Cumulus Parameterization Schemes Research Articles

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

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

Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China

The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.

Impact of cumulus parameterization schemes on summer extreme precipitation simulation in the Yellow River Basin: the 2018 case

Abstract This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) in 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic (BMJ) scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.

WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

AbstractThe 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.

Sensitivity of summer precipitation simulation to the physical parameterizations in WRF over the Tibetan Plateau: A case study of 2018

The Tibetan Plateau (TP) holds a pivotal position in regional and global climate, whereas the atmospheric characteristics vary greatly in the simulation with different physical parameterization schemes at the gray-zone scale (around 9 km). In this study, 24 sets of experiments were set up with different cumulus parameterization schemes (CPSs), microphysics parameterization schemes (MPSs) and planetary boundary layer parameterization schemes (PBLSs) using the WRF model driven by the ERA5 reanalysis to explore the sensitivity and try to summarize a combination of parameterization schemes suitable for the simulation over TP. Based on comparisons against in-situ observations, most CPS experiments showed large wet bias and overestimated occurrence of precipitation events, and CTL without CPS and CU6 with Tiedtke CPS show better performance at gray-zone scale. The influence of MPS and PBLS is less than CPS on the simulation, and Thompson MPS and UW PBLS showed a better performance. Most of the CPS experiments reach an earlier and more intensive peak compared to the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, except CTL, CU11, and CU6, which show closer but weaker peaks. CPS experiments simulate more precipitation associated with water vapor transporting into TP and anomalous cyclonic circulation against CTL. The less water vapor transport from the south edge of TP in the CTL against CU14, CU16, and CU2, leads to more local precipitation. Graupel particles and the warm-rain process play a crucial role in the precipitation simulation with different MPSs. Although with less water vapor in the low atmosphere, MP2 simulates more precipitation due to more graupel particles and warm-rain processes against CTL. PBLSs have a substantial impact on latent heat, then influence evaporation and precipitation. The lower PBL height usually leads to more latent heat and more precipitation.

Simulating Heavy Rainfall Associated with Tropical Cyclones and Atmospheric Disturbances in Thailand Using the Coupled WRF-ROMS Model—Sensitivity Analysis of Microphysics and Cumulus Parameterization Schemes

Predicting heavy rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand remains challenging. This study introduces a novel approach to enhance forecasting precision by utilizing the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), known as WRF-ROMS. We aim to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes. Three CU schemes, namely, Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (G3), and Kain-Fritsch (KF), along with three MP schemes, namely, Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy (35.1–90.0 mm) to violent (>90.1 mm) rainfall in Thailand, occurring in 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics. While the models performed well for light (0.1–10.0 mm) to moderate (10.1–35.0 mm) rainfall, forecasting heavy rainfall remained challenging. Certain parameter combinations showed promise, like BMJ and KF with LIN microphysics, but challenges persisted. Analyzing density distribution of daily rainfall, we found effective parameterizations for different sub-regions. Our findings emphasize the importance of tailored parameterizations for accurate rainfall prediction in Thailand. This customization can benefit water resource management, flood control, and disaster preparedness. Further research should expand datasets, focusing on significant heavy rainfall events and considering climate factors, for example, the Madden-Julian Oscillation (MJO) for extended-range forecasts, potentially contributing to sub-seasonal and seasonal (S2S) predictions.

Impacts of the stochastically perturbed parameterization on the precipitation ensemble forecasts of the Betts–Miller–Janjić (BMJ) scheme in Eastern China

This study investigated the impact of stochastic perturbations on precipitation forecasts generated by the Betts-Miller-Janjic (BMJ) cumulus parameterization scheme. Two types of stochastic perturbation approaches were compared. The first approach involved perturbing the temperature and humidity tendencies derived from the BMJ scheme. The second approach focused on perturbing the reference profiles of temperature and humidity estimated within the BMJ scheme. These profile perturbations led to reference profiles becoming either warmer and wetter or colder and drier. Ten precipitation cases occurring in eastern China during the summer of 2019 were selected to evaluate the different perturbation methods. The default BMJ scheme exhibited a significant wet bias at the light rain threshold due to overestimating entropy change and a dry bias at the heavier rain threshold. The tendency perturbation approach, which perturbed the temperature and humidity, did not affect the entropy change and produced precipitation placement similar to its unperturbed counterpart. These attributes resulted in a small ensemble spread and relatively low forecast skill scores. Perturbing the reference profiles, conversely, influenced the entropy change in the BMJ scheme, leading to a larger ensemble spread and higher forecast skill scores regarding the spatial distribution of precipitation. Asymmetrically perturbing the reference profiles considered the wet bias, which increased the grid points with negative entropy change and outperformed the symmetric perturbation. The asymmetric perturbation also increased the chance of heavier rain because more water was retained in the air, alleviating the corresponding dry bias.

