A Kalman filter model to estimate precipitation employing Cloud-to-Ground Lightning Events en Complex Terrain

Traditionally, precipitation has been estimated by rain gauges and radar, however these two techniques have their own strengths and weaknesses, hence none of them is free of error in estimating Quantitative Precipitation Estimation (QPE). Over the last 30 years authors have proposed several remote sensing techniques to estimate QPE, among them is the relationship between lightning and precipitation. However, the weather services over the world are still using lightning as an indicator of convective precipitation and not as an actual estimator. In this presentation we will show the development process of a model to estimate precipitation (at high space and time resolutions (5km and 5 minutes)) by using cloud-to-ground lightning events. Such a process evolves from the use of fixed time and space neighbors, to a dynamic Kalman filter which allows varying the model in time, and lastly to the newer model that considers the kalman filter and additionally considers the correct residuals- the latter which results in a significant error reduction (>50%) in QPE.