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DOE OSTI2025

CMIP6-based Dynamically Downscaled Hydroclimate Projection over the Conterminous US

This dataset presents a suite of downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled dynamically using Regional Climate Model version 4 (RegCM4). Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗

DOE OSTI2026

Modeling CO 2 flow through faulted/fractured reservoirs using tEDFM in corner-point grids

The interest in underground CO 2 storage has increased significantly over the last decade because of the rising concern about global warming due to the growing levels of greenhouse gases in the atmosphere. Considering that CO 2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is regarded as one of the most direct approaches to achieving the net zero carbon target. Although CO 2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. Here, this work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model the flow of CO 2 in faulted reservoirs, we present the first transient embedded discrete fracture model for corner-point grids (tEDFM-CPG). By comparing the results of the tEDFM-CPG to high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO 2 flow in faulted/fractured reservoirs. Finally, this work presents the use of mixed reality (MR) to efficiently observe CO 2 gas migration in the interior of these corner-point grid systems.

25 ENERGY STORAGE↗

NASA NTRS2016

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

Raschke, Ehrhard↗

NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗

NASA NTRS2018

Prediction Skill of U.S. Flash Droughts in Subseasonal Experiment (SubX) Model Hindcasts

Droughts that establish themselves over a short period of time (weeks to a few months), referred to as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict such droughts in advance would greatly enhance our preparation for them and potentially reduce their impacts. The sub-seasonal time scale at which flash droughts occur emphasizes the importance of producing forecasts at weekly or finer intervals that extend beyond the numerical weather prediction time frame. Here we assess the ability of eight global forecast systems, each participating in the Sub-seasonal Experiment project (SubX), to predict key features associated with rapidly developing droughts over the United States during the last two decades. MERRA2 reanalysis is used as observations. Prediction skill for temperature and precipitation anomalies during these events is limited to the first 1-2 weeks after initialization for most hindcasts. However, there are some hindcasts in which large anomalies are well predicted 3-4 weeks or more in advance. The physical mechanisms that are key to the development of surface anomalies, including quasi-stationary atmospheric waves, were also evaluated. Most hindcasts were unable to capture the development or progression of such drought-inducing circulation features more than 1-2 weeks in advance.

Deangelis, Anthony M.↗

DOE OSTI2024

Near-global summer circulation response to the spring surface temperature anomaly in Tibetan Plateau –– the GEWEX/LS4P first phase experiment

Subseasonal to seasonal (S2S) prediction of droughts and floods is one of the major challenges of weather and climate prediction. Recent studies suggest that the springtime land surface temperature/subsurface temperature (LST/SUBT) over the Tibetan Plateau (TP) can be a new source of S2S predictability. The project “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P)” was initiated to study the impact of springtime LST/SUBT anomalies over high mountain areas on summertime precipitation predictions. The present work explores the simulated global scale response of the atmospheric circulation to the springtime TP land surface cooling by 16 current state-of-the-art Earth System Models (ESMs) participating in the LS4P Phase I (LS4P-I) experiment. The LS4P-I results show, for the first time, that springtime TP surface anomalies can modulate a persistent quasi-barotropic Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train from the TP via the northeast Asia and Bering Strait to the western part of the North America, along with the springtime westerly jet from TP across the whole North Pacific basin. The TRC wave train modulated by the TP thermal anomaly play a critical role on the early summer surface air temperature and precipitation anomalies in the regions along the wave train, especially over the northwest North America and the southern Great Plains. The participant models that fail in capturing the TRC wave train greatly under-predict climate anomalies in reference to observations and the successful models. These results suggest that the TP LST/SUBT anomaly via the TRC wave train is the first order source of the S2S variability in the regions mentioned. Furthermore, the TP surface temperature anomaly can influence the Southern Hemispheric circulation by generating cross-equator wave trains. However, the simulated propagation pathways from the TP into the Southern Hemisphere show large inter-model differences. More dynamical understanding of the TRC wave train as well as its cross-equator propagation into the Southern Hemisphere will be explored in the newly launched LS4P phase II experiment.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

TRMM Latent Heating Retrieval and Comparisons with Field Campaigns and Large-Scale Analyses

