Abstract As numerical weather prediction (NWP) models approach hectometric resolution, they operate increasingly in a regime where urban heterogeneity, specifically the morphological heterogeneity and resulting flow heterogeneity, is only partially resolved and the assumptions underlying conventional urban canopy models (UCMs) become questionable. To address this scale gap, we apply the multi‐scale coarse‐graining framework to building‐resolving large‐eddy simulations (LES) of the University of Bristol campus, a realistic heterogeneous urban environment. Two related morphologies are considered: an original layout containing large open‐space contrasts and a modified configuration with these regions infilled. By filtering the LES fields systematically, we quantify how flow heterogeneity is partitioned between resolved and unresolved contributions as the averaging length changes and identify a characteristic urban length‐scale of flow heterogeneity at which resolved and unresolved variability are comparable. This scale is strongly morphology‐dependent, with m for the original layout and m for the modified case, demonstrating that neighbourhood‐scale organisation can remain dynamically important at resolutions relevant to next‐generation NWP. Using this framework, we perform an a priori assessment of distributed drag and turbulent‐stress parameterisations. The results show that parameterisations derived from idealised geometries perform reasonably well only at sufficiently large averaging length‐scales (, corresponding to coarse resolutions), where horizontal transport is negligible and the flow appears approximately homogeneous. At smaller averaging lengths, their fidelity degrades rapidly due to increasing heterogeneity and filter‐to‐filter variability in morphology. These limitations are more pronounced in realistic layouts than in idealised cuboid arrays. Overall, the results highlight that the applicability of urban parameterisations depends critically on the relationship between model resolution and the characteristic urban length‐scale of morphology‐dependent flow heterogeneity. The framework provides a systematic route to diagnose this scale and to guide the development of scale‐aware urban canopy models for high‐resolution NWP.
Abstract This study examines multiscale controls on the diurnal cycle of precipitation over Timika, southern Papua, where coastal processes, steep orography, and large‐scale tropical variability interact within a narrow coastal‐to‐mountain transition zone. Rainfall peak timing is identified using ground‐based gauge observations and the Global Precipitation Measurement (GPM) mission's Integrated Multi‐satellite Retrievals for GPM (IMERG) precipitation estimates for 2001–2022. GPM‐IMERG detects rainfall peaks 1–3 hr later than gauge observations, reflecting point‐to‐grid representativeness differences and the tendency of GPM‐IMERG to identify rainfall once precipitation systems become more spatially organized. Two dominant rainfall regimes are identified. Afternoon‐to‐evening rainfall (1500 h–2100 h local time, UTC + 0900) is the most frequent and is associated with thermally driven convection over land and the foothills, followed by relatively slow offshore propagation. In contrast, morning rainfall (0300 h–1200 h local time), although less frequent, is typically more intense and often originates over the adjacent ocean or coastal zone before propagating inland. These regimes are closely linked to the vertical structure of the meridional wind. Afternoon‐to‐evening rainfall is mainly influenced by lower‐ to mid‐tropospheric steering flow, especially within the 700–500 hPa layer, whereas morning rainfall is favored when low‐level onshore flow enhances inland moisture transport and coastal convergence. Large‐scale modes modulate these local regimes by altering the background meridional wind and moisture environment. Westward‐moving mixed Rossby–gravity (WMRG; also known as MRG or Yanai wave)‐related anomalies provide event‐scale perturbations to the steering flow, whereas the Madden–Julian Oscillation modifies the intraseasonal background state without imposing a deterministic phase‐to‐phase shift in rainfall peak timing. The seasonal monsoon provides the strongest background modulation: December–May conditions reinforce the dominant afternoon‐to‐evening regime, whereas June–November increases the likelihood of later evening to morning rainfall. El Niño–Southern Oscillation acts as a slower interannual influence, with its clearest signal in the afternoon‐to‐evening rainfall category. Overall, diurnal convection over southern Papua is governed by interacting processes in which local land–sea breeze and orographic forcing set the primary diurnal cycle, whereas larger‐scale modes modulate rainfall timing, propagation, and organization.
