Convective Parameterization Schemes Research Articles

Influence of convective parameterization on the simulation of tropical cyclones over the South West Indian Ocean: A case study of tropical cyclone Idai (2019)

Proper configuration of the physical parameterization scheme is important for accurate outcomes of mesoscale weather forecasting model. This study evaluates the performance of four distinct cumulus parameterization schemes: the Kain-Fritsch (KF), Grell 3D ensemble (GR3D), Modified Tiedtke (MTIEDKE) and Modified Kain-Fritsch (MKF) schemes, in simulating Tropical Cyclone (TC) Idai (2019) in the Southern Indian Ocean. Utilizing the Weather Research Forecasting (WRF) model and TC best-track and satellite precipitation datasets, the research assesses the models' efficacy in replicating Idai's observed track and intensity. Results indicate that the KF, MTIEDKE, and MKF schemes reasonably capture Idai's southwestward track, with MKF demonstrating superior accuracy in reproducing observed intensity evolution. Conversely, the GR3D simulation exhibits a northwestward deviation, leading to notable track and intensity errors. Further investigations suggest that the smaller track and intensity differences in the MKF simulation are linked to the more accurate simulation of rainfall in both the surrounding environment and the TC inner-core region. The two factors exert strong influences on the large-scale steering flow and TC secondary circulation, respectively. The findings underscore the importance of selecting appropriate convective parameterization schemes for precise tropical cyclone simulations in the Southern Indian Ocean. This research contributes valuable insights into the intricate interaction between convective processes and atmospheric dynamics, offering implications for enhancing tropical cyclone prediction models in the region.

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.

A Climatology of Convective Precipitation over Europe

Abstract The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the IMERG precipitation rate product, E-OBS ground-based precipitation gauge data, EURADCLIM climatological gauge-adjusted radar precipitation dataset, OPERA ground-based radar derived precipitation rates, and ERA5 total and convective precipitation products. ATDnet lightning data is used in conjunction with IMERG and EURADCLIM precipitation rates with an imposed threshold of 10 mm hr−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1 – 10% of the total precipitation accumulated is classified as convective, increasing to 10 – 40% over the surrounding seas. In contrast, 30 – 50 % of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40 – 60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm hr−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains.

Does a Scale‐Aware Convective Parameterization Scheme Improve the Simulation of Heavy Rainfall Events?

AbstractPrecipitation predictability using the non‐scale‐aware and scale‐aware convective parameterization schemes (CPSs) was investigated to assess the necessity of the CPSs within the gray‐zone. This study evaluates the performance of the Weather Research and Forecasting (WRF) model's CPS for 135 heavy rainfall events (HREs) over the Korean Peninsula for 10 years (i.e., 2011–2020). We tested the Kain–Fritsch (KF) scheme (non‐scale‐aware) and Multi‐scale Kain–Fritsch (MSKF) scheme (scale‐aware) in the WRF model. The MSKF scheme shows an overall improved performance of precipitation simulation compared to the KF scheme, but the precipitation forecast performance of CPS depends on the characteristics of HREs. When the HREs are characterized by synoptic‐scale atmospheric conditions with strong winds and large‐scale water vapor transport, the forecast performance of both CPSs is similar because a cloud microphysics scheme can explicitly resolve most of the precipitation. However, in the case of HREs with weak synoptic forcing conditions (e.g., moisture transport and winds) related to the localized and meso‐scale HREs, the MSKF scheme can improve overall simulated precipitation by increasing grid‐scale precipitation and reducing the overestimation of subgrid‐scale precipitation simulated in the KF scheme. Therefore, using the scale‐aware CPS in the gray‐zone can provide more accurate precipitation forecasts regardless of the environmental condition of the HREs.

