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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Cross-Measurement Comparisons for a CFD Validation Dataset on Mach 2.5 Axisymmetric Turbulent Shock-Wave/Boundary-Layer Interactions

Experimental data for a shock-wave/boundary-layer interaction has been collected using multiple measurement techniques. Unfortunately, diversity of methods for acquisition begets an aggregation of data which does not directly quantify the same properties of the flowfield. The objective in this paper was to (a) present the collection of flowfield measurements in one venue in a format more usable for those validating CFD models against the data, (b) evaluate the degree to which the experimental data support each other, and (c) highlight the differences and relative advantages/shortcomings of each measurement techniques. To present the various measured quantities into common format, CFD results from a companion paper also submitted for presentation at this meeting are utilized. For the case where the boundary layer remains attached, there is agreement between the various measurements as well as with Reynolds-averaged Navier-Stokes simulation solutions. As the impinging shock strength is increased beyond the point of separating the boundary layer, the congruity of the data wanes. Generally, agreement among the measurements exceeds the degree to which the CFD solutions agree with experiments. This suggests that unmodeled physical phenomena, such as transient motion of the reflected shock and separation bubble, give rise to the discrepancies observed in the computational results.

Shock-wave Boundary-layer interaction↗
NASA NTRS1993

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

A series of cloud and sea ice retrieval algorithms are being developed in support of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Science Team objectives. These retrievals include the following: cloud fractional area, cloud optical thickness, cloud phase (water or ice), cloud particle effective radius, cloud top heights, cloud base height, cloud top temperature, cloud emissivity, cloud 3-D structure, cloud field scales of organization, sea ice fractional area, sea ice temperature, sea ice albedo, and sea surface temperature. Due to the problems of accurately retrieving cloud properties over bright surfaces, an advanced cloud classification method was developed which is based upon spectral and textural features and artificial intelligence classifiers.

Welch, Ronald M.↗
NASA NTRS1994

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

With the growing awareness and debate over the potential changes associated with global climate change, the polar regions are receiving increased attention. Global cloud distributions can be expected to be altered by increased greenhouse forcing. Owing to the similarity of cloud and snow-ice spectral signatures in both the visible and infrared wavelengths, it is difficult to distinguish clouds from surface features in the polar regions. This work is directed towards the development of algorithms for the ASTER and HIRIS science/instrument teams. Special emphasis is placed on a wide variety of cloud optical property retrievals, and especially retrievals of cloud and surface properties in the polar regions.

Source record↗
NASA NTRS1996

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

The ASTER polar cloud mask algorithm is currently under development. Several classification techniques have been developed and implemented. The merits and accuracy of each are being examined. The classification techniques under investigation include fuzzy logic, hierarchical neural network, and a pairwise histogram comparison scheme based on sample histograms called the Paired Histogram Method. Scene adaptive methods also are being investigated as a means to improve classifier performance. The feature, arctan of Band 4 and Band 5, and the Band 2 vs. Band 4 feature space are key to separating frozen water (e.g., ice/snow, slush/wet ice, etc.) from cloud over frozen water, and land from cloud over land, respectively. A total of 82 Landsat TM circumpolar scenes are being used as a basis for algorithm development and testing. Numerous spectral features are being tested and include the 7 basic Landsat TM bands, in addition to ratios, differences, arctans, and normalized differences of each combination of bands. A technique for deriving cloud base and top height is developed. It uses 2-D cross correlation between a cloud edge and its corresponding shadow to determine the displacement of the cloud from its shadow. The height is then determined from this displacement, the solar zenith angle, and the sensor viewing angle.

Welch, Ronald M.↗
NASA NTRS2017

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

Wang, Panshi↗
NASA NTRS2001

Extraction of Airport Features from High Resolution Satellite Imagery for Design and Risk Assessment

The LPA Group, consisting of 17 offices located throughout the eastern and central United States is an architectural, engineering and planning firm specializing in the development of Airports, Roads and Bridges. The primary focus of this ARC project is concerned with assisting their aviation specialists who work in the areas of Airport Planning, Airfield Design, Landside Design, Terminal Building Planning and design, and various other construction services. The LPA Group wanted to test the utility of high-resolution commercial satellite imagery for the purpose of extracting airport elevation features in the glide path areas surrounding the Columbia Metropolitan Airport. By incorporating remote sensing techniques into their airport planning process, LPA wanted to investigate whether or not it is possible to save time and money while achieving the equivalent accuracy as traditional planning methods. The Affiliate Research Center (ARC) at the University of South Carolina investigated the use of remotely sensed imagery for the extraction of feature elevations in the glide path zone. A stereo pair of IKONOS panchromatic satellite images, which has a spatial resolution of 1 x 1 m, was used to determine elevations of aviation obstructions such as buildings, trees, towers and fence-lines. A validation dataset was provided by the LPA Group to assess the accuracy of the measurements derived from the IKONOS imagery. The initial goal of this project was to test the utility of IKONOS imagery in feature extraction using ERDAS Stereo Analyst. This goal was never achieved due to problems with ERDAS software support of the IKONOS sensor model and the unavailability of imperative sensor model information from Space Imaging. The obstacles encountered in this project pertaining to ERDAS Stereo Analyst and IKONOS imagery will be reviewed in more detail later in this report. As a result of the technical difficulties with Stereo Analyst, ERDAS OrthoBASE was used to derive aviation obstruction measurements for this project. After collecting ancillary data such as GPS locations, South Carolina Geodetic Survey and Aero Dynamics ground survey points to set up the OrthoBASE Block File, measurements were taken of the various glide path obstructions and compared to the validation dataset. This process yielded the following conclusions: The IKONOS stereo model in conjunction with Imagine OrthoBASE can provide The LPA Group with a fast and cost efficient method for assessing aviation obstructions. Also, by creating our own stereo model we achieved any accuracy better currently available commercial products.

