Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies
Authors: Sun Wei , Liu Zhiquan , Chen Dan , Zhao Pusheng , Chen Min 
Published: August 2020

Abstract. To improve the operational air quality forecasting over China, a new
aerosol or gas-phase pollutants assimilation capability is developed within the
WRFDA system using the three-dimensional variational (3DVAR) algorithm. In this first application, the interface
for the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme is built with the potential for flexible extension. Based
on the new WRFDA-Chem system, five experiments assimilating different
surface observations, including PM2.5, PM10, SO2, NO2, O3, and CO, are
conducted for January 2017 along with a control experiment without data assimilation (DA).
Results show that the WRFDA-Chem system evidently improves the air
quality forecasting. From the analysis aspect, the assimilation of surface
observations reduces the bias and RMSE in the initial condition (IC)
remarkably; from the forecast aspect, better forecast performances are
acquired up to 24 h, in which the experiment assimilating the six pollutants
simultaneously displays the best forecast skill overall. With respect to the
impact of the DA cycling frequency, the responses toward IC updating are found to be different among the pollutants. For PM2.5, PM10, SO2, and CO, the
forecast skills increase with the DA frequency. For O3, although
improvements are acquired at the 6 h cycling frequency, the advantage of
more frequent DA could be consumed by the disadvantages of the unbalanced
photochemistry (due to inaccurate precursor NOx ∕ VOC (volatile organic compound) ratios) or the changed
titration process (due to changed NO2 concentrations but not NO) from
assimilating the existing observations (only O3 and NO2, but no VOC and NO). As yet the finding is based on the 00:00 UTC forecast for this winter season only,
and O3 has strong diurnal and seasonal variations. More experiments should
be conducted to draw further conclusions. In addition, considering one
aspect (IC) in the model is corrected by DA, the deficiencies of other
aspects (e.g., chemical reactions) could be more evident. This study
explores the model deficiencies by investigating the effects of assimilating
gaseous precursors on the forecast of related aerosols. Results show that
the parameterization (uptake coefficients) in the newly added
sulfate–nitrate–ammonium (SNA)-relevant heterogeneous reactions in the model is not fully appropriate although it best simulates observed SNA aerosols
without DA; since the uptake coefficients were originally tuned under the
inaccurate gaseous precursor scenarios without DA, the biases from the two
aspects (SNA reactions and IC DA) were just compensated. In future
chemistry development, parameterizations (such as uptake coefficients) for
different gaseous precursor scenarios should be adjusted and verified with
the help of the DA technique. According to these results, DA ameliorates certain
aspects by using observations as constraints and thus provides an
opportunity to identify and diagnose the model deficiencies; it is useful
especially when the uncertainties of various aspects are mixed up and the
reaction paths are not clearly revealed. In the future, besides being used
to improve the forecast through updating IC, DA could be treated as another
approach to explore necessary developments in the model.