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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2010

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Putman, William M.↗
NASA NTRS2012

Observations of a Two-Stage Solar Eruptive Event (SEE): Evidence for Secondary Heating

We present RHESSI, SDO/AIA, SOHO/LASCO, STEREO, and GOES observations of a partially occulted solar eruptive event (SEE) that occurred at the South-West limb on 8 March, 2011. The GOES X-ray light curve shows two peaks separated by almost two hours that we interpret as two stages of a single event associated with the delayed eruption of a CME. A hot flux rope formed during the first stage and continued expanding and rising throughout the event. The speed of the flux rope decreased from approx.120 to 14 km/s during the decay phase of the first stage and increased again during the second stage to become the CME with a speed of approx.516 km/s. RHESSI and GOES data analyses show that the plasma temperature reached over 20 MK in the first stage, then decreased to approx.10 MK and increased to 15 MK in the second stage. This event provides clear evidence for a secondary heating phase. The enhanced EUV and X-ray emission came from the high corona ( approx.60 arcsec above the limb) in the second stage, approx.40 arcsec higher than the site of the initial flare emission. STEREO-A on-disk observations indicate that the post-flare loops during this stage were of larger scale sizes and spatially distinct from those in the first stage.

Su, Yang↗
NASA NTRS1979

Convective rainfall estimation from digital GOES-1 infrared data

An investigation was conducted to determine the feasibility of developing and objective technique for estimating convective rainfall from digital GOES-1 infrared data. The study area was a 240 km by 240 km box centered on College Station, Texas (Texas A and M University). The Scofield and Oliver (1977) rainfall estimation scheme was adapted and used with the digital geostationary satellite data. The concept of enhancement curves with respect to rainfall approximation is discussed. Raingage rainfall analyses and satellite-derived rainfall estimation analyses were compared. The correlation for the station data pairs (observed versus estimated rainfall amounts) for the convective portion of the storm was 0.92. It was demonstrated that a fairly accurate objective rainfall technique using digital geostationary infrared satellite data is feasible. The rawinsonde and some synoptic data that were used in this investigation came from NASA's Atmospheric Variability Experiment, AVE 7.

Sickler, G. L.↗
NASA NTRS1988

Meso-beta scale simulations of convective cloud systems over Florida utilizing synthetic data derived from GOES satellite imagery

An attempt is made to improve the simulation of the meso-alpha and meso-beta scales' precipitation distribution over the Florida peninsula during the warm season through improvement of the initialization of beta scale convective cloud systems, using GOES IR and visible satellite imagery. These data can be used to define the location and spatial extent of meso-beta scale cloud systems at a specified time. Attention is presently given to the results of the first phase of this study, in which only moisture information was synthesized from the satellite imagery.

Zack, John W.↗
NASA NTRS1988

Comparison of cloud amounts derived using GOES and Landsat data

A hybrid bispectral threshold method (HBTM) is presently used to compare cloud amounts derived from Landsat digital data over 22 regions having various cloud types with cloudiness information obtained from collocated, nearly-simultaneous 4 x 8-km GOES visible and IR data. A sensitivity analysis indicates that an rms underestimation of about 0.01 in clear sky reflectance by the HBTM increased the GOES cloud amount by 0.06, which is more than twice the decrese in cloud amount obtained by an equivalent increase in clear-sky reflectance. Landsat imagery and cloud properties derived from the Landsat data are used to explain how the partially cloud-filled GOES pixels were treated by the HBTM. It is found that the HBTM accounts for the effects of partially cloud-filled FOVs in most cases of the present study.

Minnis, Patrick↗
NASA NTRS1981

Determining economic benefits of satellite data in short-range forecasting

The chances of enhanced short term weather predictions and economic benefits from the use of GOES satellite data were examined. Results for a meteorological consulting firm before and after the introduction of GOES data were chosen as the method, and monetary benefits were selected as the measure. Services were provided for use by road and street departments, commodities dealers, and marine clients of the consulting firm. The Man-computer Interactive Data Access Program (McIDAS) was employed to furnish 1/2 hour visual or IR imagery for remote access. The commodities clients reconnected the GOES real-time imagery once the study was completed, while the consulting firm, which was personnel and not equipment intensive, did not. Further development of the flexibility of access to the GOES data and improvements in the projected grids are indicated.

Suchman, D.↗
NASA NTRS2001

Assimilation of GOES Land Surface Data: Benefits to Numerical Weather Prediction

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The technique has been employed on a semi-operational basis at the GHCC within the PSU/CAR MM5 since 1 November 1998. A one-way nested grid configuration was employed with a 75 kin CONUS domain and a 25 km grid over the southeastern United States. Initial conditions were obtained from the 12 UTC Early Eta Data Assimilation System analyses and lateral boundary conditions from the Early Eta forecast available at 3 hour intervals. The satellite-derived land surface temperature tendencies and insolation were assimilated between two and five hours (1400 and 1650 UTC) of the forecast. We performed the assimilation on the Southeastern domain only. In addition, a control run without assimilation was performed to provide insight into the performance of the assimilation technique.

