Search Results
experiment was the Weather Research and Forecasting (WRF, version 3.5) Model, a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational applications ( Skamarock et al. 2008 ). The simulations were conducted with three horizontal nested grids with grid spacing of 16, 4, and 1 km, respectively ( Figure 5 ). The coarser-resolution grid covers a good portion of the northern Pacific and western United States, and the finer-resolution grid covers the
experiment was the Weather Research and Forecasting (WRF, version 3.5) Model, a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational applications ( Skamarock et al. 2008 ). The simulations were conducted with three horizontal nested grids with grid spacing of 16, 4, and 1 km, respectively ( Figure 5 ). The coarser-resolution grid covers a good portion of the northern Pacific and western United States, and the finer-resolution grid covers the
inversion framework and how they are connected. The data and analysis steps are presented in detail in section 2 . Figure 1. Flow diagram of the framework applied in our study with the consecutive steps needed to provide the input for the Bayesian inversion. Please see text for details. 2. Data and methods 2.1. Tower network and atmospheric observation data Oregon is characterized by significant micro- to mesoscale variability in climate and vegetation characteristics. The crest of the Cascade Mountain
inversion framework and how they are connected. The data and analysis steps are presented in detail in section 2 . Figure 1. Flow diagram of the framework applied in our study with the consecutive steps needed to provide the input for the Bayesian inversion. Please see text for details. 2. Data and methods 2.1. Tower network and atmospheric observation data Oregon is characterized by significant micro- to mesoscale variability in climate and vegetation characteristics. The crest of the Cascade Mountain
understanding of the influencing factors for the rainfall variability over the Sahel could improve the predictive skill in rainfall forecasting, which will benefit the local people. Figure 1. The land-use and land-cover types of Africa from the MODIS land-cover dataset in 2001. The Sahel region is outlined in red. Sahel rainfall is known to be strongly influenced by sea surface temperature (SST), both globally and in oceans adjacent to the African continent ( Martin and Thorncroft 2014 ; Mohino et al. 2011
understanding of the influencing factors for the rainfall variability over the Sahel could improve the predictive skill in rainfall forecasting, which will benefit the local people. Figure 1. The land-use and land-cover types of Africa from the MODIS land-cover dataset in 2001. The Sahel region is outlined in red. Sahel rainfall is known to be strongly influenced by sea surface temperature (SST), both globally and in oceans adjacent to the African continent ( Martin and Thorncroft 2014 ; Mohino et al. 2011
experiment using a mesoscale two-dimensional model . J. Atmos. Sci. , 46 , 3077 – 3107 , doi: 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2 . Fischer , G. , F. N. Tubiello , H. Van Velthuizen , and D. A. Wiberg , 2007 : Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080 . Technol. Forecasting Soc. Change , 74 , 1083 – 1107 , doi: 10.1016/j.techfore.2006.05.021 . Garnaud , C. , and L. Sushama , 2015 : Biosphere-climate interactions in a
experiment using a mesoscale two-dimensional model . J. Atmos. Sci. , 46 , 3077 – 3107 , doi: 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2 . Fischer , G. , F. N. Tubiello , H. Van Velthuizen , and D. A. Wiberg , 2007 : Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080 . Technol. Forecasting Soc. Change , 74 , 1083 – 1107 , doi: 10.1016/j.techfore.2006.05.021 . Garnaud , C. , and L. Sushama , 2015 : Biosphere-climate interactions in a
al. (2014b) to undertake the first assessment of coupling strength for the austral summer [December–February (DJF)] over Australia. By applying the GLACE-1 methodology in the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), multiple simulations were conducted to understand the uncertainty associated with different model physics. Hirsch et al. (2014b) also considered the role of interannual variability of land–atmosphere coupling strength building on Northern Hemisphere
al. (2014b) to undertake the first assessment of coupling strength for the austral summer [December–February (DJF)] over Australia. By applying the GLACE-1 methodology in the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), multiple simulations were conducted to understand the uncertainty associated with different model physics. Hirsch et al. (2014b) also considered the role of interannual variability of land–atmosphere coupling strength building on Northern Hemisphere
