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, 2007 : Simulations of cumulus clouds using a spectral microphysics cloud-resolving model . J. Geophys. Res. , 112 , D04201 , https://doi.org/10.1029/2006JD007688 . Fan , J. , R. Zhang , W.-K. Tao , and K. I. Mohr , 2008 : Effects of aerosol optical properties on deep convective clouds and radiative forcing . J. Geophys. Res. , 113 , D08209 , https://doi.org/10.1029/2007JD009257 . Fan , J. , L. R. Leung , Z. Li , H. Morrison , H. Chen , Y. Zhou , Y. Qian , and
, 2007 : Simulations of cumulus clouds using a spectral microphysics cloud-resolving model . J. Geophys. Res. , 112 , D04201 , https://doi.org/10.1029/2006JD007688 . Fan , J. , R. Zhang , W.-K. Tao , and K. I. Mohr , 2008 : Effects of aerosol optical properties on deep convective clouds and radiative forcing . J. Geophys. Res. , 113 , D08209 , https://doi.org/10.1029/2007JD009257 . Fan , J. , L. R. Leung , Z. Li , H. Morrison , H. Chen , Y. Zhou , Y. Qian , and
properties [e.g., liquid water path (LWP)], which enables the algorithm to provide thermodynamic profiles in both clear and cloudy conditions. As a Bayesian retrieval, AERIoe uses a priori estimates of the mean X a and covariance a state of the atmosphere, which for this study was calculated from a 3-month radiosonde climatology generated using radiosondes regularly released at the ARM Southern Great Plains site ( Sisterson et al. 2016 ). The OE equation solves for variables [e.g., T ( z ), q ( z
properties [e.g., liquid water path (LWP)], which enables the algorithm to provide thermodynamic profiles in both clear and cloudy conditions. As a Bayesian retrieval, AERIoe uses a priori estimates of the mean X a and covariance a state of the atmosphere, which for this study was calculated from a 3-month radiosonde climatology generated using radiosondes regularly released at the ARM Southern Great Plains site ( Sisterson et al. 2016 ). The OE equation solves for variables [e.g., T ( z ), q ( z
Validation of a Water Vapor Micropulse Differential Absorption Lidar (DIAL)vapor profiles but clouds, which often occur in dynamic weather conditions, obscure the BL because clouds are usually optically thick in the infrared. Since ground-based MWRPs require periodic manual calibration, systematic biases in the calibration (e.g., Paine et al. 2014 ) can occur that impact the retrieved thermodynamic profiles (e.g., Löhnert and Maier 2012 ), and they have only limited information content, resulting in poor vertical resolution ( Löhnert et al. 2009 ). Ground-based infrared
vapor profiles but clouds, which often occur in dynamic weather conditions, obscure the BL because clouds are usually optically thick in the infrared. Since ground-based MWRPs require periodic manual calibration, systematic biases in the calibration (e.g., Paine et al. 2014 ) can occur that impact the retrieved thermodynamic profiles (e.g., Löhnert and Maier 2012 ), and they have only limited information content, resulting in poor vertical resolution ( Löhnert et al. 2009 ). Ground-based infrared
model output fields. While this is a reasonable approach in terms of single case studies, analyzing the dynamical properties of bores in large datasets spanning a large number of numerical simulations is considerably more time consuming and, additionally, prone to human errors. The algorithm framework and the attendant algorithm applications in this study are illustrated through a forecast experiment based on the 6 July 2015 PECAN case study. Using the Weather Research and Forecasting (WRF) Model
model output fields. While this is a reasonable approach in terms of single case studies, analyzing the dynamical properties of bores in large datasets spanning a large number of numerical simulations is considerably more time consuming and, additionally, prone to human errors. The algorithm framework and the attendant algorithm applications in this study are illustrated through a forecast experiment based on the 6 July 2015 PECAN case study. Using the Weather Research and Forecasting (WRF) Model
). Such a network would facilitate data assimilation (DA) efforts, allow description of the full range of bore properties and bore climatology, and yield further insight into the interaction of bores and deep convection, such as the relation between the characteristics of the ducting layer in the prebore environment and the magnitude of the bore lift. Traditionally, the Scorer parameter, which is used to identify ducting layers (e.g., Koch et al. 2008a ), has been computed from radiosonde
). Such a network would facilitate data assimilation (DA) efforts, allow description of the full range of bore properties and bore climatology, and yield further insight into the interaction of bores and deep convection, such as the relation between the characteristics of the ducting layer in the prebore environment and the magnitude of the bore lift. Traditionally, the Scorer parameter, which is used to identify ducting layers (e.g., Koch et al. 2008a ), has been computed from radiosonde
as H and k increase and as N decreases. The Bessel functions in (3.14) exhibit damped oscillations [e.g., see Fig. 9.1 of Abramowitz and Stegun (1964 , hereafter AS )] whose properties are evident in (A.2) , written here to leading order as The zero-phase lines of the functions with arguments , , and are the zeroes of the cosine in (3.16) . In view of (2.35) , we see that these phase lines are at heights , which descend with speeds . Descending phase fronts are consistent
as H and k increase and as N decreases. The Bessel functions in (3.14) exhibit damped oscillations [e.g., see Fig. 9.1 of Abramowitz and Stegun (1964 , hereafter AS )] whose properties are evident in (A.2) , written here to leading order as The zero-phase lines of the functions with arguments , , and are the zeroes of the cosine in (3.16) . In view of (2.35) , we see that these phase lines are at heights , which descend with speeds . Descending phase fronts are consistent