Abstract Northwest Indian cities (Delhi, Jaipur, and Agra) are highly prone to heatwaves (HWs) during April to June. We address the analysis of a prolonged HW event between May 24 and May 28, 2020, using in‐situ, reanalysis, and 1.5‐km resolution Weather Research and Forecasting (WRF) model products. Both observations and reanalysis revealed that warm (˜40–44°C) and dry northwesterly winds (˜10 m·s −1 ) prevailed over northwest and central India during the HW period. The WRF model simulated the evolution of surface air temperature, relative humidity, winds, and HW duration and its spatial extent fairly well. The model could depict the nighttime intensification of HWs (˜3–7°C) in urban areas and a pronounced heat dome with vertical extension of up to 2 km. The findings reveal that weaker winds, the overlong presence of the heat dome, and a nighttime urban heat island (UHI) over urban centers exacerbate the extreme heat conditions in the cities. The altered physical processes over urban areas enhances the UHI during the night and early morning hours, thereby amplifying and prolonging the persistence of heat domes, eventually contributing to the duration and intensity of HWs. Computed statistical metrics confirm that the WRF model performed satisfactorily in predicting HW over northwestern India. Furthermore, a sensitivity test revealed a reduction in UHI intensity (about 3–5°C) when urban built‐up classes were replaced by rural classes. This research outcome is valuable to urban planners and policymakers for addressing challenges related to urbanization and increasing heat stress.
Abstract Microphysical processes and their impacts on tropical cyclone (TC) intensification are not well understood. Due to limited observations and model resolution, all microphysical processes need to be parameterized within models. One such process, hydrometeor fall speed, can impact the number of hydrometeors available and rate of phase changes, thus influencing the amount of latent heat available for the TC. To address microphysical uncertainties, the representation of graupel within the Morrison scheme in the Weather Research and Forecasting (WRF) model is altered by adjusting the fall speed of the hydrometeor, or by removing it from the scheme. A more favorable large‐scale environment for rapid intensification (RI) and a less‐favorable environment are used to isolate how much the environment plays a role in the effects of microphysics. In the RI environment, the graupel adjustments have the greatest impact on intensification. With less graupel, the storms experienced delayed or limited intensification due to decreased vertical mass flux, diabatic heating, and changes in tilt and asymmetry. These differences are more subtle in the less‐favorable environment, resulting in minimal intensification differences. The results demonstrate the importance of graupel in promoting RI.
Abstract We analyse 17 Mediterranean cyclones with tropical‐like characteristics using European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) reanalysis data to investigate the role of upper‐tropospheric processes and their seasonal variations in cyclone development. Our results show that cyclones occurring in September exhibit the most intense convection, with Ianos , one of the strongest medicanes on record, representing an extreme case. Using correlation coefficients between and upper‐level relative humidity () and potential vorticity (), we find that warm‐core strengthening is systematically linked to upper‐tropospheric moistening. While is generally negative, consistent with diabatic erosion/decoupling of the upper‐level PV anomaly during warm‐core growth, both its magnitude and the level of maximum anticorrelation vary across cyclones. Using back‐trajectory analyses, we investigate the presence of dry intrusions in the early and mature cyclone phases. We show that one or more dry intrusions are common in medicanes, with an intensity that is case‐dependent. Consistently, 700‐hPa equivalent potential temperature () fields show that descending trajectories correspond to low‐ filaments wrapping around the cyclone, and that a warm‐seclusion‐like structure is visible during warm‐core onset. While dry intrusions can accelerate warm‐core formation by favouring convection, the eventual warm‐core intensity depends critically on diabatic processes near the cyclone centre, as exemplified by Ianos . In fact, despite only marginally descending flows before the main tropical‐like phase, this cyclone develops the strongest warm core among the cyclones analysed. This motivates us to examine Ianos in detail using ERA5 and a Weather Research and Forecasting (WRF) simulation with a grid spacing of 3 km, which confirms the need for high resolution to represent the cyclone intensity correctly, highlights the dominant contribution of diabatic heating to cyclone deepening, and suggests a secondary role of the weak descending branch of the PV streamer.
