Strong Surge Research Articles

A Study on the Pre-Survey and Plan for the Establishment of the Korean Typhoon Impact-Based Forecast

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean Peninsula originate and develop. The SST in these regions is increasing at a faster rate than the global average. Typhoon-related meteorological disasters are not isolated events, such as strong winds, heavy rains, or storm surges, but rather multi-hazard occurrences that can affect different areas simultaneously. As a result, preparation and evaluation must address multi-hazard disasters, rather than focusing on individual weather phenomena. This study develops the Typhoon Ready System (TRS) to improve impact-based forecasting in Korea, in response to the growing threat of multi-hazard weather disasters. By providing region-specific pre-disaster information, the TRS enables local governments and individuals to better prepare for and mitigate the impacts of typhoons. The system will be continuously refined in collaboration with the U.S. Weather-Ready Nation (WRN), which possesses advanced impact forecasting capabilities. The findings of this study offer a crucial framework for enhancing Korea’s ability to forecast and respond to the escalating threats posed by typhoons. By utilizing the TRS, it will be possible to assess the risks of various multi-hazard weather disasters specific to each region during the typhoon forecast period, and the relevant data can be efficiently applied at both the individual and local government levels for typhoon prevention efforts. The system will be continuously improved through cooperation with the U.S. WRN, leveraging their advanced impact forecasting systems. It is expected that the TRS will enhance the accuracy of typhoon impact forecasts, which have been responsible for significant damage in Korea.

Assessing Tropical Cyclone Destruction Using Landsat Satellite Imagery: A Case Study in Bangladesh

Tropical cyclones are extreme natural catastrophes and are anticipated to intensify strength and recurrence under upcoming climate change consequences in our coastal regions through Bangladesh. This study has tested and developed methods for evaluating considerable damages affected in 2020 by cyclone Amphan in Satkhira district (3817.29 km2) in Bangladesh from 30m spatial-resolution satellite imageries. Iso-cluster unsupervised classification procedures are applied to prepare maps in pre- and post-cyclone Landsat 8 imagery. A difference between the pre- and post-cyclone image classification and NDVI analysis give us evidence about the significant landscape changes at the Cyclone Amphan impact on the study area. The total accuracies for pre- and post-cyclone images classification were 70% and 68% respectively. The outcomes indicate that 35.09% of the study region was remarkably influenced by cyclone Amphan. Statistical analysis indicates that water bodies have dramatically increased 17.65% in different land cover areas due to the strong water surge. On the contrary, the classified image showed that the vegetation and agricultural land decreased 4.13% and 10.82%. Thus, land covers have inundated due to heavy flash during the cyclone. Barren land also decreased 1.80%, and low land areas were also flooded 0.36% due to heavy water flashes. NDVI analysis indicated no destruction of any vegetation species, but vegetation damage is considerable. The developed method may be imposed in future to measure the impacts produced by cyclones in Bangladesh and to make policies for the commitments of cyclone disaster controlling and management.

Examining the factors of fertilizer pricing

AbstractA strong surge in fertilizer prices in 2021 led farmers and policymakers to question the factors that drive fertilizer pricing. This study examines the relationship among natural gas, corn, and fertilizer prices, finding five structural breaks from 1997 to 2022. The pass‐through rates of natural gas and corn price changes to fertilizer prices show significant shifts after the passage of the biofuel mandate, with those changes fading as the market matured. Meanwhile, natural gas price changes are the main contributing factor in the latest period. However, the linkage among these prices continues to evolve and is likely to diminish over time.

Assessing hotspot for tropical cyclone compound rainstorms from a disaster perspective: a case study in Guangdong, China

