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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]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [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.

The reliable Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, particularly extreme precipitation, is critical for disaster prevention and mitigation, which however remains a great challenge for mission agencies and research communities. In this study, a deep learning method based on U‐Net with additional atmospheric factor forecasts included is proposed to improve S2S quantitative forecasts of summer precipitation over Southern China. The weighted loss function integrated by mean square error and threat score is introduced to capture extreme precipitation more precisely. Generally, the U‐Net model shows promising results in both general statistics and extreme events. Predictor importance analyses show that the U‐Net forecast skills at the 1‐week lead time mainly arise from synchronous precipitation forecasts, but the contributions made by atmospheric factor forecasts rise rapidly with increasing lead times. Therefore, the channel combining numerical weather prediction model and deep learning framework is demonstrated promising in S2S precipitation forecasts. Plain Language Summary: The Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, in particular of the extreme precipitation events, from 2 weeks to a season in advance is challenging despite increasing social demand and scientific interest for accurate and dependable predictions. In this study, the U‐Net based deep learning method is employed with additional atmospheric variable forecasts (e.g., wind and specific humidity at multiple levels) included to correct the S2S forecasts of summer precipitation derived from a numerical weather prediction model over Southern China. It is demonstrated that the U‐Net improves the forecast performance in both general statistics and extreme events and shows a pronounced superiority to the traditional statistical postprocessing method. Thus, combining numerical models and deep learning is very promising in subseasonal precipitation forecasts and can also be applied to the routine forecast of other atmospheric and ocean phenomena in the future. Key Points: The Subseasonal‐to‐Seasonal prediction of summer precipitation over southern China is improved with a U‐Net based deep learning methodThe U‐Net demonstrated promising performance in both general statistics and extreme events and shows superiority to the quantile mapping benchmarkThe model skills arise from precipitation itself at the early stage, while atmospheric factors play important roles at longer lead times [ABSTRACT FROM AUTHOR]

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]

Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts. Significance Statement: Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation. [ABSTRACT FROM AUTHOR]

Accurate forecasting of wind speed holds significant importance for the economic and social development of humanity. However, existing numerical weather predictions have certain inaccuracies due to various reasons. Therefore, it is highly necessary to perform statistical post-processing on forecasted results. However, traditional linear statistical post-processing methods possess inherent limitations. Hence, in this study, we employed two deep learning methods, namely the convolutional neural network (CNN) and the U-Net neural network, to calibrate the forecast of the Global Ensemble Forecast System (GEFS) in predicting 10-m surface wind speed in Southwest China with a forecast lead time of one to seven days. Two traditional linear statistical post-processing methods, the decaying average method (DAM) and unary linear regression (ULR), are conducted in parallel for comparison. Results show that original GEFS forecasts yield poorer wind speed forecasting performance in the western and eastern Sichuan provinces, the eastern Yunnan province, and within the Guizhou province. All four methods provided certain correction effects on the GEFS wind speed forecasts in the study area, with U-Net demonstrating the best correction performance. After correction using the U-Net, for a 1-day forecast lead time, the proportion of the 10-m U-component of wind with errors less than 0.5 m/s has increased by 46% compared to GEFS. Similarly, for the 10-m V-component of wind, the proportion of errors less than 0.5 m/s has increased by 50% compared to GEFS. Furthermore, we employed the mean square error-based error decomposition method to further diagnose the sources of forecast errors for different prediction models and reveal their calibration capabilities for different error sources. The results indicate that DAM and ULR perform best in correcting the Bias2, while the correction effects of all methods were variable for the distribution with the forecast lead time. U-Net demonstrated the best correction performance for the sequence. [ABSTRACT FROM AUTHOR]