A Multicloud Model for Coastal Convection

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

WRF-MODEL PARAMETERIZATION TEST FOR PREDICTING EXTREME HEAVY RAINFALL EVENT OVER KETAPANG REGENCY

Heavy rains that cause floods and landslides in the Ketapang Regency can be predicted by utilizing the weather research and forecast (WRF) model. The WRF model used, of course, needs to be configured to represent the conditions that exist in Ketapang Regency. This study evaluates the combination of cumulus and microphysics parameterization, producing the best prediction of 24-hour accumulated rainfall. The combination of cumulus and microphysics parameterization tested as many as 24 schemes which later will be obtained which combination can produce the best prediction of rainfall accumulation with the comparison of rainfall measured at the Observation Station of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Ketapang Regency. The results show that combining the KF-Scheme cumulus parameterization scheme and the Kessler-Scheme microphysics can better predict 24-hour accumulated rainfall than other tested parameterization schemes. This result is based on the root mean square error (RMSE), which shows that this combination scheme produces the smallest value and large correlation coefficient (CORR). From this research, it can also be seen that cumulus parameterization has a more dominant role than microphysics parameterization.

Sensitivity of Real‐Time Forecast for Typhoons Around Korea to Cumulus and Cloud Microphysics Schemes

AbstractIn numerical weather prediction models, the typhoon track and intensity forecast performances are highly sensitive to physics parameterization schemes. To investigate the impact of physics parameterization schemes on the real‐time short‐term forecasts, we simulated six typhoons which directly/indirectly affected the South Korea region in recent years using the Weather Research and Forecasting (WRF) model. Three cumulus parameterization schemes (CPSs) of Kain‐Fritsch (KF), Betts‐Miller‐Janjić (BMJ), modified Tiedtke (TDK), and two cloud microphysics parameterization schemes (MPSs) of WRF‐single‐moment‐microphysics class 6 (WSM6), Predicted Particle Properties (P3) 1‐category were selected for the sensitivity experiment. The results showed that there was a significant difference in simulated typhoon track and intensity performances depending on the physics schemes. On average, the typhoon forecast performances were improved when applying the KF scheme for CPS and WSM6 scheme for MPS in our experimental setup. The BMJ‐applied runs showed the worst performances, which simulated westward shifted typhoon tracks compared to other runs. Overall, the typhoon track and intensity spreads tended to be more sensitive to CPSs and MPSs, respectively. We conducted additional sensitivity experiments using the BMJ scheme with modified reference and temperature profiles. The result showed that the overactivity of the BMJ scheme at low latitudes was reasonably reduced, leading to the improved simulation of typhoons and synoptic fields.

Sensitivity of the simulation of extreme precipitation events in China to different cumulus parameterization schemes and the underlying mechanisms

The ability of climate models to capture extreme precipitation events is crucially important, but most of the existing models contain significant biases for the simulation of extreme precipitation. To understand the causes of these biases, we used five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting (CWRF) model to investigate its performance and biases in the simulation of extreme precipitation events in China. In general, the ensemble cumulus parameterization (ECP) scheme was the most skillful in reproducing the spatial distribution of the 95th percentile daily precipitation (P95) and the other four schemes either overestimated (the Kain-Fritsch Eta and Tiedtke schemes) or underestimated (the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes) P95. Compared with the observational data, ECP scheme significantly improved the simulation of extreme precipitation in China and had the highest correlation and the smallest root-mean-square error in most areas and seasons. To clarify the underlying physical processes of P95 simulation biases, we established a regression model of extreme precipitation based on ECP scheme. This showed that P95 in North China is mainly affected by moisture convergence, planetary boundary layer height and lifting condensation level (relative importance 18–32%). In Central China, the vertical upward motion of water vapor, sensible heat flux and planetary boundary layer height (relative importance 18–30%) are main factors associated with P95. In South China, the vertical upward motion and horizontal transport of water vapor are predominant (relative importance 26–37%). In addition, the net surface energy, surface and atmospheric radiation flux, total precipitable water, convective available potential energy and cloud water path also have a high correlation with P95 (the second most important factor; relative importance 14–31%). The influence of each factor on the simulation of P95 is different when using different cumulus parameterization schemes and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation. These results provide important references for future model evaluations and improvements.

