Model For Prediction Across Scales-Atmosphere Research Articles

A Global Coupled Atmosphere-Wave Model System Based on C-Coupler2. Part II: Preliminary Results

A global coupled atmosphere-wave model system (MPAS-NWW3) that consists of MPAS-A (Model for Prediction Across Scales-Atmosphere) and NWW3 (the Third Generation Wave Model, WAVEWATHCH III) with C-Coupler2 (the Community Coupler2) is demonstrated with a focus on the atmospheric results in global and the specific marine area in the year 2020. Results from the two-way coupled MPAS-NWW3 model and standalone MPAS-A are compared with reanalysis products. The coupled model performs better in terms of high wind speeds and long valid hours. This indicates that the coupled model has an obvious inhibitory effect on surface wind in areas with strong winds.

Comparing the atmospheric and ocean characteristics associated with two distinctly intensified pre‐monsoon tropical cyclones over the Bay of Bengal

AbstractTropical cyclone (TC) Amphan is analyzed in terms of the various factors that governed the intensification process associated with it and compared with Fani. Furthermore, the TC radial characteristics and ocean productivity are examined. Notably, both TCs formed in the Bay of Bengal during the pre‐monsoon seasons of 2020 and 2019, respectively. For this study, both ocean and atmospheric parameters from various sources including global analyses, satellite observations, and outputs from Model for Prediction Across Scales‐Atmosphere (MPAS‐A) and Advanced Research Weather Research and Forecasting (WRF‐ARW), are considered. The results indicate a gradual decrease in vertical wind shear during Fani. In the case of Amphan, the increase in mid‐tropospheric relative humidity values is found to be substantial. The sea surface cooling after the passage of Amphan was higher than in the case of Fani. The higher sea surface temperature in the Amphan case corresponds to the lower aerosol loading (partly because of lockdown measures) than that of Fani in the pre‐cyclone phase. And the decrease (increase) in aerosol loading coincides with an increase (decrease) in the direct radiative forcing at the ocean surface. The Madden–Julian Oscillation played a greater role in the cyclogenesis of Fani, but Kelvin waves offered a major support in the case of Amphan. The warmer sea surface, higher tropical cyclone heat potential, and conducive ocean and atmospheric setting together supported the further intensification of Amphan to the supercyclone stage. The difference in chlorophyll concentration showed a significant variation, with higher positive values seen in the case of Amphan implicating greater vertical mixing. The numerical modeling effort indicated superior performance of MPAS‐A compared to WRF‐ARW in simulating the radial parameters of the TCs.

Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3

Abstract. We describe the development of the tangent linear (TL) and adjoint models of the Model for Prediction Across Scales (MPAS)-CO2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO2 transport processes within the MPAS-CO2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and storage of the essential meteorological variables needed for CO2 transport. This strategy allows the adjoint model to undertake a long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sensitivity of atmospheric CO2 observations to surface CO2 fluxes and the initial atmospheric CO2 mixing ratio. To demonstrate the utility of the newly developed adjoint model, we conduct simulations for two types of atmospheric CO2 observations, namely the tower-based in situ CO2 mixing ratio and satellite-derived column-averaged CO2 mixing ratio (XCO2). A comparison between the sensitivity to surface flux calculated by the MPAS-CO2 adjoint model with its counterpart from CarbonTracker–Lagrange (CT-L) reveals a spatial agreement but notable magnitude differences. These differences, particularly evident for XCO2, might be attributed to the two model systems' differences in the simulation configuration, spatial resolution, and treatment of vertical mixing processes. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO2 observations due to CT-L's spatial domain limitation. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO2 conditions spread out throughout the entire Northern Hemisphere within a month. MPAS-CO2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS-CO2 TL and adjoint models, and their integration into the MPAS-CO2 system, establish the possibility of using MPAS's unique features in atmospheric CO2 transport sensitivity studies and in inverse modeling with advanced methods such as variational data assimilation.