Most standalone atmospheric models do not perform well in simulations of coastal boundary layer jets (BLJs), important weather processes that can trigger heavy rain in coastal areas by supplying both moisture and dynamic lifting. We compared 33‐year simulations with a coupled atmosphere–ocean model and its standalone atmospheric component, the REgional atmosphere MOdel (REMO), forced by the prescribed sea surface temperature (SST). We validated our results using the Tropical Rainfall Measuring Mission SST and the ERA5 hourly reanalysis data set. We found that the coupled model gave a more realistic SST standard deviation than the REMO on BLJ days and corrected the overestimated air temperature over land during the day. The coupled atmosphere–ocean model showed a lower land–sea thermal contrast in the boundary layer. This increased the effects of inertial oscillation, which caused the ageostrophic flows to veer southwest, which is the direction of the maximum wind speed on BLJ days. This reproduced a more reasonable land–sea thermal contrast in the boundary layer as a result of strong air–sea mixing in coastal weather processes, which led to a more robust inertial oscillation and a larger SST standard deviation over the central South China Sea. These findings deepen our understanding of the influence of a fully mixed air–sea boundary on coastal weather processes. These results show that operational numerical weather prediction models can be improved by applying atmosphere–ocean coupling to advance their ability to forecast the weather (e.g., BLJ events) in coastal areas. Plain Language Summary: Coastal boundary layer jets (BLJs) are important coastal weather processes and, because they are precursor signals of heavy rain, they can be used to improve operational numerical weather forecasts. Coupled atmosphere–ocean models improve the predictability of BLJs in South China, but we still need a deeper understanding of the mechanism leading to their occurrence. We compared 33‐year simulations with a coupled atmosphere–ocean model and its standalone atmospheric component forced by the prescribed sea surface temperature (SST). Through validation against remote sensing SSTs and ERA5 hourly reanalysis data, we found that the coupled model gave a more realistic SST standard deviation and corrected the overestimated air temperature over land surfaces. These improvements helped the coupled atmosphere–ocean model to reproduce a more reasonable land–sea thermal contrast in the boundary layer as a result of strong air–sea mixing in coastal weather processes, which led to a more robust inertial oscillation and a larger SST standard deviation over the central South China Sea. We conclude that the impact of the influence of a fully mixed air–sea boundary on coastal weather processes is larger than previously recognized. Key Points: The use of atmosphere‐ocean coupling can improve the predictability of coastal boundary layer jets in South ChinaThe coupled model gave a more realistic sea surface temperature standard deviation and corrected the overestimated air temperatureStrong air‐sea mixing led to a more robust inertial oscillation, veering the ageostrophic flows toward the direction of the maximum wind [ABSTRACT FROM AUTHOR]

Aiming at the rainstorm days (≥50 mm/d) in the middle and lower reaches of the Yangtze River during 2010–2020, the obliquely rotated principal component in T-mode (PCT) method is used to classify the daily mean 850 hPa geopotential height, including Type 1 (vortex/shear line), Type 2 (frontal surface), Type 3 (warm shear line), Type 4 (warm inverse trough line), Type 5 (typhoon-westerly trough), and Type 6 (easterly wave). We studied the weather system configurations of different synoptic circulation patterns, their long-term trends, and their impacts on diurnal variations of heavy precipitation and drew the following conclusions: Type 1, Type 2, or Type 3 shows balanced double-peak frequencies of the start time of heavy precipitation during 06:00–08:00 BT and around 17:00 BT, respectively. For Type 1, dynamical lifting and thermal lifting play balanced roles, while for Type 2 and Type 3, dynamical lifting plays a key role. The number of rainstorm stations for Type 1 shows a slight increasing trend, while that for Type 2 or Type 3 shows a significant increasing trend. Type 4, Type 5, or Type 6 show a significant single peak frequency of the start time during 15:00–16:00. Type 5 and Type 6 are mainly affected by dynamical lifting along with favorable cape values, which can trigger rainstorms. The number of rainstorm stations for Type 4 or Type 6 shows a decreasing trend (that for Type 4 is more significant), while that for Type 5 shows a slightly increasing trend. [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]

