Quarterly Journal of the Royal Meteorological Society

Understanding precipitation growth and decay (GD) is necessary to improve the predictability of precipitation. Investigating the precipitation GD has been mostly based on an Eulerian approach, which examines the physical characteristics in a fixed coordinate system. In contrast, a Lagrangian approach provides a way to monitor the temporal evolution following the motion of the precipitation system. In this study, we employ a semi‐Lagrangian advection framework to quantitatively retrieve radar‐based GD of precipitation in South Korea. Additionally, we explore the dependence of GD on the following factors: flow direction, flow speed, and relative geographical location to the topography and land–ocean boundary. Our findings reveal that the flow direction significantly influences the spatial distribution of GD. The growth generally tends to occur on the windward side of the mountain range and vice versa on the lee side. The flow speed affects the intensity of GD. Furthermore, we examine the diurnal variability of GD. Due to strong orographic forcing on the diurnal variation, the unique geographical features of South Korea created diurnal patterns near Seoul by land–ocean breeze circulation and on the windward side of the mountain ranges (inland) by solar heating. The GD in South Korea exhibits two diurnal peaks of growth: an early morning peak over the ocean and an afternoon peak on the land. A monthly variation is also observed, with the most intense growth taking place in August. The findings of this study will aid in improving nowcasting algorithms that account for growth and decay, while also supporting forecasters in their predictive efforts.

The integral‐correcting 4DVar method (i4DVar) is an evolution of the traditional strongly constrained 4DVar method (s4DVar), which performs much better than s4DVar without increasing computational cost and complexity, and is solved using an ensemble, adjoint‐free algorithm (i.e., NLS‐i4DVar). However, like most previous ensemble‐based methods, NLS‐i4DVar also faces the paradox of using the same set of perturbed samples to approximate the background error covariance matrix and the joint tangent linear (TL) operator, making it difficult to guarantee their accuracy. To solve this problem, we divide ensemble anomalies with a (very large) shrinkage factor ω$\omega$ to ensure the validity of the TL operator approximation, while still using the original (unshrunk) samples to approximate the background error covariance matrix, which ultimately solves the above difficulty. Finally, the advantages of the newly developed algorithm are verified by numerical evaluation experiments.

Parametrisations of unresolved gravity waves used in general circulation models can be made more computationally efficient by introducing a stochastic component to the forcing. An additional advantage of introducing stochasticity is that intermittency associated with the scheme could be tuned to resemble the intermittency of observed gravity wave sources, and could therefore act to improve the physical fidelity of the scheme. Here, it is argued that using stochastic differential equations (SDEs) to drive the stochastic component provides a natural general framework to develop such schemes. The Holton–Lindzen–Plumb model of the quasi‐biennial oscillation (QBO) is used to demonstrate the flexibility of the approach. The QBO generated in a (computationally expensive) deterministic broadband multiwave simulation is accurately reproduced using a number of quite different (cheap) stochastic schemes. The method of stochastic averaging is used to prove a matching result that shows that a wide class of such schemes, driven by different SDEs, can each reproduce the deterministic QBO provided that the characteristic time‐scale τ$\tau$ of the SDEs is sufficiently short. However, each scheme has different intermittency properties: as τ$\tau$ is increased, their QBOs are shown to diverge, despite the time‐averaged source spectrum in each case remaining unchanged. The SDE framework therefore provides great flexibility to tune a stochastic parametrisation to match observed intermittencies, meaning that future parametrisations can be developed that can account for non‐steady gravity wave forcing in a physically consistent manner.

Hailstorms are destructive and dangerous phenomena that can cause large losses, motivating better understanding of their occurrence. As climate modes of variability influence temperature and moisture and hence convective instability, they offer predictive skill for hail conditions. Here, we examine relationships between hail‐prone days across Australia and the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM). Hail‐prone days were identified using a hail proxy applied to European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) data from 1979 to 2022. Hail‐prone day anomalies were correlated with strength‐of‐mode indices. Broad areas of the country's interior show increased hail‐prone days during La Niña, negative IOD, and positive SAM in spring. The relationship with IOD and SAM is significant in winter for Brisbane and to some extent for Sydney, reversing sign in summer. Anomalies increase over Western Australia's south during El Niño and positive IOD in spring. Our work highlights potential connections between climate modes and hail‐prone conditions, investigates meteorological factors behind the observed correlations, and helps us understand annual variability to improve seasonal prediction.

