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What are the main findings? During the sudden turn of Super Typhoon Vongfong (2014), the precipitation structure also changed accordingly: the precipitation coverage expanded, convective rainfall weakened, and stratiform rainfall intensified. The intensification of stratiform precipitation was associated with enhanced warm-rain processes due to increased cloud liquid water, whereas the weakening of convective precipitation was related to weakened ice-phase processes due to decreased cloud ice content. What are the implications of the main findings? This study analyzes the evolution of precipitation during the base observation of the sudden turn of the typhoon, which can provide valuable guidance for improving flood-risk assessment, optimizing urban drainage, and emergency response planning. The findings provided observational constraints to improve the representation of cloud microphysics parameterization in typhoon prediction models and also contributed to the development of more accurate precipitation nowcasting and typhoon intensity–structure prediction tools. The sudden turn of tropical cyclones (TCs) can rapidly alter the affected disaster-prone regions and associated rainfall distributions, posing severe threats to coastal areas and creating major challenges for operational forecasting. However, most of these events occur over the open ocean, where the scarcity of in situ observations limits our understanding of how precipitation and cloud microphysical processes evolve during the sudden turning. In this study, we analyzed the precipitation evolution and associated microphysical characteristics during the sudden turn of Super Typhoon Vongfong (2014) using the latest GPM satellite observations. The main findings are as follows: (1) During the sudden-turning period, the precipitation coverage expanded significantly. Strong convective precipitation was distributed from the inner eyewall to the outer eyewall and spiral rainbands and weakened in intensity, whereas stratiform precipitation broadened in coverage and intensified. (2) The increase in stratiform precipitation was attributed primarily to increased cloud water content, which strengthened collision–coalescence processes, promoted the formation of larger and more numerous raindrops, and consequently increased precipitation efficiency and intensity. (3) The weakening of convective precipitation was related to the reduction in eyewall updrafts, which suppressed ice-phase processes and limited the development of deep convection.

Precipitation events have been occurring more frequently in the hyper-arid region of the Taklamakan Desert (TD) under recent climate change. However, in this water-limited environment, the microphysical characteristics of precipitation, as well as their link to rainfall intensity, remain unclear. To address this, this study utilizes dual-frequency precipitation radar (DPR) data of the Global Precipitation Measurement (GPM) satellite from 2014 to 2023 to analyze the microphysical characteristics of different precipitation types (stratiform and convective) in the TD during the summer. The results show that liquid water path (LWP) is a key factor influencing precipitation type: when LWP is insufficient, stratiform precipitation is more likely to occur (84.1%), while convective precipitation is difficult to occur (15.9%). Microphysical process analysis indicates that in convective precipitation, abundant low-level moisture leads to the growth of liquid particles primarily through the collision–coalescence process (59.7%), resulting in larger raindrop diameters (1.7 mm) and lower concentrations (31.9 mm−1 m−3). In contrast, stratiform precipitation, with limited LWP, primarily involves the melting and breaking-up of high-level ice-phase particles, leading to smaller raindrop diameters (1.2 mm) and higher concentrations (34.3 mm−1 m−3). The warm rain process plays a significant role in raindrop formation in both types of precipitation. The greater (lesser) the amount of LWP, the larger (smaller) the contribution of collision–coalescence (break-up) processes, and the larger (smaller) the raindrop diameter and precipitation intensity.

Marine cold spells (MCSs), characterized by pronounced seawater temperature declines, are representative marine responses to atmospheric and climatic extremes. Notably, during the boreal winter, MCSs in the Taiwan Strait (TS) have caused substantial economic losses due to the concurrence of juvenile development stages in mariculture and labor shortages during China’s New Year vacation. Understanding the spatio-temporal features of MCSs and the underlying mechanisms is imperative for mitigating the associated economic detriments. This study, for the first time, delineates two MCS hotspots in the northeastern and western TS, based on various sea surface temperature datasets. Our findings reveal a notable frequency and intensity of MCSs in the 1980s, followed by a period of suppressed phase, reaching a historic low point in 2002. However, contrary to expectations of the overall trend of global warming, MCSs have experienced a resurgence since 2002, with a pronounced focus on the western coast of the TS. Diagnostic analyses suggest that the Winter East Asian Monsoon-driven cold current and the Pacific basin scale dynamic-forced warm current jointly regulated the spatio-temporal features of the MCSs. This study contributes a novel perspective on the impacts of climate change on extreme marine events along subtropical coastal areas.

This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

The higher the atmosphere is, the larger the deviations in atmospheric temperature and humidity are between the vertical column atmosphere above the cross-section of a satellite instrument and a ray’s trajectory from the cross-section to the satellite. In general, satellite instruments that observe using cross-orbit scanning result in the difference between the observed radiance and the simulations using this method becoming incrementally larger and larger as the cross-section moves to the edge of the satellite’s orbit. The deviations depend on the distance from the column to the ray trajectory and on the horizontal gradient of variables in the distance. In fact, the horizontal gradient of water vapour is larger than the gradient of temperature in clear scenarios, which could introduce an impact of temperature and water vapour on the simulated radiance of a satellite. In this study, a new method to simulate upgoing and downgoing radiation synchronously was developed, using the observing path tracking method. The conventional vertical initial atmospheric profile (Exp.1) and the profiles along the upgoing and downgoing rays of the satellite’s observation (Exp.2) were established, in order to simulate the observed radiance of MWHS-II of FY-3D using global numerical forecasts with resolutions of 15 km and 25 km. The results showed that, for channels in the oxygen and water vapour absorption line on the microwave spectrum, deviations of the two atmospheric profiles were larger at the scan edge (0.01 K) than those at the nadir (0.001 K), and were larger in the upper atmosphere than in the lower atmosphere. The deviation was usually negative in low-latitude regions and was positive in southern high-latitude regions. Such results were obtained in experiments using both the numerical forecast method with 15 km grids and the forecast method with 25 km grids. Deviations were analysed for representative channels at 118 GHz and 183 GHz. Then, the results indicated that bigger deviations between the two experiments were observed in the water vapour absorption line than in the oxygen absorption line in the microwave spectrum. In conclusion, this indicates that, because of the greater horizontal gradient of water vapour, the stronger localisation of water vapour makes the atmospheric profile along the satellite’s observing ray have more increments in the simulated radiance at the scan edge, compared to the atmospheric column profile.