This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a 3-month scale. We analyse various drought characteristics (duration, events, frequency, intensity, and severity) for all drought months, and moderate, severe, and extreme drought conditions. The results show that drought occurs across the continent, with the equatorial regions displaying more negative SPI values, especially for moderate and severe droughts. On the other hand, Eastern Sahara and Western Southern Africa portray less negative SPI values. The study also reveals that extreme drought months have the largest interannual variability, followed by all drought months and severe drought months. The trend analysis of SPI shows a significantly increasing trend in drought episodes over most regions of Africa, especially tropical areas. Drought characteristics vary greatly across different regions of Africa, with some areas experiencing longer and more severe droughts than others. The equatorial region has the highest number of drought events, with longer durations for severe and extreme drought months. The Eastern Sahara region has a low number of drought events but with longer durations for moderate, severe, and extreme drought months, leading to an overall higher drought severity over the area. In contrast, Western Southern Africa and Madagascar display a consistently low drought severity for all categories. The study demonstrates the importance of conducting drought analysis for different drought levels instead of using all drought months. Drought management and adaptation strategies need to enhance community resilience to changing drought situations and consider drought variability in order to mitigate different impacts of drought across the continent. [ABSTRACT FROM AUTHOR]

The prediction of precipitation patterns up to 2 h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems. In this study, we are aiming to provide an efficient and easy-to-understand deep neural network – CLGAN (convolutional long short-term memory generative adversarial network) – to improve the nowcasting skills of heavy precipitation events. The model constitutes a generative adversarial network (GAN) architecture, whose generator is built upon a u-shaped encoder–decoder network (U-Net) and is equipped with recurrent long short-term memory (LSTM) cells to capture spatiotemporal features. The optical flow model DenseRotation and the competitive video prediction models ConvLSTM (convolutional LSTM) and PredRNN-v2 (predictive recurrent neural network version 2) are used as the competitors. A series of evaluation metrics, including the root mean square error, the critical success index, the fractions skill score, and object-based diagnostic evaluation, are utilized for a comprehensive comparison against competing baseline models. We show that CLGAN outperforms the competitors in terms of scores for dichotomous events and object-based diagnostics. A sensitivity analysis on the weight of the GAN component indicates that the GAN-based architecture helps to capture heavy precipitation events. The results encourage future work based on the proposed CLGAN architecture to improve the precipitation nowcasting and early warning systems. [ABSTRACT FROM AUTHOR]

Accurate precipitation nowcasting (lead time: 0–2 h), which requires high spatiotemporal resolution data, is of great relevance in many weather-dependent social and operational activities. In this study, we are aiming to construct highly accurate deep learning (DL) models to directly obtain precipitation nowcasting at 6-min intervals for the lead time of 0–2 h. The Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN) models were used as comparative DL models, and the Lucas–Kanade (LK) Optical Flow method was selected as a traditional extrapolation baseline. The models were trained with high-quality datasets (resolution: 1 min) created from precipitation observations recorded by automatic weather stations in Guizhou Province (China). A comprehensive evaluation of the precipitation nowcasting was performed, which included consideration of the root mean square error, equitable threat score (ETS), and probability of detection (POD). The evaluation indicated that the reduction of the number of missing values and data normalization boosted training efficiency and improved the forecasting skill of the DL models. Increasing the time series length of the training set and the number of training samples both improved the POD and ETS of the DL models and enhanced nowcasting stability with time. Training with the Hea-P dataset further improved the forecasting skill of the DL models and sharply increased the ETS for thresholds of 2.5, 8, and 15 mm, especially for the 1-h lead time. The PredRNN model trained with the Hea-P dataset (time series length: 8 years) outperformed the traditional LK Optical Flow method for all thresholds (0.1, 1, 2.5, 8, and 15 mm) and obtained the best performance of all the models considered in this study in terms of ETS. Moreover, the Method for Object-Based Diagnostic Evaluation on a rainstorm case revealed that the PredRNN model, trained well with high-quality observation data, could both capture complex nonlinear characteristics of precipitation more accurately than achievable using the LK Optical Flow method and establish a reasonable mapping network during drastic changes in precipitation. Thus, its results more closely matched the observations, and its forecasting skill for thresholds exceeding 8 mm was improved substantially. [ABSTRACT FROM AUTHOR]