To improve atmospheric CO2 concentration simulations in East Asia, a data assimilation (DA)–forecast system that assimilates surface CO2 concentrations was developed by modifying the Data Assimilation Research Testbed (DART) and combining it with the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem). To verify the stability of the DA–forecast system, observing system simulation experiments (OSSEs) were conducted during January and July 2019, and the DA effects according to surface CO2 observation networks were investigated. A nature run (NR; considered as the true state), a control run (CR) without DA, and assimilation runs (ARs) with DA were conducted. The stability of the DA–forecast system was investigated in AR with evenly distributed CO2 observations to avoid the influence of observation distribution. During January and July 2019, the ratios of the root‐mean‐squared error (RMSE) to the ensemble total spread of simulated CO2 concentrations were 1.00 and 0.97, respectively. By assimilating surface CO2 concentrations, the bias and RMSE of simulated CO2 concentrations were reduced by 1.23 ppm (50%) and 1.24 ppm (39%) in January and 1.41 ppm (66%) and 1.84 ppm (47%) in July, implying that the DA was performed stably in the DA–forecast system. To investigate the DA effects according to surface CO2 observation network, four AR experiments were conducted. Based on NR, in January (July), the RMSE of CR was 4.13 (5.02) ppm and the RMSEs of four ARs ranged from 3.40 (4.24) to 3.76 (4.44) ppm. The RMSE was smaller for ARs using evenly distributed observation sites, whereas the RMSEs in regions without observations were relatively high in the ARs using concentrated observation sites. The effects of the surface CO2 concentration DA using the developed system differed depending on observation networks, implying the necessity of an appropriate surface CO2 observation network to improve the CO2 concentration simulations in East Asia.

In this work we show how to extend the deterministic physical nudging scheme in order to include two important ingredients, the model and observation‐error covariance matrices, which are common features of classical data‐assimilation schemes. The method exploits the relation between a stochastic differential equation and the evolution of its probability density via the Fokker–Planck equation. Observations are introduced by evolving the posterior probability density backward in time to obtain a so‐called smoother. To obtain a computationally feasible scheme, we used the small‐time approximation, resulting in an efficient nudging scheme built from first principles. We explored the capabilities of this new nudging method with the low‐dimensional Lorenz 1963 model and a surface quasi‐geostrophic turbulence model on a 128×128$128\times 128$ grid, with many degrees of freedom. We show that the new method is more accurate than a 3DVar at similar computational cost, and is accurate and easy to implement in the high‐dimensional system. The new scheme has the potential to be used in extremely high‐dimensional systems, because ensemble integrations and adjoint models are avoided.

This study analyzes air–sea interaction in the northeast tropical Atlantic (NETA) at intraseasonal time‐scales, focusing on the feedback between sea‐surface temperature (SST) and surface winds in July, August, and September, using observational data from 2009 to 2020 (Reynolds SST and advanced scatterometer surface winds) and European Centre for Medium‐Range Weather Forecasts Reanalysis v5 data from 2000 to 2020. The analysis reveals that anomalies in the northeastern trade winds over the NETA, often associated with an African easterly wave, result in warmer SST when winds weaken and cooler SST when they intensify. In turn, this SST anomaly modulates surface wind anomalies, generating a negative feedback loop that lasts for 2–3 weeks. A secondary positive feedback loop at the northern edge of the SST anomaly contributes to this prolonged persistence and results in a slow northward migration of the coupled SST–sea‐level‐pressure gradient pattern. A warm SST anomaly between 5° N and 15° N therefore alters the propagating pattern of easterly waves by creating a negative pressure anomaly, enhancing moisture transport convergence off the West African coast and intensifying precipitation. It potentially contributes to the seasonal northward migration of the intertropical convergence zone from early to mid boreal summer. This study highlights the importance of better understanding these air–sea interaction processes and their representation in operational forecasting models to improve weather forecasts in the western Sahel region.

