Cumulus Schemes Research Articles

Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka

ABSTRACT Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.

Kilometer-scale multi-physics simulations of heavy precipitation events in Northeast China

Despite the fatal impact of heavy precipitation on people’s lives and the social economy, its accurate estimating remains challenging. In this study, we show how to address this issue by kilometer-scale simulations and how to reduce computational costs in Northeast China with the complex terrain and distribution of land and sea. Three typical heavy precipitation events are simulated at 3 km horizontal resolution, and each event is simulated with 24 combinations of schemes (with or without a scale-aware cumulus scheme, three microphysics schemes, and four planetary boundary layer schemes), which are evaluated against gauge observations. Compared to gauge observations, the ensemble mean of simulations of hourly maximum precipitation and average accumulated precipitation outperforms three widely accepted satellite products in the cold vortex and the snowstorm case, and is of comparable accuracy in the typhoon case. Overall, the microphysics scheme significantly impacts the maximum hourly precipitation, whereas the planetary boundary layer scheme has a strong control over the accumulated precipitation. The similarity among different simulations is linked to the level of convective instability's impact on heavy precipitation in each case, which also indicates that conducting 24 simulations can be not necessary. This study uses an ensemble performance estimation technique assuming the impact of different schemes is additive and finds that performing 13 rather than 24 simulations allows finding the best-performing combination of parameterization schemes, which allows for saving almost 50% of computational costs.

Improving Diurnal Precipitation Forecasts Through Coherent Coupling of Cumulus and Planetary Boundary Layer Parameterizations

AbstractThis study explores the impact of coupling cumulus and planetary boundary layer (PBL) parameterizations on diurnal precipitation forecasting during the plum rainy season in Jiangsu Province, China, using a double grid‐nesting approach. Results show that coherent coupling of cumulus (only in the 15 km grid outer domain [O]) and PBL parameterizations leads to improved forecasting of diurnal variations in the morning, afternoon, and the evening. Increasing the frequency of the Kain‐Fritsch (KF) cumulus scheme in [O] enhances subgrid precipitation while reducing grid‐scale precipitation, resulting in a more accurate representation of daytime convective activities and a reduction in over‐forecasting of evening valley and early‐morning precipitation. Additionally, coupling a suitable PBL scheme mitigates the overpredicted afternoon peak by facilitating turbulent mixing to penetrate higher altitudes with a thicker layer, thereby reducing instability energy accumulation. A higher KF frequency in [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Various PBL schemes produce distinct vertical distributions of turbulent moisture and heat transport, impacting convection and precipitation in [I] resolved by cloud microphysics processes. The coherent coupling of these parameterizations maintains a balanced supply of convective energy and water vapor, significantly improving diurnal precipitation forecasts in [I]. Isolating these parameterizations between nested grids may undermine this improvement.

Sensitivity analysis of cumulus and microphysics schemes in the WRF model in simulating Extreme Rainfall Events over the hilly terrain of Nagaland

