High-resolution NWP Research Articles

Numerical investigation of the Pedrógão Grande pyrocumulonimbus using a fire to atmosphere coupled model

Understanding the development of fire-generated thunderstorms in mega fire events is important given their high impact on the evolution of the fire fronts, where the fire spread becomes highly unpredictable and difficult to suppress. This study aims to investigate numerically the influence of strong pyro-convective activity on the local atmospheric conditions by means of a numerical simulation based on the coupled Meso-NH/ForeFire code. To our knowledge, it is the first time that the effect of wildfire spread on the local atmospheric conditions is accounted explicitly in a high-resolution NWP model to investigate pyro-convection activity. More specifically, we study numerically the Portuguese Pedrógão Grande mega fire, which was one of the most destructive and deadliest wildfire hazards affecting the Mediterranean region in the recent years. The spatio-temporal propagation of the wildfire was assigned a priori on the basis of the official investigation's reports, while the impact of the forced fire evolution and of the ensuing heat and water vapour emissions on the local atmospheric conditions is accounted explicitly. The simulation, configured with very-high spatial and temporal resolutions, was capable of resolving the intense convective column reaching the upper troposphere and the fast development of the associated cloud system. The numerical fire produced intense updraughts with vertical velocities above 15 m/s, whereas the associated pyroCb cloud was composed by five different hydrometeor species along the main convective column and reached an altitude of 10 km. It is remarkable that the numerical experiment reproduced phenomena occurring at a fine scale related to cloud microphysics, such as very-localized outflows. This study, based on a coupled numerical simulation, was capable of illustrating in detail the development of a pyroCb cloud from strong pyro-convective activity.

Evaluation of Environmental Studies using COPRAS Method

This broad area of research encompasses both the built environment and the natural environment, as well as their interactions. Environmental studies look at how people and the environment interact. Environmental studies examine transdisciplinary environmental concerns from a social, legal, managerial, and scientific perspective. Environmental issues, management, and policy are among the subjects examined in environmental studies. There is a need for strategies to lessen negative effects and encourage more sustainable activities due to the impact that humans have on their physical environment. To better comprehend the causes of environmental issues, students investigate the links between science and sociology. Ecology comes in various forms, including those related to biology, sustainability, oceanography, and marine biology. A career in environmental research can be extremely rewarding. You'll not only be employed in a sector that positively impacts the entire globe, but you'll also have the opportunity to make a good living. But you need the right education to enjoy this wonderful career. Environmental studies teach us how important our environment is, as well as how to manage natural resources more wisely and live sustainably. Understanding how organisms behave in their natural environments and how they interact with one another in populations and communities is beneficial. Although the terms "ecology" and "environmental science" are frequently used interchangeably in everyday speech, technically, ecology only refers to the study of organisms and their interactions with one another and with their surroundings. Critical environmental resource preservation and protection, as well as the preservation of priceless natural and human-made heritage, are vital for the wellbeing of society and the provision of livelihoods. COPRAS (Complex Proportional Evaluation is proposed to evaluate the possible maintenance strategy. Linguistic terms are used to evaluate the ratings and weights. The rankings of the alternatives are COPRAS Global NWP, High-resolution NWP, Hydrology, Agricultural Meteorology, Climate TOPC is Alternative and Uncertainly goal, uncertainly threshold, Spatial resolution goal, Spatial resolution threshold is evaluation. Global NWP, High-resolution NWP, Hydrology, Agricultural Meteorology, Climate TOPC. Uncertainly goal, Uncertainly threshold, Spatial resolution goal, Spatial resolution threshold. Climate-TOPC gets the first rank whereas Global NWP has the Lowest rank.

A Metamodel-Based Optimization of Physical Parameters of High Resolution NWP ICON-LAM over Southern Italy

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window.

