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1. Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud Tracking and GPM Active and Passive Microwave Measurements

   https://doi.org/10.1175/JHM-D-23-0185.1  

   Guilloteau, Clément ; Foufoula-Georgiou, Efi ( May 2024 , Journal of Hydrometeorology)

   Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPMCore Observatorysatellite are used in synergy with cloud tracking information derived from infrared imagery from theGOES-13andMeteosat-7geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

    

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2. Deep Learning for Precipitation Retrievals Using ABI and GLM Measurements on the GOES-R Series

   https://doi.org/10.1109/TGRS.2023.3322352  

   Yang, Yifan ; Chen, Haonan ; Hilburn, Kyle A ; Kuligowski, Robert J ; Cifelli, Robert ( January 2023 , IEEE Transactions on Geoscience and Remote Sensing)

   Satellite sensors have been widely used for precipitation retrieval, and a number of precipitation retrieval algorithms have been developed using observations from various satellite sensors. The current operational rainfall rate quantitative precipitation estimate (RRQPE) product from the geostationary operational environmental satellite (GOES) offers full disk rainfall rate estimates based on the observations from the advanced baseline imager (ABI) aboard the GOES-R series. However, accurate precipitation retrieval using satellite sensors is still challenging due to the limitations on spatio-temporal sampling of the satellite sensors and/or the uncertainty associated with the applied parametric retrieval algorithms. In this article, we propose a deep learning framework for precipitation retrieval using the combined observations from the ABI and geostationary lightning mapper (GLM) on the GOES-R series to improve the current operational RRQPE product. Particularly, the proposed deep learning framework is composed of two deep convolutional neural networks (CNNs) that are designed for precipitation detection and quantification. The cloud-top brightness temperature from multiple ABI channels and the lightning flash rate from the GLM measurement are used as inputs to the deep learning framework. To train the designed CNNs, the precipitation product multiradar multi-sensor (MRMS) system from the National Oceanic and Atmospheric Administration (NOAA) is used as target labels to optimize the network parameters. The experimental results show that the precipitation retrieval performance of the proposed framework is superior to the currently operational GOES RRQPE product in the selected study domain, and the performance is dramatically enhanced after incorporating the lightning data into the deep learning model. Using the independent MRMS product as a reference, the deep learning model can reduce the retrieval uncertainty in the operational RRQPE product by at least 31% in terms of the mean squared error and normalized mean absolute error, and the improvement is more significant in moderate to heavy rain regions. Therefore, the proposed deep learning framework can potentially serve as an alternative approach for GOES precipitation retrievals. 

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3. The Changing Nature of Convection over Earth’s Tropical Oceans from a Water Budget Perspective

   https://doi.org/10.1175/JCLI-D-23-0500.1  

   Leitmann-Niimi, Nicolas_M ; Kummerow, Christian_D ; Hsiao, Wei-Ting ; Maloney, Eric_D ( January 2025 , Journal of Climate)

   The water budget components of an atmospheric column are precipitation, evaporation, and horizontal water vapor divergence. This study finds that when precipitation from the Global Precipitation Climatology Project (GPCP), evaporation from the SeaFlux product, and water vapor divergence from ERA5 are employed, the degree of budget closure depends strongly on the location and time period. Variations in this error are not random, and this study seeks to better understand these biases as the climate system evolves. Errors are particularly significant over ocean regions in and near the west Pacific warm pool, where there are multiyear budget residuals of roughly 10% of the magnitude of precipitation. Biases in other tropical ocean basins are more seasonal and smaller in magnitude. Time-varying budget errors are strongly linked to variations in convective organization that vary with the large-scale environment; errors correlating with deep organized rain show coefficients of 0.62 and 0.56 in the west Pacific and central Pacific, respectively. Errors are linked to a lesser extent with cloud microphysical structures in the East Indian. Both factors affect rainfall retrievals through well-known precipitation bias mechanisms (beam-filling, convective/stratiform effects). Characteristics of the large-scale environment that produce changes in convective organization and precipitable ice water content are explored.

    

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4. Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

   https://doi.org/10.5194/amt-14-2699-2021  

   Lee, Yoonjin ; Kummerow, Christian D. ; Ebert-Uphoff, Imme ( January 2021 , Atmospheric Measurement Techniques)

   Abstract. An ability to accurately detect convective regions isessential for initializing models for short-term precipitation forecasts.Radar data are commonly used to detect convection, but radars that providehigh-temporal-resolution data are mostly available over land, and the qualityof the data tends to degrade over mountainous regions. On the other hand,geostationary satellite data are available nearly anywhere and in near-realtime. Current operational geostationary satellites, the GeostationaryOperational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible andinfrared data even without vertical information of the convective system.Existing detection algorithms using visible and infrared data look forstatic features of convective clouds such as overshooting top or lumpy cloudtop surface or cloud growth that occurs over periods of 30 min to anhour. This study represents a proof of concept that artificial intelligence(AI) is able, when given high-spatial- and high-temporal-resolution data fromGOES-16, to learn physical properties of convective clouds and automate thedetection process. A neural network model with convolutional layers is proposed to identifyconvection from the high-temporal resolution GOES-16 data. The model takesfive temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) asinputs and produces a map of convective regions. In order to provideproducts comparable to the radar products, it is trained against Multi-RadarMulti-Sensor (MRMS), which is a radar-based product that uses a rathersophisticated method to classify precipitation types. Two channels fromGOES-16, each related to cloud optical depth (channel 2) and cloud topheight (channel 14), are expected to best represent features of convectiveclouds: high reflectance, lumpy cloud top surface, and low cloud toptemperature. The model has correctly learned those features of convectiveclouds and resulted in a reasonably low false alarm ratio (FAR) and highprobability of detection (POD). However, FAR and POD can vary depending onthe threshold, and a proper threshold needs to be chosen based on thepurpose. 

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5. Precipitation Retrieval Using ABI and GLM Measurements on the Goes-R Series

   https://doi.org/10.1109/IGARSS52108.2023.10283405  

   Yang, Yifan ; Hu, Alexander C ; Chen, Haonan ; Hilburn, Kyle A ( July 2023 , IEEE)

   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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