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AbstractThe role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure but is limited by poor vertical resolution (> 2 km) and precipitation. In this study, we combine these two technologies in an optimal estimation approach, 1D variation method (1DVar), to improve the characterization of the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a radiative transfer model (RTM) that takes into account atmospheric absorption, as well as surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from large eddy simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the lower troposphere while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP (National Polar-orbiting Partnership) measurements. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System – Radio Occultation (GNSS-RO) and Microwave Radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a-priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure, but is limited by poor vertical resolution (>2 km) and precipitation. In this study we combine these two technologies in an optimal estimation approach, 1D Variation method (1DVar), to better characterize the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a Radiative Transfer Model (RTM) that takes into account atmospheric absorption, and surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from Large Eddy Simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at lower troposphere, while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a-priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP measurements. [ABSTRACT FROM AUTHOR]

Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the TKE-based Simplified Higher-Order Closure (SHOC)--a simplified version of the assumed doubled-Gaussian PDF higher-order closure method. In this paper, we implement a stochastic multiplume Mass-Flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian PDF closures and couple it to SHOC (SHOC+MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference large-eddy simulation data, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC+MF parameterization shows weak sensitivity to the vertical grid resolution and model time step. [ABSTRACT FROM AUTHOR]

Upcoming spaceborne imaging spectrometers will allow retrieval of total column water vapour (TCWV) over land at horizontal resolution of 30-80 m. Here we show how to obtain, from these retrievals, exponents describing the power-law scaling of sub-km horizontal variability in clear-sky bulk planetary boundary layer (PBL) water vapour (q). Using large-eddy simulations (LES) of shallow convective PBLs we show how sunlight entering the PBL up to several km away from the footprint location degrades estimates of these exponents. We address this by calculating exponents perpendicular to the solar azimuth, that is to say flying "across" the sunlight path rather than "towards" or "away" from the Sun. Across 23 LES snapshots, at SZA = 60° the mean bias in calculated exponent is 38 ± 12 % (95 % range) along the solar azimuth, while following our strategy it is 3 ± 9 % and no longer significant. Both bias and root-mean-square error RMSE decrease with lower SZA. We include retrieval errors from several sources including: (1) the Earth Surface Mineral Dust Source Investigation (EMIT) instrument noise model, (2) requisite assumptions about the atmospheric thermodynamic profile, and (3) spatially nonuniform aerosol distributions. This technique can be used to obtain unique information about sub-km PBL q scaling from upcoming spaceborne spectrometer missions, while mitigating errors due to challenging solar geometries. [ABSTRACT FROM AUTHOR]

A fully unified parameterization of boundary layer and moist convection (shallow and deep) is presented. The new parameterization is based on the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) approach, which distinguishes between convective plumes and nonconvective mixing. The convective plumes represent both surface-forced updrafts and evaporatively driven downdrafts. The type of convection (i.e., dry, shallow, or deep) represented by the updrafts is not defined a priori, but rather depends on the near-surface updraft properties and the stochastic interactions between the plumes and the environment through lateral entrainment. Consequently, some updrafts may contribute only to the nonlocal transport within the subcloud layer, while others may condense and form shallow or even deep convection. Such a formulation is void of trigger functions and additional closures typical of modular parameterizations. The updrafts are coupled to relatively simple warm-, mixed-, and ice-phase microphysics. Each precipitating updraft forms a downdraft driven by the evaporation of detrained precipitation. The downdrafts control the development of cold pools near the surface that can invigorate convection. The new parameterization is tested in a single-column model against large-eddy simulations (LESs) for cases representing weakly precipitating marine convection and the diurnal cycle of continental deep convection. The results of these EDMF experiments compare well with the LES reference simulations. In particular, the transitions between the different dominant convection regimes are realistically simulated. [ABSTRACT FROM AUTHOR]