Lapenta, William M.↗
NASA NTRS1987

Soil moisture estimation using GOES-VISSR infrared data - A case study with a simple statistical method

Five days of clear sky observations of Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudoindependent tests in which the test day is held out of the regression is 0.71. It is shown that a depletion coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as inferred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92 percent of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85 percent of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.

Wetzel, Peter J.↗
NASA NTRS2001

Assimilation of GOES Land Surface Data into a Mesoscale Models

A technique has been developed for assimilating Geostationary Operational Environmental Satellite (GOES)-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The assimilation technique has been applied to the Oklahoma-Kansas region during the spring-summer 2000 time period when dynamic changes in vegetation cover occur. In April, central Oklahoma is characterized by large NDVI associated with winter wheat while surrounding areas are primarily rangeland with lower NDVI. In July the vegetation pattern reverses as the central wheat area changes to low NDVI due to harvesting and the surrounding rangeland is greener than it was in April. The goal of this study is to determine if assimilating satellite land surface data can improve simulation of the complex spatial distribution of surface energy and water fluxes across this region. The PSU/NCAR NM5 V3 system is used in this study. The grid configuration consists of a 36-km CONUS domain and a 12-km nest over the area of interest. Bulk verification statistics (BIAS and RMSE) of surface air temperature and dewpoint indicates that assimilation of the satellite data results reduces both the bias and RMSE for both state variables. In addition, comparison of model data with ARM/CART EBBR flux observations reveals that the assimilation technique adjusts the bowen ratio in a realistic fashion.

Lapenta, William M.↗
NASA NTRS1997

Comparison of registration techniques for GOES visible imagery data

This paper briefly describes and then compares the effectiveness of five image registration approaches for GOES visible band imagery. The techniques compared are (1) NOAA "point" manual landmarking, (2) manual landmarking on extant feature (whole island, lake, etc.), (3) automatic phase correlation, (4) automatic spatial correlation on edge features, and (5) automatic spatial correlation of region boundaries derived from image segmentation.

Tilton, James C.↗
NASA NTRS2001

Assimilation of GOES Land Surface Data Within a Rapid Update Cycle Format: Impact on MM5 Warm Season QPF

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The focus of this paper is to examine how the satellite assimilation technique impacts simulations of near-surface meteorology on the 0-to 12-hour time scale when implemented within a local rapid update cycle (LRUC) format. The PSU/NCAR MM5 V34 is used and configured with a 36-km CONUS domain and a 12-km nest centered over the southeastern US. The LRUC format consists of a sequence of 12-hour forecasts initialized every hour between 12 and 18 UTC seven days a week. GOES skin temperature tendencies and solar insolation are assimilated in a 1-hour period prior to the start of each twelve-hour forecast. A unique aspect of the LRUC is the satellite assimilation and the continuous recycling of the adjusted moisture availability field from one forecast cycle to the next. Preliminary results for a seven-day trial period indicate that hourly LST tendencies assimilated in a 1 hour LRUC showed improved simulated air and dewpoint temperatures for all cycles on each day. The LRUC will be used during the 2001 summer months to identify the impact of the assimilation on warm season QPF Results will be presented at the meeting.

Lapenta, William M.↗
NASA NTRS1996

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Lerner, Jeffrey A.↗
NASA NTRSDate not supplied

Detecting Short Term Drought Impact in the Southwest US Using GOES-16 ABI Data

Satellite optical remote sensing has been often used for monitoring broad-region vegetation change, for example, phenology observations and the year-to-year leaf area index (LAI) responses to climate oscillations. However, rapid responses of vegetation to day-to-day weather perturbations are difficult to detect using available optical remote sensing satellites because of the low frequency of the observations. Sun-synchronous optical sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR), can observe a target area once a day. Daily observations cannot distinguish whether short-term changes in Normalized Differential Vegetation Index (NDVI) are actual LAI change or cloud contamination. NOAA’s GOES satellites make observations every 10 to 15 minutes using the Advanced Baseline Imager (ABI). In this study, we used the Geostationary-NASA Earth Exchange (GeoNEX) L1G Top-of-Atmosphere (TOA) ABI data to detect drought impact on NDVI time series in the Southwest US. We used an empirical method to cancel the BRDF effect of varying solar zenith angle. The ABI was able to detect short term drought impacts as well as an NDVI decrease in the dry season. Increased NDVI right after a rainfall followed by an immediate decrease was observed. These ABI NDVI changes were correlated with the RGB time series from PhenoCam Network data. Results indicated that the ABI can be used for short-term analysis of LAI and can detect small LAI changes caused by drought in an arid area, suggesting the potential for its use in near-real time drought monitoring applications.

ABI↗
NASA NTRS2016

Correcting GOES-R Magnetometer Data for Stray Fields

Time-varying spacecraft magnetic fields, i.e. stray fields, are a problem for magnetometer systems. While constant fields can be removed by calibration, stray fields are difficult to distinguish from ambient field variations. Putting two magnetometers on a long boom and solving for both the ambient and stray fields can help, but this gradiometer solution is more sensitive to noise than a single magnetometer. As shown here for the R-series Geostationary Operational Environmental Satellites (GOES-R), unless the stray fields are larger than the noise, simply averaging the two magnetometer readings gives a more accurate solution. If averaging is used, it may be worthwhile to estimate and remove stray fields explicitly. Models and estimation algorithms to do so are provided for solar array, arcjet and reaction wheel fields.

Magnetometer↗
NASA NTRS2001

Characterizing the Spatial and Temporal Distribution of Aerosol Optical Thickness Over the Atlantic Basin Utilizing GOES-8 Multispectral Data

In recent years, modeling and analysis efforts have suggested that the direct and indirect radiative effects of both anthropogenic and natural aerosols play a major role in the radiative balance of the earth and are an important factor in climate change calculations. The direct effects of aerosols on radiation and indirect effects on cloud properties are not well understood at this time. In order to improve the characterization of aerosols within climate models it is important to accurately parameterize aerosol forcing mechanisms at the local, regional, and global scales. This includes gaining information on the spatial and temporal distribution of aerosols, transport regimes and mechanisms, aerosol optical thickness, and size distributions. Although there is an expanding global network of ground measurements of aerosol optical thickness and size distribution at specific locations, satellite data must be utilized to characterize the spatial and temporal extent of aerosols and transport regimes on regional and global scales. This study was part of a collaborative effort to characterize aerosol radiative forcing over the Atlantic basin associated with the following three major aerosol components in this region: urban/sulfate, Saharan dust, and biomass burning. In-situ ground measurements obtained by a network of sun photometers during the Smoke Clouds and Radiation Experiment in Brazil (SCAR-B) and the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) were utilized to develop, calibrate, and validate a Geostationary Operational Environmental Satellite (GOES)-8 aerosol optical thickness (AOT) product. Regional implementation of the GOES-8 AOT product was used to augment point source measurements to gain a better understanding of the spatial and temporal distributions of Atlantic basin aerosols during SCAR-B and TARFOX.

Fox, Robert↗
NASA NTRS2006

Properties of CIRRUS Overlapping Clouds as Deduced from the GOES-12 Imagery Data

Understanding the impact of cirrus clouds on modifying both the solar reflected and terrestrial emitted radiations is crucial for climate studies. Unlike most boundary layer stratus and stratocumulus clouds that have a net cooling effect on the climate, high-level thin cirrus clouds can have a warming effect on our climate. Many research efforts have been devoted to retrieving cirrus cloud properties due to their ubiquitous presence. However, using satellite observations to detect and/or retrieve cirrus cloud properties faces two major challenges. First, they are often semitransparent at visible to infrared wavelengths; and secondly, they often occur over a lower cloud system. The overlapping of high-level cirrus and low-level stratus cloud poses a difficulty in determining the individual cloud top altitudes and optical properties, especially when the signals from cirrus clouds are overwhelmed by the signals of stratus clouds. Moreover, the operational satellite retrieval algorithms, which often assume only single layer cloud in the development of cloud retrieval techniques, cannot resolve the cloud overlapping situation properly. The new geostationary satellites, starting with the Twelfth Geostationary Operational Environmental Satellite (GOES-12), are providing a new suite of imager bands that have replaced the conventional 12-micron channel with a 13.3-micron CO2 absorption channel. The replacement of the 13.3-micron channel allows for the application of a CO2-slicing retrieval technique (Chahine et al. 1974; Smith and Platt 1978), which is one of the important passive satellite methods for remote sensing the altitudes of mid to high-level clouds. Using the CO2- slicing technique is more effective in detecting semitransparent cirrus clouds than using the conventional infrared-window method.

Chang, Fu-Lung↗
NASA NTRS2018

Strategies to Maximize Science Data Availability for the GOES-R Series of Satellites

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the next generation of Untied States geostationary weather satellites. The GOES-R series significantly improves the detection and observation of environmental phenomena that directly affect public safety, protection of property and the economic health and prosperity of the United States and all countries within the western hemisphere. Given the real-time or ''now-casting'' nature of the GOES science gathering mission, any data outage or interruption can reduce warning times or scientific fidelity for critical weather data. GOES-R mission level requirements limit key performance product outages to a total of six hours per year to maximize science data availability. Lower level requirement only allow for 120 minutes of disruption between the spacecraft bus interface to the instruments. This requirement is met using both design features of the satellite and ground system, in addition to operational strategies.

Dudley, Renee↗
DOE OSTI2024

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.

Lee, Jangho (ORCID:0000000289421092)↗