Object-based evaluation of rainfall forecasts over eastern China by eight cumulus parameterization schemes in the WRF model

Traditional pointwise verification is not fully accurate for the evaluation of rainfall forecasts due to the double-penalty problem. An alternative object-based metric, Method for Object-based Diagnostic Evaluation (MODE), was used in this study to evaluate the rainfall simulation performance of eight cumulus parameterization (CP) schemes in the Weather Research and Forecasting model. The average rainfall from May to August (MJJA) over eastern China from 2009 to 2018 and the sub-seasonal evolution of rainfall systems were mainly verified. The results showed that the heavy rainfall objects simulated by KFCuP, New Tiedtke and KSAS schemes are the closest to the observations over South China, Yangtze River Basin and North China, respectively. MODE could highlight the advantages of these schemes in the simulation of rainband physical attributes, such as centroid position, orientation, rainfall area, etc. At the sub-seasonal scale, KFCuP had the highest skill score for mimicking the observed heavy rainfall objects. To explore the reasons for the differences in rainfall simulations, the evaporation, water vapour inflow and precipitation efficiency were calculated for each scheme. It was found that members with higher precipitation generally corresponded to larger water vapour inflow and precipitation efficiency, and the water vapour inflow mainly came from the southern boundary. Further analysis found that there existed a significant positive correlation between the meridional wind and the water vapour inflow on southern boundary, which indicated that the meridional wind was an important factor affecting the regional rainfall simulation.

Tropical Cyclone Simulations with WRF Using High Performance Computing

Tropical cyclone (TC) Bud occurred over the Eastern Pacific Ocean from 9 June to 16 June 2018 reaching, a category-4 hurricane status (H4) due to its strong sustained winds and gusts. In this study, we simulate this TC to reproduce its track path, direction, and strength to determine the best model physics configurations that weather agencies could use to forecast TC tracks over the Eastern Pacific Ocean, adjacent to the Mexican coastline. To achieve this goal, the sensitivity for the impact of different microphysics and cumulus parameterization schemes is carried out through high-performance computer simulations, with the WRF model using the cluster CÓDICE B2 at Centro de Investigación en Computación (CIC) of the Instituto Politécnico Nacional (IPN). The realism of the TC for the different schemes is assessed by comparing the simulations and the best track data taken from the National Hurricane Center (NHC-NOAA). The NCEPGFS forecast data is used as initial and boundary conditions. The evolution of wind and minimum pressure at sea level for the different physics combination runs are also compared with the best track data. We found that the track paths and intensities improve when Sea Surface Temperature (SST) is allowed to evolve with the modeled atmosphere via computer simulations.

Diagnostic evaluation of extreme winter rainfall events over the Arabian Peninsula using high‐resolution weather research and forecasting simulations

AbstractThe sensitivity of different cumulus physical parameterization schemes for simulating extreme winter precipitation events over the Arabian Peninsula (AP) is investigated using a high‐resolution weather research and forecasting (WRF) model. For winters in 2001–2016, the following three parameterization schemes are examined: (i) Kain–Fritsch (KF), (ii) Betts–Miller–Janjić (BMJ), and (iii) Grell–Freitas (GF). The simulation results suggest that the AP extreme winter rainfall events are best simulated using the KF, followed by the BMJ, in terms of spatial distribution and intensity. The spatial pattern correlation coefficient between the model‐simulated and observed rainfall is highest with KF (0.94), followed by BMJ (0.91) and GF (0.76). These results are attributed to a better representation of the moisture transport associated with upper‐tropospheric cyclonic circulation and potential vorticity intrusions. By contrast, the GF scheme fails to simulate moisture convergence and updrafts, leading to an unrealistic representation of cloud hydrometeors and an improper organization of convection and associated extreme rainfall intensities. Meanwhile, the KF and BMJ also successfully simulate the dynamics and thermodynamics of extreme rainfall events that are usually driven by synoptic forcing. The study results suggest that the choice of cumulus parameterization schemes in the WRF model is critical for reliable simulation of extreme rainfall in the hyperarid AP region.

