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Skill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales. Plain Language Summary: Early warning of severe weather is particularly important in Africa, where resilience to storm hazards such as flash flooding is weak. Given large‐scale atmospheric conditions favorable for convective activity, understanding where storms will occur is challenging for conventional weather prediction models. In semi‐arid regions such as the Sahel, the spatial distribution of SM provides additional predictability of convective rain, via its impact on heating and moistening of the atmosphere. Given that convection is favored over drier soils and that storms create new SM patterns every few days during the wet season, the extent to which knowledge of today's SM aids rainfall prediction in future days is unclear. Here we use 17 years of satellite observations to document how surface properties evolve over 20 days after a storm, and how the surface influences subsequent rainfall patterns. We find that even in regions of West Africa where storms are frequent, the suppression of rain over recently‐wetted soils is evident out to 2 days. In climatologically drier regions, this predictability extends out to 8 days. Overall, the feedback between SM and rainfall enhances rainfall predictability in the short‐term (days), but effectively degrades the skill of longer‐term (weeks) forecasts. Key Points: Satellite observations over the Sahel reveal how the land surface evolves in the 20 days after a Mesoscale Convective System (MCS)After an MCS, rainfall is suppressed over wet soils for 2 days in humid regions and up to 8 days in drier areasInitially soil moisture enhances rainfall predictability, but the strong land feedback degrades skill at longer lead times [ABSTRACT FROM AUTHOR]

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification framework on a multivariate time-series data set covering solar cycles 22, 23, and 24. We implement ensemble modeling that merges the results from three proton channels (E ≥ 10 MeV, 50 MeV, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions. Our task is binary classification, such that the aim of the model is to distinguish strong SEP events from nonevents. Here, strong SEP events are those crossing the Space Weather Prediction Center's "S1" threshold of solar radiation storm and proton fluxes below that threshold are weak SEP events. In addition, we consider periods of nonoccurrence of SEPs following a flare with magnitudes ≥C6.0 to maintain a natural imbalance of sample distribution. In our data set, there are 244 strong SEP events comprising the positive class. There are 189 weak events and 2460 "SEP-quiet" periods for the negative class. We experiment with summary statistic, one-nearest neighbor, and supervised time-series forest (STSF) classifiers and compare their performance to validate our methods for prediction windows from 5 minutes up to 60 minutes. We find the STSF model to perform better under all circumstances. For an optimal classification threshold of ≈0.3 and a 60 minutes prediction window, we obtain a true skill statistic TSS = 0.850, Heidke skill score HSS = 0.878, and Gilbert skill score GSS = 0.783. [ABSTRACT FROM AUTHOR]

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little forecast skill in the troposphere: individual ensemble members are mostly uncorrelated and span a range of atmospheric evolutions that are possible for the given set of external forcings. The uncertainty of such a probabilistic forecast is then determined by this range of possible evolutions – often quantified in terms of ensemble spread. Various dynamical processes can affect the ensemble spread within a given region, including extreme events simulated in individual members. For forecasts of geopotential height at 1000 hPa (Z1000) over Europe, such extremes are mainly comprised of synoptic storms propagating along the North Atlantic storm track. We use ECMWF reforecasts from the S2S database to investigate the connection between different storm characteristics and ensemble spread in more detail. We find that the presence of storms in individual ensemble members at S2S timescales contributes about 20 % to the total Z1000 forecast uncertainty over northern Europe. Furthermore, certain atmospheric conditions associated with substantial anomalies in the North Atlantic storm track show reduced Z1000 ensemble spread over northern Europe. For example, during periods with a weak stratospheric polar vortex, the genesis frequency of Euro-Atlantic storms is reduced and their tracks are shifted equatorwards. As a result, we find weaker storm magnitudes and lower storm counts, and hence anomalously low subseasonal ensemble spread, over northern Europe. [ABSTRACT FROM AUTHOR]

