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DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

DOE OSTI2024

Volatile Organic Compounds and Meteorological Conditions in the Missouri Ozark AmeriFlux (MOFLUX) Site, 2023

This data set contains measurements of atmospheric components and meteorological conditions of central Missouri, United States during the summer of 2023. Data serve to examine the impact of meteorological conditions relevant to future climate on the emission and transformation of volatile organic compounds (VOCs). During the field campaign, researchers were also able to incorporate opportunistic analyses of the long-range transport of smoke plumes generated from extreme forest fire activities in Canada. VOC measurements were conducted at the Missouri Ozark AmeriFlux (MOFLUX) site (latitude 38.7441, longitude −92.2000) using a proton transfer reaction time of flight mass spectrometer (PTR-ToF-MS 6000 X2). The sampling campaign was conducted during the summer of 2023 (2023-06-25 to 2023-08-12) with measurements being taken at high temporal resolution (1 hour). Particular VOCs analyzed include: Methanol, Acetonitrile, Acetone, Isoprene, Methylvinyl ketone (MVK) and methacrolein (MACr), Benzene, Toluene, Catechol, and Monoterpene. Meteorological parameters included in this dataset were collected from a nearby Columbia Regional Airport (~10 km). Global solar radiation data were measured at a weather site in Ashland, MO, 5.22 km from the MOFLUX tower. The data were accessed using the MesoWest online website (https://mesowest.utah.edu/) provided by the Department of Atmospheric Sciences, University of Utah. Discussion of the data providers, database, and dissemination were highlighted in prior studies (Horel et al., 2002a; Horel et al., 2002b) . Smoke mixing ratios (in mg m−3) were estimated from the High-Resolution Rapid Refresh (HRRR) 3 km weather model for Missouri at 6-hour intervals (Dowell et al., 2022). This dataset contains three data files in comma separate (*.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

Acetone↗

DOE OSTI2024

Air temperature, Utqiagvik (Barrow), Alaska, July 15-17, 2022

Air temperature measured at 15 minute intervals using a HOBO 64K Pendant UA-001-64 housed in a solar radiation shield. Data were collected 15-27 July 2022, on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. The data package files are in .csv format, and include a data file and metadata file. These data were collected in support of stomatal response gas exchange measurements collected during this time period. See related data packages for leaf gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), and phenocamera images collected at the same time and location. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data

This dataset characterizes solifluction lobe morphology and spatial patterns using pre-existing LiDAR-derived digital elevation models of 25 sites across Norway with accompanying long term climate data for each site. Data were collected as part of an effort to better understand controls on the formation of solifluction patterns and to test the idea that they are analogous to fluid instabilities. We also provide soil velocity profiles and estimates of effective viscosity from across the world, drawn from literature. They were collected to improve our understanding of the rheology of soliflucting soil. See this article for more information on the theoretical motivation behind this dataset see "Arctic soil patterns analogous to fluid instabilities" (Glade et al., 2021). Data files arranged in a hierarchy and include image files *.tif and *.png, GIS shapefiles and geopackages (*.gpkg), *.csv (with same file as *.xlsx), and *.py (Python scripts readable with a text editor). Files also bundled into *.zip files. Note (2021-10-20): unit corrections made on two files: RR.csv and snowfall.csv. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Arctic Cut-Off High Drives the Poleward Shift of a New Greenland Melting Record

Large-scale atmospheric circulation controls the mass and energy balance of the Greenland ice sheet through its impact on radiative budget, runoff and accumulation. Here, using reanalysis data and the outputs of a regional climate model, we show that the persistence of an exceptional atmospheric ridge, centered over the Arctic Ocean, was responsible for a poleward shift of runoff, albedo and surface temperature records over the Greenland during the summer of 2015. New records of monthly mean zonal winds at 500 hPa and of the maximum latitude of ridge peaks of the 5,700+/-50 m isohypse over the Arctic were associated with the formation and persistency of a cutoff high. The unprecedented (1948-2015) and sustained atmospheric conditions promoted enhanced runoff, increased the surface temperatures and decreased the albedo in northern Greenland, while inhibiting melting in the south, where new melting records were set over the past decade. Subject terms: Earth sciences Atmospheric science Climate science

drainage↗

DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

The response of vegetation canopy conductance (g c ) to changes in moisture availability ($γ^{m}_{gc}$) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale g c observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to $γ^{m}_{gc}$ due to the local cooling effect of g c through transpiration, to develop a novel emergent constraint of $γ^{m}_{gc}$ in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate $γ^{m}_{gc}$ by ~33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce g c when soil moisture decreases. As g c controls carbon, water and energy fluxes, the misrepresentation of modeled $γ^{m}_{gc}$ contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained g c on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 μm) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor.

sea surface temperature (SST)↗

NASA NTRS2009

Improved Determination of Surface and Atmospheric Temperatures Using Only Shortwave AIRS Channels

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. AIRS is a grating spectrometer with a number of linear arrays of detectors with each detector sensitive to outgoing radiation in a characteristic frequency v(sub i) with a spectral band pass delta v(sub i) of roughly v(sub i) /1200. AIRS contains 2378 spectral channels covering portions of the spectral region 650 cm(exp -1) (15.38 gm) - 2665 cm(exp -1)' (3.752 micrometers). These spectral regions contain significant absorption features from two CO2 absorption bands, the 15 micrometer (longwave) CO2 band, and the 4.3 micrometer (shortwave) CO, absorption band. There are also two atmospheric window regions, the 12 micrometerm - 8 micrometer (longwave) window, and the 4.17 micrometer - 3.75 micrometer (shortwave) window. Historically, determination of surface and atmospheric temperatures from satellite observations was performed using primarily observations in the longwave window and CO2 absorption regions. One reason for this was concerns about the effects, during the day, of reflected sunlight and non-Local Thermodynamic Equilibrium (non-LTE) on the observed radiances in the shortwave portion of the spectrum. According to cloud clearing theory, more accurate soundings of both surface skin and atmospheric temperatures can be obtained under partial cloud cover conditions if one uses the longwave channels to determine cloud cleared radiances R(sub i) for all channels, and uses R(sub i) only from shortwave channels in the determination of surface and atmospheric temperatures. This procedure is now being used by the AIRS Science Team in preparation for the AIRS Version 6 Retrieval Algorithm. This paper describes how the effects on the radiances of solar radiation reflected by clouds and the Earth's surface, and also of non-LTE, are accounted for in the analysis of the data. Results are presented for both daytime and nighttime conditions showing improved surface and atmospheric soundings under partial cloud cover resulted from not using R(sub i) in the retrieval process for any longwave channels sensitive to cloud effects. This improvement is made possible because AIRS NEDT in the shortwave portion of the spectrum is extremely low.

Susskind,Joel↗

NASA NTRS2003

Earth Observing System: Global Observations to Study the Earth's Environment

King, Michael D.↗

NASA NTRS2001

Earth Observing System: Global Observations to Study the Earth's Environment

King, Michael D.↗

NASA NTRS2009

Ocean, Land and Meteorology Studies Using Space-Based Lidar Measurements

CALIPSO's main mission objective is studying the climate impact of clouds and aerosols in the atmosphere. CALIPSO also collects information about other components of the Earth's ecosystem, such as oceans and land. This paper introduces the physics concepts and presents preliminary results for the valueadded CALIPSO Earth system science products. These include ocean surface wind speeds, column atmospheric optical depths, ocean subsurface backscatter, land surface elevations, atmospheric temperature profiles, and A-train data fusion products.

Hu,Yongxiang↗

NASA NTRS2002

Overview of Terra

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During the last couple of years, seven EOS science missions were launched, representing observations of (i) total solar irradiance, (ii) Earth radiation budget, (iii) land cover & land use change, (iv) ocean processes (vector wind, sea surface temperature, ocean topography, and ocean color), (v) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), (v) tropospheric chemistry, (vi) sea ice concentration, and (viii) precipitation. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this lecture I will describe how scientists are using NASA's Earth science data to examine land use and natural hazards, environmental air quality, including dust storms over the world's deserts, cloud and radiation properties, sea surface temperature, and tropospheric chemistry. This lecture will describe the Terra satellite, launched in December 1999 and still operating, and each of the five sensors onboard the spacecraft. This overview will highlight the goals and objectives of this mission, and describe the contributions and unique datasets provided by each sensor. This lecture will form the background for an extensive weeklong course on Terra and all the algorithms that have been developed and implemented to process the data from this spacecraft. This lecture will include a description of the Terra orbit, launch, data communication with the spacecraft, and data processing and archival of the data.

King, Michael D.↗

NASA NTRS2003

Robotic Technology for Exploration of Venus

Venus, the "greenhouse planet", is a scientifically fascinating place. A huge number of important scientific questions remain to be answered. Venus is sometimes called Earth's "sister planet" due to the fact that it is closest to the Earth in distance and similar to Earth in size. Despite its similarity to Earth, however, the climate of Venus is vastly different from Earth's. Understanding the atmosphere, climate, geology, and history of Venus could shed considerable light on our understanding of our own home planet. The surface of Venus is a hostile environment, with an atmosperic pressure of over 90 bar of carbon dioxide, temperature of 450 C, and shrouded in sulphuric-acid clouds. Venus has been explored by a number of missions from Earth, including the Russian Venera missions which landed probes on the surface, the American Pioneer missions which flew both orbiters and atmospheric probes to Venus, the Russian "Vega" mission, which floated balloons in the atmosphere of Venus, and most recently the American Magellan mission which mapped the surface by radar imaging. While these missions have answered basic questions about Venus, telling us the surface temperature and pressure, the elevations and topography of the continents, and the composition of the atmosphere and clouds, scientific mysteries still abound. Venus is of considerable interest to terrestrial atmospheric science, since of all the planets in the solar system, it is the closest analogue to the Earth in terms of atmosphere. Yet Venus' atmosphere is an example of "runaway greenhouse effect." Understanding the history and the dynamics of Venus' atmosphere could tell us considerable insight about the workings of the atmosphere of the Earth. It also has some interest to astrobiology-- could life have existed on Venus in an earlier, pre-greenhouse-effect phase? Could life still be possible in the temperate middle-atmosphere of Venus? The geology of Venus also has interest in the study of Earth. surface robot will require new technologies; specifically, it will require electronics, scientific instruments, power supplies, and mechanical linkages designed to operate at a temperature above 450 C-hot enough to melt the solder on a standard electronic circuit board. This will require devices made from advanced semiconductor materials, such as silicon carbide, or even new approaches, such as micro-vacuum tube electronics. Such materials are now being developed in the laboratory.

Landis, Geoffrey A.↗

NASA NTRSDate not supplied

Latest Development in Radiative Transfer Models and Retrieval Algorithms Using Principal Components

The radiative transfer model (RTMs) has a wide range of applications in satellite remote sensing and atmospheric radiation applications. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast and accurate RTMs are needed to efficiently process large amount of satellite data. A Principal Component-based Radiative Transfer Model (PCRTM) was first developed in 2004 at NASA Langley Research Centre to fulfil this need. By using PC-compression, one can reduce the data dimension significantly while maintaining original information content. The PCRTM can directly compute PC-scores and their derivatives with respect to retrieved parameters. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 0.250 µm (400000 cm-1) to 2000 µm (50 cm-1) with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks (0.03 K RMS error in IR and 0.05% in solar). The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, FIRST, and CLARREO-IR in thermal IR spectral region and CLARREO-Solar, CPF, TEMPO, EMIT, OMI, and SCIAMACHY in solar spectral region. The PCRTM accuracy has been demonstrated via RTM intercomparisons and with real satellite observations from AIRS, CrIS, IASI, SCHIAMACHY, and EMIT etc. In this presentation, we will describe two PCRTM-based inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from hyperspectral sounders such as AIRS, CrIS, IASI, and NAST-I. The first one is called Single Fieldof-view Sounder Atmospheric Product (SiFSAP) algorithm. It provides L2 products with 9-times higher area spatial resolution as compared to current cloud-clearing sounder algorithms. The SiFSAP L2 and L3 products are available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The second inversion algorithm is called Climate Fingerprinting Atmospheric Product (ClimFiSP) algorithm. It is designed to produce high quality climate products (trends, anomalies, daily, and monthly profiles of temperature, water vapor, traces, clouds, and surface properties) from multiple satellite sensors such AIRS on Aqua and CrIS on multiple satellites. This product will be available at NASA GES DISC later this year. The PCRTMbased high fidelity simulators for CLARREO and CPF have been used for sensor performance trade studies, algorithm development, and inter-satellite calibrations. We have also used PCRTM generated TOA radiance spectra to train an AI-based algorithm and successfully retrieved cloud properties from EMIT solar hyperspectral imagers.

PCRTM↗

NASA NTRS2020

Long-duration Venus lander for seismic and atmospheric science

An exciting and novel science mission concept called Seismic and Atmospheric Exploration of Venus (SAEVe) has been developed which uses high-temperature electronics to enable a three-order magnitude increase in expected surface life (120 Earth days) over what has been achieved to date. This enables study of long-term, variable phenomena such as the seismicity of Venus and near surface weather, near surface energy balance and atmospheric chemical composition. SAEVe also serves as a critical pathfinder for more sophisticated landers in the future. For example, first order seismic measurements by SAEVe will allow future missions to deliver better seismometers and systems to support the yet unknown frequency and magnitude of Venus events. SAEVe is focused on science that can be realized with low data volume instruments and will most benefit for temporal operations. The entire mission architecture and operations maximize science while minimizing energy usage and physical size and mass. The entire SAEVe system including its protective entry system is estimated to be around 45 kg and approximately 0.6 m diameter. These features allow SAEVe to be relatively cost effective and be easily integrated onto a Venus orbiter mission. The technologies needed to implement SAEVe are currently in development by several funded activities. Component and system level work is ongoing under NASA’s HOTTech program and by the Long Lived Insitu Solar System Explorer (LLISSE) project. The SAEVe long duration Venus lander promises groundbreaking science and is an ideal complimentary element to many future Venus orbiter missions being proposed or planned today.

Venus↗

NASA NTRS2018

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple â€œphase learningâ€ from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

surface temperatures↗