Effects of Distant Organized Convection on Forecasts of Widespread Clear-Air Turbulence

Abstract Two cases of observed widespread moderate-or-greater (MOG) clear-air turbulence (CAT) in different synoptic patterns are investigated using a nested high-resolution NWP model. Both of these cases occurred in confluent entrance regions of upper-tropospheric–lower-stratospheric (UTLS) jet streaks, where large-scale anticyclonic outflow from distant organized moist convection strengthened the UTLS jet. Both the strength and vertical sharpness of the resulting jet influence the altitudes of MOG turbulence and the details of simulated turbulence onset mechanisms. In a strong and narrow UTLS jet downstream of a weak synoptic ridge, MOG turbulence arises from Kelvin–Helmholtz (KH) waves that overturn in opposite directions on the vertical flanks of the jet. In broader UTLS jets, MOG turbulence arising from KH waves was favored in strong vertical shear layers beneath the wind maximum, but was inhibited above it due to static stability increases near the tropopause. However, vertically propagating internal gravity waves initiated above KH wave breaking beneath the UTLS jet amplify within the lower stratosphere above the jet, constituting another possible source of turbulence. Turbulence onset mechanisms were often apparent in simulations with minimum horizontal grid spacings of Δx = 1 km. However, amplitudes of the associated grid-resolved vertical motions were unreliable when compared with simulations having minimum horizontal grid spacings of Δx = 1/3 km. In spite of this, turbulence forecasting systems driven by input from coarser-resolution operational NWP models are demonstrated to provide good diagnoses of this type of convectively influenced CAT when the NWP model accurately forecasts upstream convection. Significance Statement In this study we document the role of distant convection on observed widespread strong clear-air turbulence near upper-level jet streams in different weather patterns. Results from nested NWP model simulations, together with similar examples from previous case studies, suggest a strong association of widespread turbulence with jet stream enhancements related to outflow from upstream convection. We also demonstrate how model horizontal grid spacings of <1 km are required to adequately resolve common turbulence onset mechanisms (e.g., Kelvin–Helmholtz instability, gravity wave breaking). Nevertheless, examples are provided showing that turbulence forecasting systems driven by lower-resolution operational NWP models can provide potentially valuable guidance on these widespread turbulence events when those models accurately forecast the timing and locations of upstream convection.

A Coherent Approach to Evaluating Precipitation Forecasts over Complex Terrain

Precipitation forecasts provided by high-resolution NWP models have a degree of realism that is very appealing to most users of meteorological data. However, it is a challenge to demonstrate whether or not such forecasts contain more skillful information than their lower resolution counterparts. A verification procedure must be based on equally detailed observations that are also realistic in areas where ground observations are not available and remote sensing data can only increase the accuracy of the location of rain events at the cost of decreased accuracy in estimating the amount of rain that has actually reached the ground. Traditional verification methods based on station or grid point comparison yield poor results for high-resolution fields due to the double penalty error that is attributed to finite space and time displacement that such methods do not account for. A complete approach to evaluating precipitation forecasts over complex terrain is suggested. The method is based on realistic gridded precipitation observations generated by an interpolation method that uses long climate data series to determine the geographical characteristics that this parameter is best correlated with as well as remote sensing estimates as background information to cover the areas where observations are insufficient. Spatial verification methodologies are subsequently applied to a convective event that accentuate the relative skill of high-resolution COSMOGR forecasts in revealing characteristics in the precipitation patterns such as structure and intensity.

A Sensitivity Study on High Resolution NWP ICON—LAM Model over Italy

In this work, the results of a sensitivity study based on ICON-LAM simulations at 2.5 km of spatial resolution over the Italian area, driven by ECMWF IFS global data, are presented. The main aim is to provide a contribution to the selection of suitable parameterization schemes that result in more effective for a proper representation of the Italian climate features. Model evaluation was conducted in terms of the air temperature and precipitation for three subregions, comparing a set of 13 simulations against SCIA and E-OBS standard datasets. In addition, evaluation was also conducted against selected data stations scattered over the Italian area. We found that the ICON-LAM model was able to provide a good representation of the temperature over Italy, whereas non-negligible biases were observed for precipitation in certain regions. The model proved to be sensitive to changes in physical parameterization schemes. In particular, we found that the explicit treatment of deep convection and the “clouds as in turbulence” scheme for cloud cover allowed for a better representation of precipitation in the summer over the Alpine region. The single moment scheme is currently the best option for cloud microphysics.

