Augmenting satellite precipitation estimation with lightning information

• Published in: International journal of remote sensing Vol. 34; no. 16; pp. 5796 - 5811
• Main Authors: Mahrooghy, Majid, Anantharaj, Valentine G, Younan, Nicolas H, Petersen, Walter A, Hsu, Kuo-Lin, Behrangi, Ali, Aanstoos, James
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 2013
• Subjects: Animal, plant and microbial ecology; Applied geophysics; artificial neural network; Biological and medical sciences; Clouds; correlation; Earth sciences; Earth, ocean, space; Exact sciences and technology; freezing; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; ice; image analysis; Internal geophysics; Lightning; machine learning; neural networks; Precipitation; probability; Rain; Rainfall; remote sensing; satellite rainfall estimation; Seasons; spring; summer; Teledetection and vegetation maps; temperature; Thresholds; Winter; maps; atmospheric precipitation; Estimation; Space remote sensing; Infrared observation; neural networks; satellite measurements; clouds; classification; windows; Information; artificial intelligence; System; Rainfall rate; Relation; Self organization; brightness temperature; Lightning; imagery; Threshold
• DOI: 10.1080/01431161.2013.796100
• EISSN: 1366-5901
• ISSN: 1366-5901; 0143-1161

We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T–R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T–R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour⁻¹). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour⁻¹) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11–13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate.