Artificial Intelligence for the Earth Systems: Recent submissions
Now showing items 141-160 of 170
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Reducing Southern Ocean Shortwave Radiation Errors in the ERA5 Reanalysis with Machine Learning and 25 Years of Surface Observations
(American Meteorological Society, 2023) -
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation
(American Meteorological Society, 2023) -
A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science
(American Meteorological Society, 2023) -
Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models
(American Meteorological Society, 2023) -
Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network
(American Meteorological Society, 2023) -
Emulating Rainfall–Runoff-Inundation Model Using Deep Neural Network with Dimensionality Reduction
(American Meteorological Society, 2023) -
Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets
(American Meteorological Society, 2023) -
Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning
(American Meteorological Society, 2022)A deep learning model is presented to nowcast the occurrence of lightning at a 5-min time resolution 60 min into the future. The model is based on a recurrent-convolutional architecture that allows it to recognize and ... -
Improvements to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An Artificial Neural Network Approach
(American Meteorological Society, 2022)NOAA global surface temperature (NOAAGlobalTemp) is NOAA’s operational global surface temperature product, which has been widely used in Earth’s climate assessment and monitoring. To improve the spatial interpolation of ... -
Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?
(American Meteorological Society, 2022)In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) ... -
Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency
(American Meteorological Society, 2022)A wealth of forecasting models is available for operational weather forecasting. Their strengths often depend on the lead time considered, which generates the need for a seamless combination of different forecast methods. ... -
Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks
(American Meteorological Society, 2022)Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, ... -
Automated Identification of “Dunkelflaute” Events: A Convolutional Neural Network–Based Autoencoder Approach
(American Meteorological Society, 2022)As wind and solar power play increasingly important roles in the European energy system, unfavorable weather conditions, such as “Dunkelflaute” (extended calm and cloudy periods), will pose ever greater challenges to ... -
Editorial
(American Meteorological Society, 2022) -
Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience
(American Meteorological Society, 2022)Convolutional neural networks (CNNs) have recently attracted great attention in geoscience because of their ability to capture nonlinear system behavior and extract predictive spatiotemporal patterns. Given their black-box ... -
Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning
(American Meteorological Society, 2022)We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using ... -
Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps
(American Meteorological Society, 2022)An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations ... -
The Pairwise Similarity Partitioning Algorithm: A Method for Unsupervised Partitioning of Geoscientific and Other Datasets Using Arbitrary Similarity Metrics
(American Meteorological Society, 2022)A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, nonoverlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a ... -
Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm
(American Meteorological Society, 2022)Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these ... -
Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model
(American Meteorological Society, 2022)A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate ...