The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication. Reports in this series are issued by the NCAR Scientific Divisions ; copies may be obtained on request from the Publications Office of NCAR. Designation symbols for the series include: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

/pdf/a-description-of-the-advanced-research-wrf-version-3-o5qjsdx46x.pdf

A Description of the Advanced Research WRF Version 3

Knowledge of land surface water, energy, and carbon conditions are of critical importance due to their impact on many real world applications such as agricultural production, water resource management, and flood, weather, and climate prediction. Land Information System (LIS) is a software framework that integrates the use of satellite and ground-based observational data along with advanced land surface models and computing tools to accurately characterize land surface states and fluxes. LIS employs the use of scalable, high performance computing and data management technologies to deal with the computational challenges of high resolution land surface modeling. To make the LIS products transparently available to the end users, LIS includes a number of highly interactive visualization components as well. The LIS components are designed using object-oriented principles, with flexible, adaptable interfaces and modular structures for rapid prototyping and development. In addition, the interoperable features in LIS enable the definition, intercomparison, and validation of land surface modeling standards and the reuse of a high quality land surface modeling and computing system.

Land information system: An interoperable framework for high resolution land surface modeling

A two-moment microphysical parameterization for mixed-phase clouds was developed to improve the explicit representation of clouds and precipitation in mesoscale atmospheric models The scheme predicts the evolution of mass as well as number densities of the five hydrometeor types cloud droplets, raindrops, cloud ice, snow and graupel Since the number concentrations of all these hydrometeors are calculated explicitly, all relevant homogenous and heterogenous nucleation processes have been parameterized including the activation of cloud condensation nuclei, which is not predicted in most state-of-the-art cloud resolving models Therefore the new scheme can discriminate between continental and maritime conditions and can be used for investigations of aerosol effects on the precipitation formation in mixed-phase clouds In addition, the scheme includes turbulence effects on droplet coalescence, collisional breakup of raindrops and size-dependent collision efficiencies A new general approximation of the collection kernels and the corresponding collision integrals is introduced

A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description

Most atmospheric motions of different spatial scales and precipitation are closely related to phase transitions in clouds. The continuously increasing resolution of large-scale and mesoscale atmospheric models makes it feasible to treat the evolution of individual clouds. The explicit treatment of clouds requires the simulation of cloud microphysics. Two main approaches describing cloud microphysical properties and processes have been developed in the past four and a half decades: bulk microphysics parameterization and spectral (bin) microphysics (SBM). The development and utilization of both represent an important step forward in cloud modeling. This study presents a detailed survey of the physical basis and the applications of both bulk microphysics parameterization and SBM. The results obtained from simulations of a wide range of atmospheric phenomena, from tropical cyclones through Arctic clouds using these two approaches are compared. Advantages and disadvantages, as well as lines of future development for these methods are discussed. (Less)

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014RG000468

Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization

[1] Present-day shortcomings in the representation of upper tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global-scale measurements. With the relatively recent addition of satellite-derived products from Aura/Microwave Limb Sounder (MLS) and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their "cloud" ice and related fields. Using this information as background, we (1) discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of remaining questions and suggestions for pathways forward.

Cloud ice: A climate model challenge with signs and expectations of progress

The response of cloud systems to their environment is an important link in a chain of processes responsible for monsoons, frontal depression, El Nino-Southern Oscillation (ENSO) episodes and other climate variations (e.g., 30-60 day intra-seasonal oscillations). Numerical models of cloud properties provide essential insights into the interactions of clouds with each other, with their surroundings, and with land and ocean surfaces. Significant advances are currently being made in the modeling of rainfall and rain-related cloud processes, ranging in scales from the very small up to the simulation of an extensive population of raining cumulus clouds in a tropical- or midlatitude-storm environment. The Goddard Cumulus Ensemble (GCE) model is a multidimensional non-hydrostatic dynamic/microphysical cloud resolving model. It has been used to simulate many different mesoscale convective systems that occurred in various geographic locations. In this paper, recent GCE model improvements (microphysics, radiation and surface processes) will be described as well as their impact on the development of precipitation events from various geographic locations. The performance of these new physical processes will be examined by comparing the model results with observations. Specifically, the impact of different ice schemes (i.e., three-class ice scheme, four-class two-moment ice scheme) on precipitation processes are examined and compared. Spectral bin microphysics are used to investigate precipitation processes under clean and dirty environments. The coupled GCE-radiation model shows that the modulation of relative humidity by radiative processes is the main reason for the diurnal variation of precipitation in the tropics. The coupled GCE-land surface model is used to examine the impact of heterogeneous land surface characteristics (soil-vegetation) on precipitation processes. The effect of ocean flux algorithms (e.g., the TOGA COARE flux algorithm and a simple bulk aerodynamic method) on surface fluxes, environmental convective available potential energy (CAPE) and precipitation processes are compared. In addition, the coupled GCE-ocean mixed layer (OML) model is used to investigate the physical processes that affect the variation of sea surface temperature, mixed layer depth and salinity.

/pdf/microphysics-radiation-and-surface-processes-in-the-goddard-jv2xq7zmpo.pdf

Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model

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Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model