Hugo Kyo Lee

California Institute of Technology | CIT · Jet Propulsion Laboratory

Climate science produces a wealth of complex, high-dimensional, multivariate data from observations and numerical models. These data are critical for understanding climate changes and their socioeconomic impacts. Climate scientists are continuously evaluating output from numerical models against observations. This model evaluation process provides...

Dynamics of many complex systems, from weather and climate to spread of infectious diseases, can be described by partial differential equations (PDEs). Such PDEs involve unknown function(s), partial derivatives, and typically multiple independent variables. The traditional numerical methods for solving PDEs assume that the data are observed on a re...

Severe drought and wildfires have become grim indicators of the severity of the weather and climate extremes our planet is increasingly facing. The latest Intergovernmental Panel on Climate Change (IPCC) report describes the unprecedented rate at which the global climate has warmed in the last 200 years (UN News 2021), resulting in increasing ocean...

Virtually all aspects of our societal functioning—from food security to energy supply to healthcare—depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to...

Abstract: NASA’s Earth observing instruments have provided comprehensive observations of wildfires and smoke plumes from wildfires. At present, JPL’s Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery (SIT-FUSE), utilizes unsupervised and self-supervised deep learning (DL) to segment instances of objects like wildfires and...

Digital twins emerge as the new pivotal tools for understanding and modeling a broad range of Earth Science processes. These digital representations which intend to mirror the real world physical counterparts typically ingest Earth Science data from varied informational sources. This in turn raises the issues of quality and integrity in the consume...

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this...

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run cl...

Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Inte...

Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose...

At present, to detect and track wildfires and associated smoke plumes, instrument-specific retrieval algorithms need to be developed and applied to individual instruments. Such development is labor intensive and requires domain-specific parameters and instrument-specific calibration metrics, alongside manual efforts to track retrieved objects acros...

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages...

This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and...

At present, to detect and track wildfires and associated smoke plumes, instrument-specific retrieval algorithms need to be developed and applied to individual instruments. Such development is labor intensive and requires domain-specific parameters and instrument-specific calibration metrics, alongside manual efforts to track retrieved objects acros...

The ability of a suite of models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate observed large-scale atmospheric circulation patterns over the Pacific Northwest of North America is evaluated. Twelve representative patterns of 500-hPa geopotential height (Z500) anomalies are identified using the self...

Observation-based climate model evaluation and future projections help policymakers in developing action plans for efficient management of water resources and mitigation of the impacts of hazardous extremes. Apart from this socioeconomic importance, the scientific value cannot be overstated, especially in light of the upcoming Fifth U.S. National C...

Increasing severity of extreme heat is a hallmark of climate change. Its impacts depend on temperature but also on moisture and solar radiation, each with distinct spatial patterns and vertical profiles. Here, we consider these variables’ combined effect on extreme heat stress, as measured by the environmental stress index, using a suite of high-re...

Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting, and their resulting ensemble means, to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The analysis makes u...

Under greenhouse warming, extreme weather conditions that favor wildfire ignition are expected to occur more frequently at many locations around the world (Abtazoglou et al., 2018). Large-scale wildfires fueled by hotter and drier weather conditions can cause irreversible environmental damage, via massive emissions of smoke aerosols that can signif...

This paper proposes a visual analytics framework that addresses the complex user interactions required through a command-line interface to run analyses in distributed data analysis systems. The visual analytics framework facilitates the user to manage access to the distributed servers, incorporate data from the source, run data-driven analysis, mon...

As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the machine learning community,...

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measureme...

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is...

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distributions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, temperature, and air q...

The current study investigates the diurnal cycle of dust and dust mixture loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other sat...

The detection and tracking of objects, like smoke plumes, within a single instrument's data has long required the development of instrument-specific retrieval algorithms. Such development is labor intensive and requires domain-specific parameters and instrument-specific calibration metrics, alongside manual effort to track retrieved objects across...

Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. Thi...

The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud–Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observati...

This study utilizes Bayesian Model Averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We evaluate...

Plain Language Summary North African deserts have been reported to export ~200 million tons of dust per year to the tropical Atlantic Ocean, degrading air quality over the Caribbean Islands in boreal summer and supplying nutrients to fertilize the Amazon Rainforest in boreal winter and spring through transatlantic dust transport. It has been assume...

A 30-year climatology of lightning days and associated synoptic meteorological patterns are characterized across the interior Western United States (WUS). Locally centered composite analyses show preferred synoptic meteorological patterns with positive 500-hPa geopotential height anomalies located to the northeast and negative sea level pressure an...

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The level 2 (swath...

Abstract. The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite...

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 20...

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The Level 2 (swath...

Key spatiotemporal patterns of monthly scale temperature variability are characterized over southern South America using k‐means clustering. The resulting clusters reveal patterns of temperature variability, referred to as temperature variability states. Analysis is performed over summer and winter months separately using data covering the period 1...

Thermal infrared spectra can provide unique information on atmospheric ammonia enhancements, including those produced by agricultural, industrial, and biomass burning sources. In this work, we develop and test a quantitative ammonia retrieval algorithm using data from the airborne hyperspectral thermal emission spectrometer (HyTES) instrument over...

Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) mul...

The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed t...

This study used the nearly continuous 17-year observation record from the Multi- angle Imaging SpectroRadiometer (MISR) instrument on the National Aeronautics and Space Administration (NASA) Terra Earth Observing System satellite to determine which temporal subsets are long enough to define statistically stable speciated aerosol optical depth (AOD)...

The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed t...

Motivated by limitations in current satellite-based dust source identification techniques, this study examines dust emissions and concentrations over North Africa and the Middle East during 2000–2016 by analyzing stereo and optical products from the Multiangle Imaging SpectroRadiometer instrument on the National Aeronautics and Space Administration...

Aerosol plumes from wildfires affect the Earth's climate system through regulation of the radiative budget and clouds. However, optical properties of aerosols from individual wildfire smoke plumes and their resultant impact on regional climate are highly variable. Therefore, there is a critical need for observations that can constrain the partition...

The associations between extreme temperature months and four prominent modes of recurrent climate variability are examined over South America. Associations are computed as the percent of extreme temperature months concurrent with the upper and lower quartiles of the El Niño Southern Oscillation (ENSO), the Atlantic Niño, the Pacific Decadal Oscilla...

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by t...

Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12...

This study investigated the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA Unified-Weather Research and Forecasting (NU-WRF) model for June, July, and August of 2000....

Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article...

The Multi-angle Imaging SpectroRadiometer (MISR) Joint Aerosol (JOINT_AS) Level 3 product has provided a global, descriptive summary of MISR Level 2 aerosol optical depth (AOD) and aerosol type information for each month over 16+ years since March 2000. Using Version 1 of JOINT_AS, which is based on the operational (Version 22) MISR Level 2 aerosol...

Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975-2004 and 1955-2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over cen...

The Multi-angle Imaging SpectroRadiometer (MISR) Joint Aerosol (JOINT_AS) Level 3 product provides a global, descriptive summary of MISR Level 2 aerosol optical depth (AOD) and aerosol type information for each month between March 2000 and the present. Using Version 1 of JOINT_AS, which is based on the operational (Version 22) MISR Level 2 aerosol...

According to long-term sea level reconstruction and steric sea level data, regional sea levels in the tropical Pacific have oscillated between east and west on a decadal time scale over the past 60 years, but the oscillation has been intensified significantly in the last three decades. Using conditional composite analysis, we show that the recent i...

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies i...

Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential heigh...

Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanal...