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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Note: Coincidence measurements of 3 He and neutrons from a compact D-D neutron generator

Tagging of neutrons (2.45 MeV) with their associated 3He particles from deuterium-deuterium (D-D) fusion reactions has been demonstrated in a compact neutron generator setup enabled by a high brightness, microwave-driven ion source with a high fraction of deuterons. Energy spectra with well separated peaks of the D-D fusion reaction products, 3 He, tritons, and protons, were measured with a silicon PIN diode. The neutrons were detected using a liquid scintillator detector with pulse shape discrimination. In this work, by correlating the 3 He detection events with the neutron detection in time, we demonstrated the tagging of emitted neutrons with 3 He particles detected with a Si PIN diode detector mounted inside the neutron generator vacuum vessel.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
NASA NTRS2013

Semi-Supervised Eigenbasis Novelty Detection

Recent discoveries in high-time-resolution radio astronomy data have focused attention on a new class of events. Fast transients are rare pulses of radio frequency energy lasting from microseconds to seconds that might be produced by a variety of exotic astrophysical phenomena. For example, X-ray bursts, neutron stars, and active galactic nuclei are all possible sources of short-duration, transient radio signals. It is difficult to anticipate where such signals might appear, and they are most commonly discovered through analysis of high-time- resolution data that had been collected for other purposes. Transients are often faint and difficult to detect, so improved detection algorithms can directly benefit the science yield of all such commensal monitoring. A new detection algorithm learns a low-dimensional linear manifold for describing the normal data. High reconstruction error indicates a novel signal that does not match the patterns of normal data. One unsupervised portion of the manifold model adapts its representation in response to recent data. A second supervised portion of the model is made of a basis trained in advance using labeled examples of RFI; this prevents false positives due to these events. For a linear model, an orthonormalization operation is used to combine these bases prior to the anomaly detection decision. Another novel aspect of the approach lies in combining basis vectors learned in an unsupervised, online fashion from the data stream with supervised basis vectors learned in advance from known examples of false alarms. Adaptive, data-driven detection is achieved that is also informed by existing domain knowledge about signals that may be statistically anomalous, but are not interesting and should therefore be ignored. The method was evaluated using data from the Parkes Multibeam Survey. This data set was originally collected to search for pulsars, which are astronomical sources that emit radio pulses at regular periods. However, several non-pulsar anomalies have recently been discovered in this dataset, making it a compelling test case. By explicitly filtering known false alarm patterns, the approach yields significantly better performance than current transient detection methods.

Wagstaff, Kiri L.↗
NASA NTRS2023

A Compilation of Composite Overwrapped Pressure Vessel Research (2015–2021)

This document presents the results of a series of studies performed to develop data-driven rupture limit equations and ballistic limit equations for composite overwrapped pressure vessels. These equations can be used to differentiate between impact conditions that would result in only a small hole or crack from those that would cause catastrophic tank failure. This information would be useful in selecting tank materials to avoid catastrophic tank failure in the event of a perforating on-orbit micrometeoroid or orbital debris particle impact.

Rupture Limit Equations↗
DOE OSTI2021

Self-similar mesocrystals form via interface-driven nucleation and assembly

Crystallization by particle attachment (CPA) is a frequently occurring mechanism of colloidal crystallization that results in hierarchical morphologies. CPA has been exploited to create nanomaterials with unusual properties and is implicated in the development of complex mineral textures. Oriented attachment–a form of CPA in which particles align along specific crystallographic directions–produces mesocrystals that diffract as single crystals do, although the constituent particles are still discernible. The conventional view of CPA is that nucleation provides a supply of particles that aggregate via Brownian motion biased by attractive interparticle potentials. However, mesocrystals often exhibit regular morphologies and uniform sizes. Although many crystal systems form mesocrystals and individual attachment events have been directly visualized10, how random attachment events lead to well defined, self-similar morphologies remains unknown, as does the role of surface-bound ligands, which are ubiquitous in nanoparticle systems. Attempts to understand mesocrystal formation are further complicated in many systems by the presence of precursor nanoparticles with a phase distinct from that of the bulk. Some studies propose that such particles convert before attachment, whereas others attribute conversion to the attachment process itself and yet others conclude that transformation occurs after the mesocrystals exceed a characteristic size. Here we investigate mesocrystal formation by iron oxides, which are important colloidal phases in natural environments and classic examples of systems forming ubiquitous precursor phases and undergoing CPA accompanied by phase transformations. Combining in situ transmission electron microscopy (TEM) at 80 degrees Celsius with ‘freeze-and-look’ TEM, we tracked the formation of haematite (Hm) mesocrystals in the presence of oxalate (Ox), which is abundant in soils, where iron oxides are common. In this work, we find that isolated Hm particles rarely appear, but once formed, interfacial gradients at the Ox-covered surfaces drive Hm particles to nucleate repeatedly about two nanometres from the surfaces, to which they then attach, thereby generating mesocrystals. Comparison to natural and synthetic systems suggests that interface-driven pathways are widespread.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Multi-Model Framework for Assessing Long- and Short-Term Climate Influences on the Electric Grid