The downshear‐left enhancement of tropical cyclone rainfall has been demonstrated previously, but the radial dependence of this effect was not analyzed in detail. This study quantifies the progressive upwind shift of the wavenumber‐1 maximum rain position with radius relative to the vertical wind shear direction. This shift is visualized as a distinctive upwind spiral of the maximum. It is shown that this spiral pattern is generally observed across various storm intensities, shear strength, and ocean basins. Detailed examination revealed that the maximum downwind deflection angle of the wavenumber‐1 rain maximum relative to the shear direction is smaller for tropical storms than hurricanes, but insensitive to hurricane intensity. It is proposed that the spirality is produced by a continuous decline in angular advection of air parcels with radius. The stability of the deflection angle in hurricanes may be accounted for by a corresponding increase in vertical ascent under strengthening angular flow. Plain Language Summary: Tropical cyclones (TCs) can produce torrential rainfall that generates floods, causing significant socio‐economic losses. Understanding the spatial structure of the TC rain field is crucial for improving disaster preparedness. The TC rain field can be thought of as the combination of a symmetric and an asymmetric part. By using a technique called Fourier decomposition, we can break down the asymmetric part into individual wavenumber components. The first component, wavenumber‐1 (WN‐1), is dominant and tends to be larger in the downshear quadrants under vertical wind shear. Using 21 years of global WN‐1 rain fields, we produced composite images aligned with the shear direction. We discovered that the positions at which the WN‐1 maximum occurs progressively shift upwind with increasing distance from the TC center, forming a spiral. We provided the first quantification of the observed spirals and showed that this is a general pattern that exists across different TC intensities, shear strength, and ocean basins. We also identified detailed changes in the pattern with storm intensity and introduced simple models as a first attempt to comprehend these changes. The findings can improve weather forecasts and risk predictions, making us better prepared for hazards associated with TC rainfall. Key Points: There exists a general and progressive upwind shift in the wavenumber‐1 maximum with radius in global shear‐relative rainfall compositesMaximum downwind deflection of the wavenumber‐1 maxima rises with storm intensity up to Category 1 on the Saffir–Simpson Hurricane ScaleLinearity between the angular velocity and the vertical velocity of the storm may stabilize the downwind deflection beyond Category 1 [ABSTRACT FROM AUTHOR]

Cyclone Gabrielle passed along the northern coast of Aotearoa New Zealand in February 2023, producing historic rainfall accumulations and impacts. Gabrielle was an ex‐tropical cyclone that stalled and re‐energised off the north coast, resembling descriptions of worst case scenarios for the northeast of the country. Here we report on a comparison of the actual forecast of the storm against forecasts under conditions representative of a climate without anthropogenic interference and of a climate +2.0°C warmer than pre‐industrial (about 1.0°C cooler and warmer than present respectively). We find that regional total rainfall accumulations from a Gabrielle‐like storm are about 10% higher because of the historical anthropogenic warming, and will increase by a larger amount under similar future warming. These differences are driven by a 20% (relative to a non‐anthropogenic world) to 30% (relative to a +2.0°C world) rise in peak rainfall rates, which in turn is mainly driven by a more temporally concentrated column‐integrated moisture flux. The forecast model generates the larger increase for the +2.0°C world through greater precipitation efficiency, reflecting the importance of unresolved precipitation processes in the climate change response of rainfall extremes. Plain Language Summary: Tropical Cyclone Gabrielle formed in the Coral Sea in February 2023, then moved southeast and passed along the northern coast of Aotearoa New Zealand as an ex‐tropical cyclone. The storm's rainfall produced one of the worst natural disasters in the country's history. We compare the weather forecast of the storm against forecasts in which past anthropogenic warming is removed and in which future warming is added. We find that the storm would have dumped about 10% less total rainfall and 20% less peak hourly rainfall without human interference. A similar future amount of warming will result in a comparable total increase in storm rainfall but with about a 30% increase in the peak hourly rate. Key Points: Cyclone Gabrielle delivered large amounts of rain to northeastern Aotearoa New Zealand in February 2023Anthropogenic warming increases the total amount of rain delivered by a Gabrielle‐like storm by about 10%/°CAnthropogenic warming increases the peak amount of rain delivered by a Gabrielle‐like storm by about 20%–30%/°C [ABSTRACT FROM AUTHOR]

There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users' needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the winter storm severity index (WSSI) [operational for the contiguous United States (CONUS)] to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska-specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions, and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are the important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, and ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable. Significance Statement: Impact-based support services can assist decision-makers in prioritizing preparedness and mitigation actions related to winter storm events. The winter storm severity index adapted for specific considerations in Alaska (such as including wind and visibility components) can extend winter weather impact-based forecasting's utility. Additionally, lessons learned from the process of adapting a national product to specific regional needs may inform best practices for gathering stakeholder input and feedback. [ABSTRACT FROM AUTHOR]