Rainfall production is a fundamental process within the Earth's hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating (LH), is one of the principal sources of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The vertical distribution of LH has a strong influence on the atmosphere, controlling large-scale tropical circulations, exciting and modulating tropical waves, maintaining the intensities of tropical cyclones, and even providing the energetics of midlatitude cyclones and other mobile midlatitude weather systems. Moreover, the processes associated with LH result in significant non-linear changes in atmospheric radiation through the creation, dissipation and modulation of clouds and precipitation. Yanai et al. (1973) utilized the meteorological data collected from a sounding network to present a pioneering work on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Yanai's paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables used in LH algorithms. This paper examines the retrieval, validation, and application of LH estimates based on rain rate quantities acquired from the Tropical Rainfall Measuring Mission satellite (TRMM). TRMM was launched in November 1997 as a joint enterprise between the American and Japanese space agencies -- with overriding goals of providing accurate four-dimensional estimates of rainfall and LH over the global Tropics and subtropics equatorward of 35o. Other literature has acknowledged the achievement of the first goal of obtaining an accurate rainfall climatology. This paper describes the second major goal of obtaining credible LH estimates as well as their applications within TRMM's zone of coverage, the standard TRMM LH products, and areas for further improvement.

latent heating↗

NASA NTRS1989

Photochemistry of the atmosphere of Uranus

A detailed one-dimensional photochemical model incorporating vertical transport by eddy and molecular diffusion is used to study the importance of chemical and physical processes that control the hydrocarbon abundances in the stratosphere of Uranus. The study includes updated photochemical reaction rates and photolysis branching ratios; explicit calculation of the condensation rate of condensing species; and appropriate boundary conditions at the tropopause. It is found that the basic photochemical process generally produces low net conversion efficiencies of methane to higher hydrocarbons. However, even with these low efficiencies, the total condensation rate, which represents the net rate of conversion of methane to hydrocarbons, is roughly 2 x 10 to the -16th g/cm sec, in agreement with the upper range of the low condensation rate for the production of the stratospheric aerosol layer as determined by Pollack et al. (1987). Furthermore, a range of vertical transport rates is required to separately match the observational constraints on hydrocarbon abundances. The solar reflection data refer to the sunlit polar region of Uranus and require an eddy diffusion coefficient of K approx. = 50 sq cm/sec for globally averaged insolation or K approx = 1500 (Nh/N) exp 1/2 for overhead sun. The Voyager 2 Utraviolet Spectrometer solar occultation data refer to the equatorial regions, thus implying a large latitudinal gradient in stratospheric hydrocarbon abundance.

Summers, Michael E.↗

NASA NTRS2021

Reduced El NiñO Variability in the Mid-Pliocene According to the Pliomip2 Ensemble

The mid-Pliocene warm period (3.264–3.025 Ma) is the most recent geological period during which atmospheric CO 2 levels were similar to recent historical values (∼400 ppm). Several proxy reconstructions for the mid-Pliocene show highly reduced zonal sea surface temperature (SST) gradients in the tropical Pacific Ocean, indicating an El Niño-like mean state. However, past modelling studies do not show these highly reduced gradients. Efforts to understand mid-Pliocene climate dynamics have led to the Pliocene Model Intercomparison Project (PlioMIP). Results from the first phase (PlioMIP1) showed clear El Niño variability (albeit significantly reduced) and did not show the greatly reduced time-mean zonal SST gradient suggested by some of the proxies. In this work, we study El Niño–Southern Oscillation (ENSO) variability in the PlioMIP2 ensemble, which consists of additional global coupled climate models and updated boundary conditions compared to PlioMIP1. We quantify ENSO amplitude, period, spatial structure and “flavour”, as well as the tropical Pacific annual mean state in mid-Pliocene and pre-industrial simulations. Results show a reduced ENSO amplitude in the model-ensemble mean (−24 %) with respect to the pre-industrial, with 15 out of 17 individual models showing such a reduction. Furthermore, the spectral power of this variability considerably decreases in the 3–4-year band. The spatial structure of the dominant empirical orthogonal function shows no particular change in the patterns of tropical Pacific variability in the model-ensemble mean, compared to the pre-industrial. Although the time-mean zonal SST gradient in the equatorial Pacific decreases for 14 out of 17 models (0.2 ∘C reduction in the ensemble mean), there does not seem to be a correlation with the decrease in ENSO amplitude. The models showing the most “El Niño-like” mean state changes show a similar ENSO amplitude to that in the pre-industrial reference, while models showing more “La Niña-like” mean state changes generally show a large reduction in ENSO variability. The PlioMIP2 results show a reasonable agreement with both time-mean proxies indicating a reduced zonal SST gradient and reconstructions indicating a reduced, or similar, ENSO variability.

