Abstract
The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four-dimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of “analysis nudging” developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1°×1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2m temperature, 2m water vapor mixing ratio, and 10m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.
Highlights
Combining data at various times in a dynamical model to provide time continuity and dynamic balance among the model fields was first suggested by Charney et al (1969) and has become known as four-dimensional data assimilation (FDDA)
Nudging strongly toward the target fields should produce good agreement with those fields. The intent of these first comparisons was to verify that using nudging coefficients for temperature, humidity, and wind similar to those used in Weather Research and Forecasting (WRF) would constrain MPASA simulations in a reasonable manner
To further test the capabilities of FDDA in Model for Prediction Across Scales – Atmosphere (MPAS-A), simulated surface-level data for temperature, humidity, and wind speed from both the standard and modified Model for Prediction Across Scales (MPAS)-A models were compared to observational data from the Meteorological Assimilation Data Ingest System (MADIS)
Summary
Combining data at various times in a dynamical model to provide time continuity and dynamic balance among the model fields was first suggested by Charney et al (1969) and has become known as four-dimensional data assimilation (FDDA). Known as Newtonian relaxation, involves the use of special terms in forecast equations to “nudge” an atmospheric model simulation toward observations or some reference state. It was originally employed for dynamic initialization (Anthes, 1974; Hoke and Anthes, 1976). Air quality models are often applied with relatively coarse horizontal resolution on hemispheric and global scales to provide boundary information for nested, higher-resolution regional models (Bullock Jr. et al, 2008; Jacobson and Ginnebaugh, 2010; Schere et al, 2012; Mathur et al, 2014). Once the required “target” fields (i.e., reference data for nudging) are defined to match the MPAS-A prognostic variable array, analysis nudging in MPAS-A is similar to its ancestral implementations
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