A comprehensive study is carried out to investigate the sensitivity of various combinations between cumulus (CU) and microphysics (MP) schemes using the Weather Research and Forecasting (WRF) model for simulating Extreme Rainfall Events (EREs) over the hilly terrain of Nagaland (25.1 oN – 27.2 oN; 93.2 oE– 95.3 °E). The study explores fifty-four distinct simulation combinations resulting from six CU and nine MP schemes. The simulated rainfall is compared with ground-based rain gauge network and Global Precipitation Measurement (GPM) satellite observations. The investigation focuses primarily on finding the optimal CU-MP scheme combination based on spatiotemporal precision achieved by the identified schemes. The C14 (New Simplified Arakawa Schubert) cumulus scheme is found to be the optimum individual CU scheme. Similarly, the M14 (WDM5) microphysics scheme performs as the optimum individual MP scheme. The study highlights the significance of evaluating the performance of combined schemes over individual ones. Among the fifty-four combinations, sensitivity analysis identified six top-performing combinations, out of which the C11-M8 (Multiscale Kain Fritsch - New Thompson) combination stands out to be optimum with a robust correlation coefficient (CC) of 0.75, lower standard deviation (STD) of 0.76 mm, and RMSD of 0.51 mm. A variation in lead/lag time is found to vary from (−) 3.2 to (+) 5.0 h, with a median value of (+) 1.3 h, suggesting that, on average, the model tends to predict the occurrence of peak rainfall events slightly earlier than observed one. The absolute difference in latitudes (longitudes) of peak rainfall variation ranges from 0.42o - 0.8o (0 o - 0.51o) with a median value of 0.66o (0.37o), indicating that the model could better represent the longitudinal peak rainfall positions with notable accuracy. The spatial correlation coefficient (SCC) ranges between 0.31 and 0.68 with a median value of 0.50. From the overall analysis, it is also found that double moment MP schemes (New Thompson, Morrison (2 M), WDM5, WDM6) consistently perform better than the single moment MP schemes (Kessler, Lin et al., WSM3, WSM5, WSM6), whether in combination with CU schemes or individually. Spatiotemporal variations observed across different cases underscore the pivotal role of accurate topography representation in complex terrains, emphasizing the significance of terrain data in shaping rainfall patterns.

Validation of WRF Rainfall Model during the November 4th, 2021 Flash Flood Event in Batu City – Indonesia

Flash floods in Batu City, Indonesia on November 4th, 2021, caused damage to property, agricultural land, settlements, death of livestock, and loss of human life. One of the important factors triggering this flash flood is heavy rainfall. This study aims to analyse the spatial and temporal dynamics of rainfall as a trigger for the event. Rainfall modelling was conducted using the WRF model with 2 microphysical schemes and 3 cumulus schemes. The data used is GFS data on October 30th, 2021, with a resolution of 0.25 x0.25 degrees which is used to predict rain events until November 4, 2021. Contingency table verification, RMSE value verification, and verification using tolerance limits are used to verify the results of modelling data based on BMKG observation data. The results of this study show that this model can predict rain and non-rain events very well. However, this model is not good enough to predict the thickness of rain until November 4, 2021. The best scheme in modelling rainfall is scheme 5 (Lin and Betts Miller Janjic). Scheme 5 has the smallest RMSE value of 77, the data fit is appropriate and the medium error rate in scheme 5 is 84%. Scheme 5 with GFS data input started from 02 November 2021 can record rain events on 04 November 2021 with extreme rain categories. This extreme rain is influenced by convective clouds. The temperature of the cloud tops changes significantly from 13.00 WIB to 14.00 local time, namely from −4 degrees, and decreases to −76 degrees Celsius.

Improving Estimates of Land–Atmosphere Coupling Through a Novel Framework of Land Aridity Classification

AbstractThe evapotranspiration (ET) regime is an illustration of the water‐energy interactions at the land surface and is important for regional climate. In this study, we propose a revised framework for land aridity classification based on the ET regime and the conditional mutual information method. The proposed framework effectively captures the influences of short‐term water and energy supply on ET and highlights their potential roles in extreme events. This framework can be a supplement to the traditional classification schemes that rely on long‐term mean climate. Furthermore, we examine the sensitivity of this land aridity framework to different model physics parameterization schemes. The cumulus schemes are found to have the most important impact, followed by the radiation and microphysics schemes, while the planetary boundary layer schemes have the weakest impact. These findings provide valuable insights for the identification of land aridity and its relationship with land–atmosphere interactions.

