Publications

  1. DeHaan, L. L., Wilson, A. M., Kawzenuk, B., Zheng, M., Delle Monache, L., Wu, X., Lavers, D. A., Ingleby, B., Tallapragada, , Pappenberger, F., and Ralph, F. M., 2023. Impacts of dropsonde observations on forecasts of atmospheric rivers and associated precipitation in the NCEP GFS and ECMWF IFS models. Weather and Forecasting, conditionally accepted
  2. Cobb, A., Steinhoff, D., Weihs, R., DeHaan, L., Reynolds, D., Delle Monache, L., Cannon, F., Kawzenuk, B., Papadopolous, C., and Ralph, F. M., 2023. West-WRF 34-year reforecast: description and validation. Journal of Hydrometeorology, conditionally accepted
  3. Cobb, A., Ralph, F., Tallapragada, V., Wilson, A. W., Davis, C., Delle Monache, L., Doyle, J., Pappenberger, F., Reynolds, C., Subramanian, A., Black, P., Cannon, F., Castellano, C. M., Cordeira, J., Haase, J., Hecht, C., Kawzenuk, B., Lavers, D., Murphy, M., Parrish, J. P., Rickert, R., Rutz, J., Torn, R., Wu, X., and Zheng, M., 2023. Atmospheric River Reconnaissance 2021: A Review. Weather and Forecasting, accepted
  4. Higgins, T. B., Subramanian, A. C., Graubner, A., Kapp-Schwoerer, L., Watson, P. A. G., Sparrow, S., Kashinath, K., Kim, S., Delle Monache, L., and Chapman, W., 2023. Using deep learning for an analysis of atmospheric rivers in a high-resolution large ensemble climate data set. Journal of Advances in Modeling Hearth Systems, 15, e2022MS003495
  5. Hu, W., Ghazvinian, M., Chapman, W. E., Sengupta, A., Ralph, F. M., and Delle Monache, L., 2023. Deep learning forecast uncertainty for precipitation over Western US. Monthly Weather Review, MWR-D-22-0268.1
  6. Hu, W., Cervone G., Young, G., and Delle Monache, L., 2023. Machine learning weather analogs for near-surface variables. Boundary Layer Meteorology, 186, 711–735
  7. Lu, Y., Delle Monache, L., Weil, J., Ngan, K., Li, Q., 2023. Predictability of passive scalar dispersion in atmospheric surface layers with urban-like roughness: A large-eddy simulations study. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4445
  8. Badrinath, A., Delle Monache, L., Hayatbini, N., Chapman, W., Cannon, F., and Ralph, M. F., 2023. Improving precipitation forecasts with convolutional neural networks. Weather and Forecasting, 22, 291–306
  9. 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., and Lin, Hai, 2023. Multi-model subseasonal prediction skill assessment of water vapor transport associated with atmospheric rivers over the Western U.S. Journal of Geophysical Research, 128, e2022JD037608
  10. 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., and Ralph, F. M., 2023. Winter wet–dry weather patterns driving atmospheric rivers and Santa Ana winds provide evidence for increasing wildfire hazard in California. Climate Dynamics, 60, 1729–1749
  11. 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., and 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, 128, e2022JD037360
  12. Sun, W., Liu, Z., Davis, C. A., Ralph, F. M., Delle Monache, L., and Zheng, M., 2022. Impacts of dropsonde and satellite observations on the forecasts of two atmospheric-river-related heavy rainfall events. Atmospheric Research,  278, 106327
  13. 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., García-Solórzano, A. M., Gibson, P. B., Goddard, L., González Romero, C., Graham, R. J., Graham, R. M., Grams, C. M., Halford, A., Katty Huang, W. T., Jensen, K., Kilavi, M., Lawal, K. A., Lee, R. W., MacLeod, D., Manrique-Suñén, A., Martins, , E. S. P. R., Maxwel, C., J., Merryfield, W. J., Muñoz, A. G., Olaniyan, E., Otieno, G., Oyedepo, J. A., Palma, L., Pechlivanidis, I. G., Pons, D., Ralph, F. M., Reis Jr., D. S., Remenyi, T. A., Risbey, J. S., Robertson, D. J. C., Robertson, A. W., Smith, S., Soret, A., Sun, T., Todd, M. C., Tozer, C. R., Vasconcelos Jr., F. C., Vigo, I., Waliser, D. E., Wetterhall, F., and Wilson, R. G., 2022. Advances in the application and utility of subseasonal-to-seasonal predictions. Amer. Meteor. Soc., 103, E1448–E1472
  14. Wilson, A. M., Cobb, A. C., Ralph, F. M., Tallapragada, V., Davis, C., Doyle, J., Delle Monache, L., Pappenberger, F., Reynolds, C., Subramanian, A., Cannon, F., Cordeira, J., Haase, J., Hecht, C., Lavers, D., Rutz, J. J., and Zheng, M., 2022. Atmospheric River Reconnaissance Workshop Promotes Research and Operations Partnership. Amer. Meteor. Soc., 103, E810-E816.