Abstract This study investigates the structure and dynamics of simulated tropical cyclone boundary layers (TCBLs) using a moist potential vorticity () framework. in the boundary layer of tropical cyclones is related to the distribution of equivalent potential temperature () and absolute angular momentum (), and can therefore respond to air–sea transfers of moist entropy and momentum. We examine the distribution of using axisymmetric simulations of tropical cyclones and study the effects of changing both surface entropy and surface momentum fluxes on the distribution. The simulated TCBL is characterized uniquely as a region of negative with a robust and coherent layer of high‐magnitude , extending from the surface in the inner core to the top of the TCBL in the outer core. This layer is referred to as the potential vorticity minimum layer (PVML). Detailed budget analyses indicate that both diabatic heating of and frictional dissipation contribute to the generation of large negative in the inner core. On the other hand, diabatic heating is the primary mechanism responsible for the maintenance of the PVML in the outer core, with friction playing a smaller indirect role.
Abstract Nonlinear relationships between model state variables and complex observations such as radar reflectivity or satellite radiances can degrade data assimilation schemes that rely on linear assumptions and Gaussian error statistics. This study investigates likelihood tempering coupled with the ensemble Kalman filter (TEnKF) to address these nonlinearities by assimilating observations iteratively with adjusted error covariances. Using the Lorenz‐96 model and a strongly nonlinear observation operator designed to mimic the inherent challenges of radar reflectivity, we evaluate sensitivity to the number of iterations, ensemble size, and relative amount of information assimilated at each iteration. Results show that the TEnKF improves stability and performance with respect to the standard EnKF for nonlinear operators at modest computational cost. Larger improvements are found in challenging regimes such as small ensembles, sparse observing networks, and small observation‐error variance. Moreover, most of the beneficial impact of tempering is obtained with two to three iterations, thus limiting additional computational overhead. Furthermore, a comparison with adaptive observation‐error inflation reveals that, while both methods improve filter stability, the information‐preserving nature of the TEnKF yields superior accuracy in sparse observing networks.
The skill of seasonal forecasts of midlatitude atmospheric circulation is notoriously intermittent and might be modest on average. The use of seasonal forecasts for real-time applications can therefore benefit greatly from approaches providing an a priori indication of how skillful a single forecast will be at capturing circulation anomalies. This work introduces a methodology to predict the skill of such a single ensemble forecast. It consists of verifying the ensemble mean against each of its individual members before averaging, in order to provide a "perfect model" estimate of forecast skill. This methodology is applied to seasonal forecasts from the Copernicus Climate Change Service (C3S), for a selection of four variables (mean sea-level pressure, geopotential height at 500 and 200 hPa, and streamfunction at 200 hPa) characterizing the atmospheric circulation in three domains covering the Northern Hemisphere midlatitudes: Pacific and North America, North Atlantic, and Asia. Skill is defined as the Anomaly Correlation Coefficient (ACC), which quantifies how well the predicted spatial patterns of atmospheric circulation match the observed ones. Results show that the prediction of forecast skill is quite successful for upper-troposphere variables but is diversely successful in the mid-troposphere and the surface, depending on the region under study. Moreover, the capacity to predict forecast skill exhibits seasonal dependence, with generally better performance in boreal summer and winter and an even greater summer peak for the North Atlantic region. These results are very similar across the six C3S models under consideration and their multi-model combination, which is an indication of robustness.
The structure and governing processes associated with fast and slow Madden-Julian Oscillation (MJO) propagation speed are examined. On the basis of spectral analysis and three-dimensional normal-mode decomposition, we find that slow MJOs exhibit a shorter zonal scale relative to faster MJOs, in agreement with previous work. Thermodynamic distinctions between the two MJO types are then considered by evaluating four criteria that so-called moisture modes should satisfy, and by examining their column-integrated moist static energy (MSE) budget. Slow MJO events align more closely with the moisture-mode criteria than fast events, satisfying all four criteria throughout the broad Indo-Pacific warm pool, whereas fast MJOs only meet these conditions over the Indian Ocean. These results imply that moisture governs the thermodynamics of slow MJOs, while temperature and moisture play comparable roles in fast MJO thermodynamics. When examining the column MSE budget, we find that the slow MJO MSE anomalies propagate eastward primarily via horizontal MSE advection, with zonal moisture advection accounting for up to 50% of the total MSE tendency over the Indo-Pacific warm pool. In contrast, vertical MSE advection plays a larger role in fast MJOs, especially east of the Maritime Continent. Lastly, we compare the vorticity budget of fast and slow MJOs. The vorticity anomalies in both fast and slow MJOs propagate eastward via vortex stretching. However, advection of planetary vorticity opposes this vortex stretching and this process is stronger in slow MJOs. Collectively, these findings indicate that MJO governing processes vary with propagation speed and region.
