Publications
Submitted / Preprints
Toride, K., M. Newman, A. Hoell, A. Capotondi, J. Schlör, D. Amaya, Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts, arXiv Preprint, 2404.15419 (source code)
Schneider, M., K. Toride, F. Khosrawi, F. Hase, B. Ertl, C. J. Diekmann, K. Yoshimura, Assessing the potential of free tropospheric water vapour isotopologue satellite observations for improving the analyses of latent heating events, EGUsphere Priprint, doi: 10.5194/egusphere-2023-1121
Peer-reviewed Publications
13. Wang, X., K. Toride, K. Yoshimura (2023), Impact of Gaussian Transformation on Cloud Cover Data Assimilation for Historical Weather Reconstruction, Monthly Weather Review, 151(10), 2701-2716, doi: 10.1175/MWR-D-22-0315.1
12. Wang, X., K. Toride, K. Yoshimura (2022), Historical atmospheric analysis by weather category assimilation using Gaussian transformation, Journal of Japan Society of Civil Engineers, Ser. B1(Hydraulic Engineering), 78(2), I_691-I_696, doi: 10.2208/jscejhe.78.2_I_691
11. Toride, K., G. J. Hakim (2022), What distinguishes MJO events associated with atmospheric rivers?, Journal of Climate, 35(18), 6135-6149, doi: 10.1175/JCLI-D-21-0493.1
10. Tada, M., K. Yoshimura, K. Toride (2021), Improving Weather Forecasting by Assimilation of Water Vapor Isotopes, Scientific Reports, 11, 18067, doi: 10.1038/s41598-021-97476-0 (University of Tokyo press release)
9. Toride, K., G. J. Hakim (2021), Influence of low-frequency PNA variability on MJO teleconnections to North American atmospheric river activity, Geophysical Research Letters, 48, e2021GL094078, doi: 10.1029/2021GL094078
8. Toride, K., K. Yoshimura, M. Tada, C. Diekmann, B. Ertl, F. Khosrawi, M. Schneider (2021), Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability, Geophysical Research Letters, 48, e2020GL091698, doi: 10.1029/2020GL091698 (Science Editors' Choice)
7. Toride, K., Y. Sawada, K. Aida, T. Koike (2019), Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model, Sensors, 19, 3924, doi: 10.3390/s19183924
6. Toride, K., Y. Iseri, M. D. Warner, C. D. Frans, A. M. Duren, J. F. England, M. L. Kavvas (2019), Model-based Probable Maximum Precipitation estimation: How to estimate the worst-case scenario induced by atmospheric rivers?, Journal of Hydrometeorology, 20(12), 2383-2400, doi: 10.1175/JHM-D-19-0039.1
5. Toride, K., Y. Iseri, A. M. Duren, J. F. England, M. L. Kavvas (2019), Evaluation of physical parameterizations for atmospheric river induced precipitation and application to long-term reconstruction based on three reanalysis datasets in Western Oregon, Science of the Total Environment, 658, 570-581, doi: 10.1016/j.scitotenv.2018.12.214
4. Toride, K., D. L. Cawthorne, K. Ishida, M. L. Kavvas, M. L. Anderson (2018), Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed, Science of the Total Environment, 626, 244-254, doi: 10.1016/j.scitotenv.2018.01.004
3. Sawada, Y., T. Koike, K. Aida, K. Toride, J. P. Walker (2017), Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness, IEEE Transactions on Geoscience and Remote Sensing, 1-12, doi: 10.1109/TGRS.2017.2722468
2. Toride, K., P. Neluwala, H. Kim, K. Yoshimura (2017), Feasibility Study of the Reconstruction of Historical weather with Data Assimilation, Monthly Weather Review, 145(9), 3563-3580, doi: 10.1175/MWR-D-16-0288.1
1. Kennedy, A. B., N. Mori, Y. Zhang, T. Yasuda, S. Chen, Y. Tajima, W. Pecor, K. Toride (2016), Observations and Modeling of Coastal Boulder Transport during Super Typhoon Haiyan, Coastal Engineering Journal, 58(01), 1640004, doi: 10.1142/S0578563416400040
Presentations (selected)
15. Interpretable ENSO forecasting using hybrid deep learning analog approach (FEWS NET Science Meeting 2023 | AMS 2024 | CESM ESPWG 2024 | PSL 2024 | AOFD 2024)
14. Improving weather forecasting by satellite-observed water vapor isotopes (IsoNet 2022)
13. Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability (Fugaku meeting 2021)
12. MJO teleconnection to North American atmospheric river and role of the extratropical state (U Washington 2021 | PSL 2023)
11. The influence of the MJO diversity on midlatitude teleconnection patterns (AGU 2020)
10. Impacts of assimilating high-resolution IASI water vapor isotopic observations on weather forecasts (AGU 2019)
9. The impacts of assimilating IASI water vapor isotope on weather forecasts (Water Isotopes and Climate Workshop 2019)
8. Model-based Probable Maximum Precipitation Estimation: How to Maximize an Atmospheric River? (U Tokyo 2018)
7. Evaluation of dynamically downscaled historical precipitation in Western Oregon based on three reanalysis datasets (World Environmental and Water Resources Congress 2018)
6. Coupling of Noah-MP and upscaled unsaturated flow model to account for spatial heterogeneity in various scales (AGU 2017)
5. Maximum Precipitation Estimation over Shasta Dam Watershed by Means of Atmospheric Boundary Condition Shifting Method (World Environmental and Water Resources Congress 2017)
4. Climate Change Trend Analysis on Extreme Precipitation over the Shasta Dam Watershed Based on 159-Year Long-Term Dynamic Downscaling (World Environmental and Water Resources Congress 2017)
3. Development of an Algorithm for Soil Moisture with High Spatial- and Temporal- Resolution (Catchment Hydrological Modeling and Data Assimilation (CAHMDA VI) and Hydrologic Ensemble Prediction Experiment (HEPEX-DAFOH III) Joint workshop 2014)
2. Toward Reconstruction of Historical Weather with Data Assimilation: Idealized Experiments to Investigate the Feasibility (The Association of Japanese Geographers 2014)
1. Toward Reconstruction of Historical Weather with Data Assimilation: Present Day Experiments using Reanalysis Data (13th International Regional Spectral Model Workshop 2014)