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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport that are responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares nine global AR detection algorithms from the perspective of AR lifecycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980–2017. Uncertainties in AR lifecycle characteristics, including event number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum AR origin (termination) frequency is located over the western (eastern) portion of ocean basins, significant disagreements appear in regional distribution. Spreads are large in AR lifetime and intensity. Here, the number of landfalling AR events produced by the algorithms can vary from 16 to 80 events per year, although the agreement improves for stronger ARs. By examining the ARs' connections with the Madden-Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfall activity) to climate variability are consistent among algorithms.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

MODIS Solar Reflective Calibration Traceability

Long-term climate data records often consist of observations made by multiple sensors. It is, therefore, extremely important to have instrument overlap, to be able to track instrument stability, to quantify, measurement uncertainties, and to establish absolute scale traceable to the International System of Units (SI). The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument for both the Terra and Aqua missions, which were launched in December 1999 and May 2002, respectively. It has 20 reflective solar bands (RSB) with wavelengths from 0.41 to 2.2 micrometers and observes the Earth at three nadir spatial resolutions: 0.25km, 0.5km, and 1km. MODIS RSB on-orbit calibration is reflectance based with reference to the bidirectional reflectance factor (BRF) of its on-board solar diffuser (SD). The SD BRF characterization was made pre-launch by the instrument vendor using reference samples traceable directly to the National Institute of Standards and Technology (NIST). On-orbit SD reflectance degradation is tracked by an on-board solar diffuser monitor (SDSM). This paper provides details of this calibration chain, from prelaunch to on-orbit operation, and associated uncertainty assessments. Using MODIS as an example, this paper also discusses challenges and key design requirements for future missions developed for accurate climate studies.

Xiong, Xiaoxiong↗
NASA NTRS2009

Observations of Infrared Radiative Cooling in the Thermosphere on Daily to Multiyear Timescales from the TIMED/SABER Instrument

We present observations of the infrared radiative cooling by carbon dioxide (CO2) and nitric oxide (NO) in Earth s thermosphere. These data have been taken over a period of 7 years by the SABER instrument on the NASA TIMED satellite and are the dominant radiative cooling mechanisms for the thermosphere. From the SABER observations we derive vertical profiles of radiative cooling rates (W/cu m), radiative fluxes (W/sq m), and radiated power (W). In the period from January 2002 through January 2009 we observe a large decrease in the cooling rates, fluxes, and power consistent with the declining phase of solar cycle. The power radiated by NO during 2008 when the Sun exhibited few sunspots was nearly one order of magnitude smaller than the peak power observed shortly after the mission began. Substantial short-term variability in the infrared emissions is also observed throughout the entire mission duration. Radiative cooling rates and radiative fluxes from NO exhibit fundamentally different latitude dependence than do those from CO2, with the NO fluxes and cooling rates being largest at high latitudes and polar regions. The cooling rates are shown to be derived relatively independent of the collisional and radiative processes that drive the departure from local thermodynamic equilibrium (LTE) in the CO2 15 m and the NO 5.3 m vibration-rotation bands. The observed NO and CO2 cooling rates have been compiled into a separate dataset and represent a climate data record that is available for use in assessments of radiative cooling in upper atmosphere general circulation models.

Mlynczak, Martin G.↗
DOE OSTI2015

Hydrogeologic modeling and the development of preliminary remediation goals for the Guterl Specialty Steel Site, New York - 15431

