Combinations Of Parameterization Schemes Research Articles

Effect of wave-dependent mechanisms on storm surge and current simulation during three extreme weather systems

Abstract The wave effect is crucial to coastal ocean dynamics, but the roles of the associated wave-dependent mechanisms, such as the wave-enhanced surface stress, wave-enhanced bottom stress, and three-dimensional wave force, are not yet fully understood. In addition, the parameterizations of each mechanism vary and need to be assessed. In this study, a coupled wave-current model based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model system was established to identify the effect of the wave-dependent mechanism on storm surges and currents during three typical extreme weather systems, i.e., cold wave, extratropical cyclone, and typhoon systems, in a semi-enclosed sea. The effects of the three coupled mechanisms on the surface or bottom stress, in terms of both the magnitude and direction, were investigated and quantified separately based on numerical sensitive analysis. A total of seven parameterizations is used to evaluate these mechanisms, resulting in significant variations in the storm surge and current vectors. The similarities and differences of the wave-induced surge and wave-induced current among the various mechanisms were summarized. The change in the surface stress and bottom stress and the excessive momentum flux due to waves were found to mainly occur in shallow nearshore regions. Optimal choice of the combination of parameterization schemes was obtained through comparison with measured data. The wave-induced current in the open waters with a deep-water depth and complex terrain could generate cyclonic or anticyclonic current vorticities, the number and intensity of which always increased with the enhanced strength and rotation of the wind field increased.

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast.

Fidelity of WRF model in simulating heat wave events over India

The evaluation of Weather Research and Forecasting (WRF) model has been performed for simulating episodic Heat Wave (HW) events of 2015 and 2016 with varied horizontal resolutions of 27 km for the entire India (d01), 9 km for the North West (NW (d02)) and South East (SE (d03)) domain. Study compares the maximum temperature (Tmax) simulated by WRF model, using six different combination of parameterization schemes, with observations from the India Meteorological Department (IMD) during the HW events. Among the six experiments, Exp2 (i.e., combination of WSM6 microphysics (MP) together with radiation parameterization CAM, Yonsei (PBL), NOAH land surface and Grell-3D convective schemes) is found closest to the observations in reproducing the temperature. The model exhibits an uncertainty of ± 2 °C in maximum temperature (Tmax) for both the regions, suggesting regional temperature is influenced by the location and complex orography. Overall, statistical results reveal that the best performance is achieved with Exp2. Further, to understand the dynamics of rising HW intensity, two case studies of HW days along with influencing parameters like Tmax, RH and prevailing wind distribution have been simulated. Model simulated Tmax during 2015 reaches up to 44 °C in NW and SE part of India. In 2016, HW is more prevailing towards NW, while in SE region Tmax reaches upto 34–38 °C with high RH (60–85%). The comparative research made it abundantly evident that these episodic events are unique in terms of duration and geographical spread which can be used to assess the WRF performance for future projections of HW.

Sensitivity analysis of different parameterization schemes of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall events over the Mahi River Basin, India

A reliability-based assessment of heavy rainfall events can provide insights for developing appropriate measures and predictions to control flood risk. One of the contemporary challenges is to model heavy rainfall events, which depend on different synoptic conditions and model configurations. In this study, the regional mesoscale Weather Research and Forecasting (WRF) model was used to evaluate the applicability of different parameterization schemes for simulating rainfall events over the Mahi River basin between 22 and 26 August 2020 with the maximum rainfall occurring on 23 August 2020. A high-resolution triple-nested (27, 9, and 3 km) WRF model was configured, and data for initial boundary conditions were obtained from NCEP/NCAR FNL with a resolution of 1° × 1° (six-hourly). The model results were evaluated using gridded rainfall of ECMWF Reanalysis v5 (ERA5) and India Meteorological Department (IMD) at a spatial resolution of 0.25° × 0.25°. Further to verify the model output, GPM-IMERG was used to compute the skill score (bias, threat score, probability of detection, and accuracy) metrics at 24 hourly and 3 hourly scales. Eight sensitivity tests were conducted to identify the optimal sets of microphysics (WSM6 and Goddard), cumulus (Kain and BMJ), and planetary boundary layer (YSU and ACM2) parameterization schemes for rainfall simulation of the selected events. The Kain-Goddard-YSU and Kain-Goddard-ACM2 parameterization schemes demonstrated the optimal performance for simulating the rainfall event. Relative humidity and reflectivity were used for analyzing moisture and cloud brightness temperature, respectively. The model score revealed that the Kain-Goddard scheme outperformed other schemes in case of heavy precipitation, with a threshold of 60 mm/day over the Mahi basin. The findings of this study showed that the simulation of heavy rainfall events with a set of selected combinations of parameterization schemes can reproduce the structure of convective organization as well as prominent synoptic features associated with heavy rainfall events over the Mahi basin. Moreover, this information may help to better understand the future forecast trend of precipitation and explore solutions for sustainable water management to support decision-makers and operational water managers.