mid-Pliocene warm period↗

NASA NTRS1993

The national geomagnetic initiative

The Earth's magnetic field, through its variability over a spectrum of spatial and temporal scales, contains fundamental information on the solid Earth and geospace environment (the latter comprising the atmosphere, ionosphere, and magnetosphere). Integrated studies of the geomagnetic field have the potential to address a wide range of important processes in the deep mantle and core, asthenosphere, lithosphere, oceans, and the solar-terrestrial environment. These studies have direct applications to important societal problems, including resource assessment and exploration, natural hazard mitigation, safe navigation, and the maintenance and survivability of communications and power systems on the ground and in space. Studies of the Earth's magnetic field are supported by a variety of federal and state agencies as well as by private industry. Both basic and applied research is presently supported by several federal agencies, including the National Science Foundation (NSF), U.S. Geological Survey (USGS), U.S. Department of Energy (DOE), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and U.S. Department of Defense (DOD) (through the Navy, Air Force, and Defense Mapping Agency). Although each agency has a unique, well-defined mission in geomagnetic studies, many areas of interest overlap. For example, NASA, the Navy, and USGS collaborate closely in the development of main field reference models. NASA, NSF, and the Air Force collaborate in space physics. These interagency linkages need to be strengthened. Over the past decade, new opportunities for fundamental advances in geomagnetic research have emerged as a result of three factors: well-posed, first-order scientific questions; increased interrelation of research activities dealing with geomagnetic phenomena; and recent developments in technology. These new opportunities can be exploited through a national geomagnetic initiative to define objectives and encourage coordination of efforts among federal and state agencies, academic institutions, and industry to systematically characterize the spatial and temporal behavior of the Earth's magnetic field on local, regional, and global scales in order to understand the physical processes in the solid earth and geospace environment, and to apply this understanding to a variety of scientific problems and to technical and societal needs.

Source record↗

NASA NTRSDate not supplied

Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System

Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.

GMI↗

DOE OSTI2025

E3SM‐Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.

Energy Exascale Earth System Model (E3SM)↗

DOE OSTI2026

Benchmarking soil moisture and its relationship to ecohydrologic variables in Earth System Models

Soil moisture (SM) is a key regulator of ecosystem biogeophysics, influencing plant water relations and land-atmosphere energy exchanges. We evaluate the representation of SM in 16 Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using the International Land Model Benchmarking (ILAMB) framework, focusing on surface (0–5, 0–10 cm) and rootzone (0–100 cm) depths, as well as key ecohydrological variables like gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET), and their coupling. Models are benchmarked against multiple observational and assimilated datasets to assess both state variables and cross-variable relationships. Surface SM is generally well represented (r > 0.87), while rootzone SM variability is systematically overestimated (normalized standard deviation > 1). ET shows strong agreement with observations (r > 0.9), whereas GPP and LAI exhibit larger inter-model spread. Skill in individual variables does not guarantee realistic SM–ecohydrology coupling, which varies strongly across models and depends on the reference dataset. Köppen-based regional analyses reveal strong regime dependence, with several models performing well in Tropical and Temperate regions but degrading in Continental (high-latitude) zones. Across both global and regional benchmarks, models cluster by land surface framework, indicating that structural choices in soil hydrology and soil–plant coupling exert a first-order control on performance. These results provide process-relevant benchmarks and suggest that improving the representation of vertical soil structure, rooting depth distributions, and soil–plant hydraulic coupling will be central to advancing soil moisture realism in next-generation Earth system models.

