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Urbanisation significantly alters the interaction between land surface and the lower troposphere, impacting occurrences of natural hazards. The influence of urbanisation on natural hazards like heatwaves, hailstorms, and flooding remains debated. However, it is well established that impervious surfaces in urban areas can lead to flooding amplification. Singapore, amidst rapid urbanisation, experiences frequent flooding, exacerbated by its tropical-monsoon climate and climate change. Utilising high-temporal-resolution rainfall data from 2017 onwards, we examined the dynamics of urban flooding in Singapore. In total, 108 flooding events were reported for the period 2017–2023, all of a transient nature, primarily linked to cloudbursts. Based on the unique precipitation characteristics associated with urban flash flooding, the term ‘burst flooding’ is introduced to refer to urban floods caused by intense, short-duration rainfall events. A notable increase in cloudburst occurrences in November and December during La Niña years emphasises the role of global climate phenomena in local weather.

Two distinct spatial patterns of coastal boundary layer jets in the northern South China Sea (CBLJ-NSCS) in June 2015–22 are investigated using hourly output data from the Weather Research and Forecasting Model with a horizontal grid spacing of 9 km and ERA5 data. Spatial pattern 1 shows a high-incidence core on the east side of Hainan Island, while pattern 2 features dual high-incidence cores covering both the east side of Hainan Island and the waters south of Guangdong, with the latter being stronger and more extensive. Unique diurnal cycles have been observed in different high-incidence cores of CBLJ-NSCS: the western core, on the east side of Hainan Island, peaks at night and shows a secondary afternoon subpeak, while the eastern core, located south of Guangdong, reaches its maximum intensity at night with a morning subpeak. Inertial oscillations triggered by large-scale perturbation wind circulation and momentum propagation from upstream Indochina Peninsula CBLJs explain the nocturnal enhancement of both cores. The thermal effect exerted by Hainan Island largely contributes to the afternoon enhancement in the western core. The formation of the morning subpeak of the eastern core results from strong convergence and lifting in the northeast region of it. When upstream CBLJs along the Annamite Range intensify, more momentum is propagated to the eastern core instead of the western one due to wind field changes, which promotes the transition from spatial pattern 1 to pattern 2. Significance Statement: Two spatial patterns of coastal boundary layer jets in the northern South China Sea have been identified. Spatial pattern 1 shows a high-incidence core on the east side of Hainan Island, while pattern 2 features dual high-incidence cores covering both the east side of Hainan Island and the waters south of Guangdong. The transition between these two spatial patterns is related to low-level westerly winds along the coast of the Annamite Range. This study has uncovered the novel spatial pattern of coastal boundary layer jets in the northern South China Sea and provides new insights into precipitation research in southern China, considering the substantial influence of these jets on coastal weather. [ABSTRACT FROM AUTHOR]

Accurate predictions of monthly extremes assume paramount importance in enabling proactive decision‐making, which however are lacked in skills even for state‐of‐the‐art dynamical models. Taking the extreme precipitation prediction over the mid‐to‐lower reaches of the Yangtze River, China, as an instance, a multi‐predictor U‐Net deep learning approach is designed to enhance the prediction over the European Center for Medium‐Range Weather Forecasts (ECMWF) model, with the single‐predictor U‐Net parallelly examined as the benchmark. Focusing on the precipitation extremes, an extreme associated component is incorporated into the model loss function for optimization. Besides, predictions composed by daily outputs with multiple lead times are imported as a comprehensive set in the training phase to augment the deep learning sample size and to emphasize enhancements in predictions at the monthly timescale as a whole. Results indicate that the multi‐predictor U‐Net effectively improves predictions of extreme summer precipitation frequency, showing distinct superiority to the raw ECMWF and the single‐predictor U‐Net. Multiple evaluation metrics indicate that the model shows a significant positive improvement ratio ranging from 65.1% to 80.0% across all grids compared to the raw ECMWF prediction, which has also been validated through applications in the two extreme summer precipitation cases in 2016 and 2020. Besides, a ranking analysis of feature importance reveals that factors such as humidity and temperature play even more crucial roles than precipitation itself in the multi‐predictor extreme precipitation prediction model at the monthly timescale. That is, in such a deep learning approach, the monthly prediction on extreme precipitation benefits significantly from the inclusion of multiple associated predictors. Plain Language Summary: Predicting extreme precipitation is crucial for disaster prevention. This study introduces a new deep learning model using a multi‐predictor U‐Net approach in the mid‐to‐lower Yangtze River in China, aiming to improve the accuracy of monthly extreme precipitation predictions over existing models from the European Center for Medium‐Range Weather Forecasts (ECMWF). The enhanced model integrates additional variables like humidity and temperature, which are crucial for predicting extreme events. By analyzing summer precipitation extremes from 2016 to 2020, the multi‐predictor model shows a significant improvement, increasing prediction accuracy by 65.1%–80.0% of the total grids over the ECMWF model. This research demonstrates that incorporating multiple variables can significantly boost the effectiveness of monthly extreme precipitation forecasts, providing a valuable tool for better preparing for potential disasters. Key Points: A multi‐predictor U‐Net with extreme‐associated loss component is established to enhance monthly range extreme precipitation predictionsThe multi‐predictor U‐Net outperforms European Center for Medium‐Range Weather Forecasts (ECMWF) with conspicuous improvements by 65.1%–80.0%, which is also demonstrated by case analysesHumidity and temperature are crucial predictors boosting the multi‐predictor U‐Net performance in predicting extreme precipitation [ABSTRACT FROM AUTHOR]

Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes.