Climate change influences many aspects of the electric grid, but prior work and industry practices often ignore the potential effects of changing climate, or they only consider a single effect or individual effects in isolation. Challenges vary with each grid and include adapting to long-term trends such as changing temperature and precipitation or shorter-term events such as drought or storms that could increase in frequency or intensity. Here we present a multi-model framework designed to analyze the effects of long and short-term climate impacts in combination. This framework couples capacity expansion and production cost models with hydrologic models and future climate scenario data to analyze alternative climate and energy futures at high spatial, temporal, and process resolutions. Furthermore, we constructed and evaluated the results of a suite of simulated scenarios exploring climate impacts on capacity investment and stress-tested the resulting future infrastructures using hourly dispatch modeling under alternative drought and load conditions. We demonstrate the approach through a case study of the U.S. Western Interconnection, where climate impacts depend on interactions between temperature-induced load, water availability for hydropower, technology competitiveness, and demand flexibility. Changes in 2038 generating capacity range from -8.5-16.6 GW, and changes in 2038 transmission capacity range from -1-2 GW. Capacity increases are driven by higher load from higher temperatures, while capacity reductions can be achieved in scenarios with higher future hydropower availability and increased demand flexibility. Scenarios requiring additional capacity cost an additional $\$5$-$\$17$ billion (discounted) from 2018 to 2038; however, scenarios with capacity reductions cost $\$1$-$\$18$ billion less. Stress tests on four 2038 infrastructures demonstrated that the identified systems were able to serve at least 99.999% of load and 99.96% of reserves. However, drought and unexpected high-load conditions can result in reduced capacity to respond to contingency events we did not model. Although these results are system and scenario specific, they highlight the importance of considering multiple climate change impacts simultaneously in long-term planning efforts and demonstrate a multi-model, multiscale approach that can be flexibly applied to any system and set of climate change concerns.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Data-Driven Modeling of Electron Recoil Nucleation in PICO C$_3$F$_8$ Bubble Chambers

The primary advantage of moderately superheated bubble chamber detectors is their simultaneous sensitivity to nuclear recoils from weakly interacting massive particle (WIMP) dark matter and insensitivity to electron recoil backgrounds. A comprehensive analysis of PICO gamma calibration data demonstrates for the first time that electron recoils in C3F8 scale in accordance with a new nucleation mechanism, rather than one driven by a hot spike as previously supposed. Using this semiempirical model, bubble chamber nucleation thresholds may be tuned to be sensitive to lower energy nuclear recoils while maintaining excellent electron recoil rejection. The PICO-40L detector will exploit this model to achieve thermodynamic thresholds as low as 2.8 keV while being dominated by single-scatter events from coherent elastic neutrino-nucleus scattering of solar neutrinos. In one year of operation, PICO-40L can improve existing leading limits from PICO on spin-dependent WIMP-proton coupling by nearly an order of magnitude for WIMP masses greater than 3 GeV c-2 and will have the ability to surpass all existing non-xenon bounds on spin-independent WIMP-nucleon coupling for WIMP masses from 3 to 40 GeV c-2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

MAGNETIC FLUX CANCELATION AS THE TRIGGER OF SOLAR QUIET-REGION CORONAL JETS

We report observations of 10 random on-disk solar quiet-region coronal jets found in high-resolution extreme ultraviolet (EUV) images from the Solar Dynamics Observatory ( SDO )/Atmospheric Imaging Assembly and having good coverage in magnetograms from the SDO /Helioseismic and Magnetic Imager (HMI). Recent studies show that coronal jets are driven by the eruption of a small-scale filament (called a minifilament ). However, the trigger of these eruptions is still unknown. In the present study, we address the question: what leads to the jet-driving minifilament eruptions? The EUV observations show that there is a cool-transition-region-plasma minifilament present prior to each jet event and the minifilament eruption drives the jet. By examining pre-jet evolutionary changes in the line of sight photospheric magnetic field, we observe that each pre-jet minifilament resides over the neutral line between majority-polarity and minority-polarity patches of magnetic flux. In each of the 10 cases, the opposite-polarity patches approach and merge with each other (flux reduction between 21% and 57%). After several hours, continuous flux cancelation at the neutral line apparently destabilizes the field holding the cool-plasma minifilament to erupt and undergo internal reconnection, and external reconnection with the surrounding coronal field. The external reconnection opens the minifilament field allowing the minifilament material to escape outward, forming part of the jet spire. Thus, we found that each of the 10 jets resulted from eruption of a minifilament following flux cancelation at the neutral line under the minifilament. These observations establish that magnetic flux cancelation is usually the trigger of quiet-region coronal jet eruptions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Boundary layer control of nocturnal convection associated with a synoptic scale system