Robinson, Chris↗
NASA NTRS2021

Australian tidal currents – assessment of a barotropic model (COMPAS v1.3.0 rev6631) with an unstructured grid

While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The mean absolute error (MAE) of the modelled M2 height amplitude is 8.8 cm, or 12 % of the 73 cm mean observed amplitude. The MAE of phase (10°), however, is significant, so the M2 mean magnitude of vector error (MMVE, 18.2 cm) is significantly greater. The root sum square over the eight major constituents is 26 % of the observed amplitude. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations). For the M2 major axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 to 156 cm s −1 , is 6.9 cm s −1 , or 22 % of the 31.7 cm s −1 observed mean. This nationwide average result is encouraging, but it conceals a very large regional variation. Relative errors of the tidal current amplitudes on the narrow shelves of New South Wales (NSW) and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents, so the tidal errors are of little practical significance. Looking nationwide, we show that the model has predictive value for much of the 79 % of Australia's shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, the Kimberley to Arnhem Land, and southern Great Barrier Reef regions. There is limited observational evidence to confirm that the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing “unofficial” tidal current predictions for chosen regions in the near future based on both our COMPAS model and the validation dataset we have assembled.

Tidal currents↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
NASA NTRSDate not supplied

Geophysical Retrievals and Cloud Analyses from Merged Airborne Radiometer Datasets Covering 10–684 GHz

Airborne microwave radiometers provide insight about numerous aspects of Earth’s atmosphere and yield critical validation datasets for spaceborne radiometers. Three radiometers that are important to NASA’s airborne remote-sensing arsenal include: the Advanced Microwave Precipitation Radiometer (AMPR), covering 10–85 GHz; the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR), covering 50–183 GHz; and the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR), covering 170–684 GHz. The NASA field campaigns of interest to this study include: the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, the Olympic Mountains Experiment and Radar Definition Experiment (OLYMPEX/RADEX) in 2015–2016, the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) in 2020–2023, and the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) in 2023. To provide a more comprehensive perspective on clouds and precipitation observed during these airborne field campaigns, AMPR data were merged spatiotemporally with CoSMIR data for IPHEx, OLYMPEX/RADEX, and IMPACTS (2020 and 2022), while considering differences in instrument characteristics and operations, providing brightness temperature (Tb) values from 10–183 GHz in a common background grid throughout each flight. AMPR and CoSSIR data were similarly merged for IMPACTS (2023) and ALOFT, providing a common background grid with Tb values covering 10–684 GHz throughout each flight. These merged Tb data were employed in geophysical retrievals using the Community Radiative Transfer Model (CRTM), an Eddington radiative transfer model, and a one-dimensional variational (1DVAR) inversion method. Retrievals of cloud liquid water path were of primary interest. This presentation will include an overview of the methods for the radiometer data mergers, the radiative transfer methods, the geophysical retrievals, and detailed results from examining trends in Tb and cloud liquid water path in clouds, precipitation, and cloud-to-precipitation transition zones.

Corey G Amiot↗
NASA NTRS2016

Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats

The use of autonomous proling oats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reectance in the ocean is examined. This effort includes comparing quantities estimated from oat and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the oat estimates. This study had 65 occurrences of coincident high-quality observations from oats and MODIS Aqua and 15 occurrences of coincident high-quality observations oats and Visible Infrared Imaging Radi-ometer Suite (VIIRS). The oat estimates of remote sensing reectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the oatsatellite comparisons is similar to the variability of in situsatellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of oat-based and satellite-based quantities, suggests that oats are likely a good platform for validation of satellite-based estimates of remote sensing reectance.

In Situ Oceanic Observations↗
NASA NTRS1993

Validated environmental and physiological data from the CELSS Breadboard Projects Biomass Production Chamber. BWT931 (Wheat cv. Yecora Rojo)

This KSC database is being made available to the scientific research community to facilitate the development of crop development models, to test monitoring and control strategies, and to identify environmental limitations in crop production systems. The KSC validated dataset consists of 17 parameters necessary to maintain bioregenerative life support functions: water purification, CO2 removal, O2 production, and biomass production. The data are available on disk as either a DATABASE SUBSET (one week of 5-minute data) or DATABASE SUMMARY (daily averages of parameters). Online access to the VALIDATED DATABASE will be made available to institutions with specific programmatic requirements. Availability and access to the KSC validated database are subject to approval and limitations implicit in KSC computer security policies.

