58 results on '"Jin, Jianbing"'
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2. Reply on RC1
3. Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001–2018)
4. A decadal atmospheric ammonia reanalysis product in China
5. A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
6. An improved hourly-resolved atmospheric NOx emission inventory of industrial sources based on Continuous Emission Monitoring System data: Case of Jiangsu Province, China
7. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter
8. Reply on RC2
9. Reply on RC1
10. 4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals
11. Supplementary material to "EnKF-based fusion of site-available machine learning air quality predictions from RFSML v1.0 and gridded chemical transport model forecasts from GEOS-Chem v13.1.0"
12. EnKF-based fusion of site-available machine learning air quality predictions from RFSML v1.0 and gridded chemical transport model forecasts from GEOS-Chem v13.1.0
13. How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent
14. Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data
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17. Composited analyses of the chemical and physical characteristics of co-polluted days by ozone and PM2.5 over 2013–2020 in the Beijing–Tianjin–Hebei region
18. Clustering-based spatial transfer learning for short-term ozone forecasting
19. Supplementary material to "How aerosol size matters in AOD assimilation and the optimization using Ångström exponent"
20. How aerosol size matters in AOD assimilation and the optimization using Ångström exponent
21. Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
22. Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data
23. Supplementary material to "Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data"
24. Composited analyses of the chemical and physical characteristics of co-polluted days by ozone and PM2.5 over 2013–2020 in the Beijing–Tianjin–Hebei region
25. Supplementary material to "Composited analyses of the chemical and physical characteristics of co-polluted days by ozone and PM2.5 over 2013–2020 in the Beijing–Tianjin–Hebei region"
26. Reply on RC2
27. Reply on RC1
28. Supplementary material to "Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China"
29. Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
30. Future Co‐Occurrences of Hot Days and Ozone‐Polluted Days Over China Under Scenarios of Shared Socioeconomic Pathways Predicted Through a Machine‐Learning Approach
31. Inverse modeling of the 2021 spring super dust storms in East Asia
32. Supplementary material to "Inverse modeling of the 2021 spring super dust storms in East Asia"
33. Inverse modeling of the 2021 spring super dust storms in East Asia
34. Model‐Reduced Adjoint‐Based Inversion Using Deep‐Learning: Example of Geological Carbon Sequestration Modeling
35. Improved gridded ammonia emission inventory in China
36. Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0
37. Reply on RC2
38. Reply on RC1
39. Improved gridded ammonia emission inventory in China
40. Supplementary material to "Improved gridded ammonia emission inventory in China"
41. Position correction in dust storm forecast using LOTOS-EUROS v2.1: grid distorted data assimilation v1.0
42. Evaluating the influence of COVID-19 pandemic on NO2 concentration variation in selected regions in China using TROPOMI data, surface measurements and modeling approaches
43. Machine learning based bias correction for numerical chemical transport models
44. Mono-mercapto-functionalized pillar[5]arene: a host–guest complexation accelerated reversible redox dimerization
45. Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China
46. Response to the comments of Referee #3
47. Response to the comments of Referee #4
48. Source backtracking for dust storm emission inversion using adjoint method: case study of northeast China
49. Supplementary material to "Source backtracking for dust storm emission inversion using adjoint method: case study of northeast China"
50. Machine learning for observation bias correction with application to dust storm data assimilation
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