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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Overview of the NASA TROPICS CubeSat Constellation Mission

Recent technology advances in miniature microwave radiometers that can be hosted on very small satellites has made possible a new class of affordable constellation missions that provide very high revisit rates of tropical cyclones and other severe weather. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture-Instrument (EVI-3)program and is now in development with planned launch readiness in late 2019. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS will provide rapid-refresh microwave measurements (median refresh rate better than 60 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm life cycle. TROPICS will comprise a constellation of at least six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz (gigahertz) oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles and low-level moisture. This observing system offers an unprecedented combination of horizontal and temporal resolution in the microwave spectrum to measure environmental and inner-core conditions for TCs on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data. Here, we provide an overview of the mission and an update on current status,with a focus on unique characteristics of the Cubesat system, recent performance simulations on a range of observables to be provided by the constellation, and a summary of science applications.

Passive Microwave↗
NASA NTRS2011

Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat fluxes. A slight increase in precipitation coverage was noted over Lake Erie due to a decrease in ice cover. Both the RTG SST and the GLST products predicted the precipitation south of the actual location of precipitation. This single case study is the first part of an examination to determine how MODIS data can be applied to improve model forecasts in the Great Lakes region.

Cipullo, Michelle↗
NASA NTRS2012

The View from the Top: CALIOP Ice Water Content in the Uppermost Layer of Tropical Cyclones

NASA's CALIPSO satellite carries both the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Imaging Infrared Radiometer (IIR). The lidar is ideally suited to viewing the very top of tropical cyclones, and the IIR provides critical optical and microphysical information. The lidar and the IIR data work together to understand storm clouds since they are perfectly co-located, and big tropical cyclones provide an excellent complex target for comparing the observations. There is a lot of information from these case studies for understanding both the observations and the tropical cyclones, and we are just beginning to scratch the surface of what can be learned. Many tropical cyclone cloud particle measurements are focused on the middle and lower regions of storms, but characterization of cyclone interaction with the lowermost stratosphere at the upper storm boundary may be important for determining the total momentum and moisture transport budget, and perhaps for predicting storm intensity as well. A surprising amount of cloud ice is to be found at the very top of these big storms.

Avery, Melody A.↗
NASA NTRS1989

False-color display of special sensor microwave/imager (SSM/I) data

Displays of multifrequency passive microwave data from the Special Sensor Microwave/Imager (SSM/I) flying on the Defense Meteorological Satellite Program (DMSP) spacecraft are presented. Observed brightness temperatures at 85.5 GHz (vertical and horizontal polarizations) and 37 GHz (vertical polarization) are respectively used to 'drive' the red, green, and blue 'guns' of a color monitor. The resultant false-color images can be used to distinguish land from water, highlight precipitation processes and structure over both land and water, and detail variations in other surfaces such as deserts, snow cover, and sea ice. The observations at 85.5 GHz also add a previously unavailable frequency to the problem of rainfall estimation from space. Examples of mesoscale squall lines, tropical and extra-tropical storms, and larger-scale land and atmospheric features as 'viewed' by the SSM/I are shown.

Negri, Andrew J.↗
NASA NTRS2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement–Southern Great Plains Site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to get a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near boundary layer top. We also explore how differences in retrievals may impact results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval showed some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

boundary-layer observation↗
DOE OSTI2026

Evaluating the role of green infrastructure features in post-disaster recovery – Case Study of Beaumont, Texas after tropical storm imelda

While green infrastructure (GI) can provide multiple environmental benefits, its role in post-disaster economic and social recovery remains relatively underexplored. This article investigates whether different characteristics of GI, such as size, shape, connectivity, and amenities, affect the resilience of local businesses following Tropical Storm Imelda in Beaumont, Texas. The study utilizes SafeGraph mobility data to analyze foot traffic patterns to local businesses before, during, and after the disaster. FRAGSTATS indices measure GI characteristics (e.g., area, shape index, fractal dimension, proximity) while park features such as sports facilities, playgrounds, water features, and accessibility are cataloged through manual observation. Ordinary Least Squares regression models assess the relationship between park characteristics and post-recovery business performance, controlling for demographic variables including income, race, and poverty levels. Results indicate that certain GI attributes significantly enhance business recovery. Points of interest within walking distance (0.5 miles) of parks demonstrated better post-recovery status compared to those beyond this range. Specifically, parks with larger areas (p < 0.01) and more complex shapes measured by fractal dimension index (p < 0.01) had the strongest positive impact on surrounding businesses' recovery. Interestingly, playgrounds showed a negative correlation with recovery (p < 0.05), likely due to flood damage rendering them unusable during the immediate recovery period. Social vulnerability factors, including higher poverty rates and minority populations, negatively affected recovery outcomes despite park proximity.

