Physical Parameterization Schemes Research Articles

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

BackgroundThe Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind.MethodsThis study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy.By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined.Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi.Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. ConclusionThese maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

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

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

Investigation of model forecast biases and skilful prediction for Assam heavy rainfall 2022

Extreme rainfall events (ERE) during the summer monsoon season have been occurring over most parts of India resulting in flooding and immense socio-economic loss. These extremes are becoming a frequent norm in the hilly and mountainous regions of the country such as Assam. Assam received one of the most historical EREs from 14–June 17, 2022. The present study analyses the performance of a suite of high-resolution ensemble model forecasts for this extreme event in terms of its intensity, and distribution with a lead time of up to 96 h. Furthermore, the 36 numerical experiments are carried out using two different land use and land cover (LULC) data sets (i.e. ISRO and USGS) and three different sets of parameterization schemes (i.e. planetary boundary layer, cumulus, and microphysics).Rainfall distributions in the case of USGS LULC are relatively less coherent and underestimated (60–260 mm/day) against IMD (80–300 mm/day) including the rainfall categories heavy (HR), very heavy (VHR), and extremely heavy (EHR) rainfall throughout the day-1 to day-4. Among all the ensembles (E1-E10), USGS (E6 - E10) has underestimated rainfall (140–260 mm/day) compared to ISRO (150–280 mm/day), specifically in MR and HR categories over the upper Assam (UAD) and lower Assam (LAD) divisions. Further, the Bias Correction Ensemble (BCE) technique is applied to minimize the forecast errors. A rigorous statistical analysis in terms of frequency distribution, Taylor diagram, and benchmark skill scores is carried out to elucidate the model biases. The set of the model ensembles using ISRO (E1- E5) and USGS (E6- E10) reasonably captured the HR, VHR, and EHR. In addition, throughout the forecast hour, BCE E5 (E10) is noted with the distinct realistic (underestimated) representation of model bias (5–20 %) (10–30 %) over all the subdivisions of Assam. Our results suggest that the combined efforts of ensembles of physical parameterization schemes, along with proper LULC, and the BCE approach are required to overcome challenges to improve the skills of rainfall events, particularly over complex terrains such as Assam.

Impact of a New Radiation Scheme on Simulated Climate in the Global–Regional Integrated SysTem Model under Varying Physical Parameterization Schemes

In this study, the radiation scheme BCC-RAD (Beijing Climate Center RADiative transfer model) developed for global climate models is implemented into the Global–Regional Integrated SysTem (GRIST) model as an alternative to the default RRTMG (general circulation model (GCM) version of the Rapid Radiative Transfer Model) scheme. Its impact on the simulated climate is comprehensively evaluated under different physics parametrization packages, in comparison with both the CERES (partly from ERA5 reanalysis) observations and multi-model results from CMIP6. The results indicate that under the default physics parameterization package of GRIST (PhysC), BCC-RAD improved the simulated global mean cloud cover by ~3% and the clear-sky outgoing longwave radiation by ~5.6 W/m2. Upon the inclusion of the PhysCN parameterization package, BCC-RAD exhibited further improvement in simulated cloud cover and radiative forcing (particularly longwave radiative forcing, the bias of which decreases from −9.2 W/m2 to −1.8 W/m2), leading it to be closer to observations than RRTMG. Additionally, BCC-RAD improved the simulation of atmospheric temperature and hence notably diminished the apparent overestimation of atmospheric humidity seen in RRTMG. This study demonstrates the advantages of BCC-RAD over RRTMG in certain aspects of the GRIST-simulated climate, verifying its capability for the climate-oriented configuration of GRIST.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Impacts of physical parameterization scheme of snow and soil properties over the Tibetan Plateau on the simulation of summer rainfall in China