  15. Chapman, W. E., Delle Monache, L., Alessandrini, S., Subramanian, A. C., Ralph, F. M., Xie, S.P., Lerch, S., and Hayatbini, N., 2022. Probabilistic predictions from deterministic atmospheric river forecasts with Deep Learning. Monthly Weather Review, 150, 215–234
  16. Haase, J. S., Murphy, M. J., Cao, B., Ralph, F. M., Zheng, M., and Delle Monache, L., 2021. Multi-GNSS Airborne Radio Occultation observations as a complement to dropsondes in Atmospheric River Reconnaissance. Journal of Geophysical Research, 126, e2021JD034865
  17. Calovi, M., Hu, W., Cervone, G., and Delle Monache, L., 2021. WRF-NAM temperature downscaling using personal weather stations to study urban temperature heat hazards. GeoHazards, 2, 257–276
  18. DeHaan, L. L., Martin, A., Weihs, R., Delle Monache, L., and Ralph, F. M., 2021. Object-based verification of atmospheric river predictions in the Northeast Pacific. Weather and Forecasting, 36, 1575–1587
  19. Cobb, A., Delle Monache, L., Cannon, F., and Ralph, F. M., 2021. Representation of dropsonde-observed atmospheric river conditions in reanalyses. Geophysical Research Letters, 48, e2021GL093357
  20. Gibson, P. B., Chapman, W. E., Altinok, A., Delle Monache, L., DeFlorio, M. J., and Waliser, D. E., 2021. Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Nature Communications Earth and Environment, 2, 159, https://doi.org/10.1038/s43247-021-00225-4
  21. Zheng, M., Delle Monache, L., Cornuelle, B. D., Ralph, F. M., Tallapragada, V. S., Subramanian, A., Haase, J. S., Zhang, Z., Wu, X., Murphy, M. J., Higgins, T. B., and DeHaan, L., 2021. Improved forecast skill through assimilating dropsonde observations from Atmospheric River Reconnaissance. Journal of Geophysical Research, 126, e2021JD03496.
  22. Meech, S., Alessandrini, S., Chapman, W., and Delle Monache, L., 2021. Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood. Bulletin of Atmospheric Science and Technology, 1, 373–385
  23. Raman, A., Arellano, A. F., Delle Monache, L., Alessandrini, S., and Kumar, R., 2021. Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem. Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2020.118134
  24. Kumar, R. Mitchell, D. A., Steinhoff, D. F., Saide, P., Kosovic, B., Downey, N.,Blewitt, D., and Delle Monache, L., Evaluating the Mobile Flux Plane (MFP) Method to Estimate Methane Emissions using Large Eddy Simulations (LES). Journal of Geophysical Research, https://doi.org/ 10.1029/2020JD032663
  25. Zardi, D., Falocchi, M., Giovannini, L., Tirler, W., Tomasi, E., Antonacci, G., Ferrero, E., Alessandrini, S., Jimenez, P. A., Kosovic, B., and Delle Monache, L., 2021. The Bolzano Tracer Experiment (BTEX). Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-19-0024.1
  26. Zheng, M., Delle Monache, L., Wu, X., Ralph, F. M., Cornuelle, B., Tallapragada, V., Haase, J. S., Wilson, A. M., Mazloff, M., Subramanian, A., and Cannon, F., 2021. Data Gaps within Atmospheric Rivers over the Northeastern Pacific. Bulletin of the American Meteorological Society, https://doi.org/ 10.1175/BAMS-D-19-0287.1
  27. Cobb, A., Michaelis, A., Iacobellis, S., Ralph, F. M., and Delle Monache, L., 2021. Atmospheric river sectors: definition and characteristics observed using dropsondes from 2014-2020 CalWater and AR Recon. Monthly Weather Review, https://doi.org/10.1175/MWR-D-20-0177.1
  28. Gibson, P., Waliser, D. E., Goodman, A., DeFlorio, M. J., Delle Monache, L., and Molod, A., 2020. Subseasonal-to-seasonal hindcast skill assessment of ridging events related to drought over the Western United States. Journal of Geophysical Research, 125, e2020JD033655
  29. Delaney, C. J., Hartman, R. K. Mendoza, J., Dettinger, M., Delle Monache, L., Jasperse, J., Ralph, F. M., Talbot, C., Brown, J., Reynolds, D., and Evett, S., 2020. Forecast Informed Reservoir Operations using ensemble streamflow predictions for a multipurpose reservoir in Northern California. Water Resources Research, 56, e2019WR026604.
