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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Total Ozone Trends from 1979 to 2016 Derived from Five Merged Observational Datasets - The Emergence into Ozone Recovery

We report on updated trends using different merged datasets from satellite and ground-based observations for the period from 1979 to 2016. Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. Merged datasets used here include NASA MOD v8.6 and National Oceanic and Atmospheric Administration (NOAA) merge v8.6, both based on data from the series of Solar Backscatter UltraViolet (SBUV) and SBUV-2 satellite instruments (1978–present) as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone (GTO) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (1995-present), mainly comprising satellite data from GOME, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and GOME-2A. The fifth dataset consists of the monthly mean zonal mean data from ground-based measurements collected at World Ozone and UV Data Center (WOUDC). The addition of four more years of data since the last World Meteorological Organization (WMO) ozone assessment (2013-2016) shows that for most datasets and regions the trends since the stratospheric halogen reached its maximum (approximately 1996 globally and approximately 2000 in polar regions) are mostly not significantly different from zero. However, for some latitudes, in particular the Southern Hemisphere extratropics and Northern Hemisphere subtropics, several datasets show small positive trends of slightly below +1 percent decade(exp. -1) that are barely statistically significant at the 2 Sigma uncertainty level. In the tropics, only two datasets show significant trends of +0.5 to +0.8 percent(exp.-1), while the others show near-zero trends. Positive trends since 2000 have been observed over Antarctica in September, but near-zero trends are found in October as well as in March over the Arctic. Uncertainties due to possible drifts between the datasets, from the merging procedure used to combine satellite datasets and related to the low sampling of ground-based data, are not accounted for in the trend analysis. Consequently, the retrieved trends can be only considered to be at the brink of becoming significant, but there are indications that we are about to emerge into the expected recovery phase. However, the recent trends are still considerably masked by the observed large year-to-year dynamical variability in total ozone.

column ozone trends↗
NASA NTRS1987

A survey of nested grid techniques and their potential for use within the MASS weather prediction model

A survey of various one- and two-way interactive nested grid techniques used in hydrostatic numerical weather prediction models is presented and the advantages and disadvantages of each method are discussed. The techniques for specifying the lateral boundary conditions for each nested grid scheme are described in detail. Averaging and interpolation techniques used when applying the coarse mesh grid (CMG) and fine mesh grid (FMG) interface conditions during two-way nesting are discussed separately. The survey shows that errors are commonly generated at the boundary between the CMG and FMG due to boundary formulation or specification discrepancies. Methods used to control this noise include application of smoothers, enhanced diffusion, or damping-type time integration schemes to model variables. The results from this survey provide the information needed to decide which one-way and two-way nested grid schemes merit future testing with the Mesoscale Atmospheric Simulation System (MASS) model. An analytically specified baroclinic wave will be used to conduct systematic tests of the chosen schemes since this will allow for objective determination of the interfacial noise in the kind of meteorological setting for which MASS is designed. Sample diagnostic plots from initial tests using the analytic wave are presented to illustrate how the model-generated noise is ascertained. These plots will be used to compare the accuracy of the various nesting schemes when incorporated into the MASS model.

Koch, Steven E.↗
NASA NTRS1988

DMSP-SSM/1 NASA algorithm validation using primarily LANDSAT and secondarily DMSP and/or AVHRR visible and thermal infrared satellite imagery

The approach to the DMSP SSMI (Defense Meteorological Satellite Program; Special Sensor Microwave Imager) sea-ice validation effort is to demonstrate a quantitative relationship between the SSMI-derived sea ice parameters and those same parameters derived from other data sets including visible and infrared satellite imagery, aerial photographic and high-resolution microwave aircraft imagery. The question to be addressed is to what accuracy (relative to these other observations) can the following ice parameters be determined: (1) position of the ice boundary; (2) total sea ice concentration; and (3) multiyear sea ice concentration. Specific tasks include: (1) a study of the interrelationship of surface information content and sensor spatial and spectral resolution in order to establish relationships between ice surface features and the manner in which they are expressed in the satellite observations; and (2) apply these relationships to map the sea ice features which can be used to evaluate NASA's proposed SSM/1 sea ice algorithms. Other key points to be addressed include the accuracy to which these parameters can be determined in different regions (marginal ice zone such as Bering Sea, Arctic ocean, such as Beaufort Sea); the accuracy of these parameters for different seasons; the accuracy of the algorithms weather filter under different weather conditions; and the effectiveness of the 85.5 GHz channels to locate the ice edge.

Steffen, K.↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
DOE OSTI2021

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

17 WIND ENERGY↗
DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Regularization for Atmospheric Temperature Retrieval Problems

Passive remote sensing of the atmosphere is used to determine the atmospheric state. A radiometer measures microwave emissions from earth's atmosphere and surface. The radiance measured by the radiometer is proportional to the brightness temperature. This brightness temperature can be used to estimate atmospheric parameters such as temperature and water vapor content. These quantities are of primary importance for different applications in meteorology, oceanography, and geophysical sciences. Depending on the range in the electromagnetic spectrum being measured by the radiometer and the atmospheric quantities to be estimated, the retrieval or inverse problem of determining atmospheric parameters from brightness temperature might be linear or nonlinear. In most applications, the retrieval problem requires the inversion of a Fredholm integral equation of the first kind making this an ill-posed problem. The numerical solution of the retrieval problem requires the transformation of the continuous problem into a discrete problem. The ill-posedness of the continuous problem translates into ill-conditioning or ill-posedness of the discrete problem. Regularization methods are used to convert the ill-posed problem into a well-posed one. In this paper, we present some results of our work in applying different regularization techniques to atmospheric temperature retrievals using brightness temperatures measured with the SSM/T-1 sensor. Simulation results are presented which show the potential of these techniques to improve temperature retrievals. In particular, no statistical assumptions are needed and the algorithms were capable of correctly estimating the temperature profile corner at the tropopause independent of the initial guess.

Velez-Reyes, Miguel↗
DOE OSTI2023

Influence of data uncertainty on cold season threshold-based climate indices

Climate indices are used to reduce the complex climate system and its changes to simple measures. The data basis – whether observational data or climate model data – to which the climate indices are applied, is usually subject to uncertainties. For threshold-based climate indices, the data uncertainty influences the threshold value, and, hence, the uncertainty can influence the values for the climate index. What the actual impacts of these uncertainties are on threshold-based climate indices is examined in this paper. The focus is not only on the climate model uncertainty, but also on the observational data uncertainty. The general sensitivity of each of the chosen climate indices to arbitrary changes in the threshold is studied. This shows a higher sensitivity of indices assessing extremes (ice days, heavy precipitation days) to changes in the threshold than indices that integrate a quantity over a given time interval (coldsum, consecutive days). For assessing an ensemble of climate model data with respect to their ability to reproduce the index values for current climate, the reference data uncertainty is applied to the chosen threshold-based climate indices by changing their threshold value by its corresponding uncertainty. It is shown that the climate model uncertainty can be within the range of the reference data uncertainty. When using threshold-based climate indices to assess changes in future climate periods, uncertainties should always be taken into account and ideally corrected in an appropriate way. This is especially important for indices that assess extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1945

Statistical Analysis of the Characteristics of Repeated Gusts in Turbulent Air

Statistical methods were applied to acceleration and airspeed data obtained with the XC-35 airplane during flights in turbulent air within convective clouds in order to determine the characteristics of repeated or closely spaced gusts pertinent to design problems. Results indicated that, in turbulent air within convective cloud, gusts tend to be contiguous and are seldom found isolated in space. Over-all average spacing between repeated gusts was in good agreement with twice the average gust-gradient distance of 10 chords used in present design.

AUTHORS (2)↗
NASA NTRS2007

Open-Lattice Composite Design Strengthens Structures

Advanced composite materials and designs could eventually be applied as the framework for spacecraft or extraterrestrial constructions for long-term space habitation. One such structure in which NASA has made an investment is the IsoTruss grid structure, an extension of a two-dimensional "isogrid" concept originally developed at McDonnell Douglas Astronautics Company, under contract to NASA's Marshall Space Flight Center in the early 1970s. IsoTruss is a lightweight and efficient alternative to monocoque composite structures, and can be produced in a manner that involves fairly simple techniques. The technology was developed with support from NASA to explore space applications, and is garnering global attention because it is extremely lightweight; as much as 12 times stronger than steel; inexpensive to manufacture, transport, and install; low-maintenance; and is fully recyclable. IsoTruss is expected to see application as utility poles and meteorological towers, for the aforementioned reasons and because its design offers superior wind resistance and is less susceptible to breaking and woodpeckers. Other applications, such as reinforcement for concrete structures, stand-alone towers, sign supports, prostheses, irrigation equipment, and sporting goods are being explored

Source record↗
NASA NTRS2019

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

Tzallas, Vasileios↗
NASA NTRS2008

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Adelfang, Stanley I.↗
DOE OSTI2021