The Former Guterl Specialty Steel Corporation Site (Guterl Site) is located 32 kilometers (20 miles) northeast of Buffalo, New York, in Lockport, Niagara County, New York. Between 1948 and 1952, up to 15,875 metric tons (35 million pounds) of natural uranium metal (U) were processed at the Guterl Site. The resulting milling dust, shavings, thermal scale, and associated on-site land disposal contaminated both the facility and soils. These combined manufacturing and disposal processes promoted the development of a large-scale uranium plume in the groundwater underlying the site. Site soils are anthropogenic fill and re-worked, glacially-derived sediments that vary between 0.6 meters (m) and 3 meters (2.0 to 10.0 feet [ft]) in thickness. These soils blanket a highly permeable weathered and fractured zone in the upper Lockport Dolostone bedrock. Aerial recharge through the thin soils flushes soluble constituents to groundwater, as exemplified by variations in specific conductivity and groundwater levels in the bedrock during precipitation events. Seasonally higher groundwater levels also promote contact with uranium contamination in deeper soil, thus complicating the transport characteristics of the site (e.g., oxidizing metallic uranium in the soil that increases leach rates). The Seasonal Soil (SESOIL) compartment model was used to simulate a series of site soil conditions and predict the leaching of uranium to groundwater. The SESOIL input included physical soil characteristics, uranium profiles common in five soil areas, climate data, and an assumed oxidation-state (or chemical valence) for metallic uranium in the soil. The iterative modeling process evaluated site-specific variables to achieve a final deterministic set of input that was applied to the five soil areas. The leaching simulations estimated time-dependent influx rates for uranium, which were input to a numerical groundwater flow (MODFLOW) and transport (MT3DMS) model. The combined modeling effort produced spatially and temporally variable transport conditions that were simulated over a 1,000-year period. The leaching models predicted a preliminary remediation goal (PRG) for uranium of 11 milligrams per kilogram (mg/kg) that is protective of groundwater. However, this low uranium PRG has field implementation concerns, so a modified direct-exposure PRG of 70 mg/kg was developed and evaluated in the flow and transport model. The primary difference in the PRGs is the longevity and location of the uranium plume, which will affect the remedial costs and potential stakeholder acceptance. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

The Effects of Different Footprint Sizes and Cloud Algorithms on the Top-Of-Atmosphere Radiative Flux Calculation from the Clouds and Earth’s Radiant Energy System (CERES) Instrument on Suomi National Polar-Orbiting Partnership (NPP)

Only one Clouds and Earth’s Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W/sq. m for SW (shortwave) and less than 1.0 W/sq. m for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W/sq. m when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W/sq. m between them; the biases of daytime and night- time LW flux are close to zero with RMS errors of 0.8 and 0.2 W/sq. m. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W/sq. m in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W/sq. m and the RMS error increases to 5.2 W/sq. m. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W/sq. m in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record.

Su, Wenying↗
DOE OSTI2024

Optimizing DOE Opportunities to Research Land–Atmosphere Interactions in the U.S. Southeast (Workshop Report)

The southeastern United States (Southeast), with its complex and varied environments, is an area of tremendous economic, ecological, and societal importance to the country. The region is characterized by heterogeneous landscapes (i.e., geology and soil type) and a long history of human land use coupled with a warm temperature regime and high precipitation. As a result, soil erosion and deposition are pronounced, vegetation recovery is rapid, and human modification is extensive across the region. To better understand land–atmosphere interactions in this important and complex region, research communities supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) program identified the Southeast as a priority region of interest. In fall 2024, the third Atmospheric Radiation Measurement Mobile Facility (AMF3), one of three mobile monitoring facilities designed to collect atmospheric and climate data from undersampled regions around the world, will begin operations in northwestern Alabama’s Bankhead National Forest (BNF). The AMF3-BNF 5-year deployment, from 2024–2029, will monitor the effects of feedbacks among aerosols, clouds, and precipitation on plant physiology and canopy-scale fluxes. It will also focus on scale aggregation to resolve the role of local forcing on larger-scale processes. To enable broader AMF3 involvement by the science community, the BER Environmental System Science (ESS) program organized the Southeast Land– Atmosphere Research Opportunities (SELARO) workshop in August 2023. The purpose was to identify gaps in scientific understanding of terrestrial processes in the Southeast (defined as states bounded by the Gulf of Mexico to the south, the Atlantic Ocean to east, the Mississippi River to the west, and extending through Tennessee and North Carolina to the north) and explore opportunities to use the AMF3-BNF deployment to coordinate and leverage research efforts across the region. Many parts of the Southeast have experienced repeated anthropogenic forcings. Farming, hunting, burning, and settlement of the region by Indigenous Peoples first shaped the distribution of plant communities, which in turn influenced European colonization patterns. Timber harvesting was common during the expansion of European settlements, and production forestry continues today. Agricultural production was extensive and then waned through the 20th century, creating a period of afforestation following agricultural abandonment. Today, many formerly agricultural landscapes are undergoing rapid urbanization and suburbanization. Overlying these patterns of anthropogenic land use are frequent disturbances from hurricanes, tornadoes, wildfires, drought, flooding, ice storms, and the occasional blizzard. An additional characteristic of the Southeast is its overall landscape complexity. Unlike the western United States, where broad expanses may share similar characteristics, Southeast topography, drainage patterns, vegetation, and development patterns vary widely across relatively small spatial scales (<1 km). This is due to the region’s underlying geology and soil development, species biodiversity patterns, and land ownership and use coupled with strong forces of erosion, weathering, and rapid plant growth in the warm, wet climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Increase in winter haze over eastern China in recent decades: Roles of variations in meteorological parameters and anthropogenic emissions: INCREASE IN WINTER HAZE IN EASTERN CHINA