CMIP6↗

NASA NTRSDate not supplied

Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of Oci

Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. A critical requirement for OCI is the high-contrast or spatial cross-talk specification (also referred to as in-field stray-light response). The requirement states that for global top-of-atmosphere radiances based on measured MODIS radiances, the global average residual contamination shall be less than 0.15% for any multispectral band. Accurate resolution of high contrast in TOA radiance images is important to estimate stray light contamination due to clouds, for studying small scale features like ocean fronts and for working in coastal and estuarine areas where the scales are 1km. This occurs in all wavelengths in the spatial direction. Knowledge of high contrast resolution makes up part of the artifact budget. Accurate measurement of the high-contrast performance of OCI requires laboratory Ground Support Equipment (GSE) that projects a scene of sufficient quality that the unwanted stray light of the GSE itself is not confused with the stray light response of the telescope. This paper concerns the development, analyses and test of the GSE to ensure the quality of the projected image is sufficient to verify the OCI requirements. Optical models were developed for both the instrument as well as the GSE and laboratory environment. Simulation of various non-ideal parameters were critical to accurately predict performance. Measurements using COTS cameras and lenses were also made of the projected GSE image to reasonably verify the optical model predictions. Measured and modelled results from OCI are discussed.

OCI↗

NASA NTRSDate not supplied

Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of OCI

OCI, PACE, LSF, PSF, Stray-light, Contrast, Cross-↗

NASA NTRS2016

The New Sun-Sky-Lunar Cimel CE318-T Multiband Photometer - A Comprehensive Performance Evaluation

This paper presents the new photometer CE318-T, able to perform daytime and night-time photometric measurements using the sun and the moon as light source. Therefore,this new device permits a complete cycle of diurnal aerosol and water vapour measurements valuable to enhance atmospheric monitoring to be extracted. In this study wehave found significantly higher precision of triplets when comparing the CE318-T master instrument and the Cimel AErosol RObotic NET work (AERONET) master (CE318-AERONET) triplets as a result of the new CE318-T tracking system. Regarding the instrument calibration, two new methodologies to transfer the calibration from a reference instrument using only daytime measurements (Sun Ratio and Sun-Moon gain factor techniques) are presented and discussed. These methods allow the reduction of the previous complexities inherent to nocturnal calibration. A quantitative estimation of CE318-T AOD uncertainty by means of error propagation theory during daytime revealed AOD uncertainties (u(sup D)(sub AOD)) for Langley-calibrated instruments similar to the expected values for other reference instruments (0.002-0.009). We have also found u(sup D)(sub AOD) values similar to the values reported in sun photometry for field instruments (approximately 0.015). In the case of the night-time period, the CE318-T-estimated standard combined uncertainty (u(sup N)(sub AOD)) is dependent not only on the calibration technique but also on illumination conditions and the instrumental noise. These values range from 0.011-0.018 for Lunar Langley-calibrated instruments to 0.012-0.021 for instruments calibrated using the Sun Ratio technique. In the case of moon-calibrated instruments using the Sun-Moon gain factor method and sun calibrated using the Langley technique, we found u(sup N)(sub AOD) ranging from 0.016 to 0.017 (up to 0.019 in 440 nm channel), not dependent on any lunar irradiance model. A subsequent performance evaluation including CE318-T and collocated measurements from independent reference instruments has served to assess the CE318-T performance as well as to confirm its estimated uncertainty. Daytime AOD evaluation, performed at Izana station from March to June 2014, encompassed measurements from a reference CE318-T, a CE318-AERONET master instrument, a Precision Filter Radiometer (PFR) and a Precision Spectroradiometer (PSR) prototype, reporting low AOD discrepancies between the four instruments (up to 0.006). The nocturnal AOD evaluation was performed using CE318-T- and starphotometer-collocated measurements and also by means of a day/night coherence transition test using the CE318-T master instrument and the CE318 daytime data from the CE318-AERONET master instrument. Results showed low discrepancies with the star photometer at 870 and 500 nm channels(less tna or equal to 0.013) and differences with AERONET daytime data (1 h after and before sunset and sunrise) in agreement with the estimated u(sup N)(sub AOD) values at all illumination conditions in the case of channels within the visible spectral range, and only for high moon's illumination conditions in the case of near infrared channels. Perceptible water vapour (PWV) validation showed a good agreement between CE318-T and Global Navigation Satellite System (GNSS) PWV values for all illumination conditions, within the expected precision for sun photometry. Finally, two case studies have been included to highlight the ability of the new CE318-T to capture the diurnal cycle of aerosols and water vapour as well as short-term atmospheric variations, critical for climate studies.