Forecasts of MJO during DYNAMO in a coupled tropical channel model, Part I: Impact of parameterization schemes

AbstractA coupled tropical channel model is developed to forecast the Madden–Julian Oscillation (MJO) event during the Dynamics of the MJO (DYNAMO) campaign period. Eight sets of 30‐day hindcast experiments are performed using different combinations of cumulus parameterization and planetary boundary layer (PBL) schemes. The results indicate that a well‐simulated mean state is a prerequisite for MJO forecast, and the model's ability to reproduce the mean state is sensitive to the parameterization schemes. The experiment with Tiedtke (TDK) cumulus parameterization and University of Washington (UW) PBL schemes can successfully predict the MJO event. Over the equatorial Indian Ocean, this experiment has a good representation of zonal sea surface temperature (SST) gradient and low‐level westerly wind, favouring a reasonable atmospheric circulation. As a result of boundary‐layer convergence in the eastern Indian Ocean, more water vapour is gathered in the low‐middle troposphere by updrafts, contributing to the PBL moistening ahead of the MJO convection and the eastward‐propagation of MJO precipitation. Further analysis reveals that the high cloud is accompanied by strong convection. A better simulation in tropical high cloud fraction helps trap the cloud radiation heating to warm middle‐upper troposphere, which promotes the MJO propagation. Our results suggest that the selection of proper parameterization schemes with a better‐simulated mean state is crucial to predicting the MJO event in the tropical channel model.

Assessing the Impact of Cumulus Parameterization Schemes on Simulated Summer Wind Speed over Mainland China

Wind speed is an important meteorological parameter, whose simulation is influenced by various physical process parameterizations. However, the impact of cumulus parameterization schemes (CPSs) on wind speed simulation at the climate scale has not been sufficiently investigated in previous studies. Using the Advanced Research version of the Weather Research and Forecasting model (ARWv3) and hydrostatic wind speed change equation, we assessed the effects of four CPSs on a 10 m wind speed simulation over mainland China in the summer of 2003. In general, different CPSs can reproduce the wind speed distribution. Meanwhile, the sensitivity of wind speed simulation to CPSs was found to be the highest in East and southern China, followed by the Tibetan Plateau, and then Northwest China. We found that the main physical processes influencing wind speed (i.e., the pressure gradient (PRE), diffusion (DFN), and convection (CON) terms) vary greatly with sub-regions. CPSs mainly affect the secondary CON that regulates the balance between the dominant terms PRE and DFN, and also has a significant effect on PRE. For example, for CON, the difference index (DIF) between the Kain–Fritsch (KF) and previous KF (pKF) CPSs is larger than 20%, corresponding to a PRE DIF of about 14%. The term of local wind speed change (Vt) is significantly more sensitive to the CPSs than the other terms with a DIF of 283% over the Tibetan Plateau, suggesting high CPS sensitivity of the simulated wind speed. In addition, we explained the causes of the CPS-induced sensitivities. This work helps understand the Weather Research and Forecasting model (WRF) performance and emphasizes the importance of the CPS choice in simulating/forecasting wind speed.

