Reply on AC1

doi:10.5194/gmd-2021-253-rc3

Abstract

Well-estimated air pollutant concentration fields through data fusion are critically important to compensate the observations that are only sparsely available, especially over non-urban areas. Previous data fusion methods generally used statistical models to relate target observations and supporting data variables at known stations. In this study, we built a new data fusion paradigm by designing a dedicated deep learning framework to learn multi-variable spatial correlations from Chemical Transport Model (CTM) simulations, before using it to estimate PM2.5 reanalysis fields from station observations. The model was composed of two modules, which include an explainable PointConv operation to pre-process isolated observations and a regression grid-to-grid network to reflect correlations among multiple variables. The model was evaluated in two aspects of reproducing PM2.5 CTM simulations and generating reanalysis/fused PM2.5 fields. First, the fusion model was able to well reproduce CTM simulations from sampled station CTM data items with an average R2 = 0.94. Second, the fusion model achieved good performance with R2 = 0.77 and R2 = 0.83 respectively evaluated at the stringent city-level and station-level. The generated reanalysis PM2.5 fields have complete spatial coverage within the modelling domain and at daily time scale. One significant benefit of our fusion framework is that the model training does not rely on observations, which can be used to predict PM2.5 fields in newly-setup observation networks such as those using portable sensors. The fusion model has high computing efficiency (< 1 s/day) in predicting PM2.5 concentrations due to acceleration using GPU. As an alternative to generate chemical/meteorological reanalysis fields, the method can be readily applied for other simulated variables that with measurements available.

Highlights

Pollutant concentration fields with high accuracy are important for evaluating health effects, climate changes and agricultural studies (Bell et al, 2007; Donkelaar et al, 2015; Gao et al, 2017)
The model was evaluated in two aspects of reproducing PM2.5 Chemical Transport Model (CTM) simulations and generating reanalysis/fused PM2.5 fields
The simulated PM2.5 and other meteorological variables in 2016~2020 were produced using a modeling system that consists of three major components: The meteorology component (WRFv3.4.1) provides meteorological fields, the emission component provides gridded estimates of hourly emissions rates of primary pollutants that matched to model species, and the CTM 70 component (CMAQ v5.0.2(Byun and Schere, 2006)) solves the governing physical and chemical equations to obtain 3-D pollutant concentrations fields at a horizontal resolution of 12 km

Summary

Introduction

Pollutant concentration fields with high accuracy are important for evaluating health effects, climate changes and agricultural studies (Bell et al, 2007; Donkelaar et al, 2015; Gao et al, 2017). Even though many high-quality datasets have been developed through deliberately designed statistical models and abundant explanatory variables, there are scientific gaps following this paradigm to develop air pollutant fields These models usually rely on long-term and large-scale station observations for training, especially those complex time and space resolved 45 models (Feng et al, 2020; Huang et al, 2021). In near-real-time operational data fusion applications, adjoint models are often required to be 55 running simultaneously (Friberg et al, 2016) To address these scientific gaps, this study developed a new deep-learning-based model framework to estimate reanalysis from station observations by learning spatio-temporal correlations from deterministic models. The model framework is fundamentally an alternative of generating chemical/meteorological reanalysis fields but without rerunning CTMs with data assimilation

Data and Methods

Ground Observations

Model Parameters