9129767 3BVIFSK4 items 1 0 date desc year Ralph 18 https://mralph.scrippsprofiles.ucsd.edu/wp-content/plugins/zotpress/
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Lavers, D. A., Torn, R. D., Davis, C., Richardson, D. S., Ralph, F. M., & Pappenberger, F. (2023). Forecast evaluation of the North Pacific jet stream using AR Recon dropwindsondes. Quarterly Journal of the Royal Meteorological Society, qj.4545. https://doi.org/10.1002/qj.4545
Shulgina, T., Gershunov, A., Hatchett, B. J., Guirguis, K., Subramanian, A. C., Margulis, S. A., Fang, Y., Cayan, D. R., Pierce, D. W., Dettinger, M., Anderson, M. L., & Ralph, F. M. (2023). Observed and projected changes in snow accumulation and snowline in California’s snowy mountains. Climate Dynamics. https://doi.org/10.1007/s00382-023-06776-w
Zhang, Z., DeFlorio, M. J., Delle Monache, L., Subramanian, A. C., Ralph, F. M., Waliser, D. E., Zheng, M., Guan, B., Goodman, A., Molod, A. M., Vitart, F., Kumar, A., & Lin, H. (2023). Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S. Journal of Geophysical Research: Atmospheres, 128(7), e2022JD037608. https://doi.org/10.1029/2022JD037608
Shields, C. A., Payne, A. E., Shearer, E. J., Wehner, M. F., O’Brien, T. A., Rutz, J. J., Leung, L. R., Ralph, F. M., Marquardt Collow, A. B., Ullrich, P. A., Dong, Q., Gershunov, A., Griffith, H., Guan, B., Lora, J. M., Lu, M., McClenny, E., Nardi, K. M., Pan, M., … Zarzycki, C. (2023). Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High‐Resolution Global Warming Experiment. Geophysical Research Letters, 50(6), e2022GL102091. https://doi.org/10.1029/2022GL102091
Castellano, C. M., DeFlorio, M. J., Gibson, P. B., Delle Monache, L., Kalansky, J. F., Wang, J., Guirguis, K., Gershunov, A., Ralph, F. M., Subramanian, A. C., & Anderson, M. L. (2023). Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity. Journal of Geophysical Research: Atmospheres, 128(6), e2022JD037360. https://doi.org/10.1029/2022JD037360
Hu, W., Ghazvinian, M., Chapman, W. E., Sengupta, A., Ralph, F. M., & Delle Monache, L. (2023). Deep Learning Forecast Uncertainty for Precipitation over Western US. Monthly Weather Review. https://doi.org/10.1175/MWR-D-22-0268.1
Guan, B., Waliser, D. E., & Ralph, F. M. (2023). Global Application of the Atmospheric River Scale. Journal of Geophysical Research: Atmospheres, 128(3), e2022JD037180. https://doi.org/10.1029/2022JD037180
Badrinath, A., Delle Monache, L., Hayatbini, N., Chapman, W., Cannon, F., & Ralph, M. (2023). Improving Precipitation Forecasts with Convolutional Neural Networks. Weather and Forecasting. https://doi.org/10.1175/WAF-D-22-0002.1
Lord, S. J., Wu, X., Tallapragada, V., & Ralph, F. M. (2023). The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part II: Dynamic Variables and Humidity. Weather and Forecasting, 38(5), 721–752. https://doi.org/10.1175/WAF-D-22-0072.1
Howard, I. M., Stahle, D. W., Dettinger, M. D., Poulsen, C., Ralph, F. M., Torbenson, M. C. A., & Gershunov, A. (2023). A 440-Year Reconstruction of Heavy Precipitation in California from Blue Oak Tree Rings. Journal of Hydrometeorology, 24(3), 463–477. https://doi.org/10.1175/JHM-D-22-0062.1
Lord, S. J., Wu, X., Tallapragada, V., & Ralph, F. M. (2023). The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part I: Precipitation. Weather and Forecasting, 38(1), 17–45. https://doi.org/10.1175/WAF-D-22-0036.1
Reynolds, C. A., Stone, R. E., Doyle, J. D., Baker, N. L., Wilson, A. M., Ralph, F. M., Lavers, D. A., Subramanian, A. C., & Centurioni, L. (2023). Impacts of Northeastern Pacific Buoy Surface Pressure Observations. Monthly Weather Review, 151(1), 211–226. https://doi.org/10.1175/MWR-D-22-0124.1
Corringham, T. W., McCarthy, J., Shulgina, T., Gershunov, A., Cayan, D. R., & Ralph, F. M. (2022). Climate change contributions to future atmospheric river flood damages in the western United States. Scientific Reports, 12(1), 13747. https://doi.org/10.1038/s41598-022-15474-2
Guirguis, K., Gershunov, A., Hatchett, B., Shulgina, T., DeFlorio, M. J., Subramanian, A. C., Guzman-Morales, J., Aguilera, R., Clemesha, R., Corringham, T. W., Delle Monache, L., Reynolds, D., Tardy, A., Small, I., & Ralph, F. M. (2022). Winter wet-dry weather patterns driving atmospheric rivers and Santa Ana winds provide evidence for increasing wildfire hazard in California. Climate Dynamics, 21. https://doi.org/10.1007/s00382-022-06361-7
Collow, A. B. M., Shields, C. A., Guan, B., Kim, S., Lora, J. M., McClenny, E. E., Nardi, K., Payne, A., Reid, K., Shearer, E. J., Tome, R., Wille, J. D., Ramos, A. M., Gorodetskaya, I. V., Leung, L. R., O’Brien, T. A., Ralph, F. M., Rutz, J., Ullrich, P. A., & Wehner, M. (2022). An overview of ARTMIP’s Tier 2 Reanalysis Intercomparison: Uncertainty in the detection of atmospheric rivers and their associated precipitation. Journal of Geophysical Research-Atmospheres, 127(8), 20. https://doi.org/10.1029/2021jd036155
Michaelis, A. C., Gershunov, A., Weyant, A., Fish, M. A., Shulgina, T., & Ralph, F. M. (2022). Atmospheric river precipitation enhanced by climate change: A case study of the storm that contributed to California’s Oroville Dam crisis. Earths Future, 10(3), 12. https://doi.org/10.1029/2021ef002537
Fish, M. A., Done, J. M., Swain, D. L., Wilson, A. M., Michaelis, A. C., Gibson, P. B., & Ralph, F. M. (2022). Large-Scale Environments of Successive Atmospheric River Events Leading to Compound Precipitation Extremes in California. Journal of Climate, 35(5), 1515–1536. https://doi.org/10.1175/jcli-d-21-0168.1
O’Brien, T. A., Wehner, M. F., Payne, A. E., Shields, C. A., Rutz, J. J., Leung, L. R., Ralph, F. M., Collow, A., Gorodetskaya, I., Guan, B., Lora, J. M., McClenny, E., Nardi, K. M., Ramos, A. M., Tome, R., Sarangi, C., Shearer, E. J., Ullrich, P. A., Zarzycki, C., … Zhou, Y. (2022). Increases in future AR count and size: Overview of the ARTMIP Tier 2 CMIP5/6 experiment. Journal of Geophysical Research-Atmospheres, 127(6), 15. https://doi.org/10.1029/2021jd036013
de Orla-Barile, M., Cannon, F., Oakley, N. S., & Ralph, F. M. (2022). A climatology of narrow cold-frontal rainbands in Southern California. Geophysical Research Letters, 49(2), 10. https://doi.org/10.1029/2021gl095362
Chapman, W. E., Delle Monache, L., Alessandrini, S., Subramanian, A. C., Ralph, F. M., Xie, S. P., Lerch, S., & Hayatbini, N. (2022). Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning. Monthly Weather Review, 150(1), 215–234. https://doi.org/10.1175/mwr-d-21-0106.1
Afzali Gorooh, V., Shearer, E. J., Nguyen, P., Hsu, K., Sorooshian, S., Cannon, F., & Ralph, M. (2022). Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California. Journal of Hydrometeorology, 23(12), 1899–1911. https://doi.org/10.1175/JHM-D-22-0066.1
Stewart, B. E., Cordeira, J. M., & Ralph, F. M. (2022). Evaluating GFS and ECMWF Ensemble Forecasts of Integrated Water Vapor Transport along the U.S. West Coast. Weather and Forecasting, 37(11), 1985–2004. https://doi.org/10.1175/WAF-D-21-0114.1
Sun, W., Liu, Z., Davis, C. A., Ralph, F. M., Monache, L. D., & Zheng, M. (2022). Impacts of dropsonde and satellite observations on the forecasts of two atmospheric-river-related heavy rainfall events. Atmospheric Research, 278, 106327. https://doi.org/10.1016/j.atmosres.2022.106327
White, C. J., Domeisen, D. I. V., Acharya, N., Adefisan, E. A., Anderson, M. L., Aura, S., Balogun, A. A., Bertram, D., Bluhm, S., Brayshaw, D. J., Browell, J., Büeler, D., Charlton-Perez, A., Chourio, X., Christel, I., Coelho, C. A. S., DeFlorio, M. J., Delle Monache, L., Di Giuseppe, F., … Wilson, R. G. (2022). Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions. Bulletin of the American Meteorological Society, 103(6), E1448–E1472. https://doi.org/10.1175/BAMS-D-20-0224.1
Voss, K. K., Evan, A. T., & Ralph, F. M. (2021). Evaluating the meteorological conditions associated with dusty atmospheric rivers. Journal of Geophysical Research-Atmospheres, 126(24), 14. https://doi.org/10.1029/2021jd035403
Woodside, G. D., Hutchinson, A. S., Ralph, F. M., Talbot, C., Hartman, R., & Delaney, C. (2021). Increasing stormwater capture and recharge using Forecast Informed Reservoir Operations, Prado Dam. Groundwater, 7. https://doi.org/10.1111/gwat.13162
Haase, J. S., Murphy, M. J., Cao, B., Ralph, F. M., Zheng, M., & Delle Monache, L. (2021). Multi-GNSS airborne radio occultation observations as a complement to dropsondes in atmospheric river reconnaissance. Journal of Geophysical Research-Atmospheres, 126(21), 24. https://doi.org/10.1029/2021jd034865
Mascioli, N. R., Evan, A. T., & Ralph, F. M. (2021). Influence of dust on precipitation during landfalling atmospheric rivers in an idealized framework. Journal of Geophysical Research-Atmospheres, 126(22), 17. https://doi.org/10.1029/2021jd034813
Zheng, M. H., Delle Monache, L., Cornuelle, B. D., Ralph, F. M., Tallapragada, V. S., Subramanian, A., Haase, J. S., Zhang, Z. H., Wu, X. R., Murphy, M. J., Higgins, T. B., & DeHaan, L. (2021). Improved forecast skill through the assimilation of dropsonde observations from the atmospheric river reconnaissance program. Journal of Geophysical Research-Atmospheres, 126(21), 25. https://doi.org/10.1029/2021jd034967
Prince, H. D., Gibson, P. B., DeFlorio, M. J., Corringham, T. W., Cobb, A., Guan, B., Ralph, F. M., & Waliser, D. E. (2021). Genesis locations of the costliest atmospheric rivers impacting the Western United States. Geophysical Research Letters, 48(20), 11. https://doi.org/10.1029/2021gl093947
Cobb, A., Delle Monache, L., Cannon, F., & Ralph, F. M. (2021). Representation of dropsonde-observed atmospheric river conditions in reanalyses. Geophysical Research Letters, 48(15), 11. https://doi.org/10.1029/2021gl093357
DeHaan, L. L., Martin, A. C., Weihs, R. R., Delle Monache, L., & Ralph, F. M. (2021). Object-based verification of atmospheric river predictions in the Northeast Pacific. Weather and Forecasting, 36(4), 1575–1587. https://doi.org/10.1175/waf-d-20-0236.1
Michaelis, A. C., Martin, A. C., Fish, M. A., Hecht, C. W., & Ralph, F. M. (2021). Modulation of atmospheric rivers by mesoscale frontal waves and latent heating: Comparison of two US West Coast events. Monthly Weather Review, 149(8), 2755–2776. https://doi.org/10.1175/mwr-d-20-0364.1
Chapman, W. E., Subramanian, A. C., Xie, S. P., Sierks, M. D., Ralph, F. M., & Kamae, Y. (2021). Monthly modulations of ENSO teleconnections: Implications for potential predictability in North America. Journal of Climate, 34(14), 5899–5921. https://doi.org/10.1175/jcli-d-20-0391.1
Cao, Q., Shukla, S., DeFlorio, M. J., Ralph, F. M., & Lettenmaier, D. P. (2021). Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal western US Watersheds. Journal of Hydrometeorology, 22(6), 1535–1552. https://doi.org/10.1175/jhm-d-20-0219.1
Pagano, T. J., Waliser, D. E., Guan, B., Ye, H. C., Ralph, F. M., & Kim, J. (2021). Extreme surface winds during landfalling atmospheric rivers: The modulating role of near-surface stability. Journal of Hydrometeorology, 22(6), 1681–1693. https://doi.org/10.1175/jhm-d-20-0165.1
Ramos, A. M., Roca, R., Soares, P. M. M., Wilson, A. M., Trigo, R. M., & Ralph, F. M. (2021). Uncertainty in different precipitation products in the case of two atmospheric river events. Environmental Research Letters, 16(4). https://doi.org/10.1088/1748-9326/abe25b
Cordeira, J. M., & Ralph, F. M. (2021). A summary of GFS ensemble integrated water vapor transport forecasts and skill along the US West Coast during water years 2017-20. Weather and Forecasting, 36(2), 361–377. https://doi.org/10.1175/waf-d-20-0121.1