  30. Lewis, W. E., Rozoff, C., Alessandrini, S., and Delle Monache, L., 2020. Performance of the HWRF Rapid Intensification Analog Ensemble (HWRF RI-AnEn) during the 2017 and 2018 HFIP real-time demonstrations. Weather and Forecasting, 35, 841–856
  31. Candido, S., Singh, A., and Delle Monache, L., 2020. Improving wind forecasts in the lower stratosphere by distilling an analog ensemble into a deep neural network. Geophysical Research letters, https://doi.org/ 10.1029/2020GL089098
  32. Sumargo, E., Wilson, A. M., Ralph, F. M., Weihs, R., White, A., Jasperse, J., Asgari-Lamjiri, M., Turnbull, S., Downer, C., and Delle Monache, L., 2020. The hydrometeorological observation network in California’s Russian River watershed. Development, characteristics, and key findings from 1997 to 2019. Bulletin of the American Meteorological Society, 101, E1781–E1800
  33. Gibson, P. B., Waliser, D. E., Goodman, A., DeFlorio, M. J., Delle Monache, L., and Molod A. 2020. Subseasonal-to-seasonal hindcast skill assessment of ridging events related to drought over the Western United States. Journal of Geophysical Research, 125, https://doi.org/ 10.1029/2020JD033655.
  34. Delle Monache, L., Alessandrini, S., Djalalova, I., Wilczak, J., Knievel, J. C., , and Kumar, R., 2020. Improving air quality predictions over the United States with an analog ensemble. Weather and Forecasting, 35, 2145–2162
  35. Ralph, F. M., Cannon, F., Tallapragada, V., Davis, C. A., Doyle, J. D., Pappenberger, F., Subramanian, A., Wilson, A. M., Lavers, D. A., Reynolds, C. A., Haase, J. S., Centurioni, L., Ingleby, B., Rutz, J. J., Cordeira, J. M., Zheng, M., Hecht, C., Kawzenuk, B., and Delle Monache, L., 2020. West coast forecast challenges and development of atmospheric river reconnaissance. Bulletin of the American Meteorological Society, doi:10.1175/bams-d-19-0183.1
  36. Guirguis, K., Gershunov, A., DeFlorio,
 J., Shulgina, T., Delle Monache, L., Subramanian, A. C., Corringham, T. W., and 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, https://doi.org/10.1029/2020GL087609
  37. Kosovic, B., Haupt, S. E., Adriaansen, D., Alessandrini, S., Wiener, G., Delle Monache, L., Liu, Y., Linden, S., Jensen, T., Cheng, W., Politovich, M., and Prestopnik, P. A., 2020. Comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction. Energies, 13, 1372
  38. Wu, E., Zamora Zapata, M., Delle Monache, L., and Kleissl, J., 2019. Observation-based analog ensemble solar forecast in coastal California. 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC). 2440-2444.