Simulation of a Hailstorm Event over Bangladesh using WRF Model

An attempt is made to simulate a pre-monsoon hailstorm event over Bangladesh using the high-resolution Weather Research and Forecasting (WRF) model. A severe hailstorm event embedded with multi-cell thunderstorm occurred over the North-central region of Bangladesh and adjacent West Bengal state of India on 30 March 2018, which moved towards the South-East direction up to its highest intensity stage during 0900-1030 UTC is selected for the study. The WRF model is used to investigate the dynamical and thermodynamical characteristics of the selected hail producing thunderstorm event. The model is run on a double nested domain at 9 and 3 km horizontal resolutions using the Kain-Fritsch (new Eta) cumulus and Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2) planetary boundary layer parameterization schemes. The analysis of the accumulated hail, mean sea level pressure, vertical wind shear, vertical (w) component of wind, relative humidity along with low level wind, convective available potential energy (CAPE) and convective inhibition energy (CINE), rainfall (mm) parameters are investigated. Rainfall is compared with that of observed value. The high amount of relative humidity (>90%), CAPE (>3000 JKg-1) and vertical wind shear (>40 ms-1) help to form severe convective storm which vertically developed upto 150 hPa level. In addition, the high value of w component of winds (~0.4 ms-1) helps updraft process which is favorable for hail formation. The present combination of parameterization schemes of model is found suitable to capture the selected hailstorm event reasonably well, but more similar hailstorm events need to be studied to make any final conclusions. The Dhaka University Journal of Earth and Environmental Sciences, Centennial Special Volume June 2022: 123-130

Global Evaluation of the Noah‐MP Land Surface Model and Suggestions for Selecting Parameterization Schemes

AbstractThis study examines the overall performance of the Noah with multiparameterization (Noah‐MP) land surface model in simulating key land‐atmosphere variables at a global scale and explores the feasibility of running Noah‐MP with regionally different combinations of parameterization schemes. We conducted Noah‐MP ensemble simulations and evaluated the annual means and seasonal cycles of the simulated latent heat flux, net radiation (RN), runoff, soil moisture, snow water equivalent, land surface temperature (LST), leaf area index (LAI), and gross primary productivity (GPP) against a wide variety of global products. The results show that the global patterns of the modeled annual means of these variables generally agree with those of the reference data sets. By evaluating the best simulations in the ensemble, we show that Noah‐MP performs very well in simulating global LST and RN but produces biases in annual mean LAI and GPP by more than 40% in most herbaceous regions. Overall, the main disagreements between Noah‐MP and the reference data sets occurred in the tropical, polar, high‐altitude, and hyperarid regions. This study also highlights the potential of land‐cover‐specific combinations of parameterization schemes to produce optimal modeling results over different land‐cover types. In addition, we strongly suggest the use of multi‐objective optimization of the key parameterizations and parameters to further improve the Noah‐MP's overall performance.

WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone

The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.

Quantitative assessment of the parameterization sensitivity of the Noah-MP land surface model with dynamic vegetation using ChinaFLUX data

