Search Results

You are looking at 1 - 8 of 8 items for :

  • 12th International Precipitation Conference (IPC12) x
  • Refine by Access: All Content x
Clear All
Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

, P. J. Neiman , and D. Cayan , 2011 : Atmospheric rivers, floods, and the water resources of California . Water , 3 , 445 – 478 , https://doi.org/10.3390/w3020445 . 10.3390/w3020445 Ebtehaj , A. M. , and E. Foufoula-Georgiou , 2013 : On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization . Water Resour. Res. , 49 , 5944–5963 , https://doi.org/10.1002/wrcr.20424 . 10.1002/wrcr.20424 Ebtehaj , A. M. , E

Open access
Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

TB attributes were collocated with the snowband over Lake Ontario and along the axis of the Saint Lawrence River. Both 89- and 166-GHz TB trends over Lake Ontario followed similar patterns as the previous case study, with warmer emission signals against the cold lake background evolving into scattering signals. Reduced ΔTB 89 and ΔTB 166 magnitudes over Lake Ontario surfaces also coincided with lake-effect clouds and snowbands. Interestingly, 166-GHz and ΔTB 183.31 scattering signatures

Restricted access
Yingzhao Ma, V. Chandrasekar, Haonan Chen, and Robert Cifelli

Tono Dam of West Africa. Wehbe et al. (2019) addressed the potential of WRF-Hydro in the short-term hydrological and meteorological predictions in an extreme weather event in an arid region of the United Arab Emirates. Furthermore, Lin et al. (2018) implemented a vector-based river network into the WRF-Hydro to enhance the flood discharge simulation in a hurricane storm event. Arnault et al. (2019) developed a joint soil–vegetation–atmospheric water tagging procedure with WRF-Hydro to assess

Restricted access
Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

the performance of PDIR-Now in capturing the seasonal cycle of precipitation, based on monthly precipitation, with GPCP 1DD precipitation being used as a baseline for evaluation. We carry out the analysis at two regions with distinct rainfall seasonal cycles. The first region (location 1) is a rectangular region bounded by the latitudes (1°–6°N) and the longitudes (16°–21°E). This region lies at the northwestern part of the Congo River basin with elevation in the range of 0–700 m above sea level

Open access
Allison E. Goodwell

summer, TE is dominantly from north or west, with very broad distributions in some regions, reflecting the large variety of precipitation causes in this season ( Kunkel et al. 2012 ). Southwest (California, Lower Colorado, Rio Grande): Here, TE is dominantly from south or southwest in winter. This reflects precipitation events due to atmospheric rivers that convey moisture from the Pacific Ocean during this season ( Gershunov et al. 2017 ). In the summer when conditions are relatively drier in most

Restricted access
Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

. 2015 ). These can result from tropical cyclones, mesoscale convective systems, orographic uplift, and atmospheric rivers that interact with the region’s complex terrain ( Barros et al. 2014 ; Mahoney et al. 2016 ; Moore et al. 2015 ). Hurricane Frances was followed by Hurricane Ivan within a 2-week period in September 2004, for example, and caused approximately 400 landslides, 11 deaths, and widespread property damage ( Boyle 2014 ). Landslides continue to pose a threat to the region, with three

Free access
F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

. Yang , N. K. Biswas , S. H. Rahat , and T. J. Neelam , 2020 : Machine learning-based error modeling to improve GPM IMERG precipitation product over the Brahmaputra river basin . Forecasting , 2 , 248 – 266 , https://doi.org/10.3390/forecast2030014 . 10.3390/forecast2030014 Boukabara , S. A. , and Coauthors , 2011 : MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system . IEEE Trans. Geosci. Remote Sens. , 49 , 3249 – 3272 , https://doi.org/10.1109/TGRS

Restricted access
Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

.1175/JHM-D-15-0094.1 Mahoney , K. , and Coauthors , 2016 : Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States . Mon. Wea. Rev. , 144 , 1617 – 1632 , https://doi.org/10.1175/MWR-D-15-0279.1 . 10.1175/MWR-D-15-0279.1 Matsui , T. , and Coauthors , 2014 : Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling . J. Geophys. Res. Atmos. , 119 , 8450 – 8475 , https://doi.org/10.1002/2013JD021424 . 10

Restricted access