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations

Abstract. An ensemble of 3D ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble-mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF). The experiment using En-3DEnVar produces a similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting, although there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance, and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

Tropical Cyclones and Equatorial Waves in a Convection‐Permitting Aquaplanet Simulation With Off‐Equatorial SST Maximum

AbstractTropical weather phenomena—including tropical cyclones (TCs) and equatorial waves—are influenced by planetary‐to‐convective‐scale processes; yet, existing data sets and tools can only capture a subset of those processes. This study introduces a convection‐permitting aquaplanet simulation that can be used as a laboratory to study TCs, equatorial waves, and their interactions. The simulation was produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) using a variable resolution mesh with convection‐permitting resolution (i.e., 3‐km cell spacing) between 10°S and 30°N. The underlying sea‐surface temperature is given by a zonally symmetric profile with a peak at 10°N, which allows for the formation of TCs. A comparison between the simulation and satellite, reanalysis, and airborne dropsonde data is presented to determine the realism of the simulated phenomena. The simulation captures a realistic TC intensity distribution, including major hurricanes, but their lifetime maximum intensities may be limited by the stronger vertical wind shear in the simulation compared to the observed tropical Pacific region. The simulation also captures convectively coupled equatorial waves, including Kelvin waves and easterly waves. Despite the idealization of the aquaplanet setup, the simulated three‐dimensional structure of both groups of waves is consistent with their observed structure as deduced from satellite and reanalysis data. Easterly waves, however, have peak rotation and meridional winds at a slightly higher altitude than in the reanalysis. Future studies may use this simulation to understand how convectively coupled equatorial waves influence the multi‐scale processes leading to tropical cyclogenesis.

Three-Dimensional Structure of Convectively Coupled Equatorial Waves in Aquaplanet Experiments with Resolved or Parameterized Convection

Abstract Accurate simulations of convectively coupled equatorial waves (CCEWs) are key to properly forecasting rainfall and weather patterns within (and outside) the tropics. Many studies have shown that global numerical weather prediction (NWP) models usually do not accurately simulate CCEWs; however, it is unclear if this problem can be alleviated with a better representation of deep convection in the models. To this end, this study investigates the representation of multiple types of CCEWs in the Model for Prediction Across Scales-Atmosphere (MPAS-A). The simulated structure of CCEWs is analyzed from three MPAS-A aquaplanet experiments with horizontal cell spacing of 30, 15, and 3 km, respectively. Using a wave-phase composite technique, the simulated structure is compared against observed CCEWs as represented by satellite and reanalysis data. All aquaplanet experiments capture the overall structure of gravity wave–type equatorial waves (e.g., Kelvin waves and inertio-gravity waves). Those waves are more realistic in the 3-km experiment, particularly in terms of the vertical structure of temperature, water vapor, and wind anomalies associated with the waves. The main reason for this improvement is a more realistic diabatic heating profile; the experiment with resolved convection produces stronger heating (or weaker cooling) below the melting level during the convectively active phase of Kelvin and inertio-gravity waves. Intriguingly, the rainfall and lower-tropospheric structure associated with easterly waves show pronounced discrepancies between the aquaplanet experiments and reanalysis. Resolved deep convection primarily affects the intensity and propagation speeds of these waves.

African Easterly Wave Evolution and Tropical Cyclogenesis in a Pre‐Helene (2006) Hindcast Using the Model for Prediction Across Scales‐Atmosphere (MPAS‐A)

AbstractTropical cyclogenesis (TCG) remains an elusive phenomenon partly due to the limited understanding of complex water vapor‐convection‐wave interactions. The Model for Prediction Across Scales‐Atmosphere (MPAS‐A) was used to study the TCG of the African easterly wave (AEW) that became Hurricane Helene (2006). The two main objectives were: (a) evaluate the capability of MPAS‐A to simulate TCG from an AEW by comparing MPAS‐A—initialized with the Integrated Forecasting System (IFS) and the Global Forecast System (GFS)—with observations together with reanalysis and, (b) use the hindcast to investigate the role of moisture in the mechanisms that led to Helene's TCG. The more intense GFS‐initialized pre‐Helene was slower propagating and was associated with a wetter and stronger monsoon when compared to both the IFS‐initialized simulation and observed. TCG occurred when net moisture flux within the boundary layer toward the center of the wave increased persistently. The reanalysis pre‐genesis top‐heavy vertical mass flux profile transitioned to a bottom‐heavy profile during TCG, whereas the simulations had top‐heavy and bottom‐heavy profiles simultaneously, resulting from a more‐intense and fast‐occurring TCG than in the reanalysis. Moisture‐vortex instability helped explain the vertical mass fluxes and the co‐location of convection, moisture and wave vortex demonstrating to be an applicable theoretical model for TCG. Moisture mode was tested as a diagnostic tool for AEW evolution and TCG. The case exhibited some moisture mode properties, and it is proposed that AEWs become more moisture‐mode like once reaching western Africa and during TCG. An AEW TCG pathway is proposed.