In Guangdong Province, China, known as a hotspot for tropical cyclone (TC) and rainstorm disasters, investigating the role of hazard factors for both TC and rainstorm disasters is of great scientific and operational importance. Daily observational data from meteorological stations along with rainstorm/TC disaster loss data at the county level in Guangdong during 2001–2020 were analyzed to compare the characteristics of rainstorm and TC disasters. During the study period, although non-TC rainstorm rainfall in Guangdong was generally greater than TC rainstorm rainfall, TC disaster losses were double those of rainstorm disasters. Non-TC rainstorms and their losses predominantly occurred during the first flood season, whereas TC disaster primarily occurred in the second flood season. Although regional-average TC rainstorm rainfall was similar to non-TC rainstorm rainfall during the second flood season in Guangdong, it was greater than non-TC rainstorm rainfall in western Guangdong. Additionally, the coastal areas were frequently affected by TC strong winds, resulting in greater TC disaster losses than rainstorm disaster losses. The maximum daily precipitation was significantly correlated with rainstorm disaster losses. In areas influenced by both TC strong winds and rainstorms, TC disaster losses exhibited a significant positive correlation with the daily maximum wind speed, and the combined effect of strong winds and rainstorms led to greater disaster losses than with TC rainstorms alone. These findings emphasize the effects of TC strong wind and storm surges, besides those of heavy rainfall, in coastal areas of China when assessing the risks related to TC disasters.

Supply vs Demand Factors Influencing Prices of Manufactured Goods

The strong surge and rapid retreat of U.S. goods price inflation during 2021-2023 has occupied the forefront of economic policy discussions, and debate on the primary causes continues. Some commentators point to widespread supply bottlenecks and adverse geo-political events that caused significant disruption to the production and availability of manufactured goods.

Spatiotemporal changes in hurricane-force wind risk assessment in the Yucatan Peninsula, Mexico

Tropical cyclones are one of the most dangerous natural phenomena. These extreme events involve various hazards, such as strong winds, severe precipitation, storm surge, flooding, and landslides. In Mexico, tropical cyclones are the most frequent natural threats and have a high cost to affected populations. This research aimed to characterise the spatial and temporal changes in risk associated with hurricane winds on the Yucatan Peninsula. This effort included a comprehensive analysis of three integral risk components (hazard, vulnerability, and exposure) for three distinct time intervals (1950–2000, 1950–2010, and 1950–2020). This analytical process was executed utilising a fine-resolution hexagonal grid. Hazard was estimated by calculating the probabilities of occurrence of winds related to various hurricane categories after estimating wind fields from the International Best Track Archive for Climate Stewardship (IBTrACS) data with a parametric model. Vulnerability was approximated by constructing indicators with sociodemographic data from the National Population and Housing Census issued by Mexico’s National Institute of Statistics and Geography. With these indicators, a factor analysis was performed, and a weighted index was constructed. Finally, exposure was estimated from population density. Each of these indices was aggregated at the hexagonal level, allowing the calculation of the risk associated with hurricane-force wind. The results showed high-risk levels associated with high-hazard levels, e.g. in coastal areas such as the Riviera Maya. Similarly, high-risk levels are related to high marginalisation, i.e. vulnerability, in the northeastern zone of the Yucatan Peninsula. The increased frequency of tropical cyclones combined with high population densities has recently led to higher risk levels in this region of Mexico.

Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI

The clean energy market analysis has seen a strong surge in the post-Paris agreement owing to its undeniable environmental sustainability. The present work investigates the predictability of clean energy investment in the US market by selecting eight sectoral stock indices. The predictive exercise is carried out separately during pre-COVID-19 and COVID-19 timelines to draw key behavioral aspects of the underlying sectors. After scrutiny of previous research, we identify a set of technical and macroeconomic variables as explanatory constructs. We then utilize Facebook's Prophet and NeuralProphet to find future figures for the clean energy indices. Finally, Explainable Artificial Intelligence (XAI) is used to draw deeper insights into the contribution patterns of the constituent explanatory variables and obtain their relative importance. The findings suggest that the future movements of the sectoral clean energy assets can be predicted with a very high level of accuracy. Also, the predictability marginally improves during the COVID-19 pandemic despite the unprecedented uncertainty. Technical indicators appear to be the dominant features, while market sentiment and fear exert significant influence.

