Sub-daily extreme precipitation in Jakarta exhibits trends that are related to local temperature, seasonal tropical monsoon circulations, and other environmental drivers. An analysis of 81 years of hourly rainfall between 1900 and 2010 shows a significant increase of about doubling the number of short-duration rainfall events in the wet season. In recent decades, rainfall is found to be higher in intensity and shorter in duration relative to preceding decades. These short-duration rain showers develop typically between afternoon and late night or during early morning hours. Changing short-duration rainfall characteristics throughout the last century are partly attributed to changes in the surface environment of urban Jakarta. A recent temperature increase and land surface drying in the city, in combination with a small increase in the atmospheric moisture content, promote intensified atmospheric convection. A combination of rain gauge data with upper-air observations collected during 2002–2016 reveals that surface warming in the urbanized city accompanied by enhanced availability of moisture results in an increase in convective available potential energy, which contributes to enhanced intense precipitation. Super Clausius–Clapeyron scaling (CC) of high-intensity rainfall is attributed to high near-surface temperature and atmospheric moisture content in the morning. This super-CC scaling is present in a relatively small range of surface temperature values. Results of this study are in agreement with earlier findings exploring the intensification of extreme morning precipitation and a temporal shift of the diurnal convective maximum from late afternoon to late night/early morning in response to local warming. For a delta city such as Jakarta with abundant convection and heavy precipitation, a well-maintained rainfall database is crucial to assist urban flood early warning.

This study investigated tidal records and landfall tropical cyclone (TC) best tracks from 1980 to 2019 to determine changes in storm surge heights in coastal regions of Central Japan, including Tokyo. The results indicate that annual mean storm surge heights have increased in the last 20 years (2000–2019) compared with those in 1980–1999, and that these changes are noteworthy, particularly in Tokyo Bay. The TC wind intensity and size during landfall have become stronger and larger, respectively, corresponding to increasing storm surge magnitudes from 1980 to 2019. The increased frequency of TCs with more northeastward tracks is another factor that may have contributed to the increased surge hazards around Tokyo. Additionally, a positive correlation between surge heights and a hazard index supports these statistical findings. The central coast of Japan will likely experience increasing numbers of extreme storm surge events in the future if the current increasing tendency continues.

Extremely hot days in Japan are known to persist for a week or more, and they are measured by the temperature anomaly at 850 hPa averaged over Japan derived from the JRA-55 reanalysis data, denoted as T850JP. Severe high-temperature anomalies are often accompanied by multiple teleconnection patterns that affect the weather in Japan, but their relative contribution to individual heat wave events has not yet been quantified. In this study, we examined the effects of three major teleconnection patterns, namely, the Pacific-Japan (PJ), circumglobal teleconnection (CGT), and Siberian patterns, on T850JP in July and August from 1958–2019 using daily low-pass-filtered anomalies with 8 days cutoff timescale derived from the reanalysis.

A linear regression analysis demonstrated that T850JP tended to show a large positive anomaly one or two days after the peak of these patterns. On the basis of this relationship, we reconstructed a daily T850JP time series using a multivariate statistical model wherein the parameters were estimated using regression analyses between T850JP and the indices of the three teleconnection patterns. The reconstructed T850JP showed that the three teleconnection patterns together accounted for 50 % of the total variance of T850JP for extremely hot summers, to which each of the three teleconnection patterns were found to have a similar degree of contribution. The statistical model reproduces the interannual variability along with the long-term T850JP trend. The PJ pattern has the largest effect on the interannual variability of T850JP, probably due to the PJ teleconnection occurring over a longer timescale compared with the other two patterns. The reconstructed T850JP also displays a warming trend associated with an increasing trend in the CGT index, which may be a factor, along with the direct thermodynamic effects due to global warming, to explain the long-term increase in the heat wave frequency in Japan.

Continuous simulations from the middle of the 20th century to the end of the 21st century were performed using a 20-km atmospheric general circulation model (AGCM), and a 60-km AGCM with dynamical downscaling via a 20-km regional climate model (RCM), to explore the transitional changes in regional extreme events. The representative scenario simulations by the AGCMs followed the protocol of the High Resolution Model Intercomparison Project experiments. In addition, ensemble simulations using four emission scenarios were conducted using the 60-km AGCM with 20-km RCM downscaling.

Regardless of the emission scenario used, the global-mean relative increase in annual maximum daily precipitation (Rx1d) was roughly proportional to the increase in the global-mean surface air temperature (SAT), consistent with previous results from coarser-resolution climate models. It means that the relationship is also valid for a higher-resolution model. A similar correlation between Rx1d and SAT was seen also in the values averaged over the Japanese land area in the 20-km AGCM and the 20-km RCM simulations after applying a 10-year running mean. However, it was not so clear in the 60-km AGCM, mainly because of insufficient grid points over land in Japan in the 60-km AGCM owing to too large noise. This suggests that transitional changes in Rx1d at regional scales such as the Japanese land area can only be represented by using a model resolution as high as 20 km, unless using ensemble simulations.