Two high-impact extreme precipitation events during the Meiyu season: simulations and their sensitivity to a scale-aware convective parameterization scheme

In this study, we investigate the impact of a scale-aware convective parameterization scheme (CPS) on simulating two high-impact extreme precipitation events during the Meiyu season at a 1 km resolution. Compared with explicit resolving simulation, applying scale-aware CPS mostly affects the atmospheric environment before convection is triggered and the regions outside the convective cell (e.g., stratiform regions) by consuming convective available potential energy (CAPE), which further inhibits precipitation. For the 2019 event, since the explicit resolving simulation underestimates precipitation, applying scale-aware CPS further inhibits precipitation and reduces the skill for the heavy rainfall category. In contrast, for the 2020 event, as the explicit resolving simulation overestimates precipitation, using scale-aware CPS inhibits precipitation and improves the skill of the model. When scale-aware CPS is applied, increasing σ1, which represents the effect of horizontal resolution, the CPS precipitation is reduced and the grid-scale precipitation is increased, but the overall effect inhibits the total precipitation. However, with the increase in σ1, although the number of stations with heavy rainfall (≥50 mm) is reduced, the average precipitation among these stations is increased. Further analysis of those stations with the largest 24 h precipitation and cross sections over regions with large radar reflectivity shows that increasing σ1 reduces the CAPE consumed by CPS and provides a more favorable environment for strong convections and strengthens precipitation. The results of this study may provide useful information for operational model application and may be beneficial to society.

Inclusion of the radiative effect of deep convective clouds in the Eta model simulations

AbstractConvective clouds play an important role in the local energy budget by directly interacting with solar and terrestrial radiation. However, radiation parameterization schemes of atmospheric models generally consider clouds produced from microphysics schemes or some other grid saturation criteria. Deep convective parameterization schemes tend to rain out the convective cloud before the radiation scheme perceives its water load. This may be a source of the positive bias of the incoming solar radiation at the surface. The objective of this work is to include the effects of deep convective clouds in the radiation scheme of the regional Eta model and to evaluate the impacts on the net radiative energy and other meteorological variables. The radiation scheme is the Rapid Radiative Transfer Model. The work is developed in four stages. The positive bias in the incoming solar radiation was diagnosed in the first stage. In the second stage, the parameters of the convective parameterization scheme were modified to increase convective precipitation. In the third stage, the parameters of the microphysics scheme were modified to increase explicit clouds. In the fourth and last stage, in addition to the previous modifications, the condensates from the convective parameterization were input into the radiation scheme. The runs were performed for a period of one summer rainy month with intense convective activity over South America. Including deep convective cloud condensates into the radiation scheme improved the cloud cover, the diurnal cycle of the surface net radiation, and the 2‐m temperature. However, the reduction of the net radiation at the surface caused the reduction of the available energy for convective instability and, consequently, the precipitation reduction. The results show the importance of including cumulus cloud water load in the radiative scheme for bias reduction in the radiative energy components.

Dynamically computed characteristic adjustment time scale for Zhang–McFarlane convective parameterization scheme

AbstractCharacteristic adjustment time scale τ is an important parameter in convective parameterization scheme. In this paper, we propose a dynamic calculation formula of τ in the Zhang–McFarlane scheme based on convective available potential energy (CAPE) in order to improve the simulation of precipitation. The performance of using the new formula is tested and evaluated using the Community Atmosphere Model (CAM5) for numerical experiments. The results indicate that the root mean square errors of mean precipitation amount over the Northern Hemisphere eastern equatorial Pacific Ocean, the central Indo-Pacific Warm Pool, the Tibetan Plateau, and northern South America are reduced by 20.15, 16.40, 12.56, and 6.45% in summer, respectively, after a dynamic τ is adopted. The dynamically computed τ improves the Pacific Walker circulation by strengthening the western ascending and eastern descending, and increases the intensity of deep convective precipitation in tropical areas by at least 1 mm/day. Dynamic τ also increases the frequency of large deep convective precipitation in the equatorial Pacific by almost 50%, and decreases the frequency of light deep convective precipitation in many other regions. The dynamic τ has positive effects in the areas with large CAPE and spatiotemporal variability, and also leads to an increase in deep convective precipitation and a significant decrease in large-scale precipitation over high-altitude regions of windward slopes. Therefore, the generation of exaggerated precipitation in the highlands is reduced in the CAM5. Overall, the dynamic calculation of characteristic adjustment time scale can improve precipitation simulation.

