1.3 Lightning Observations from ISS LIS and GOES-R GLM and Applications in Rainfall Estimation

Wednesday, 10 January 2018: 12:00 AM
Ballroom G (ACC) (Austin, Texas)
Weixin Xu, Colorado State Univ., Fort Collins, CO; and S. A. Rutledge and R. Adler

It is widely recognized and accepted that spaceborne lightning observations provide a wealth of information regarding the dynamical and microphysical characteristics of convection. Lightning information also has great potential for improving the estimation of convective rainfall. The recent launches of the Global Lightning Mapper (GLM) onboard the GOES-R (now GOES-16) and the Lightning Imaging Sensor (LIS) to the ISS provide an excellent opportunity to apply lightning observations for enhancing satellite rainfall estimation. We have quantified the relationships between lightning, convective rain volume, cold cloud coverage, and bulk precipitation microphysics for both tropical and mid-latitude convection (warm season) based on observations from TRMM. These statistical relationships have been used to develop a lightning-enhanced IR (LenIR) rainfall estimation algorithm. The application of the LenIR algorithm to an independent TRMM IR and lightning database showed significant improvement compared to IR-based rainfall estimation when validated by TRMM passive microwave rainfall estimates. We are now refining the LenIR algorithm by using lightning data from GLM and ISS-LIS and IR data from the GOES-16 Advanced Baseline Imager (ABI). LenIR algorithm is further improved by including environmental variables such as wind shear between 850 and 200 hPa, CAPE, and total precipitable water (850-500 hPa) as derived from reanalysis data. Our updated lightning-enhanced rainfall estimation algorithm will be applied to an independent GOES-16 ABI/GLM and ISS-LIS dataset and validated against rain estimates from GPM, rain gauge, and ground-based radar (using the NEXRAD NMQ product). This effort will make the geosynchronous satellite precipitation information uniquely important, providing more accurate (and highly time resolved) precipitation estimates for supplementing ground-based and orbital satellite-based estimates, especially in remote mountainous areas and over the open oceans.
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