An offline methodology is applied to estimate parameters of a subgrid-scale convective gravity-wave scheme using observations from constant-level balloons. The approach integrates the ensemble Kalman filter (EnKF) with an iterative method based on the expectation-maximization (EM) algorithm. The meteorological fields required for the parameterization are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) reanalysis, corresponding to the instantaneous meteorological conditions found underneath the Strateole-2 balloon observations made in the lower tropical stratosphere. Compared with optimizations with fixed parameters with an uncertainty quantification using Bayesian inference from an ensemble of simulations, our analysis demonstrates that the EnKF/EM method characterizes the launching amplitudes and altitudes of the parameterized gravity waves effectively and quantifies their uncertainties. We also show that the method can help improve the realism of the scheme: for example, by incorporating background-wave activity. By allowing parameters to vary in time, the ENKF/EM approach also makes it possible to pinpoint processes that the scheme under-represents.
The presence of Arctic clouds plays a crucial role in the evolution of the surface temperature of Arctic sea ice. However, large biases in cloud representation remain in state-of-the-art weather and climate models. In this study, we use observational data from the one-year Arctic ship campaign Multi-disciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to evaluate the Integrated Forecasting System (IFS), the global weather prediction model of the European Centre for Medium-range Weather Forecasts (ECMWF). We find seasonal biases in the cloud liquid water path, which is underestimated in winter and overestimated in summer. We show that the occurrence of supercooled liquid clouds with strong supercooling (below ) is underestimated, while the occurrence of liquid-containing clouds close to freezing temperature is overestimated. Focusing on winter, we investigate the sensitivity of supercooled liquid clouds to different model uncertainties in a single-column model setup. We find the strongest sensitivity is to uncertainties in cloud microphysics and show that reducing the assumed ice particle number concentration (within the bounds of uncertainty) reduces the ice deposition rate and improves the underestimation of supercooled liquid clouds significantly. As a result, the long-wave downward radiation at the surface improves. Positive effects on the boundary-layer structure are shown in a case study. Furthermore, we document the sensitivity of clouds to ice-particle fall speed and to small fractions of open ocean in the sea ice, which are often present in the model when satellite observations show full sea-ice cover. Both sensitivities are minor compared with the sensitivity to uncertain assumptions that affect the ice deposition rate directly. Our results can guide future model development to reduce Arctic cloud biases.
Observations from microwave temperature sounders such as the Advanced Microwave Sounding Unit-A (AMSU-A) provide some of the largest contributions to forecast skill in numerical weather prediction. Currently, AMSU-A radiances are assimilated under all-sky conditions at operational centres without accounting explicitly for spatial observation-error correlations. To mitigate the impact of unrepresented correlated errors, strategies like spatial thinning (reducing observation density) and inflated observation-error variances are commonly used. Here we present new estimates of spatial observation-error correlations for the AMSU-A all-sky systems from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office, using diagnostics from background and analysis departures. Results are presented for three different cases: when all data are considered and when data are separated by surface type and cloud cover. High spatial observation-error correlations are seen particularly for tropospheric channels (4-8) over land, with correlation length-scales ranging from 75 to 125 km. We hypothesise that these correlations originate primarily from inadequacies in the modelling of surface emissivity, surface skin temperature, clouds, or precipitation. Our findings suggest that an increase in forecast skill could be achieved by following a pragmatic strategy of increasing assimilated observation density for stratospheric channels (9-14) and all channels 4-14 over the ocean, due to the negligible error correlations in these situations. In contrast, for tropospheric channels over land, exploiting the available data fully through reduced thinning requires accounting for spatial observation-error correlations.