  39. DeFlorio, M. J., Waliser, D. E., Ralph, F. M., Guan, B., Goodman, A., Gibson, P. B., Asharaf, S., Delle Monache, L., Zhang, Z., Subramanian, A. C., Vitart, F., Lin, H., and Kumar , A., 2019. Experimental subseasonal‐to‐seasonal (S2S) forecasting of atmospheric rivers over the Western United States. Journal of Geophysical Research, 124, 11242–11265
  40. Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., and Ralph, F. M., 2019. Improving atmospheric river forecasts with machine learning. Geophysical Research Letters, 46, 10627–10635
  41. Shahriari, M., Cervone, G., Clemente-Harding, L., and Delle Monache, L., 2020. Using the analog ensemble method as a proxy measurement for wind power predictability. Accepted to appear on Renewable Energy, 146, 789-801
  42. Cloud, K. A., Reich, B. J., Rozoff, C. M., Alessandrini, S., Lewis, W. E., and Delle Monache, L., 2019. A feed forward neural network based on model output statistics for short-term hurricane intensity prediction. Weather and Forecasting, https://doi.org/10.1175/WAF-D-18-0173.1
  43. Kumar, R., Delle Monache, L., Bresch, J., Saide, P. E., Tang, Y., Liu, Z., da Silva, A. M., Alessandrini, S., Pfister, G., Edwards, D., Lee, P., and Djalalova, I., 2019. Toward improving short‐term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals. Journal of Geophysical Research, https://doi.org/ 10.1029/2018JD029009
  44. Kumar, R., Lee, J. A., Delle Monache, L., and Alessandrini, S., 2019. Effect of meteorological variability on fine particulate matter simulations over the contiguous United States. Journal of Geophysical Research, https://doi.org/10.1029/2018JD029637
  45. Tomasi, E., Giovannini, L., Falocchi, M., Antonacci, G., Jiménez, P. A., Kosovic, B., Alessandrini, S., Zardi, D., Delle Monache, L., and Ferrero, E., 2019. Turbulence parameterizations for dispersion in sub-kilometer horizontally non-homogeneous flows. Atmospheric Research, 228, 122–126
  46. Saide, P. E., Steinhoff, D. F., Kosovic, B., Weil, J., Downey, N., Blewitt, D., Hanna, S. R., and Delle Monache, L., 2018. Evaluating methods to estimate methane emissions from oil and gas production facilities using LES simulations. Sci. Technol., 52, 19, 11206-11214
  47. Gant, S., Weil, J., Delle Monache, L., McKenna, B., Garcia, M. M., Tickle, G., Tucker, H., Stewart, J., Kelsey, A., McGillivray, A., Batt, R., Witlox, H., and Wardman, M., 2018. Dense gas dispersion model development and testing for the Jack Rabbit II phase 1 chlorine release experiments. Atmospheric Environment, 192, 218-240
  48. Yang, J., Astitha, M., Delle Monache, L., and Alessandrini, A., 2018. An analog technique to improve storm wind speed prediction using a dual NWP model approach. Monthly Weather Review, 146, 4057–4077
  49. Pan, L., Liu, Y., Knievel, J., Delle Monache, L., and Roux, G., 2018. Evaluations of WRF sensitivities in surface simulations with an ensemble prediction system. Atmosphere, 9, 106, doi:10.3390/atmos9030106
  50. Alessandrini, S., Delle Monache, L., Rozoff, C., and Lewis, W., 2018. Probabilistic prediction of tropical cyclone intensity with an analog ensemble. Monthly Weather Review, 146, 1723–1744
  51. Odak Plenkovic, I., Delle Monache, L., Horvarth, K., and Hrastinski, M., 2018. Deterministic wind speed predictions with analog-based methods over complex topography. Journal of Applied Meteorology and Climatology, 57, 2047–2070
  52. Kumar, R., Barth, M., Pfister, G., Delle Monache, L., Lamarque, J. F., Archer-Nicholas, S., Tilmes, S. Ghude, D., Wiedinmyer, C., Jones, B., Neill, B. O., Naja, M., and Walters, S., 2018. How will air quality change by 2050 in South Asia? Journal of Geophysical Research, 123, 1840–1864
  53. Tang, Y., Pagowski, M., Chai, T., Pan, L., Lee, P., Baker, B., Kumar, R., Delle Monache, L., Tong, D., and Kim, H.-C., 2018. A Case study of aerosol data assimilation with the Community Multi-Scale Air Quality model over the contiguous United States using 3D-Var and optimal interpolation methods. Geoscientific Model Development, 10, 4743–4758
  54. Frediani, M., Hopson, T., Hacker., J., Anagnostou, E., Delle Monache, L., and Vandenberghe, F., 2017. Object-based analog forecasts for surface wind speed. Monthly Weather Review, 145, 5083–5102