High‐resolution mesoscale model simulations of two archetypal quasi‐linear convective systems producing outbreaks of three or more tornadoes in the United Kingdom are performed to determine vortexgenesis mechanisms. Type 1 events are associated with north–south‐oriented cold fronts with regularly spaced misocyclones along them. In one type 1 event, a near‐surface vortex sheet broke down into near‐equally spaced misovortices having a wavelength of about 7.5 times the width of the shear zone. Rayleigh's and Fjørtoft's instability criteria were met preceding the development of the vortices, suggesting the presence of horizontal shearing instability (HSI). Lagrangian calculations of vorticity tendency showed that parcels entered the misovortices at lower heights, acquired their vorticity via tilting, before being further enhanced by stretching as the parcel ascended. These results implied HSI was the initial mechanism for the amplification of perturbations along the vortex sheet in the type 1 event. In contrast, type 2 events are associated with west–east‐oriented cold fronts with disorganized, elongated cyclonic–anticyclonic vorticity couplets evolving into a small number of cyclonic and anticyclonic misovortices with irregular misovortices. In one type 2 event, Fjørtoft's instability criterion was not met. Lagrangian vorticity‐tendency calculations showed that parcels acquired vorticity similar to type 1 events, where parcels entered the misovortices at lower heights, acquired their vorticity via tilting, before being further enhanced by stretching as the parcel ascended. However, the magnitude of tilting was typically larger in the type 2 event. Comparing these two events showed two possible mechanisms for misovortexgenesis in UK tornado outbreaks: misovortices in type 1 events form and grow via HSI along the front, whereas misovortices in type 2 events are not due to HSI.

For the majority of data assimilation (DA) applications, a Gaussian assumption is made to model the behaviour of errors associated with the specific situation. This assumption is generally false in geoscience fields, especially for variables that are positive (semi‐)definite. The three‐dimensional variational (3DVar) data‐assimilation method was traditionally generated through Bayes' theorem with Gaussian multivariate probability density functions; however, over the last 15 years this assumption has been modified to allow for a lognormal distribution, as well as combining the Gaussian and lognormal distributions to form a mixed Gaussian–lognormal distribution. In this article, we adapt this assumption and allow these errors to be reverse‐lognormally distributed. This is done by applying the Bayesian probability framework to derive a reverse‐lognormal 3DVar cost function. With the new reverse‐lognormally distributed 3DVar, we use machine‐learning methods to detect which 3DVar method (Gaussian, lognormal, reverse‐lognormal) is suitable to minimise the errors in that region of the Lorenz 1963 model.

A persistent low‐level cloud cover (LCC) is a major climatic feature of western equatorial Africa during the long dry season (June–September). We investigate the ability of the mesoscale atmospheric model Meso‐NH at 2‐km horizontal grid spacing, forced by ERA5 reanalysis data, to simulate the LCC features. We chose a six‐day period in June 2008, presenting temporal changes in cloud cover, to better understand the atmospheric mechanism associated with the LCC formation. The main meteorological variables of the model are firstly validated extensively against tri‐hourly station data and ERA5. The LCC diurnal cycle shows an early‐morning maximum consistent with station data, then a decrease in the afternoon which matches the evolution of the stratiform cloud cover, but some of the transition to cumulus clouds is missed. Discrepancies are found with respect to satellite data, but the latter have issues of reliability. Over the ocean, Meso‐NH overestimates the LCC compared to satellite data, while ERA5 underestimates it. Over land, Meso‐NH enabled the depiction of subregionally coherent LCC dynamics in relatively good agreement with observations, though discrepancies often occurred on individual days. This result does not depend on methodological issues such as the cloud overlap assumption or the low‐level cloud fraction threshold retained to define cloudy observations. Dynamical analysis suggested that local and regional‐scale wind direction changes, related to the synoptic weather pattern, are key to the maintenance or clear‐up of the LCC in the afternoon. The LCC persistence is associated with surface westerlies advecting cooler maritime air increasing the lower‐tropospheric stability. Moreover, the moistening of the top of the Planetary Boundary Layer promotes the formation of the LCC. These local atmospheric changes are driven by a mid‐tropospheric easterly wave further north, a weakening of the Kalahari High over southern Africa and the weakening of the transequatorial flow.

