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The dataset includes 15-minute average wind speed and direction records from 10 m to 250 m above ground level (AGL) in 5-m range gates. Data were collected by a Scintec SFAS wind profiler installed at the Condon State Airport in Oregon, about 1.8 km northeast of the center of Condon, Ore., and are intended for validating WFIP2 model improvements.
The dataset includes 15-minute average wind speed and direction records from 10 m to 250 m above ground level (AGL) in 5-m range gates. Data were collected by a Scintec SFAS wind profiler installed at the Condon State Airport in Oregon, about 1.8 km northeast of the center of Condon, Ore., and are intended for validating WFIP2 model improvements.
A 7.5-year (July 1987-December 1994) dataset of daily surface specific humidity and turbulent fluxes (momentum, latent heat, and sensible heat) over global oceans has been retrieved from the Special Sensor Microwave/Imager (SSM/I) data and other data. It has a spatial resolution of 2.0 deg.x 2.5 deg. latitude-longitude. The retrieved surface specific humidity is generally accurate over global oceans as validated against the collocated radiosonde observations. The retrieved daily wind stresses and latent heat fluxes show useful accuracy as verified by those measured by the RV Moana Wave and IMET buoy in the western equatorial Pacific. The derived turbulent fluxes and input variables are also found to agree generally with the global distributions of annual-and seasonal-means of those based on 4-year (1990-93) comprehensive ocean-atmosphere data set (COADS) with adjustment in wind speeds and other climatological studies. The COADS has collected the most complete surface marine observations, mainly from merchant ships. However, ship measurements generally have poor accuracy, and variable spatial coverages. Significant differences between the retrieved and COADS-based are found in some areas of the tropical and southern extratropical oceans, reflecting the paucity of ship observations outside the northern extratropical oceans. Averaged over the global oceans, the retrieved wind stress is smaller but the latent heat flux is larger than those based on COADS. The former is suggested to be mainly due to overestimation of the adjusted ship-estimated wind speeds (depending on sea states), while the latter is suggested to be mainly due to overestimation of ship-measured dew point temperatures. The study suggests that the SSM/I-derived turbulent fluxes can be used for climate studies and coupled model validations.
The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.
NASA's Global Precipitation Measurement Mission (GPM) has conducted as series of Ground Validation (GV) studies to assist algorithm development for the GPM core satellite. Characterizing the drop size distribution (DSD) for different types of precipitation systems is critical in order to accurately estimate precipitation across the majority of the planet. Thus far, GV efforts have sampled DSDs in a variety of precipitation systems from Finland to Oklahoma. This dataset consists of over 33 million raindrops sampled by GPM GV's two‐dimensional video disdrometers (2DVD) and includes RSD observations from the LPVEx, MC3E, GCPEx, HyMEx and IFloodS campaigns as well as from GV sites in Huntsville, AL and Wallops Island, VA. This study focuses on the larger end of the raindrop size spectrum, which greatly influences radar reflectivity and has implications for moment estimation. Thus knowledge of the maximum diameter is critical to GPM algorithm development. There are over 24,000 raindrops exceeding 5 mm in diameter contained within this disdrometer dataset. The largest raindrops in the 2DVD dataset (>7‐8 mm in diameter) are found within intense convective thunderstorms, and their origins are believed to be hailstones. In stratiform rainfall, large raindrops have also been found to fall from lower and thicker melting layers. The 2DVD dataset will be combined with that collected by dual‐polarimetric radar and aircraft particle imaging probes to "follow" the vertical evolution of the DSD tail (i.e., retrace the large drops from the surface to their origins aloft).
Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.
Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.
Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.
The ICARUS Liquid Argon Time Projection Chamber (LArTPC) is used to investigate short-baseline neutrino oscillations and the possible existence of sterile neutrinos. To aid in identifying and rejecting cosmic ray backgrounds, the system is outfitted with external Cosmic Ray Taggers (CRTs). The CRT is able to measure more tracks and at a wider range of angles, useful in further detailing future calibrations. This project compared the most probable values (MPVs) of charge deposition between CRT tagged and TPC tagged tracks. The Y-Z maps of charge scale for both datasets show a strong correlation, not just in their overall structure, but also in how they reflect known detector effects. Both reconstruction paths showed similar structures in their charge distribution, such as less effective wire section or mechanical support, confirming that CRT-tagged tracks, despite being external, respond to the same calibration landscape as Pandora tracks, validating previous Pandora reconstructions and values. Overall, the analysis supports the idea that the CRT tags are a useful addition to ICARUS calibration efforts. Their broad angular range and independence from Pandora reconstruction make them a helpful secondary tool and a potential asset in extending calibration across the detector volume.
WINDPROF provides 10-minute wind and turbulence profiles that integrate scanning Doppler lidars, a profiling lidar, a 915 MHz radar wind profiler, and a sonic anemometer across Northeast U.S. coastal and offshore sites during the WFIP3 campaign. Variables include wind speed, wind direction, vertical velocity, turbulence intensity, and turbulent kinetic energy, each with per-instrument quality control and inter-instrument agreement validation. Profiles are mapped to a standardized height grid – a dedicated near-surface level at the sonic measurement height (5 m AGL), 20 m spacing to 100 m, and 30 m spacing above – and carry component and derived uncertainty estimates. Heights are reported above ground level; the site's ground elevation is stored separately. The Nantucket dataset covers 1 February 2024 – 8 September 2025.
Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.
Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a key role in supporting many of these advances. Improvements in satellites and in computational and data-handling techniques have yielded high quality, readily accessible data. However, rapid increases in data volume have also led to large and complex datasets that pose significant challenges in data analysis (NRC, 2007). Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need to reduce the uncertainty in remotely sensed climate records and projections. The approaches currently used to quantify the uncertainty in remotely sensed data, including statistical methods used to calibrate and validate satellite instruments, lack an overall mathematically based framework.
Between 2007 and 2010, Japan's Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) captured dual polarization HH and HV data across the tropics and sub-tropics. A pan tropical dataset of Japanese Earth Resources Satellite (JERS-1) SAR (HH) data was also acquired between 1995 and 1998. The provision of these comparable cloud-free datasets provided an opportunity for observing changes in the extent of coastal mangroves over more than a decade. Focusing on nine sites distributed through the tropics, this paper demonstrates how these data can be used to backdate and update existing baseline maps of mangrove extent. The benefits of integrating dense timeseries of Landsat sensor data for both validating assessments of change and determining the causes of change are outlined. The approach is evaluated for wider application across the geographical range of mangroves in order to advance the development of JAXA's Global Mangrove Watch (GMW) program.
Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.
Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.
The ICARUS Liquid Argon Time Projection Chamber (LArTPC) is used to investigate short-baseline neutrino oscillations and the possible existence of sterile neutrinos. To aid in identifying and rejecting cosmic ray backgrounds, the system is outfitted with external Cosmic Ray Taggers (CRTs). The CRT is able to measure more tracks and at a wider range of angles, useful in further detailing future calibrations. This project compared the most probable values (MPVs) of charge deposition between CRT tagged and TPC tagged tracks. The Y-Z maps of charge scale for both datasets show a strong correlation, not just in their overall structure, but also in how they reflect known detector effects. Both reconstruction paths showed similar structures in their charge distribution, such as less effective wire section or mechanical support, confirming that CRT-tagged tracks, despite being external, respond to the same calibration landscape as Pandora tracks, validating previous Pandora reconstructions and values. Overall, the analysis supports the idea that the CRT tags are a useful addition to ICARUS calibration efforts. Their broad angular range and independence from Pandora reconstruction make them a helpful secondary tool and a potential asset in extending calibration across the detector volume.
Chemical weathering over geological timescales acts as a source or sink of atmospheric carbon dioxide (CO 2 ), while influencing long-term redox cycling and atmospheric oxygen (O 2 ) at Earth's surface. There is a growing recognition that the oxidative weathering of rock organic carbon (OC petro ) can release more CO 2 than is locally drawn down by silicate weathering, and may vary due to changes in erosion and climate. The element rhenium (Re) has emerged as a proxy to track the oxidative weathering of OC petro , yet uncertainties in its application remain namely that we lack a systematic assessment of the comparative mobility of Re and OC petro during sedimentary rock weathering. Here we measure Re and OC petro loss across gradients in rock weathering at 9 global sites, spanning a range of initial OC petro values from ~0.2 % to >10 %. We use titanium to account for volume changes during weathering and assess Re and OC petro loss alongside major elements that reflect silicate (Na, Mg), carbonate (Ca, Mg) and sulfide (S) weathering. Across the dataset, Re loss is correlated with OC petro loss but not with loss of any other major element. Further, across the weathering profiles, the average molar ratio of OC petro to Re loss was 0.84 ± 0.15, with 8 out of 9 sites having a ratio >0.74. At one site (Marcellus Shale), the average ratio was lower at 0.58 ± 0.11. The excess loss of Re matches expectations that, typically, between ~0 and 20 % of the Re liberated by sedimentary rock weathering derives from silicate or sulfide phases, while some OC petro may be physically or chemically protected from weathering. Overall, our measurements provide validation for the Re proxy of OC petro oxidation and allow future work to further improve our knowledge of regional and global-scale rates of this important source of CO 2 in the geochemical carbon cycle.
Federated Learning (FL) has become a ubiquitous approach for training machine learning models on decentralized data, addressing the myriad privacy concerns inherent in traditional centralized methods. However, the efficiency of FL depends on effective client selection and robust privacy preservation mechanisms. Inadequate client selection may lead to suboptimal model performance, while insufficient privacy measures risk exposing sensitive data. This paper proposes a client selection framework for FL that integrates differential privacy and fault tolerance. Our adaptive approach dynamically adjusts the number of selected clients based on model performance and system constraints, ensuring privacy through calibrated noise addition. We evaluate our method on a network anomaly detection use case using the UNSW-NB15 and ROAD datasets. Results show up to a 7% increase in accuracy and a 25% reduction in training time compared to FedL2P. Moreover, we highlight the trade-offs between privacy budgets and model performance, with higher privacy budgets reducing noise and improving accuracy. Our fault tolerance mechanism, while causing a slight performance drop, enhances robustness to client failures. Statistical validation using Mann-Whitney U tests confirms the significance of these improvements (p < 0.05).