Comparing the Convective Structure and Microphysics in Two Sahelian Mesoscale Convective Systems: Radar Observations and CRM Simulations

Abstract

Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. This study was undertaken to determine the performance of the cloud-resolving model in representing distinct convective and microphysical differences between the two MCSs over a tropical continental location. Simulations are performed using 1-km horizontal grid spacing, a lower limit on current embedded cloudresolving models within a global multiscale modeling framework. Simulated system convective structure and microphysics are compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Vertical distributions of ice hydrometeors indicate underestimation at the mid-and upper levels, partially due to the inability of the model to produce adequate system heights. The abundance of high-reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles show that high reflectivity is present at greater heights than the simulations produced, thought to be a result of using a single-moment microphysics scheme. Relative trends in the population of simulated hydrometeors are in agreement with observations, though a secondary convective burst is not well represented. Despite these biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model has some skill in capturing observed differences between the two MCSs.

High‐resolution NU‐WRF simulations of a deep convective‐precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations

“…Both 4ICE schemes produce much less graupel than the Graupel scheme, as a significant fraction of frozen supercooled water becomes hail. The end result is that the modified 4ICE scheme has very little graupel in its stratiform region, which is largely dominated by snow consistent with both in situ measurements [e.g., Stith et al ., ] and radar HID analyses [e.g., Lerach et al ., ; Guy et al ., ] of MCSs and also similar to the Hail scheme except that the total amount of stratiform snow is much greater in the modified 4ICE.…”

Implementation of an aerosol–cloud‐microphysics–radiation coupling into the NASA unified WRF: Simulation results for the 6–7 August 2006 AMMA special observing period

“…Therefore, the 24 h‐accumulated precipitation derived from the MIT radar data at Niamey was adopted to provide a quantitative comparison with the model rainfall forecast. Following Guy et al (2013), reflectivities from the MIT radar were converted to rain rates (mm h −1 ) via a Z – R (reflectivity–rain) relationship. The rain values were then summed throughout the period of interest at each grid point.…”

“…In Figure 7, the predicted reflectivity (only results from Exp. AM are shown as all four experiments produced similar structures) is compared with the observed reflectivity collected with the MIT C-band radar at Niamey (13.49 • N, 2.17 • E) during the AMMA SOP (Guy et al, 2013). The model produces a linear system as observed; however, reflectivities in the leading edge of the second (larger) cloud band are stronger than observations, and the orientation is more north-south as compared to the observed northwest-southeast.…”

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

“…The development of these products is motivated by a demand for high-quality rainfall-accumulation maps to act as a key constraint for continuous climate-modelforcing datasets (e.g., Xie et al 2004. There is extensive support for rainfall-accumulation products to evaluate multiple scales of modeling capabilities for the capture of convective cloud life cycle, precipitation regime or diurnal behaviors, and diabatic-heating implications for organized convective systems (Houze et al 1989;Schumacher et al 2004;Dai 2006;Milbrandt and Yau 2006;Matsui et al 2010;Wapler et al 2010;Bukovsky and Karoly 2011;Dirmeyer et al 2012;Varble et al 2011;Iguchi et al 2012;Del Genio et al 2012;Caine et al 2013;Guy et al 2013).…”