No abstract

A well-known bias common to many bulk microphysics schemes currently being used in cloud-resolving models is the tendency to produce excessively large reflectivity values (e.g., 40 dBZ) in the middle and upper troposphere in simulated convective systems. The Rutledge and Hobbs-based bulk microphysics scheme in the Goddard Cumulus Ensemble model is modified to reduce this bias and improve realistic aspects. Modifications include lowering the efficiencies for snow/graupel riming and snow accreting cloud ice; converting less rimed snow to graupel; allowing snow/graupel sublimation; adding rime splintering, immersion freezing, and contact nucleation; replacing the Fletcher formulation for activated ice nuclei with that of Meyers et al.; allowing for ice supersaturation in the saturation adjustment; accounting for ambient RH in the growth of cloud ice to snow; and adding/accounting for cloud ice fall speeds. In addition, size-mapping schemes for snow/graupel were added as functions of temperature and mixing ratio, lowering particle sizes at colder temperatures but allowing larger particles near the melting level and at higher mixing ratios. The modifications were applied to a weakly organized continental case and an oceanic mesoscale convective system (MCS). Strong echoes in the middle and upper troposphere were reduced in both cases. Peak reflectivities agreed well with radar for the weaker land case but, despite improvement, remained too high for the MCS. Reflectivity distributions versus height were much improved versus radar for the less organized land case but not for the MCS despite fewer excessively strong echoes aloft due to a bias toward weaker echoes at storm top.

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar–gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%.