The increase in winter haze over eastern China in recent decades due to variations in meteorological parameters and anthropogenic emissions was quantified using observed atmospheric visibility from the National Climatic Data Center Global Summary of Day database for 1980–2014 and simulated PM2.5 concentrations for 1985–2005 from the Goddard Earth Observing System (GEOS) chemical transport model (GEOS-Chem). Observed winter haze days averaged over eastern China (105–122.5°E, 20–45°N) increased from 21 d in 1980 to 42 d in 2014, and from 22 to 30 d between 1985 and 2005. The GEOS-Chem model captured the increasing trend of winter PM2.5 concentrations for 1985–2005, with concentrations averaged over eastern China increasing from 16.1 μg m -3 in 1985 to 38.4 μg m -3 in 2005. Considering variations in both anthropogenic emissions and meteorological parameters, the model simulated an increase in winter surface-layer PM2.5 concentrations of 10.5 (±6.2) μg m -3 decade -1 over eastern China. The increasing trend was only 1.8 (±1.5) μg m -3 decade -1 when variations in meteorological parameters alone were considered. Among the meteorological parameters, the weakening of winds by -0.09 m s -1 decade -1 over 1985–2005 was found to be the dominant factor leading to the decadal increase in winter aerosol concentrations and haze days over eastern China during recent decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗
DOE OSTI2023

Scaling of Floods With Geomorphologic Characteristics and Precipitation Variability Across the Conterminous United States

Abstract Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15% over 40 years, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Climate change↗
DOE OSTI2023

Multi-scenario Extreme Weather Simulator application to heat waves: Ko’olauloa community resilience hub

Heat waves are increasing in severity, duration, and frequency. The Multi-Scenario Extreme Weather Simulator (MEWS) models this using historical data, climate model outputs, and heat wave multipliers. In this study, MEWS is applied for planning of a community resilience hub in Hau'ula, Hawaii. The hub will have normal operations and resilience operations modes. Both these modes were modeled using EnergyPlus. The resilience operations mode includes cutting off air conditioning for many spaces to decrease power requirements during emergencies. Results were simulated for 300 future weather files generated by MEWS for 2020, 2040, 2060, and 2080. Shared socioeconomic pathways 2-4.5, 3-7.0 and 5-8.5 were used. The resilience operations mode results show two to six times increase of hours of exceedance beyond 32.2 °C from present conditions, depending on climate scenario and future year. The resulting decrease in thermal resilience enables an average decrease of energy use intensity of 26% with little sensitivity to climate change. The decreased thermal resilience predicted in the future is undesirable, but was not severe enough to require a more energy-intensive resilience mode. Instead, planning is needed to assure vulnerable individuals are given prioritized access to air-conditioned parts of the hub if worst-case heat waves occur.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

An integrated phenology modelling framework in r

Abstract Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network ( USA ‐ NPN ), the Pan European Phenology Project ( PEP 725), MODIS phenology ( MCD 12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared ( RMSE ) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.

