Cumulus Parameterization Research Articles

The Non-Monotonic Response of Cumulus Congestus to the Concentration of Cloud Condensation Nuclei

This study uses idealized simulations to investigate the impact of cloud condensation nuclei (CCN) on a cumulus congestus. Thirteen cases with the initial CCN_C, which is the CCN concentration at 1% supersaturation with respect to water, from 10 to 10,000 cm−3 are simulated. The analysis focuses on the liquid phase due to the negligible ice phase in this study. A non-monotonic response of cloud properties and precipitation to CCN concentration is observed. When CCN_C is increased from 10 to 50 cm−3, the enhanced condensation due to the more numerous droplets invigorates the cumulus congestus. The delayed precipitation formation due to the smaller droplets also facilitates the cloud development. The two processes together lead to a higher liquid water path (LWP), higher cloud top, and heavier precipitation. The cumulus congestus has the highest cloud top, the strongest updraft, and the most accumulated precipitation and at CCN_C = 50 cm−3. When CCN_C is increased from 50 to 500 cm−3, the condensation near the cloud base is further enhanced and the precipitation is further delayed, both of which lead to more liquid water remaining in the cloud, and thus an even higher LWP and heavier precipitation rate in the later stage. However, the significantly enhanced evaporation near the cloud top limits the vertical development of the cumulus congestus, leading to a lower cloud top. When CCN_C is further increased to be higher than 1000 cm−3, the cumulus congestus is strongly suppressed, and no precipitation forms. The ratio of the precipitation production rate to vertical cloud water flux in the updraft is not a constant, as is generally assumed in cumulus parameterization schemes, but decreases significantly with increasing CCN concentration. It is also found that the CCN effect on the cumulus congestus relies on which parameters are used to describe the cloud strength. In this study, as CCN_C increases, the LWP and the maximum precipitation rate peak at CCN_C = 500 cm−3, while the cloud top height, maximum updraft, and accumulated precipitation amount peak at CCN_C = 50 cm−3.

Aircraft Observations Reveal the Relationship Between Cumulus Entrainment Rate and Aerosol Loading

AbstractThe influence of entrainment, a key process characterized by the entrainment rate in cumulus parameterization, on aerosol‐cloud interactions has been widely recognized. However, despite qualitative links established between entrainment and aerosol loading, a quantitative relationship based on observational evidence remains elusive. This study utilizes aircraft observations of cumulus clouds during two field campaigns to determine the quantitative relationship between entrainment rate and aerosol loading. In both campaigns, the entrainment rate is negatively correlated with aerosol loading. It is speculated that increased aerosol loading enhances cloud edge droplet evaporation, which leads to increased buoyancy and vertical velocity within the cloud, thereby reducing the entrainment rate. Further analysis shows that the response of entrainment rate to aerosol perturbations is more significant in smaller cumulus clouds with weak buoyancy and less pronounced under opposite conditions. These findings shed new light on improving the description of aerosol‐cloud interactions in cumulus parameterizations.

Analysis of WRF-solar in the estimation of global horizontal irradiation in Amapá, northern Brazil

Global horizontal irradiance (GHI) was simulated using version 4.3.1 of the WRF-Solar model over the state of Amapá in the northern region of Brazil. The performance of the Fast All-sky Model for Solar Applications (FARMS) algorithm, called WsolarAPf, was evaluated. The algorithm proposed by the Baseline Surface Radiation Network was used to treat hourly data from the automatic meteorological station of the National Institute of Meteorology in the city of Macapá-AP. Two cumulus parameterizations were needed to represent the rainy and less rainy periods. The modeling used input data from ERA5 reanalysis. Using cluster analysis, it was possible to estimate the typical meteorological year representative for a large area. The use of the FARMS algorithm reduced the relative RMSE of the GHI during winter (JJA) and austral spring (SON), from 26 % to 16 % in austral winter and from 17 % to 12 % in austral spring. The average daily maximum of GHI occurs at 13:00 local time during all seasons, and WRF-Solar showed it at 13:00 and 14:00 in the rainy season and 12:00 and 13:00 in the less rainy season. The suggestion to explore the development of an optimal combination of the two model predictions can be implemented using FARMS.