Economic resilience↗
NASA NTRS1981

Global weather requirements for precipitation measurements

Results of a radar study of summertime convection in the high plains of Kansas are presented which demonstrate the importance of the small scale structure of precipitation to the overall production of precipitation in a storm. The smaller scale structure must be modeled to develop valid relationships between satellite observables and precipitation amount. The Kansas results suggest that just the observation of the number and spacings of the active regions of convection (thunderstorms) is sufficient to provide an estimate of water flux with an uncertainty of less than a factor of two.

Clark, J. H. E.↗
NASA NTRS1992

Deuterium in North Atlantic storm tops

During the ERICA project in 1989, ice crystals were collected from the tops of two winter storms and one broad cirrus cloud. Deuterium concentration in the storm ice samples, together with a model of isotope fractionation, are used to determine the temperature where the ice was formed. Knowledge of the ice formation temperature allows us to determine whether the ice has fallen or been lofted to the altitude of collection. In both storms, the estimated fall distance decreases upward. In the 21 January storm, the fall distance decreases to zero at the cloud top. In the 23 January storm, the fall distance decreases to zero at a point 2 km below the cloud top and appears to become negative above, indicating lofted ice. Cloud particle data from the cloud tops show an ice-to-vapor ratio greater than one and indicate the presence of particles with small terminal velocities; both observations support the idea of ice lofting. The satellite-derived cloud tops lie well below the actual cloud top (e.g., 2.5 km below on 23 January), indicating that the lofted ice in winter storms may not be detectable from space using IR radiance techniques. A comparison of deuterium in cloud-top ice and clear-air vapor suggests that even in winter, when vertical air motions are relatively weak, lofted ice crystals are the dominant source of water vapor in the upper troposphere.

Smith, Ronald B.↗
NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
NASA NTRS2011

Use of Dual-Polarization Radar Variables to Assess Low-Level Wind Shear in Severe Thunderstorm Near-storm Environments in the Tennessee Valley

The upgrade of the National Weather Service (NWS) network of S ]band dual-polarization radars is currently underway, and the incorporation of polarimetric information into the real ]time forecasting process will enhance the forecaster fs ability to assess thunderstorms and their near ]storm environments. Recent research has suggested that the combination of polarimetric variables differential reflectivity (ZDR) and specific differential phase (KDP) can be useful in the assessment of low level wind shear within a thunderstorm. In an environment with strong low ]level veering of the wind, ZDR values will be largest along the right inflow edge of the thunderstorm near a large gradient in horizontal reflectivity (indicative of large raindrops falling with a relative lack of smaller drops), and take the shape of an arc. Meanwhile, KDP values, which are proportional to liquid water content and indicative of a large number of smaller drops, are maximized deeper into the forward flank precipitation shield than the ZDR arc as the smaller drops are being advected further from the updraft core by the low level winds than the larger raindrops. Using findings from previous work, three severe weather events that occurred in North Alabama were examined in order to assess the utility of these signatures in determining the potential for tornadic activity. The first case is from October 26, 2010, where a large number of storms indicated tornadic potential from a standard reflectivity and velocity analysis but very few storms actually produced tornadoes. The second event is from February 28, 2011, where tornadic storms were present early on in the event, but as the day progressed, the tornado threat transitioned to a high wind threat. The third case is from April 27, 2011, where multiple rounds of tornadic storms ransacked the Tennessee Valley. This event provides a dataset including multiple modes of tornadic development, including QLCS and supercell structures. The overarching goal of examining these three events is to compare dual ]polarization features from this larger dataset to previous work and to determine if these signatures can be a useful indication of the potential for tornadic activity associated with the amount of low ]level wind shear in the near ]storm environment.