The Noah land surface model with multiparameterization options (Noah-MP) depicts the water, heat, and carbon exchange processes occurring between the land and atmosphere. Including dynamic vegetation in the model gives tens of thousands of possible model parameterization combinations, making it difficult to select the optimal scheme for practical applications. Quantitative assessment of parameterization sensitivity can mitigate this difficulty. In this study, we combined summertime observation data from eight sites in the ChinaFLUX network with the multiple parameterization schemes of four physical subprocesses to design 72 simulation tests for each site to assess the model. Subsequently, Sobol sensitivity analysis was performed to quantitatively evaluate the sensitivity of the four physical subprocesses in simulating sensible heat flux (SHF), latent heat flux (LHF), and net ecosystem exchange (NEE). These subprocesses include the soil moisture factor which controls stomatal resistance (BTR), surface turbulence exchange coefficient (SFC), runoff and groundwater (RUN), and radiation transfer (RAD). Finally, the role of the sensitive subprocess parameterization schemes on the simulation results was explored. The results showed that a combination of parameterization schemes reasonably simulated diurnal variations in SHF and LHF, although a significant difference was shown in simulated NEE diurnal variation. The SFC and RAD subprocess parameterizations were sensitive to simulated SHF at most sites, as were the BTR and SFC subprocesses to NEE. With respect to LHF, the sensitive processes were not consistent at each site. Comparison of the parameterization schemes of the sensitive processes showed that RAD and SFC process parameterization affected heat, water, and carbon flux mainly via the canopy and ground temperature, BTR by transpiration and carbon assimilation, and RUN by soil moisture.

Evaluating and improving the sand storm numerical simulation performance in Northwestern China using WRF-Chem and remote sensing soil moisture data

This paper aims to test the ability of the WRF-Chem model to simulate sand storms in Northwest China and improve the simulation results by introducing remote sensing soil moisture data into WRF-Chem. We conducted sensitivity tests, including parameterization scheme tests and soil moisture tests using WRF-Chem. By comparing various combinations of parameterization schemes, which have considerable impacts on sand emissions, the most suitable parameterization scheme for WRF-Chem to simulate sand storms in Northwest China was selected for establishing models in future research. Based on the optimal combination of parameterization schemes, the sensitivity of sand emissions to soil moisture was tested, indicating that soil moisture has a substantial influence on sand emissions and revealing a nonlinear relationship between sand emissions and soil moisture. Although sand simulations are sensitive to soil moisture, the sand emission volume does not always increase with decreasing soil moisture content. Moreover, the soil moisture content simulated by WRF-Chem is quite different from that observed by satellites. Therefore, we selected ten sand storms that occurred in Northwest China in 2009–2018, including weak, moderate and strong sand storms, to explore the responses of sand storms of different intensities to soil moisture. Some experiments were conducted in two scenarios: scenario A was simulated only by WRF-Chem, and scenario B was designed by replacing the initial soil moisture field in WRF-Chem with soil moisture satellite data from AMSR2 and AMSR-E. Comparing the simulation results with in-situ ground-based and satellite-based measurements indicates that WRF-Chem can capture strong sand storms and some moderate sand storms, but the ability to simulate weak sand storms needs to be greatly improved. After introducing remote sensing soil moisture data into WRF-Chem, the simulation of sand emissions becomes more accurate, substantially improving the simulation results. Among the ten simulated sand storms, the AOD (aerosol optical depth) and PM10 simulation accuracies are improved for five and seven sand storms, respectively. After this improvement, the AOD correlation coefficient can reach 0.63, and the PM10 correlation coefficient can reach 0.60.

High-resolution precipitation simulation with WRF for the Three-River Headwaters region in China

The Three-River Headwater Region (TRHR), also known as China “Water Tower”. It is a sensitive region to climate change and plays a critical role in the hydrology circulation and ecological conservation of the Tibet Plateau. Climate change are greatly impacting the climate of this region. However, there exists large biases in the precipitation simulation for this region. In this study, we explored the impact of resolution and physical schemes on precipitation simulation over the TRHR. The results show that a high resolution is necessary for the precipitation modelling over the TRHR with a complex terrain. The precipitation modelling over the TRHR are sensitive to microphysics schemes and cumulus schemes. The microphysics scheme mainly determines the magnitude of simulated precipitation over the TRHR. The cumulus schemes largely influence the spatial distribution of simulated precipitation over the TRHR. Based on the sensitivity tests, we proposed an optimized combination of parameterization schemes that can remarkably improve the accuracy of simulated precipitation over the TRHR. This study is helpful to understand the mechanism of precipitation formation over the TRHR.

