744 Propagation of JPSS Precipitation Retrievals using Near Real-Time Lightning Data

Wednesday, 13 January 2016
Patrick C. Meyers, Univ. of Maryland, College Park, MD; and R. R. Ferraro and S. Rudlosky

The high latency of low earth orbit (LEO) satellite data often dissuades forecasters from using LEO precipitation observations for near real-time applications. To address this issue, a lightning-based advection scheme for passive microwave precipitation retrievals promises increased utilization of LEO satellite observations in operational monitoring and forecasting for strong convective systems. A recently observed rain field can be propagated to near real-time by using low-latency lightning activity data. A proof-of-concept has been demonstrated using high temporal and spatial resolution lightning data from the Washington DC Lightning Mapping Array. Lightning coincides with strong convective systems where ice aloft promotes charge separations. The radiometric scattering of surface emissions due to the ice particles is easily identified in passive microwave observations. The lightning tracking algorithm identifies local maxima of lightning activity using a 10-minute accumulation of lightning flashes in a 2 km grid. An iterative search determines the most likely direction and translational speed of the lightning feature by minimizing the root mean square difference of two time-lagged scenes. The estimated motion vectors for each convective core are interpolated to a common grid to propagate the observed rain swath. Additionally, the initial precipitation field can be augmented in new areas with intense lightning. The product is demonstrated for ATMS and AMSR2, but is easily adaptable for any 2-dimensional LEO precipitation field. This approach is scalable for application over a larger domain, including global observations using Earth Networks Total Lightning Network and Vaisala's Global Lightning Dataset. These high resolution ground-based systems will serve as proxy data for the GOES-R Geostationary Lightning Mapper (GLM), which will help determine the optimal feature tracking strategy for GLM's sampling characteristics. The resulting precipitation estimates will provide a radar-like loop, rather than a single static image, which would be beneficial to forecasters in regions with limited radar coverage (i.e. NWS Pacific Region, Tropical Analysis & Forecast Branch, and Ocean Prediction Center). Figure: (Top) Lightning density observed using the DCLMA on 13 JUN 2013. Red barbs indicate the estimated motion of the primary convective cores. (Bottom) A propagated AMSR2 precipitation retrieval approximately 45 minutes after initial satellite overpass.

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