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The paper discusses some refinements to the SL-AV atmospheric model, which have made it possible to substantially reduce the systematic errors in reproducing the averaged characteristics of the atmospheric circulation. The main focus of the paper is the wind gustiness parameterization and its role in the calculation of the turbulence fluxes in the atmosphere boundary layer. The statistical significance of the influence of the considered changes on the accuracy of atmospheric circulation simulation is evaluated based on the analysis of the quality of retrospective long-range forecasts with a lead time up to four months (for different seasons) and on the study of averaged characteristics of the model atmosphere in comparison with the ERA5 reanalysis. [ABSTRACT FROM AUTHOR]

In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. [ABSTRACT FROM AUTHOR]

The paper describes the configuration of the COSMO-Ru6Sib system with a horizontal grid spacing of 6.6 km adapted to regional conditions. The COSMO-Ru2Sib configuration with a horizontal grid spacing of 2.2 km is also considered. The results of modifying this configuration using weather radar data assimilation as well as urban canopy parameterization are presented and discussed. The results of numerical experiments for the COSMO-Ru6Sib with the COSMO and ICON-LAM models as compute kernels are also presented. [ABSTRACT FROM AUTHOR]

The Equilibrium Simplified Aerosol Model for Climate version 12 (EQSAM4Clim-v12) has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. EQSAM4Clim is based on an analytical concept that is not only sufficiently fast for chemical weather prediction applications but also free of numerical noise, which also makes it attractive for air quality forecasting. EQSAM4Clim allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases and can therefore provide important information about the acidity of the aerosols. Here we provide a comprehensive description of the recent changes made to the aerosol acidity parameterization (referred to as a version 12) which builds on the original EQSAM4Clim. We evaluate the pH improvements using a detailed box model and compare them against previous model calculations and both ground-based and aircraft observations from the USA and China, covering different seasons and scenarios. We show that, in most cases, the simulated pH is within reasonable agreement with the reference results of the Extended Aerosol Inorganics Model (E-AIM) and of satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method. [ABSTRACT FROM AUTHOR]

The charge structure in thunderstorms may be strongly affected by different secondary ice production (SIP) processes, but has not been well understood. In this study, the impacts of three SIP mechanisms on microphysics and electrification in a squall line are investigated using model simulation, including the rime‐splintering, ice‐ice collisional breakup, and shattering of freezing drops. The parameterization of the three SIP mechanisms, a noninductive and an inductive charging parameterization are implemented in the spectral bin microphysics. The results show that with SIP processes included, the modeled radar reflectivity is more consistent with observation. It is found that both the mass and concentrations of graupel/hail are enhanced by SIP processes, while the diameter decreases. The mixing ratio of ice/snow decreases due to the rime‐splintering, and increases in mixing ratio are due to the shattering of freezing drops. Particle charging is significantly affected by SIP, leading to a dipole structure of the total charge density, which includes a lower negative and an upper positive charge region. With both the noninductive and inductive charging considered, the charge carried by graupel/hail changes from negative to a bipolar structure, and the charge sign carried by ice/snow is inverted due to the SIP. The modeled lightning activity is enhanced by implementing all three SIP processes, while if only considering the rime‐splintering process, the flash rate would be suppressed. The insights obtained from this study highlight the importance of considering different mechanisms of SIP in modeling the charge structure and lightning activity in thunderstorms. Plain Language Summary: Electrification in thunderstorms is strongly related to microphysics, especially the ice‐phase processes. However, the ice microphysics in deep convective clouds is very complicated, and the impacts of different ice‐phase processes on cloud electrification are not well understood. One of the unresolved questions is how the charge structure in thunderstorms can be affected by various secondary ice production (SIP) mechanisms. Until now, only a small number of studies have investigated this issue, and most of them focused on a single SIP (rime‐splintering process). However, recent studies have shown that other SIP mechanisms, especially the shattering of freezing drops and ice‐ice collisional breakup, can greatly enhance ice generation in convective clouds. Therefore, the various mechanisms of SIP may potentially have strong impacts on the charge structure in thunderstorms. In this study, the parameterizations of three different mechanisms of SIP, as well as the noninductive and inductive charge parametrization are implemented in a spectral bin microphysics model scheme in WRF (Weather Research and Forecasting model). A squall line is modeled to investigate the impacts of the three mechanisms of SIP on charge structure and flash rate. The results highlight the importance of considering various mechanisms of SIP in modeling cloud electrification and lightning. Key Points: The impacts of three secondary ice production (SIP) mechanisms on the electrification of a squall line is investigated using a modelThe SIP processes have strong impacts on the cloud microphysics, resulting in significant modification of charge structureThe flash rate is suppressed by rime‐splintering, while enhanced by ice‐ice collisional breakup and shattering of freezing drops [ABSTRACT FROM AUTHOR]

The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (ZDR), specific differential phase (KDP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, ZDR, KDP and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) is reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even if the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the Weather Research and Forecasting (WRF) model using a high-resolution spatial grid (1 km) with various combinations of physical parameterization packages to simulate a severe event in August 2021 in the southeastern Brazilian coast. After determining the optimal set of physical parameterizations for representing wind patterns during this event, a year-long evaluation was conducted, covering forecast horizons of 24, 48, and 72 h. The simulation results were compared with observational wind data from four weather stations. The findings highlight variations in the efficacy of different physical parameterization sets, with certain sets encountering challenges in accurately depicting the peak of the severe event. The most favorable results were achieved using a combination of Tiedtke (cumulus), Thompson (microphysics), TKE (boundary layer), Monin-Obukhov (surface layer), Unified-NOAH (land surface), and RRTMG (shortwave and longwave radiation). Over the one-year forecasting period, the WRF model effectively represented the overall wind pattern, including forecasts up to three days in advance (72-h forecast horizon). Generally, the statistical metrics indicate robust model performance, even for the 72-h forecast horizon, with correlation coefficients consistently exceeding 0.60 at all analyzed points. While the model proficiently captured wind distribution, it tended to overestimate northeast wind speed and gust intensities. Notably, forecast accuracy decreased as stations approached the ocean, exemplified by the ATPM station. [ABSTRACT FROM AUTHOR]