AbstractAccurate representation of soil and snow state variables in the land surface model is crucial for good simulation results. In order to investigate the potential impacts of the overestimated snow cover over the Tibetan Plateau (TP) on precipitation over China, we designed comparative experiments over TP with and without updated soil texture data, soil hydrological parameters, and the fresh snow density scheme to address this issue with the Weather Research and Forecasting Model (WRF). The results show that snow cover fraction (SCF) and surface albedo in winter on the TP were overestimated in the WRF control experiment (CTL), with albedo bias exceeding 0.4 in the western TP. The SCF in the WRF improved experiment (WRFIM) is about 8% lower than that of the CTL, and the bias is decreased to 0.2–0.3 in the interior of the TP. The thermal and dynamic effects of the TP in winter have a significant impact on the weather and climate of East Asia during summer. Thus, the simulated summer rainfall bias is significantly reduced by WRFIM in North China, and the mid-lower reaches of the Yangtze River, along with the spatial correlation coefficient between the observed-based and WRF-simulated rainfall increased from 0.76 in CTL to 0.88 in WRFIM. The WRFIM-simulated reductions in snow cover and albedo cause a decrease in upward shortwave radiation and eventually increase net surface radiation. The horizontal temperature gradient of the atmosphere is increased in the WRFIM compared to the CTL, and the increased temperature in the lower and middle layers accommodates more water vapor, which is not conducive to precipitation formation. Correspondingly, the Western Pacific Subtropical High at 500 hPa in the middle troposphere weakened. The low-level anticyclonic circulation at 850 hPa also weakened to the east, affecting water vapor transport in eastern China. It leads to a decrease in the water vapor transported northward by southeasterly from the western Pacific Ocean and a decrease in the water vapor flux reaching North China. This work indicates that accurate characterization of soil properties and snow cover on the TP is crucial for improving the simulation bias of summer rainfall in China.

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.

Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)

Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)

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

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

Evaluate WRF-based Lightning Potential Index (LPI) lightning parameterization over Sri Lanka during second inter-monsoon in 2018

It is important to develop accurate and reliable lightning prediction system that can be contributed towards the safety of life, both concerning forecasting for the public safety and safety of aviation and electrical power. This study aims to evaluate WRF-based Lightning Potential Index(LPI) lightning parameterization and its applicability for predicting lightning over Sri Lanka during the second inter-monsoon. The WRF-ARW model 3.9.1 was used to produce predictions for three lightning events with various physical parameterization schemes with two nested domains with a resolution of 12km and 4km respectively. The model simulated LPI values were evaluated using the Earth Networks Global Lightning (ENGLN) dataset. Results show corresponding lightning simulations were produced with spatial distribution aligned with ground-based lightning data. Results consistently show a high correlation of the LPI index with an hourly CG flash rate over the three cases. Moreover, the WRF model was able to capture the lightning using LPI in Sri Lanka, suggesting that it can be used operationally to predict lightning potential region.

Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada

Abstract. Regional-scale surface energy balance (SEB) models of glacier melt require forcing by coarse-gridded data from reanalysis or global climate models that need to be downscaled to glacier scale. As on-glacier meteorological observations are rare, it generally remains unknown how exact the reanalysis and downscaled data are for local-scale SEB modeling. We address this question by evaluating the performance of reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5 and ERA5-Land reanalysis), with and without downscaling, at four glaciers in western Canada with available on-glacier meteorological measurements collected over different summer seasons. We dynamically downscale ERA5 with the Weather Research and Forecasting (WRF) model at 3.3 and 1.1 km grid spacing. We find that our SEB model, forced separately with the observations and the two reanalyses, yields less than 10 % difference in simulated total melt energy and shows strong correlations (0.86) in simulated time series of daily melt energy at each site. The good performance of the reanalysis-derived melt energy is partly due to cancellation of biases between overestimated incoming shortwave radiation and substantially underestimated wind speed and subsequently turbulent heat fluxes. Downscaling with WRF improves the simulation of wind speed, while other meteorological variables show similar performance to ERA5 without downscaling. The choice of WRF physics parameterization schemes is shown to have a relatively large impact on the simulations of SEB components but a smaller impact on the modeled total melt energy. The results increase our confidence in dynamical downscaling with WRF for long-term glacier melt modeling in this region.

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

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

Delayed onset of the tropical Asian summer monsoon in CMIP6 can be linked to the cold bias over the Tibetan Plateau

Most global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate a delayed onset of the tropical Asian summer monsoon of 3–6 pentads when compared with the observations. However, a clear explanation of this model bias has yet to be developed. This study indicates that 23 of the 31 of CMIP6 models generate both the Tibetan Plateau (TP) cold bias and the delayed monsoon onset across tropical Asia. The aloft TP cold air temperature associated with these models tends to reduce the land–sea thermal contrast and monsoon circulation, and hence it generates a delayed onset for the tropical summer monsoon. Two sensitivity experiments based on a coupled ocean–atmosphere–land GCM, together with additional data analysis, further confirm the underlying connection between monsoon onset and temperature anomaly over the TP. Therefore, it is of great importance that we attempt to reduce the model bias associated with the simulation of monsoon onset by improving the physical process parameterization scheme related to the TP temperatures.

Performance of the WRF Model at the Convection‐Permitting Scale in Simulating Snowfall and Lake‐Effect Snow Over the Tibetan Plateau

AbstractThis study investigated the performance of the Weather Research Forecasting (WRF) model at 4‐km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor–Yamada–Janjic), no cumulus and multi‐scale Kain‐Fritsch, two land surface models (Noah and Noah‐MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%–53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3°C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37–0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (∼0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow‐related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection‐permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions.

