A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling

Autor(es)

Grell, G. A. ^[1] ; Freitas, S. R. ^[2]

Afiliação do(s) autor(es)

^[1] NOAA, Earth Syst Res Lab, Boulder, CO 80305 - USA

^[2] INPE, Ctr Weather Forecasting & Climate Studies, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Atmospheric Chemistry and Physics; v. 14, n. 10, p. 5233-5250, 2014.
• Citações Web of Science: 225

Resumo

A convective parameterization is described and evaluated that may be used in high resolution non-hydrostatic mesoscale models as well as in modeling system with unstructured varying grid resolutions and for convection aware simulations. This scheme is based on a stochastic approach originally implemented by Grell and Devenyi (2002). Two approaches are tested on resolutions ranging from 20 km to 5 km. One approach is based on spreading subsidence to neighboring grid points, the other one on a recently introduced method by Arakawa et al. (2011). Results from model intercomparisons, as well as verification with observations indicate that both the spreading of the subsidence and Arakawa's approach work well for the highest resolution runs. Because of its simplicity and its capability for an automatic smooth transition as the resolution is increased, Arakawa's approach may be preferred. Additionally, interactions with aerosols have been implemented through a cloud condensation nuclei (CCN) dependent autoconversion of cloud water to rain as well as an aerosol dependent evaporation of cloud drops. Initial tests with this newly implemented aerosol approach show plausible results with a decrease in predicted precipitation in some areas, caused by the changed autoconversion mechanism. This change also causes a significant increase of cloud water and ice detrainment near the cloud tops. Some areas also experience an increase of precipitation, most likely caused by strengthened downdrafts. (AU)

• Processo FAPESP: 12/13575-9 - Participação no experimento de campo SAMBBA, análise dos dados coletados e modelagem
• Beneficiário: Karla Maria Longo de Freitas
• Modalidade de apoio: Auxílio à Pesquisa - Regular