The Next-Generation Goddard Convective-Stratiform Heating Algorithm: Addressing Higher Latitude, Cold Season, and Synoptic Systems

The Goddard Convective-Stratiform Heating (or CSH) algorithm is used to retrieve estimates of cloud heating over the global Tropics using TRMM rainfall data and a set of look-up-tables (LUTs) derived from a series of multi-week cloud-resolving model (CRM) simulations using the Goddard Cumulus Ensemble model (or GCE). These simulations link satellite observables (i.e., surface rainfall and stratiform fraction) with cloud heating profiles, which are not directly observable. The current CSH LUTs are differentiated with respect to surface rainfall characteristics, which is effective for tropical and continental summertime environments. However, with the launch of GPM in 2014, the range over which such algorithms can be applied has been extended from the Tropics and mid-latitudes to higher latitudes, including cold season and synoptic weather systems. Accordingly, the CSH algorithm and LUTs need to be updated for higher latitude events. In this study, NU-WRF was employed at 1 km to simulate winter systems in the US. A, new methodology has been adopted to construct LUTs utilizing satellite-observable 3D intensity fields, such as radar reflectivity. The new methodology/LUTs can be then applied to simulated radar fields to derive cloud heating for comparison against the model simulated heating. The model heating is treated as the `truth' as it is self-consistent with the simulated radar fields. This `consistency check' approach is a common well-established first step in algorithm development (e.g., the earlier CSH). The LUTs will be improved by iterating the consistency checks to quantitatively evaluate the similarities between the retrieved and simulated heating. The evaluations will be performed for different weather events, including northeast winter storms and atmospheric rivers.