Recent Developments in Reanalysis at NASA GMAO From MERRA-2 to GEOS-R21C and MERRA-3
Explore the source record for details and available documents.
Amal El Akkraoui↗
Engineering topics
William Robert Mccarty
Publications and source records attributed to William Robert Mccarty.
Assimilation of microwave satellite observations over the rainbands of tropical cyclones
The potential of microwave observations in improving the weather forecasts is limited by the accuracy of all-sky radiative transfer calculations. We introduce a novel Bayesian MonteCarlo technique to improve the assimilation of microwave observations over the rainbands of tropical cyclones. The BMCI technique eliminates the need for a forward model in the data assimilation system. The technique includes three steps, (i) generating a comprehensive dataset using in-situ cloud measurements and atmospheric profiles, (ii) generating synthetic ATMS observations from the training dataset, and (iii) using real observations to estimate the geophysical variables over the rainbands of tropical cyclones. The retrieved profiles of temperature, relative humidity, and cloud water content as well assurface information such as SST were then assimilated into the model. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns.
Monte Carlo↗
GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery
Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.
Geostationary satellites↗
Deep Learning System for Efficient Processing of Geostationary Satellite Imagery
Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.
Thomas Vandal↗
Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow
Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.
Atmospheric winds↗