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Derek J. Posselt

Abstract

Deep convective cloud content, precipitation distribution and rate, dynamics, and radiative fluxes are known to be sensitive to the details of liquid- and ice-phase cloud microphysical processes. Previous studies have explored the multivariate convective response to changes in cloud microphysical parameter values in a framework that isolated the cloud and radiation schemes from the thermodynamic and dynamic environment. This study uses a Bayesian Markov chain Monte Carlo (MCMC) algorithm to generate sets of cloud microphysical parameters consistent with a specific storm environment in a three-dimensional cloud-system-resolving model. These parameter sets, and the corresponding large ensemble of model simulations, contain information about the univariate model sensitivity, as well as parameter–state and parameter–parameter interactions. Examination of the relationships between cloud parameters and in-cloud vertical motion and latent heat release provides information about the influence of microphysical processes on the in-cloud environment. Exploration of the joint dependence of microphysical properties and clear-air relative humidity and temperature allows an assessment of the influence of cloud microphysics on the near-cloud environment. Analysis of the MCMC results indicates the model output is sensitive to a small subset of the parameters. In addition, constraint of cloud microphysics using bulk observations of the hydrologic cycle and TOA radiative fluxes uniquely constrains vertical velocity, latent heat release, and the environmental temperature and relative humidity.

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Fei He and Derek J. Posselt

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This study advances the understanding of how parameterized physical processes affect the development of tropical cyclones (TCs) in the Community Atmosphere Model (CAM) using the Reed–Jablonowski TC test case. It examines the impact of changes in 24 parameters across multiple physical parameterization schemes that represent convection, turbulence, precipitation, and cloud processes. The one-at-a-time (OAT) sensitivity analysis method quantifies the relative influence of each parameter on TC simulations and identifies which parameters affect six different TC characteristics: intensity, precipitation, longwave cloud radiative forcing (LWCF), shortwave cloud radiative forcing (SWCF), cloud liquid water path (LWP), and ice water path (IWP). It is shown that TC intensity is mainly sensitive to the parcel fractional mass entrainment rate (dmpdz) in deep convection. A decrease in this parameter can lead to a change in simulated intensity from a tropical depression to a category-4 storm. Precipitation and SWCF are strongly affected by three parameters in deep convection: tau (time scale for consumption rate of convective available potential energy), dmpdz, and C0_ocn (precipitation coefficient). Changes in physical parameters generally do not affect LWCF much. In contrast, LWP and IWP are very sensitive to changes in C0_ocn. The changes can be as large as 10 (5) times the control mean value for LWP (IWP). Further examination of the response functions for the subset of most sensitive parameters reveals nonlinear relationships between parameters and most output variables, suggesting that linear perturbation analysis may produce misleading results when applied to strongly nonlinear systems.

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Derek J. Posselt and Tomislava Vukicevic

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This study explores the functional relationship between model physics parameters and model output variables for the purpose of 1) characterizing the sensitivity of the simulation output to the model formulation and 2) understanding model uncertainty so that it can be properly accounted for in a data assimilation framework. A Markov chain Monte Carlo algorithm is employed to examine how changes in cloud microphysical parameters map to changes in output precipitation, liquid and ice water path, and radiative fluxes for an idealized deep convective squall line. Exploration of the joint probability density function (PDF) of parameters and model output state variables reveals a complex relationship between parameters and model output that changes dramatically as the system transitions from convective to stratiform. Persistent nonuniqueness in the parameter–state relationships is shown to be inherent in the construction of the cloud microphysical and radiation schemes and cannot be mitigated by reducing observation uncertainty. The results reinforce the importance of including uncertainty in model configuration in ensemble prediction and data assimilation, and they indicate that data assimilation efforts that include parameter estimation would benefit from including additional constraints based on known physical relationships between model physics parameters to render a unique solution.

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Derek J. Posselt and Craig H. Bishop

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This paper explores the temporal evolution of cloud microphysical parameter uncertainty using an idealized 1D model of deep convection. Model parameter uncertainty is quantified using a Markov chain Monte Carlo (MCMC) algorithm. A new form of the ensemble transform Kalman smoother (ETKS) appropriate for the case where the number of ensemble members exceeds the number of observations is then used to obtain estimates of model uncertainty associated with variability in model physics parameters. Robustness of the parameter estimates and ensemble parameter distributions derived from ETKS is assessed via comparison with MCMC. Nonlinearity in the relationship between parameters and model output gives rise to a non-Gaussian posterior probability distribution for the parameters that exhibits skewness early and multimodality late in the simulation. The transition from unimodal to multimodal posterior probability density function (PDF) reflects the transition from convective to stratiform rainfall. ETKS-based estimates of the posterior mean are shown to be robust, as long as the posterior PDF has a single mode. Once multimodality manifests in the solution, the MCMC posterior parameter means and variances differ markedly from those from the ETKS. However, it is also shown that if the ETKS is given a multimode prior ensemble, multimodality is preserved in the ETKS posterior analysis. These results suggest that the primary limitation of the ETKS is not the inability to deal with multimodal, non-Gaussian priors. Rather it is the inability of the ETKS to represent posterior perturbations as nonlinear functions of prior perturbations that causes the most profound difference between MCMC posterior PDFs and ETKS posterior PDFs.

