Kain-Fritsch Research Articles

Sensitivity of Cumulus Physics in ARW Model on Track of Tropical Cyclones ‘Amphan’ and ‘Bulbul’ over the Bay of Bengal

The Advanced Research Weather Research and Forecasting (ARW) model performance can be improved by analyzing the sensitivity of the physical parameterization schemes. In order to revalidate the performance of the ARW model in relation to the selection of Cumulus Physics (CP) schemes, the sensitivity of CP schemes for Tropical Cyclones (TCs) over the Bay of Bengal (BoB) region has been examined. National Centre for Environment Prediction (NCEP)’s Final Reanalysis (FNL) data (1010) have been used as lateral and initial conditions in the ARW model. The model has been configured in a single domain and runs for four different initial conditions in simulating TC ‘Amphan’ and for three different initial conditions in simulating TC ‘Bulbul’. In this study, average track errors have been found between 40-50 km for Kain-Fritsch Cumulus Potential (KFCP) and Multi-Scale Kain-Fritsch (MSKF) schemes for TC Amphan and below 60 km for the Kain Fritsch (KF) scheme for TC Bulbul. Lower landfall position error has been found below 60 km for the KF scheme for TC Amphan, and 40 km for KFCP and MSKF schemes for TC Bulbul. Journal of Engineering Science 15(1), 2024, 119-129

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.

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

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

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

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

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

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

Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant role. In this regard, physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the Weather Research and Forecasting (WRF) model as CPM to the use of different physics schemes on Andalusia, a complex orography region in the southern part of the Iberian Peninsula (IP). To do that, a set of 1-year WRF simulations was completed based on two “one-way” nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, characterized to be a very wet year, were compared with observational datasets from different sources to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations (from annual to hourly). In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the “best” configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region.

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.

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 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.

The Importance of Cumulus Parameterization and Resolution in Simulating Rainfall over Peninsular Malaysia

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

Impacts of Cumulus Parameterizations on Extreme Precipitation Simulation in Semi-Arid Region: A Case Study in Northwest China

In the context of climate change, extreme precipitation in semi-arid region happens frequently. How well models simulate extreme precipitation in semi-arid region remains unclear. Based on a WRF v4.3 simulation of a rainstorm event that occurred in Qingyang, China on 21 July 2019, applying Kain–Fritsch (KF), Grell–Devenyi (GD) and Bullock–Wang (BW) schemes, the impacts of different cumulus parameterizations on extreme precipitation simulations in semi-arid region were analyzed, and the possible causes of precipitation biases were explored. The results showed that the WRF with the three schemes essentially reproduced the location and structure of precipitation, but the intensity of precipitation in the central region was underestimated. Based on the structure-amplitude-location (SAL) method, the KF scheme exhibited better performance in precipitation simulation than the other two schemes, while there were significant intensity and location deviations of rain band occurrence between simulations using the GD, BW schemes and observations. Convection simulation using the GD and BW schemes was less effective than that using the KF scheme, compared to the observations. As a result, the GD and BW schemes simulated a larger geopotential height at 500 hPa over Qingyang and weaker upper-level low troughs than simulations using the KF scheme. This led to simulation of less water vapor transport into the front of the trough, resulting in a deficit in simulated precipitation. The study results highlight the impacts of convection on large-scale atmospheric circulation linked to extreme precipitation in semi-arid region.

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.

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

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

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.

The Hourly Precipitation Frequencies in the Tropical-Belt Version of WRF: Sensitivity to Cumulus Parameterization and Radiation Schemes

AbstractThe sensitivity of hourly precipitation to cumulus parameterization and radiation schemes is explored by using the tropical-belt configuration of the Weather Research and Forecasting (WRF) Model. The domain covers the entire tropical region from 45°S to 45°N with a grid spacing of about 45 km. A series of 5-yr simulations with four cumulus parameterization schemes [new Tiedtke (NT), Kain–Fritsch (KF), new SAS (NS), and Tiedtke (TK)] and two radiation schemes (RRTMG and CAM) are carried out. We focus on the frequencies of hourly precipitation above three thresholds (0.02 mm h−1 = light drizzle rate; 0.2 mm h−1 = moderate rate; and 2 mm h−1 = heavy rate) between the observed CMORPH products and simulations. The sensitivity is higher for precipitation frequency than amount, and frequency is dominated by the cumulus parameterization. Frequencies above the moderate rate are well reproduced, whereas frequencies above the other two rates present large deviations. No combination of physical schemes is found to perform best in reproducing the frequencies above all thresholds. Simulations using the NT and NS schemes show higher precipitation frequencies above the light drizzle rate and lower precipitation frequencies above the heavy rate than those simulations using the KF and TK schemes. Precipitation frequency is higher when reproduced by experiments using the RRTMG scheme than those using the CAM scheme, except for frequencies above the light rate over oceans. The overestimation of frequency is mainly caused by too-frequent convective rainfall. The results imply that the triggering based on the vertical velocity may increase the occurrence of a rain event and that CAPE-based closure may increase the heavy precipitation frequency in the cumulus parameterization.