KAZR Hydrometeor and Insect Masks

The Ka-band ARM zenith pointing radar (aka, KAZR) is so sensitive that it detects cloud particles and individual insects. While detecting insects with Ka-band radar is beneficial and desirable to advance radar entomology, this sensitivity can be detrimental to radar meteorology because insects could be interpreted as clouds or precipitation. For example, misclassifying insects as clouds has been a problem for the ARM Active Remote Sensing of Clouds (ARSCL) Value Added Product since its inception (Clothiaux et al. 2000). Based on cloud particle and insect radar scattering properties, an algorithm was developed that identifies clouds, raindrops, ice particles, and insects in KAZR co- and cross-polarimeteric Doppler velocity spectra. The algorithm produces affirmative masks in the KAZR native time and height resolution indicating time-height locations of hydrometeors and insects. The hydrometeor mask contains binary information (e.g., yes/no hydrometeor presence), and the insect mask includes a proxy for insect activity that increases when more insects are detected in the Doppler velocity spectra. The algorithm was developed using KAZR medium sensitivity mode (MD) observations and was applied to two summer seasons of KAZR observations at the Southern Great Plains (SGP) Central Facility: May-October 2018 and 2019. In the future, this data set will be expanded to include other KAZR operating modes and observations from other ARM field sites. Details of the algorithm and data set can be found in: Williams, C.R., K.L. Johnson, S.E. Giangrande, J. C. Hardin, R. Oktem, and D. M. Romps, 2021: Identifying Insects, Clouds, and Precipitation using Vertically Pointing Polarimetric Radar Doppler Velocity Spectra. Atmospheric Measurement Techniques, submitted 6-Feb-2021. https://amt.copernicus.org/preprints/amt-2021-27/#discussion.For more information on the ARSCL VAP, see Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000; J. Appl. Meteor., 39, 645-665.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Superboom Caustic Analysis and Measurement Program (SCAMP) Final Report

The objectives of the Superboom Caustic Analysis and Measurement (SCAMP) Program were to develop and validate, via flight-test measurements, analytical models for sonic boom signatures in and around focal zones as they are expected to occur during commercial aircraft transition from subsonic to supersonic flight, and to apply these models to focus boom prediction of low-boom aircraft designs. The SCAMP program has successfully investigated sonic boom focusing both analytically and experimentally, while gathering a comprehensive empirical flight test and acoustic dataset, and developing a suite of focused sonic boom prediction tools. An experimental flight and acoustic measurement test was designed during the initial year of the SCAMP program, with execution of the SCAMP flight test occurring in May 2011. The current SCAMP team, led by Wyle, includes partners from the Boeing Company, Pennsylvania State University, Gulfstream Aerospace, Eagle Aeronautics, and Central Washington University. Numerous collaborators have also participated by supporting the experiment with human and equipment resources at their own expense. The experiment involved precision flight of a McDonnell Douglas (now Boeing) F-18B executing different maneuvers that created focused sonic booms. The maneuvers were designed to center on the flight regime expected for commercial supersonic aircraft transonic transition, and also span a range of caustic curvatures in order to provide a variety of conditions for code validations. The SCAMP experiment was designed to capture concurrent F-18B on-board flight instrumentation data, high-fidelity ground-based and airborne acoustic data, and surface and upper air meteorological data. Close coordination with NASA Dryden resulted in the development of new experimental instrumentation and techniques to facilitate the SCAMP flight-test execution, including the development of an F-18B Mach rate cockpit display, TG-14 powered glider in-flight sonic boom measurement instrumentation and "Where's the Focus?" (WTF) software for near-real time way-point computation accounting for local atmospherics. In May 2011, 13 F-18B flights were conducted during 5 flying days over a 2 week period. A densely populated 10,000 ft-long ground acoustic array with 125-ft microphone spacing was designed to capture pre-, focus, and post-focus regions. The ground-based acoustic array was placed in a nominally east-west orientation in the remote Cuddeback lakebed region, north of Edwards AFB. This area was carefully selected to avoid placing focused booms on populated areas or solar power facilities. For the SCAMP measurement campaign, approvals were obtained to temporarily extend the Black Mountain supersonic corridor northward by three miles. The SCAMP flight tests successfully captured 70 boom events, with 61 focus passes, and 9 calibration passes. Seventeen of the focus passes and three of the calibration passes were laterally offset; with the others being centerline flights. Airborne incoming sonic boom wave measurements were measured by the TG-14 for 10 of the F-18B flight passes including one maximum focus signature, several N-u combinations, several overlapped N-u signatures, and several evanescent waves. During the 27-month program, the SCAMP team developed a suite of integrated computer codes with sonic boom focusing predictive capabilities: PCBoom, Lossy Nonlinear Tricomi Equation Method (LNTE) and the Nonlinear Progressive wave Equation (NPE) method. PCBoom propagates the rays through the atmosphere and, in addition to legacy focus signature prediction based on the Gill-Seebass method, provides input source characteristics and propagation parameters to LNTE and NPE. LNTE, a Tricomi solver that incorporates atmospheric losses, computes the focus signature at the focus, and computes the focus signature in the vicinity of the focal zone, including the evanescent and post-focus zones. LNTE signature auralization from low-boom vehicle designs has been demonstrated in the NASA Langley Interior Effects Room (IER). The NPE has also been validated for use in prediction of focused ground boom signatures in sonic boom focal zones. The NPE formulation has the capability to incorporate atmospheric turbulence in the predictions. This has been applied to sonic boom propagation in the past. Prediction of turbulence effects on focal zone signatures was not, however, explored during the SCAMP program.

