08 June 2015

Simulating Convective Properties using Physical Spectral-bin and Parameterized Bulk Microphysical Models


Clouds play an important role in the climate system’s global energy and water cycles. Representation of clouds, especially cumulus clouds, remains a great modeling challenge due to their variability in time and space, and the coarse grid-spacing in regional and global climate models. Even at the cloud-resolving scale, models with bulk (computationally inexpensive) microphysical formulas have difficulties simulating the convective (updraft) properties of cumulus clouds. Using the Weather Research and Forecasting (WRF) model, researchers at the Department of Energy’s Pacific Northwest National Laboratory found that compared to observations, the spectral-bin (more physical, more computationally expensive) microphysics method provides better simulations of precipitation and vertical velocity of the cumulus convective cores than two methods that use double-moment (mass and number) bulk microphysics. The spectral-bin microphysics method reproduces the observed updraft intensity well, alleviating much of the overestimation of updraft speeds produced by the bulk method. This suggests that a cloud microphysical method can improve model simulation of convective cloud properties. The researchers then used the spectral-bin microphysics model output as benchmark simulations for their study of scale-dependence of convection transport (Part II of the study). They also discovered that mass flux (rate of mass flow), a quantity on which cumulus representations are based, is very sensitive to different microphysical methods for tropical convection, indicating strong microphysics modification to convection. But, the modeled mass fluxes of cloud systems in the mid-latitudes are not sensitive to the choice of microphysics methods. Cloud microphysical measurements of rain, snow, and graupel in convective cores will be critically important to further understand and elucidate performances of cloud microphysics methods.

Richard Grotjahn
University of California at Davis (UC Davis)

Support for this work was provided through Scientific Discovery through Advanced Computing (SciDAC) program funded by U.S. Department of Energy Office of Advanced Scientific Computing Research and Office of Biological and Environmental Research. The Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. Argonne National Laboratorys (ANL) work was supported by the Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), under contract DE-AC02-06CH11357 as part of the ARM Program. Kuan-Man Xu was supported by NASA Modeling, Analysis and Prediction program. Xiquan Dong was supported by DOE ASR project with award number DE-SC0008468 at University of North Dakota. The modeling data can be obtained by contacting Jiwen Fan (Jiwen.Fan@pnnl.gov). NARR reanalysis data were from the NOAA/OAR/ESRL Colorado, at the website http://www.esrl.noaa.gov/ psd/. NCEP FNL Operational Model Global Tropospheric Analyses were obtained by National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (2000), http://dx.doi.org/ 10.5065/D6M043C6. CPOL radar data and derived products were provided by Peter May at the Centre for Australian Weather and Climate Research and the Australian Bureau of Meteorology; 3-D multi-Doppler wind field from the MC3E were provided by Kirk North at McGill University, Canada; 3-D dual-Doppler wind field from the TWPICE were developed by Scott Collis at Argonne National Laboratory. Aircraft measurement and NEXRAD radar were provide by Xiquan Dong at University of North Dakota; ABRFC precipitation data were download from ARM Data Archive, http://www.archive.arm.gov/ armlogin/login.jsp.