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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

EPIC Calibration/Validation Experiment Field Campaign Report

A field exercise involving several different kinds of Unmanned Aerial Systems (UAS) and supporting instrumentation systems provided by DOE/ARM and NOAA/NSSL was conducted at the ARM SGP site in Lamont, Oklahoma on 29-30 October 2016. This campaign was part of a larger National Oceanic and Atmospheric Administration (NOAA) UAS Program Office program awarded to the National Severe Storms Laboratory (NSSL). named Environmental Profiling and Initiation of Convection (EPIC). The EPIC Field Campaign (Test and Calibration/Validation) proposed to ARM was a test or “dry-run” for a follow-up campaign to be requested for spring/summer 2017. The EPIC project addresses NOAA’s objective to “evaluate options for UAS profiling of the lower atmosphere with applications for severe weather.” The project goal is to demonstrate that fixed-wing and rotary-wing small UAS have the combined potential to provide a unique observing system capable of providing detailed profiles of temperature, moisture, and winds within the atmospheric boundary layer (ABL) to help determine the potential for severe weather development. Specific project objectives are: 1) to develop small UAS capable of acquiring needed wind and thermodynamic profiles and transects of the ABL using one fixed-wing UAS operating in tandem with two different fixed rotary-wing UAS pairs; 2) adapt and test miniaturized, high-precision, and fast-response atmospheric sensors with high accuracy in strong winds characteristic of the pre-convective ABL in Oklahoma; 3) conduct targeted short-duration experiments at the ARM Southern Great Plains site in northern Oklahoma concurrently with a second site to be chosen in “real-time” from the Oklahoma Mesonet in coordination with the (National Weather Service (NWS)-Norman Forecast Office; and 4) gain valuable experience in pursuit of NOAA’s goals for determining the value of airborne, mobile observing systems for monitoring rapidly evolving high-impact severe weather conditions not observed with current operational systems. OU operated three UAS at the Lamont SGP site – the OU CopterSonde, the OU Iris, and the Meteomatics Meteodrone – under a blanket Federal Aviation Administration (FAA) Certificate of Authorization (COA) allowing flights up to 400 feet above ground level (AGL). The mission for the rotary-wing UAS involved four aircraft, three from the ARM Lamont site and one from an Oklahoma Mesonet site located at Medford, involving a vertical ascent to an altitude of 400 ft (130 m) AGL at ~ 30 minute intervals for ~ 5 hours duration on each of the two experiment days. This operation was conducted in close coordination with NSSL-launched rawinsonde balloons at the two sites, and operation of a fixed-wing UAS from the University of Colorado called the TTwistor that flew mission flight legs between the Lamont site and Medford. The NSSL operation at Medford (outside of ARM) also involved use of their Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) ground-based remote-sensing system for measuring atmospheric profiles of temperature, moisture, and winds with atmospheric emitted radiance interferometer (AERI), microwave radiometer, and Doppler wind lidar systems. The ARM Facility supported the project by providing access to their instrumented tower data at Lamont (at three levels), as well as AERI and Doppler wind lidar data obtained from systems quite similar to those used by CLAMPS. These non-UAS data from both ARM and NSSL provided the observations used to validate the experimental UAS observations.

atmospheric boundary layer↗
DOE OSTI2018

Arctic Methane, Carbon Aerosols, and Tracers Study Field Campaign Report

The Arctic Methane, Carbon Aerosols, and Tracers Study was a measurement campaign at the National Oceanic and Atmospheric Administration (NOAA) Barrow Observatory and U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility North Slope of Alaska (NSA) sites in Barrow (formerly known as Utqiagvik) that involved the deployment of instruments to measure CH4, black carbon (BC), and source tracers. The campaign ran from September 1, 2014 to September 1, 2016 and was extended until July 30, 2017. We deployed an in situ instrument to measure methane, the ratio of its isotopologues 13CH4/12CH4, and ethane. We deployed a second instrument to measure carbon monoxide, carbonyl sulfide (COS), carbon dioxide, and water vapor. We also deployed an in situ instrument to measure BC, a single-particle soot photometer (SP2), at the nearby NOAA Barrow Observatory, co-located with the other NOAA aerosol instruments. Changes in emissions across different regions and seasons will be inferred using atmospheric transport and inverse modeling techniques.

North Slope of Alaska, trace gases, black carbon, ↗
DOE OSTI2019

The De-Icing Comparison Experiment – ARM Contribution (DICEXACO) Field Campaign Report

Longwave and shortwave radiative fluxes are fundamental quantities regularly observed globally using broadband radiometers. In regions conducive to frost, rime, and snow, ice frequently builds up on sensor windows, contaminating measurements. Since icing occurs under particular meteorological conditions, associated data loss constitutes a climatological bias. Furthermore, the signal caused by ice is difficult to distinguish from that of clouds, hampering efforts to identify contaminated data in post-processing. Because of the sensitivity of radiometers to internal temperature instabilities, there are limitations to using heat as a de-icing method, and consequently substantial amounts of data are lost. The De-Icing Comparison Experiment (D-ICE) was a campaign carried out at $Utqia\dot{g}vik$ (formerly known as Barrow) and Oliktok Point, Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational stations run by the NOAA Global Monitoring Division (GMD) at NOAA’s Barrow Atmospheric Baseline Observatory. D-ICE also evaluated the sky radiometer (SKYRAD) systems at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) and Oliktok Point (OLI; third ARM Mobile Facility [AMF3]) observatories. In total, 34 systems were evaluated (8 of which were SKYRAD radiometers). All radiometers were monitored continuously using cameras, and a total of more than one million images of sensor domes were archived collectively between the stations. Data collected as part of D-ICE by NOAA can be found through the project web page, https://www.esrl.noaa.gov/psd/arctic/d-ice/. The DICEXACO component of D-ICE (https://www.arm.gov/research/campaigns/nsa2017dicexaco) are the images collected at 10-min intervals at both OLI and NSA and are the subject of this report. Two cameras were installed at each ARM station, one directed towards the SKYRAD tracker and the other towards the SKYRAD global precision spectral pyranometer (PSP).

Oliktok Point, North Slope of Alaska, SKYRAD, pyra↗
DOE OSTI2025

NANT Site - Microwave Radiometer Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous brightness temperature measurements at 35 channels observed with a microwave radiometer MP3000A operated by NOAA Physical Sciences Laboratory on Nantucket Island for WFIP3. Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the MWR housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

NANT Site - ASSIST / Thermodynamic retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009) operated by NOAA Physical Sciences Laboratory on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the ASSIST housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

BLOC Site - ASSIST Thermodynamic retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009) operated by NOAA Physical Sciences Laboratory on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Radiances are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the ASSIST housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗
DOE OSTI2016

ACAPEX – Ship-Based Ice Nuclei Collections Field Campaign Report

Measurements were sought to evaluate a hypotheses that sea-spray-sourced ice-nucleating particles (INPs) are of biological origin and represent a distinctly different INP population in comparison to long-range-transported desert or urban and regional land-sourced INP, and that the layering of marine within other aerosol layers feeding orographic storms over the mountains of California and the Western United States thereby leads to common and quantifiable scenarios that influence precipitation over the region. Aerosol collections on the National Oceanic and Atmospheric Administration (NOAA) research vessel (RV) Ronald H. Brown, for subsequent processing of INP immersion freezing activation temperature spectra and composition analyses, added a valuable measurement component to the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) and related CalWater2 (NOAA) studies for use in parameterizing and modeling the impacts of marine boundary layer and other aerosols on climate and radiation via aerosol indirect effects on mixed-phase clouds. Twenty-five nominally 24-hour collections were made and have been processed for immersion freezing INP number concentrations versus temperature in the mixed-phase cloud temperature regime from -10 to -27°C. The similarity of INP number concentrations compared to typical marine boundary layer values attributed to sea-spray aerosols was noted. Nevertheless, variability of INP concentrations of up to 50 times was noted at individual temperatures over the course of the study. A particular analysis possible with this data set is to examine INP budgets over oceans inside versus outside of atmospheric river conditions. These INP measurements supplemented multiple airborne INP measurements on the ARM Aerial Facility (AAF), and others on the ground during ACAPEX and CalWater2, to provide extensive spatial and temporal analyses of INP immersion freezing spectra during winter storm periods. Future analyses will use thermal sensitivity to examine INP compositions as organic versus inorganic in these marine boundary layer samples. Data set integration is occurring under funding from an Atmospheric System Research (ASR) proposal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Wind Forecasting Improvement Project In Complex Terrain Near the Columbia River Gorge

In 2014 a multi-institution team led by Vaisala, Inc. was selected by the Department of Energy (DOE) to partner with multiple DOE and National Oceanic and Atmospheric Administration (NOAA) laboratories on a project designed to improve the quality of wind power forecasts in areas of complex terrain. This was the second Wind Forecast Improvement Project (WFIP2) funded by DOE and it extended from late 2014 through the middle of 2018. It encompassed an 18-month observational field campaign, numerical weather prediction (NWP) model development, extensive analysis of data and NWP output, and the creation of decision support tool algorithms to convey forecast information to end users in the wind industry. WFIP2 focused on improvements to the representation of near-surface and boundary-layer physics in NOAA’s High-Resolution Rapid Refresh (HRRR) model. Improvements to HRRR, which is run operationally over the continental United States, benefit the wind industry in multiple ways. Forecasts from the operational HRRR are used directly by wind power forecast vendors and the operators of wind plants. In addition, because HRRR is built using the widely-used Weather Research and Forecasting (WRF) model, improvements to its parameterizations become available to commercial and research institutions using WRF for a myriad of purposes. The geographic area studied by WFIP2 was a region of the Columbia River basin located to the east of the Cascade Mountains between Oregon and Washington. Home to over 6 GW of installed capacity for wind energy production, this area also hosts a variety of atmospheric phenomena either unique to or augmented by complex topography. This makes it an attractive test-bed for the analysis of wind forecast in complex terrain, though results found are should be applicable in any area of topographic complexity. WFIP2 succeeded as a collaborative effort, and while this report focuses on the activities of the team led by Vaisala, the work described here is part of a larger whole. The Vaisala team accomplished a number of specific tasks as described in this report, while also contributing to this larger effort.

