UMBC Joint Center for Earth Systems Technology (JCET)

Permanent URI for this collectionhttp://hdl.handle.net/11603/7732

The Joint Center for Earth Systems Technology (JCET) was formed under a Cooperative agreement between the Earth Science Division of NASA Goddard Space Flight Center (GSFC) and the University of Maryland, Baltimore County (UMBC) in 1995. The JCET family consists of its business staff, students, research faculty, GSFC Sponsors, appointed fellows and affiliated tenured/tenure-track faculty. JCET is an innovative center where these scientists interact in a seamless fashion with the support of an efficient business and administrative unit.

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Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible
(MDPI, 2025-03-17) Hou, Weizhen; Liu, Xiong; Wang, Jun; Chen, Cheng; Xu, Xiaoguang
In satellite remote sensing, mixed pixels commonly arise in medium- and low-resolution imagery, where surface reflectance is a combination of various land cover types. The widely adopted linear mixing model enables the decomposition of mixed pixels into constituent endmembers, effectively bridging spectral resolution gaps by retrieving the spectral properties of individual land cover types. This study introduces a method to enhance multispectral surface reflectance data by reconstructing additional spectral information, particularly in the visible spectral range, using the TROPOMI BRDF product generated by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. Employing non-negative matrix factorization (NMF), the approach extracts spectral basis vectors from reference spectral libraries and reconstructs key spectral features using a limited number of wavelength bands. The comprehensive test results show that this method is particularly effective in supplementing surface reflectance information for specific wavelengths where gas absorption is strong or atmospheric correction errors are significant, demonstrating its applicability not only within the 400–800 nm range but also across the broader spectral range of 400–2400 nm. While not a substitute for hyperspectral observations, this approach provides a cost-effective means to address spectral resolution gaps in multispectral datasets, facilitating improved surface characterization and environmental monitoring. Future research will focus on refining spectral libraries, improving reconstruction accuracy, and expanding the spectral range to enhance the applicability and robustness of the method for diverse remote sensing applications.
Key Governance Practices That Facilitate the Use of Remote Sensing Information for Wildfire Management: A Case Study in Spain
(MDPI, 2025-2-14) Prados, Ana; Allen, Mackenzie
We present results from a comprehensive analysis on the use of Earth Observations (EO) in Spain for wildfire risk management. Our findings are based on interviews with scientists, firefighters, forest engineers, and other professionals from government and private sector organizations in nine autonomous regions in Spain. Our aim is to identify the key governance practices facilitating or hindering the use of remote sensing (RS) information and to provide recommendations for improving their integration into landscape management and fire suppression activities to reduce wildfire risk. We share several case studies detailing activities and institutional arrangements facilitating the translation of satellite science and research into decision-making environments, with a focus on how this knowledge flows among the various stakeholder categories. Among the barriers faced by fire management teams in Spain, we identified institutional silos, lack of technical skills in satellite data processing and analysis, and the evolving acceptance of satellite data by decision makers.
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
(2025-03-08) Khan, Md Azim; Gangopadhyay, Aryya; Wang, Jianwu; Erbacher, Robert F.
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially when required to perform efficiently in real environments. This research presents a novel vision language model (VLM) framework that leverages frequency domain transformations and low-rank adaptation (LoRA) to enhance feature extraction, scalability, and efficiency. Unlike traditional VLMs, which rely solely on spatial-domain representations, our approach incorporates Discrete Fourier Transform (DFT) based low-rank features while retaining pretrained spatial weights, enabling robust performance in noisy or low visibility scenarios. We evaluated the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise. Quantitative results demonstrate that our model achieves evaluation metrics comparable to state-of-the-art VLMs, such as CLIP ViT-L/14 and SigLIP. Qualitative analysis further reveals that our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV).
Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction
(Cambridge University Press, 2025-03-10) Chakraborty, Sudip; Devnath, Maloy Kumar; Jabeli, Atefeh; Kulkarni, Chhaya; Boteju, Gehan; Wang, Jianwu; Janeja, Vandana
This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (~ 33,000 km²) during the presence of BC AARs as compared to ~13,000 km² during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.
ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS
(NASA GHRC, 2024-05-04) Guimond, Stephen
The ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS dataset consists of horizontal wind components, uncertainties in the horizontal wind components, and radar reflectivity collected by the EXRAD instrument onboard the NASA ER-2 aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic CoastThreatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023, No deployments occurred in 2021 due to COVID-19). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD 3D Winds IMPACTS dataset files are available from January 25 through February 7, 2020 in netCDF-3 format.
Enterprise Risk Magazine - Winter 2024
(Issuu, 2024-12-06) Byatt, Gareth; Kelman, Ilan; Prados, Ana
Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get them in front of Issuu’s millions of monthly readers. Title: Enterprise Risk Magazine - Winter 2024, Author: Institute of Risk Management, Length: 40 pages, Page: 1, Published: 2024-12-06
Dynamic Impact of the Southern Annular Mode on the Antarctic Ozone Hole Area
(MDPI, 2025-02-27) Lee, Jae N.; Wu, Dong L.
This study investigates the impact of dynamic variability of the Southern Hemisphere (SH) polar middle atmosphere on the ozone hole area. We analyze the influence of the southern annular mode (SAM) and planetary waves (PWs) on ozone depletion from 19 years (2005–2023) of aura microwave limb sounder (MLS) geopotential height (GPH) measurements. We employ empirical orthogonal function (EOF) analysis to decompose the GPH variability into distinct spatial patterns. EOF analysis reveals a strong relationship between the first EOF (representing the SAM) and the Antarctic ozone hole area (γ = 0.91). A significant negative lag correlation between the August principal component of the second EOF (PC2) and the September SAM index (γ = -0.76) suggests that lower stratospheric wave activity in August can precondition the polar vortex strength in September. The minor sudden stratospheric warming (SSW) event in 2019 is an example of how strong wave activity can disrupt the polar vortex, leading to significant temperature anomalies and reduced ozone depletion. The coupling of PWs is evident in the lag correlation analysis between different altitudes. A “bottom-up” propagation of PWs from the lower stratosphere to the mesosphere and a potential “top-down” influence from the mesosphere to the lower stratosphere are observed with time lags of 21–30 days. These findings highlight the complex dynamics of PW propagation and their potential impact on the SAM and ozone layer. Further analysis of these correlations could improve one-month lead predictions of the SAM and the ozone hole area.
Developing Advanced Cloud Retrievals for PACE: Building a Joint Spectro-Polarimetric Cloud Microphysics Retrieval
(NASA, 2024-12) Miller, Daniel J.; Meyer, Kerry; Platnick, Steven E.; Zhang, Zhibo; Ademakinwa, Adeleke; Sinclair, Kenneth; Alexandrov, Mikhail; Geogdzhayev, Igor; van Diedenhoven, Bastiaan
Developing a Lagrangian Frame Transformation on Satellite Data to Study Cloud Microphysical Transitions in Arctic Marine Cold Air Outbreaks
(2025-03-13) Seppala, Hannah; Zhang, Zhibo; Zheng, Xue
Arctic marine cold air outbreaks (CAOs) generate distinct and dynamic cloud regimes due to intense air-sea interactions. To understand the temporal evolution of CAO cloud properties and compare different CAO events, a Lagrangian perspective is particularly useful. We developed a novel technique that enables the conversion of inherently Eulerian satellite data into a Lagrangian framework, combining the broad spatiotemporal coverage of satellite observations with the advantages of Lagrangian tracking. This technique was applied to eight CAO cases associated with a recent field campaign. Our results reveal a striking contrast among the cases in terms of cloud-top phase transitions, providing new insights into the evolution of CAO cloud properties.