Environmental and Storm-Scale Controls on Close Proximity Supercells Observed by TORUS on 8 June 2019

Abstract The Targeted Observation by Radars and UAS of Supercells (TORUS) field project observed two supercells on 8 June 2019 in northwestern Kansas and far eastern Colorado. Although these storms occurred in close spatial and temporal proximity, their evolutions were markedly different. The first storm struggled to maintain itself and eventually dissipated. Meanwhile, the second supercell developed just after and slightly to the south of where the first storm dissipated, and then tracked over almost the same location before rapidly intensifying and going on to produce several tornadoes. The objective of this study is to determine why the first storm struggled to survive and failed to produce mesocyclonic tornadoes while the second storm thrived and was cyclically tornadic. Analysis relies on observations collected by the TORUS project—including unoccupied aircraft system (UAS) transects and profiles, mobile soundings, surface mobile mesonet transects, and dual-Doppler wind syntheses from the NOAA P-3 tail Doppler radars. Our results indicate that rapid changes in the low-level wind profile, the second supercell’s interaction with two mesoscale boundaries, an interaction with a rapidly intensifying new updraft just to its west, and the influence of a strong outflow surge likely account for much of the second supercell’s increased strength and tornado production. The rapid evolution of the low-level wind profile may have been most important in raising the probability of the second supercell becoming tornadic, with the new updraft and the outflow surge leading to a favorable storm-scale evolution that increased this probability further.

Твердый сток реки Дон и поступление взвеси в дельту при нагонах: статистическое моделирование и сопоставление в период маловодья

Statistical modeling of the Don River solid runoff based on water discharge and turbidity measurements obtained at the hydrological station in the village of Razdorskaya for the twelve-year period 2009–2020 was carried out. The WRTDS (Weighted Regressions on Time, Discharge, and Season) and WRTDSKalman (WRTDS with Kalman filtering) methods were applied. The developed statistical model is aimed at solving the problem of the imbalance between the regularity of collecting data on water discharge and data on the concentration of suspended matter by “restoring” the concentration values on days without measurements based on data on the most “similar” days with measurements in terms of time, discharge and season and does not claim to describe a relationship between the concentration of the constituent of interest and discharge. The quality of the developed statistical model and its modification were checked. The average daily concentrations and fluxes of suspended matter were compared with estimates of the volumes of suspended material deposited during periods of recurring strong surge phenomena. A comparative assessment of sea and river factors contributions to the transport and sedimentation of suspended matter in the Don River delta was fulfilled.

Research on the Impact of Climate Change on Insurance Companies

The global climate is changing with more and more extreme weather, including typhoons, extreme temperatures and other disasters. Climate change has an extraordinary impact on the insurance industry. However, according to research, a third of insurers are not paying enough attention to the impacts of climate change. This paper analyzes and demonstrates the impact of weather changes from two parts of non-life insurance and life insurance through cases. Taking the typhoon as the cut-in perspective, the impact on the climate is analyzed from three aspects: strong wind, storm surge and heavy rain. Additionally, this article offers some advice to insurers on how to benefit from global events. The findings suggest that insurers should recalculate all catastrophe risks and rebuild risk models. Catastrophe bonds can also help businesses raise capital. Insurers need to recognize how far-reaching climate change will affect the insurance sector, and how risks present opportunities.

Improving the short‐range forecast of storm surges in the southwestern Atlantic continental shelf using 4DEnSRF data assimilation

In this study, the assimilation of tide gauge and altimetry data into a two‐dimensional barotropic numerical model for the southwestern Atlantic continental shelf (SWACS) was developed. To do this, the preoperative 4‐day storm surges ensemble prediction system developed by Dinápoli et al. (2021, Journal of the Royal Meteorological Society 147: 557–572) was implemented for the SWACS. This new configuration, called “Model for Storm Surge Simulations” (M3S), considers a curvilinear grid that covers the SWACS with higher resolution along the shoreline (from 2 to 10 km). M3S was forced with an ensemble of 60 members conformed by the combination of perturbations of the eight principal tidal constituents and of the atmospheric products derived from the Global Ensemble Forecast System. Tidal gauge and altimetry data were assimilated in an asynchronous mode using the four‐dimensional‐ensemble square‐root filter (4DEnSRF). The system was developed and validated forecasting two strong positive storm surges. Results show that 4DEnSRF's innovations produce a positive impact upon the forecast skill up to 2 days. Hence, the 4‐day forecast can be divided into two parts: the first 2 days with a stronger dependence on the initial conditions and the other 2 days purely driven by external forcing. It was found that a symmetric assimilation window of 12 hr length produces the best initial condition. Under this configuration, 4DEnSRF removes biases and improves the timing of the M3S forecasted solutions. The largest improvements were observed at the northern SWACS, where more chaotic processes, such as the atmospheric circulation, explain a large part of the sea‐surface height variability. No significant improvements were found at the southern SWACS, which can be attributed to the strong tidal dynamics that characterise the zone. Our results show that the incorporation of 4DEnSRF into M3S can significantly improve the forecast in the SWACS and also the accuracy of the short‐range detection of storm surges.