Stutte, G. W.↗
NASA NTRS2011

The NASA CloudSat/GPM Light Precipitation Validation Experiment (LPVEx)

Ground-based measurements of cool-season precipitation at mid and high latitudes (e.g., above 45 deg N/S) suggest that a significant fraction of the total precipitation volume falls in the form of light rain, i.e., at rates less than or equal to a few mm/h. These cool-season light rainfall events often originate in situations of a low-altitude (e.g., lower than 2 km) melting level and pose a significant challenge to the fidelity of all satellite-based precipitation measurements, especially those relying on the use of multifrequency passive microwave (PMW) radiometers. As a result, significant disagreements exist between satellite estimates of rainfall accumulation poleward of 45 deg. Ongoing efforts to develop, improve, and ultimately evaluate physically-based algorithms designed to detect and accurately quantify high latitude rainfall, however, suffer from a general lack of detailed, observationally-based ground validation datasets. These datasets serve as a physically consistent framework from which to test and refine algorithm assumptions, and as a means to build the library of algorithm retrieval databases in higher latitude cold-season light precipitation regimes. These databases are especially relevant to NASA's CloudSat and Global Precipitation Measurement (GPM) ground validation programs that are collecting high-latitude precipitation measurements in meteorological systems associated with frequent coolseason light precipitation events. In an effort to improve the inventory of cool-season high-latitude light precipitation databases and advance the physical process assumptions made in satellite-based precipitation retrieval algorithm development, the CloudSat and GPM mission ground validation programs collaborated with the Finnish Meteorological Institute (FMI), the University of Helsinki (UH), and Environment Canada (EC) to conduct the Light Precipitation Validation Experiment (LPVEx). The LPVEx field campaign was designed to make detailed measurements of cool-season light precipitation by leveraging existing infrastructure in the Helsinki Precipitation Testbed. LPVEx was conducted during the months of September--October, 2010 and featured coordinated ground and airborne remote sensing components designed to observe and quantify the precipitation physics associated with light rain in low-altitude melting layer environments over the Gulf of Finland and neighboring land mass surrounding Helsinki, Finland.

Petersen, Walter A.↗
NASA NTRSDate not supplied

Fundamental Test of a Hovering Rotor: Comprehensive Measurements for CFD Validation

A model-scale hover test of a 4-bladed, 11.08-ft diameter rotor was recently completed inside the National Full-Scale Aerodynamics Complex 80- by 120-Foot Wind Tunnel test section. The primary objective of the test was to acquire key experimental data for a hovering rotor of sufficient quality and quantity to allow validation of state-of-the-art analysis codes. A comprehensive measurement set has been acquired, including rotor performance, blade airloads, flow transition locations, blade deflections, and wake geometry for a range of tip Mach numbers and collective settings. The present paper provides an overview of the test, including detailed descriptions of the hardware, instrumentation, and measurement systems. In addition, the specific test objectives, approach, and sample results are presented. The full test database, as well as detailed rotor geometry information, will ultimately be shared openly on a NASA-sponsored website to serve as a benchmark validation dataset.

Hover↗
NASA NTRS2024

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (R rs ) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and R rs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

signal-to-noise ratio↗
NASA NTRS2016

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗
NASA NTRS1994

Arctic ocean radiative fluxes and cloud forcing estimated from the ISCCP C2 cloud dataset, 1983-1990

Radiative fluxes and cloud forcings for the ocean areas of the Arctic are computed from the monthly cloud product of the International Satellite Cloud Climatology Project (ISCCP) for 1983-90. Spatially averaged short-wave fluxes are compared well with climatological values, while downwelling longwave fluxes are significantly lower. This is probably due to the fact that the ISCCP cloud amounts are underestimates. Top-of-the-atmosphere radiative fluxes are in excellent agreement with measurements from the Earth Radiation Budget Experiment (ERBE). Computed cloud forcings indicate that clouds have a warming effect at the surface and at the top of the atmosphere during winter and a cooling effect during summer. The net radiative effect of clouds is larger at the surface during winter but greater at the top of the atmosphere during summer. Overall the net radiative effect of clouds at the top of the atmosphere is one of cooling. This is in contrast to a previous result from ERBE data showing arctic cloud forcings have a net warming effect. Sensitivities to errors in input parameters are generally greater during winter with cloud amount being the most important paarameter. During summer the surface radiation balance is most sensitive to errors in the measurements of surface reflectance. The results are encouraging, but the estimated error of 20 W/sq m in surface net radiative fluxes is too large, given that estimates of the net radiative warming effect due to a doubling of CO2 are on the order of 4 W/sq m. Because it is difficult to determine the accuracy of results with existing in situ observations, it is recommended that the development of improved algorithms for the retrieval of surface radiative properties be accompanied by the simultaneous assembly of validation datasets.

Schweiger, Axel J.↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