All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

The Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) allows for high-spatiotemporal-resolution observations of the local severe weather systems. The capabilities for assimilating the all-sky AGRI infrared radiances are explored with the WRF three-dimensional variational data assimilation system (WRF-3DVAR) to improve the forecast accuracy of an extremely heavy rainfall event over the Henan Province of China in this study. In particular, data assimilation experiments are conducted with different physical parameterization schemes, including microphysical schemes and cumulus parameterizations. The results show that the all-sky AGRI radiance data assimilation experiments based on the Purdue Lin microphysics scheme and the Kain–Fritsch cumulus scheme can lead to better forecasts of the rainfall intensity and location, respectively. Meanwhile, it is found that the analyzed cloud top temperature fits the observations better when cloudy radiances from the AGRI are assimilated using the Purdue Lin scheme. This finding suggests that proper physical processes could facilitate the effective use of AGRI cloud-affected observations. On the other hand, positive forecast impacts from assimilating the all-sky AGRI radiance data have been confirmed when compared to the experiments that assimilate only the clear-sky AGRI radiances or only conventional observations. The assimilation of the all-sky AGRI water vapor channel is conducive to humidifying the initial conditions in the middle and low troposphere with more realistic analysis increments of water vapor, cloud water, and cloud ice, resulting in improved intensity forecasts of the heavy rainfall system and better forecast skill scores.

Performance of Different Parameterization Configurations of WRF-ARW Model during Heavy Rainfall over Mi Oya River Basin

Short-term intense precipitation is one of the hallmarks of climate change. Mi Oya River basin experiences severe seasonal floods annually, but the damage can be lessened by developing a numerical weather forecasting (NWF) model for the entire basin and incorporating it with effective reservoir management. Several areas in Sri Lanka have undergone NWF studies, however they are insufficient to determine the best physics schemes for the basin. This study investigates the Weather Research and Forecasting (WRF-ARW) model's predictability with varying three microphysics and two cumulus schemes to discover the optimal set of physics parameters for predicting heavy rainfall occurrences throughout the Southwest and Northeast monsoon seasons within a nested domain configuration. The WRF model's forecasting results at 3 km grid resolution were compared with four rainfall gauging stations in the basin for three rainfall events in May 2016, April 2018, and November 2015. Total Model Performance was derived for the evaluation utilizing bias, MAE, RMSE, Correlation Coefficient, and slope of each model's output data with observed rainfall data. After comparing the model output to data, WSM6 microphysics and Betts-Miller-Janjic cumulus with other default physics settings were determined to be the optimal physics combination to forecast weather across the region.

Seasonal dependent suitability of physical parameterizations to simulate precipitation over the Himalayan headwater

The Himalayan ecosystem is fragile and needs robust management strategies for sustainability of natural resources such as water and vegetation. Therefore, reliable precipitation estimation becomes quite important from operational and regulation standpoints. It is crucial for numerous activities including policy/planning, agriculture, reservoir operations, disaster management, and others. In addition, reliable information on temporal variability of precipitation is also crucial for various applications such as agricultural and hydrological. The western Himalaya receives two distinct weather systems during summer and winter. Summer is responsible (largely) for rainfall and winter is for snowfall. Therefore, we hypothesize that there may not be a single set of parameterization schemes that can represent well both the weather systems. To investigate, we set up the WRF modeling system and performed six experiments with a combination of three microphysics (MP3, MP3, and WSM6) and two cumulus schemes (KF, and BMJ). It was found that the precipitation along the Himalayan foothills (near to basin terminal) is underestimated in four out of six experiments. Only experiments with BMJ cumulus scheme along with WSM6 and MP8 microphysics were able to show a considerable amount of precipitation along these foothills. It was noted that all six experiments showed high precipitation in the upstream region and over the mountain peaks and ridges in North-Western Himalaya. For DJF, each experiment was found to have large biases and none of them represented the observation with high confidence. However, the selection of observation reference data itself is a challenging task because of data paucity in this region. Therefore, the closest experiment to the most appropriate observation was selected as the reliable configuration (MP8_KF: MP8 microphysics and KF cumulus scheme) for DJF precipitation simulation. In this study we have, for the first time, reported the role of seasonal sensitivity for the climate scale simulations as we found that different schemes were suitable for different weather systems.