  55. Sperati, S., Alessandrini, S., and Delle Monache, L., 2017. Gridded probabilistic forecasts with an analog ensemble. Quarterly Journal of the Royal Meteorological Society, 143, 2874–2885
  56. Cervone, G., Clemente-Harding, L., Alessandrini, S., Delle Monache, L., 2017. Short-term photovoltaic power forecasts using Artificial Neural Networks and an analog ensemble. Renewable Energy, 108, 274–286
  57. Huang, J., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., Shafran, P., Allured, D., Lee, P., Pan, L., Tong, D., Huang, H.-C., DiMego, G., Upadhayay, S., and Delle Monache, L., 2017. Improving NOAA NAQFC PM5 predictions with a bias correction approach. Weather and Forecasting, 32, 407–421
  58. Keller, J., Delle Monache, L., and Alessandrini, S., 2017. Statistical downscaling of a high-resolution precipitation reanalysis using the analog ensemble method. Journal of Applied Meteorology and Climatology, 56, 2081–2095
  59. Lee, J., Hacker, J., Delle Monache, L., Kosovic, B., Clifton, A., Vandenberghe, F., and Sanz Rodrigo, J., 2017. Improving wind predictions in the marine atmospheric boundary layer through parameter estimation in a single-column model. Monthly Weather Review, 145, 5–24
  60. Jimenez, P., Alessandrini, S., Haupt, S., Deng, A., Kosovic, B., Lee, J., and Delle Monache, L., 2016. The role of unresolved clouds on short-range global horizontal irradiance predictability. Monthly Weather Review, 144, 3099–3107
  61. Davo’, F., Alessandrini, S., Sperati, S., and Delle Monache, L., Airoldi, D., and Vespucci, M., 2016. Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Solar Energy, 134, 327–338
  62. Sperati, S., Alessandrini, S., and Delle Monache, L., 2016. An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting Solar Energy. Solar Energy, 133, 437–450
  63. Ferruzzi, G., Cervone, G., Delle Monache, L., Graditi, G., and Jacobone, F., 2016. Optimal bidding in a day-ahead energy market for micro grid under uncertainty in renewable energy production. Energy, 106, 194–202
  64. Che, Y., Peng, X., Delle Monache, L., Kawaguchi, T., and Xiao, F, 2016. A wind power forecasting system based on the WRF model and Kalman filtering over a wind farm in in Japan. Journal of Renewable and Sustainable Energy, 8, 013302-1–013302-17
  65. Eckel, F. A., and Delle Monache, L., 2016. A Hybrid NWP-Analog Ensemble. Monthly Weather Review, 144, 897–911
  66. Junk, C., Delle Monache, L., and Alessandrini, S., 2015. Analog-based ensemble model output statistics. Monthly Weather Review, 143, 2909–2917
  67. Zhang, J., Draxl, C., Hopson, T., Delle Monache, L., and Hodge, B.-M., 2015. Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods. Applied Energy, 156, 528–541
  68. Tushaus, S., Posselt, D., Miglietta, M., Rotunno, R., and Delle Monache, L., 2015. Bayesian exploration of multivariate orographic precipitation sensitivity for moist stable and neutral flows. Monthly Weather Review, 143, 4459–4475
  69. Alessandrini, S., Delle Monache, L., Sperati, S., and Cervone, G., 2015. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 157, 95–110
  70. Junk, C., Späth, S., von Bremen, L., Delle Monache, L., 2015. Comparison and combination of regional and global ensemble prediction systems for probabilistic predictions of hub-height wind speed. Weather and Forecasting, 30, 1234–1253
  71. Djalalova, I., Delle Monache, L., and Wilczak, J., 2015. PM2.5 analog forecast and Kalman filtering post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmospheric Environment, 119, 431–442
  72. Junk, C., Delle Monache, L., Alessandrini, S., von Bremen, L., and Cervone, G., 2015. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble. Meteorologische Zeitschrift, 24, 361-379
  73. Nagarajan, B., Delle Monache, L., Hacker, J., Rife, D., Searight, K., Knievel, J., and Nipen, T., 2015. An evaluation of analog-based post-processing methods across several variables and forecast models. Weather and Forecasting, 30, 1623–1643
  74. Alessandrini, S., Delle Monache, L., Sperati, S., and Nissen, J, 2015. A novel application of an analog ensemble for short-term wind power forecasting. Renewable Energy, 76, 768-781
  75. Vanvyve, E., Delle Monache, L., Rife, D., Monaghan, A., Pinto, J., 2015. Wind resource estimates with an analog ensemble approach. Renewable Energy, 74, 761-773
