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diurnal cycle of deep convective clouds. Using observations from both microwave and infrared satellites, Hong et al. (2006) found a two hour delay in the diurnal cycle of convection due to cirrus clouds and an enhancement of the diurnal variations in the blended 11- μ m areal fraction of 210 K (cloud tops above 13.5 km) deep convective clouds in non-TC environments. One mechanism by which the cirrus canopy can modulate the diurnal cycle of convection is through modification of the location and
diurnal cycle of deep convective clouds. Using observations from both microwave and infrared satellites, Hong et al. (2006) found a two hour delay in the diurnal cycle of convection due to cirrus clouds and an enhancement of the diurnal variations in the blended 11- μ m areal fraction of 210 K (cloud tops above 13.5 km) deep convective clouds in non-TC environments. One mechanism by which the cirrus canopy can modulate the diurnal cycle of convection is through modification of the location and
location and amount of precipitation generated by the model disagreed with observations. Only by including both a typical monsoonal wind profile (westerlies in the low levels, easterlies aloft) and ocean surface fluxes did the model simulations fall in accord with observations. In other model simulations, Xie et al. (2006) found that even the relatively small and narrow mountain ranges of Myanmar cause enough impedance of the monsoon flow to create the convergence necessary to generate convection and
location and amount of precipitation generated by the model disagreed with observations. Only by including both a typical monsoonal wind profile (westerlies in the low levels, easterlies aloft) and ocean surface fluxes did the model simulations fall in accord with observations. In other model simulations, Xie et al. (2006) found that even the relatively small and narrow mountain ranges of Myanmar cause enough impedance of the monsoon flow to create the convergence necessary to generate convection and
. First, precipitation data come from version one of the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ; Xie et al. 2017 ). The data are available as 30-min total precipitation accumulation estimates at 8-km spatial resolution, covering 60°S–60°N. The CMORPH method takes precipitation rate estimates from passive microwave satellite retrievals and then uses cloud-motion vectors derived from infrared satellites to morph and interpolate through space and time to other
. First, precipitation data come from version one of the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ; Xie et al. 2017 ). The data are available as 30-min total precipitation accumulation estimates at 8-km spatial resolution, covering 60°S–60°N. The CMORPH method takes precipitation rate estimates from passive microwave satellite retrievals and then uses cloud-motion vectors derived from infrared satellites to morph and interpolate through space and time to other