The extratropical stage of Hurricane Ida (2021) brought extreme subdaily rainfall and devastating flooding to parts of eastern Pennsylvania, New Jersey, and New York. We investigate the predictability and character of this event using 31-member ensembles of perturbed initial condition hindcasts with the Tropical Atlantic version of GFDL's System for High-resolution prediction on Earth-to-Local Domains (T-SHiELD), a ∼13-km global weather forecast model with a ∼3-km nested grid. At lead times of up to 4 days, the ensembles are able to capture the most extreme observed hourly and daily rainfall accumulations but are negatively biased in the spatial extent of heavy precipitation. Large intraensemble differences in the magnitudes and locations of simulated extremes suggest that although impacts were highly localized, risks were widespread. In Ida's tropical stage, interensemble spread in extreme hourly rainfall is well predicted by large-scale moisture convergence; by contrast, in Ida's extratropical stage, the most extreme rainfall is governed by mesoscale processes that exhibit chaotic and diverse forms across the ensembles. Our results are relevant to forecasting and communication in advance of extratropical transition and imply that flood preparedness efforts should account for the widespread possibility of severe localized impacts. Significance Statement: After making landfall in Louisiana, Hurricane Ida (2021) transitioned to an extratropical storm which brought extreme rainfall and unprecedented flooding to parts of the northeastern United States. To what extent were these impacts knowable in advance? We use a numerical weather model with very high resolution to produce ensemble hindcasts—simulations of a past weather event initialized with tiny perturbations to the initial conditions, representing dozens of equally plausible versions of Ida's extratropical stage. We find that the observed hourly and daily rainfall maxima fall within the simulated outcomes of ensembles initialized with lead times of about 4 days or less. The location and intensity of the heaviest rainfall vary widely across these ensembles, suggesting that many locations across the Northeast were exposed to some likelihood of extreme rainfall. [ABSTRACT FROM AUTHOR]

According to the U.S. Drought Monitor, drought coverage in the Lower 48 States decreased slightly from February 27 to April 2. March storms helped replenish soil moisture in the Plains and Midwest, while the middle and northern Atlantic states experienced their wettest March on record. California received above-average rainfall and snowpack, ending its drought. The eastern half of the country experienced warmer temperatures, while the Pacific Coast and High Plains had near- or below-normal temperatures. Notable weather events included tornado outbreaks, heavy rainfall, and wildfires in certain areas. In April, Alaska had near to above normal snowpack, and the Bering Sea had slightly higher sea ice extent than the previous year. The month also saw severe thunderstorms, a high number of tornadoes, and a mix of extreme temperatures across the United States. Texas faced heavy rainfall and flooding, while Alaska experienced above-average precipitation and significant snowfall. [Extracted from the article]

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods. [ABSTRACT FROM AUTHOR]

The diurnal cycle of total hydrometeor availability and its associated patterns of atmospheric circulation is studied over a connected Andes–Amazon (A–A) system in the central region of Peru during the summer season. Surface precipitation depends on the amount of hydrometeors that occur in the atmosphere and its atmospheric dynamics. Hydrometeors and the precipitation efficiency index were estimated using radar of the core satellite of the GPM system (N-GPM) for the period 2014–2022. The atmospheric dynamics were analyzed using the regional Weather Research and Forecasting (WRF) model. According to the results, the Andes mountain range produces precipitation at a surface level more efficiently during the afternoon and early evening hours (12–19 LT) due to the convergence of the thermal mesoscale circulations transporting moisture fluxes from the east and west. Both generate convective multicells along the Andes mountain range. The circulation from the west intensifies during the day, causing the displacement of the chain of convective multicells towards the east and producing hydrometeors and intense precipitations in the inter-Andean valleys. The A–A transition zone is more efficient in producing precipitation during the early hours of the day (00–07 LT) due to an increase in the northern circulation associated with the low-level jets and a change in the magnitude of the horizontal winds. Northerly winds enter the A–A transition zone with increased intensity and leave with reduced intensity. This mechanism is driven by the effect of the topographical barrier and the masses of cold air located in high areas on the eastern flank of the Andes. These factors generate significant updrafts and, therefore, the formation of storm clouds with high concentrations of hydrometeors and precipitation on the surface. [ABSTRACT FROM AUTHOR]

Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy ( R 2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system. [ABSTRACT FROM AUTHOR]

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model's humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm's water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm's development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain's terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment. [ABSTRACT FROM AUTHOR]

