An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

Abstract

A lightning-precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial-and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

Algorithm for Improved QPE over Complex Terrain Using Cloud-to-Ground Lightning Occurrences

“…In [23], the authors improved their methodology for QPE estimation by using lightning at a space and time resolution of 5 km and 5 min. In order to address the time and space displacement that occurs when LPR is correlated at high resolutions, a linear space and time-invariant model was developed, leading to improved results in comparison to the two parameter model in the 2012 study.…”

“…With the aim of contributing to the development and promoting the use of lightning for estimating precipitation, this article presents, in detail, the algorithm developed by Minjarez and co-authors [23] to estimate QPE by cloud-to-ground lightning, as well as the methodology developed to test the model and select the parameters that were employed in the 2017 study.…”

“…To these data, others have added climatologies of orographic precipitation [21] to produce a multi-sensor QPE (MQPE) and improve QPE accuracy. However, even with this combination of all these data sources and error reduction methodologies, precipitation estimation still suffers from problems with accuracy in regions with complex terrain [21][22][23].…”

“…In the last three decades, other techniques, such as satellite-derived precipitation, have been proposed and incorporated for QPE. Satellite-derived precipitation is not limited by coverage problems in mountainous regions as radars are, but it has its deficiencies [23]. Mehran and AghaKouchak [24], in comparing satellite QPE with products like PERSIANN [25], CMORPH [26], and TRMM Multi-Satellite Precipitation Analysis Real Time (TMPA-RT) [27] revealed errors for strong precipitation events associated with topography; that is, precisely where multi-sensor methods also produce inaccuracies.…”

“…Another limitation is that, at least for quantitative precipitation forecasting (QPF), its temporal resolution and latency (which is about 15 min) make these products useful for short-term QPF [28], but not for better precipitation nowcasts. Even with existing modern techniques and methods for QPE, the problems with precipitation estimation persist, especially in regions with poor sensor coverage such as mountainous terrain [22][23][24] where rainfall can be more frequent and copious.…”