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Accurate precipitation retrieval using satellite sensors is still challenging due to the limitations on spatio-temporal sampling of the applied parametric retrieval algorithms. In this research, we propose a deep learning framework for precipitation retrieval using the observations from Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM) on GOES-R satellite series. In particular, two deep Convolutional Neural Network (CNN) models are designed to detect and estimate the precipitation using the cloud-top brightness temperature from ABI and lightning flash rate from GLM. The precipitation estimates from the ground-based Multi-Radar/Multi-Sensor (MRMS) system are used as the target labels in the training phase. The experimental results show that in the testing phase, the proposed framework offers more accurate precipitation estimates than the current operational Rainfall Rate Quantitative Precipitation Estimate (RRQPE) product from GOES-R.
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Correcting for Undercounting of Lightning Flashes by Space‐Based Optical Sensors
Abstract Accurate measurements of global lightning are essential for understanding present and future atmospheric electricity, composition, and climate. The latest space‐based lightning detector, the Geostationary Lightning Mapper (GLM), was the first to be placed in geostationary orbit, with a continuous view of most of the American continents. Prior to the GLM, the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite collected lightning measurements from which numerous lightning climatologies have been developed, including those used in global models. However, this study finds that both the GLM and a second, similar LIS placed on the International Space Station (ISS) in 2017 detect lightning at similar rates and are undercounting lightning compared to ground‐based Lightning Mapping Arrays (LMAs). The GLM undercounts lightning by an average factor of 7.0, reaching a maximum over 120 as a function of satellite zenith angle, radar reflectivity at a height where the temperature is −10°C, flash height, and thunderstorm polarity. The LIS is estimated to undercount lightning by an average factor of 5.6, reaching a maximum of 75.0 as a function of radar reflectivity at the −10°C level, flash height, and thunderstorm polarity. Preliminary predictive equations for the GLM and LIS lightning undercount factor, or scaling factor (SF), use ice‐water content, equilibrium level, flash height, and satellite zenith angle, all of which can be derived in models. These equations are developed to encourage updating lightning parameterizations within global models and will likely increase modeled lightning's effects on atmospheric electrical circuits, composition, chemistry, and climate change.
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- Award ID(s):
- 2134961
- PAR ID:
- 10480206
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 128
- Issue:
- 24
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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