Non‐Uniqueness in ITCZ Latitude Due To Radiation‐Circulation Coupling in an Idealized GCM

AbstractAn idealized aquaplanet moist global atmospheric model with realistic radiative transfer but no clouds and no convective parameterization is found to possess multiple climate equilibria. When forced symmetrically about the equator, in some cases the Inter Tropical Convergence Zone (ITCZ) migrates to an off‐equatorial equilibrium position. Mechanism denial experiments prescribing relative humidity imply that radiation‐circulation coupling is essential to this instability. The cross‐equatorial asymmetry occurs only when the underlying slab ocean is sufficiently deep and the atmosphere's spectral dynamical core is sufficiently coarse (∼T170 or less with our control parameters). At higher resolutions, initializing with an asymmetric state indicates metastability with very slow (thousands of days) return to hemispheric symmetry. There is some sensitivity to the model timestep, which affects the time required to transition to the asymmetric state, with little effect on the equilibrium climate. The instability is enhanced when the planetary boundary layer scheme favors deeper layers or by a prescribed meridional heat transport away from the equator within the slab. The instability is not present when the model is run with a convective parameterization scheme commonly utilized in idealized moist models. We argue that the instability occurs when the asymmetric heating associated with a spontaneous ITCZ shift drives a circulation that rises poleward of the perturbed ITCZ. These results serve as a warning of the potential for instability and non‐uniqueness of climate that may complicate studies with idealized models of the tropical response to perturbations in forcing.

Evaluation of WRF Performance in Simulating an Extreme Precipitation Event over the South of Minas Gerais, Brazil

Extreme precipitation events are becoming increasingly frequent and intense in southeastern Brazil, leading to socio-economic problems. While it is not possible to control these events, providing accurate weather forecasts can help society be better prepared. In this study, we assess the performance of the Weather Research and Forecasting (WRF) model in simulating a period of extreme precipitation from 31 December 2021 to 2 January 2022 in the southern region of Minas Gerais (SMG) state in southeastern Brazil. We conducted five simulations using two nested grids: a 12 km grid (coarse resolution) and a 3 km grid (high resolution). For the coarse resolution, we tested the performance of five cumulus convection parameterization schemes: Kain–Fritsch, Betts–Miller–Janjic, Grell–Freitas, Grell–Devenyi, and New Tiedke. We evaluated the impact of these simulations on driving the high-resolution simulations. To assess the performance of the simulations, we compared them with satellite estimates, in situ precipitation measurements from thirteen meteorological stations, and other variables from ERA5 reanalysis. Based on the results, we found that the Grell–Freitas scheme has better performance in simulating the spatial pattern and intensity of precipitation for the studied region when compared with the other four analyzed schemes.

How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?

Abstract. Cloud-radiative heating (CRH) within the atmosphere and its changes with warming affect the large-scale atmospheric winds in a myriad of ways, such that reliable predictions and projections of circulation require reliable calculations of CRH. In order to assess the sensitivities of upper-tropospheric midlatitude CRH to model settings, we perform a series of simulations with the ICOsahedral Nonhydrostatic Model (ICON) over the North Atlantic using six different grid spacings, parameterized and explicit convection, and one- versus two-moment cloud microphysics. While sensitivity to grid spacing is limited, CRH profiles change dramatically with microphysics and convection schemes. These dependencies are interpreted via decomposition into cloud classes and examination of cloud properties and cloud-controlling factors within these different classes. We trace the model dependencies back to differences in the mass mixing ratios and number concentrations of cloud ice and snow, as well as vertical velocities. Which frozen species are radiatively active and the broadening of the vertical velocity distribution with explicit convection turn out to be crucial factors in altering the modeled CRH profiles.