Well defined nocturnal convection, together with distinct maximum precipitation events in Nebraska, were associated with strong diurnal wind oscillations at 850 mb in Oklahoma, Kansas and Missouri in the present study of pronounced precipitation and low level circulation variations over the Great Plains on May 5-10, 1979. A numerical prediction model, used to relate boundary layer flow modulations to synoptic scale flow for this case, is driven by a diurnal cycle. Experimental forecasts displayed diurnal wind modulations at 500 m, with rising motions of the order of 4 cm/sec at the 2200 m level, along the leading edge of the nocturnal jet.

Astling, E. G.↗
NASA NTRS2004

Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data

The Autonomous Sciencecraft Experiment (ASE) is a JPL-led, New Millennium Program mission containing new technology in the form of software to be flown on the Earth Observer-1 (EO-1) satellite in early 2004. This new technology will facilitate an artificially intelligent machine with autonomous science-driven capabilities. Among the ASE flight software is a set of onboard science algorithms designed for autonomous data processing, primarily based on change detection from observation to observation. Using the output from these algorithms, ASE has the ability to autonomously modify the EO-1 observation plan, retargeting itself for a more in-depth observation of a scientific event in progress. Furthermore, intelligent and selective information down-linking will maximize return of the most valuable scientific data. Among the algorithms developed for use on ASE is a Lava-Vegetation (L-V) detection algorithm. This algorithm can effectively identify the initial location and extent of lava and vegetation coverage based on spectral shape. Comparison of several different observations, all classified via this algorithm, can make change detection possible.

Lee, R. J.↗
NASA NTRS2017

Maven Observations of Tail Current Sheet Flapping at Mars

The Martian magnetotail is a complex regime through which atmospheric particles are lost to space. Our current understanding of Mars' tail continues to develop with the comprehensive particle and field data collected by Mars Atmosphere and Volatile EvolutioN (MAVEN). In this work, we identify periods when MAVEN encounters multiple current sheet crossings through a single tail traversal in order to understand tail dynamics. We apply an analysis technique that has been developed and validated by using multipoint measurements in order to separate the spatial and temporal properties associated with current sheet flapping. Events are classified into periods of steady flapping, due to a global motion of the current sheet, and kink-like flapping, resulting from localized wave propagation along the tail current sheet. Out of 106 periods during which multiple current sheet crossings were observed, 20 were due to steady flapping and 10 from kink-like flapping. A majority of the kink-like events resulted from waves propagating in the opposite direction of the solar wind convection electric field, regardless of their location in the tail, unlike at Earth and Venus. This finding suggests that possible magnetosphere energy sources, whereby plasma is accelerated and removed from the Martian environment, are not located in the central magnetotail; rather, these waves may be driven by a source located at the tail flank based on the direction of the solar wind electric field. Therefore, by identifying potential sources of impulsive energy release in the tail, we may better understand mechanisms that drive atmospheric loss at Mars.

Atmospheric Particles↗
NASA NTRS2017

MAVEN observations of tail current sheet flapping at Mars

The Martian magnetotail is a complex regime through which atmospheric particles are lost to space. Our current understanding of Mars' tail continues to develop with the comprehensive particle and field data collected by Mars Atmosphere and Volatile EvolutioN (MAVEN). In this work, we identify periods when MAVEN encounters multiple current sheet crossings through a single tail traversal in order to understand tail dynamics. We apply an analysis technique that has been developed and validated by using multipoint measurements in order to separate the spatial and temporal properties associated with current sheet flapping. Events are classified into periods of steady flapping, due to a global motion of the current sheet, and kink‐like flapping, resulting from localized wave propagation along the tail current sheet. Out of 106 periods during which multiple current sheet crossings were observed, 20 were due to steady flapping and 10 from kink‐like flapping. A majority of the kink‐like events resulted from waves propagating in the opposite direction of the solar wind convection electric field, regardless of their location in the tail, unlike at Earth and Venus. This finding suggests that possible magnetosphere energy sources, whereby plasma is accelerated and removed from the Martian environment, are not located in the central magnetotail; rather, these waves may be driven by a source located at the tail flank based on the direction of the solar wind electric field. Therefore, by identifying potential sources of impulsive energy release in the tail, we may better understand mechanisms that drive atmospheric loss at Mars.