SwMPAS-A: Scaling MPAS-A to 39 Million Heterogeneous Cores on the New Generation Sunway Supercomputer

With the computing power of High-Performance Computing (HPC) systems having stepped into the exascale era, more complex problems can be solved with scientific applications on a large scale. However, due to the significant performance gap between computing nodes and storage subsystems, suboptimal design for the Input/Output (I/O) module will significantly impede the efficiency of scientific applications, especially for the ubiquitous atmosphere applications. Two-phase I/O implemented in N-to-1 mode creates a serious bottleneck that hinders the scalability for the Model for Prediction Across Scales-Atmosphere (MPAS-A) on the new generation Sunway supercomputer. To address the I/O problem, we apply a custom data reorganization method to enable N-to-M I/O mode to exploit the parallel file system's performance and limit the data transfer among MPI ranks to a restricted scope to alleviate communication overhead. Moreover, we have conducted several methods to accelerate the computations, including the redesign for tracer transport, a hybrid buffering scheme, and a three-level parallelization scheme, which allows MPAS-A to use all heterogeneous computing resources efficiently. Experimental results show admirable scalability and efficiency of our I/O method, which achieves speedups of 41× and 58.9× for input and output compared with the raw I/O method on 30,000 MPI ranks. By scaling MPAS-A to 39 million heterogeneous cores, we demonstrate the necessity of a well-constructed I/O module for a real-world atmosphere application. Speed tests show that our optimization methods obtain good results for computations, and MPAS-A achieves a speed of 0.82 Simulated Day per Hour (SDPH) and 0.76 parallel efficiency of strong scaling with 600,000 MPI ranks.

Evaluation of the Forecast Performance for Week-2 Winter Surface Air Temperature from the Model for Prediction Across Scales–Atmosphere (MPAS-A)

Abstract The forecast skill for week-2 wintertime surface air temperature (SAT) over the Northern Hemisphere by the Model for Prediction Across Scales–Atmosphere (MPAS-A) is evaluated and compared with operational forecast systems that participate in the Subseasonal to Seasonal Prediction project (S2S). An intercomparison of the MPAS against the China Meteorological Administration (CMA) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model was performed using 10-yr reforecasts. Comparing the forecast skill for SAT and atmospheric circulation anomalies at a lead of 2 weeks among the three models, the MPAS shows skill lower than the ECMWF model but higher than the CMA model. The gap in skills between the MPAS model and CMA model is not as large as that between the ECMWF model and MPAS model. Additionally, an intercomparison of the MPAS model against 10 S2S models is presented by using real-time forecasts since 2016 stored in the S2S database. The results show that the MPAS model has forecast skill for week-2 to week-4 wintertime SAT comparable to that in most S2S models. The MPAS model tends to be at an intermediate level compared to current operational forecast models.

Numerical Weather Prediction at 200 m Local Resolution Based on an Unstructured Grid Global Model

AbstractIt is a great challenge to reach sub‐km resolution in numerical weather prediction models without using domain nesting techniques. The Model for Prediction Across Scales – Atmosphere (MPAS‐A) uses an unstructured grid to achieve smooth transition of resolution in a global model. However, a number of model physical parameterization schemes and methods to enhance computational efficiency assume resolution down to about 3 km only. We extended the MPAS‐A model to support resolution down to 200 m and carried out idealized and hindcast numerical experiments over a domain covering Hong Kong. The Shin‐Hong Planetary Boundary Layer scheme was integrated to parameterize partially resolved turbulence in a scale‐aware sense. The quality of static geographical data and land‐water boundary for a coastal urban locale are found to be vital to the forecast accuracy of ground temperature and humidity forecast. Resolving terrain to 200 m, the model is found to be able to simulate the sheltering effect of mountains as well as high wind speed at hill top comparable to station observation. Our results indicate that global model simulations with an unstructured grid can realistically reproduce local weather conditions, suitable for ultra‐high‐resolution predictions and studies.