Spatial Characteristics of Deep-Developed Boundary Layers and Numerical Simulation Applicability over Arid and Semi-Arid Regions in Northwest China

The spatial distribution and long-time variation of the deep-developed boundary layer are not well understood in arid and semi-arid regions of northwest China. ERA-Interim (ECMWF Reanalysis data, ECMWF: European Centre for Medium-Range Weather Forecasts) were used to study the deep-developed boundary layer in the five representative areas in summer and then the Weather Research Forecast (WRF) model was applied to simulate and verify its applicability. The results show that the boundary layer heights in the five representative areas are higher in late spring and summer (the highest is 2485~3502 m in June) and lower in autumn, winter and early spring (the lowest is 758~907 m in December). The seasonal variations of the boundary layer height are smaller at 02:00 BJT and 08:00 BJT, while the variations are relatively larger at 14:00 BJT and 20:00 BJT. The atmospheric boundary layer, with heights over 4000 m, generally exists in late spring and summer. The boundary layer heights are higher in the arid region than in the semi-arid region and the deep-developed boundary layer lasts longer in the arid region. The boundary layer heights present reductions from the northwest to the southeast, except for Minqin in the middle north. The numerical simulation results show that there is a significant difference between different combinations of parameterization schemes to simulate the deep-developed boundary layer in these areas. The combination Goddard+SLAB+ACM2 performs better in the extreme arid area, Dunhuang, and the arid areas, Jiuquan and Minqin, whereas the simulation effect of the combination Dudhia+Noah+ACM2 is better in the semi-arid areas, Yuzhong and Lanzhou. The difference between the schemes is related to the determination of the boundary layer height.

Weather research and forecasting model simulations over the Pearl River Delta Region

Pearl River Delta (PRD), located in south-eastern coast of mainland China, is one of the regions affected by heavy particulate matter (PM) levels. Notwithstanding the potential use of meteorological and air quality modelling to characterize the air pollution problems, little attention has been paid to meteorological model configuration and its impact on air quality modelling applications over the region. Aiming to find the most suitable set of parameterization schemes of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for air quality modelling applications over the PRD region, a performance experiment was performed. Three tests, with different combinations of parameterization schemes, were created and evaluated. For the best configuration modelling setup, meteorological simulations for a winter (i.e. January) and summer (i.e. July) periods are provided. The meteorological model showed a clockwise deviation for the wind direction and tends to overestimate the temperature and wind speed. It is expected that the present work could reduce the meteorological and air quality modelling uncertainty over the PRD region.

Analysis of sensitivity to different parameterization schemes for a subtropical cyclone

A sensitivity analysis to diverse WRF model physical parameterization schemes is carried out during the lifecycle of a Subtropical cyclone (STC). STCs are low-pressure systems that share tropical and extratropical characteristics, with hybrid thermal structures. In October 2014, a STC made landfall in the Canary Islands, causing widespread damage from strong winds and precipitation there. The system began to develop on October 18 and its effects lasted until October 21. Accurate simulation of this type of cyclone continues to be a major challenge because of its rapid intensification and unique characteristics. In the present study, several numerical simulations were performed using the WRF model to do a sensitivity analysis of its various parameterization schemes for the development and intensification of the STC. The combination of parameterization schemes that best simulated this type of phenomenon was thereby determined. In particular, the parameterization combinations that included the Tiedtke cumulus schemes had the most positive effects on model results. Moreover, concerning STC track validation, optimal results were attained when the STC was fully formed and all convective processes stabilized. Furthermore, to obtain the parameterization schemes that optimally categorize STC structure, a verification using Cyclone Phase Space is assessed. Consequently, the combination of parameterizations including the Tiedtke cumulus schemes were again the best in categorizing the cyclone's subtropical structure. For strength validation, related atmospheric variables such as wind speed and precipitable water were analyzed. Finally, the effects of using a deterministic or probabilistic approach in simulating intense convective phenomena were evaluated.

Assessing uncertainties in the Noah‐MP ensemble simulations of a cropland site during the Tibet Joint International Cooperation program field campaign

AbstractDespite the widespread use of the latest community Noah with multiparameterization (Noah‐MP) land surface model, it has not been rigorously evaluated over the complex Tibetan Plateau. This study assessed uncertainties in Noah‐MP simulations of a cropland site using observations from the 2008 Joint International Cooperation program field campaign. Such an assessment was conducted in the context of performing a total number of 4608 Noah‐MP physics ensemble simulations using two analysis methods: the natural selection approach and Tukey's test, where the impacts of uncertainties in atmospheric forcing conditions, vegetation parameters, and subprocesses on model simulations were identified. Uncertainty in precipitation data exerts greater influence on the general behavior of Noah‐MP ensemble simulations than that in the leaf area index (LAI). However, using a more realistic seasonal LAI improves the seasonal variations of surface heat fluxes. Combining a better precipitation forcing data set and Moderate Resolution Imaging Spectroradiometer monthly LAI significantly reduces the uncertainty range of the ensemble mean of surface heat fluxes. The uncertainty analysis results using the natural selection method are largely similar to that from Tukey's test but show some subtle differences. Both methods reveal greater uncertainties in the following subprocess schemes: canopy resistance, soil moisture threshold for evaporation, runoff and groundwater, and surface‐layer parameterization for this cropland site. The uncertainty analysis identifies the parameterization schemes that demonstrably degrade model performance. The uncertainties in ensemble simulations were significantly reduced when those schemes were excluded, and it was possible to configure an optimal combination of parameterization schemes to obtain similar performance to the ensemble mean of the “best” ensemble experiment.