Precipitation 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. Plain Language Summary: In this study, we evaluate the predictability of precipitation using two types of convective parameterization schemes (CPS), non‐scale‐aware and scale‐aware, in the Weather Research and Forecasting (WRF) model. The scale‐aware CPS utilizes scale‐aware parameters to adjust convection processes based on horizontal resolution. We focus on heavy rainfall events (HREs) over the Korean Peninsula for 10 years from 2011 to 2020. A HRE is defined as rainfall greater than 110 mm in 12 hr. The results show that the scale‐aware CPS improved the precipitation forecast performance compared to the non‐scale‐aware CPS, and the precipitation forecast performance of CPS differs depending on the characteristics of HREs. For HREs characterized by the synoptic forcing conditions with strong winds and large‐scale horizontal advection of water vapor, both CPSs perform well, but their forecast performance decreases for HREs with weak synoptic forcing conditions. We also find that for localized heavy rain cases, using the scale‐aware CPS helps improve precipitation forecasts at 4‐km horizontal resolution. Key Points: We evaluate the impacts of the non‐scale‐aware and scale‐aware convective parameterization scheme (CPS) in the gray‐zone using the Weather Research and ForecastingThe precipitation forecast performance of CPS depends on the type of heavy rainfall event (HRE) and environmental conditionsThe scale‐aware CPS in the gray‐zone can provide more accurate precipitation forecasts regardless of the environmental condition of the HREs [ABSTRACT FROM AUTHOR]

The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology toward novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the nonresolved processes, i.e., usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parameterizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier–Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g., artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed. [ABSTRACT FROM AUTHOR]

Three cumulus and five microphysics parameterization schemes of the Weather Research and Forecasting model (WRF) are the basis for simulating ten specific meteorological events of the Paraíba do Sul River Basin (PSRB) in Southeast Brazil. The cases studied are frontal wave systems, thermodynamic instability, and the South Atlantic Convergence Zone (SACZ). Each parameterization combination generated 15 simulations for each event, resulting in 150 tests. The primary domain has a horizontal resolution of 8.0 km and the nested 2.6 km resolution. Three analysis tools underlie the study: (i) punctual verification of the first 24 h of precipitation forecast, using the Taylor diagram; (ii) verification of the prediction of precipitation using categorical binary variable and (iii) the Model´s ability to reproduce patterns of the spatial distribution of precipitation. The Taylor diagram suggests that the combination of the Morrison Double moment and Multiscale Kain–Fritsch schemes produce the best results. The categorical verification indicates that, for dynamic/convective events, the Morrison Double moment and Multiscale Kain–Fritsch and WRF Double Moment 6–class sets showed the best indices. Some configurations presented reliable results for exclusively convective events, and WRF Single–moment 6–class and Grell–Freitas Ensemble is the best combination. The Morrison Double moment and Multiscale Kain–Fritsch parameterizations yielded the best performance for the spatial distribution. Overall, the schemes tested perform better for the upstream region, i.e., the area of greater water uptake for the basin. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river‐related precipitation (ARP) event with 206 and 57 mm, highest and area‐averaged precipitation (AAP) per 24‐h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4‐km domain), both point‐scale and grid‐scale comparisons were performed against gauge‐ and satellite‐based observational data at three accumulation time‐scales (12‐, 18‐, and 24‐h) and in three distinct sub‐regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain‐Fritsch, KF‐CuP, and Grell‐3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang‐McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the "momentum transport" option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low‐level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24‐h accumulation time‐scale than at the 12‐ and 18‐h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time‐scale decrease and the distance from the core of the precipitation field increases. [ABSTRACT FROM AUTHOR]

A new version of the Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and the one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula. [ABSTRACT FROM AUTHOR]

Numerical weather modelling has piqued the attention of the hydrological community because precise predictions from the models might lessen the extreme hydrological repercussions. Despite the paucity of existing studies, significant tropical storms frequently affect the Asian island of Sri Lanka. This research investigates the Weather Research Forecast (WRF-ARW) model's cumulus parameterization condition and physical parameterization schemes for a 2019 northeast monsoon event over the Badulu Oya Basin, Sri Lanka. Three cumulus schemes (Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ) and Multi-scale Kain–Fritsch (MKF)) and four microphysics schemes (WRF single-moment 5-class (WSM5), WRF single-moment 6-class (WSM6), Kessler (KSL) and WRF double moment 6-class (WDM6)) were evaluated for their impact on modelled rainfall. The model performances were assessed using 24-hr accumulated model rainfall and observed rainfall with various model configurations at a horizontal grid resolution of 3 km using categorical and two quantitative comparison techniques. The study concluded that the activated KF scheme with a finer domain resolution (3 km) would be preferred for cumulus parameterization in the study region. The KF-WSM5 combination was the best since it produced the highest statistics: ETS is 0.38, B is 0.95, r is 0.76, NSD is 1.06, NRMSE is 0.72, and CCPA is 75%. Research highlights: We simulated an extreme northeast monsoon precipitation event over the Badulu Oya catchment, Sri Lanka. Simulations were performed using the Weather Research and Forecasting model (WRF-ARW). Sensitivity studied to cumulus parameterization condition and microphysics schemes (CPSs- KF, BMJ, MKF and MPSs-WSM5, WSM6, KSL and WDM6). The activated Kain–Fritsch cumulus scheme at 3 km resolution was found to be the most accurate. Combination of KF-WSM5 with activated cumulus scheme in the finer domain could be preferable option for heavy rainfall simulations over the study area. [ABSTRACT FROM AUTHOR]

This study examines for the first time the impacts of the different physical parameterization schemes in the Weather Research and Forecasting (WRF-ARW) model on the quality of heavy rain prediction during the transitional season in eastern Algeria. Various physical model configurations are examined and verified by considering 144 numerical experiences using different combinations of cumulus and microphysical schemes compared to rain records of a network of thirty specialized meteorological stations for the period from August 1 until the last of October 2022. The most reliable forecasts have been obtained from the Kain-Fritsch cumulus scheme (KF). However, the impact of microphysical schemes was not truly identified in our study cases. [ABSTRACT FROM AUTHOR]