17 WIND ENERGY↗
DOE OSTI2025

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗
DOE OSTI2025

Harnessing land-atmosphere interactions to enhance subseasonal-to-seasonal predictability

2025 Advancing Understanding of Land-Atmosphere Interactions and Processes on S2S Predictability Workshop What: 227 registered workshop participants gathered in person (43%) and online (57%) to discuss state-of-the-art scientific understanding and modeling of land-atmosphere interactions and related processes in the context of subseasonal-to-seasonal (S2S) predictability. Topics covered sources of S2S predictability, land model initialization methods, model diagnosis and evaluation metrics, AI/ML analysis and applications, and coordination of future community multi-model S2S forecast focused experiments. To advance the science, this community workshop, organized by NSF NCAR, NOAA, NASA, and DOE, aimed to 1) identify process- and application-oriented metrics for assessing S2S prediction skill and 2) develop experimental protocols for coordinated experiments to isolate, quantify, and understand the role of land-atmosphere interactions in S2S predictability. When: June 16-18, 2025 Where: Boulder, CO, USA, and online.

Land-Atmosphere Interaction↗
DOE OSTI2016

RAM‐SCB simulations of electron transport and plasma wave scattering during the October 2012 “double‐dip” storm

Abstract Mechanisms for electron injection, trapping, and loss in the near‐Earth space environment are investigated during the October 2012 “double‐dip” storm using our ring current‐atmosphere interactions model with self‐consistent magnetic field (RAM‐SCB). Pitch angle and energy scattering are included for the first time in RAM‐SCB using L and magnetic local time (MLT)‐dependent event‐specific chorus wave models inferred from NOAA Polar‐orbiting Operational Environmental Satellites (POES) and Van Allen Probes Electric and Magnetic Field Instrument Suite and Integrated Science observations. The dynamics of the source (approximately tens of keV) and seed (approximately hundreds of keV) populations of the radiation belts simulated with RAM‐SCB is compared with Van Allen Probes Magnetic Electron Ion Spectrometer observations in the morning sector and with measurements from NOAA 15 satellite in the predawn and afternoon MLT sectors. We find that although the low‐energy ( E < 100 keV) electron fluxes are in good agreement with observations, increasing significantly by magnetospheric convection during both SYM‐H dips while decreasing during the intermediate recovery phase, the injection of high‐energy electrons is underestimated by this mechanism throughout the storm. Local acceleration by chorus waves intensifies the electron fluxes at E ≥50 keV considerably, and RAM‐SCB simulations overestimate the observed trapped fluxes by more than an order of magnitude; the precipitating fluxes simulated with RAM‐SCB are weaker, and their temporal and spatial evolutions agree well with POES/Medium Energy Proton and Electron Detectors data.

58 GEOSCIENCES↗
DOE OSTI2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Investigation of the N 2 O emission strength in the U. S. Corn Belt

Nitrous oxide (N 2 O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N 2 O budget. To date, studies on local surface N 2 O emissions and the atmospheric N 2 O budget have commonly used Lagrangian models. Here, in the present study, we used an Eulerian model - Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N 2 O emissions in the Corn Belt and observed atmospheric N 2 O mixing ratios. We derived a simple equation to relate the emission strengths to atmospheric N 2 O mixing ratios, and used the derived equation and hourly atmospheric N 2 O measurements at the KCMP tall tower in Minnesota to constrain agricultural N 2 O emissions. The modeled spatial patterns of atmospheric N 2 O were evaluated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N 2 O mixing ratios monitored at the KCMP tower. Agricultural N 2 O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emissions in the Corn Belt for June 1–20, 2010 - a peak emission period. Optimized mean N 2 O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m -2 s -1 for June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N 2 O mixing ratios after optimization were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns suggest that the underestimate of emissions using IPCC (Inter-governmental Panel on Climate Change) inventory methodology is not dependent on tower measurement location.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Cross-scale modeling of storm surge, tide, and inundation in Mid-Atlantic Bight and New York City during Hurricane Sandy, 2012