Performance of an Improved TKE-Based Eddy-Diffusivity Mass-Flux (EDMF) PBL Scheme in 2021 Hurricane Forecasts from the Hurricane Analysis and Forecast System

Abstract Continuous development and evaluation of planetary boundary layer (PBL) parameterizations in hurricane conditions are crucial for improving tropical cyclone (TC) forecasts. A turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF-TKE) PBL scheme, implemented in NOAA’s Hurricane Analysis and Forecast System (HAFS), was recently improved in hurricane conditions using large-eddy simulations. This study evaluates the performance of HAFS TC forecasts with the original (experiment HAFA) and modified EDMF-TKE (experiment HAFY) based on a large sample of cases during the 2021 North Atlantic hurricane season. Results indicate that intensity and structure forecast skill was better overall in HAFY than in HAFA, including during rapid intensification. Composite analyses demonstrate that HAFY produces shallower and stronger boundary layer inflow, especially within 1–3 times the radius of maximum wind (RMW). Stronger inflow and more moisture in the boundary layer contribute to stronger moisture convergence near the RMW. These boundary layer characteristics are consistent with stronger, deeper, and more compact TC vortices in HAFY than in HAFA. Nevertheless, track skill in HAFY is slightly reduced, which is in part attributable to the cross-track error from a few early cycles of Hurricane Henri that exhibited ∼400 n mi (1 n mi = 1.852 km) track error at longer lead times. Sensitivity experiments based on HAFY demonstrate that turning off cumulus schemes notably reduces the track errors of Henri while turning off the deep cumulus scheme reduces the intensity errors. This finding hints at the necessity of unifying the mass fluxes in PBL and cumulus schemes in future model physics development.

Sensitivity of WRF multiple parameterization schemes to extreme precipitation event over the Poyang Lake Basin of China

Global warming leads to more frequent and more violent extreme weather events, which cause severe natural disasters. The sensitivity of physical schemes in numerical weather prediction models for extreme precipitation is a significant challenge. To simulate a heavy precipitation process from 1 July 2020 to 10 July 2020 over the Poyang Lake Basin, where floods occur frequently, the Weather Research and Forecast model (WRF) was employed. The observation (OBS) from 92 meteorological stations was applied to evaluate WRF performance. To assess the optimal parameter, 27 combinations of multiphysics schemes based on three cumulus schemes (CUs), three microphysical schemes (MPs) and three land surface schemes (LSMs) were employed in WRF simulation. The Euclid distance (ED) was derived to evaluate the performance of the modelled total cumulative precipitation (TCP). The results showed that the simulation generally reproduced the spatial distribution of precipitation and captured the storm centre. In general, WRF underestimated the observation for most areas but overestimated the observation in the northeastern part of the basin. For total cumulative precipitation (TCP), the spatial correlation coefficients ranged from 0.6 to 0.8. M11 had the highest value of 0.796. The scores (TS, POD, FAR) of M15 were 0.66, 0.79, and 0.21, respectively. With the maximum similarity of 0.518, M7 (BMJ-WDM6-Noah) showed the best performance in ED based on six evaluation metrics. The mean values of OBS and M19 were 259.34 and 218.33 mm, respectively. M19 (NT-Thompson-Noah) was closest to the OBS for the range of the TCP. In terms of daily precipitation, the CC of M3 was maximum (0.96), and the RMSE of M24 was minimum (11.9 mm. day-1). The minimum error between the simulation and OBS was found for M3 (NT-Thompson-Noah). Therefore, with a comprehensive evaluation, five optimal combinations of physical schemes (M22, M19, M9, M3, M21) were found for the PLB, which is of great significance for extreme precipitation simulation and flood forecasting.