Based on three-dimensional lightning data and an S-band Doppler radar, a strong relationship was identified between lightning activity and the radar volume of squall lines. A detailed analysis of the squall line investigates the relationship following an exponential relationship. According to the correlation between lightning and the radar volume, three radar-volume-based lightning parameterization schemes, named the V30dBZ, V35dBZ, and V40dBZ lightning schemes, have been established and introduced into the weather research and forecasting (WRF) model. The performance of lightning precondition by different lightning parameterization schemes was evaluated, including the radar-volume-based schemes (V30dBZ, V35dBZ, and V40dBZ), as well as existing lightning schemes (PR92_1, PR92_2, and the Lightning Potential Index (LPI)). The evaluation shows that the simulated spatial lightning density and temporal lightning frequency by the radar-volume-based lightning schemes are more consistent with the observations. While the two PR_92 lightning schemes significantly underestimated the magnitude of lightning density. The radar-volume-based lightning parameterization schemes are proven to be more reliable in estimating lightning activity than other lightning schemes. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) simulated at gray-zone resolution (9 km) in the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) are evaluated over the Western North Pacific (WNP) during 11-year (2008–2018) TC seasons (June–November). The effects of the cumulus parameterization (CPS) are tested with two sets of numerical experiments: one involving CPS (ICPS) and one without (NICPS). Through comparisons with the observation and reanalysis, it is shown that the ICPS experiment yields good skills in simulating TC frequency due to more realistic large-scale mean states, including stronger low-tropospheric circulation, wetter mid-tropospheric environment, and stronger ascending motion over the TC main development region (MDR). On the other hand, the NICPS experiment is found to better reproduce intense TCs (Saffir–Simpson hurricane categories 3, 4 and 5) in terms of intensity, accumulated cyclone energy (ACE), and inner-core size. NICPS can also reasonably capture TC inner-core structure with stronger radial inflow in the boundary layer, rising motion around the eyewall and outflow in the mid to upper troposphere. Nevertheless, NICPS significantly underestimates TC outer size due to rapidly decay of TC outer wind field, suggesting limited severe convective activities in the outer region. [ABSTRACT FROM AUTHOR]

We investigate the sensitivity of modeled tropical precipitation accumulation, intensity and structures to the extent of convection‐permitting limited area model (LAM) domain size. Our comparison focusses on two LAM domains, with identical physical parameterization schemes and using 2.2 km grid spacing. One LAM domain spans almost the full tropical belt while the other focusses on southeast Asia. We show that the LAMs both capture the complex diurnal cycle of precipitation and that the timing and intensity of precipitation are comparable with satellite observations. Systematic differences between the LAMs are largest within ∼1,000 km of the western and eastern boundaries of the southeast Asia LAM. This is due to convective spin‐up at the western boundary of the southeast Asia LAM and a lack of propagating deep convection. We highlight that showing the added value of global storm‐resolving models by comparing with LAMs will help to accelerate their operational implementation. Plain Language Summary: Accurately capturing the location, timing and intensity of heavy tropical precipitation is key to providing early warnings of meteorological hazards that impact lives and livelihoods in the tropics. Here we investigate whether new high‐resolution weather forecast model simulations that almost fully span the tropics provide additional information (for precipitation), compared with smaller regional forecast models. We find that both versions of our model show similar intensity and timing of precipitation events in southeast Asia—and compare reasonably well with satellite derived observations. Differences between our simulations are largest close to the boundaries of the southeast Asia region, we explore and discuss the causes of these differences. We also discuss the need for more studies focusing on how new higher resolution global forecasts provide added value compared with current approaches to obtaining regional weather forecast information. Key Points: We compare precipitation in a limited area model (LAM) to a novel 2.2 km resolution Tropical Channel domain, focusing on southeast AsiaA LAM can adequately reproduce the precipitation characteristics of a Tropical wide simulation at 15% of the costFurther work within our model framework will investigate the upscale impacts of organized convection within the Tropical Channel [ABSTRACT FROM AUTHOR]

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount. [ABSTRACT FROM AUTHOR]

To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study. Significance Statement: Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning. [ABSTRACT FROM AUTHOR]

By using Weather Research and Forecasting Model (WRF) to simulate a southwest vortex precipitation process, this work studies the correlations between entrainment rate (λ) and dynamical parameters in the cloud and further fit λ. We relate the probability density distribution (PDF) to the parameterization of λ and find that the greater the probability, the larger the slope of the logarithmic liner function. The slope of the log-linear fitting function in fitting decreases for developing and enhancing cumulus clouds, which is related to the increase in updraft motion and the decrease in λ. Then, we group clouds according to cloud top heights and calculate average λ and dynamic parameters, and the results indicate that when only one dynamic parameter is used, vertical wind velocity (w) is more suitable than buoyancy (B) to be used to fit λ. The fitting functions combing one single parameter and more parameters by principal components regression are compared with two traditional schemes, and we found that λ obtained by our fitting schemes are between the two traditional schemes. Because the principal component regression method takes into account the interaction between more dynamic factors and entrainment, the fitting function, including w and B, is suitable to be applied to fit λ in the parameterization scheme for cumulus clouds. [ABSTRACT FROM AUTHOR]

Projecting the future extreme high-temperature risk under the background of global warming and urbanization is essential to the collaborative development of Beijing, Tianjin, and Hebei (BTH). In this study, based on the global climate simulation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the fine land-cover data, we use the Weather Research and Forecast (WRF) model coupled with the building effect parameterization (BEP) and building energy model (BEM) at 3-km grid spacing to project the changes in the intensity, frequency, and risk of extreme high temperature over the BTH urban agglomeration. The results show that under the future shared socioeconomic pathway scenario (SSP245), the average extreme high-temperature intensity (EHI) in the BTH will increase by 0.71 °C and 2.12 °C in the middle and late twenty-first century, respectively, compared with that in the reference period (2005–2014), which are 0.23 ℃ and 0.58 ℃ more than that only considering global warming, respectively. The average extreme high-temperature frequency (EHF) will increase by 99 h and 200 h, 53 h and 72 h more than that considering only climate change, respectively. The average high-temperature risk in the BTH for 20-year and 50-year return periods will increase by 1.9 times and 2.4 times in the middle twenty-first century, respectively, and expand to 8.0 times and 12.9 times in the late twenty-first century, respectively. Therefore, it is necessary to take adaptation approaches to reduce the future risk of extreme high-temperature events in the BTH. [ABSTRACT FROM AUTHOR]

