Extending the Capabilities of High-Frequency Rainfall Estimation from Geostationary-Based Satellite Infrared via a Network of Long-Range Lightning Observations

Abstract

An algorithm for real-time precipitation estimation that combines satellite infrared with long-range lightning network observations is developed. The emphasis is on enhancing current capabilities in continuous rainfall monitoring over large regions at high spatiotemporal resolutions and in separating precipitation type into its convective and stratiform components. Lightning information is retrieved from an experimental long-range very low frequency radio receiver network named the Sferics Timing and Ranging Network. Parameterizations for delineating the total rain area and its convective portion as well as convective and stratiform rain-rate relationships are obtained for lightning (LTG) and lightning-free (NLTG) clouds. The procedure accounts for differences in land versus ocean and for various levels of cloud system maturity. The parameters are evaluated using as reference the most definitive precipitation fields and rain classification estimates derived from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The algorithm is evaluated based on independent PR estimates and measurements from a rain gauge network in Florida. Overall, the algorithm underestimates rain area with respect to PR for LTG and NLTG clouds by about 20%, while for the rain volume there is an overestimation of ?19% for LTG and ?12% for NLTG clouds. Comparison of hourly estimates with rain gauges revealed an overall overestimation of 6% at 0.1° scale. At monthly scales, the biases are 2.4% and 0.27% for 1° and 2° resolutions. The significance of lightning information on rainfall estimation accuracy is investigated by applying the proposed technique without lightning information. The hypothesis made is that lightning measurement that is associated with ice aloft can provide better identification of the convective area, which could contribute to improving precipitation estimation. Indeed, comparisons with the PR showed that in rain area determination there is an overall bias reduction of 31% by using lightning information. In rain gauge comparisons, the bias reduction from incorporating lightning data is 87% for the hourly 0.1° estimates. In regards to correlation, the increase in hourly estimates varies from 0.13 to 0.03 for scales ranging from 0.1° to 1°.