Enhancing Operational Nowcasting through an Enterprise Environmental Data Fusion and Assimilation System

The key requirement for operational nowcasting of extreme weather is timely and reliable environmental intelligence gathered from a variety of sources, including various remotely sensed observations from space, surface-based radar, in-situ observations, and model analyses and forecasts, to name a few. The volume of data available along with the disparities across data sources presents many challenges to the forecaster. For instance, traditional global data assimilation (DA) analyses are only available, at best, every 6 hours, exclude many satellite observations (e.g. radiances impacted by precipitation), and are highly weighted toward the forecast background. Although regional DA analyses provide better temporal and spatial resolution compared to the global systems, often they exclude even more satellite observations. Also, although remote sensing products provide observations directly from the satellite to the user, the volume of data from a large number of sources and algorithms make it difficult to parse for information relevant to specific weather threats and understand error characteristics associated with the products. Current data fusion and blending approaches have mitigated these issues to some degree, but at the expense of losing physical consistency across parameters and decreasing the fit to the original observations. As a result, a large proportion of available environmental information is not analyzed or used. To address these challenges, a new Enterprise Environmental Data Fusion (EDF) system has been developed at NOAA/NESDIS/STAR with the objective of providing half-hourly, global, high-resolution analyses which unifies together all available remote sensing (both LEO and GEO constellations) and in-situ observations with model fields. The EDF concept involves a preprocessing framework developed for integrating remote sensing algorithms to precondition a background forecast through displacement correction, executing an enhanced global DA system, and a post-processing framework developed to integrate remote sensing algorithms which derive products from the DA analysis. The result is a comprehensive, 4D cube EDF analysis where all environmental parameters are physically consistent with both the input observations as well as model dynamic and thermodynamic fields. In this study, we will present the overall EDF framework and concept of how the preprocessing, DA, and post-processing components interface, as well as the impact of the preprocessing on creating an observation-weighted analysis. Additionally, the benefits for operational nowcasting will be illustrated, leveraging efforts to integrate EDF products into AWIPS2. This will include a demonstration of the wide range of EDF geophysical products including sounding, QPE, stability indices, and dynamical parameters, as well as quality control metrics to help users better interpret regions where data is trustable, using case studies of extreme precipitation and severe weather events. Finally some comments and considerations for operational deployment will be made.