This paper reports analysis of eddy-covariance data collected during the WFIP2 field campaign in the complex-terrain of the US Pacific Northwest. A 31-day period representative of the region's dry season was used to address the following questions: (1) To what extent does the Constant-Flux Layer (CFL) assumption hold? (2) What is the spatial variability of turbulent and momentum fluxes over km scales? and (3) How skilful are the surface-layer parameterizations of mesoscale models? These questions are directly relevant to subgrid parameterization studies of mesoscale models. Results show that the efficacy of the CFL concept and the spatial variability of turbulent and momentum fluxes are dependent on: (i) the turbulent parameter being analysed, (ii) the measurement's location, (iii) the atmospheric stability regime (determined by the flow and vertical stratification), and (iv) the magnitude of the flux. Finally, the skill of the physics formulation of an often-used surface-layer parameterization scheme available in the Weather Research and Forecasting (WRF) model was also evaluated. Meteorological conditions associated with the highest and the lowest errors were identified. A metric to quantify (time-dependent) flow heterogeneity is proposed, which appears to be a good candidate to predict the skill of idealized surface-layer parameterization schemes in complex terrain. [ABSTRACT FROM AUTHOR]

The momentum roughness length (z0) significantly impacts wind predictions in weather and climate models. Nevertheless, the impacts of z0 parameterizations in different wind regimes and various model configurations on the hurricane size, intensity, and track simulations have not been thoroughly established. To bridge this knowledge gap, a comprehensive analysis of 310 simulations of 10 real hurricanes using the Weather Research and Forecasting (WRF) Model is conducted in comparison with observations. Our results show that the default z0 parameterizations in WRF perform well for weak (category 1–2) hurricanes; however, they underestimate the intensities of strong (category 3–5) hurricanes. This finding is independent of model resolution or boundary layer schemes. The default values of z0 in WRF agree with the observational estimates from dropsonde data in weak hurricanes while they are much larger than observations in strong hurricanes regime. Decreasing z0 close to the values of observational estimates and theoretical hurricane intensity models in high wind regimes (≳45 m s−1) led to significant improvements in the intensity forecasts of strong hurricanes. A momentum budget analysis dynamically explained why the reduction of z0 (decreased surface turbulent stresses) leads to stronger simulated storms. [ABSTRACT FROM AUTHOR]

Traditional atmospheric surface layer theory assumes homogeneous surface conditions. Regardless, nearly all surface layer parameterization schemes employed within numerical weather prediction models utilize the same techniques within highly heterogeneous coastal regimes as for homogeneous environments. We compare predicted surface weather and fluxes of momentum, heat, and moisture—focusing mainly on momentum—from regional simulations using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model to observations collected from offshore buoys, inland flux towers, and radiosonde profiles during the Coastal Land-Air-Sea Interaction (CLASI) project throughout the summer of 2021 around Monterey Bay, California. Results reveal that modeled cross-coastal surface flux gradients are spuriously discontinuous, leading to systematically overestimated fluxes and weak winds inland of the coastline during onshore flow periods. Additionally, contrary to observations, modeled surface exchange coefficients are insensitive to wind direction on both sides of the coast, which degrades predictive skill downstream from the coastline. Over the central bay, prediction degrades when near-surface wind directions deviate from the prevailing flow direction as the parameterized stress–wind relationship fails during these cases. Predictive skill over the bay is therefore linked to variations in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifurcations in drag coefficients based on wind direction were measured here as well. Last, increasing the horizontal grid spacing from 333 m to 3 km does not significantly affect surface layer prediction. This work highlights the need to reevaluate surface layer parameterization methods for modeling within coastal regions. Significance Statement: Understanding surface layer weather is critical for many purposes, such as infrastructure design and weather forecasting. Within the context of numerical modeling and weather prediction, skillful forecasts of surface winds and temperature rely on accurate portrayal of the surface layer. By comparing observations collected during the Coastal Land-Air-Sea Interaction field program to numerical model solutions, we show that prediction of the surface layer fluxes of momentum, heat, and moisture break down near the coastline, which leads to biases in the predicted surface layer weather both inland and over the water. As surface layer parameterization methods across nearly all numerical models are rooted in the same practices, our results call into question the use of traditional methods near the coastline. [ABSTRACT FROM AUTHOR]

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM2.5 by a chemical transport model. [ABSTRACT FROM AUTHOR]

Typhoons and typhoon waves can cause disasters in coastal areas around the world. The Taiwan Strait often experiences typhoons, especially in summer. Numerical models have been adopted to predict typhoons and reduce losses. The Weather Research and Forecasting (WRF) model is widely used in typhoon simulations, and the Simulating WAves Nearshore (SWAN) model performs well in wave simulations. However, significant uncertainty remains in terms of choosing suitable WRF physical parameterizations in different situations. To evaluate the effect of WRF physical parameterizations on wind and wave simulations, 27 simulation experiments were designed. Three typhoon events (Goni, Dujuan, and Meranti) with different tracks that influenced the Taiwan Strait were simulated. Three parameters (wind speed, wind direction and significant wave height) were assessed using Taylor diagrams, and it was found that the best simulation experiment changed according to typhoon tracks and physical parameters. In wind speed simulation, the best simulation experiment is 12 for typhoon Dujuan and 19 for typhoon Goni and Meranti. From the perspective of wind direction simulation, experiments 26, 23, and 2 performed best for typhoons Goni, Dujuan, and Meranti. And experiments 19, 1, and 20 had the best performances in significant wave height simulation for typhoons Goni, Dujuan, and Meranti. The WRF-SWAN model using the best simulation experiment reduced the error and exhibited good performance in the wind and wave simulations. Skill scores of three parameters were all over 70 for typhoon Goni and 80 for typhoon Dujuan and Meranti. The applicability of the best simulation experiments was demonstrated in typhoon simulations with similar tracks. The accuracy of the wave simulation depended on wind speed, wind direction, and their interaction. In addition, a scheme's sensitivity changed with different typhoon tracks. This study provides references for designing physical parameterizations for use with the WRF-SWAN model, which may help to simulate typhoons and typhoon waves in the Taiwan Strait more accurately in the future. [ABSTRACT FROM AUTHOR]