Обзор методов пространственной верификации и их применение для ансамблевых прогнозов

State-of-the-art high-resolution NWP models simulate mesoscale systems with a high degree of detail, with large amplitudes and high gradients of fields of weather variables. Higher resolution leads to the spatial and temporal error growth and to a well-known double penalty problem. To solve this problem, the spatial verification methods have been developed over the last two decades, which ignore moderate errors (especially in the position), but can still evaluate the useful skill of a high-resolution model. The paper refers to the updated classification of spatial verification methods, briefly describes the main methods, and gives an overview of the international projects for intercomparison of the methods. Special attention is given to the application of the spatial approach to ensemble forecasting. Popular software packages are considered. The Russian translation is proposed for the relevant English terms. Keywords: high-resolution models, verification, double penalty, spatial methods, ensemble forecasting, object-based methods

A Square-Root, Dual-Resolution 3DEnVar for the AROME Model: Formulation and Evaluation on a Summertime Convective Period

AbstractA three-dimensional ensemble-variational (3DEnVar) data assimilation algorithm has been developed for the high-resolution AROME NWP system. Building on previous work on 3DEnVar for AROME, we describe a formulation of the 3DEnVar that is based on the traditional square-root preconditioning. The localization may be performed either in gridpoint or spectral space, and allows for cross-covariances between surface pressure and the three-dimensional variables. The scheme has capacity for dual resolution, with the ensemble running at a lower 3.2-km spatial resolution than the deterministic AROME running at 1.3 km. This formulation is compatible with the variational bias correction scheme used in AROME. Hybrid covariances are implemented with climatological covariances at 1.3-km resolution being combined with ensemble perturbations that are interpolated to high resolution on the fly in the computation of the gradient. Hybrid 3DEnVar has an increased computational cost compared to 3DVar, which is partly mitigated by the use of dual resolution and the adoption of a flexible convergence criterion in the minimization. To get the full benefit from the ensemble scheme, it is recommended 1) to increase ensemble size from 25 to 50 members and 2) to decrease the localization length scale for the benefit of high-density radar observations. With those changes, the 3DEnVar outperforms the operational AROME-France 3DVar by a significant margin on the first 12 h of forecast range, as evidenced by a 3-month summer experiment. Finally, a case study reports on the improved prediction of heavy rainfall that frequently occurs in the Mediterranean region.

Seamless Multimodel Postprocessing for Air Temperature Forecasts in Complex Topography

AbstractStatistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multimodel EMOS approach (“mixed EMOS”) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8%–12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF); however, calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single-model postprocessing, it also enables us to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity.

Densification of the Ground-Based GNSS Observation Network in the Southwest Indian Ocean: Current Status, Perspectives, and Examples of Applications in Meteorology and Geodesy

In the frame of the research project IOGA4MET, a new ground-based GNSS network is being deployed in the Southwest Indian Ocean (SWIO) since November 2017. This new network opens up for new research opportunities in geodesics and atmospheric sciences in this area regularly exposed to natural hazards, and significantly expands the International GNSS System (IGS) network, which only includes eight stations so far in the SWIO. After a description of the current status of IOGA4MET, examples of regional applications in meteorology (climatology and numerical modelling) and geodesy (monitoring of ground deformations) are presented using data collected in northern Madagascar and small neighboring islands in 2018-2020. The observed water vapor cycle is shown to exhibit different characteristics and important variability at all locations. At the seasonal scale, maximum IWV values are similar everywhere, minimum values decrease away from the equator and a sharp IWV gradient can be noticed during the wet-dry transition period. At this time scale, the seasonal IWV cycle analyzed with the high-resolution NWP system AROME-IO matches observations with little discrepancies . At the diurnal scale, IWV characteristics show different behavior on each side of Madagascar, which emphasizes the important role of this island in driving the regional circulation. Finally, examination of ground displacements measured by GNSS stations in relationship with the ongoing Mayotte seismic-volcanic crisis indicates that ground deformation associated with the current off-shore volcanic activity east of Mayotte does not extend up to 250 km, or is currently too low to be detected in very far field.

Dust induced radiative perturbations during an episode of long-range dust transport over Delhi, India: a high-resolution regional NWP model study

