Common Community Physics Package: Fostering Collaborative Development in Physical Parameterizations and Suites
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54 ENVIRONMENTAL SCIENCES↗
Engineering topics
Firl, Grant
Publications and source records attributed to Firl, Grant.
Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements
PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons’ RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.
54 ENVIRONMENTAL SCIENCES↗
Large-Scale Forcing Impact on the Development of Shallow Convective Clouds Revealed From LASSO Large-Eddy Simulations
Real-world large-eddy simulations (LES) are driven by time-varying large-scale forcings (LSF) - e.g., temperature advection, moisture advection, and subsidence - derived from large-scale weather models. This study investigates the impact of the uncertainty in LSF on real-world LES in terms of the development of shallow convection at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory for the 11 June 2016 case using LES provided by the U.S. Department of Energy's LES ARM Symbiotic Simulation and Observation (LASSO) activity. The LASSO dataset provides an ensemble of LES for the selected case, which consists of LES runs that were driven by different LSF. The two contrasting LES runs investigated here generate different types of convective clouds, i.e., nonprecipitating shallow clouds and precipitating cumulus congestus, mainly due to the difference of LSF in temperature advection in the free troposphere. The temperature advection modulates the strength of the capping inversion and therefore the buoyancy of the air parcels rising from the atmospheric boundary layer (ABL). The inversion, together with large-scale updrafts, controls the penetration of the ABL thermals into the free troposphere, leading to cumulus congestus in the case of a weaker inversion. In contrast, clouds remain shallow in the case of a strong inversion. Differences between the two simulations are amplified over time, as mixed-phase clouds are formed near the top of the congestus in the weaker inversion case. Furthermore, this high dependency of LES results to LSF stresses the importance of accurate LSF by large-scale models to real-world LES simulations.
54 ENVIRONMENTAL SCIENCES↗