Numerical Investigation of Track and Intensity Evolution of Typhoon Doksuri (2023)

This study utilized the WRF model to investigate the track evolution and rapid intensification (RI) of Typhoon Doksuri (2023) as it moved across the Luzon Strait and through the South China Sea (SCS). The simulation results indicate that Doksuri has a smaller track sensitivity to the use of different physics schemes, while having a greater intensity sensitivity. Sensitivity numerical experiments with different physics schemes can well capture its northwestward movement in the first two days, but they predict less westward track deflection as the typhoon moves across the Luzon Strait and through the SCS. Moreover, all the experiments successfully simulated Doksuri’s RI, albeit with quite different rates and a time lag of 12 h. Among different combinations of physics schemes, there exists an optimal set of cumulus parameterization and cloud microphysics schemes for track and intensity predictions. Doksuri’s track changes as the typhoon moved across the Luzon Strait and through the SCS were influenced by the topographic effects of the terrain of the Philippines and Taiwan, to different extents. The track changes of Doksuri are explained by the wavenumber-one potential vorticity (PV) tendency budget from different physical processes, highlighting that the horizontal PV advection dominates the PV tendency throughout most of the simulation time due to the offset of vertical PV advection and differential diabatic heating. In addition, this study applies the extended Sawyer–Eliassen (SE) equation to compare the transverse circulations of the typhoon induced by various forcing sources. The SE solution indicates that radial inflow was largely driven in the lower-tropospheric vortex by strong diabatic heating, while being significantly enhanced in the lower boundary layer due to turbulent friction. All other physical forcing terms were relatively insignificant for the induced transverse circulation. The coordinated radial inflow at low levels may have led to the eyewall development in unbalanced dynamics. Intense diabatic heating thus was vital to the severe RI of Doksuri under a weak vertical wind shear.

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.

Performance Evaluation of TGFS Typhoon Track Forecasts over the Western North Pacific with Sensitivity Tests on Cumulus Parameterization

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.

Understanding the initial conditions contributing to the rapid intensification of typhoons through ensemble sensitivity analysis

While steady improvements have been achieved for the track forecasts of typhoons, there has been a lack of improvement for intensity forecasts. One challenge for intensity forecasts is to capture the rapid intensification (RI), whose nonlinear characteristics impose great difficulties for numerical models. The ensemble sensitivity analysis (ESA) method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts, especially with RI. Six RI processes from five typhoons (Chaba, Haima, Meranti, Sarika, and Songda) in 2016, are applied with ESA, which also gives a composite initial condition that favors subsequent RI. Results from individual cases have generally similar patterns of ESA, but with different magnitudes, when various cumulus parameterization schemes are applied. To draw the initial conditions with statistical significance, sample-mean azimuthal components of ESA are obtained. Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels, intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels, a prominent warm core at around 300 hPa, and increased humidity at low levels. As the forecast lead time increases, the patterns of ESA are retained, while the sensitivity magnitudes decay. Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes, appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.摘要针对台风强度特别是快速增强 (RI) 的预报难点, 本文对2016年5个台风的6次RI过程应用集合敏感性分析 (ESA), 以分析有利于RI的初始条件. 对于不同的个例和积云参数化方案, ESA获得的有利于台风RI的集合敏感性相似但量级存在差异. 复合敏感性揭示了经历RI的台风所需的初始条件, 其有更强的主环流, 更显著的暖心及增加的低层湿度. ESA估计的集合敏感性随预报时长增加而减弱. 通过采样复合敏感性可有望改进集合初始条件及台风强度预报.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance Statement Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.

