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NASA NTRS2014

The Goddard Cumulus Ensemble Model (GCE): Improvements and Applications for Studying Precipitation Processes

Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is amulti-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes.

GCE↗

NASA NTRS2019

Evaluating Precipitation Features and Rainfall Characteristics in a Multi-scale Modeling Framework

Cloud and precipitation systems over the tropics and subtropics are simulated with a multi-scale modeling framework (MMF) and compared against the TRMM radar precipitation features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to analyze simulated cloud precipitating structures from the embedded two-dimensional cloud-resolving models (CRMs) within an MMF. Despite the two-dimensionality of the CRMs, the simulated RPFs population distribution, and horizontal and vertical structure are in good agreement with TRMM observations. However, some deficits are also found in the model simulations. The model tends to overestimate mean convective precipitation rates for RPFs with a size less than 100 km, contributing to the excessive precipitation biases in the warm pool and western Pacific, western and northern India Ocean, and eastern Pacific commonly found in most MMFs. For large features with a size greater than 150 km, both convective and stratiform rain rates are underestimated. The distribution of maximum radar echo top heights as a function of RPF size is well simulated except the model tends to underestimate the occurrence frequency of maximum heights greater than 15 km. The maximum echo top heights for convective cells embedded within large RPFs with a size greater than 150 km are also underestimated. The cyclic lateral boundary with a limited model domain generates artificial occurrences for RPFs with a size close to the model domain size, producing a significant contribution to the total rainfall due to their sizes. This cyclic lateral boundary effect can be easily identified and quantified in both probability and cumulative distribution functions of RPFs. The geophysical distribution of the population of the largest RPFs in the control experiment shows they are mainly located in the Subtropics but also partially contribute to the common MMF biases of excessive precipitation in the Tropics. Sensitivity experiments using CRMs with different domain sizes and different grid spacings show larger domains (higher resolution) tend to shift the RPFs distribution to large (small) sizes. The cyclic lateral boundary biases increase as CRM domain size decreases. The impacts of model horizontal and vertical resolution on simulated convective systems are also investigated.

Chern, Jiun-Dar↗