCM60). The projections at the end of the twenty-first century under the Representative Concentration Pathway 8.5 (RCP8.5) scenario indicate an overall increase in EASM precipitation but with large sub-seasonal and regional variations. In June, the Meiyu-Baiu rainband is projected to strengthen, with its eastern part (i.e., the Baiu rainband) shifted southward relative to its present-day position. This result is robust within the ensemble simulations. In July and August, the simulations consistently project a significant increase in precipitation over the northern East Asian continent and neighboring seas; however, there is a lack of consensus on the projection of the Meiyu-Baiu rainband in July. A small change in precipitation over the Pacific is another feature in August.

Results of sensitivity experiments with the MRI-AGCM60 reveal that the precipitation changes in early summer are dominated by the effects of sea surface temperature (SST) warming (i.e., uniform warming and the tropical pattern change), inducing an increase in atmospheric moisture and a strengthening and southward shift of the upper-level East Asian westerly jet (EAJ), especially over the Pacific. On the other hand, the influence of land warming and successive large SST warming in the extratropics is apparent in the precipitation changes in late summer. These late summer effects oppose and exceed the early summer effects through changes in the EAJ and low-level monsoon winds. These results suggest that the competition between the opposing factors makes the signal of the Meiyu-Baiu rainband response smaller in July than in June. Therefore, there tends to be a larger spread among simulations regarding the future tendency of the rainband in July.

Presently, satellite-derived precipitation estimates have been widely used as a supplement for real precipitation observation. Detailed evaluations of a satellite precipitation estimate are the prerequisite for using it effectively. On the basis of the daily precipitation observation from 91 rain gauges throughout Thailand during a 15-yr period, this study evaluated the performances of daily precipitation data of Climate Prediction Centre morphing technique (CMORPH) and TRMM (3B42 version 7) in an interpolating-grid-points-into-stations manner. This filled in the deficiencies of the current evaluations of TRMM-3B42v7's performances over Thailand made the first evaluation of CMORPH in this region and showed the first report of relative performances of two datasets.

For the entire Thailand, a total of 35 factors (including precipitation intensity, spatial distribution pattern, and duration/interval) were used in the evaluation. It is found that only 12 of them (including annual and monthly variations of precipitation, conditional rain rate in the rainy season, rainfall interval in an entire year, non-precipitation days, etc.) were reproduced credibly (i.e., the relative error was less than 20 %) by the two datasets. Both TRMM-3B42v7 and CMORPH displayed similarly poor performances in representing the intensity and spatial distribution of extreme precipitation. Comparisons based on the 35 factors indicate that TRMM-3B42v7 displayed a better overall performance than CMORPH for the entire Thailand.

For each region of Thailand, CMORPH/TRMM-3B42v7 showed different performances in different regions (a total of 19 factors was used). The CMORPH/TRMM-3B42v7 data made credible estimates over all five regions of Thailand in terms of daily precipitation intensity and monthly variation of precipitation, whereas, in terms of precipitation day fraction, conditional rain rate during the dry season, and interval/duration of rainfall events during the rainy season, it showed notable errors in all regions. Overall, TRMM-3B42v7 exhibited superior performances to CMORPH for the North, Northeast, East, and South of Thailand, whereas CMORPH and TRMM-3B42v7 displayed similar performances for the Central Thailand.

A rotating shadow-band spectroradiometer system is a powerful tool for surveying light in an environment. It can provide the following spectral components of the solar irradiance without using any traditional solar tracking tool: direct normal irradiance (spDNI), diffuse horizontal irradiance (spDHI), and global horizontal irradiance (spGHI). Both irradiances, spDNI and spDHI, are derived from the combination of spGHI observations at different shadow-band positions. The shadow-band system induces basic errors caused by the imperfect corrections of the diffuse irradiance shadowed by band. To restrict the basic errors to < 2 %, the band slant-angle should be < 72° for a usual operating condition of the MS-700 spectroradiometer manufactured by EKO Instruments Co., Ltd., with the MB-20 shadow-band system for MS-700. The errors in the spDNI and spDHI estimation are evaluated quantitatively by using realistic models that consider instrumental and atmospheric conditions. Estimates of spDNI can result in optical depth errors. The relative error in this estimation is described by using a correction coefficient C_fwd defined by the ratio of the true diffuse irradiance simulated by the shadowed irradiance to the approximate value observed. The value of C_fwd depends on the magnitude of the aerosol optical depth as well as the aerosol type. This error analysis should help in improving the accuracy of this system of measurements.

The regional data assimilation system at the Japan Meteorological Agency uses a variational data assimilation system based on the non-hydrostatic model ASUCA (named ASUCA-Var). This paper reviews the configurations and current status of ASUCA-Var. To consider the consistency of analysis and prognostic variables, the control variables of ASUCA-Var include soil variables and basic atmospheric variables. The background-errors based on the control variables are calculated every 3 h for land and sea grid points to better reflect the representative error covariance structure, considering daily variations and differences in structure on land and sea. Although the cost function is designed to be a perfect quadratic form, the basic field update method in the optimization process allows the nonlinearity of the observation operator and numerical weather prediction model to be incorporated into the solution of optimization problem in the incremental four-dimensional variational (4D-Var) method. The outer/inner models used in the incremental 4D-Var method are based on ASUCA, with suitable configurations according to each resolution and applied linearization. Observation operators are implemented for various kinds of observations used, with unified interfaces encapsulating external simulators. Variational quality control and variational bias correction are also introduced for advanced observation handling within the variational system. Parallelization is introduced to enhance computational efficiency, including adjoint calculations. To assess the impact of assimilated observations, degrees of freedom for signal are also available. Additionally, as a system for operational use, ASUCA-Var is designed for sustainable development. The meso-scale analysis and local analysis workflows are presented as operational implementations of ASUCA-Var. ASUCA-Var enhances forecasting in a wide range of validation indices. The major future improvements of ASUCA-Var include the introduction of the flow-dependent background-error and extension of the control variable to hydrometeors, which are expected to enhance the prediction accuracy of the operational regional model.