Sensitivity analysis of cumulus parameterization scheme and data sources to simulate thunderstorms over Bangladesh using WRF model

To optimize weather research and forecasting (WRF) model, a sensitivity analysis has been performed for test thunderstorm events to determine the best combination of cumulus physics (CU) and data source options to be used for simulating thunderstorms over Bangladesh. Six combinations for three cumulus physics options namely, Kain-Fritsch Scheme (KF) (CU-1), Betts-Miller-Janjic Scheme (BMJ) (CU-2) and Grell-Dévényi (GD) Ensemble Scheme (CU-3) and two different data sources namely, National Center for Environmental Prediction (NCEP) Final Analysis (FNL) and the Global Forecast System (GFS) data have been used for this purpose. WRF model has been run on a single domain of 9 km horizontal resolution utilizing the combinations of these cumulus physics and six hourly GFS/FNL datasets for 48 h; from 0000 UTC of 14 May 2017 to 0000 UTC of 16 May 2017 and from 0000 UTC of 29 March 2018 to 0000 UTC of 31 March 2018 as initial and lateral boundary conditions for test event-1 and test event-2 respectively. Model simulated output for rainfall, relative humidity; mean sea level pressure and temperature have been compared with the observed data of Bangladesh Meteorological Department (BMD). Then absolute error of each simulated parameter has been calculated. On the basis of root mean square error (RMSE) for each parameter, better cumulus physics and data source combination has been selected. It is found that, CU-1 and GFS dataset combination exhibits less RMS error in 4 sections out of 12 sections for event-1 and in 5 sections out of 12 sections for event-2. So, it may be expected that Cu_1 and GFS dataset combination may be used for simulating thunderstorm over Bangladesh. After finalizing model physics options and data sets, another test case of TS has been simulated and different parameters are analyzed for justification of WRF setup. It is found that WRF model captured MSLP, temperature and relative humidity very well. But in case of rainfall the simulated value is much smaller than the observed value. For more accuracy more case studies may be carried out including for fair weather days as well as severe TS days.

WRF numerical simulation of summer precipitation and its application over the mountainous southern Tibetan Plateau based on different cumulus parameterization schemes

The extreme scarcity of meteorological observation data caused by harsh natural environments greatly limits our understanding of precipitation and related atmospheric processes over the mountainous regions of the southern Tibetan Plateau and its surrounding areas (hereafter mountainous southern TP). Precipitation simulation has been challenging in this region due to the complex topography and large elevation differences. Considering the high sensitivity of precipitation to the choice of cumulus parameterization scheme (CPS) over the TP, this study systematically evaluated the precipitation simulation performance of 11 CPSs in July 2018 using the WRF model. The characteristics of atmospheric circulation and gradient variation of precipitation were also revealed based on the optimal CPS over this region. The results indicated that the scale-aware schemes (KIAPS SAS [KSAS] and multi-scale Kain–Fritsch [MSKF]) demonstrated excellent potential for precipitation simulations. Due to the reasonable expression of atmospheric stability and the resulting accurate simulation of the timing and amount of regional heavy precipitation events, the KSAS scheme was superior to the MSKF scheme. The output of the KSAS-configured experiment revealed two main water vapor transport channels for the mountainous southern TP in summer, namely the southern monsoonal channel and the western westerly channel. Based on the precipitation profiles along the typical paths of these two channels (longitudinal path of 95.5°E and latitudinal path of 29.5°N), two precipitation belts and one precipitation belt existed on the southern and western slopes of the TP, respectively.

Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation

AbstractEntrainment and detrainment rates (ε and δ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate ε and δ for cumulus clouds, overcoming the limitation of other observation‐based approaches that solely yield ε without offering insights into δ. This study aims to comprehensively evaluate the reliability of this new approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates the capability of the new approach to back‐calculate predetermined ε and δ based on the physical properties before and after the entrainment mixing. Second, evaluation using large‐eddy simulations illustrates that the new approach yields consistent ε and δ profiles compared to the traditional approach. Sensitivity tests indicate a weak sensitivity of the estimated δ with the new approach to the entrained air source. A decrease in the proportion of cloudy air in the assumed detrained air leads to a reduction in the estimated δ, while ε remains unaffected. Finally, the most appropriate assumptions for entrained and detrained air are discussed. Estimating ε for cumulus parameterizations involves acquiring ambient air more than 500 m away from the cloud edge as entrained air. Due to implicit mean field approximations in the traditional approach, determining the optimal assumption for detrained air properties proves challenging. This study confirms the reliability of the new approach in estimating ε and δ, providing confidence in its application to extensive observational data and advancement in parameterization.