The principal modes of the diurnal cycle of rainfall (DCR) over South China during the presummer rainy season are examined using 23-yr satellite observations and reanalysis data. Three distinctly different DCR modes are identified via empirical orthogonal function analysis, that is, the early-afternoon precipitation (EAP) mode, the late-afternoon precipitation (LAP) mode, and the morning precipitation (MP) mode. Under the EAP mode, the rainfall starts to increase from midnight and reaches its peak in the early afternoon. The nocturnal to morning rainfall generally concentrates on the northeastern Pearl River delta (PRD) and along the coastline. The coastal rainfall is initiated from the convergence zone induced by the strong onshore wind and is further enhanced via the establishment of a land breeze in the early morning. The northeastern PRD center is mainly attributed to the windward mechanical lifting associated with the strong low-level wind. The afternoon rainfall is pronounced over inland areas and exhibits significantly regional diversity. The eastern inland rainfall develops from the early-morning rainfall over the northeastern PRD, whereas the eastward-propagating rain belts associated with frontal activities are responsible for the formation of western inland rainfall. The LAP mode features a late-afternoon peak, which is triggered and developed locally with favorable thermal–dynamic conditions over western inland South China. The MP mode exhibits a single early-morning peak. Nocturnal to morning rainfall is prominent on the northeastern PRD and near-offshore region. The near-offshore rainfall is basically induced by the convergence between the onshore wind and land breeze in the early morning, which further propagates far offshore in the morning due to effects of gravity waves. [ABSTRACT FROM AUTHOR]

Based on the China Meteorological Administration Land Data Assimilation System (CLDAS) reanalysis data and 12–72 h forecasts of the surface (2-m) air temperature (SAT) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three numerical weather prediction (NWP) models of the China Meteorological Administration (CMA-GFS, CMA-SH, and CMA-MESO), multi-model ensemble forecasts are conducted with a convolutional neural network (CNN) and a feed-forward neural network (FNN) to improve the SAT forecast in Henan Province, China. The results show that there are large errors in the 12–72 h forecasts of SAT from the CMA, while the ECMWF outperforms the other raw NWP models, especially in eastern and southern Henan. The CNN has the best short-term forecasting skills. The difference in the geographical distribution of the CNN forecast errors is small, without any apparent large-value areas. The CNN shows its advantages in its bias correction in the mountainous region (western Henan), indicating that the CNN can capture the spatial features of the atmospheric fields and is therefore more robust in regions with varied topography. In addition, the CNN can extract data features through the convolution kernel and focus on local features; it can assimilate the local features at a higher level and obtain global features. Therefore, the CNN takes advantage of the four models in the SAT forecast and further improves the forecast skill. [ABSTRACT FROM AUTHOR]

The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China's offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites' SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors. [ABSTRACT FROM AUTHOR]

The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In this study, we are aiming to provide an efficient and easy-to-understand model - CLGAN, to improve the nowcasting skills of heavy precipitation events with deep neural networks for video prediction. The model constitutes a Generative Adversarial Network (GAN) architecture whose generator is built upon an u-shaped encoder-decoder network (U-Net) equipped with recurrent LSTM cells to capture spatio-temporal features. A comprehensive comparison among CLGAN, and baseline models optical flow model DenseRotation as well as the advanced video prediction model PredRNN-v2 is performed. We show that CLGAN outperforms in terms of scores for dichotomous events and object-based diagnostics. The ablation study indicates that the GAN-based architecture helps to capture heavy precipitation events. The results encourage future work based on the proposed CLGAN architecture to improve the precipitation nowcasting and early-warning systems. [ABSTRACT FROM AUTHOR]

Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society. [ABSTRACT FROM AUTHOR]