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- Author or Editor: Samson Hagos x
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Abstract
Radar-based latent heating retrievals typically apply a lookup table (LUT) derived from model output to surface rain amounts and rain type to determine the vertical structure of heating. In this study, a method has been developed that uses the size characteristics of precipitating systems (i.e., area and mean echo-top height) instead of rain amount to estimate latent heating profiles from radar observations. This technique [named the convective–stratiform area (CSA) algorithm] leverages the relationship between the organization of convective systems and the structure of latent heating profiles and avoids pitfalls associated with retrieving accurate rainfall information from radars and models. The CSA LUTs are based on a high-resolution regional model simulation over the equatorial Indian Ocean. The CSA LUTs show that convective latent heating increases in magnitude and height as area and echo-top heights grow, with a congestus signature of midlevel cooling for less vertically extensive convective systems. Stratiform latent heating varies weakly in vertical structure, but its magnitude is strongly linked to area and mean echo-top heights. The CSA LUT was applied to radar observations collected during the DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/ARM MJO Investigation Experiment (AMIE) field campaign, and the CSA heating retrieval was generally consistent with other measures of heating profiles. The impact of resolution and spatial mismatch between the model and radar grids is addressed, and unrealistic latent heating profiles in the stratiform LUT, namely, a low-level heating peak, an elevated melting layer, and net column cooling, were identified. These issues highlight the need for accurate convective–stratiform separations and improvement in PBL and microphysical parameterizations.
Abstract
Radar-based latent heating retrievals typically apply a lookup table (LUT) derived from model output to surface rain amounts and rain type to determine the vertical structure of heating. In this study, a method has been developed that uses the size characteristics of precipitating systems (i.e., area and mean echo-top height) instead of rain amount to estimate latent heating profiles from radar observations. This technique [named the convective–stratiform area (CSA) algorithm] leverages the relationship between the organization of convective systems and the structure of latent heating profiles and avoids pitfalls associated with retrieving accurate rainfall information from radars and models. The CSA LUTs are based on a high-resolution regional model simulation over the equatorial Indian Ocean. The CSA LUTs show that convective latent heating increases in magnitude and height as area and echo-top heights grow, with a congestus signature of midlevel cooling for less vertically extensive convective systems. Stratiform latent heating varies weakly in vertical structure, but its magnitude is strongly linked to area and mean echo-top heights. The CSA LUT was applied to radar observations collected during the DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/ARM MJO Investigation Experiment (AMIE) field campaign, and the CSA heating retrieval was generally consistent with other measures of heating profiles. The impact of resolution and spatial mismatch between the model and radar grids is addressed, and unrealistic latent heating profiles in the stratiform LUT, namely, a low-level heating peak, an elevated melting layer, and net column cooling, were identified. These issues highlight the need for accurate convective–stratiform separations and improvement in PBL and microphysical parameterizations.
Abstract
The isolation of the Amazon rain forest makes it challenging to observe precipitation forming there, but it also creates a natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Observations were collected upwind and downwind of Manaus, Brazil, during the “Observations and Modeling of the Green Ocean Amazon 2014–2015” experiment (GoAmazon2014/5). Besides aircraft, most of the observations were point measurements made in a spatially heterogeneous environment, making it hard to distinguish anthropogenic signals from naturally occurring spatial variability. In this study, 15 years of satellite data are used to examine the spatial and temporal variability of deep convection around the GoAmazon2014/5 sites using cold cloud tops (infrared brightness temperatures colder than 240 K) as a proxy for deep convection. During the rainy season, convection associated with the inland propagation of the previous day’s sea-breeze front is in phase with the diurnal cycle of deep convection near Manaus but is out of phase a few hundred kilometers to the east and west. Convergence between the river breezes and the easterly trade winds generates afternoon convection up to 10% more frequently (on average ~4 mm day−1 more intense rainfall) at the GoAmazon2014/5 sites east of the Negro River (T0e, T0t/k, and T1) relative to the T3 site, which was located west of the river. In general, the annual and diurnal cycles of precipitation during 2014 were similar to climatological values that are based on satellite data from 2000 to 2013.
Abstract
The isolation of the Amazon rain forest makes it challenging to observe precipitation forming there, but it also creates a natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Observations were collected upwind and downwind of Manaus, Brazil, during the “Observations and Modeling of the Green Ocean Amazon 2014–2015” experiment (GoAmazon2014/5). Besides aircraft, most of the observations were point measurements made in a spatially heterogeneous environment, making it hard to distinguish anthropogenic signals from naturally occurring spatial variability. In this study, 15 years of satellite data are used to examine the spatial and temporal variability of deep convection around the GoAmazon2014/5 sites using cold cloud tops (infrared brightness temperatures colder than 240 K) as a proxy for deep convection. During the rainy season, convection associated with the inland propagation of the previous day’s sea-breeze front is in phase with the diurnal cycle of deep convection near Manaus but is out of phase a few hundred kilometers to the east and west. Convergence between the river breezes and the easterly trade winds generates afternoon convection up to 10% more frequently (on average ~4 mm day−1 more intense rainfall) at the GoAmazon2014/5 sites east of the Negro River (T0e, T0t/k, and T1) relative to the T3 site, which was located west of the river. In general, the annual and diurnal cycles of precipitation during 2014 were similar to climatological values that are based on satellite data from 2000 to 2013.