Impacts of Radio Occultation Data on Typhoon Forecasts as Explored by the Global MPAS-GSI System

Global Navigation Satellite System (GNSS) radio occultation (RO) provides plentiful sounding profiles over regions lacking conventional observations. The Gridpoint Statistical Interpolation (GSI) hybrid system for assimilating RO data is integrated in this study with the Model for Prediction Across Scales–Atmosphere (MPAS) to improve tropical cyclone forecasts. After the MPAS-GSI assimilation cycles, dynamical vortex initialization (DVI) that may effectively spin up the initial inner typhoon vortex through cycled model integration is implemented to improve the initial analysis fit to the best track position as well as maximum wind or pressure intensity for Typhoon Nepartak (2016) that moved northwestward toward southern Taiwan. During the cycling assimilation, assimilation with RO data improves the temperature and moisture analysis, and largely reduces the forecast errors compared to those without RO data assimilation. The two RO operators that assimilate local bending angle or refractivity produce similar analyses, but the temperature and moisture increments from bending angle assimilation are slightly larger than those from refractivity assimilation. The MPAS forecasts at 60-15 km resolution show that the typhoon track prediction is improved with RO data, especially using bending angle data. The reduction in track deviations is explained by the wavenumber-one potential vorticity budget for several forecasts associated with the track deflection near southern Taiwan. Assimilation of RO data has fewer impacts on the typhoon intensity forecast compared to the DVI that largely improves the initial and thus forecasted intensity of the typhoon but at the cost of a slightly degraded track. Use of the enhanced 3 km resolution in the typhoon path also further improved the forecasts with and without the DVI. The feasible performance of the MPAS-GSI system with the RO data impact is also illustrated for Typhoon Mitag (2019), that passed around northern Taiwan.

Modulation of Atmospheric Rivers by Mesoscale Frontal Waves and Latent Heating: Comparison of Two U.S. West Coast Events

AbstractA complex and underexplored relationship exists between atmospheric rivers (ARs) and mesoscale frontal waves (MFWs). The present study further explores and quantifies the importance of diabatic processes to MFW development and the AR–MFW interaction by simulating two ARs impacting Northern California’s flood-vulnerable Russian River watershed using the Model for Prediction Across Scales-Atmosphere (MPAS-A) with and without the effects of latent heating. Despite the storms’ contrasting characteristics, diabatic processes within the system were critical to the development of MFWs, the timing and magnitude of integrated vapor transport (IVT), and precipitation impacts over the Russian River watershed in both cases. Low-altitude circulations and lower-tropospheric moisture content in and around the MFWs are considerably reduced without latent heating, contributing to a decrease in moisture transport, moisture convergence, and IVT. Differences in IVT are not consistently dynamic (i.e., wind-driven) or thermodynamic (i.e., moisture-driven), but instead vary by case and by time throughout each event. For one event, AR conditions over the watershed persisted for 6 h less and the peak IVT occurred 6 h earlier and was reduced by ~17%; weaker orographic and dynamic precipitation forcings reduced precipitation totals by ~64%. Similarly, turning off latent heating shortened the second event by 24 h and reduced precipitation totals by ~49%; the maximum IVT over the watershed was weakened by ~42% and delayed by 18 h. Thus, sufficient representation of diabatic processes, and by inference, water vapor initial conditions, is critical for resolving MFWs, their feedbacks on AR evolution, and associated precipitation forecasts on watershed scales.

