Kyle Hilburn
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View article: Transparent Machine Learning: Training and Refining an Explainable Boosting Machine to Identify Overshooting Tops in Satellite Imagery
Transparent Machine Learning: Training and Refining an Explainable Boosting Machine to Identify Overshooting Tops in Satellite Imagery Open
An Explainable Boosting Machine (EBM) is an interpretable machine learning (ML) algorithm that has benefits in high risk applications but has not yet found much use in atmospheric science. The overall goal of this work is twofold: (1) expl…
View article: Measuring Sharpness of AI-Generated Meteorological Imagery
Measuring Sharpness of AI-Generated Meteorological Imagery Open
AI-based algorithms are emerging in many meteorological applications that produce imagery as output, including for global weather forecasting models. However, the imagery produced by AI algorithms, especially by convolutional neural networ…
View article: Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone Applications
Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone Applications Open
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC)…
View article: SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale Open
We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims to enhance short-term convective-scale forecasts of high-im…
View article: Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts Open
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides t…
View article: Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS
Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS Open
Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and …
View article: An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability across the Continental United States
An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability across the Continental United States Open
Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution R…
View article: Analysis of methods for assimilating fire perimeters into a coupled fire-atmosphere model
Analysis of methods for assimilating fire perimeters into a coupled fire-atmosphere model Open
Correctly initializing the fire within coupled fire-atmosphere models is critical for producing accurate forecasts of meteorology near the fire, as well as the fire growth, and plume evolution. Improperly initializing the fire in a coupled…
View article: Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts Open
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides t…
View article: Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN
Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN Open
Convolutional neural networks (CNNs) are opening new possibilities in the realm of satellite remote sensing. CNNs are especially useful for capturing the information in spatial patterns that is evident to the human eye but has eluded class…
View article: Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks
Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks Open
Atmospheric processes involve both space and time. Thus, humans looking at atmospheric imagery can often spot important signals in an animated loop of an image sequence not apparent in an individual (static) image. Utilizing such signals w…
View article: Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks Open
Atmospheric processes involve both space and time. This is why human analysis of atmospheric imagery can often extract more information from animated loops of image sequences than from individual images. Automating such an analysis require…
View article: Application of the GOES-16 Advanced Baseline Imager: Morphology of a Preconvective Environment on 17 April 2019
Application of the GOES-16 Advanced Baseline Imager: Morphology of a Preconvective Environment on 17 April 2019 Open
Thunderstorms formed in the afternoon of 17 April 2019 over northern Mexico. Satellite data are used to highlight several features associated with convective preconditioning over portions of Coahuila, Mexico. Satellite imagery was used to …
View article: CIRA Guide to Custom Loss Functions for Neural Networks in Environmental\n Sciences -- Version 1
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental\n Sciences -- Version 1 Open
Neural networks are increasingly used in environmental science applications.\nFurthermore, neural network models are trained by minimizing a loss function,\nand it is crucial to choose the loss function very carefully for environmental\nsc…
View article: CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1 Open
Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choose the loss function very carefully for environmental scien…
View article: Machine Learning Estimation of Fire Arrival Time from Level-2 Active Fires Satellite Data
Machine Learning Estimation of Fire Arrival Time from Level-2 Active Fires Satellite Data Open
Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progressi…
View article: Simple finite elements and multigrid for efficient mass-consistent wind downscaling in a coupled fire-atmosphere model
Simple finite elements and multigrid for efficient mass-consistent wind downscaling in a coupled fire-atmosphere model Open
We present a simple finite element formulation of mass-consistent approximation, and a fast multigrid iterative method with adaptive semicoarsening, which maintains the convergence of the iterations over a range of grids and penalty coeffi…
View article: Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations
Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations Open
The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one appr…
View article: Evaluating Geostationary Lightning Mapper Flash Rates Within Intense Convective Storms
Evaluating Geostationary Lightning Mapper Flash Rates Within Intense Convective Storms Open
The Geostationary Lightning Mapper (GLM) marks the first time that lightning observations at storm‐scale resolution are operationally available from geostationary orbit. We evaluate GLM detection efficiency (DE) for a special class of conv…
View article: Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications Open
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translatio…
View article: Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery
Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery Open
Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction…
View article: Geostationary Lightning Mapper and Earth Networks Lightning Detection Over the Contiguous United States and Dependence on Flash Characteristics
Geostationary Lightning Mapper and Earth Networks Lightning Detection Over the Contiguous United States and Dependence on Flash Characteristics Open
This study compared the detection capabilities of the Geostationary Lightning Mapper (GLM) and ground‐based Earth Networks Total Lightning Network (ENL) over the contiguous United States (CONUS) from 25 April 2017 to 5 May 2018. GLM detect…
View article: Towards Objective Identification and Tracking of Convective Outflow Boundaries in Next-Generation Geostationary Satellite Imagery
Towards Objective Identification and Tracking of Convective Outflow Boundaries in Next-Generation Geostationary Satellite Imagery Open
Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction…
View article: Retrieving Fire Perimeters and Ignition Points of Large Wildfires from Satellite Observations
Retrieving Fire Perimeters and Ignition Points of Large Wildfires from Satellite Observations Open
We present a new statistical interpolation method to estimate fire perimeters from Active Fires detection data from satellite-based sensors, such as MODIS, VIIRS, and GOES-16. Active Fires data is available at varying temporal and spatial …
View article: Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm
Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm Open
A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is co…
View article: Retraction: “AMSR2 calibration: Intercomparison of RSS and JAXA brightness temperatures” by Kyle A. Hillburn and Chelle L. Gentleman
Retraction: “AMSR2 calibration: Intercomparison of RSS and JAXA brightness temperatures” by Kyle A. Hillburn and Chelle L. Gentleman Open
This article has been retracted by the authors because necessary permission to use the RSS data reported in Table 1 and Figure 2 was not obtained.
View article: An intensified seasonal transition in the Central U.S. that enhances summer drought
An intensified seasonal transition in the Central U.S. that enhances summer drought Open
In the long term, precipitation in the Central U.S. decreases by 25% during the seasonal transition from June to July. This precipitation decrease has intensified since 1979 and such intensification could have enhanced spring drought occur…
View article: The Observed State of the Energy Budget in the Early Twenty-First Century
The Observed State of the Energy Budget in the Early Twenty-First Century Open
New objectively balanced observation-based reconstructions of global and continental energy budgets and their seasonal variability are presented that span the golden decade of Earth-observing satellites at the start of the twenty-first cen…
View article: The Observed State of the Water Cycle in the Early Twenty-First Century
The Observed State of the Water Cycle in the Early Twenty-First Century Open
This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first an…