Page, Juliet↗
NASA NTRS1999

Project ATLANTA (Atlanta Land use Analysis: Temperature and Air Quality): Use of Remote Sensing and Modeling to Analyze How Urban Land Use Change Affects Meteorology and Air Quality Through Time

This paper presents an overview of Project ATLANTA (ATlanta Land use ANalysis: Temperature and Air-quality) which is an investigation that seeks to observe, measure, model, and analyze how the rapid growth of the Atlanta, Georgia metropolitan area since the early 1970's has impacted the region's climate and air quality. The primary objectives for this research effort are: (1) To investigate and model the relationships between land cover change in the Atlanta metropolitan, and the development of the urban heat island phenomenon through time; (2) To investigate and model the temporal relationships between Atlanta urban growth and land cover change on air quality; and (3) To model the overall effects of urban development on surface energy budget characteristics across the Atlanta urban landscape through time. Our key goal is to derive a better scientific understanding of how land cover changes associated with urbanization in the Atlanta area, principally in transforming forest lands to urban land covers through time, has, and will, effect local and regional climate, surface energy flux, and air quality characteristics. Allied with this goal is the prospect that the results from this research can be applied by urban planners, environmental managers and other decision-makers, for determining how urbanization has impacted the climate and overall environment of the Atlanta area. Multiscaled remote sensing data, particularly high resolution thermal infrared data, are integral to this study for the analysis of thermal energy fluxes across the Atlanta urban landscape.

Quattrochi, Dale A.↗
DOE OSTI2025

Using ground-based lidar data to investigate the water–vapor budget in the daytime atmospheric boundary layer

The moisture advection term in the water–vapor budget equation is investigated with a combination of a vertically-staring water–vapor lidar and Doppler lidar systems. These instruments make it possible to get the mean profile of moisture tendency and the latent heat flux (LHF) divergence. We use data of the Land–Atmosphere Feedback Experiment (LAFE) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site, Oklahoma, USA, collected on 30 August 2017 between 15 and 24 UTC, which corresponds to 09 to 18 LT. The lidars provide turbulence resolving profiles of moisture and vertical wind fluctuations. The LHF profile is derived from the covariance of these moisture and vertical wind fluctuations. The mean boundary layer height z i is determined from the peak of the moisture variance. The results demonstrate that the combination of two remote sensing instruments can be applied for determining the dominant water–vapor budget terms, namely moisture tendency, latent heat flux divergence and moisture advection.

Advection↗
NASA NTRS1985

The form of Newtonian cooling in atmospheric tidal theory

The analytical results of Zurek (1986) are applied to compare the vertical-structure equations obtained by Dickinson and Geller (1968) and Lindzen and McKenzie (1967) in solving the classical tidal equations for planetary atmospheres when Newtonian cooling (NC) is included as part of diabatic tidal forcing. The two diabatic forcings are found to be different when the basic state temperature varies with height, and it is recommended that a log-pressure NC formulation be used whenever temperature is made a function of pressure in the radiative-damping calculation. Scaling arguments are presented to show that the choice of NC formulation has significant effects only in cases where the radiative time constants are short (such as the thin CO2 atmosphere of Mars). Numerical computations for that case indicate a difference of 20 percent in amplitude and 15 deg in phase for the (probably meteorologically significant) 4.1-d wavenumber-one Rossby tidal mode.

Zurek, Richard W.↗