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Annareli Morales, Hugh Morrison, and Derek J. Posselt

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This study explores the sensitivity of clouds and precipitation to microphysical parameter perturbations using idealized simulations of moist, nearly neutral flow over a bell-shaped mountain. Numerous parameters are perturbed within the Morrison microphysics scheme. The parameters that most affect cloud and precipitation characteristics are the snow fall speed coefficient As, snow particle density ρs, rain accretion (WRA), and ice–cloud water collection efficiency (ECI). Surface precipitation rates are affected by A s and ρ s through changes to the precipitation efficiency caused by direct and indirect impacts on snow fall speed, respectively. WRA and ECI both affect the amount of cloud water removed, but the precipitation sensitivity differs. Large WRA results in increased precipitation efficiency and cloud water removal below the freezing level, indirectly decreasing cloud condensation rates; the net result is little precipitation sensitivity. Large ECI removes cloud water above the freezing level but with little influence on overall condensation rates. Two environmental experiments are performed to test the robustness of the results: 1) reduction of the wind speed profile by 30% (LowU) and 2) decreasing the surface potential temperature to induce a freezing level below the mountain top (LowFL). Parameter perturbations within LowU result in similar mechanisms acting on precipitation, but a much weaker sensitivity compared to the control. The LowFL case shows ρ s is no longer a dominant parameter and A s now induces changes to cloud condensation, since more of the cloud depth is present above the freezing level. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations.

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Derek J. Posselt and Jonathan E. Martin

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The effect of latent heat release on the development of the occluded thermal structure in a major winter storm is examined through comparison of full physics (FP) and no-latent-heat-release (NLHR) simulations of the event performed using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Though both simulations possess a well-developed occluded thermal ridge near the surface, the 3D structure of their respective occluded quadrants is quite different. In particular, the FP simulation depicts the canonical, troposphere-deep warm occluded thermal structure, whereas the NLHR simulation produces only a shallow, poorly developed one. Consistent with these differences in tropospheric thermal structure, the FP cyclone displays a robust treble clef potential vorticity (PV) distribution in the upper troposphere in its postmature phase, while a considerably less robust version characterizes the NLHR simulation. The PV minimum of the treble clef overlies a poleward sloping column of warm, weakly stratified air that extends through the depth of the troposphere and is a signature of the trowal, the essential structural feature of warm occluded cyclones. Consequently, examination of the role played by latent heat release in production of the occluded thermal structure in this case is made through consideration of its influence on the evolution of the upper-tropospheric PV morphology.

It is found that direct dilution of upper-tropospheric PV by midtropospheric latent heat release initiates formation of a local, upper-tropospheric PV minimum, or low PV tongue, to the northwest of the surface cyclone center. The production of this PV minimum initiates a cutting off of the upper-tropospheric PV anomaly associated with the surface development. The upper-tropospheric circulation associated with this cutoff anomaly, in turn, forces the advection of low (<1 PVU) values of PV into the developing PV trough. This combination of kinematic and diabatic processes acts to produce both the tropopause PV treble clef as well as the underlying warm occluded thermal structure in the FP simulation. In contrast, though an adiabatic kinematic tendency for production of a treble clef PV morphology operates in the NLHR simulation, the resulting PV and thermal structures are weaker and slower to evolve than those produced in the FP simulation. Thus, it is suggested that latent heat release plays an indispensable role in the production of the characteristic occluded thermal structures observed in nature.

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Annareli Morales, Derek J. Posselt, and Hugh Morrison

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This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θ sfc), and the snow fall speed coefficient (A s). RH, U, and A s exhibit interdependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial subregions and given different environmental conditions. In particular, high θ sfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

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Derek J. Posselt and Craig H. Bishop
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Derek J. Posselt and Gerald G. Mace

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Collocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.

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Juan A. Crespo and Derek J. Posselt

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Clouds are both produced by and interact with the mesoscale and synoptic-scale structure of extratropical cyclones (ETCs) in ways that are still not well understood. Cloud-scale radiative and latent heating modifies the thermal environment, leading to a response in the dynamics that can in turn feed back on cloud distribution and microphysical properties. Key to the structure of ETCs is the warm conveyor belt (WCB); the poleward-ascending airstream that produces the bulk of the clouds and precipitation. This paper examines a long-lived WCB that persisted over the western North Atlantic Ocean in nearly the same location for several days. During this time, the storm was sampled multiple times by NASA’s A-Train satellite constellation, and a clear transition from stratiform to convective clouds was observed. Examination of coincident temperature and water vapor data reveals destabilization of the thermodynamic profile after the cyclone reached maturity. CloudSat radar reflectivity from two sequential overpasses of the warm front depicts a change from stratiform to convective cloud structure, and high-frequency microwave data reveal an increase in the amount of ice hydrometeors. The presence of convection may serve to strengthen the warm frontal trough while slowing the movement of the primary low pressure center. The stratiform–convective transition cannot be detected from passive measurements of cloud-top pressure. The results demonstrate the effectiveness of multivariate satellite observations for examining the outcome of dynamic processes in ETCs, and highlight the need for more rapid temporal profiling in future remote sensing observing systems.

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