Timothy J Lang

and 21 more

The Lightning Imaging Sensor (LIS) was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm-scale horizontal resolution during both day and night. ISS LIS data are available beginning 1 March 2017. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground-based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51-75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low-Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers. ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as realtime lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long-term global climatology of lightning from space, and is the only recent platform that extends the global record to higher latitudes (± 55). The global spatial distribution of lightning from ISS LIS is broadly similar to previous datasets, with globally averaged seasonal/annual flash rates about 5-10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed.

Talha Khan

and 7 more

Airborne dust, including Dust storms and weaker dust traces, can have deleterious and hazardous effects on human health, agriculture, solar power generation, and aviation. Although earth observing satellites are extremely useful in monitoring dust using visible and infrared imagery, dust is often difficult to visually identify in single band imagery due to its similarities to clouds, smoke, and underlying surfaces. Furthermore, night-time dust detection is a particularly difficult problem, since radiative properties of dust mimic those of the cooling, underlying surface. The creation of false-color red-green-blue (RGB) composite imagery, specifically the EUMETSAT Dust RGB, was designed to enhance dust detection through the combination of single bands and band differences into a single composite image. However, dust is still often difficult to identify in night-time imagery even by experts. We developed a Deep Learning, UNET image segmentation model to identify airborne dust at night leveraging six GOES-16 infrared bands, with a focus on infrared and water vapor bands.The UNET model architecture is an encoder-decoder Convolutional Neural Network that does not require large amounts of training data, localizes and contextualized image data for precise segmentation, and provides fast training time for high accuracy pixel level prediction. This presentation highlights collection of the training database, development of the model, and preliminary model validation. With further model development, validation, and testing in a real-time context, probability-based dust prediction could alert weather forecasters, emergency managers, and citizens to the location and extent of impending dust storms.

Laura Jewell

and 12 more