Adding Four Dimensional Data Assimilation by Analysis Nudging to
the Model for Prediction Across Scales – Atmosphere (Version 4.0)

Bullock, O. Russell; Foroutan, Hosein; Gilliam, Robert C.; Herwehe, Jerold A.

doi:10.5194/gmd-2017-246

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…This study expands upon Bullock et al. (2018), only conducted for a winter and a summer month, by showing that MPAS can now be ran continuously with FDDA with no error growth at any level of the atmosphere.…”

Section: Resultssupporting

confidence: 62%

“…The MPAS configuration used here improves upon Bullock et al. (2018) by the implementation of the P‐X LSM and its indirect soil nudging scheme, as well as the ACM2 PBL scheme and the Pleim surface layer model. MPAS compares well with WRF over the CONUS for some variables and certain times of the year, but there are some instances where WRF has consistently lower error.…”

Section: Discussionmentioning

confidence: 99%

“…For the 2016 annual MPAS simulation, FDDA based on analysis nudging (Bullock et al., 2018) leveraged analyses from the National Centers for Environmental Prediction (NCEP) Global Forecast System’s (GFS) Data Assimilation System (GDAS). GDAS starts with a 6‐h forecast and blends observations using three‐dimensional variational data assimilation (3DVar) with Gridpoint Statistical Interpolation (GSI) to produce an analysis where most observations fit within specified error bounds.…”

Section: Methodsmentioning

confidence: 99%

“…As MPAS‐AQ is developed and refined, preparing MPAS for retrospective meteorological modeling has followed a parallel track. Initially, the WRF analysis‐nudging‐based FDDA module was implemented in MPAS (Bullock et al., 2018). In this context, FDDA allows for continuous retrospective simulations where errors are mitigated by constraining the model toward meteorological analyses.…”

Section: Introductionmentioning

confidence: 99%

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Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

et al. 2021

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The U.S. EPA (United States Environmental Protection Agency) is leveraging recent advances in meteorological modeling to construct an air quality modeling system to allow consistency from global to local scales. The Model for Prediction Across Scales-Atmosphere (MPAS-A or MPAS) has been developed by the National Center for Atmospheric Research (NCAR) as a global complement to the Weather Research and Forecasting model (WRF). Patterned after a regional coupled system with WRF, the Community Multiscale Air Quality (CMAQ) modeling system has been coupled within MPAS to explore global-to-local chemical transport modeling. Several options were implemented into MPAS for retrospective applications. Nudging-based data assimilation was added to support continuous simulations of past weather to minimize error growth that exists with a weather forecast configuration. The Pleim-Xiu land-surface model, the Asymmetric Convective Model 2 boundary layer scheme, and the Pleim surface layer scheme were added as the preferred options for retrospective air quality applications with WRF. Annual simulations were conducted using this EPA-enhanced MPAS configuration on two different mesh structures and compared against WRF. MPAS generally compares well with WRF over the conterminous United States. Errors in MPAS surface meteorology are comparable to WRF throughout the year. Precipitation statistics indicate MPAS performs slightly better than WRF. Solar radiation in MPAS is higher than WRF and measurements, suggesting fewer clouds in MPAS than WRF. Upper-air meteorology is well-simulated by MPAS, but errors are slightly higher than WRF. These comparisons lend confidence to use MPAS for retrospective air quality modeling and suggest ways it can be further improved in the future. Plain Language SummaryThe US EPA analyses and performs research on the past, present and future air quality of the United States using the Community Multiscale Air Quality model (CMAQ). Historically, the modeling was focused on the U.S. as regulations and impact are first order local issues. Global modeling is becoming more attainable and common as computer potential has increased, modeling systems advanced and air quality viewed as a global issue. This research demonstrates that we now have a meteorological modeling system that is capable of modeling air quality from global to local scales. The more comprehensive air quality modeling will directly address research issues on the link between air quality and human/ecological health. Specific results presented with this work indicate that the meteorology from the new global model is on par with the meteorology we have been using for the last decade. This suggests that the global air quality simulations will not be significantly restricted by poor driving meteorology and we can move forward with some confidence. GILLIAM ET AL.

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Section: Resultssupporting

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

et al. 2021

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“…However, one should note that in the interest of a fair comparison, WRF in this study was used without the data assimilation framework of MeteoGroup that is applied in operational forecasting. The use of a data assimilation with MPAS is still under development with some early fruitful results (Ha et al 2017;Bullock et al 2018), but this may pose limitations to regular users for employing MPAS in an operational setup straight away. Additionally, the WRF model uses 39 vertical layers, while the MPAS model uses 55.…”

Section: Discussionmentioning

confidence: 99%

Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations

Kramer

Heinzeller

Hartmann³

et al. 2018

Clim Dyn

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Regional weather forecasting models like the Weather Research and Forecasting (WRF) model allow for nested domains to save computational effort and provide detailed results for mesoscale weather phenomena. The sudden resolution change by nesting may cause artefacts in the model results. On the contrary, the novel global Model for Prediction Across Scales (MPAS) runs on Voronoi meshes that allow for smooth resolution transition towards the desired high resolution in the region of interest. This minimises the resolution-related artefacts, while still saving computational effort. We evaluate the MPAS model over Europe focussing on three mesoscale weather events: a synoptic gale over the North Sea, a föhn effect in Switzerland, and a case of organised convection with hail over the Netherlands. We use four different MPAS meshes (60 km global refined to-3 km (60-3 km), analogous 30-3 km, 15-3 km, global 3 km) and compare their results to routine observations and a WRF setup with a single domain of 3 km grid spacing. We also discuss the computational requirements for the different MPAS meshes and the operational WRF setup. In general, the MPAS 3 km and WRF model results correspond to the observations. However, a global model at 3 km resolution as a replacement for WRF is not feasible for operational use. More importantly, all variable-resolution meshes employed in this study show comparable skills in short-term forecasting within the high-resolution area at considerably lower computational costs.

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Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0)

Abstract: Abstract. The Model for Prediction Across Scales -Atmosphere (MPAS-A

Cited by 2 publications

References 9 publications

Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations

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