We propose a local identification method for mixed Rossby–gravity (MRG) waves, which include both eastward mixed Rossby–gravity (EMRG) waves, also known as n=1 n=1 eastward inertia–gravity (EIG1) waves, and westward mixed Rossby–gravity (WMRG) waves. The method identifies MRG waves at specific longitudes using only a pair of EMRG and WMRG meridional structures. A criterion is proposed to estimate WMRG wave dominance within the identified MRG wave field. Propagation of WMRG waves can then be studied at specific longitudes. The method has the main advantages of (1) identifying MRG waves in data from a single longitude and pressure level without need for global data and (2) requiring very low computational cost to apply. Finally, it is found that MRG waves alone explain more than 90% of the variance of the latitudinal average of the equatorial meridional wind at 850 hPa and more than 95% at 200 hPa.

The relationship between meridional displacement of the Asian jet and Indian summer monsoon (ISM) is examined in the observations and 40 state‐of‐the‐art Coupled Model Intercomparison Project, phase 6 (CMIP6) historical simulations. The southward displacement of the Asian jet (SWDAJ) is found to be linked with reduced rainfall over central and north India including monsoon trough region in both the observations and most of the CMIP6 models. In general, models are able to simulate the characteristics of the SWDAJ during summer with some differences in zonal asymmetry. Detailed analysis elucidated two observed pathways that connect the SWDAJ with ISM rainfall variability. The first is the west Asian jet–easterly jet pathway, where the SWDAJ is linked with the southeastward shift of the South Asian high, contributing to the weakening of tropical easterly jet. This pattern aligns with changes in low‐level ISM circulation and contributed to reduced rainfall over India. The west Asian jet–easterly jet pathway is well represented by most of the CMIP6 models. The second pathway involves the East Asian jet–Pacific Japan pattern, in which the SWDAJ over East Asia strengthens the western North Pacific anticyclone through the barotropic–baroclinic coupling. The anomalous westward extended easterlies originating from the western North Pacific anticyclone hinder the southwesterly monsoon flow and suppress the rainfall. It is found that CMIP6 models largely failed to capture the East Asian jet–Pacific Japan pathway, likely due to the models' tendency to overestimate the simulation of the Indian Ocean dipole, and westward extension of El Niño‐related sea‐surface temperature anomalies in most of the models. Further, the combined influence of El Niño and the Pacific decadal oscillation and El Niño alone on the SWDAJ and ISM rainfall is also discussed in detail. Results obtained in this study are useful to understand the ability of CMIP6 models in representing the midlatitude circulation associated with ISM rainfall variability.

Low‐level jets (LLJs) are sensitive to continental‐scale pressure gradients. Soil moisture influences these gradients by altering turbulent flux partitioning and near‐surface temperatures, thereby affecting LLJ characteristics. The Turkana jet, a strong southeasterly LLJ flowing through a channel between the Ethiopian and East African Highlands, is an important feature of the East African water cycle. Previous work has shown that the jet is sensitive to soil‐moisture‐induced pressure gradients driven by the Madden–Julian oscillation. Here, we build on this finding through using convection‐permitting UK Met Office Unified Model simulations to isolate the role of soil moisture in shaping jet characteristics. Modelling experiments reveal that the Turkana jet is highly sensitive to soil‐moisture‐induced temperature gradients across the channel's exit. Prescribing realistic dry soils intensifies the local surface‐induced thermal low and strengthens the jet. A maximum jet sensitivity of up to 8m·s−1$8\kern0.3em \mathrm{m}\cdotp {\mathrm{s}}^{-1}$ occurs when comparing dry and wet surface states within 750 km downstream of the exit, highlighting the significant influence of soil moisture on jet dynamics, given typical speeds of 8–14m·s−1$14\kern0.3em \mathrm{m}\cdotp {\mathrm{s}}^{-1}$. The impact of soil moisture on the jet is most pronounced when synoptic forcing is weak and skies are clear. Notably, despite a substantial impact on LLJ strength, we find a minor sensitivity of the vertically integrated moisture transport. We speculate that this minimal sensitivity is linked to model errors in the representation of boundary‐layer turbulence, which affects midtropospheric moisture and the strength of elevated nocturnal inversions. This study highlights that the Turkana channel is a hotspot for surface–jet interactions, due to the strong sensitivity of surface fluxes to soil moisture near a topographically constrained LLJ. Future research should continue examining surface‐driven predictability, particularly in regions where land–atmosphere interactions influence dynamical atmospheric conditions, and evaluate such processes in weather prediction models.