Preliminary evaluation of all-sky radiance assimilation scheme with POD-3DEnVar method

To meet the challenges in satellite data assimilation involving cloud and precipitation processes, an all-sky radiance assimilation scheme (referred to as the ‘scheme’) that includes hydrometeor analysis is constructed based on the Proper Orthogonal Decomposition (POD)-3DEnVar method. Three key elements of the scheme are: (1) four hydrometeor mixing ratios for cloud-water, cloud-ice, rain, and snow in the control variables of the POD-3DEnVar method; (2) an extension of the Community Radiative Transfer Model interface to include hydrometeor profiles; and (3) multiple physical process model forecasts that use multiple microphysical and cumulus parameterization schemes as ensemble samples to estimate the flow-dependent background error covariance of the control variables, including those for hydrometeors.To evaluate the performance of the scheme, single-observation experiments and observation system simulation experiments (OSSEs) were designed for a binary typhoon case. Results for the single observation experiments show that the scheme can accurately assimilate satellite radiance near typhoon centers and produce reasonable increments for the hydrometeor variables. The background error covariances of the hydrometeor variables have reasonable flow-dependent characteristics. Results for the OSSEs have root-mean-square errors for all control variables of the assimilation analysis field with respect to the ‘true’ field that are lower than those obtained without assimilation. This indicates that the scheme effectively reduces error for all control variables, including those for hydrometeors. Forecasting experiments reveal that these improvements in the assimilation of control variables result in improved forecasts. However, improvements in the hydrometeor variables are of shorter duration than are those of the other control variables. The impact of improvements in the assimilation on typhoon track and intensity are obvious in the forecasting field.

Evaluation of WRF Cumulus Parameterization Schemes for the Hot Climate of Sudan Emphasizing Crop Growing Seasons

High spatiotemporal resolution climate data are essential for climate-related impact studies. The Weather Research and Forecasting (WRF) model is widely used to downscale climate data for different regions with regional-specific physics configurations. This study aimed to identify robust configurations of the WRF model, especially cumulus parameterization schemes, for different climatic zones of Sudan. We focused on wet season (June–September) rainfall and dry season (November–February) temperature, which are determinants of summer crop and irrigated wheat yields, respectively. Downscaling experiments were carried out to compare the following schemes: Betts–Miller–Janjic (BMJ), improved Kain–Fritch (KFT), modified Tiedtke (TDK), and Grell–Freitas (GF). Results revealed that the BMJ performed better for wet season rainfall in the hyper-arid and arid zones; KFT performed better for rainfall in July and August in the semi-arid zone where most summer crops are cultivated. For dry season temperature, the BMJ and TDK outperformed the other schemes in all three zones, except that the GF performed best for the minimum temperature in December and January in the arid zone, where irrigated wheat is produced, and in the semi-arid zone. Specific parameterization schemes therefore need to be selected for specific seasons and climatic zones of Sudan.

Assessing the performance of the WRF model cumulus parameterization schemes for the simulation of five heavy rainfall events over the Pol-Dokhtar, Iran during 1999–2019

Assessing the performance of the WRF model cumulus parameterization schemes for the simulation of five heavy rainfall events over the Pol-Dokhtar, Iran during 1999–2019

Enhanced Feedback between Shallow Convection and Low-Level Moisture Convergence Leads to Improved Simulation of MJO Eastward Propagation

Abstract Recent study indicates that the non-instantaneous interaction of convection and circulation is essential for evolution of large-scale convective systems. It is incorporated into cumulus parameterization (CP) by relating cloud-base mass flux of shallow convection to a composite of subcloud moisture convergence in the past 6 h. Three pairs of 19-yr simulations with original and modified CP schemes are conducted in a tropical channel model to verify their ability to reproduce the Madden–Julian oscillation (MJO). More coherent tropical precipitation and improved eastward propagation signal are observed in the simulations with the modified CP schemes based on the non-instantaneous interaction. It is found that enhanced feedback between shallow convection and low-level moisture convergence results in amplified shallow convective heating, and then generates reinforced moisture convergence, which transports more moisture upward. The improved simulations of eastward propagation of the MJO are largely attributed to higher specific humidity below 600 hPa in the free troposphere to the east of maximum rainfall center, which is related to stronger boundary layer moisture convergence forced by shallow convection. Large-scale horizontal advection causes asymmetric moisture tendencies relative to rainfall center (positive to the east and negative to the west) and also gives rise to eastward propagation. The zonal advection, especially the advection of anomalous specific humidity by mean zonal wind, is found to dominate the difference of horizontal advection between each pair of simulations. The results indicate the vital importance of non-instantaneous feedback between shallow convection and moisture convergence for convection organization and the eastward MJO propagation.