Storm-Scale Dynamical Changes of Extratropical Transition Events in Present-Day and Future High-Resolution Global Simulations

AbstractTropical cyclones (TCs) propagating into baroclinic midlatitude environments can transform into extratropical cyclones, in some cases resulting in high-impact weather conditions far from the tropics. This study extends analysis of extratropical transition (ET) changes in multiseasonal global simulations using the Model for Prediction Across Scales–Atmosphere (MPAS-A) under present-day and projected future conditions. High resolution (15 km) covers the Northern Hemisphere; TCs and ET events are tracked based on sea level pressure minima accompanied by a warm core and use of a cyclone phase space method. Previous analysis of these simulations showed large changes in ET over the North Atlantic (NATL) basin, with ET events exhibiting a 4°–5° northward latitudinal shift and ~6-hPa strengthening of the post-transition extratropical cyclone. Storm-relative composites, primarily representing post-transformation cold-core events, indicate that this increase in post-transition storm intensity is associated with an intensification of the neighboring upper-level trough and downstream ridge, and a poleward shift in the storm center, conducive to enhanced trough–TC interactions after ET completion. Additionally, the future composite ET event is located in the right-jet entrance of an outflow jet that is strengthened relative to its present-day counterpart. Localized impacts associated with ET events, such as heavy precipitation and strong near-surface winds, are significantly enhanced in the future-climate simulations; 6-hourly precipitation for NATL events increases at a super-Clausius–Clapeyron rate with area-average precipitation increasing over 30%. Furthermore, intensified precipitation contributes to enhanced lower-tropospheric potential vorticity and stronger upper-tropospheric outflow, implying the potential for more extreme downstream impacts under the future climate scenario.

Development and evaluation of CO<sub>2</sub> transport in MPAS-A v6.3

Abstract. Chemistry transport models (CTMs) play an important role in understanding fluxes and atmospheric distribution of carbon dioxide (CO2). They have been widely used for modeling CO2 transport through forward simulations and inferring fluxes through inversion systems. With the increasing availability of high-resolution observations, it has been become possible to estimate CO2 fluxes at higher spatial resolution. In this work, we implemented CO2 transport in the Model for Prediction Across Scales – Atmosphere (MPAS-A). The objective is to use the variable-resolution capability of MPAS-A to enable a high-resolution CO2 simulation in a limited region with a global model. Treating CO2 as an inert tracer, we implemented in MPAS-A (v6.3) the CO2 transport processes, including advection, vertical mixing by boundary layer scheme, and convective transport. We first evaluated the newly implemented model's tracer mass conservation and then its CO2 simulation accuracy. A 1-year (2014) MPAS-A simulation is evaluated at the global scale using CO2 measurements from 50 near-surface stations and 18 Total Carbon Column Observing Network (TCCON) stations. The simulation is also compared with two global models: National Oceanic and Atmospheric Administration (NOAA) CarbonTracker v2019 (CT2019) and European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). A second set of simulation (2016–2018) is used to evaluate MPAS-A at regional scale using Atmospheric Carbon and Transport – America (ACT-America) aircraft CO2 measurements over the eastern United States. This simulation is also compared with CT2019 and a 27 km WRF-Chem simulation. The global-scale evaluations show that MPAS-A is capable of representing the spatial and temporal CO2 variation with a comparable level of accuracy as IFS of similar horizontal resolution. The regional-scale evaluations show that MPAS-A is capable of representing the observed atmospheric CO2 spatial structures related to the midlatitude synoptic weather system, including the warm versus cold sector distinction, boundary layer to free troposphere difference, and frontal boundary CO2 enhancement. MPAS-A's performance in representing these CO2 spatial structures is comparable to the global model CT2019 and regional model WRF-Chem.

Persistent Anomaly Changes in High-Resolution Climate Simulations

AbstractPersistent anomalies (PAs) are associated with a variety of impactful weather extremes, prompting research into how their characteristics will respond to climate change. Previous studies, however, have not provided conclusive results, owing to the complexity of the phenomenon and to difficulties in general circulation model (GCM) representations of PAs. Here, we diagnose PA activity in ten years of current and projected future output from global, high-resolution (15-km mesh) time-slice simulations performed with the Model for Prediction Across Scales-Atmosphere (MPAS-A). These time slices span a range of ENSO states. They include high-resolution representations of sea-surface temperatures and GCM-based sea ice for present and future climates. Future projections, based on the RCP8.5 scenario, exhibit strong Arctic amplification and tropical upper warming, providing a valuable experiment with which to assess the impact of climate change on PA frequency. The MPAS-A present-climate simulations reproduce the main centers of observed PA activity, but with an eastward shift in the North Pacific and reduced amplitude in the North Atlantic. The overall frequency of positive PAs in the future simulations is similar to that in the present-day simulations, while negative PAs become less frequent. Although some regional changes emerge, the small, generally negative changes in PA frequency and meridional circulation index indicate that climate change does not lead to increased persistence of midlatitude flow anomalies or increased waviness in these simulations.