Sun, R., Subramanian, A. C., Cornuelle, B. D., Mazloff, M. R., Miller, A. J., Ralph, F. M., Seo, H., & Hoteit, I. (2021). The role of air-sea interactions in atmospheric rivers: Case studies using the SKRIPS regional coupled model. Journal of Geophysical Research-Atmospheres, 126(6). https://doi.org/10.1029/2020jd032885
Eiras-Barca, J., Ramos, A. M., Algarra, I., Vazquez, M., Dominguez, F., Miguez-Macho, G., Nieto, R., Gimeno, L., Taboada, J., & Ralph, F. M. (2021). European West Coast atmospheric rivers: A scale to characterize strength and impacts. Weather and Climate Extremes, 31. https://doi.org/10.1016/j.wace.2021.100305
Nguyen, P., Ombadi, M., Gorooh, V. A., Shearer, E. J., Sadeghi, M., Sorooshian, S., Hsu, K. L., Bolvin, D., & Ralph, M. F. (2020). PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A near-real-time, quasi-global satellite precipitation dataset. Journal of Hydrometeorology, 21(12), 2893–2906. https://doi.org/10.1175/jhm-d-20-0177.1
Norris, J. R., Ralph, F. M., Demirdjian, R., Cannon, F., Blomquist, B., Fairall, C. W., Spackman, J. R., Tanelli, S., & Waliser, D. E. (2020). The observed water vapor budget in an atmospheric river over the Northeast Pacific. Journal of Hydrometeorology, 21(11), 2655–2673. https://doi.org/10.1175/jhm-d-20-0048.1
Voss, K. K., Evan, A. T., Prather, K. A., & Ralph, F. M. (2020). Dusty atmospheric rivers: Characteristics and origins. Journal of Climate, 33(22), 9749–9762. https://doi.org/10.1175/JCLI-D-20-0059.1
Sumargo, E., McMillan, H., Weihs, R., Ellis, C. J., Wilson, A. M., & Ralph, F. M. (2020). A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events. Hydrological Processes. https://doi.org/10.1002/hyp.13998
Zheng, M., Delle Monache, L., Wu, X., Ralph, F. M., Cornuelle, B., Tallapragada, V., Haase, J. S., Wilson, A. M., Mazloff, M., Subramanian, A., & Cannon, F. (2020). Data gaps within atmospheric rivers over the northeastern Pacific. Bulletin of the American Meteorological Society, 1–1. https://doi.org/10.1175/BAMS-D-19-0287.1
Delaney, C. J., Hartman, R. K., Mendoza, J., Dettinger, M., Delle Monache, L., Jasperse, J., Ralph, F. M., Talbot, C., Brown, J., Reynolds, D., & Evett, S. (2020). Forecast informed reservoir operations using ensemble streamflow predictions for a multipurpose reservoir in Northern California. Water Resources Research, 56(9). https://doi.org/10.1029/2019wr026604
Cannon, F., Oakley, N. S., Hecht, C. W., Michaelis, A., Cordeira, J. M., Kawzenuk, B., Demirdjian, R., Weihs, R., Fish, M. A., Wilson, A. M., & Ralph, F. M. (2020). Observations and predictability of a high-impact narrow cold-frontal rainband over Southern California on 2 February 2019. Weather and Forecasting, 1–40. https://doi.org/10.1175/waf-d-20-0012.1
Guirguis, K., Gershunov, A., DeFlorio, M. J., Shulgina, T., Delle Monache, L., Subramanian, A. C., Corringham, T. W., & Ralph, F. M. (2020). Four atmospheric circulation regimes over the North Pacific and their relationship to California precipitation on daily to seasonal timescales. Geophysical Research Letters, 47(16). https://doi.org/10.1029/2020gl087609
Cao, Q., Gershunov, A., Shulgina, T., Ralph, F. M., Sun, N., & Lettenmaier, D. P. (2020). Floods due to atmospheric rivers along the US West Coast: The role of antecedent soil moisture in a warming climate. Journal of Hydrometeorology, 21(8), 1827–1845. https://doi.org/10.1175/jhm-d-19-0242.1
Lavers, D. A., Ingleby, N. B., Subramanian, A. C., Richardson, D. S., Ralph, F. M., Doyle, J. D., Reynolds, C. A., Torn, R. D., Rodwell, M. J., Tallapragada, V., & Pappenberger, F. (2020). Forecast errors and uncertainties in atmospheric rivers. Weather and Forecasting, 35(4), 1447–1458. https://doi.org/10.1175/waf-d-20-0049.1