Recent progress in the particle filter has made it possible to use it for nonlinear or non-Gaussian data assimilation in high-dimensional systems, but a relatively large ensemble is still needed to outperform the ensemble Kalman filter (EnKF) in terms of accuracy. An alternative ensemble data assimilation method based on deep learning is presented, in which deep neural networks are locally embedded in the EnKF. This method is named the deep learning-ensemble Kalman filter (DL-EnKF). The DL-EnKF analysis ensemble is generated from the DL-EnKF analysis and the EnKF analysis deviation ensemble. The performance of the DL-EnKF is investigated through data assimilation experiments in both perfect and imperfect model scenarios using three versions of the Lorenz 96 model and a deterministic EnKF with an ensemble size of 10. Nonlinearity in data assimilation is controlled by changing the time interval between observations. Results demonstrate that despite being such a small ensemble, the DL-EnKF is superior to the EnKF in terms of accuracy in strongly nonlinear regimes and that the DL-EnKF analysis is more accurate than the output of deep learning because of positive feedback in assimilation cycles. Even if the target of training is an EnKF analysis with a large ensemble or a simulation by an imperfect model, the improvement introduced by the DL-EnKF is not very different from the case where the target of training is the true state.

We applied tracer transport simulations using the Taiwan vector vorticity equation cloud-resolving model (TaiwanVVM) to evaluate the effects of the local circulation associated with the lee vortex and the planetary boundary layer development on the transport and accumulation of the pollutants on a diurnal time scale in central Taiwan. The wind directions of crucial synoptic northeast monsoon are idealized as the initial conditions of the simulations to examine the impact of the lee vortex on the pollutant transport. The primary local nontraffic emission sources are taken as the tracer emission sites so that the experiment results could be a good proxy of the realistic scenarios. With the local circulation over complex topography being resolved explicitly, the impact of the boundary layer development on the tracer transport of the Puli basin is discussed. The simulation results clarify the contribution of the sea breeze and the lee vortex to the tracer transport in central Taiwan. We conclude that the high tracer concentration at Puli at night is due to the tracer being trapped by the thinning of the mixed-layer depth in the evening. The sensitivity of the local tracer transport to the change of the synoptic wind direction shows that under northeasterly due east (due north) environment, the pollutant transports from the southern source (northern source) of central Taiwan are most likely to induce a high concentration in Puli at night. This is the first study to distinguish the contribution of the sea breeze and the lee vortex in pollutant transport in Taiwan. The results obtained from idealized experiments provide the possible mechanism of pollutant transport, which could be taken as an insight to interpret the observations and guide the design of the field experiment to further establish the fundamental principles of the pollution transports in central Taiwan.

The Dynamical–Statistical–Analog Ensemble Forecast model for landfalling typhoon precipitation (the DSAEF_LTP model) identifies tropical cyclones (TCs) from history data that are similar to a target TC and then assembles the precipitation amounts and distributions of those identified to obtain those of the target TC. Two original ensemble methods in the DSAEF_LTP model, namely, mean and maximum, tend to under- and over- forecast TC precipitation, respectively. In addition, these two methods are unable to forecast precipitation at stations beyond their maxima. To overcome the shortcomings and improve the forecast performance of the DSAEF_LTP model, the following five new ensemble methods are incorporated: optimal percentile, fuse, probability-matching mean, equal difference-weighted mean, and TC track Similarity Area Index-weighted mean. Then, model experiments for landfalling TCs over China in 2018 are conducted to evaluate the forecast performance of the DSAEF_LTP model with the new ensemble methods. Results show that the overall performance of the optimal percentile (the 90th percentile) ensemble method is superior, with the false alarm rate lower than that of the original ensemble methods. As compared to five operational numerical weather prediction models, the improved DSAEF_LTP model shows advantages in predicting accumulated rainfall, especially with rainfall of over 250 mm. When implementing the experiments, above results, however, it is found that the model forecast performance varies, depending on the type of TC tracks. That is, the accumulated rainfall forecast for westbound TCs is significantly better than that of northbound TCs. To address this issue, different schemes are used to forecast the accumulated rainfall of TCs with the two different track types. The precipitation forecast performance for westbound and northbound TCs, using the 90th percentile and the probability-matching ensemble mean ensemble method, respectively, is much better than that using a single ensemble method for all TCs.