A Fully Compressible Nonhydrostatic Deep-Atmosphere Equations Solver for MPAS

AbstractA solver for the nonhydrostatic deep-atmosphere equations of motion is described that extends the capabilities of the Model for Prediction Across Scales-Atmosphere (MPAS-A) beyond the existing shallow-atmosphere equations solver. The discretization and additional terms within this extension maintain the C-grid staggering, hybrid height vertical coordinate, and spherical centroidal Voronoi mesh used by MPAS, and also preserve the solver’s conservation properties. Idealized baroclinic wave test results, using Earth-radius and reduced-radius sphere configurations, verify the correctness of the solver and compare well with published results from other models. For these test cases, the time evolution of the maximum horizontal wind speed, and the total energy and its components, are presented as additional solution metrics that may allow for further discrimination in model comparisons. The test case solutions are found to be sensitive to the configuration of dissipation mechanisms in MPAS-A, and many of the differences among models in previously published test case solutions appear to arise because of their differing dissipation configurations. For the deep-atmosphere reduced-radius sphere test case, small-scale noise in the numerical solution was found to arise from the analytic initialization that contains unstable lapse rates in the tropical lower troposphere. By adjusting a parameter in this initialization, the instability is removed and the unphysical large-scale overturning no longer occurs. Inclusion of the deep-atmosphere capability in the MPAS-A solver increases the dry dynamics cost by less than 5% on CPU-based architectures, and configuration of either the shallow- or deep-atmosphere equations is controlled by a simple switch.

Performance of MPAS-A and WRF in predicting and simulating western North Pacific tropical cyclone tracks and intensities

Performances of the Model for Prediction Across Scales-Atmosphere (MPAS-A) in predicting and the Weather Research and Forecasting (WRF) model in simulating western North Pacific (WNP) tropical cyclone (TC) tracks and intensities have been compared. Parallel simulations of the same historical storms that made landfall over southern China, namely, TCs Hope (1979), Gordon (1989), Koryn (1993), Imbudo (2003), Dujuan (2003), Molave (2009), Hato (2017) and Mangkhut (2018), were carried out using WRF and MPAS-A, with initial conditions (and, for WRF, lateral boundary conditions as well) taken from ERA-interim. For MPAS-A, the model was integrated using a standard 60-to-3-km variable-resolution global grid mesh and also on 160-to-2-km grids customized to cover the TC tracks with the highest resolution mesh. The WRF model was integrated using a 15-km/3-km nested domain. No TC bogus scheme was applied when initializing the MPAS-A and WRF simulations. It was found that while TC tracks were reasonably captured by the two models configured variously, the storm intensities were underestimated in general. Given MPAS-A runs were initial value predictions whereas WRF runs were dynamically downscaled from ERA-interim, the finding that MPAS-A has comparable (or slightly better) performance as (than) WRF is noteworthy. To further examine the sensitivity of the MPAS-A TC forecasts to the initial data, additional experiments were carried out for TCs Molave and Hope using ERA5 reanalysis as initial conditions. The ERA5 initialized runs showed significant (slight) improvement in intensity (track) evolution, suggesting that the underestimated TC intensity is likely related to inferior representation of storms in the ERA-interim initial fields. Furthermore, additional runs using another customized 60-to-2-km mesh showed a reasonable improvement in capturing the TC tracks, suggesting that the track forecast accuracy of MPAS-A in TC can be sensitive to the grid resolution in the coarsest part of the variable-resolution mesh used.