Sensitivity analysis of atmospheric phenomena models for precipitation assessment on the Paraíba do Sul River Watershed

This paper is aimed at performing a group of experiments to evaluate the sensitivity to cumulus and microphysics schemes, as represented in numerical simulations of the Weather Research and Forecasting (WRF) model. The convective schemes of Kain-Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell-Devenyi (GD), Grell-Freita (GF), Grell 3D (G3D), Tiedtke and New Tiedtke (NT) were tested in association with the microphysics schemes of Kessler, Purdue Lin, WSM3, WSM5, WSM6, ETA (Ferrier) and Goddard (totaling forty-nine experiments) in order to identify the combination which best represents the cumulative rainfall distribution in the Paraíba do Sul watershed. In order to evaluate the best performance experiments, they were submitted to statistical tests of bias (BIAS), root mean square error (RMSE), absolute mean error (MAE) and Coefficient of Determination (R2). Results show that combinations WSM5 and GD; Goddard and G3D; Perdue Lin and G3D; WSM5 and G3D form a group of four physical configurations statistically similar and able to predict well the mean rainfall in the Paraiba do Sul watershed. It was noticed also that the cumulus scheme has a greater weight than microphysics in rainfall simulations being GD3 the best performing.

Evaluation of Three Genesis Potential Indices for Tropical Cyclogenesis in the Arabian Sea: Two Case Studies Using WRF and ERA5

Abstract Tropical cyclones (TCs) generated over the Arabian Sea can cause significant damage to infrastructure, human lives, landfall, and property near inshore and maritime trade route areas. A key to successful prediction of TCs is a skillful prediction of potential cyclogenesis locations. This study focuses on evaluating three genesis potential indices (GPIs) derived from a global reanalysis (ERA5) and dynamically downscaling using a regional model (WRF) for two TC cases: Gonu in 2007 and Kyarr in 2019, selected by analyzing the accumulated cyclone energy trend from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset over the period of 1981–2019. The two TCs belong to category 4 and above on the Saffir–Simpson scale. To test the sensitivity of downscaling to cumulus parameterizations, two WRF experiments were conducted using the Kain–Fritsch and New Tiedke cumulus schemes, respectively. The calculated genesis locations with help of the three GPIs were compared with IBTrACS. The results show that 1) all indices have reasonable skills in reproducing genesis locations, although their performances differ somewhat; 2) the dynamic downscaling with two WRF experiments added value to the study by comparing two numerical schemes for estimating genesis locations; and 3) WRF with the New Tiedke and Kain–Fritsch schemes showed good skill in reproducing the spatial distribution of the most relevant dynamical parameters. The pattern correlations are well correlated with environmental parameters of untransformed GPI and higher correlations with binary logarithmic transformed GPI. The applicability to other cyclones is also tested (e.g., TC Nilofar in 2014) with encouraging results. This study demonstrates the usefulness of GPIs for forecasting TC genesis in the region. Significance Statement The trend analysis of accumulated cyclone energy (ACE) over the Arabian Sea (AS) shows an increase over the period of 1981–2019 with the highest ACE values for 2019. The genesis potential indices (GPI) show strong ability for use as a forecasting tool for tropical cyclone genesis, and hence, are helpful for providing a reference for future studies. WRF experiments were able to reproduce the GPI with slight differences from the observations and ERA5. WRF schemes show good performance in reproducing key meteorological fields. The analysis of the GPI and WRF schemes shows the potential to be implemented for maritime forecasts of the tropical cyclones in the region. This study will be helpful scientifically and strategically with a significant impact on socioeconomic activities in the region.

Assessment of WRF microphysics and cumulus parameterizations in simulating heavy rainfall events over Badulu Oya catchment, Sri Lanka

Abstract Extreme rainfall events leading to severe hydrological impacts warrant an accurate prediction of such events not only on time but also in magnitude. Sri Lanka is a South Asian country that is frequently affected by severe tropical storms. The primary aim of this study was to improve heavy rainfall events forecast during the North-East monsoon over the Badulu Oya catchment, Sri Lanka. This aim was accomplished by simulating precipitation for two extreme North-East monsoon rainfall events using the Weather Research and Forecasting (WRF-ARW) model. A detailed comparison was made between the 24-h spatial distribution of model rainfall and observations obtained from rainfall gauges. Verification was evaluated based on three deterministic approaches. Each rainfall event was simulated multiple times using 15 different parameterization scheme combinations including six microphysics and four cumulus schemes at a 3 km grid resolution. The filtered best model combinations were validated using observations from another two heavy North-East monsoon rainfall events. The key finding from these evaluations was that model configurations with WSM5, WSM6, Kessler and WDM6 microphysics, and KF, BMJ and MKF cumulus schemes displayed the overall best performances. Therefore, these combinations have a good potential for operational use in numerical weather prediction over the said catchment.