This paper studies the current evaluation system for the online training program in meteorology and hydrology. CIPP model that includes context evaluation, input evaluation, process evaluation and product evaluation differs from Kirkpatrick model including reactions evaluation, learning evaluation, transfer evaluation and results evaluation in that the subject of evaluation is different. We take the advantages of the CIPP model and Kirkpatrick model in constructing an evaluation system for online training programs in meteorology and hydrology held by the WMO Regional Training Centre Nanjing, China so as to improve the effectiveness of the training programs and meet the demand of the national meteorological and hydrological services.

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China's offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China's offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. [ABSTRACT FROM AUTHOR]

Previous studies indicated that the air quality was improved in Wuhan during COVID-19 lockdown. However, the impact of atmospheric general circulation on the changes of air quality has not been taken into account. The present study aims to discuss the improvement of air quality in Wuhan and its possible reasons during COVID-19 lockdown. The results showed that all air pollutants except O3 decreased in Wuhan during early 2020. The occurrence days of A, C, W and NW types’ circulation pattern during early 2020 are more than those during the same period of 1979-2020. The occurrence days of SW type’s circulation pattern is slightly less than those during early 1979-2020. With more occurrence days of these dominant atmospheric circulation patterns, the number of polluted days could rise in Wuhan during early 2020. Nevertheless, this scenario didn’t occur. The COVID-19 lockdown did improve the air quality in Wuhan during early 2020.

In December 2021, Super Typhoon Rai caused significant devastation to the South Philippines and East Malaysia. In the meantime, an unprecedented flood event occurred in Peninsular Malaysia at 2,000 km west of the typhoon's path, causing comparable socioeconomic impacts as Rai. Record‐breaking 3‐day precipitation was received by Peninsular Malaysia during 16–18 December. Based on the storm tracking results, this study identified two mesoscale convective systems (MCSs) that were directly responsible for the flooding. The two MCSs were directly initiated by a tropical depression and sustained by an elongated easterly water vapor corridor originating from the Super Typhoon Rai. The return period and joint frequency analysis of key drivers indicate that the 3‐day downpour was more severe than a "once‐in‐a‐century" event. Historical records suggest such anomalous moisture channel has become more frequent in Southeast Asia, which alarms heightened attention in forecasting winter flood. Plain Language Summary: On 16–18 December, Peninsular Malaysia received a record‐shattering 3‐day precipitation, resulting in catastrophic socioeconomic impacts. Due to the temporal coincidence with Super Typhoon Rai but far away in space, there were speculations that there might be a teleconnection between the two events. Our results reveal that their relationship could be more straightforward. Based on the analyses of storm tracking database and synoptic data records, we found that two consecutive mesoscale convective systems were responsible for the heavy precipitation, which were produced by a tropical depression that hovered over the peninsula. Meanwhile, Super Typhoon Rai provided a long‐range water vapor transport, akin to adding fuel (i.e., moisture) to the engine (i.e., the tropical depression), and therefore, the precipitation over the peninsula was significantly enhanced. Such long‐range moisture transport has become more frequent during the boreal winter season, posing an increasing risk of flooding in Southeast Asia. Key Points: A stretched moisture channel from Typhoon Rai and a strong tropical depression are key synoptic drivers for the flooding eventReturn period and joint probability of key drivers indicate that the 2021 Peninsular Malaysia flood was more severe than "once‐in‐a‐century"There is an increasing trend in such anomalous moisture channel, suggesting a rising risk of severe flooding in Southeast Asia [ABSTRACT FROM AUTHOR]

The accuracy of different types of El Niño-Southern Oscillation (ENSO) predictions is sensitive to initial errors in different key areas of the Pacific Ocean. To improve the accuracy of the forecast, assimilation techniques can be utilized to eliminate these initial errors. However, limited studies have measured the extent to which assimilating ocean temperature data from different key regions in the Pacific Ocean can enhance two types of ENSO predictions. In previous research, three critical regions were identified as having initial errors in ocean temperature most interfering with two types of El Niño predictions, namely the North Pacific for Victoria Mode-like initial errors, the South Pacific for South Pacific Meridional Mode-like initial errors, and the subsurface layer of the western equatorial Pacific. Based on these initial error patterns, we quantified the effect of assimilating ocean temperature observation datasets in these three key regions using the particle filter method. The result indicates that ocean temperature initial accuracy in the tropical western area near the thermocline region is important for improving the prediction skill of CP-El Niño compared with the other two sensitive areas. However, three key areas are all important for EP-El Niño predictions. The most critical area varies among different models. Assimilating observations from the north and south Pacific proves to be the most effective for improving both types of El Niño predictions compared to the other two areas' choices. This suggests that the initial accuracy of ocean temperature in these two regions is less dependent on each other for enhancing El Niño predictions. Additionally, assimilating observations from all three sensitive areas has the best results. In conclusion, to enhance the accuracy of two types of El Niño predictions, we need to ensure the initial accuracy of ocean temperature in both tropical and extratropical regions simultaneously. [ABSTRACT FROM AUTHOR]