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the mixing ratio of rainwater (interval of 0.5 g kg −1 ) in LDR. The difference between LDR and CTL in water vapor flux is shown in Fig. 8 . A positive flux area southeast of MCS A is consistent with the area of modified horizontal wind. This enhancement in water vapor flowing into MCS A accounts for the heavy rainfall event. In addition, the difference in water vapor flux south of MCS B, generated by the improvement in the modified wind field, appears to improve the forecasting. Because these
the mixing ratio of rainwater (interval of 0.5 g kg −1 ) in LDR. The difference between LDR and CTL in water vapor flux is shown in Fig. 8 . A positive flux area southeast of MCS A is consistent with the area of modified horizontal wind. This enhancement in water vapor flowing into MCS A accounts for the heavy rainfall event. In addition, the difference in water vapor flux south of MCS B, generated by the improvement in the modified wind field, appears to improve the forecasting. Because these
subdaily time scales to our knowledge) to make sure surface fluxes were applied at the correct time and that the restart time of the model was correct. We should also retune the error covariances because of the change in the time window. However, because errors do not grow linearly, there is no simple scaling that can be applied so this work is left for the future, when we will do a full reestimation of the error covariances in the coupled model (probably using the output of the runs presented here
subdaily time scales to our knowledge) to make sure surface fluxes were applied at the correct time and that the restart time of the model was correct. We should also retune the error covariances because of the change in the time window. However, because errors do not grow linearly, there is no simple scaling that can be applied so this work is left for the future, when we will do a full reestimation of the error covariances in the coupled model (probably using the output of the runs presented here
m. The first six fields are input directly into the ocean model or used in calculating components of the heat and buoyancy fluxes, while the last four fields are used to compute surface wind stress with temperature- and humidity-based stability dependence. NOGAPS forcing is available on the FNMOC 0.5 degree resolution application grid and extends out to 120 h (i.e., the length of the HYCOM/NCODA forecast). The global HYCOM forecast system includes a built-in energy loan thermodynamic ice model
m. The first six fields are input directly into the ocean model or used in calculating components of the heat and buoyancy fluxes, while the last four fields are used to compute surface wind stress with temperature- and humidity-based stability dependence. NOGAPS forcing is available on the FNMOC 0.5 degree resolution application grid and extends out to 120 h (i.e., the length of the HYCOM/NCODA forecast). The global HYCOM forecast system includes a built-in energy loan thermodynamic ice model
-4DVAR. Thus, we would need to take into consideration the difference in the IAU period when we compare results of the two experiments. Both experiments start from the same initial state on 1 January 2000, which is obtained from a reanalysis experiment using MOVE-3DVAR-WNP ( Usui et al. 2006 ). The model is driven by daily wind stress and heat fluxes from the Japanese 25-yr Reanalysis and JMA climate data assimilation system ( Onogi et al. 2007 ). Latent and sensible heat fluxes are calculated by
-4DVAR. Thus, we would need to take into consideration the difference in the IAU period when we compare results of the two experiments. Both experiments start from the same initial state on 1 January 2000, which is obtained from a reanalysis experiment using MOVE-3DVAR-WNP ( Usui et al. 2006 ). The model is driven by daily wind stress and heat fluxes from the Japanese 25-yr Reanalysis and JMA climate data assimilation system ( Onogi et al. 2007 ). Latent and sensible heat fluxes are calculated by
;2 . Risien, C. M. , and Chelton D. B. , 2008 : A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data . J. Phys. Oceanogr. , 38 , 2379 – 2413 , doi: 10.1175/2008JPO3881.1 . Takahashi, T. , and Coauthors , 2009 : Climatological mean and decadal change in surface ocean pCO 2 , and net sea–air flux over the global oceans . Deep-Sea Res. II , 56 , 554 – 577 , doi: 10.1016/j.dsr2.2008.12.009 . Tollefson, J. , 2014 : El Niño
;2 . Risien, C. M. , and Chelton D. B. , 2008 : A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data . J. Phys. Oceanogr. , 38 , 2379 – 2413 , doi: 10.1175/2008JPO3881.1 . Takahashi, T. , and Coauthors , 2009 : Climatological mean and decadal change in surface ocean pCO 2 , and net sea–air flux over the global oceans . Deep-Sea Res. II , 56 , 554 – 577 , doi: 10.1016/j.dsr2.2008.12.009 . Tollefson, J. , 2014 : El Niño