Driven by high-resolution NAM (North America Mesoscale) and ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric fields, a 3-D unstructured grid SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model) was applied to simulate total water level in the Mid-Atlantic Bight during Hurricane Sandy in 2012. The simulated storm surge, tide, significant wave height, and wave peak period results compared favorably with NOAA observations along US East Coast, Long Island Sound, and New York Harbor. The maximum total water level at The Battery in the New York Harbor was accurately simulated with an absolute error of less than 0.08 m (out of 2.9 m peak surge) and a timing difference less than 10 min, which includes the effects on the order of 0.1–0.3 m (5–10%) from the coastal setup induced by the surface waves. The scenario comparisons of (1) “NAM” versus “ECMWF”, (2) “2-D” versus “3-D”, and (3) “with” versus “without” wind wave model were examined. The 3-D barotropic model forced by ECMWF including the effects of wind waves performs the best, attributed to wave-induced radiation stress and reduction of bottom stress when the 3-D version is used. Simultaneously, the ELCIRC-Sub model, using a 5-m topography/bathymetry sub-grid and a regular 200-m resolution finite volume computational model grid, was developed to simulate the street-level inundation in New York City. The momentum and mass fluxes calculated by the coarser base grid model were effectively coupled with the sub-grid so that running a full-blown high-resolution base model is not required. The ELCIRC-Sub model uses MPI (Message Passing Interface) parallel computing to enlarge the coverage of the land surface and an efficient non-linear solver to improve the accuracy of the wetting-and-drying processes. The temporal comparisons of modeled water level with NOAA's tidal stations and USGS' rapid-deployed gauges showed overall performance with an average error of 0.1 m. Particularly, ELCIRC-Sub captured the profile of the highest peak surge (3.9 m), which rose 2 m within 3 hour at Kings Point, NY. The spatial comparisons of the modeled peak water level at 80 surveyed locations showed an average error less than 0.13 m. The modeled maximum inundation extent also matched well with an 80% hit rate against FEMA's Hurricane Sandy maximum flooding extent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Taxonomy of USA east coast fishing communities in terms of social vulnerability and resilience

Increased concern with the impacts that changing coastal environments can have on coastal fishing communities led to a recent effort by NOAA Fisheries social scientists to develop a set of indicators of social vulnerability and resilience for the U.S. Southeast and Northeast coastal communities. A goal of the NOAA Fisheries social vulnerability and resilience indicator program is to support time and cost effective use of readily available data in furtherance of both social impact assessments of proposed changes to fishery management regulations and climate change adaptation planning. The use of the indicators to predict the response to change in coastal communities would be enhanced if community level analyses could be grouped effectively. This study examines the usefulness of combining 1130 communities into 35 relevant subgroups by comparing results of a numerical taxonomy with data collected by interview methods, a process herein referred to as “ground-truthing.” The validation of the taxonomic method by the method of ground-truthing indicates that the clusters are adequate to be used to select communities for in-depth research. - Highlights: • We develop a taxonomy of fishing communities based on vulnerability indicators. • We validate the community clusters through the use of surveys (“ground-truthing”). • Clusters differ along important aspects of fishing community vulnerability. • Clustering communities allows for accurate and timely social impact assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) es- timates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

58 GEOSCIENCES↗
DOE OSTI2018

Developing Intensity-Duration-Frequency (IDF) Curves From Satellite-Based Precipitation: Methodology and Evaluation

Given the continuous advancement in the retrieval of precipitation from satellites, it is important to develop methods that incorporate satellite-based precipitation data sets in the design and planning of infrastructure. This is because in many regions around the world, in situ rainfall observations are sparse and have insufficient record length. A handful of studies examined the use of satellite-based precipitation to develop intensity-duration-frequency (IDF) curves; however, they have mostly focused on small spatial domains and relied on combining satellite-based with ground-based precipitation data sets. In this study, we explore this issue by providing a methodological framework with the potential to be applied in ungauged regions. This framework is based on accounting for the characteristics of satellite-based precipitation products, namely, adjustment of bias and transformation of areal to point rainfall. The latter method is based on previous studies on the reverse transformation (point to areal) commonly used to obtain catchment-scale IDF curves. The paper proceeds by applying this framework to develop IDF curves over the contiguous United States (CONUS); the data set used is Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR). IDFs are then evaluated against National Oceanic and Atmospheric Administration (NOAA) Atlas 14 to provide a quantitative estimate of their accuracy. Results of this work show that median errors are in the range of (17–22%), (6–12%), and (3–8%) for one-day, two-day and three-day IDFs, respectively, and return periods in the range (2–100) years. Furthermore, a considerable percentage of satellite-based IDFs lie within the confidence interval of NOAA Atlas 14.

54 ENVIRONMENTAL SCIENCES↗