Seasonal variation of the Indo-Pacific warm pool (IPWP) plays an important role in oceanographic and climatological processes. While expansion of the IPWP under greenhouse warming has been widely discussed, the response of IPWP seasonality to climate change has received limited attention. In this study, we found an obvious seasonal diversity in expansion of the IPWP from 1950 to 2020, with a maximum (minimum) expansion trend of 0.28 × 10 7 km 2 /decade in winter (0.17 × 10 7 km 2 /decade in spring), which consequently reduces the seasonality amplitude of the variation in IPWP size. This is primarily attributed to the seasonal difference in the climatological spatial sea surface temperature (SST) pattern over the Indo-Pacific Ocean, especially that over the tropical Indian Ocean, which determines the capacity for IPWP expansion. Heat budget analyses show that the seasonal shortwave radiation and latent heat fluxes are the major factors controlling the capacity for change in IPWP size across seasons. The presented analyses emphasize the significant weakening of the seasonality of IPWP size, which may have great impacts on the ecological environment of the IPWP and the tropical climate system, and remind us that the intrinsic properties of the climate background of Indo-Pacific SST hold important clues about IPWP expansion under climate change.

In order to investigate the impact of increasing the vertical resolution of the initial field on the 12–24 h forecasts of the TRAMS (Tropical Regional Atmosphere Model System) model, this study conducted numerical experiments focusing on a typical coastal warm sector rainfall event that occurred in the South China. The findings indicate that increasing the vertical resolution of the initial field led to improved simulation of coastal convection during the 0–12 h period. However, spurious convection was observed over the sea surface and continued to intensify in the 12–24 h period. Subsequent analysis revealed that the spurious convection is primarily associated with the hydrostatic adjustment of initial potential temperature in the TRAMS model. The hydrostatic adjustment leads to a reduction in the stability of the initial temperature stratification in the lower layers of the model, particularly when the number of vertical layers in the initial field increased from 17 to 32. A noticeable spurious unstable layer emerged between 0–200 m over the sea surface, triggering false convection. Further investigation revealed that the area where this unstable stratification occurs over the sea is situated below the height of the lowest level of the input analysis field (1000 hPa), indicating that the spurious disturbances are caused by an unreasonable vertical extrapolation process. Therefore, the findings of this study indicate that the extrapolation calculations using cubic splines in the initialization module of the TRAMS model introduce significant errors. Moreover, these errors increase with the enhancement of the vertical resolution of the initial field, which limits the improvement in model forecasting that could be achieved by increasing the vertical resolution of the initial field. We found that increasing the vertical resolution of the initial field in the TRAMS model led to spurious convection. This spurious convection is triggered by a false unstable layer near the surface. The computational errors during the hydrostatic adjustment of the initial perturbed potential temperature resulted in this false unstable layer.

Given its high population density and degree of urbanization, the eastern United States (US) is a region vulnerable to the impacts from hailstorms. Small changes in hail activity may indicate large impacts on the potential hail risks faced by the region. While contrasting hailstorm-favorable environmental changes between the northeastern and southeastern US have been documented, the meridional shift of hail activity in the eastern US has not been directly revealed based on observed hailstorm records. In this letter, using the official hailstorm database, we find a significant northward migration of hail activity (+0.33° N decade −1 ) in the eastern US since 2000, which is mainly contributed by the increasing proportion of large hailstorm events (hail size 0.75–2.0 inch) hitting the northeast in July and August (+0.93° N decade −1 ). The spatially inhomogeneous climatic mean state changes over the past two decades contribute a leading role: the intensified Bermuda High and the eastward shift of upper-level jet stream over the central US tended to moisten (dry) the atmosphere over the northeastern (southeastern) US by enhancing the low-level poleward moisture transport. This not only provides more moisture for hailstorm formation in the northeast but also destabilizes (stabilizes) the atmosphere in the northeast (southeast) under an overall increase in dry instability over the eastern US. These factors together lead to a northward shift of large hailstorms toward the northeastern US, where hailstorms were relatively seldom reported. Incorporating this shift in knowledge may improve contingency and risk management strategies of both the public and private sectors in the future.

Landfalling typhoons can cause disasters over large regions. The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position, while slight timing error can be ignored. The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand, according to the forecasts and observation of historical cases. In the evaluation of landfalling typhoon track, the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed. Consequently, the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation. The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach. In this new method, the timing error was lessened to highlight the importance of the position error during the landfall of typhoon. The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases. Results demonstrated that the new method is not sensitive to the sampling frequency, and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate (between 15–30 km h −1 ). The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error.

The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intraseasonal signals (10–60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (R2 > 0.8). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering.