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NASA NTRSDate not supplied

New Releases and Upgrades of Giant Planet Global Reference Atmospheric Models (GRAMs)

• GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of atmospheric properties for numerous planetary destinations – Outputs atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path – Provide mean values and variability for any point in an atmosphere – Includes seasonal, geographic, and altitude variations – Used by engineering community because of their ability to create realistic atmospheric dispersions; can be integrated into high fidelity flight dynamic simulations of launch, entry, descent, and landing (EDL), aerobraking, and aerocapture • GRAMs are not forecast models • GRAMs are available for: Earth, Mars, Venus, Neptune, Titan, Jupiter, and Uranus • Available through the NASA Software Catalog https://software.nasa.gov/software/MFS-33888-1

atmospheric models↗
NASA NTRS2003

Temperature Data Assimilation with Salinity Corrections: Validation for the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 1993-1998

The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an Ocean data assimilation system to initialize the quasi-isopycnal ocean model used in our experimental coupled-model forecast system. Initial tests of the system have focused on the assimilation of temperature profiles in an optimal interpolation framework. It is now recognized that correction of temperature only often introduces spurious water masses. The resulting density distribution can be statically unstable and also have a detrimental impact on the velocity distribution. Several simple schemes have been developed to try to correct these deficiencies. Here the salinity field is corrected by using a scheme which assumes that the temperature-salinity relationship of the model background is preserved during the assimilation. The scheme was first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity and temperature is used to cross-validate two data assimilation experiments run for the 6-year period 1993-1998. In these two experiments only subsurface temperature observations are used, but in one case the salinity field is also updated whenever temperature observations are available.

Troccoli, Alberto↗
NASA NTRS2014

The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part 1: African Wave Circulation

The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.

variability↗
NASA NTRS1992

Potential climate impact of Mount Pinatubo eruption

The GISS global-climate model is used to make a preliminary estimate of Mount Pinatubo's climate impact. Assuming the aerosol optical depth is nearly twice as great as for the 1982 El Chichon eruption, the model forecasts a dramatic but temporary break in recent global warming trends. The simulations indicate that Pinatubo occurred too late in the year to prevent 1991 from becoming one of the warmest years in instrumental records, but intense aerosol cooling is predicted to begin late in 1991 and to maximize late in 1992. The predicted cooling is sufficiently large that by mid 1992 it should even overwhelm global warming associated with an El Nino that appears to be developing, but the El Nino could shift the time of minimum global temperature into 1993. The model predicts a return to record warm levels in the later 1990s. The effect is estimated of the predicted global cooling on such practical matters as the severity of the coming Soviet winter and the dates of cherry blossoming next spring.

Hansen, James↗
NASA NTRS2018

Impact of Assimilating Cloud-Cleared and Adaptively Thinned Infrared Hyperspectral Data on Tropical Cyclones in a Global Data Assimilation and Forecast Framework

A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS) and Infrared Atmospheric Sounding Interferometer (IASI) radiances is evaluated through a combination of Observing System Experiments (OSEs) and adjoint methodologies. In addition, the impact of cloud-cleared radiances for AIRS is also evaluated. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. The adaptive strategy uses a denser coverage in a moving domain centered around tropical cyclones (TCs), sparser everywhere else.The OSEs consist of three sets of data assimilation runs that cover the period from September 1st to 10 November 2014, with the first 20 days discarded for spin-up. All sets assimilate conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS, CrIS, and IASI radiances at different densities, another AIRS cloud-cleared radiances, and CrIS and IASI clear-sky radiances, and the third adaptively thinned AIRS, CrIS and IASI radiances. Daily 10-day forecasts are initialized from all these analyses and evaluated with focus on TCs over the Atlantic and the Pacific.Evidence is provided that this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies, increases the global forecast skill and improves tropical cyclone representation and intensity forecast. In addition, the impact of AIRS cloud-cleared radiances is demonstrated to be particularly strong on TCs. The implications are that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could be operationally used with large gain in TC forecasting and no loss of global skill.

Reale, Oreste↗
NASA NTRS2005

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Gottschalck, Jon↗
NASA NTRS1987

Transport models for numerical forecast

The explosive growth of computing power, coupled with scientific and technological emphasis on a national scale, has led to significant major advances in operational numerical weather prediction (NWP) during the last two decades. There are about half a dozen major centers around the world running global NWP models operationally. Many more countries have operational hemispheric or limited-area models which provide weather forecasts. The global models typically have several hundred kilometer resolution, while the limited-area models usually have horizontal spacing of 50 to 100 km. Given the pace of burgeoning growth in this area, it seems warranted to occasionally take an overview of aspects of the field common to all modelers. Here, a brief look is taken at the nature of subgrid scale turbulence transport parameterization, and some of the difficulties pertaining thereto, with particular emphasis on operational NWP models.

Burk, Stephen D.↗
NASA NTRS2015

Results of Lunar Impact Observations During Geminid Meteor Shower Events

Meteoroids are natural particles with origins from comets, asteroids, and planets from within the solar system. On average, 33 metric tons (73,000 lb) of meteoroids hit Earth everyday with velocities ranging between 20 and 72 km/s. However, the vast majority of these meteoroids disintegrate in the atmosphere and never make it to the ground. The Moon also encounters the same meteoroid flux, but has no atmosphere to stop them from striking the surface. At such speeds even a small meteoroid has incredible energy. A meteoroid with a mass of only 5 kg can excavate a crater over 9 m across, hurling 75 metric tons (165,000 lb) of lunar soil and rock on ballistic trajectories above the lunar surface. Meteoroids with particle sizes as small as 100 micrometer (1 Microgram) can do considerable damage to spacecraft in Earth's orbit and beyond. Impacts can damage thermal protection systems, radiators, windows, and pressurized containers. Secondary effects might include partial penetration or pitting, local deformation, and surface degradation that can cause a failure upon reentry. The speed, mass, density, and flux of meteoroids are important factors for design considerations and mitigation during operations. Lunar operations (unmanned and manned) are also adversely affected by the meteoroid flux. Ejecta from meteoroid impacts is also part of the lunar environment and must be characterized. Understanding meteoroid fluxes and the associated risk of meteoroids impacting spacecraft traveling in and beyond Earth's orbit is the objective of the Meteoroid Environment Office (MEO) located at Marshall Space Flight Center (MSFC). One of the MEO's programs is meteoroid impact monitoring of the Moon. The large collecting area of the night side of the lunar disk provides statistically significant counts of meteoroids that can provide useful information about the flux of meteoroids in the hundreds of grams to kilograms size range. This information is not only important for characterizing the lunar environment associated with larger lunar impactors, but also provides statistical data for verification and improving meteoroid prediction models. Current meteoroid models indicate that the Moon is struck by a sporadic meteoroid with a mass greater than 1 kg over 260 times per year. This number is very uncertain since observations for objects in this mass range are few. Factors of several times, higher or lower, are easily possible. Meteor showers are also present to varying degrees at certain times of the year. The Earth experiences meteor showers when encountering the debris left behind by comets, which is also the case with 2 the Moon. During such times, the rate of shower meteoroids can greatly exceed that of the sporadic background rate for larger meteoroids. Looking for meteor shower impacts on the Moon at about the same time as they occur on Earth will yield important data that can be fed into meteor shower forecasting models, which can then be used to predict times of greater meteoroid hazard on the Moon. The Geminids are one such meteor shower of interest. The Geminids are a major meteor shower that occur in December with a peak intensity occurring usually during the 13th and 14th of the month and appearing to come from a radiant in the constellation Gemini. The Geminids are interesting in that the parent body of the debris stream is an asteroid, which along with the Quadrantids, are the only major meteor showers not originating from a comet. The Geminids parent body, 3200 Phaethon, is about 5 km in diameter and has an orbit that has a 22deg inclination which intersects the main asteroid belt and has a perihelion less than half of Mercury's perihelion distance. Thus, its orbit crosses those of Mars, Earth, Venus, and Mercury. The Geminid debris stream is by far the most massive as compared to the others. When the Earth passes through the stream in mid-December, a peak intensity of approx. equal 120 meteors per hour can be seen. Because of the Geminids' relatively large intensity and unique origin, it is important to monitor and gain information about the Geminids so as to improve their forecasts and understand their contribution to the meteoroid environment in Earth's orbit and at the Moon. It is the purpose of this Technical Memorandum (TM) to document two lunar observing periods coinciding with the Geminid meteor showers that occurred in 2006 and 2010.

Suggs, R. J.↗
NASA NTRS1991

A simplified wind vector algorithm for satellite scatterometers

A simplified algorithm is derived for retrieving wind vectors from microwave scatterometer observations. The azimuthal dependence of the sea-surface radar cross section is modeled by a double-cosine function, rather than the traditional second-order cosine expansion. The algorithm is tested using the aircraft scatterometer observations obtained during the AAFE-RADSCAT Experiment in 1975 and 1976. There is little difference between the performance of the simplified algorithm and the more involved least-squares searching algorithm. The AAFE-RADSCAT aircraft observations are sampled so as to simulate the three-look satellite scatterometer NSCAT that will be launched on the Japanese Advanced Earth Observing Satellite. The results of the retrievals indicate that it is probably not possible to uniquely determine the wind direction using just NSCAT observations because of a 180 degree ambiguity. However, if forecast models can predict the wind direction to an accuracy of +/- 90 deg, thereby eliminating the ambiguity, then the results indicate that NSCAT can determine the wind direction to an accuracy of about +/- 20 deg. Better performance is obtained when the three observations are the same polarization (either v-pol or h-pol), as compared to using a mix of v-pol and h-pol observations.

Wentz, Frank J.↗
NASA NTRS2000

Chandra X-Ray Observatory's Radiation Environment and the AP-8/AE-8 Model

The Chandra X-ray Observatory (CXO) was launched on July 23, 1999 and reached its final orbit on August 7, 1999. The CXO is in a highly elliptical orbit, approximately 140,000 km x 10,000 km, and has a period of roughly 63.5 hours (approx. 2.6 days). It transits the Earth's Van Allen belts once per orbit during which no science observations can be performed due to the high radiation environment. The Chandra X-ray Observatory Center (CXC) currently uses the National Space Science Data Center's "near Earth" AP-8/AE-8 radiation belt model to predict the start and end times of passage through the radiation belts. However, our scheduling software only uses a simple dipole model of the Earth's magnetic field. The resulting B, L magnet coordinates, do not always give sufficiently accurate predictions of the start and end times of transit of the Van Allen belts. We show this by comparing to the data from Chandra's on-board radiation monitor, the EPHIN (Electron, Proton, Helium Instrument particle detector) instrument. We present evidence that demonstrates this mis- of the radiation belts as well as data that also demonstrate the significant variability of one radiation belt transit to the next as experienced by the CXO. We present an explanation for why the dipole implementation of the AP-8/AE-8 gives inaccurate results. We are also investigating use of the Magnetospheric Specification and Forecast Model (MSM) - a model that also accounts for radiation belt variability and geometry.

Virani, S. N.↗
NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗
NASA NTRSDate not supplied

New York Ecological Forecasting: Utilizing NASA Earth Observations to Map Ash Distribution and Inform Emerald Ash Borer Control

Since their first sightings in the U.S. in 2002, emerald ash borer beetles (Agrilus planipennis; EAB) have killed millions of native ash (Fraxinus spp.) trees across 35 states. Infected ash stands frequently exhibit complete mortality, with the predicted result being the functional extinction of native ash in U.S. forests. In August of 2020, EAB was discovered in the 6.1-million-acre Adirondack Park. The team’s partners at the Adirondack Park Invasive Plant Program (APIPP) desired ash tree distribution and EAB susceptibility information to help improve EAB bio-control efficiency and apply the methodology to future invasive programs. To assist, the team mapped ash tree distribution using NASA Earth observations from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Shuttle Radar Topography Mission (SRTM), along with hyperspectral imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data from the Monitoring and Managing Ash (MaMA) project, iMapInvasives and iNaturalist databases, and the New York State Department of Environmental Conservation (NYSDEC) provided ground truthing for mapping and modeling. Results indicate that for ash detection, the team’s Spectral Angle Mapping (SAM) hyperspectral classification is slightly more sensitive but less accurate than multispectral Random Forest (RF) classification, though neither method was above a ~20% detection rate. End products include maps of ash extent derived from both imagery types, a model forecasting future spread scenarios based on current EAB presence, and outreach materials. These products inform APIPP’s management decisions and facilitate public awareness of EAB’s threat to communities within the region.

Liam Megraw↗
NASA NTRS1991

Observing system simulation experiments for the laser atmospheric wind sounder using global spectral model

Fundamental to improving the understanding of the total Earth system are increased and improved observations. In the coming decade several spaceborne instrumented platforms will be constructed and implemented. These platforms will, in large, be housing the NASA Earth Observing System (EOS) instrument suite. One of the proposed instruments is a wind profiling system which is currently referred to as the Laser Atmospheric Wind Sounder (LAWS). This instrument will use a CO2 Doppler lidar wind profiler to give wind measurements with a vertical and horizontal resolution which has yet to be seen globally. The LAWS instrument is now a candidate for launch on a NASA EOS-B platform and is fundamental to increasing our understanding of Earth system science. The LAWS data sets will form an integral component of the temporally continuous data base needed for research of the coupled climate systems. This instrument's observations will aid in giving an improved description of the atmospheric circulation, including the transports of energy, momentum, moisture, trace gases, and aerosols. Also, the wind data will be assimilated and used as the initial state for many global forecast models at various operational centers. Results of system simulation experiments are discussed, and future experiments are described.

Rohaly, Gregg↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗
NASA NTRS1994

Investigation of water vapor motion winds from geostationary satellites

Water vapor imagery from geostationary satellites has been available for over a decade. These data are used extensively by operational analysts and forecasters, mainly in a qualitative mode (Weldon and Holmes 1991). In addition to qualitative applications, motions deduced in animated water vapor imagery can be used to infer wind fields in cloudless regimes, thereby augmenting the information provided by cloud-drift wind vectors. Early attempts at quantifying the data by tracking features in water vapor imagery met with modest success (Stewart et al. 1985; Hayden and Stewart 1987). More recently, automated techniques have been developed and refined, and have resulted in upper-level wind observations comparable in quality to current operational cloud-tracked winds (Laurent 1993). In a recent study by Velden et al. (1993) it was demonstrated that wind sets derived from Meteosat-3 (M-3) water vapor imagery can provide important environmental wind information in data void areas surrounding tropical cyclones, and can positively impact objective track forecasts. M-3 was repositioned to 75W by the European Space Agency in 1992 in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the current GOES-7 (G-7) satellite (positioned near 112W), the M-3 water vapor channel contains a superior horizontal resolution (5 km vs. 16 km ). In this paper, we examine wind sets derived using automated procedures from both GOES-7 and Meteosat-3 full disk water vapor imagery in order to assess this data as a potentially important source of large-scale wind information. As part of a product demonstration wind sets were produced twice a day at CIMSS during a six-week period in March and April (1994). These data sets are assessed in terms of geographic coverage, statistical accuracy, and meteorological impact through preliminary results of numerical model forecast studies.

Velden, Christopher S.↗
NASA NTRS2018

TPSAS-NF1676L-30290-DND

The Summer 2017 Ozone Water Land Environmental Transition Study (OWLETS) mission set out to compare the differences in ozone concentrations between inland measurements of air quality, specifically at Langley Research Center (LaRC), and measurements taken over water, specifically on the Chesapeake Bay Bridge Tunnel (CBBT), as well as the vertical profiles above each location. Current weather models often predict a gradient in ozone concentration between land and ocean and this campaign attempted to capture this difference using hand-held Personal Ozone Monitors (POMs) in correspondence to ozonesondes and LIDAR measurements. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) is the first geostationary satellite that will take hourly measurements to monitor air pollutants across North America using solar backscatter. The small footprint allows for higher spatial resolution readings of many parameters including O3, NO2 , and aerosol. TEMPO’s higher resolution readings would benefit from validation techniques on the ground. Validation methods usually include comparison to air quality monitoring stations, but they could also incorporate other forms of validation such as comparison to small sensors. In cooperation with TEMPO, OWLETS aims to provide the user community high resolution temporal and spatial, both horizontal and vertical, variability of O3 simultaneously over the land and water during various air quality events to improve forecast models and air quality satellite retrievals. Together, these missions will improve spatial resolution and capture temporal variability of air quality over North America.

Emily Gargulinski↗
NASA NTRS1998

Evolution of the 1997-98 El Nino and La Nina: the Role of Altimetry in ENSO Studies and Prediction

El Nino and the Southern Oscillation (ENSO) are known to be a quasi-periodic coupled ocean-atmosphere phenomenon occurring roughly every 3-7 years. Its largest oceanic manifestations are found in the equatorial Pacific, but it is of global climatic significance. During the flight of the TOPEX]Poseidon altimeter, the equatorial Pacific was in the midst of an unusual period of prolonged ENSO warming in the mid-1990's. In 1997, the tropical Pacific witnessed the development of a major El Nino event, rivaling in strength the 1982-83 El Nino. The intensity of this El Nino, and its rapid intensification early in the calendar year, caught the scientific community by surprise. None of the forecast models, statistical or dynamical, captured these features of the 1997 El Nino, although those models that assimilated in situ ocean observations did better than most in predicting that the event would occur in 1997. The onset of the El Nino coincided with the occurrence of several westerly wind events in the western Pacific rather than to delayed oscillator physics. However, western Pacific sea levels were anomalously elevated a year and a half prior to the onset, which may have helped precondition the system to a particularly strong ENSO episode. The coming of La Nina was foreshadowed by large-scale drops in sea level in the western tropical Pacific and cyclonic wind stress curl anomalies far beyond the +\- 8 degrees latitude bounds of the TAO mooring array. In past El Nino events, the evolution of the coupled system could only be studied from the perspective of in situ observations. The 1997-1998 El Nino event will be the first time a major El Nino event will have been observed from start to finish via remotely-sensed measurements of sea surface topography, sea surface temperature, sea surface winds, ocean color, and precipitation. These observations have considerable potential for advancing coupled climate forecasts on time scales from seasons to decades. This presentation will address questions along the lines of: (1) What are the scientific problems in ENSO that can benefit most from altimetry data? (2) Are there any precursors in the global ocean-atmosphere system for the 1997-98 extreme event? (3) Is there a unique role of altimetry in detecting potential precursors?(4) Has Topex/Poseidon data been adequately used to improve ENSO forecasts? Is there any firm evidence? (6) What is the status of the transition of El Nino into a La Nina?

Busalacchi, Antonio J.↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