This study conducted idealized simulations to compare stratospheric gravity waves (GWs) generated in two‐dimensional (2D) convective systems with those in three‐dimensional (3D) convective systems. Our results indicate that the efficiency of GW production, measured by total vertical momentum and energy fluxes normalized by rain rate, is significantly higher in 2D systems than in 3D systems. This increased efficiency is attributed to greater convective organization within the 2D systems. By analyzing the domain‐averaged entrainment rate, we identified a strong correlation between convective organization and GW production efficiency, which may be useful for GW parameterizations in climate models. Additionally, we observed a distinct component in the ground‐relative ω–k power spectrum with relatively high frequency and a slope matching local mean flow speed. This feature demonstrates the presence of northward‐ and southward‐moving waves that propagate perpendicular to the zonal flow, which also cannot exist in 2D simulations. The results show the importance of using 3D simulations to fully understand the behavior and impact of GWs generated by atmospheric convection.

Accurate meteorological forecasts from the surface to troposphere layers are crucial for dust storm predictions, as even small uncertainties in meteorological conditions can influence the transportation of dust particles, thereby significantly affecting dust storm forecasts. Typically, a greater quantity and higher quality of meteorological observations result in more accurate meteorological outcomes. However, meteorological stations, especially the stations which monitor tropospheric meteorological variables, are sparsely distributed and may not be sufficient for high‐quality meteorological forecasts. To address this shortfall, this study investigates the sensitive areas for target observation to enhance meteorological forecasts for dust storm events that struck the Beijing–Tianjin–Hebei (BTH) area from 2021 to 2023, using the Conditional Nonlinear Optimal Perturbation (CNOP) method, which fully considers the impact of nonlinearity. For comparison, the First Singular Vector (FSV) method, which is widely used in operational target observation field campaigns, is also employed to identify the sensitive areas. Results show that although the sensitive areas identified by the two methods are both distributed in the northwest direction of the BTH region, the FSV‐based sensitive areas are much closer to the BTH region. By conducting observing system experiments for each dust storm event, we verified numerically and explained physically the advantages of CNOP in determining the sensitive areas in target observation. The result highlights the importance of considering nonlinearity when identifying the sensitive areas for target observation and may provide a theoretical foundation for establishing upper‐air radiosonde sites or planning practical field observation campaigns.

The intraseasonal variability (ISV) of the stratiform cloud cover over Western Equatorial Africa (WEA) is analysed during the dry season (June–September, JJAS 1971–2019). Each JJAS daily sequence of a regional‐scale index of the stratiform cloud cover could be assimilated as a red‐noise process, without any significant recurrent periodicities. At local scale, cloudier conditions than usual are preceded by and synchronised with sustained easterly wind and warm anomalies near the top (850 hPa) and above the stratiform cloud deck, indicating higher stability. Anomalous easterlies or northeasterlies bring also anomalously moist air from either the Congo Basin or the West‐African rainbelt region to WEA. At low levels (<850 hPa), there is a clear switch between antecedent warm easterlies and synchronous cool and dry westerlies from the cold‐tongue area over the equatorial Atlantic. This switch may reflect a negative feedback loop, operating on a short time‐scale (i.e., ˜3–5 days) and involving low‐level thermal and geopotential gradients, zonal winds between the cold‐tongue area and the Congo Basin, as well as the deep convection over the latter region. Kelvin waves appear to be a possible trigger of this loop, which could be sustained internally. Another main process operates at longer time‐scales (i.e., ˜6–10 days) and involves a near‐standing Rossby wave over the South Atlantic and adjacent southern Africa. A ridge over the central South Atlantic and downstream trough over southern Africa lead to an overall strengthening of the St. Helena high, and increased thermal and geopotential gradients between the southeast South Atlantic and southern Africa. The ridge–trough couple is also related to an anomalous warming over equatorial Africa in the middle troposphere associated with increased lower‐tropospheric stability over WEA a few days later. All these mechanisms contribute to the knowledge regarding the intraseasonal atmospheric variations over equatorial Africa during boreal summer.