The Rossby wave train along the wintertime subtropical jet over the Asian continent extends from Western Europe to Japan, and it significantly influences the East Asian climate. Two primary excitation sources of the subtropical wave train are the European vorticity anomaly related to the North Atlantic Oscillation (NAO) and tropical anomalous convection associated with the Indo-Pacific Walker circulation. The wave trains excited solely by the European vorticity anomaly exhibit large amplitudes near Europe but diminish downstream. However, as they reach the Asian domain, the waves can amplify through vorticity advection linked with convective activity in the Indo-Western Pacific. The coherency of the NAO-related European vorticity and the anomalous Indo-Pacific Walker circulation plays a key role in the excitation of the subtropical wave train. To investigate their interference, statistical analyses were carried out based on reanalysis data, and additional numerical experiments were performed using a linear baroclinic model. Results show that when the cyclonic vorticity injection associated with the NAO in Western Europe coincides with the anomalous meridional divergent wind over South China associated with the enhanced Indo-Pacific Walker circulation, constructive interference occurs, leading to a significant low-pressure anomaly in the Far East and colder winters. On the other hand, when the NAO-related vorticity forcing and tropical convective activity are out of phase, destructive interference arises, suppressing the propagation of Rossby waves downstream of South Asia and ensuing a weaker impact on the East Asian climate. The findings reveal that the phase combination of the NAO-related European vorticity and Indo-Pacific Walker circulation determines the eastern extent of the wave train along the subtropical jet and its associated climate impacts on the broader Asian region.
Equatorial waves are a driver of heavy rainfall events in Southeast (SE) Asia. These heavy rainfall events can be very damaging, resulting in landslides and flooding, but remain challenging to predict. Previous studies have demonstrated the role single equatorial waves play in heavy rainfall and the usefulness of this information in aiding forecasting. Less is known about the impacts when multiple equatorial waves co-occur. Here, dynamically identified equatorial waves are used to construct a "compound" equatorial wave phase space to explore statistically how different combinations of the phases of Kelvin, equatorial Rossby, and westward-moving mixed Rossby-gravity (WMRG) waves are related to the intensity of precipitation in the SE Asia region. A demonstration of the methodology focuses on heavy rainfall in boreal winter in Peninsular Malaysia in relation to Kelvin and WMRG co-occurrence. We show that heavy rainfall is significantly increased in Peninsular Malaysia during coincident Kelvin and WMRG waves, with heavy rainfall probability increased by up to seven times relative to climatology, compared with twice when activity from only one wave type is considered. Other regions profoundly impacted by wave co-occurrence include the South Philippines, Sumatra, and Borneo; rainfall in 14 of the 15 regions examined showed significant relationships between heavy rainfall and compound wave occurrence. Moreover, it is found that in some cases the occurrence of both waves together, rather than a single wave alone, is a requirement for increased heavy rainfall probability. A table summarising the findings for all individual regions of SE Asia is produced and can aid forecasters in forecasting extreme weather events.
Extreme wildfire events are characterised by strong interactions between the convective wildfire plume and the atmosphere, often resulting in erratic and difficult-to-control fire behaviour. Previous (modelling) studies on pyroconvection have focused mostly on the plume's thermodynamics and induced circulation. The dynamic interactions between these convective plumes and mesoscale weather effects are less studied and are complex, due to the different scales involved. It therefore remains unclear how mesoscale effects, such as sea breezes, interact with a convective wildfire plume, and especially whether resolving mesoscale atmospheric motions is necessary to simulate wildfire plume dynamics. In this study, we present a set of large-eddy simulations (LESs), nested in a mesoscale simulation, of the Santa Coloma de Queralt wildfire in northeastern Spain (July 24, 2021). We study the interaction between a low-level moisture front and the wildfire plume and how this is dependent on the nesting of the LES, comparing our simulations with an in-plume radiosounding. We find that the LES nested in a mesoscale simulation produces a sharply defined density current of moisture, as opposed to a gradual moistening of the atmospheric boundary layer for the LES without the mesoscale nesting. This results in a different timing of pyrocumulus formation. Both LES setups produce wildfire plumes that are concurrent with the in-plume radiosounding. We also show how the front influences the circulations around the plume, resulting in a downwind rotor-like circulation. This case study therefore shows that the main added value from resolving the mesoscale explicitly is in the timing of pyrocumulus formation and circulation changes. The characteristics of the convective plume itself are not substantially different between the simulations.
