## 4 Discussion
Another concern of ASDM/ASDMTE is its test robustness. To stabilize \(\hat{f}\) at test time, we trained different ASDM/ASDMTE models for each season of a year and separately for different regions/countries, as shown in Section 2.2.3. The shorter period of time and smaller target domain simplified the learning task of each model and at the same time, simplified the domain to which the model needed to generalize, so we obtained more robust results when testing. In addition, ASDM/ASDMTE was designed to solve a supervised downscaling problem, that is, to downscale coarse-scale data and validate against fine-scale data. It requires the presence of some fine-scale data and ASDM/ASDMTE can computationally efficiently extend its temporal range by utilizing the within-scale temporal association to downscale. In the absence of fine-scale data, ASDM/ASDMTE cannot be applied. A further research direction would be to stabilize the sequential downscaling performance in the presence of shorter temporal range of fine-scale data to account for predicting over a long time series. As shown in Appendix Figures A1-A4, ASDM/ASDMTE can have good downscaling performances and their performances can even recover from previous bad downscaled results, but the performance still shows a temporally decreasing trend. Our future research will focus on improved learning of stable temporal associations to improve sequential downscaling performance for long time series prediction. Conceptualization, M.F. and M.W. ; methodology, M.W. ; validation, M.W. ; formal analysis, M.W. ; resources, M.F. ; data curation, M.F. ; writing--original draft preparation, M.W., M.F. ; writing--review and editing, M.F., L.L. ; visualization, M.W. ; supervision, M.F. ; project administration, M.F. ; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript. This research was funded by National Aeronautics and Space Administration grant number 80 NSSC19K0225. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Analysis data are available upon request. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} ANN & Artificial Neural Network \\ AOD & Aerosol Optical Depth \\ ASDM & Artificial Neural Network Sequentially Downscaling Method \\ ASDMTE & ASDM with Transfer Learning Enhancement \\ CNN & Convolutional Neural Network \\ CS & Coarse-Scale \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ FC & Fully Connected \\ FS & Fine-Scale \\ G5 NR & GEOS-5 Nature Run \\ GAM & Generalized Additive Model \\ GCM & General Circulation Model \\ GEOS-5 & Goddard Earth Observing System Model, Version 5 \\ GEOS-5 AGCM & GEOS-5 Atmospheric General Circulation Model \\ GMAO & Global Modeling and Assimilation Of-44 fice \\ GMTED2010 & The Global Multi-resolution Terrain Elevation Data 2010 \\ LM & Linear Regression Model \\ MERRA-2 & Modern-Era Retrospective analysis for8 Research and Applications, Version 2 \\ MSE & Mean Square Error \\ NGA & Geospatial-Intelligence Agency \\ OSSEs & Observing System Simulation Experiments \\ ReLU & Rectified Linear Unit \\ RMSE & Root Mean Square Error \\ SD & Standard Deviation \\ SRDRN & Super Resolution Deep Residual Network \\ USGS & U.S. Geological Survey \\ UAE & United Arab Emirates \\ \end{tabular}   
Appendix A Supplemental Results: Downscaling PerformanceFigure A3: Downscaling performance of ASDM, ASDMTE, SRDRN, dissever GAM and dissever LM in Season 3. Please refer to Figure 4 and Section 2.2.3 for the definition of Season 3. Figure A5: Downscaled (by method) and GSNR data over the study region on 23 October 2006: (a) ASDMTE, (b) ASDM, (c) SRDRN, (d) dissever GAM, (e) dissever LM, (f) GSNR.