  76. Alessandrini, S., Davo’, F., Sperati, S., Benini, M., Delle Monache, L., 2014. Comparison of the economic impact of different wind power forecast systems for producers. Advance in Science and Research, 11, 49-53
  77. Archer, C., Delle Monache, L., and Rife, D., 2014. Airborne wind energy: Optimal locations and variability. Renewable Energy, 64, 180-186
  78. Pinto, J., Monaghan, A., Vanvyve, E., Delle Monache, L., and Rife, D., 2014. Regional assessment of a targeted random sampling technique for more efficient dynamical climate downscaling. Journal of Climate, 27, 1524-1538
  79. Archer, C. L., Colle, B., Delle Monache, L., Dvorak, M., Lundquist, J., Bailey, B. H., Beaucage, P., Churchfield, M. J., Fitch, A. C., Kosovic, B., Lee, S., Moriarty, P. J., Simao, H., Stevens, R. J. A. M., Veron, D., and Zack, J., 2014. Meteorology for coastal/offshore wind energy in the United States: Recommendations and research needs for the next 10 years. Bulletin of the American Meteorological Society, 95, 515-519
  80. Williams, J., Maxwell, R., Delle Monache, L., 2013. Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model. Journal of Advances in Modeling Earth Systems, 5, 1-16
  81. Delle Monache, L., Eckel, T., Rife, D., Nagarajan, B., and Searight, K., 2013. Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141, 3498-3516
  82. Lozano-Fuentes, S., Hayden, M.-H., Welsh-Rodriguez, C., Ochoa-Martinez, C., Tapia-Santos, B., Kobylinski, K. C., Uejio, C. K., Zielinski-Gutierrez, E., Delle Monache, L., Monaghan, A. J., Steinhoff, D. F., and Eisen, 2012: Dengue virus mosquito vectors at high elevation in Mexico. J. Trop. Med. Hyg., 87, 902-909
  83. Lozano-Fuentes, S., Welsh-Rodriguez, C., Hayden, M. H., Tapia-Santos, B., Ochoa-Martinez, C., Kobylinski, K. C., Uejio, C. K., Zielinski-Gutierrez, E., Delle Monache, L., Monaghan, A. J., Steinhoff, D. F., Eisen, L., 2012. Aedes (Ochlerotatus) epactius Dyar & Knab along an elevation and climate gradient in Veracruz and Puebla States, México. Journal of Medical Entomology, 49, 1244-1253
  84. Mahoney, B., Parks, K., Wiener, G., Liu, Y., Myers, W., Sun, Juanzhen, Delle Monache, L., Hopson, T., Johnson, D., Haupt, S., 2012. A wind power forecasting system to optimize grid integration. IEEE Transactions on Sustainable Energy, 3, 670-682
  85. Hirschberg , P., Abrams, E., Bleistein, A., Bua, W., Delle Monache, , Dulong, T., Gaynor, J., Glahn, B., Hamill, T., Hansen, J., Hilderbrand, D., Hoffman, R., Morrow, B., Philips, B., Sokich, J., Stuart, N., 2011. A weather and climate enterprise strategic implementation plan for generating and communicating forecast uncertainty information. Bulletin of the American Meteorological Society, 92, 1651-1666
  86. Delle Monache, L., Nipen, T., Liu, Y., Roux, G., Stull, R., 2011. Kalman filter and analog schemes to post-process numerical weather predictions. Monthly Weather Review, 139, 3554-3570
  87. Djalalova, I., Wilczak, J., McKeen, S., Grell, G., Peckham, S., Pagowski, M., Delle Monache, L., McQueen, J., Lee, P., Tang, Y., McHenry, J., Gong, W., Bouchet, V., Marthur, R., 2010. Ensemble and bias-correction techniques for probabilistic forecast of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006. Atmospheric Environment, 44, 455-467
  88. Delle Monache, L., Weil, J., Simpson, M., Leach, M., 2009. A new urban boundary layer and dispersion parameterization for the LLNL modeling system: tests with the Joint Urban 2003 data set. Atmospheric Environment, 43, 5807-5821
  89. Delle Monache, L., 2009. Reconstruction of an atmospheric source from downwind measurements: a method based on Bayesian inference and Markov Chain Monte Carlo sampling. Rivista di Meteorologia (in Italian), 1, 12-21
  90. Delle Monache, L., Lundquist, J., Kosovic, B., Johannesson, G., Dyer, K., Aines, R., Chow, F., Belles, R., Hanley, W., Larsen, S., Loosmore, G., Nitao, J., Sugiyama, G., Vogt, P., 2008. Bayesian inference and Markov Chain Monte Carlo to reconstruct a contaminant source at continental scale. Journal of Applied Meteorology and Climatology, 47, 2600-2613
  91. Pryor, S. C., Barthelmie, R. J., Schoof, J. T., Binkowski, F. S., Delle Monache, L., and Stull, R. B., 2008. Modeling the impact of sea-spry on particle concentrations in a coastal city. Science of the Total Environment, 391, 132-142
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