Wave breaking under strong wind conditions in tropical cyclones (TCs) can generate sea spray droplets, which, during their suspension in air, release sensible heat due to the air‐sea temperature difference while absorb sensible heat from the environment when they evaporate and release latent heat to the environment. Since the spray mass flux is a function of surface drag coefficient (CD), the effect of spray on TC evolution should depends on CD parameterization, while this has not been addressed so far. This study examines the effects of sea spray on the simulated TC evolution with two different CD parameterizations (the Weather Research and Forecasting (WRF) default scheme and the Donelan scheme). Results show that during the primary intensification stage, the TC with spray effect becomes stronger than that without spray when the WRF CD scheme is used, but becomes weaker when the Donelan CD scheme is used. This occurs because CD is maximum outside the radius of maximum wind (RMW) with the Donelan scheme, which produces relatively large spray‐mediated latent heat flux outside the RMW, which is unfavorable for TC intensification. The difference is enlarged by a feedback between spray and TC intensification involving the inertial stability and surface friction‐induced radial inflow. However, in the mature stage, the simulated TCs with spray become stronger no matter which CD scheme is used. In addition, the spray effect on the TC inner‐core size evolution also weakly depends on the drag parameterization. When CD is relatively greater outside the RMW, the inclusion of the spray effect would lead to the inner‐core size increase. Plain Language Summary: Wave breaking under strong wind conditions, such as in tropical cyclones (TCs), can generate abundant sea‐spray droplets, which, during their suspension in air, release sensible heat due to the air‐sea temperature difference while absorb sensible heat from the environment when they evaporate and release latent heat to the environment. This will mediate the air‐sea enthalpy transfer and affect the TC intensification. As the spray mass flux is closely related with sea surface drag coefficient (CD), we investigated how the spray effects on TC intensity evolution depend on the CD scheme used in idealized numerical simulations. Two CD schemes were used to perform four numerical experiments. Results show that the sea spray effect on TC intensity evolution depends on the CD scheme used and the stages of the TC lifetime, largely due to the different wind speed dependence of CD and its effect on the radial distribution of the spray‐mediated latent flux. However, the finding demonstrates that caution should be given to surface drag parameterization when the sea spray effects on TC evolution are studied using numerical models. It is also suggested that efforts to measure spray properties under TC conditions should be conducted to validate/improve spray parameterization in the future. Key Points: The sea spray effect on tropical cyclone (TC) intensity evolution depends on the surface drag (CD) scheme used and the stages of the TC lifetimeDifferent wind speed dependence of CD can affect the radial distribution of the spray mass flux and thus spray‐mediated enthalpy fluxAlthough spray can speed up or slow down the intensification rate, it contributes positively to the mature‐stage TC intensity [ABSTRACT FROM AUTHOR]

Following the current understanding of gravity waves (GWs) and especially mountain waves (MWs), they have high potential of horizontal propagation from their source. This horizontal propagation and therefore the transport of energy is usually not well represented in MW parameterizations of numerical weather prediction and general circulation models. The lack thereof possibly leads to shortcomings in the model's prediction as e.g. the cold pole bias in the Southern Hemisphere and the polar vortex breaking down too late. In this study we present a mountain wave model (MWM) for quantification of the horizontal propagation of orographic gravity waves. This model determines MW source location and associates their parameters from a fit of idealized Gaussian shaped mountains to topography data. Propagation and refraction of these MWs in the atmosphere is modeled using the ray-tracer GROGRAT. Ray-tracing each MW individually allows for an estimation of momentum transport due to both vertical and horizontal propagation. This study presents the MWM itself and gives validations of MW induced temperature perturbations to ECMWF IFS numerical weather prediction data and estimations of gravity wave momentum flux (GWMF) compared to HIRDLS satellite observations. The MWM is capable of reproducing the general features and amplitudes of both of these data sets and, in addition, is used to explain some observational features by investigating MW parameters along their trajectories. [ABSTRACT FROM AUTHOR]

The current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature (Tscreen$$ {T}_{\mathrm{screen}} $$) simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of Tscreen$$ {T}_{\mathrm{screen}} $$ is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model. [ABSTRACT FROM AUTHOR]

Recent work has shown that bulk-Richardson (Rib) parameterizations for friction velocity, sensible heat flux, and latent heat flux have similar, and in some instances better, performance than long-standing parameterizations from Monin–Obukhov similarity theory (MOST). In this work, we expanded upon new Rib parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), υ (meridional), and w (vertical) wind components (i.e., σu, συ, and σw, respectively), which allowed us to derive Rib-based parameterizations of turbulent kinetic energy (e), and standard deviations in the 30-min temperature and moisture measurements (σθ and σq, respectively). We used datasets from three 10-m micrometeorological towers installed during the Land Atmosphere Feedback Experiment (LAFE) conducted in Oklahoma from 1 to 31 August 2017 and evaluated the new parameterizations by comparing them against parameterizations from MOST. We used the LAFE datasets and fully independent datasets obtained from two micrometeorological towers installed in Alabama between February 2016 and April 2017 to evaluate the performance of the parameterizations. Based on the slope of the relationship between the observed and parameterized turbulence statistics (mb) and the coefficient of correlation (r), we found that the Rib relationships generally performed better than MOST at parameterizing συ, σw, σθ, and σq, and the Rib relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Rib parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Rib-based parameterizations in weather forecasting models. Significance Statement: Deficiencies in Monin–Obukhov similarity theory (MOST) are well known, yet MOST forms the basis in weather forecasting models for describing heat, moisture, and momentum transfer between the land surface and atmosphere. We expanded upon previous work suggesting a MOST alternative called the bulk-Richardson approach. We used data collected from meteorological towers installed in Oklahoma and compared the bulk-Richardson approach with MOST. We evaluated these two approaches using data from meteorological towers installed in Oklahoma and Alabama and found that, overall, the bulk-Richardson approach performed better than MOST in determining the 30-min variability in temperature, moisture, and wind. This result provides additional motivation to use a bulk-Richardson approach in weather forecasting models because doing so will likely yield improved forecasts. [ABSTRACT FROM AUTHOR]