Urban impacts on spatial and temporal rainfall variabilities present significant challenges for effective urban flood mitigation and adaptation strategies. However, the physical mechanisms underlying these impacts remain unclear. In this study, we conducted modeling analyses using the Weather Research and Forecast (WRF) model, combined with thunderstorm identification and tracking algorithms, to investigate the influence of urban areas on warm-season thunderstorms in Nanjing, China. Our findings reveal divergent urban impacts on the structure and evolution properties of thunderstorms based on different pre-storm synoptic conditions. When the synoptic conditions are weak, the urban heat island effect dominates, enhancing convective activities over urban areas. This leads to a reduction in the number of storm cells but an expansion in spatial coverage, ultimately resulting in increased rainfall over downtown areas. Conversely, when the synoptic conditions are strong, the urban canopy effect becomes prominent, slowing down storm movement and increasing the frequency of small storm cells over urban regions. These storm cells exhibit distinct “sharp” structures in terms of rainfall distribution and tend to intensify over downwind areas due to moist convergence. As a result, both downtown and downwind regions experience enhanced rainfall. This study improves our understanding of urban rainfall modification and offers valuable insights for storm nowcasting algorithms and the design of urban-specific rainfall events. [ABSTRACT FROM AUTHOR]

In this study, we present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms with in disturbance storm time (Dst) amplitudes ranging from Dst = - 64 nT to Dst = - 422 nT. In order to determine the solar wind (SW) parameters that mainly influence the main phase development of a GS, which can best describes the SW-magnetosphere coupling, we divided our selected GSs into four groups based on main phase duration. Superposed epoch analysis was performed on the selected geomagnetic indices, SW plasma and field parameters, and their derivatives separately for each group. To that end, the dynamics of GS main phase development is mainly guided by interplanetary driver's magnetic field southward component, (-Bz). It has been determined that there is a temporal difference between the peak values of Bz and Dst. As a result, Dst is delayed from Bz by 1 – 4 h, which is crucial for space weather forecasting. Our findings support the theory that the arrival of high-speed magnetic field structures in front of the Earth's magnetosphere is responsible for the development of GS, which begins a few hours later. The peak of Dst has a direct relationship with the amplitude of storm sudden commencement (SSC) and an inverse relationship with the duration of SSC. The inter-relationship between the peaks of the three indices (Dst, AE, and ap) during GS, is also obtained. Dst is found to be more closely related to ap than AE. To determine the best fit SW parameter to the geomagnetic activity indices, we used a linear correlation between the peak values of individual geomagnetic indices and SW plasma and field parameters and their derivatives. An electric field related function involving speed and IMF ( v 4 3 Bz) when coupled with a viscous term ( ρ 1 2 ) ( v 4 3 Bz ρ 1 2 ) correlates very well with the intensity of the GS (Dst min or Δ Dst) and the magnitude of (ap max) and (AE max) during storms. However, a related function ( v 4 3 B ρ 1 2 ) represents slightly better the peak of AE max during the storms. We strongly encourage higher-resolution observations to confirm and reach a thorough conclusion. [ABSTRACT FROM AUTHOR]

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán. [ABSTRACT FROM AUTHOR]

The atmospheric-hydrological coupling systems are essential in flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal relies on a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluated the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully-distributed WRF-Hydro and a simi-distributed Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) modeling systems were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China, characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performances of the coupled systems with different complexities. The coupled WRF/HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF/HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF/WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF/HEC-HMS, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric-hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics. [ABSTRACT FROM AUTHOR]

Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC's R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement: Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions. [ABSTRACT FROM AUTHOR]

The objective of this study is to simulate the rainfall-runoff process of mountain torrents in the Shou River Basin located in the southern part of Wenchuan County, Sichuan Province. The basin encountered a significant flood disaster on August 20, 2019, resulting in substantial human and economic losses, as well as limited hydrological data availability. This research employs a unidirectional coupling approach by integrating the Weather Research and Forecasting Model (WRF) with the Storm Water Management Model (SWMM) to accurately simulate rainfall-runoff processes. To account for the non-uniform distribution of precipitation within mountainous watersheds, high-precision rainfall data generated by WRF are extracted and utilized as input for the SWMM model. The quantitative comparison between the evolution of spatial distribution of rainstorms and hourly precipitation processes demonstrates that the WRF model effectively captures dynamic changes in rainstorms. Analysis results indicate a trend of rainfall center movement from midstream to downstream, with sub-watersheds' superimposed effect on rainfall response leading to increased flood peak response, duration, and destructive power. This study provides an effective method and reference for predicting and assessing mountain flood disasters in data-scarce areas such as the Shou River Basin. [ABSTRACT FROM AUTHOR]