Effects of the built-up index and land surface temperature on the mangrove area change along the southern Sumatra coast

Mangrove trees have a crucial role in providing habitat and breeding for various aquatic species and averting coastal erosion caused by strong waves, tidal surges, and storm surges. Currently, the mangrove area along the Sungsang coast in South Sumatra, Indonesia, has been degraded by human activities like agriculture, fish ponds, and other infrastructure developments. In this study, we used the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and land surface temperature (LST) analysis for observing urban development and vegetation cover in the Sungsang mangrove area. This study aimed to analyze the effects of NDBI and NDVI on the LST change in the Sungsang mangrove area. Results revealed that the mangrove vegetation significantly reduced (by 23%) during 2013–2022 years, in contrast to the built-up development and the LST gradually increased. The area with a temperature value greater than 20 °C was characterized by mangrove degradation, expanding around the peripheral area. There was a strong positive correlation between LST and NDBI (r = 0.90, p < 0.01), and a moderate correlation between LST and NDVI (r = 0.74, p < 0.05). Thus, the built-up index greatly influenced the vegetation area within the study site. This study assists the authority in comprehending their recent mangrove status and promoting sustainable actions for mangrove vegetation and urban management.

Sensing fear: fast and precise threat evaluation in human sensory cortex.

Animal models of threat processing have evolved beyond the amygdala to incorporate a distributed neural network. In human research, evidence has intensified in recent years to challenge the canonical threat circuitry centered on the amygdala, urging revision of threat conceptualization. A strong surge of research into threat processing in the sensory cortex in the past decade has generated particularly useful insights to inform the reconceptualization. Here, synthesizing findings from both animal and human research, we highlight sensitive, specific, and adaptable threat representations in the sensory cortex, arising from experience-based sculpting of sensory coding networks. We thus propose that the human sensory cortex can drive smart (fast and precise) threat evaluation, producing threat-imbued sensory afferents to elicit network-wide threat responses.

COMPARISON ON STUDY OF LITHIUM ION AND LEAD ACID CHARGING AND DISCHARGING CHARACTERISTICS

A lithium-ion or Li-ion battery is a type of rechargeable battery which uses the reversible reduction of lithium ions to store energy. It is the predominant battery type used in portable consumer electronics and electric vehicles. Energy density: 250–693 Wh/L (0.90–2.49 MJ/L) Specific energy: 100–265 Wh/kg (0.360–0.954 MJ/kg) Charge/discharge efficiency: 80–90% Cycle durability: 400–1,200 cycles Self-discharge rate: 0.35% to 2.5% per month depending on state of charge Specific power: c. 250–340 W/kg Lithium batteries, as opposed to alkaline, are capable of giving off a strong energy surge after a long period of low discharge. This makes them ideal for fire alarms. Alkaline batteries provide good, long-term power, but they lose strength over time. On the other side , the Lead acid battery are inexpensive compared to newer technologies, lead–acid batteries are widely used even when surge current is not important and other designs could provide higher energy densities. Energy density: 80–90 Wh/L Nominal cell voltage: 2.1 V Self-discharge rate: 3%–20%/month Specific energy: 35–40 Wh/kg Charge temperature interval: Min. −35°C, max. 45°C Charge/discharge efficiency: 50%–95% Cycle durability: &lt;350 cycle In order to observe the characteristic nature of Lead acid &amp; Lithium ion battery , a review is made on the subject using Matlab Simulink and Simscape.