Hufkens, Koen↗
DOE OSTI2026

Fast and Accurate Intersections on a Sphere

We introduce a fast, high-precision algorithm for calculating intersections between great circle arcs and lines of constant latitude on the unit sphere. We first propose a simplified intersection point formula with improved speed and numerical robustness over the ones traditionally implemented in geoscience software. We then show how algorithms based on the concept of error-free transformations (EFT) can be applied to evaluate this formula within a relative error bound that is on the order of machine precision. Here, we demonstrate that, with a vectorized and parallelized implementation, this enhanced accuracy is achieved with no compute time overhead compared to a direct calculation in hardware floating point, making our algorithm suitable for performance-sensitive applications like regridding of high-resolution climate data. In contrast, evaluating our formula using high-precision data types like quadruple precision and arbitrary precision, or using the robust intersection computation routines from the Computational Geometry Algorithms Library, leads to significant computational overhead, especially since these alternatives inhibit vectorization. More generally, our work demonstrates how EFT techniques can be combined and extended to implement nontrivial geometric calculations with high accuracy and speed.

Environmental sciences↗
DOE OSTI2023

Longitudinal Effects on Plant Species Involved in Agriculture and Pandemic Emergence Undergoing Changes in Abiotic Stress

In this work we identify changes in high-resolution zones across the globe linked by environmental similarity that have implications for agriculture, bioenergy, and zoonosis. We refine exhaustive vector comparison methods with improved similarity metrics as well as provide multiple methods of amalgamation across 744 months of climatic data. The results of the vector comparison are captured as networks which are analyzed using static and longitudinal comparison methods to reveal locations around the globe experiencing dramatic changes in abiotic stress. Specifically we (i) incorporate updated similarity scores and provide a comparison between similarity metrics, (ii) implement a new feature for resource optimization, (iii) compare an agglomerative view to a longitudinal view, (iv) compare across 2-way and 3-way vector comparisons, (v) implement a new form of analysis, and (vi) demonstrate biological applications and discuss implications across a diverse set of species distributions by detecting changes that affect their habitats. Species of interest are related to agriculture (e.g., coffee, wine, chocolate), bioenergy (e.g., poplar, switchgrass, pennycress), as well as those living in zones of concern for zoonotic spillover that may lead to pandemics (e.g., eucalyptus, flying foxes).

Cashman, Mikaela↗
DOE OSTI2016

Stride Search v. 1.0

Stride Search provides a flexible tool for detecting storms or other extreme climate events in high-resolution climate data sets saved on uniform latitude-longitude grids in standard NetCDF format. Users provide the software a quantitative description of a meteorological event they are interested in; the software searches a data set for locations in space and time that meet the user’s description. In its first stage, Stride Search performs a spatial search of the data set at each timestep by dividing a search domain into circular sectors of constant geodesic radius. Data from a netCDF file is read into memory for each circular search sector. If the data meet or exceed a set of storm identification criteria (defined by the user), a storm is recorded to a linked list. Finally, the linked list is examined and duplicate detections of the same storm are removed and the results are written to an output file. The first stage’s output file is read by a second program that builds storm. Additional identification criteria may be applied at this stage to further classify storms. Storm tracks are the software’s ultimate output and routines are provided for formatting that output for various external software libraries for plotting and tabulating data.

Bosler, Peter↗
DOE OSTI2017

Xanthos

Xanthos is a Python package designed to quantify and analyze global water availability in history and in future at 0.5° × 0.5° spatial resolution and a monthly time step under a changing climate. Its performance was also tested through real applications. It is open-source, extendable and convenient to researchers who work on long-term climate data for studies of global water supply, and Global Change Assessment Model (GCAM). This package integrates inherent global gridded data maps, I/O modules, Water-Balance Model modules and diagnostics modules by user-defined configuration.

Source record↗