Mean Climate and Tropical Rainfall Variability in Aquaplanet Simulations Using the Model for Prediction Across Scales‐Atmosphere

AbstractAquaplanet experiments are important tools for understanding and improving physical processes simulated by global models; yet, previous aquaplanet experiments largely differ in their representation of subseasonal tropical rainfall variability. This study presents results from aquaplanet experiments produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A)—a community model specifically designed to study weather and climate in a common framework. The mean climate and tropical rainfall variability simulated by MPAS‐A with varying horizontal resolution were compared against results from a recent suite of aquaplanet experiments. This comparison shows that, regardless of horizontal resolution, MPAS‐A produces the expected mean climate of an aquaplanet framework with zonally symmetric but meridionally varying sea‐surface temperature. MPAS‐A, however, has a stronger signal of tropical rainfall variability driven by convectively coupled equatorial waves. Sensitivity experiments with different cumulus parameterizations, physics packages, and vertical grids consistently show the presence of those waves, especially equatorial Kelvin waves, in phase with lower‐tropospheric convergence. Other models do not capture such rainfall‐kinematics phasing. These results suggest that simulated tropical rainfall variability depends not only on the cumulus parameterization (as suggested by previous studies) but also on the coupling between physics and dynamics of climate and weather prediction models.

Impacts of tiled land cover characterization in the Model for Prediction Across Scales-Atmosphere (MPAS-A).

Parameterization of subgrid-scale variability of land cover characterization (LCC) is an active area of research, and can improve model performance compared to the dominant (i.e., most abundant tile) approach. The "Noah" land surface model implementation in the global Model for Predictions Across Scales-Atmosphere (MPAS-A), however, only uses the dominant LCC approach that leads to oversimplification in regions of highly heterogeneous LCC (e.g., urban/suburban settings). Thus, in this work we implement a subgrid tiled approach as an option in MPAS-A, version 6.0, and assess the impacts of tiled LCC on meteorological predictions for two gradually refining meshes (92-25 and 46-12 km) focused on the conterminous U.S for January and July 2016. Compared to the dominant approach, results show that using the tiled LCC leads to pronounced global changes in 2-m temperature (July global average change ~ -0.4 K), 2-m moisture, and 10-m wind speed for the 92-25 km mesh. The tiled LCC reduces mean biases in 2-m temperature (July U.S. average bias reduction ~ factor of 4) and specific humidity in the central and western U.S. for the 92-25 km mesh, improves the agreement of vertical profiles (e.g., temperature, humidity, and wind speed) with observed radiosondes; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic temperature biases found in numerical weather prediction models, particularly those that employ a dominant LCC approach.

Evaluation of a Customized Variable‐Resolution Global Model and its Application for High‐Resolution Weather Forecasts in East Asia

AbstractThe performance of a variable‐resolution global model, based on the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) framework and with customized 160‐to‐1 km resolution grid mesh, was tested by simulating idealized flow fields as well as forecasting the evolution of actual weather systems. The mesh contains five levels of refinement, with 20, 15, 9, 3, and 1 km resolution covering central to East Asia, central to southern China, southeastern China, and Greater Bay Area/Hong Kong, respectively. Using a shallow‐water solver and MPAS‐A's solver, the mesh was evaluated against standard circularly refined meshes in simulating idealized steady‐state flows. Conservation properties and error growths in the 160‐to‐1 km and counterpart simulations were fairly comparable. By perturbing the steady flow, realistic baroclinic wave evolutions could be captured. Initialized by Global Forecast System (GFS), parallel experiments were further conducted with new‐Tiedtke (nTDK), Kain‐Fritsch (KF), and Tiedtke (TDK) cumulus schemes and a convection‐permitting suite (CP). Experiments showed that the model can give reasonable 5‐day outlooks and evolution of synoptic‐scale weather systems typically found in East Asia in various seasons. In particular, it can reproduce the mesoscale precipitation related to the Meiyu rainband (cold fronts) in summer (winter). When compared with station data, promising skills in predicting local temperature, humidity, and wind changes were found. It also performed slightly better using nTDK, KF, and TDK schemes, than adopting CP. Overall, by capturing multiscale features concurrently, these experiments gave reasonable global, regional, and local weather predictions, thereby demonstrating the practicality of using customized variable‐resolution meshes for high‐resolution short‐range weather forecasts under MPAS‐A framework.