Analysis of the different influence between initial/boundary and physical perturbation during ensemble forecast of fog

To explore the different effects of initial/boundary condition (ICBC) and physical perturbation during fog ensemble forecast, ensemble forecast experiments are done for a heavy fog episode from December 31, 2016 to January 2, 2017. Three ensemble schemes [ICBC, multi-physics (MPY), and a combination of ICBC and MPY (COM)] were compared. Their forecast performances are analyzed in detail and compared with the reference deterministic forecast. The results show that all ensemble schemes, especially the COM, are able to noticeably improve fog prediction. The TS score of ensemble-based fog forecast with 50% probability threshold is higher than that of the control deterministic prediction by ~ 26%. Compared with the ICBC scheme, the MPY scheme can produce a larger ensemble spread and has more skill in fog and the near-surface variables forecast. When ICBC and MPY are combined (the COM scheme), the ensemble spread is enhanced and the prediction performance is also further improved. The sensitivity experiments of different physical parameterization schemes are also analyzed among microphysics, planetary boundary layer, and land surface. The fog forecast is found to be most sensitive to the land surface scheme, followed by planetary boundary layer scheme, and the least sensitive to microphysics.

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

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

An Evaluation of Non-Gaussian Data Assimilation Methods in Moist Convective Regimes

Abstract Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks. Significance Statement The accurate prediction of severe storms using numerical weather models depends on effective parameterization schemes for small-scale processes and the assimilation of incomplete observational data in a manner that faithfully represents the probabilistic state of the atmosphere. Current generation methods for data assimilation typically assume a standard form for the error distributions of relevant quantities, which can introduce bias that not only hinders numerical prediction, but that can also confound the characterization of errors from the model itself. The current study performs data assimilation using a novel method that does not make such assumptions and explores characteristics of resulting model fields and forecasts that might make such a method useful for improving model parameterization schemes.

A Multivariable Convolutional Neural Network for Forecasting Synoptic-Scale Sea Surface Temperature Anomalies in the South China Sea

Abstract The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, and the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than a single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.

Probing the Skill of Random Forest Emulators for Physical Parameterizations Via a Hierarchy of Simple CAM6 Configurations

AbstractMachine learning approaches, such as random forests (RF), have been used to effectively emulate various aspects of climate and weather models in recent years. The limitations to these approaches are not yet known, particularly with regards to varying complexity of the underlying physical parameterization scheme within the climate model. Utilizing a hierarchy of model configurations, we explore the limits of random forest emulator skill using simplified model frameworks within NCAR's Community Atmosphere Model, version 6 (CAM6). These include a dry CAM6 configuration, a moist extension of the dry model, and an extension of the moist case that includes an additional convection scheme. Each model configuration is run with identical resolution and over the same time period. With unique RF being optimized for each tendency or precipitation rate across the hierarchy, we create a variety of “best case” emulators. The random forest emulators are then evaluated against the CAM6 output as well as a baseline neural network emulator for completeness. All emulators show significant skill when compared to the “truth” (CAM6), often in line with or exceeding similar approaches within the literature. In addition, as the CAM6 complexity is increased, the random forest skill noticeably decreases, regardless of the extensive tuning and training process each random forest goes through. This indicates a limit on the feasibility of RF to act as physics emulators in climate models and encourages further exploration in order to identify ideal uses in the context of state‐of‐the‐art climate model configurations.

Unstructured grid dynamical modeling of planetary atmospheres using planetMPAS: The influence of the rigid lid, computational efficiency, and examples of Martian and Jovian application

We present a new planetary global circulation model, planetMPAS, based on the state-of-the-art NCAR MPAS General Circulation Model. Taking advantage of the cross compatibility between WRF and MPAS, planetMPAS includes most of the planetWRF physics parameterization schemes for terrestrial planets such as Mars and Titan. PlanetMPAS also includes a set of physics that represents radiative transfer, dry convection, moist convection and its associated microphysics for the Jovian atmosphere. We demonstrate that, despite the rigid-lid approximation, planetMPAS is suitable to simulate the climate systems in Martian and Jovian atmospheres with potential application to slow-rotating planets such as Titan. Simulations using planetMPAS show that the new model can reproduce many aspects of the observed features on Mars and Jupiter, such as the seasonal CO2 cycle, polar argon enrichment, zonal mean temperature, and qualitative dust opacity on Mars, as well as the equatorial superrotation and banded zonal wind patterns on Jupiter.