Impact of genesis conditions on regional simulations of extreme rainfall : A convection parameterization sensitivity study

Approximated and simplified real-atmospheric process impact in physical parameterization is a primary correspondent of biases in the model, particularly for extreme events. The present study discusses how event genesis in the small and large-scale quintessential environment is incongruously simulated within a set of multiple convection parameterizations. Despite a few inherent errors, most of the selected convective parameterization schemes could indicate 10-15 days in advance the Uttarakhand heavy rains resulted from large-scale background interaction. The runs without any convection scheme, followed by new-Tiedtke and BMJ schemes, outperform in this case. Further, almost all schemes except new-Tiedtke flunked for the case of Mount-Abu flood originated from relatively local-scale interaction even from 5-day advance initialization. Results are further extended for a few other cases using best performers of both extreme events and new-Tiedtke found to be more efficient. The better representation of convection (especially the shallow) and low clouds in this scheme makes it superior to other schemes for simulating extreme precipitation events.

Sensitivity analysis of convective and PBL parameterization schemes for Luban and Titli tropical cyclones

Sensitivity analysis of convective and PBL parameterization schemes for Luban and Titli tropical cyclones

Quantification of model uncertainty in sub-daily extreme precipitation projections

Sub-daily precipitation is projected to intensify more rapidly with global warming, that in turn may lead to frequent disasters such as flash floods, landslides, etc. However, the precipitation projections from Global Climate Model (GCMs) are known to be highly uncertain, with an increasing magnitude at finer spatio-temporal scales. Hence, realistic quantification of the inherent uncertainty is vital to improve the confidence in these projections, for the mitigation/adaptation applications. In the present study, the model uncertainty in sub-daily (3-hourly) precipitation extremes is estimated, by considering 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, during four different seasons for the near (2026–45) and far-future (2081–99) periods. An uncertainty quantification approach using “mutual information based independence weight” is proposed in this study, to account the linear and nonlinear dependence between GCMs along with the equitability property. The CMCC-CM model is found to be the most independent model having highest independence weight (0.064) in the ensemble. The uncertainty in northern hemisphere is found to be more during June to August period, whereas in southern hemispheres during December to February period. Overall, there is an increase in model uncertainty in far-future, especially over the equatorial regions. However, the behaviour of different indices is not in agreement with each other. For example, extreme characteristics such as monthly maximum 3hourly-precipitation has increasing uncertainty; whereas the mean characteristic like, simple hourly-precipitation intensity index possesses unchanging uncertainty. Model uncertainty is most sensitive to the precipitation characteristics of the regions, followed by elevation, a significant geophysical factor. The study also highlighted the need for model improvement over the grids having elevation <2 km and above 4 km. The study identifies the possible regions, seasons, factors etc. where model improvement can be made. Moreover, it is anticipated that improvement of resolution, updation of convective parameterization schemes and land surface parameterization schemes in GCMs may enhance the skill of GCMs and can improve the confidence in the projection of precipitation on sub-daily scale. • Seasonal model uncertainty is estimated to assess the reliability in the projections of sub-daily precipitation extremes. • Mutual information-based weighting technique is used to establish the independence weight of models. • Different aspects of sub-daily precipitation exhibit differing uncertainty, in terms of magnitude and also temporal evolution. • Precipitation intensity remains the dominant factor behind model uncertainty, but geophysical influences cannot be ignored. • Reliability on the sub-daily mean characteristics improves with time; whereas that on extreme characteristics declines.