Simulation of tropical cyclones over the Bay of Bengal during 1999-2013 : Impact of physical parameterization schemes

Tropical Cyclone (TC) is one of the most devastating and deadly weather phenomena and causes loss of life and property. The Bay of Bengal (BoB) is a potentially energetic region for the development of cyclonic storms, accounting about 6% of the global annual total number of cyclonic storms. Accurate prediction of these storms is essential to avoid loss of lives and damage to properties. In this regard, the reduction of errors associated with the track and intensity forecast of the TCs is an important aspect to disseminate more reliable information to the disaster management. The present study examined the performance of Advanced Research Weather Research and Forecasting (WRF-ARW) model for the simulation of track and intensity of TCs over BoB during 1999-2013. The numerical simulations were carried out for 38-cyclones (during 1999-2013) using different microphysical parameterization (MP), cumulus parameterization (CP) and planetary boundary layer (PBL) schemes of the WRF-ARW model. The total 1133 simulations were performed by using 11 combinations with four MPs [Lin et al. (LIN), Ferrier (new Eta) (FERR), WSM 6-class Scheme graupel scheme (WSM6) and Thompson (THOMP)], four CPs [Kain-Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell-Devenyi (GD) and New Grell Scheme (NG)] and three PBLs (Yonsei University scheme (YSU), Mellor-Yamada-Janjic (Eta) TKE (MYJ) and Asymmetrical Convective Model version 2 (ACM2)] to identify the best logical combination of these schemes to predict the track and intensity of TCs over BoB. The best combination of CP and PBL schemes were more influenced the simulations of intensity and track of the cyclones compared to the MP schemes. Further, the simulations were conducted for intense storms with different initial conditions and suggested that the combination of KF + LIN + YSU provided better simulations of the storms for 72h over the BoB and also observed the decreased track error from 1999 to 2013 cyclones. This clearly indicated that the improvement in the initial conditions and parameterization schemes of the WRF-ARW model. This study is very useful for the cyclone warning centers, disaster management centers, real-time governance system and researchers to provide better management for coastal community during the cyclone events.

Precipitation Simulation from the Cumulus Convection Parameterization Schemes Based on the WRF Model in the Weihe River Basin, china

Two cumulus convection parameterization schemes, i.e., Kain-Fritsch (K-F) and Grell-3 based on the V4.1.2 Weather Research and Forecasting (WRF) model, were used to simulate the summer precipitation in the Weihe River Basin using hourly precipitation data at the resolution of 5 km and 2 km. the precipitation experiment results at different resolution were compared and analyzed. The root square error and correlation coefficient were used to verify and evaluate the simulation results. The results show that in K-F and Grell-3, the two cumulus model simulations with a resolution of 5 km can explain the spatial distribution pattern of precipitation and the diurnal variation process, and the experimental simulation results are consistent with the actual observations. The summer precipitation simulation in June, July, and August is highly sensitive to the two cumulus parameter schemes of K-F and Grell-3. The simulated precipitation value is higher than the actual value, and the root mean square errors (RMSEs) of K-F and Grell-3 schemes are 5.49-13.29 and 5.69-10.88, respectively. In terms of precipitation simulation, Grell-3 scheme is better than K-F scheme, especially in areas with heavy rainfall. When the resolution is increased to 2 km, the regions with heavy precipitation can be displayed more finely. The influence of water vapor flux and vertical velocity changes on precipitation in the Weihe River is analyzed from two aspects, i.e., water conditions and atmospheric instability. From the simulation results by the K-F scheme, the vertical velocity fluctuations are unstable and strong in the convective area, and the convective rainfall is relatively high. From the simulation results by Grell-3, the vertical velocity is large and increased rapidly, which is conducive to the generation of large-scale heavy precipitation. Both schemes are very sensitive to the simulation of water vapor transport and vertical velocity. This study provides a basis for the research on the adaptability of regional precipitation simulation.

