Abstract. Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the cloud-resolving model (CRM) called the System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness–radiation as well as the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2∘ S, −60.6∘ W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux as well as (b) to determine the relationship between the integrated cloud fraction, radiative fluxes and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAM simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have a meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from the previous day can be used to estimate the cloud fraction as well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud–radiation as well as dynamic and thermodynamic variables of the atmosphere during the periods of this study, being able to obtain atmospheric variables that are impossible to obtain in an observational way.
Abstract. Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the Cloud-Resolving Model (CRM) called System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness-radiation and the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2° S, −60.6° W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux; and (b) to determine the relationship between the integrated cloud fraction, radiative fluxes, and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAMS simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from previous day can be used to estimate the clouds fraction. As well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud-radiation and dynamic and thermodynamic variables of the atmosphere during the periods of this study, being indicated to obtain atmospheric variables that are impossible to obtain in an observational way.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.