Convective Initiation and 0-6 hr Storm Nowcasting for GOES-R

Over the past few years, research has focused on the use of the Geostationary Operational Environmental Satellite (GOES) series to forecast which cumulus clouds could potentially produce future thunderstorms, so-called convective initiation (CI). A product of this research is the GOES-R Algorithm Working Group (AWG) CI method as developed at the University of Alabama in Huntsville (UAHuntsville). The algorithm will use a series of interest fields comprised of static and temporal infrared (IR) channel differences from the Advanced Baseline Imager (ABI) aboard GOES-R to diagnose areas of potential CI [Algorithm Theoretical Basis Document (ATBD) 2011]. The GOES-R AWG CI algorithm will present crucial information on the growth characteristics of cumulus clouds as depicted within these interest fields. Unfortunately, the current series of GOES does not have the temporal resolution, nor the necessary spectral channels to create the robust CI forecasts that the GOES-R AWG algorithm will provide. However, a proxy algorithm has been created to take advantage of the spectral channels currently available, and has been tested and evaluated within National Weather Service Forecast Offices (NWSFO) since 2010. Routine very short-term (0-2 hour) CI forecasts are currently produced by the proxy AWG CI algorithm however, the meteorological community desires accurate convective forecasts on the order of 1-6 hours. Numerical weather prediction (NWP) models provide these longer-term forecasts, yet CI placement, timing, and occurrence remains a significant problem, which if wrong, leads to deleterious downstream impacts on the larger-scale forecast, sometimes lasting days within a given model simulation. Therefore, the research hypotheses guiding this work are:

(1) Information and techniques learned from the creation of an improved proxy AWG CI algorithm can be transferred into improved strategies for direct assimilation of current and future satellite datasets (that describe the CI process), leading to increased accuracy in NWP of the onset of new convective storms.

(2) The assimilation of the raw satellite-based observation fields into rapid-update NWP models such as the Rapid Refresh (RAP) and High Resolution Rapid Refresh (HRRR) will provide the models with a better description of initiating convective storms, leading to beneficial improvements to short- (0-6 hrs) and subsequently longer-range mesoscale forecasts (6-18 hrs).

The HRRR will operate at 3 km resolution and is scheduled to become a National Center for Environmental Prediction (NCEP) operational model in 2014, and hence this activity represents a natural research–to–operational (R2O) transition for GOES-R products into NWP. The overarching theme is improving the short-term mesoscale forecasts, where the assimilation of GOES-R data into the HRRR will provide better timing and locations of future CI, along with the coincident development of an improved proxy AWG CI algorithm toward increasing detection of early-onset storm initiation in advance of radar echoes by up to 45-60 minutes or more.

Shown during this presentation will be recent results of assimilating the GOES-R CI-based latent heating profiles into the RAP mode, that then go on to form new convective storms. Also, analysis involving the use of ~15 NWP fields together with satellite datasets, in a logistic regression model, that produces probabilistic CI nowcasts will be shown (along with feedback from the Hazardous Weather Testbed). Furthermore, analysis will be presented on how satellite-derived cloud property fields improve the GOES-R CI algorithm's performance.