Pectoral fin swimmers turn to save energy in coral reef waves

It takes all sorts to make a coral reef community. From elegant shimmering Hawaiian flagtails (Kuhlia xenura) to vivid kole tangs (Ctenochaetus strigosus) and stripy sergeant major fish (Abudefduf vaigiensis), these creatures all make their homes in the perpetual swell of the coral reef waters off Hawaii. ‘The waves over shallow reefs create constantly changing water flows that may help explain the great variety of fish body shapes and types of swimming we see among reef fishes’, says Keith Korsmeyer from Hawaii Pacific University, USA. Some fish swim sinuously, gently beating their tails from side to side, while others manoeuvre and hover deftly by wiggling their wing-like pectoral fins. But all must be able to withstand strong surges and remain in place, even when the turbulence of the strongest storms blows through. Yet it wasn't clear just how much of an effort it takes for fish with different builds and swimming styles to hold their own in turbulent coral reef waters. Korsmeyer teamed up with Mathias Schakmann (Hawaii Pacific University) and the pair recreated the wavey conditions on a reef to find how much effort it takes fish to stay in place and how much swimming style affects their exertions.‘I went fishing at the local pier and was lucky to catch fish with interesting differences in body shape and how they swim’, says Schakmann, who came back with Hawaiian flagtails, kole tangs, sergeant major fish and saddle wrasse (Thalassoma duperrey). He then set up a fish ‘treadmill’ in the lab with water flowing through a tube, where he could measure the fish's oxygen consumption to calculate the energy they used as they swam at different steady speeds, in addition to how hard they had to swim when the water washed to and fro as he simulated coral reef waves. ‘All the fish quickly adjusted to each wave surge and kept swimming into the flow’, says Korsmeyer.It was quickly clear that the fish switched direction each time a simulated wave washed through, speeding up and slowing down to remain in place as the waves surged past. Although all of the fish swam harder to stay put in the wavey conditions, when Schakmann compared the fish's swimming styles, it was evident that the pectoral fin wiggling swimmers were far more efficient than the tail-beating Hawaiian flagtails. The flagtail's energy use increasing 2.5-fold as the waves increased from one every 10 s to one every 3.3 s, while the fin-flapping saddle wrasse only increased their exertions by 1.5-fold and the disc-shaped fin-wiggling sergeant major fish barely made any additional effort (0.5-fold increase). ‘The fish that swam with flapping pectoral fins could deal with the increasing wave motion more easily, which would save energy while living in these wave-swept habitats’, says Korsmeyer.However, when Schakmann compared the exertions of the three pectoral fin swimmers, the deepest-bodied kole tangs and sergeant major fish swam more efficiently than the slimmer saddle wrasse as they switched back and forth. ‘Our results suggest that more rounded disc-shaped bodies seem to be the most efficient for dealing with staying put in a wave surge water flow’, says Schakmann.So disc-like fish that depend on their pectoral fins for manoeuvrability are best suited for life on wave-swept coral reefs, turning on a dime as each wave washes through, and it takes all sorts to make a community, although some are better suited to steady swimming in the calmer deep waters off coral reefs.

Upper tropospheric cloud‐radiation interaction induced by monsoon surge over the South China Sea

AbstractWe have investigated cloud formation over the South China Sea in the upper troposphere and its impacts on the radiation budget of the troposphere and ocean surface under monsoon surges using satellite and reanalysis products and an atmospheric numerical model. During strong surges (SS), the upper tropospheric (UT) cloud formation in central South China Sea shows a clear diurnal cycle, peaking around 12–14 local solar time when the incoming solar radiation is the strongest. The enhanced UT clouds attenuate the incoming solar radiation in the upper troposphere reducing the net downward shortwave radiation flux at the surface by approximately 28% in SS compared with no surge case. In contrast the downward longwave radiation flux is enhanced mainly from the middle to upper troposphere in SS. The cooling because of the diminished incoming shortwave radiation overwhelms the warming due to the longwave radiation at the ocean surface. This cloud radiative forcing is observed partly over the cold tongue in sea surface temperature (SST) in the South China Sea. Numerical simulations with a 1‐dimensional slab ocean model suggests that the monsoon‐induced UT cloud radiative forcing contributes partially to maintain and reinforce the SST cold tongue with a tendency of about 0.08 K in 6 days or about 1/6 of one standard deviation of the Cold Tongue Index in December. The latent heat flux due to the monsoon surge is still the most significant factor maintaining the cold tongue.

A study of the wave effects on the current circulation in Galway Bay, using the numerical model COAWST

A model coupling waves and currents is set up for Galway Bay, Ireland, using COAWST. The impact of the coupling on the accuracy of the model is shown to be marginal on the overall statistics. The model is in good agreement with the available in-situ observations that are used for the validation. However a closer look at the data during storms shows a deterioration of the agreement, which is barely improved by the coupling. A special analysis of the different processes ruling the ocean circulation and wave propagation is conducted during Storm Hector (2018/06/14). It shows a strong wave-induced surge by 7cm in the back of Galway Bay, and a strong response in terms of currents along the coast of Clare of about 30cm/s. Most of the surge is attributed to the impact of the conservative wave-induced forcing terms, but the main process involved is not identified. The response in currents is partly attributed to the conservative wave-induced forcing terms, but mostly to the wave-enhanced surface roughness.