In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that machine learning (ML) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physics-based schemes. However, as the ML models are commonly implemented using the ML libraries written in Python, very few ML-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the ML-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather Research and Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the ML model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the ML-based radiation emulators are trained and coupled with the WRF model successfully. [ABSTRACT FROM AUTHOR]

On seanonal timescale, the variation of Earth rotation is mainly regulated by angular momentum exchanges between the solid Earth and the fluidal atmosphere, ocean and hydrosphere. In the 2nd EOP PCC, we developed Dill2019's method for polar motion prediction, using piecewise autoagressive parameters. The maximum prediction errors within 90 days are 36 and 16 mas for polar motion x and y components, respectively. Compared with Bulletin A, the mean absolute error of polar motion y prediction is improved by 20% in all timescale, and with a maximum improvement of 49% on the 5th day. Whereas, for polar motion x, the performance is slightly better (2% - 8%) within 30 days but worse (−7%~ −19%) within 30~90 days. We found that the prediction accuracy is very sensitive to the quality of the angular momentum data. For example, on average, the prediction of polar motion y is around 2 times better than polar motion x. In addition, we found the accuracy of 30-90 days prediction is dramatically decreased in the year 2020. We suspect that such deterioration might be due to the pandemic of coronavirus COVID-19, which suppressed global airline activities by more than 60%, then result in a lose of air-borne meteorological data, which are important for weather forecast. [ABSTRACT FROM AUTHOR]

Using the Advanced Research Weather Research and Forecasting (WRF) model, a series of numerical experiments are conducted to examine the sensitivity of the Typhoon Mangkhut intensification simulation to different air–sea flux parameterization schemes (isftcflx option), including option 0 (OPT0), option 1 (OPT1), and option 2 (OPT2). The results show that three schemes basically reproduce tropical cyclone (TC) track and intensity of observation, and the simulated exchange coefficient of three schemes is consistent with theoretical results. Using the same upper limit of C d as OPT0 and OPT2, OPT1 has much larger C k than the other two options, which leads to larger latent heat (and sensible heat) flux and produces stronger inflow (within boundary layer) and updrafts (around eyewall), and thus stronger TC intensity. Meanwhile, the results that larger C k / C d corresponds with stronger TC in the mature stage are consistent with Emanuel's potential intensity theory. The fact that C k in OPT1 is evidently larger than the C k from previous studies leads to produce a better TC intensity simulation. Generally, we should use more reasonable air–sea flux parameterization based on observation to improve TC intensity simulation. [ABSTRACT FROM AUTHOR]

An intercomparison of raindrop mean diameter frequency distribution (RDFD) is performed for numerical simulations of precipitating cloud systems using an array of models and microphysics schemes. This includes results from the Regional Atmospheric Modeling System (RAMS) double-moment microphysics, the Hebrew University Cloud Model bin microphysics (HUCM) interfaced to the RAMS parent model, and the Weather Research and Forecasting (WRF) Model with the Thompson, Morrison, double-moment 6-class (WDM6), and National Severe Storms Laboratory (NSSL) double-moment schemes. Simulations are examined with respect to the raindrop size distribution (DSD) volume-number mean diameter (Dm) and intercept parameter (Nw). When compared to a suite of disdrometer observations, the RDFD resulting from each microphysics scheme exhibits varying degrees of mean drop size constraints and peaks in the frequency distribution of Dm. A more detailed investigation of the peaked RDFD from the RAMS simulations suggests that the parameterization of raindrop collisional breakup can impose strong limitations on the evolution of simulated drop growth. As such, a summary and comparison of the drop breakup parameterizations among the aforementioned microphysics schemes is presented. While some drop breakup parameterizations are adjusted toward the observations by modifying the threshold diameter for the onset of breakup, this study explores the use of a modified maximum breakup efficiency. This method permits the parameterization to retain its threshold breakup diameter, while limiting the strength of drop breakup and permitting a broader range of drop sizes. As a result, the simulated mean drop sizes are in better agreement with observations. [ABSTRACT FROM AUTHOR]

Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the wake recovers faster in the LES, whereas for the TKE both types of simulations show similar levels. From the various configurations, the impact of inversion height and strength is small for these heights and inversion levels. The highest impact for the one-turbine simulations appears under the low-speed case due to the higher thrust, whereas the impact of resolution is low for the large-eddy simulations but high for the mesoscale simulations. Our findings demonstrate that higher-fidelity simulations are needed to validate wind-farm parameterizations. [ABSTRACT FROM AUTHOR]

In this study, five simulations were conducted using the weather research and forecasting (WRF) model with different cumulus parameterizations schemes (CPSs) for the period from 2013 until 2018. A one-year simulation of 2013 with three different horizontal resolutions of 25, 5, and 1.6 km was also performed. The CPSs used were Kain–Fritsch (KF), Grell–Devenyi (GR), Betts–Miller–Janjic (BM), and a non-parameterized scheme (NC). In assessments of model resolutions, both the 25 and 5 km resolutions depicted a strong negative bias in the northeastern part of Peninsular Malaysia during December–January–February (DJF), with marginal differences between the two simulations. Among all 5 km experiments, the best performing scheme was the BM scheme for almost all seasons. Furthermore, the 5 km simulation did not exhibit significant differences relative to the 25 km of the diurnal cycle. The 1.6 km simulation showed significant added value as it was the only simulation that was able to simulate the high precipitation intensity in the morning and a precipitation peak during the evening. The 1.6 km resolution was also the only resolution capable of picking up the precipitation signals in the R4 region (South Peninsular Malaysia) compared to the other two resolutions. While both CPSs and resolutions are important for accurate predictions, the role of CPSs became less significant in a higher resolution simulation. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso- and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017 to 2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation. [ABSTRACT FROM AUTHOR]

