Search Results
>2.0.CO;2 . Hong , X. , C. H. Bishop , T. Holt , and L. W. O’Neill , 2011 : Impacts of sea surface temperature uncertainty on the western North Pacific subtropical high (WNPSH) and rainfall . Wea. Forecasting , 26 , 371 – 387 , doi: 10.1175/WAF-D-10-05007.1 . Huddleston , J. N. , and B. W. Stiles , 2000 : A multidimensional histogram rain-flagging technique for SeaWinds on QuikSCAT. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symp. 2000 , Honolulu, HI, Institute
>2.0.CO;2 . Hong , X. , C. H. Bishop , T. Holt , and L. W. O’Neill , 2011 : Impacts of sea surface temperature uncertainty on the western North Pacific subtropical high (WNPSH) and rainfall . Wea. Forecasting , 26 , 371 – 387 , doi: 10.1175/WAF-D-10-05007.1 . Huddleston , J. N. , and B. W. Stiles , 2000 : A multidimensional histogram rain-flagging technique for SeaWinds on QuikSCAT. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symp. 2000 , Honolulu, HI, Institute
that the cyclone type that appeared and rapidly developed over the northwestern Pacific Ocean, the so-called Pacific Ocean–ocean (PO–O) cyclones, was more reinforced by the effect of latent heating than were other types. This may be because the PO–O cyclones occur under moister environments. From the viewpoint of forecasting explosive cyclone development, Kuwano-Yoshida and Enomoto (2013) demonstrated that the underestimation of latent heat release in a numerical model is a primary factor in PO
that the cyclone type that appeared and rapidly developed over the northwestern Pacific Ocean, the so-called Pacific Ocean–ocean (PO–O) cyclones, was more reinforced by the effect of latent heating than were other types. This may be because the PO–O cyclones occur under moister environments. From the viewpoint of forecasting explosive cyclone development, Kuwano-Yoshida and Enomoto (2013) demonstrated that the underestimation of latent heat release in a numerical model is a primary factor in PO
description We utilize the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007b , 2014 ). SCOAR currently couples one of two weather models, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) or the Regional Spectral Model (RSM; Juang and Kanamitsu 1994 ), to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). This study uses the WRF–ROMS version of SCOAR ( Seo et al. 2014 ). The interacting boundary layer
description We utilize the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007b , 2014 ). SCOAR currently couples one of two weather models, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) or the Regional Spectral Model (RSM; Juang and Kanamitsu 1994 ), to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). This study uses the WRF–ROMS version of SCOAR ( Seo et al. 2014 ). The interacting boundary layer
, experiments, and data a. Model description The Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007a , 2014 , 2016 ; http://hseo.whoi.edu/scoar ) is a regional coupled climate model that couples the Weather Research and Forecast (WRF; Skamarock et al. 2008 ) Model to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). The interacting boundary layer is based on the bulk formula ( Fairall et al. 1996 ; 2003 ), which calculates the
, experiments, and data a. Model description The Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007a , 2014 , 2016 ; http://hseo.whoi.edu/scoar ) is a regional coupled climate model that couples the Weather Research and Forecast (WRF; Skamarock et al. 2008 ) Model to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). The interacting boundary layer is based on the bulk formula ( Fairall et al. 1996 ; 2003 ), which calculates the
the accumulated storm-track value as the month proceeds. This yields a finescale temporal resolution metric that does not require copious model output. This filtering method can also be used on observations that are available only at a daily resolution (e.g., Guo et al. 2009 ). For one component of the analysis, we utilize the technique of Booth et al. (2010) to calculate an estimated surface storm track defined as the region of overlap of the upper quantiles of and T DIFF . Note that Booth
the accumulated storm-track value as the month proceeds. This yields a finescale temporal resolution metric that does not require copious model output. This filtering method can also be used on observations that are available only at a daily resolution (e.g., Guo et al. 2009 ). For one component of the analysis, we utilize the technique of Booth et al. (2010) to calculate an estimated surface storm track defined as the region of overlap of the upper quantiles of and T DIFF . Note that Booth
mechanism. A minimum of surface divergence does not by itself provide the necessary forcing to achieve a deep atmospheric response. In this case, if the large-scale fields are interpreted as evidence of forcing by storms and small-scale SST, then storms are still necessary to explain the “anchoring” of convergence and upward motion over the Gulf Stream. Finally, it is noted that none of these techniques perfectly remove all traces of storms from the instantaneous divergence, so the divergence minima
mechanism. A minimum of surface divergence does not by itself provide the necessary forcing to achieve a deep atmospheric response. In this case, if the large-scale fields are interpreted as evidence of forcing by storms and small-scale SST, then storms are still necessary to explain the “anchoring” of convergence and upward motion over the Gulf Stream. Finally, it is noted that none of these techniques perfectly remove all traces of storms from the instantaneous divergence, so the divergence minima