Barreto, Africa↗

NASA NTRS1993

Precision orbit determination using TOPEX/Poseidon TDRSS observations

The TOPEX/Poseidon (T/P) Mission carries a variety of packages to support experimental, precision and operational orbit determination. Included are a GPS transponder, laser retro-reflectors, the French-developed Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Doppler tracking system and a Tracking Data Relay Satellite System (TDRSS) transponder. Presently, TDRSS tracking is used for operational orbit support and is processed with force and measurement modeling consistent with this purpose. However, the low noise and extensive geographical coverage of the TDRSS/TOPEX data allows an assessment of TDRSS Precision Orbit Determination (POD) capabilities by comparison to the T/P precision orbit determination. The Geodynamics (GEODYN) Orbit Determination System is used to process laser and DORIS data to produce the precision orbits for the T/P Project. GEODYN has been modified recently to support the TDRSS observations. TDRSS data analysis can now benefit from the extensive force modeling and reference frame stability needed to meet the orbit determination (OD) goals of the T/P Mission. This analysis has concentrated on the strongest of the TDRSS measurement types, its two-way average range rate. Both the TDRSS and T/P orbits have been assessed in combination with the global satellite laser ranging (SLR) data and by themselves. These results indicate that significant improvement in the TDRSS ephemerides is obtained when the T/P orbit is well determined by SLR, and the TDRSS/TOPEX Doppler link is used to position TDRSS. Meter-level TDRSS positioning uncertainty is achieved using this approach. When the TDRSS orbit location is provided by this approach, the two-way range rate from a single TDRSS (i.e. West only) can provide T/P orbits with sub-meter radial accuracies and two meter RMS total position agreement with SLR defined orbits. These preliminary results indicate improved modeling of the TDRSS measurement through the elimination of heretofore neglected effects like the motion of the T/P TDRSS antenna and improved modeling of ionospheric and atmospheric refractive effects, and the inclusion of TDRSS East in the analysis are warranted. Through these improvements, TDRSS can make a significant contribution to geopotential recovery and precision OD.

Teles, Jerome↗

NASA NTRS2015

Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impacts, Vulnerability, and Adaptation: Chapter - 5

The global change research community has recognized that new pathway and scenario concepts are needed to implement impact and vulnerability assessment where precise prediction is not possible, and also that these scenarios need to be logically consistent across local, regional, and global scales. For global climate models, representative concentration pathways (RCPs) have been developed that provide a range of time-series of atmospheric greenhouse-gas concentrations into the future. For impact and vulnerability assessment, new socio-economic pathway and scenario concepts have also been developed, with leadership from the Integrated Assessment Modeling Consortium (IAMC).This chapter presents concepts and methods for development of regional representative agricultural pathways (RAOs) and scenarios that can be used for agricultural model intercomparison, improvement, and impact assessment in a manner consistent with the new global pathways and scenarios. The development of agriculture-specific pathways and scenarios is motivated by the need for a protocol-based approach to climate impact, vulnerability, and adaptation assessment. Until now, the various global and regional models used for agricultural-impact assessment have been implemented with individualized scenarios using various data and model structures, often without transparent documentation, public availability, and consistency across disciplines. These practices have reduced the credibility of assessments, and also hampered the advancement of the science through model intercomparison, improvement, and synthesis of model results across studies. The recognition of the need for better coordination among the agricultural modeling community, including the development of standard reference scenarios with adequate agriculture-specific detail led to the creation of the Agricultural Model Intercomparison and Improvement Project (AgMIP) in 2010. The development of RAPs is one of the cross-cutting themes in AgMIP's work plan, and has been the subject of ongoing work by AgMIP since its creation.

agriculture↗