Testing the Sensitivity of a WRF-Based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated. Significance Statement Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

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.

Reproducibility of Equatorial Kelvin Waves in a Super‐Parameterized MIROC: 2. Linear Stability Analysis of In‐Model Kelvin Waves

AbstractWhile low‐resolution climate models at present struggle to appropriately simulate convectively coupled large‐scale atmospheric disturbances such as equatorial Kelvin waves (EKWs), superparameterization helps better reproduce such phenomena. To evaluate such model differences based on physical mechanisms, a linearized theoretical framework of convectively coupled EKWs was developed in a form readily applicable to model evaluation by allowing background stability and diabatic heating to have arbitrary vertical profiles rather than assuming simplified ones. A system of linearized equations of convection‐coupled gravity waves was derived as a two‐dimensional model of the convectively coupled EKWs. In this work, the basic states are taken from observations, CTL‐MIROC and SP‐MIROC experiments introduced in Part 1. The tendency of convectively coupled gravity waves to grow faster under top‐heavy heating is confirmed for realistic stratification profiles, as found in previous studies under constant stratifications. A comparison of linear unstable solutions with basic states taken from SP‐MIROC and CTL‐MIROC shows that the top‐heavy heating profile in SP‐MIROC largely contributes to the enhancement of the EKW‐like unstable modes, while subtle differences of stratification profiles considerably affect EKW behaviors. The bottom‐heavy heating bias in the CTL‐MIROC likely originates from insufficient modeling of subgrid stratiform precipitation in tropical organized systems. It is desirable to incorporate such stratiform components in cumulus parameterizations to achieve better EKW reproducibility.

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) during 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 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.

Implementation and evaluation of a spectral cumulus parametrization for simulating tropical cyclones in JMA‐GSM

AbstractA spectral cumulus parametrization (spectral scheme) developed using a cloud‐resolving model simulation was implemented in Global Spectral Model of the Japan Meteorological Agency (JMA‐GSM, GSM hereafter). The performance of the spectral scheme in terms of mean state, variability, and tropical cyclone (TC) properties was evaluated using atmosphere‐only model experiments by comparing results of the original convection scheme (based on Arakawa–Schubert scheme, AS scheme) and the spectral scheme. Parameter tunings were first conducted for the spectral scheme, through which the climatological errors of the cloud cover in the Southern Ocean and temperature were greatly improved. The spectral scheme showed comparable climatological errors to the AS scheme in the increased resolutions. The spectral scheme greatly improved tropical variability, that is, response to El Niño/Southern Oscillation and Madden–Julian Oscillation. Analyses on TC statistical data revealed that the spectral scheme improved TC properties in terms of genesis frequency, intensity, and the response of TC to tropical variability. Specifically, significant improvements were seen in the Northern Hemisphere. Further analyses on the TC structure revealed that higher TC intensity was sustained for longer times by the spectral scheme than by the AS scheme. The reason for this is that the spectral scheme better simulates convective heating by considering coexistence of different cloud types with parametrized vertically varying entrainment rates, especially from low to mid‐altitudes. The convective heating leads to a sharper vertical‐radial circulation in the TC structure, causing stronger incoming moisture flux toward the TC centre, and resulting in a stronger TC intensity. Consequently, the spectral scheme can simulate a comparable mean state, better variability, and better TC properties compared with the original scheme by simulating a more detailed convective structure. Therefore, it has a potential to increase the TC forecast skill for operational GSM.

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.

Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

AbstractDiurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.