Evaluation of several cumulus parameterization schemes for daily rainfall predictions over Palembang City

The daily rainfall prediction calculated from four Cumulus schemes i.e.: Kain-Fritsch (KF), New Tiedtke (NT), Grell 3D ensemble (G3D), and Betts-Miller-Janjic (BMJ) had been tested for compliance with daily observation data for 90 days (1 Dec 2018 to 28 Feb 2019) from three observer stations in Palembang city: BMKG-Kenten (Kenten), BMKG-SMB2 (SMB2) and Unsri Bukit Besar (UBB). The test results show that the BMJ cumulus scheme provides better suitability for 19 out of 45 unique indicators for rainfall event indicator (REI) groups and for as many as 13 out of 27 unique indicators for rainfall intensity indicator (RII) groups. The four schemes are able to provide the good performance for each on different indicators and stations, however, in general the BMJ scheme gives the best performance in predicting daily rainfall over Palembang city.

Sensitivity of tropical cyclones to convective parameterization schemes in RegCM4

This study investigates the sensitivity of simulated tropical cyclones (TC) affecting the Philippines to convective parameterization schemes (CPS) in the Regional Climate Model Version 4 (RegCM4). Five ERA-Interim driven RegCM4 simulations at 25-km horizontal resolution were conducted utilizing the CPS of Grell with Arakawa–Schubert closure (GR), Emanuel (EM), Kain–Fritsch (KF), Tiedtke (TE), and a combined Grell scheme over land and Emanuel over the ocean (GR-EM). Comparisons made between the observed and RegCM4-simulated TCs covering a 30-year period (1981–2010) indicate that the EM scheme yields an annual-mean TC frequency that is closest to observations. The GR-EM scheme, on the other hand, closely reproduces the observed seasonal patterns of TC tracks, spatial patterns of TC track density and TC-associated rainfall, and TC lifespan. The KF scheme is the only CPS that was able to simulate intense TCs (maximum wind speed > 40 m s–1) within the domain. In contrast, both GR and TE schemes largely underestimated the TC frequency, and were only able to simulate weak TCs. Such underestimation in the TC frequency and intensity in the GR and TE simulations can be attributed to the dry mid-tropospheric environment and the absence of a large area with positive low-level relative vorticity over the Pacific Ocean inhibiting TC formation and further development over the area. These findings would be helpful in selecting the more appropriate CPS for TC-related model simulations over the Philippines and in further model improvements, given the climate modeling imperfections and associated biases.

Role of convection representation across the gray zone in forecasting warm season extreme precipitation over Shanghai from two typical cases

This study investigates the role of convection representation across the gray zone in the multigrid nesting Weather Research and Forecasting (WRF) model based on two extreme precipitation events in Shanghai during the warm season. The precipitation forecast sensitivity and physical processes are examined using three convective treatments: (1) the traditional Kain-Fritsch (KF), (2) scale-aware Grell-Freitas (GF) cumulus parameterization, and (3) explicit convection (EC) solution and different nesting configurations consisting of grid spacings of 27, 15, 9, 5, 3, and 1 km using single, double, and triple nestings. At coarser grids (> 10 km), GF produces wet biases with delayed rainfall peaks, while KF produces dry biases with largely underestimated rainfall peaks. At a 9-km grid spacing, these biases were largely reduced, and using cumulus parameterizations (CUPs) shows specific advantages over the EC in the diurnal variation in extreme precipitation. GF performs better in simulating primary rainfall peak intensity, but KF performs better in simulating diurnal timing, albeit with underestimated intensity. The utilization of CUPs is beneficial at the 5-km grid in simulating both extreme intensity and diurnal variations, but it shows mixed effects at 3 km. When the 1-km grid with explicit convection was nested directly into their outermost 15-km or 9-km grids using KF CUP, avoiding treatment in the gray zone (5 km or 3 km), the primary rainfall peak at noon was best reproduced, but a secondary peak with a weak forcing was not detected. This peak was identified with local moisture divergence and a stable atmosphere.