Impacts of Cumulus Convection and Turbulence Parameterizations on the Convection-Permitting Simulation of Typhoon Precipitation

Abstract Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model. Significance Statement Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.

Sensitivity of simulating Typhoon Haiyan (2013) using WRF: the role of cumulus convection, surface flux parameterizations, spectral nudging, and initial and boundary conditions

Abstract. 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.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Modeling the Shallow Cumulus-Topped Boundary Layer at Gray Zone Resolutions

Abstract This study investigates simulated fair-weather shallow cumulus-topped boundary layer (SCTBL) on kilometer- and subkilometer-scale horizontal resolutions, also known as the numerical gray zone of boundary layer turbulence. Based on a priori analysis of a simulated classic SCTBL with large-eddy simulation, its gray zone scale is determined. The dominant length scale of the cloud layer (CL) is found to be the effective cloud diameter, while that of the underlying mixed layer (ML) is the size of organized convection. The two scales are linked by a simple geometric argument based on vertically coherent updrafts, and are quantified through spectral analysis. Comparison to a simulated dry convective boundary layer (CBL) further reveals that the ML gray zone scale does not differentiate between clear and cloudy conditions with the same bulk stability. A posteriori simulations are then performed over a range of resolutions to evaluate the performance of a recently developed scale-adaptive planetary boundary layer (PBL) scheme. Simulation results suggest indifferences to the scale-adaptive capability. Detailed analyses of flux partition reveal that, in the absence of a shallow cumulus scheme, overly energetic resolved fluxes develop in the CL at gray zone and coarse resolutions, and are responsible for overpredicted resolved convection in the ML. These results suggest that modifications are needed for scale-adaptive PBL schemes under shallow cumulus-topped conditions. Significance Statement Shallow cumulus (ShCu) clouds play an important role in the dynamical and radiative processes of the atmospheric boundary layer. As the grid resolution of modern numerical weather prediction models approach kilometer and subkilometer scales, also known as the gray zone, accurate modeling of ShCu clouds becomes challenging due to difficulties in their parameterization. This study identifies the spatial scale that sets the gray zone of ShCu clouds, providing the key to building better parameterizations. Performance of existing parameterizations developed for clear-sky conditions is evaluated for cloudy conditions, exposing deficiencies and motivating further development.

Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China

The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes.

Assessment of heavy rainfall forecasts over the southern Vietnam by using WRF-ARW with different physical parameterization schemes

This research aims to investigate heavy rainfall forecasting over southern Vietnam (SVN) by using the Weather Research and Forecasting with the Advanced Research dynamical solver (WRF-ARW) model. The selection of 34 heavy rainfall events from 2012 to 2016 over SVN during the active phase of the Southeastern Asian summer monsoon was studied. Sixty different WRF-ARW configurations are determined from three cumulus schemes (Kain–Fritsch with three different types of trigger mechanisms, Grell–Devenyi and Betts– Miller–Janjic), three planetary boundary schemes (Yonsei University, Mellor–Yamada–Janjic and Bougeault-Lacarrère) and four cloud microphysics schemes (Lin, Thompson and two WRF microphysics schemes with five and six moments). Typical skill scores are calculated based on the surface observation data and spatial verifications are carried out using the fraction skill score (FSS) and grid rainfall observations from the satellite-based GSMaP product. It is clear that the most sensitive to heavy rainfall forecasts were cumulus scheme selections, especially at thresholds below 50 mm/24 h. The configurations using the Kain–Fritsch scheme have wet bias issues. However, overall, in terms of a high probability of detection (POD) and high threat scores (CSI), the most skillful configuration was the use of the Kain–Fritsch scheme with the third trigger mechanism in combination with local diffusion planetary boundary schemes.