The complexities of convection–circulation coupling challenge understanding of the tropical atmosphere. This coupling is manifested in the vertical component of vorticity, which both aids and is modulated by convection. This study, for the first time, investigates the structure, maintenance, and propagation of vorticity associated with precipitating convection at various intraseasonal timescales (low‐frequency (LF), high‐frequency (HF), and synoptic‐scale events) for global tropical ocean basins during the boreal summer. Examining the vertically resolved vorticity budget for the European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), we show that the vorticity associated with precipitating convection intensifies progressively and becomes vertically uniform away from the Equator. For convective events closer to the Equator, vorticity is weakly associated with rainfall both temporally and spatially. In contrast, for convection sufficiently away from the Equator, rainfall and vorticity are spatially collocated and temporally in phase. Larger values of absolute vorticity and consequently higher boundary‐layer vortex stretching drive this stronger association farther away from the Equator. Maintenance of a vertically uniform vorticity structure is achieved by boundary‐layer vortex stretching and convection‐induced vertical advection of vorticity into the free troposphere. Despite quantitative differences, these findings hold across global ocean basins, and the dominance of vortex stretching and vertical advection persists across timescales. In contrast, the propagation characteristics of these vortices differ. LF events primarily propagate northward, while HF and synoptic events move northwestward. The mechanism for the northward propagation of LF vorticity in the Bay of Bengal is latitude‐dependent. Consistent with previous theories, vortex tilting makes substantial contributions to the propagation of vorticity close to the Equator. However, a different mechanism involving enhanced contributions from horizontal advection dominates when convection is farther poleward. This systematic evolution of convection–circulation coupling across timescales provides an important benchmark for covariation of precipitation, vertical vorticity, and velocity in climate models.

Prediction of significant changes in tropical‐cyclone (TC) intensity, particularly the early‐stage initial intensification, has been a long‐standing challenge. Because most TCs are born and develop over tropical oceans with limited in‐situ observation networks and infrequent low Earth‐orbiting satellite overpasses, geostationary satellite observations often provide the sole source of information on the TC lifecycle. This study examines the impact of assimilating radiances in clear and cloudy regions from the latest generation of NOAA's Geostationary Operational Environmental Satellites (GOES‐16) on the prediction of TC intensification onset in the 2017 hurricane season. It is found that assimilation of all‐sky satellite radiances made a significant contribution to the forecast improvement of early‐stage TC intensification onset. This study highlights the potential for all‐sky satellite radiance assimilation to improve the representation of the inner‐core structures of TCs, resulting in more accurate prediction.

This study numerically investigates the impact of local topographic relief on the three‐dimensional near‐surface wind field over actual hilly terrain. The numerical simulations are performed with a computational fluid dynamics (CFD) code using the Reynolds‐averaged Navier–Stokes equations over a hilly island off the southeast coast of China. A novel evaluation method which selects ERA‐5 reanalysis data as the background wind field to match the CFD inlet is proposed to validate the performance of the simulated wind field. The assessment results show that the simulated CFD wind field exhibited satisfactory performance, as indicated by the small values of the root‐mean‐squared errors of the wind speed (0.40 m·s⁻¹) and wind direction (16.31°). Variations in wind speed ratios (S) with different slope angles and horizontal slope lengths are examined and compared with current building load codes. It is found that the wind speed acceleration (S > 0) and deceleration (S < 0) are more pronounced near the surface than at upper levels, and the vertical range of the simulated wind field influenced by the actual hilly terrain far exceeds that considered in all load codes. At a given horizontal slope length, the horizontal wind speed ratio first increases and then decreases after reaching a critical slope angle in both uphill and downhill conditions. The current load codes are suggested to be optimized for areas with steep slopes and gentle downhill slopes.