Evaluation of parameterization schemes in the Weather Research and Forecasting (WRF) model: A case study for the Kaiga nuclear power plant site

In this study, the Weather Research and Forecasting (WRF) model performance with various combinations of parameterization schemes is analyzed in predicting surface and upper air meteorology around the Kaiga nuclear power plant site. The case studies selected for simulation correspond to occurrences of annual maximum, minimum temperature and maximum wind speed in the years (2004–2007). Based on the collective performance of the various combinations of the schemes in reproducing observed winds, temperature and relative humidity at Kaiga site, the most suitable combination of parameterization schemes are identified. For temperature and relative humidity, the combination consisting of Asymmetric Convective Model (ACM2) as the Planetary Boundary Layer (PBL) scheme, the Monin Obhukhov as the surface layer (SL) scheme and the 5 layer thermal diffusion model as the land surface model (LSM) is found to be better than other combinations whereas the combination consisting of Mellor Yamada Janjic (Eta) as the PBL scheme, Monin Obhukhov Janjic (Eta) as the SL scheme and Noah LSM performs reasonably well in reproducing the observed wind conditions. This indicates that the selection of parameterization schemes may depend on the intended application of the model for a given site.

Evaluation of Parameterization Schemes in the WRF Model for Estimation of Mixing Height

This paper deals with the evaluation of parameterization schemes in the WRF model for estimation of mixing height. Numerical experiments were performed using various combinations of parameterization schemes and the results were compared with the mixing height estimated using the radiosonde observations taken by the India Meteorological Department (IMD) at Mangalore site for selected days of the warm and cold season in the years 2004–2007. The results indicate that there is a large variation in the mixing heights estimated by the model using various combinations of parameterization schemes. It was seen that the physics option consisting of Mellor Yamada Janjic (Eta) as the PBL scheme, Monin Obukhov Janjic (Eta) as the surface layer scheme, and Noah land surface model performs reasonably well in reproducing the observed mixing height at this site for both the seasons as compared to the other combinations tested. This study also showed that the choice of the land surface model can have a significant impact on the simulation of mixing height by a prognostic model.

The role of snow‐surface coupling, radiation, and turbulent mixing in modeling a stable boundary layer over Arctic sea ice

To enhance the understanding of the impact of small‐scale processes in the polar climate, this study focuses on the relative role of snow‐surface coupling, radiation and turbulent mixing in an Arctic stable boundary layer. We extend the GABLS1 (GEWEX Atmospheric Boundary‐Layer Study 1) model intercomparison for turbulent mixing with the other relevant physical processes in the stable boundary layer over sea ice. We use the Single Column Model (SCM) version of the Weather Research and Forecasting (WRF) mesoscale meteorological model and run different combinations of boundary layer and radiation schemes, using a state‐of‐the art land surface scheme. With this intercomparison of schemes, we confirm a wide variety in the state of the atmosphere and the surface variables for the selected parameterization schemes. To understand this variety, a sensitivity analysis for one particular combination of parameterization schemes is performed, using a novel analysis method of process diagrams. The variation between the sensitivity runs indicates a relative orientation of model sensitivities to variations in each of the governing processes and these can explain the variety of model results obtained in the intercomparison of different parameterization schemes. Moreover, we apply the same method for several geostrophic wind speeds to represent a large range of synoptic conditions. Results indicate a shift in process significance for different wind regimes. For low wind regimes, the model sensitivity is larger for surface coupling and radiation, while for high wind speeds, the largest sensitivity is found for the turbulent mixing process. An interesting non‐linear feature was found for turbulent mixing for frequently occurring wind speeds and low wind speed cases, where the 2 m temperature increases for decreased amounts of mixing.