Dust-induced modifications to the radiative fluxes/heating during a light-blocking haze episode over Delhi, India, 7–9th Nov 2017—triggered by long range transport from a severe Saudi Arabian dust storm—have been investigated with a regional NWP model, NCUM-R with prognostic dust-radiation feedbacks. The study employs ‘Double Radiation Calls’, wherein parallel runs of the radiation scheme ‘with’ (prognostic) and ‘without’ (diagnostic) dust radiative effects—while prognostic fields drive the forecast—isolate the dust-induced perturbations. The forecasted dust optical depth agreed spatially with the AOD from MODIS with Angstrom Exponent > 0.5, indicating that the dust was well mixed with the fine mode anthropogenic aerosols upwind. The ‘downward shortwave (SW) flux’ was diminished (upto − 12.9 Wm−2) in layers sampling (i) near-surface (L1Avg), (ii) well-mixed layer within the planetary boundary layer (PBL, L2Avg) and (iii) free-troposphere (FT, L3Avg). Dust-induced ‘Solar heating’ dominated in FT (upto 9.5 × 10–7 Ks−1) and the patches below (in L2Avg) exhibited a cooling, leading to thermal dipoles. The ‘upward longwave (LW) flux’ in FT was reduced and ‘LW heating’ prevailed in all levels—peak (2.5 × 10–6 Ks−1) in L2Avg—along with well-defined cooling zones in L1Avg. The dust–radiation interaction in turn influenced the boundary layer meteorology, manifested as (i) shallow PBLs that spatially correlate with dust-induced cooling of the boundary layer column, (ii) enhanced surface humidity and (iii) reduced visibility. The study is an instance of prognostic dust-radiation feedbacks improving the skill of NWP models in dust-laden regions.

Use of radar rainfall to model deposition of radionuclides

Accurate precipitation information is necessary for modelling the deposition of radionuclides following an accidental release of radioactive material to the atmosphere. The increase in resolution of numerical weather prediction models (NWP) has led to precipitation which looks more realistic but performs poorly when compared to observations using traditional grid-point statistics. Dispersion models have the ability to use precipitation information derived from radars. This study looks at the differences in estimates of air activity and deposition when precipitation information from a high resolution NWP is replaced by precipitation information from radar. Using a hypothetical release and a year's worth of meteorological data the results show that in around a third of cases there is a significant difference in the amount of deposition estimated when NWP precipitation is used compared to cases where radar precipitation is used. The results also show a small number of cases where air activity concentrations vary significantly between the runs.

Evalution of the TSUNM3 high-resolution mesoscale NWP model

Evalution of the TSUNM3 high-resolution mesoscale NWP model

Assessment of Doppler Radar Radial Wind Observation Quality from Different Echo Sources for Assimilation during the Sydney 2014 Forecast Demonstration Project

AbstractA new quality control system, primarily using a naïve Bayesian classifier, has been developed to enable the assimilation of radial velocity observations from Doppler radar. The ultimate assessment of this system is the assimilation of observations in a pseudo-operational numerical weather prediction system during the Sydney 2014 Forecast Demonstration Project. A statistical analysis of the observations assimilated during this period provides an assessment of the data quality. This will influence how observations will be assimilated in the future, and what quality control and errors are applicable. This study compares observation-minus-background statistics for radial velocities from precipitation and insect echoes. The results show that with the applied level of quality control, these echo types have comparable biases. With the latest quality control, the clear air observations of wind are apparently of similar quality to those from precipitation and are therefore suitable for use in high-resolution NWP assimilation systems.

Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations

The UK Met Office Unified Model (UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ∼1.5 km [e.g., at the UK Met Office and the Korea Meteorological Administration (KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma (quasi-stationary) front, and Typhoon Sanba (2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice (crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.

Use of rainfall forecast from a high-resolution global NWP model in a hydrological stream flow model over Narmada river basin during monsoon

Use of NWP model rainfall forecast is useful for hydrological modelling for river basins for flood and water management. In this study, the SWAT model is applied to the Narmada river basin to estimate the surface runoff. The capability of the model is tested for the south west monsoon season (June–September) of 2016 and validated against the NCUM global NWP model forecast data up to Day-5. Two heavy rainfall events are used for verification of the global model forecast with the observed data. The two verification events occurring during 1 July to 15 July (mean of 15 days) and 30 Aug to 11 Sept (mean of 12 days) are considered. Using Arc GIS sub watershed boundaries, the drainage network, slope, land use map and soil map are generated for the whole Narmada river basin. The surface runoff is computed by forcing with observed rainfall data as well as the NCUM model rainfall forecasts data as input to the SWAT model. Model simulations are compared at five stations of Narmada namely—Balkheri, Sandia, Handia, Hoshangabad and Garudeshwar. Initially, the NCUM model rainfall forecast is compared with the actual observation to evaluate the model performances in terms of bias estimation. Then the surface runoff is computed using the observed rainfall data and NCUM forecast data up to Day-5 and made a comparison to evaluate the SWAT model performances. It is shown that the estimated surface runoff during the two active phases is simulated well with some overestimation during the season. The skill of hydrological simulations is examined during different length of forecasts. It is seen that the SWAT model output performance is related to the NWP rainfall forecast skills. NCUM global NWP model is overestimating rainfall amount (wet bias) by 50–70 mm/day over the basin during the two active phases. Further, in all the days’ forecasts, the model is showing wet bias over the upper part of the Narmada river basin and a dry bias over the lower part of the basin. The dry bias is seen to be around 5–35 mm/day.