Within a meso‐γ‐scale convective storm, dynamic processes play a pivotal role in extreme rainfall production. However, there are still large unexplained gaps in understanding the effects of dynamic processes on the generation of extreme short‐term rainfalls. In this study, a nocturnal rainfall event with an extreme hourly rainfall (EHR) of 184 mm on 7 May 2017 over the coastal city of Guangzhou is examined based on cloud‐permitting simulations, focusing on the generation of the EHR. Results reveal that the EHR is featured by obvious horizontally delivered rainwater (qr) from the front to the rear within a meso‐γ‐scale convective storm. The horizontally delivered qr from the front of the storm overlayed on the qr produced by cloud microphysical processes locally overhead in the rear of the storm, leading to a deep qr layer with values over 4 g·kg⁻¹ at the lowest 0–4 km levels above the ground. Thus, huge qr poured down in a short time, resulting in the EHR. According to statistical results, at least 80 mm qr was provided by horizontal delivery for the majority of grid points with hourly rainfall over 120 mm. This dynamic delivery mechanism is further confirmed by a trajectory analysis of raindrops. We argue that this mechanism may play a decisive role in EHR formation in particular scenarios while admitting that EHR can also be produced sometimes mainly via cloud microphysical processes. The formation mechanism of EHR proposed herein may help further understand and forecast localized extreme short‐term rainfall.

While the positive effect of trees on thermal comfort is well‐established, particularly in urban street canyons, their impact on air quality remains questionable, especially in the case of pollutants emitted by heavy traffic at the pedestrian level. Complex microscale models of an urban boundary layer with a high spatial resolution (down to 1 m) enable a deeper understanding of most processes at street‐level scale and can simulate selected variables related to air quality and bio‐meteorology with high precision and fidelity. In this study, scenarios with different percentages of tree coverage of two streets were simulated under different atmospheric stratifications to investigate the problem. Real geography and quasi‐real meteorology were used as a background. Results of the Parallelized Large‐eddy Simulation Model (PALM) model simulations, which utilised a large‐eddy simulation (LES) core, showed the spatio‐temporal variability of the thermal comfort and dust concentration at the pedestrian level. The findings indicate that the effect of trees on the local microclimate is crucial and complex and cannot be omitted during the planning of urban mitigation measures. The study demonstrates a notable improvement in thermal comfort, with a significant decrease in the thermal index in shaded areas beneath trees during the hottest part of the day, as well as a cooling effect of urban greenery just after sunset. However, the analysis also revealed a significant downside: in narrower streets, PM10${\mathrm{PM}}_{10}$ concentrations increased by more than 100% compared with tree‐free scenarios. The slowdown and vertical shift of the primary vortex within the street caused by the trees can mostly explain the changes in pollution dispersion. This indicates a potential trade‐off between thermal comfort and air quality in densely built urban environments.

Numerical weather prediction is becoming increasingly reliant on the assimilation of cloud‐affected satellite observations. Their assimilation implicitly linearizes nonlinear observation operators in ensemble Kalman filters. The linearization causes the posterior to deviate from its linear approximation, which is often used for analysis verification. We investigate the linearization error for visible and infrared radiances in the ensemble adjustment Kalman filter (EAKF) using observing‐system simulation experiments (OSSEs). We found that increments can be detrimental for small first‐guess departures, but they are beneficial on average. The increments were typically about half of their linear approximation. Similarly, the ensemble spread reduction was smaller than its linear approximation, and sometimes negative (spread increase), but overall the mean nonlinear variance adjustment was consistent with the mean nonlinear squared error reduction. Lastly, the linear approximation overestimated the analysis mean absolute error (MAE) reduction by 37% for visible reflectance and 71% for infrared brightness temperature. Thus, the linear approximation of the posterior of observed variables, such as satellite radiances, should not be used for verification.