Typhoon (TY) Haiyan was one of the most intense and highly destructive tropical cyclones (TCs) to affect the Philippines. As such, it is regarded as a baseline for extreme TC hazards. Improving the simulation of such TCs will not only improve the forecasting of intense TCs but will also be essential in understanding the potential sensitivity of future intense TCs with climate change. In this study, we investigate the effects of model configuration in simulating TY Haiyan using the Weather Research Forecasting (WRF) Model. Sensitivity experiments were conducted by systematically altering the choice of cumulus schemes, surface flux options, and spectral nudging. In addition to using the European Centre for Medium-Range Weather Forecasts Reanalysis fifth-generation (ERA5) single high-resolution realization as initial and boundary conditions, we also used 4 of the 10 lower-resolution ERA5 data assimilation system (EDA) ensemble members as initial and boundary conditions. Results indicate a high level of sensitivity to cumulus schemes, with a trade-off between using Kain–Fritsch and Tiedtke schemes that have not been mentioned in past studies of TCs in the Philippines. The Tiedtke scheme simulates the track better (with a lower mean direct positional error, DPE, of 33 km), while the Kain–Fritsch scheme produces stronger intensities (by 15 hPa minimum sea level pressure). Spectral nudging also resulted in a reduction in the mean DPE by 20 km, and varying the surface flux options resulted in the improvement of the simulated maximum sustained winds by up to 10 m s -1. Simulations using the EDA members initial and boundary conditions revealed low sensitivity to the initial and boundary conditions, having less spread than the simulations using different parameterization schemes. We highlight the advantage of using an ensemble of cumulus parameterizations to take into account the uncertainty in the track and intensity of simulating intense tropical cyclones. [ABSTRACT FROM AUTHOR]

In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that deep learning (DL) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physic-based schemes. However, as the DL models are commonly implemented using the DL libraries written in Python, very few DL-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the DL-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather and Research Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the DL model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the DL-based radiation emulators are trained and coupled with the WRF model successfully. [ABSTRACT FROM AUTHOR]

In mesoscale Weather Research and Forecasting (WRF) model, several urban‐modeling options exist, such as Single‐layer Urban Canopy Model, Building Effect Parameterization, and Building Energy Model. However, these models have limitations in terms of the choice of land surface models (LSMs) and planetary boundary layer (PBL) schemes, the associated computational expenses, and other constraints. Recently, Dy et al. (2019, https://doi.org/10.1029/2018JD029333) included the urban‐momentum drag effect for wind‐speed modeling by developing a new multilayer model based on a modified nonlocal Asymmetric Convective Model version‐2 (ACM2) PBL scheme. This urban‐based ACM2 (UACM) has demonstrated a significantly improved ability over the base‐ACM2 (BACM) to predict wind speed near the urban ground surface and shown an inflection point at roof level in the vertical profile of horizontal wind. In this study, urban thermal and moisture components are introduced into the Pleim‐Xiu (PX) LSM and coupled with UACM. The diurnal variation in street, walls, and roof‐surface temperatures is modeled using the two‐layer force‐restore algorithm. Simple radiation treatment is considered to account for shadowing on streets based on the solar zenith angle and building morphology. Heat and moisture flux evolution are considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The idealized and real urban data case WRF simulations are performed over the Pearl River Delta region in southern China to test the new UACM. The computationally efficient UACM is expected to perform faster for operational forecasting runs. Key Points: Urban thermal and moisture effects are included in the Pleim‐Xiu land surface model, and connected to the Urban Asymmetric Convective ModelAbility of new UACM is confirmed by performing idealized simulations to analyze model sensitivity to various urban morphological parametersImprovement in the urban wind, temperature, and moisture‐prediction is demonstrated with the new UACM by conducting real‐data case studies [ABSTRACT FROM AUTHOR]

A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean. Plain Language Summary: A stochastic cloud population model is coupled with a Weather Research and Forecasting model for applications as a parameterization of cumulus convection. The model predicts the evolution of the cloud‐base mass flux distribution via a transition function obtained using the machine learning algorithm trained by a 1‐km grid‐spacing convection permitting model simulation. The predicted mass flux statistics is then provided to an entraining plume model to obtain heating and moistening tendencies. The potential of the approach as a path to improvement of statistics and variability of precipitation in models is examined over multiple regimes. Key Points: A stochastic cloud population model is coupled with a mesoscale model for applications as a parameterization of cumulus convectionThe model predicts the evolution of the cloud‐base mass flux distribution via a transition function obtained using machine learningThe potential of the approach as a path to improvement of statistics and variability of precipitation in models is demonstrated [ABSTRACT FROM AUTHOR]

nerating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δx) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δx ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional meso- scale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. [ABSTRACT FROM AUTHOR]

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. • The sensitivity of the simulation of Southern Indian OceanTC to four distinct cumulus schemes is assessed based on TC Idai (2019). • The MKF produces the least track and intensity error due to precise simulation of rainfall in the environment and TC inner-core. • The more accurate large-scale and inner-core rainfall results in less biased steering flow and TC secondary circulation. [ABSTRACT FROM AUTHOR]