Verification of heavy rainfall in NWP models : A case study

Forecasting of heavy rainfall events is still a challenge even for the most advanced state-of-art high resolution NWP modelling systems. Very often the models fail to accurately predict the track and movement of the low pressure systems leading to large spatial errors in the predicted rain. Quantification of errors in forecast rainfall location and amounts is important for forecasters (to choose a forecast and interpret) and modelers for monitoring the impact of changes and improvements in model physics and dynamics configurations. This study aims to quantify and summarize errors in rainfall forecast for heavy rains associated with a Bay of Bengal (BOB) low pressure systems. The verification analysis is based on three heavy rain events during June to September (JJAS) 2015. The performance of the three deterministic models, NCMRWF’s Global Forecast Systems (NGFS), NCMRWF’s Unified Model (NCUM) and Australian Community Climate and Earth-System Simulator – Global (ACCESS-G) in predicting these heavy rainfall events has been analysed. In addition to standard verification metrics like RMSE, ETS, POD and HK Score, this paper also uses new family of scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and Symmetric EDI with special emphasis on verification of extreme rainfall to bring out the relative performance of the models for these three rainfall events. The results indicate that Unified modeling framework in NCUM and ACCESS-G by and large performs better than NGFS in rainfall forecasts over India specially at higher lead times. Relatively improved skill in NCUM forecasts can be attributed to (i) improved resolution (~17 km) and (ii) END Game dynamics of NCUM.

Toward Scatterometer Winds Assimilation in the Mesoscale HARMONIE Model

Data assimilation (DA) experiments have been conducted with the high-resolution limited-area model HirLAM Aladin Regional Mesoscale Operational NWP In Euromed (HARMONIE), which is operational at most weather centers, which are part of the European HirLAM consortium. Recently, the assimilation of scatterometer ocean surface winds was introduced, showing limited forecast skill improvement. Possible explanations are discussed. These include model bias and the time mismatch between observation and analysis time, which introduces nonnegligible correlated errors in a three-dimensional (3-D) variational assimilation system. Also, ignoring the time mismatch increases the innovation, i.e., the observation minus background (model short-term forecast), by about 20% for scatterometer winds. The use of observations as point observations in most DA systems needs reconsideration for mesoscale DA. The introduction of observation operators, taking into account the instrument footprint, would improve the innovation by about 5% for scatterometer winds. Additional directions for improved use of observations in HARMONIE are discussed based on the notice that DA is an inherent deterministic concept. Hence, the selection of the spatial scale for deterministic DA should depend primarily on the 4-D observation coverage rather than the effective model resolution.

Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in Alabama

Abstract As part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h-long update system demonstrate the potential of a frequently updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF-type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.

The DYMECS Project: A Statistical Approach for the Evaluation of Convective Storms in High-Resolution NWP Models

Abstract A new frontier in weather forecasting is emerging by operational forecast models now being run at convection-permitting resolutions at many national weather services. However, this is not a panacea; significant systematic errors remain in the character of convective storms and rainfall distributions. The Dynamical and Microphysical Evolution of Convective Storms (DYMECS) project is taking a fundamentally new approach to evaluate and improve such models: rather than relying on a limited number of cases, which may not be representative, the authors have gathered a large database of 3D storm structures on 40 convective days using the Chilbolton radar in southern England. They have related these structures to storm life cycles derived by tracking features in the rainfall from the U.K. radar network and compared them statistically to storm structures in the Met Office model, which they ran at horizontal grid length between 1.5 km and 100 m, including simulations with different subgrid mixing length. The authors also evaluated the scale and intensity of convective updrafts using a new radar technique. They find that the horizontal size of simulated convective storms and the updrafts within them is much too large at 1.5-km resolution, such that the convective mass flux of individual updrafts can be too large by an order of magnitude. The scale of precipitation cores and updrafts decreases steadily with decreasing grid lengths, as does the typical storm lifetime. The 200-m grid-length simulation with standard mixing length performs best over all diagnostics, although a greater mixing length improves the representation of deep convective storms.