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
(2025-03-03) Hossain, Emam; Ferdous, Muhammad Hasan; Wang, Jianwu; Subramanian, Aneesh; Gani, Md Osman
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ causal discovery algorithms with a hybrid deep learning architecture. Using 43 years (1979-2021) of daily and monthly Arctic Sea Ice Extent (SIE) and ocean-atmospheric datasets, our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency. Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times. Beyond SIE prediction, the proposed framework offers a scalable solution for dynamic, high-dimensional systems, advancing both theoretical understanding and practical applications in predictive modeling.
Using Earth observations to avoid disasters
(UNDRR, 2024-10-11) Kelman, Ilan; Byatt, Gareth; Prados, Ana
Words Into Action requires evidence of disaster risk reduction (DRR) approaches and solid communication of that evidence through effective engagement with everyone in the science-policy-society ecosystem: policy makers, scientists and researchers, the private and non-profit sectors, the media, and ordinary people. Through our Disasters Avoided project, we are demonstrating the benefits of proactive DRR for everyone, including through the effective use of Earth observations, so that no one is left behind. Examples profiled here are wildland fires in Australia, cyclones in Bangladesh, and earthquakes across the east coast of the U.S.A. They show the importance of compiling, verifying, and sharing compelling good news of potential disasters which could have happened, but did not, because action was proactively taken based on knowledge and sound Earth observations data—as well as work remaining. We hope that these examples will inspire continuing action, based on repeated words, across all parts of society, including collaborative activities between Earth scientists and policy makers.
The airborne LUnar Spectral Irradiance (air-LUSI) Mission
(NASA, 2018-10-08) Turpie, Kevin; Brown, Steve; Woodward, John; Maxwell, Steve; Larason, Thomas; Zarobila, Clarence; Grantham, Steve; Gadsden, Andrew; Cataford, Andrew; Stone, Tom
The airborne LUnar Spectral Irradiance (air-LUSI) mission is a NASA Airborne Instrument Technology Transition (AITT) project. The goal of the AITT program is to mature airborne instruments from the demonstration phase to science-capable instruments.The USGS RObotic Lunar Observatory (ROLO) model represents the most precise knowledge of lunar spectral irradiance and is used frequently as a relative calibration standard for Earth observation by space-borne sensors (Keiffer and Stone, 2005). However, apparent phase-dependent biases in ROLO limits its application for absolute radiometric calibration. The objective of air-LUSI is to provide NASA a capability to improve ROLO by measuring exo-atmospheric lunar spectral irradiance with unprecedented accuracy. Careful characterization of the Moon from above the atmosphere will make it a stable and consistent SI-traceable absolute calibration reference. This could revolutionize lunar calibration for some Earth observing satellites and would be especially beneficial to ocean color missions. Because of the high sensitivity of aquatic remote sensing to calibration (Turpie et al., 2015), improvement of lunar calibration could directly affect upcoming PACE and JPSS (VIIRS) missions, and retrospectively for the SeaWiFS, EOS (MODIS), and S-NPP (VIIRS) data records.
Key Governance Practices That Facilitate the Use of Remote Sensing Information for Wildfire Management: A Case Study in Spain
(MDPI, 2025-01) Prados, Ana; Allen, Mackenzie
We present results from a comprehensive analysis on the use of Earth Observations (EO) in Spain for wildfire risk management. Our findings are based on interviews with scientists, firefighters, forest engineers, and other professionals from government and private sector organizations in nine autonomous regions in Spain. Our aim is to identify the key governance practices facilitating or hindering the use of remote sensing (RS) information and to provide recommendations for improving their integration into landscape management and fire suppression activities to reduce wildfire risk. We share several case studies detailing activities and institutional arrangements facilitating the translation of satellite science and research into decision-making environments, with a focus on how this knowledge flows among the various stakeholder categories. Among the barriers faced by fire management teams in Spain, we identified institutional silos, lack of technical skills in satellite data processing and analysis, and the evolving acceptance of satellite data by decision makers.
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
(45692) Shaw, Jonah K.; Swales, Dustin J.; Desouza-Machado, Sergio; Turner, David D.; Kay, Jennifer E.; Schneider, David P.