Gina A. DiBraccio↗
DOE OSTI2020

Automating the interpretation of PM 2.5 time–resolved measurements using a data–driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM 2.5 time-resolved data and differentiating PM 2.5 emissions resulting from indoor and outdoor sources. Here, we use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM 2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

ARE THE FAINT STRUCTURES AHEAD OF SOLAR CORONAL MASS EJECTIONS REAL SIGNATURES OF DRIVEN SHOCKS?

Recently, several studies have assumed that the faint structures ahead of coronal mass ejections (CMEs) are caused by CME-driven shocks. In this study, we have conducted a statistical investigation to determine whether or not the appearance of such faint structures depends on CME speeds. For this purpose, we use 127 Solar and Heliospheric Observatory/Large Angle Spectroscopic COronagraph (LASCO) front-side halo (partial and full) CMEs near the limb from 1997 to 2011. We classify these CMEs into two groups by visual inspection of CMEs in the LASCO-C2 field of view: Group 1 has the faint structure ahead of a CME and Group 2 does not have such a structure. We find the following results. (1) Eighty-seven CMEs belong to Group 1 and 40 CMEs belong to Group 2. (2) Group 1 events have much higher speeds (average = 1230 km s{sup –1} and median = 1199 km s{sup –1}) than Group 2 events (average = 598 km s{sup –1} and median = 518 km s{sup –1}). (3) The fraction of CMEs with faint structures strongly depends on CME speeds (V): 0.93 (50/54) for fast CMEs with V ≥ 1000 km s{sup –1}, 0.65 (34/52) for intermediate CMEs with 500 km s{sup –1} ≤ V < 1000 km s{sup –1}, and 0.14 (3/21) for slow CMEs with V < 500 km s{sup –1}. We also find that the fraction of CMEs with deca-hecto metric type II radio bursts is consistent with the above tendency. Our results indicate that the observed faint structures ahead of fast CMEs are most likely an enhanced density manifestation of CME-driven shocks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources: Preprint

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

contingencies↗
DOE OSTI2018

Understanding the Driver of Energetic Electron Precipitation Using Coordinated Multisatellite Measurements

Magnetospheric plasma waves play a significant role in ring current and radiation belt dynamics, leading to pitch angle scattering loss and/or stochastic acceleration of the particles. During a nonstormtime dropout event on 24 September 2013, intense electromagnetic ion cyclotron (EMIC) waves were detected by Van Allen Probe A (RBSP-A). We quantitatively analyze a conjunction event when RBSP-A was located approximately along the same magnetic field line as MetOp- 01, which detected simultaneous precipitation of >30 keV protons and energetic electrons over an unexpectedly broad energy range (>~30 keV). Multi-point observations together with quasilinear theory provide direct evidence that the observed electron precipitation at higher energy (>~700 keV) is primarily driven by EMIC waves. However, the newly observed feature of the simultaneous electron precipitation extending down to ~30 keV is not supported by existing theories and raises an interesting question on whether EMIC waves can scatter such low-energy electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Connections Between Mean North Pacific Circulation and Western US Precipitation Extremes in a Warming Climate

Abstract Extreme precipitation has widespread impacts over the western US (WUS), which are expected to be exacerbated in the future given thermodynamically driven increases in atmospheric moisture and shifts in atmospheric circulation patterns. However, internal climate variability complicates how these factors affect future projections of precipitation changes. In this study, we investigate late 21st‐century changes in mean‐state responses over the North Pacific Ocean and associated WUS precipitation extremes using the Multi‐Model Large Ensemble Archive, which is a recent and valuable resource to disentangle the effects of model structural differences from internal variability. We found that inter‐model differences in precipitation extremes over the WUS are large: from south to north (here, from California to Washington), models show different levels of increases for both the frequency and intensity of heavy precipitation. In general, the Pacific Northwest sees a more consistent signal than California. We show that wet‐season mean circulation influences extreme precipitation, by using a decomposition of the relative contributions of thermodynamic (atmospheric humidity) and dynamic (large‐scale circulation) driven changes. The thermodynamic contribution dominates, however, the dynamic contribution varies with latitude and differs substantially between model ensembles. Inter‐model differences affect the spread in not only the magnitude, but also the sign of the dynamic contribution. This implies that mean circulation changes contribute to uncertainty in quantifying the regional structure of concentrated moisture flux and future extreme events. This result highlights the need to more accurately constrain projected North Pacific coupled circulation changes to narrow the spread in projections of future precipitation extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information

Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.

54 ENVIRONMENTAL SCIENCES↗