Can we unlock deep-tech in Indian startups for long-term success?

PurposeInnovation in technology is nothing new. However, a strong surge of inventions centered on digital apps and platforms has emerged over the past decade and has come to be associated with the tech sectors. The purpose of this research is to know whether deep-tech can be unlocked in startups for their long-term success.Design/methodology/approachPapers and websites that included the term deep-tech startups were considered for this study.FindingsThe authors have seen no explanation why India cannot develop several global deep-tech firms in the coming five years, similar to what China did in the previous 10 years, if Indian deep-tech entrepreneurs receive solid support from domestic industry giants, supportive regulatory changes and prompt access to finance. The authors think that the epidemic has made conditions for this to pick up speed that is highly favorable.Research limitations/implicationsThis research is limited to a few numbers of papers and websites.Practical implicationsDue to the unanticipated disruption, the pandemic caused, clever robotics and hands-free technology were urgently needed. The development of smart, self-monitoring, simple, tailored and potential on-the-go improvement solutions are the talk of the day. Deep-tech solutions have the ability to provide limitless benefits in a variety of fields to the world's 1.3 billion inhabitants.Social implicationsThis research will help startups to know more about deep-tech and implement them to be the first-mover advantage in this competitive and turbulent environment.Originality/valueThe novelty of this research is based on its presentation of an organized and thorough evaluation, which defines the current state of the art with regard to deep-tech startups. Not many studies have been performed in the area of deep-tech startups. In order to create a sustainable startup, a thorough study on how deep-tech can be unlocked in startups and its opportunities, challenges and funding is presented.

Modeling the Sea Level Response of the Northern East China Sea to Different Types of Extratropical Cyclones

AbstractSea level response to extratropical cyclones, one of the most disastrous weather systems, varies depending on cyclone types and tracks. This study presents a systematic modeling assessment of storm surge induced by extratropical cyclones in the northern East China Sea using an ocean‐wave coupled model, based on the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport (COAWST) model system. We identified the type of extratropical cyclone most likely to cause disasters and the areas likely to suffer the worst effects. We conducted sensitivity experiments to analyze the driving mechanisms (such as wind, air pressure, tide, and wave) and influencing mechanisms (such as local and remote meteorological effect and Ekman effect) of storm surge. During strong winds, positive and negative storm surges were consistent with the wind direction, and formed source of coastal trapped waves. When an abnormally high water level occurred in a semi‐enclosed sea area, it propagated mainly along the cyclone direction. The energy of coastal trapped waves was easy to dissipate in case of caps and other locations with large topographic changes. Tide‐surge‐wave interaction and propagation were also greatly affected by coastal trapped waves.

Delayed effects of large-scale cold surge on winter heavy rainfall in southern China

In winter, large-scale cold surges usually invade southern China and cause heavy rainfall in that region. They provide the baroclinic instability and dynamic conditions favorable for the formation of heavy rainfall. Through examining an extreme wintertime heavy rainfall event in southern China, this study demonstrates that a large amount of moisture transportation that originates from the tropical region can be driven by the northern cold surges. Results show that a strong cold surge invade southern China 4 days prior to the heavy rainfall. The residual cold airmass of the cold surge provides an important convergent lifting slope for the formation of heavy rainfall as well as determines its location. The moisture for the heavy rainfall is provided by the southerly flow along the western part of the Philippine Sea anticyclone that owes to the cold surge. As the cold surge intrudes into the Philippine Sea, decreases in temperature and humidity are expected in the lower troposphere, enhancing large-scale downdrafts and sea level pressure and eventually triggering an anticyclone. The Philippine Sea anticyclone reaches its strongest 4 days after the cold surge and activates the low-level moisture pooling in the region of interest. The findings suggest that the large-scale cold surge indirectly stimulates the wintertime heavy rainfall in southern China by a delayed effect of moisture pooling through forming the Philippine Sea anticyclone.