Radially outward-propagating, diurnal pulses in tropical cyclones (TCs) are associated with TC intensity and structural changes. The pulses are observed to feature either cloud-top cooling or warming, so-called cooling pulses (CPs) or warming pulses (WPs), respectively, with CPs posing a greater risk for hazardous weather because they often assume characteristics of tropical squall lines. The current study evaluates the characteristics and origins of simulated CPs using various convection-permitting Weather Research and Forecasting (WRF) Model simulations of Hurricane Dorian (2019), which featured several CPs and WPs over the tropical Atlantic Ocean. CP evolution is tested against choice of microphysics parameterization, whereby the Thompson and Morrison schemes present distinct mechanisms for CP creation and propagation. Specifically, the Thompson CP is convectively coupled and propagates outward with a rainband within 100–300 km of the storm center. The Morrison CP is restricted to the cirrus canopy and propagates radially outward in the upper-level outflow layer, unassociated with any rainband, within 200–600 km of the storm center. The Thompson simulation better represents the observations of this particular event, but it is speculated that CPs in nature can resemble characteristics from either MP scheme. It is, therefore, necessary to evaluate pulses beyond just brightness temperature (e.g., reflectivity, rain rate), especially within simulations where full fields are available. Significance Statement: Tropical cyclone size and structure are influenced by the time of day. Identifying and predicting such characteristics is critical for evaluating hazardous weather risk of storms close to land. While satellite observations are valuable for recognizing daily fluctuations of tropical cyclone clouds as seen from space, they do not reliably capture what occurs at the surface. To investigate the relationship between upper-level cloud oscillations and rainbands, this study analyzes simulations of a major hurricane along the coast of Florida. The results show that rainbands are not always tied to changes in cloud tops, suggesting multiple pathways toward the daily oscillation of upper-level tropical cyclone clouds. [ABSTRACT FROM AUTHOR]

Northward tropical cyclones over the Bay of Bengal (BoB TCs) often interact with atmospheric circulation, transporting large amounts of water vapor to the Tibetan Plateau (TP), causing extreme precipitation. The BoB surrounded by land on three sides and the complex topography of the TP bring challenges to implementing numerical simulation in these regions. However, the scarcity of data in the two areas makes it necessary to find a technological process to perform practicable numerical simulations on the BoB TC and its induced extreme precipitation to carry out further research. In this study, the WRF 3.9.1 is used to perform many simulation experiments on a northward BoB TC Rashmi (2008) from 24 to 27 October 2008 associated with a record-breaking extreme precipitation on the TP, indicating that the selection of the simulation region, the source of initial-boundary conditions, and the cumulus convection schemes are three important factors influencing the results. We examined and compared the simulation of Rashmi with 10 experiments that were generated by combining The Final Operational Global Analysis (FNL) reanalysis data and the European Centre for Medium-Range Weather Forecasting 5(th) generation reanalysis (ERA5) data as initial-boundary conditions with five cumulus convection schemes. Most of the experiments can predict Rashmi and precipitation in the TP to a certain degree, but present different characteristics. Compared with FNL, the ERA5 performs well regarding Rashmi's intensity and thermal structure but overestimates Rashmi's moving speed. For the extreme precipitation in the TP, experiments suffice to reproduce the heavy rainfall (>25 mm/day) in the TP, with TS and ETS scores above 0.3 and most HSS scores greater than 0.4. The optimal experiments of three stations with extreme precipitation deviated from the actual precipitation by less than 15%. The ERA5 TDK scheme is recommended as the optimal solution for balancing the simulation of Rashmi and its extreme precipitation in the TP. [ABSTRACT FROM AUTHOR]

The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the "training line" systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics. Significance Statement: Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia's current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases. [ABSTRACT FROM AUTHOR]

Floods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project. [ABSTRACT FROM AUTHOR]

In October 1878, a powerful storm known as the Gale of '78 disrupted the nation's weather service by knocking down telegraph lines between Baltimore and New York. The storm, recognized today as a Category 2 hurricane, caused significant damage along the Atlantic coast, with over 700 buildings destroyed in Philadelphia alone. The storm resulted in the loss of over 70 lives and caused extensive damage to ships and coastal areas. This event highlights the importance of accurate and timely weather information for forecasting and warning purposes. [Extracted from the article]

This article discusses the characteristics and behavior of Pacific weather systems that move through the Midwest and Great Lakes regions. These systems can range from benign to intense, depending on the temperature contrasts between Canada and the Gulf of Mexico. The article explains how forecasters analyze weather charts to identify air masses, fronts, and other features that contribute to the development of winter storms, blizzards, and tornadic storms. It also provides information on the classification of air masses and the identification of the dryline, which is a boundary associated with thunderstorm formation. The article includes weather charts and explanations of the symbols used to represent different weather conditions. [Extracted from the article]