Assimilation of GLM Data Together with Ground-Based Lightning Observations for Improved Storm Spin-Up in the High Resolution Rapid Refresh

In the convection-allowing High Resolution Rapid Refresh (HRRR) system, convective features are initialized via a latent-heating scheme, relying primarily on radar reflectivity observations, to provide realistic and accurate short-term severe weather forecasts. Radar reflectivity observations are augmented by cloud-to-ground (CG) lightning observations from ground-based sensing networks (NLDN and GLD360) to enhance heating at convective cores and to supplement areas of low radar coverage.

The Geostationary Lightning Mapper (GLM) instruments onboard the GOES-16 and GOES-17 satellites in geostationary orbit over the Americas provide continuous detection of in-cloud, cloud-to-cloud, and CG lightning throughout the HRRR domain and surrounding areas at about a 10-km resolution. In contrast to the currently assimilated ground-based detections of CG lightning, the total lightning detected by GLM can anticipate convective activity several minutes earlier than CG or radar reflectivity alone. The satellite data also complement reflectivity and ground-based lightning detections for storms over data sparse regions (primarily oceanic regions adjacent to North America for the RAP/HRRR systems), improving the initialization of convective storms in these areas. For the HRRR-Caribbean experimental domain, where radar reflectivity data are lacking, lightning data also serves as a novel measurement against which to verify forecasts.

For ingest into HRRR, GLM lightning detections may be combined with those that are already ingested from ground-based sensors to provide optimal coverage. Case studies contrast forecast skill due to the assimilation of ground-based detections only, satellite detections only, and the union of the two datasets. Results of the latest assimilation experiments will be presented, including new summer 2019 case studies, tropical/oceanic cases, and the incorporation of data from the new GOES-17 satellite.