Despite the rapid development of global‐storm‐resolving models, computational expense hampers widespread deployment of these for operational forecasting. Thus, regional models at convection‐permitting resolutions still need to be deployed to take advantage of explicitly representing smaller‐scale processes that improve the forecast. Often, the choice of domain size is made subjectively, despite both domain size and location with respect to prevailing meteorology significantly impacting how constrained the regional model is to its driving model. This has implications on error growth, upscale impacts of mesoscale variability, along with ensemble spread. Here, we introduce a novel diagnostic designed to characterise lateral boundary spin‐up by quantifying the age of air, or the time since the air entered the model through the lateral boundaries. We apply this diagnostic to a variety of case studies over regional domains in Australia, and contrast this to a larger pan‐Australia domain, demonstrating that larger‐domain models exhibit more realistic atmospheric structures and reduced spin‐up at the boundaries, directly correlated with the age‐of‐air metric. Additionally, we show that large‐domain models demonstrate quicker spin‐up of mesoscale kinetic energy and subsequent upscale energy growth, evidenced by power spectral analysis. We further generalise the age‐of‐air diagnostic by applying it to reanalysis, providing climatological perspectives of the age of air which aid future applications such as characterising error growth in regional versus driving models in different weather regimes, and determining optimal time scales to blend regional and global model fields in data assimilation.

Heavy precipitation events (HPEs) are high‐impact weather phenomena, the prediction of which over complex terrain remains a substantial challenge for the scientific community. This work focused on the bias in prediction of HPEs over the Sichuan Basin (SCB) in China in the summers of 2019–2022. Three synoptic weather patterns (SWPs) were identified using a modified T‐mode principal components analysis of 37 HPEs with the western Pacific subtropical high (WPSH) located to the southeast (WPSH‐SE), east (WPSH‐E) and south (WPSH‐S) of SCB. We found that the position of the WPSH could substantially influence the occurrence of HPEs among the two major SWPs (WPSH‐SE, WPSH‐E). To further understand HPE prediction bias, we compared observed precipitation with that forecast by the operational numerical weather prediction (NWP) mesoscale model of the China Meteorological Administration (CMA‐MESO). Results revealed that overestimation of the precipitation area occurred in the morning, whereas underestimation of the precipitation amount occurred at night, with minimal differences between the two major SWPs. Additionally, the precipitation bias of both main SWPs was lower during the afternoon (0400–0800 UCT, i.e. 1200–1600 Beijing time). Further analysis revealed a strong relationship between the integrated water vapor flux deviation and the precipitation bias in the SCB. Major contributors to this deviation were identified as the overestimated wind speed and clockwise deviation in the wind direction in the boundary layer over the mountainous Yunnan–Guizhou Plateau, possibly attributable to lack of subgrid orographic drag. The findings of this study contribute to better comprehension of the relationship between HPEs and low‐level winds over complex terrain, thereby establishing a foundation for further improvement in the subgrid orographic and boundary layer schemes of operational NWP models.

Feng Yun 3G (FY‐3G) spaceborne Ku‐band radar observations were assimilated for the first time using one‐dimensional (1D) Bayesian retrieval and a three‐dimensional variational (3D‐Var) method for a rainfall event on 28 July 2023, in Chongqing, China. Ground‐based radars were used to validate the spaceborne radar observations, and comparisons using the volume match method demonstrate that the FY‐3G Ku‐band radar observations are reliable. In this study, a spaceborne radar forward operator based on the T‐matrix method is used, and the melting and attenuation processes are also taken into account. Pseudo‐observations of relative humidity profiles were retrieved from FY‐3G Ku‐band radar reflectivity, and subsequently assimilated. In the 1D Bayesian retrieval procedure, not only vertical but also horizontal similarity is considered. Comparison with radiosonde observations indicates that this method is capable of retrieving reasonable relative humidity profiles. By assimilating pseudo‐observations, the forecast of the rainfall event by the China Meteorological Administration Mesoscale model (CMA‐MESO) has been improved. The pattern and magnitude of the echo more closely matched the observations after assimilation and the forecasting improvement was maintained for approximately six hours. The Critical Success Index (CSI) scores increased, although the Frequency Bias (FBIAS) scores also increased. Although there was an increase in positional deviation over time, the hourly precipitation patterns closely match the rain gauge observations after assimilation. It was found that the humidity in the east and southeast of the rainfall region increased, accompanied by a cyclonic pattern in the wind increment after assimilation. These changes are beneficial for the development and maintenance of the convective system. This study proves the value of assimilating FY‐3G Ku‐band radar in improving the forecast.