Infrared spectral radiation fields observed by satellites make up an information-rich, multi-decade record with continuous coverage of the entire planet. As direct observations, spectral radiation fields are also largely free from uncertainties that accumulate during geophysical retrieval and data assimilation processes. Comparing these direct observations with earth system models (ESMs), however, is hindered by definitional differences between the radiation fields satellites observe and those generated by models. Here, we present a flexible, computationally efficient tool called COSP-RTTOV for simulating satellitelike radiation fields within ESMs. Outputs from COSP-RTTOV are consistent with instrument spectral response functions and orbit sampling, as well as the physics of the host model. After validating COSP-RTTOV's performance, we demonstrate new constraints on model performance enabled by COSP-RTTOV. We show additional applications in climate change detection using the NASA AIRS instrument, and observing system simulation experiments using the NASA PREFIRE mission. In summary, COSP-RTTOV is a convenient tool for directly comparing satellite radiation observations with ESMs. It enables a wide range of scientific applications, especially when users desire to avoid the assumptions and uncertainties inherent in satellite-based retrievals of geophysical variables or in atmospheric reanalysis.
Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System (I): Assimilation Framework
(AMS, 2025-01-31) Zhu, Y.; Arnold, N. P.; Yang, E.-G.; Ganeshan, M.; Salmun, H.; Palm, S.; Santanello, J.; McGrath-Spangler, E. L.; Lewis, Jasper; Molod, A.; Wu, D.; Lei, T.; Akkraoui, A. El; Sienkiewicz, M.
In this study, a strategy and framework are developed to build a global Planetary Boundary Layer (PBL) height (PBLH) analysis and monitoring capability from multiple observing systems in the NASA Global Earth Observing System (GEOS) data assimilation system. To facilitate this effort, PBLH are derived from radiosonde and Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity data. As PBLH can be sensitive to potentially disparate observables and retrieval algorithms, new model PBLH definitions consistent with each observation type are added to the forecast model for the calculation of first guess departures from observations (OmF). These model definitions are augmented to the control variable vector, interacting with other control variables through flow-dependent ensemble background error covariance component. Moreover, to capture capping inversions, methods are explored using PBLH data to improve background error covariance through inflation of ensemble spread and adjustment of vertical localization length scale for virtual temperature and relative humidity variables. Experiments are conducted to assess the separate and combined impacts of these methods and the correlation relationships between PBLH and other control variables in the background error covariance. Preliminary results show that these changes are beneficial to the assimilation of other observations to improve the PBL thermodynamic structure.
Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression
(IEEE, 2025-01-16) Zhang, Olivia; Grissom, Brianna; Pulido, Julian; Munoz-Ordaz, Kenia; He, Jonathan; Cham, Mostafa; Jing, Haotong; Qian, Weikang; Wen, Yixin; Wang, Jianwu
Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods
Signal Processing of Images for Convective Boundary Layer Height Estimation from Radar (SPICER) and multi-instrument verification
(IEEE, 2025-01-13) Porta, Delia Tatiana Della; Demoz, Belay
The study of the planetary boundary layer (PBL) is one of the main topics of the atmospheric community. The current study presents a new algorithm for PBL height determination using a publicly available but unexplored data source, the Weather Service Radar (WSR-88D). The diurnal evolution of the PBL is also known as Convective Boundary Layer (CBL), key in the study of convection and precipitation. This paper presents the Signal Processing of Images for Convective Boundary Layer Height Estimation (SPICER) algorithm that can automatically detect the CBL Height (CBLH) for all of the 159 radar locations across the United States during clear days. The present work is the first step to applying SPICER to a network of Next Generation Radars (NEXRAD) with continuous countrywide coverage. With the possible combination with the Automated Surface Observing System network (ASOS), a source of ceilometer profile data, a validated dataset of CBLH estimates can be expected soon. The algorithm treats averaged differential reflectivity vs range as an image and applies filtering plus Canny edge detection to estimate the CBLH. In addition, another algorithm is presented to automate the detection of the mixing layer height (MLH), a proxy for CBLH from Raman Lidar and a 915 MHz wind profiler. A comparison of CBLH estimates vs widely used methods in meteorology (Radiosondes, Raman Lidar, ceilometer, 915 MHz wind profiler, and Doppler Lidar-based derived Value-Added Product (VAP) ) is performed to validate the NEXRAD detected CBLH using SPICER. The SPICER algorithm shows over 0.9 correlation with radiosonde measurements.