Hailstorms pose a significant hazard, although how hail frequency may respond to climate change is still uncertain and varies by region. In addition, hail is spatially and temporally sporadic and depends on ground-based reports, so observational samples of hail events are difficult to collect. To address this issue, this study proposes a deep-learning-based hail-diagnosis model (DHDM). We developed a seven-layer convolutional neural network with residual blocks and a masking mechanism. This model enhances hail-detection capability under limited sample conditions. The results show that the DHDM has a hit rate of 80.7% and a false-alarm rate of 50.9% for hail, under the condition of a dataset constructed with a 1:4 ratio of hail to non-hail samples. Model interpretability analysis reveals that the incorporation of the masking mechanism enhances its focus on circulation patterns at 500- and 850-hPa levels, thereby improving its capability to learn key features at these critical pressure levels. This suggests that the masking mechanism acts as a regularization and data augmentation strategy that encourages the model to learn more robust and distributed representations, rather than relying on fixed local patterns. This study highlights the potential of deep learning in severe convective weather forecasting and provides a reference for integrating masking mechanisms into meteorological applications to address the issue of learning with limited samples.
This study investigates the effects of trapped lee wave interference on wave clouds over the southern Kanto region of Japan using camera images, satellite images, meteorological observations, a model analysis dataset, and numerical simulations based on Weather Research and Forecasting models. The wave clouds of interest had a NNE-SSW strike with cloud rows separated by approximately 15 km. They exhibited a lenticular irregular cloud base in the target area. The results of the numerical simulation and terrain-modification experiments suggested that the wave clouds of the observed shapes were not formed by trapped lee waves excited by individual mountain ranges alone, but by constructive and destructive interference among trapped lee waves originating from multiple mountain ranges. In particular, wave clouds developed in regions where constructive interference locally strengthened the upward motion, whereas areas where destructive interference reduced the wave amplitude lacked cloud formation, resulting in an uneven, irregular cloud base. Nonlinear interactions further influence wave behaviour in multiple ways: latent heat release within clouds strengthens vertical motion, amplifies waves, and alters their phase, whereas excessive growth triggers convective overturning and Kelvin-Helmholtz instabilities, leading to wave breaking and significant amplitude reduction. The coexistence of interference and nonlinear effects produces localised zones of amplification and dissipation, shaping complex cloud bases and spatial wave patterns.
Earth-system and weather forecasting models are moving to km-scale resolutions to provide more pertinent information to society on extreme events or the impacts of climate change. As some parametrized processes can be represented explicitly, increasing spatial resolution is expected to be beneficial for the atmosphere and oceanic components. It is not obvious that the same benefits will be achieved for land-surface models (LSMs), as landscape organizing processes start to play a role. To evaluate the consequences of increasing resolution, six LSMs driven by 3-km resolution forcings are compared with their reference simulation at 50-km resolution. These high-resolution atmospheric forcing data are developed over a region covering all catchments flowing off the Pyrenees. It is shown that these forcings capture the contrasts in atmospheric conditions between mountainous areas and valleys absent at coarser resolutions. At finer resolution, the LSMs display reduced evaporation over semi-arid catchments, which cannot be explained by differences in the atmospheric forcings. The cause has to be sought in the lack of spatial redistribution of water within the catchments. The observed diurnal amplitude of land-surface temperature shows that the models do not reproduce the local minima along rivers and in irrigated areas caused by increased evaporation. We conclude that, at km-scale resolution, lateral transfers of water that organize landscapes play an important role in predicting evaporation correctly. At resolutions of a few deca-kilometres, the contribution of grid-cell lateral flows to evaporation can be neglected. However, at higher resolutions, groundwater, riparian recharge, and human water management for irrigation need to be simulated to represent realistic spatial contrasts in the surface fluxes that drive the atmosphere. We call upon the community to invest in the development of representation of these processes in LSMs, so that they are ready for higher resolution applications.