The impact of eddy diffusivities in the Mellor–Yamada–Nakanishi–Niino (MYNN) boundary layer parameterization on the fine‐scale turbulence and intensity of tropical cyclone (TC) simulations at gray‐zone resolution was investigated through comparison with large‐eddy simulation (LES) and observations. A series of idealized experiments were performed with varying vertical mixing lengths using the Advanced Research version of the Weather Research and Forecasting model. For enhanced eddy diffusivity cases, the storm intensity was sensitive to the eddy diffusivities and reducing the overestimation of the eddy diffusivities caused a significant increase in intensity. For the reduced eddy diffusivity cases, the TC intensity was marginally influenced by the eddy diffusivities. However, the reduction in eddy diffusivities effectively reproduced the fine‐scale turbulence. The stronger the resolved fine‐scale turbulence that could be established, the more frictional tendencies near the location of maximum wind were provided to the TC, the closer it was to the reference LES and observations. The resolved turbulence also had consequences in terms of preventing the wind gradient from increasing, which was one of the primary reasons for the discrepancies in the nonlinear intensity and fine‐scale features. The results of this study demonstrate that the modification of eddy diffusivities in the MYNN scheme can measure or guide improvements in the simulation of TC boundary layer fine‐scale features and intensity when operating at the gray‐zone resolution. Plain Language Summary: Eddy diffusivities—key parameters in planetary boundary layer parameterization—are not well understood owing to a lack of sufficient data on the tropical cyclone (TC) boundary layer (TCBL). Moreover, their effects on the TCBL may be further complicated at the turbulent gray‐zone resolution when turbulence starts to be resolved. Therefore, the role of eddy diffusivities in the simulation of TC fine‐scale features and intensity needs to be further investigated at the gray‐zone resolution. In this study, we focused on how boundary layer fine‐scale turbulence and TC intensity are influenced by eddy diffusivities in a widely used boundary layer scheme at resolutions down to 500 and 166 m. The results indicated that the eddy diffusivities were overestimated in the boundary layer scheme. The reduction of eddy diffusivities reproduced better fine‐scale features and TC intensity, and a closer vertical structure to a reference large‐eddy simulation and observations. In conclusion, artificial modification of eddy diffusivities can serve as a measure or as guidance toward improving the simulation of TC boundary layer fine‐scale features and intensity when operating at gray‐zone resolutions. Key Points: Direct modification of KM is applicable to improve a turbulent kinetic energy planetary boundary layer scheme, which can improve the tropical cyclone (TC) turbulence simulation at gray‐zone resolutionImproving the resolved turbulence provides considerable frictional tendency near the height of maximum wind and reduces the vertical gradientFor TC simulation at gray‐zone resolution, the resolved turbulence is one of the key reasons for the nonlinear effect of KM on TC intensity [ABSTRACT FROM AUTHOR]

In 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-scaleaware), 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. Plain Language Summary Based on the increase in computational resources, numerical weather prediction models are operating in the "gray-zone" for horizontal grid spacing in the range of 1-10 km in which both cumulus parameterizations and explicitly resolved deep convection are problematic. In this study, the effect of the scale-aware convective parameterization scheme (CPS) on the simulation of convective cellrelated heavy precipitation across the gray-zone was investigated using the Weather Research and Forecasting (WRF) model. The scale-aware CPS uses a scale-aware parameter, which modulates the convective process as a function of the horizontal grid spacing. We found that simulating convection processes in the gray-zone without the CPS is inadequate. Furthermore, the CPS without scale-awareness generates an erroneous precipitation simulation due to the exaggeration of convection and distortion of synoptic fields. In contrast, the scale-aware CPS improves the simulation of convection cell-related heavy rainfall in the gray-zone by reducing the role of the CPS and increasing that of explicitly resolved precipitation with the decrease in the grid spacing. Our results indicate that using a scale-aware CPS and managing convective processes are crucial in controlling the CPS and microphysics parameterization scheme (MPS) in the gray-zone. [ABSTRACT FROM AUTHOR]

Ozone (O 3) dry deposition is a major O 3 sink. As a follow-up study of Huang et al. (2021), we quantify the impact of satellite soil moisture (SM) on model representations of this process when different dry-deposition parameterizations are implemented, based on which the implications for interpreting O 3 air pollution levels and assessing the O 3 impacts on human and ecosystem health are provided. The SM data from NASA's Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization (Noah-MP) land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry (WRF-Chem) regional-scale simulations covering the southeastern US. Major changes in the modeling system used include enabling the dynamic vegetation option, adding the irrigation process, and updating the scheme for the surface exchange coefficient. Two dry-deposition schemes are implemented, i.e., the Wesely scheme and a "dynamic" scheme, in the latter of which dry-deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the dynamic scheme is applied, the simulated O 3 dry-deposition velocities vd and their stomatal and cuticular portions, as well as the total O 3 fluxes Ft , are larger overall; vd and Ft are 2–3 times more sensitive to the SM changes due to the data assimilation (DA). Further, through case studies at two forested sites with different soil types and hydrological regimes, we highlight that, applying the Community Land Model type of SM factor controlling stomatal resistance (i.e., β factor) scheme in replacement of the Noah-type β factor scheme reduced the vd sensitivity to SM changes by ∼75 % at one site, while it doubled this sensitivity at the other site. Referring to multiple evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O 3 concentrations shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy-relevant O 3 metrics as well as their matching exposure–response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range of 1 %–17 %. Their sensitivities to the model's dry-deposition scheme and the implementation of SM DA are discussed. [ABSTRACT FROM AUTHOR]

Reliable simulation of wind patterns that form under stable weather conditions is vital to prevent air pollution. Here, we investigated how different physical parameterization schemes impact surface wind simulations over the coastal regions of North China using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 0.5 km. We performed 640 ensemble simulations using multiple combinations of 10 planetary boundary layer (PBL), 16 microphysics (MP), and four longwave/shortwave (LW/SW) radiation schemes. Model performance was evaluated using measurements from weather station observations. These data show that WRF model can reproduce the temporal variation of wind speed and direction to a high degree of accuracy. The simulated wind speed is most sensitive to the PBL schemes, followed by LW/SW radiation schemes and MP schemes, while wind direction is less sensitive to variation of the physical parameterizations. Among all PBL schemes, the MYJ scheme shows the strongest correlation with observation data, while the YSU scheme had the smallest model bias. A combined Dudhia and RRTM scheme and MYDM7 scheme show the best model performances out of all LW/SW radiation and MP schemes, respectively. Our results show that the interactions among physical components also play an important role in wind simulations. Further investigations indicate that land type has a profound influence on model sensitivity. For example, for the weather stations located in coastal regions. The MYNN scheme showed the highest correlation among all PBL schemes, while LES and YSU has the smallest model errors, the RRTMG and Goddard schemes showed the highest correlations and smallest biases out of all LW/SW radiation schemes. Our results indicate the roles that various parameterization schemes play in wind simulations under stable weather conditions, and provide a valuable reference for further research in this region and in other locations around the world. [ABSTRACT FROM AUTHOR]