Using Layered Precipitable Water and Other Satellite Derived Datasets to Anticipate High Impact Weather Events. Part I: Heavy Precipitation Applications

In anticipation of the first Geostationary Operational Environmental Satellite R series (GOES-R) launch, scheduled for March, 2016, the National Weather Service (NWS) seeks to provide forecaster training, prior to the platforms becoming operational. This initiative will be challenging, in that it will continue to be difficult to balance the training needs, priorities, and resources of satellite, dual-polarization radar, and numerical weather prediction topics. In light of several recent high impact weather events and findings from both National and Regional level service assessments, it remains clear that additional remote sensing products/techniques are needed to help anticipate and better forecast high impact weather events.

The purpose of this presentation is to highlight the use of new satellite datasets, which can assist in isolating locations favorable for excessive precipitation development, during both the warm and cold seasons. Several case studies will be shown using the experimental CIRA SPoRT Layered Precipitable Water (LPW) product, which has the ability to track individual layers of moisture into an area of developing heavy precipitation. LPW data will also be compared with Total-column Blended Precipitable Water (TPW) data, in order to highlight the benefits of using both datasets in tandem. Additionally, data from the experimental CIMSS NearCast model (theta-e and precipitable water difference fields) will be investigated, in order to show how the combination of real-time analyses from GOES sounder channels, along with numerical weather projections, can be assessed to pinpoint/track areas where deep moisture and convective instability (key flash flood ingredients) may co-exist. It is hoped that continued evaluation and documentation of key benefits will increase visibility and foster operational implementation of these products. Operational meteorologists will also benefit from exposure to these products, especially when even higher resolution datasets become available with the launch of the GOES-R platforms.