High-resolution regional climate modeling of warm-season precipitation over the Tibetan Plateau: Impact of grid spacing and convective parameterization

The Tibetan Plateau (TP) features high elevation and complex terrain, which together shape the region's unique precipitation with high spatial heterogeneity and strong diurnal variations. Due to low spatial resolution, deficient convective parameterization (CP) and uncertainties in other physical assumptions, global climate models and traditional regional models tend to produce excessive precipitation with an earlier diurnal timing. In this work, two sets of numerical experiments with 4- and 10-km grid spacing, respectively, were performed using the Weather Research and Forecasting model to simulate the seasonal precipitation for an average warm season (April–September 2017) over the TP. Results showed that the 4-km experiment with microphysics only (i.e., without CP) reproduced the precipitation amount, frequency, intensity and diurnal cycle reasonably well on regional and local scales, as well as the nocturnal propagating convection at the lee side. The 10-km experiments with scale-aware CP schemes significantly overestimated the precipitation amount and frequency, underestimated the intensity, and failed to capture the diurnal propagation signature. The 10-km experiment with microphysics only had the poorest skill in representing the spatial distribution of precipitation. The scale-aware CP schemes on the 4-km grid caused a too-frequent occurrence of light precipitation even though their overall contribution to the total precipitation amount was rather small, and thus had little benefit to the model performance. A common deficiency in all experiments was excessive afternoon precipitation across the high elevation of the central Himalaya possibly due to the excessive surface insolation, but the satellite-based data may not accurately capture the afternoon precipitation. This research highlights the value of the 4-km model (the so-called convection-permitting model) and the limited benefit of the tested convective parameterizations over the TP.

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases.

Effect of a Scale‐Aware Convective Parameterization Scheme on the Simulation of Convective Cells‐Related Heavy Rainfall in South Korea

AbstractIn this study, the effect of a scale‐aware convective parameterization scheme (CPS) on the simulation of heavy precipitation in the gray‐zone was investigated using the Weather Research and Forecasting (WRF) model. We performed WRF simulations with the Kain–Fritsch (KF) scheme (non‐scale‐aware), Multiscale Kain–Fritsch (MSKF) scheme (scale‐aware), and explicit convection (i.e., no CPS). The MSKF scheme uses a scale‐aware parameter that modulates the convective available potential energy (CAPE) timescale and entrainment process in the KF scheme as a function of the horizontal grid spacing. The results of this study show that WRF simulations using explicitly resolved convection lead to overestimations and erroneous precipitation locations in the gray‐zone because the convection and atmospheric instability cannot be appropriately triggered and reduced. The CPS without scale‐awareness in the gray‐zone exaggerates the convection and distorts synoptic fields, leading to the erroneous simulation of heavy precipitation at high resolution. The MSKF scheme with scale‐awareness improves the simulation of convective cells‐related heavy rainfall by removing the atmospheric instability in the gray‐zone, reducing the role of the CPS, and increasing the role of the microphysics parameterization scheme (MPS) with decreasing grid spacing. In addition, the results of sensitivity experiments show that reducing the CAPE timescale leads to the faster development of convective cells, whereas decreasing the entrainment leads to precipitation overestimation. The modulated parameters in the scale‐aware MSKF scheme play a crucial role in balancing the effects of the CPS and MPS in the gray‐zone.

Parameterizing Convective Organization Effects With a Moisture‐PDF Approach in Climate Models: Concept and a Regional Case Simulation

AbstractWe propose a parameterization scheme of convective organization effects based on a moisture‐distribution approach, which can reflect aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free‐troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the grid‐mean condition, which provides a “shielding” effect favorable for deeper convective updrafts. Such effects are represented quantitatively via utilizing a schematic diagram about the geometric interpretation of convective cluster, dry area, and their contact area, in which both the convective cluster size and dry area fraction increase during the development of convection. Our sensitivity analyses indicate that the new scheme performs well in capturing the mean precipitation features. Excluding the convective organization effects in the model leads to a considerable reduction in the simulated precipitation magnitude. Notably, there exists an inverse relationship between background mean moisture condition and sub‐grid moisture variability for a given rain rate, suggesting that with a high spatial heterogeneity in free‐troposphere moisture, convection can still develop and maintain its strength under relatively dry background conditions, consistent with the convection‐permitting model simulation. Overall, our new parameterization of convective organization effects can successfully reproduce the relationship between precipitation and sub‐grid moisture variability, which is a missing element in traditional convection parameterization schemes but important for the simulations of precipitation variability at various scales in climate models.