Predicting the Frequency of Low Cloud Mesoscale Morphologies in Southern Ocean Extratropical Cyclones Using Cloud Controlling Factors
(2025-01-19) Tong, Shuoyun; Wood, Robert; Yuan, Tianle
Shortwave radiation biases over the Southern Ocean (SO) stem largely from a poor understanding of low clouds in the cold sectors of extratropical cyclones, where rapid transitions between low cloud mesoscale morphologies are frequent. Stratus dominates the poleward regime of the cyclones. It transitions into closed mesoscale cellular convection (MCC) downstream and then to open MCC in the cold sector of cyclones. Clustered and suppressed cumulus are often found in the warm sector. Principal component (PC) analysis is applied to a set of cloud controlling factors to characterize properties of the entire extratropical cyclone that are critical to low cloud mesoscale morphologies. The first two PCs are strongly related to cyclone intensity and sea surface temperature averaged over the cyclone domain, respectively. Daily average insolation at the top of the atmosphere, which has large seasonal and latitudinal variability over the SO, is used as an additional independent predictor. Closed and open MCC are negatively correlated with insolation, while disorganized MCC and clustered cumulus are positively correlated with insolation. In stronger cyclones, closed MCC, open MCC, and clustered cumulus tend to be more frequent, whereas stratus and suppressed cumulus tend to be less common. In cyclones over a colder sea surface, closed MCC and stratus are more abundant, and clustered cumulus and suppressed cumulus are less abundant. These results deepen the current understanding of low cloud processes and provide insights of transitions between morphologies, and thus changes in cloud radiative effects, over the SO in a changing climate.
NEWS Integrated Analysis (NEWS-IA) Dataset: Budgets and Input Data
(NTRS, 2024-11-20) Roberts, J. Brent; Olson, William S.
This documentation provides a summary of the underlying physical and mathematical descriptions of the global energy and water cycle budgets used to produce the NASA Energy and Water Cycle Study (NEWS) Integrated Analysis. In addition to discussion of the budget equations, a summary is provided concerning NEWS regions, input data sources, and data fields found in the NEWS-IA input data file.
Improved characterization of Dome Concordia for tracking calibration changes in MODIS reflective solar bands
(SPIE, 2024-11-20) McBride, Brent; Twedt, Kevin; Wu, Aisheng; Geng, Xu; Xiong, Xiaoxiong
Dome Concordia (Dome C) in Antarctica is an excellent calibration site for polar-orbiting Earth observation instruments due to its spectral, spatial, and temporal uniformity. These instruments also observe Dome C multiple times a day and at a variety of geometries. The MODIS Characterization Support Team uses regular observations of Dome C by Aqua and Terra MODIS to help validate and improve the calibration of the detector gain and response versus scan angle of the reflective solar bands used to generate NASA’s Level 1B reflectance products. The reflectance trends at Dome C are typically assessed on a yearly basis, due to a six-month sunlit observation period. In this work, we increase the temporal resolution of the trends from yearly to bi-monthly and reduce measurement noise using a reflectance-based snow BRDF model. We show results for Terra and Aqua MODIS BRDF-normalized reflectance using the Collection 7 calibration for bands 1-4, 8-9, and 17. The BRDF model significantly reduces the variations in the bi-monthly reflectance trends with the best results observed near nadir and for the blue bands 3, 8, and 9. The higher temporal sampling allows for better real-time identification of any calibration errors during the sunlit season. In addition, due to its polar location, Dome C is largely insensitive to the recent orbit drift of the Terra and Aqua satellites which has created challenges for MODIS calibration based on other on-board and Earth targets. Combined, these advantages will make Dome C a particularly important calibration reference target during the final years of the Terra and Aqua missions.

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