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exert an important influence on the global heating rates and the nonlinear cloud–climate interactions. Very light precipitation events over ocean bodies clearly exceed those over land surfaces. These findings are in sharp contrast to the other two methods. The 2C-PC method (middle panel), tuned only to liquid precipitation, shows a decrease in precipitation events as we move poleward, due to an increase in solid precipitation events. As expected, the AMSR-E PMW–based algorithm (bottom panel) fails
exert an important influence on the global heating rates and the nonlinear cloud–climate interactions. Very light precipitation events over ocean bodies clearly exceed those over land surfaces. These findings are in sharp contrast to the other two methods. The 2C-PC method (middle panel), tuned only to liquid precipitation, shows a decrease in precipitation events as we move poleward, due to an increase in solid precipitation events. As expected, the AMSR-E PMW–based algorithm (bottom panel) fails
decades. There are general agreements on the trend of global temperature, but there is less consensus on changes in global precipitation ( Folland et al. 2001 ; Hegerl et al. 2007 ; Karl and Trenberth 2003 ; Allen and Ingram 2002 ; Gu et al. 2007 ). Wentz et al. (2007) showed a 1.4 ± 0.5% increase in global precipitation and a 7% increase in the total amount of water in the atmosphere in response to a 1°C change in surface temperature from satellite observations. Their oceanic precipitation data
decades. There are general agreements on the trend of global temperature, but there is less consensus on changes in global precipitation ( Folland et al. 2001 ; Hegerl et al. 2007 ; Karl and Trenberth 2003 ; Allen and Ingram 2002 ; Gu et al. 2007 ). Wentz et al. (2007) showed a 1.4 ± 0.5% increase in global precipitation and a 7% increase in the total amount of water in the atmosphere in response to a 1°C change in surface temperature from satellite observations. Their oceanic precipitation data
shortwave schemes recently added into WRF, and discussed in section 2 , were adopted to provide longwave and shortwave parameterizations that interact with the atmosphere. The planetary boundary layer parameterization for this study was the Mellor–Yamada–Janjić ( Mellor and Yamada 1982 ; coded and modified by Dr. Janjić for the NCEP Eta Model) level-2 turbulence closure model for the full range of atmospheric turbulent regimes. The surface heat and moisture fluxes (from both ocean and land) were
shortwave schemes recently added into WRF, and discussed in section 2 , were adopted to provide longwave and shortwave parameterizations that interact with the atmosphere. The planetary boundary layer parameterization for this study was the Mellor–Yamada–Janjić ( Mellor and Yamada 1982 ; coded and modified by Dr. Janjić for the NCEP Eta Model) level-2 turbulence closure model for the full range of atmospheric turbulent regimes. The surface heat and moisture fluxes (from both ocean and land) were
parts. Subcloud evaporation, rainfall suppression by desert aerosols, and surface effects are among the possible factors. The atmosphere is normally very dry over most of this region. Figure 10 compares mean relative humidity (RH) at the 1000- and 500-hPa levels over the current validation region and surrounding areas. These data are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay et al. 1996 ). The atmosphere
parts. Subcloud evaporation, rainfall suppression by desert aerosols, and surface effects are among the possible factors. The atmosphere is normally very dry over most of this region. Figure 10 compares mean relative humidity (RH) at the 1000- and 500-hPa levels over the current validation region and surrounding areas. These data are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay et al. 1996 ). The atmosphere
RTTOV. Land surface emissivity is particularly difficult to simulate because of the complex interaction of electromagnetic radiation with soil, vegetation, and snow cover as a function of a large number of unknown state variables. Therefore, land emissivity was modeled based on climatologies derived from SSM/I observations and integrated NWP and satellite products ( Prigent et al. 1997 ). Surface emissivity maps retrieved from all seven SSM/I channels were employed to interpolate SSM/I emissivities
RTTOV. Land surface emissivity is particularly difficult to simulate because of the complex interaction of electromagnetic radiation with soil, vegetation, and snow cover as a function of a large number of unknown state variables. Therefore, land emissivity was modeled based on climatologies derived from SSM/I observations and integrated NWP and satellite products ( Prigent et al. 1997 ). Surface emissivity maps retrieved from all seven SSM/I channels were employed to interpolate SSM/I emissivities
( Swift et al. 1985 ) and a high-resolution land–sea mask is applied. 1) Evaporation The HOAPS-3 evaporation is calculated with the Coupled Ocean–Atmosphere Response Experiment (COARE) 2.6a bulk flux algorithm ( Fairall et al. 1996 , 2003 ), requiring retrievals of wind speed ( u ), sea surface saturation specific humidity ( q s ), and surface atmospheric specific humidity ( q a ) as input: The evaporation ( E ) follows with where ρ is moist air density (calculated using q a , the estimated air
( Swift et al. 1985 ) and a high-resolution land–sea mask is applied. 1) Evaporation The HOAPS-3 evaporation is calculated with the Coupled Ocean–Atmosphere Response Experiment (COARE) 2.6a bulk flux algorithm ( Fairall et al. 1996 , 2003 ), requiring retrievals of wind speed ( u ), sea surface saturation specific humidity ( q s ), and surface atmospheric specific humidity ( q a ) as input: The evaporation ( E ) follows with where ρ is moist air density (calculated using q a , the estimated air
fields. SSM/I has seven channels at frequencies from 19 to 85 GHz, where the atmosphere is semitransparent in clear skies and most absorption comes from total column water vapor, due to both water vapor continuum absorption and the 22-GHz line. In cloudy skies, the lower-frequency channels are sensitive to rain and cloud water and the higher-frequency channels are sensitive to snow and cloud ice as well. SSM/I observations have been corrected at ECMWF for the scan-dependent biases found by Colton
fields. SSM/I has seven channels at frequencies from 19 to 85 GHz, where the atmosphere is semitransparent in clear skies and most absorption comes from total column water vapor, due to both water vapor continuum absorption and the 22-GHz line. In cloudy skies, the lower-frequency channels are sensitive to rain and cloud water and the higher-frequency channels are sensitive to snow and cloud ice as well. SSM/I observations have been corrected at ECMWF for the scan-dependent biases found by Colton
winds. As well, synoptic and mesoscale cyclones can also intensify katabatic winds, produce or enhance a barrier wind along strong topography, or generate flow around obstacles that can lead to an increase or a decrease in precipitation ( Parish and Bromwich 1998 ). These winds, and in particular the katabatic wind flow, can frequently reach wind speeds in excess of a wind speed threshold, above which particles at the surface can be lofted into the atmosphere to produce blowing or drifting snow
winds. As well, synoptic and mesoscale cyclones can also intensify katabatic winds, produce or enhance a barrier wind along strong topography, or generate flow around obstacles that can lead to an increase or a decrease in precipitation ( Parish and Bromwich 1998 ). These winds, and in particular the katabatic wind flow, can frequently reach wind speeds in excess of a wind speed threshold, above which particles at the surface can be lofted into the atmosphere to produce blowing or drifting snow