WRF ensemble dynamical downscaling of precipitation over China using different cumulus convective schemes

Ensemble dynamical downscaling of precipitation over China was conducted based on simulations of the Weather and Research Forecasting model using both Kain–Fritsch and Grell cumulus convective parameterization schemes. The simulations were driven by ERA-Interim reanalysis data with 25-km horizontal resolution for the period 1980–2015. Results indicated that superior performance was achieved by the ensemble based on the two different schemes because certain observed signals were captured complementarily by each scheme over distinct regions. For climatological mean precipitation, the ensemble improved the annual mean pattern, probability distribution, and seasonal evolution of precipitation. With regard to interannual variation, the ensemble showed the highest skill in representing the series of precipitation anomalies at regional scales for all subregions. Improvement was also evident in the spatial patterns of the dominant precipitation variability modes and the corresponding temporal variations. For extreme precipitation, several indices were selected, and the ensemble was better able to capture the main features with higher spatial pattern correlations and closer magnitudes. The advantages of the ensemble are due to its appropriate regime specific weights derived from the Kain–Fritsch and Grell schemes

Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

This work documents the performances of diagnostic and explicit lightning forecasts for selected high impact weather events over Bangladesh. The lightning flashes are calculated by three methods: (1) a diagnostic lightning parameterization that bases its algorithm on the level of neutral buoyancy from a convective parameterization scheme, (2) lightning potential index (LPI) using local graupel and ice contents, and (3) explicit prediction of electric fields and lightning initiation from WRF-ELEC. Five lightning events that occurred on 02 April 2019, 17 May 2019, 20 April 2020, 26 May 2020, and 20 May 2021 were examined to evaluate the forecasting capability of each scheme for the lightning over a 24-h forecast period. Prior to diagnosing lightning activity, the composite radar reflectivity and the neighborhood score metrics (ETS, FSS) for hourly rainfall were analyzed to determine if the simulated precipitation structure is consistent with the observations. Analyses based on performance diagrams were also included for a better illustration of how simulations perform on predicting precipitation. The spatial distribution of LPI and lightning flashes is compared against the NASA Lightning Imaging Sensor (LIS) dataset. The qualitative results show that in most of the studied cases, there is a good agreement between the observations and the WRF-ELEC-based simulations in terms of detecting the primary regions of lightning activity. The LPI based predictions perform also considerably well. The results from this endeavor constitute an essential initiative towards the implementation of an effective operational lightning warning system to mitigate lightning-related hazards in Bangladesh and northeast India.

Convection Modeling of Pure-steam Atmospheres

Abstract Condensable species are crucial to shaping planetary climate. A wide range of planetary climate systems involve understanding nondilute condensable substances and their influence on climate dynamics. There has been progress on large-scale dynamical effects and on 1D convection parameterization, but resolved 3D moist convection remains unexplored in nondilute conditions, though it can have a profound impact on temperature/humidity profiles and cloud structure. In this work, we tackle this problem for pure-steam atmospheres using three-dimensional, high-resolution numerical simulations of convection in postrunaway atmospheres. We show that the atmosphere is composed of two characteristic regions, an upper condensing region dominated by gravity waves and a lower noncondensing region characterized by convective overturning cells. Velocities in the condensing region are much smaller than those in the lower, noncondensing region, and the horizontal temperature variation is small. Condensation in the thermal photosphere is largely driven by radiative cooling and tends to be statistically homogeneous. Some condensation also happens deeper, near the boundary of the condensing region, due to triggering by gravity waves and convective penetrations and exhibits random patchiness. This qualitative structure is insensitive to varying model parameters, but quantitative details may differ. Our results confirm theoretical expectations that atmospheres close to the pure-steam limit do not have organized deep convective plumes in the condensing region. The generalized convective parameterization scheme discussed in Ding &amp; Pierrehumbert is appropriate for handling the basic structure of atmospheres near the pure-steam limit but cannot capture gravity waves and their mixing which appear in 3D convection-resolving models.