Crowe, Christina C.↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Tree Inventory

This is the tree inventory (diameter, species, and live/dead status) data from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field Measurements and Experiments; see https://compass.pnnl.gov/FME/COMPASSFME) project. It addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in eastern Maryland, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.This dataset includes:- An overall dataset README file.- The tree inventory data in both "wide" and "long" forms. These contain the same information but are structured differently, with the former more useful for human viewers and the latter more amenable for programmatic analyses.- A key to the species/genus codes used, which follow the U.S. Department of Agriculture's PLANTS schema (https://plants.usda.gov/).- A copy of the R code used to generate the wide- and long-form data files.All files are comma-separated value (CSV) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. Furthermore, we also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Moisture convergence from a combined mesoscale moisture analysis and wind field for 24 April 1975

Precipitable water values inferred from the Vertical Temperature Profile Radiometer data of the polar orbiting NOAA-4 satellite are used in conjunction with wind-field analyses obtained from Synchronous Meteorological Satellite visible-channel data to study the moisture convergence in the boundary layer immediately preceding a storm. This combination of data simulates the information that will be available from the Visible and Infrared Spin-Scan Radiometer on board the GOES-D satellite, which is scheduled to begin operation in the 1980s. Serviceable representations of boundary layer flow are developed through analysis of the satellite infrared cumulus velocities, although the flow representations are not exactly located in the vertical.

Negri, A. J.↗
NASA NTRS1982

Water vapor in the Martian atmosphere - A discussion of the Viking data

A summary of calculations describing the Martian atmosphere water vapor content based on data from the Mars Atmospheric Water Detectors carried by the Viking landers is presented. The water column has been observed to vary with season, time of day, and locality. Over 5 yr of continuous data collection has permitted modeling of the Martian year into 24 seasonal periods of planetocentric solar longitude, with gaps in the model due to the presence of dust storms. The vapor content is asymmetric pole-to-pole, but symmetric latitudinally with respect to the equator. Low elevation areas display a higher vapor content, especially with rapid height changes in nearby terrain. Dust storms reduced the total atmospheric vapor, with concentration shifts tending toward the north, from where it is expected renewed balances will be reinstated. Consideration is also given to diurnal variations, and variations due to temperature, composition, and wind velocity.

Doms, P. E.↗
NASA NTRS1993

Mars Operational Environmental Satellite (MOES): A post-Mars Observer discovery mission

Mars Operational Environmental Satellite (MOES) is a Discovery concept mission that is designed to observe the global short-term weather phenomena on Mars in a systematic fashion. Even after the Mariner, Viking, and, soon, Mars Observer missions, crucial aspects of the martian atmosphere will remain unobserved systematically. Achieving a better understanding of the cycles of dust, water vapor, and ices on Mars requires detailed information about atmospheric transports of those quantities associated with the weather systems, particularly those arising in mid latitudes during fall and winter. It also requires a quantitive understanding of the processes responsible for the onset and evolution of dust storms on all scales. Whereas on Earth the system of geosynchronous and polar orbiting satellites provides continuous coverage of the weather systems, on Mars the time history of important events such as regional and global dust storms remains unobserved. To understand the transport of tracers in the martian atmosphere and particularly to identify their sources and sinks, it is necessary to have systematic global, synoptic observations that have yet to be attained. Clearly these requirements are not easy to achieve from a single spacecraft in orbit, but if we focus on specific regions of the planet, e.g., polar vs. low and mid latitudes, then it is possible to attain a nearly ideal coverage at a reasonable spatial and temporal resolution with a system of just two satellites. Mars Observer is about to yield good coverage of the polar latitudes, so we focus initially on the region not covered well in terms of diurnal coverage, and in terms of desired observations will provide the initial data for the numerical models of the martian weather and climate that can be verified only with better temporal and spatial data.

Limaye, Sanjay S.↗
NASA NTRS1995

Urban remote sensing applications: TIMS observations of the City of Scottsdale

A research program has been initiated between Arizona State University and the City of Scottsdale, Arizona to study the potential applications of TIMS (Thermal Infrared Multispectral Scanner) data for urban scene classification, desert environmental assessment, and change detection. This program is part of a long-term effort to integrate remote sensing observations into state and local planning activities to improve decision making and future planning. Specific test sites include a section of the downtown Scottsdale region that has been mapped in very high detail as part of a pilot program to develop an extensive GIS database. This area thus provides excellent time history of the evolution of the city infrastructure, such as the timing and composition of street repavement. A second area of study includes the McDowell intensive study by state and local agencies to assess potential sites for urban development as well as preservation. These activities are of particular relevance as the Phoenix metropolitan area undergoes major expansion into the surrounding desert areas. The objectives of this study in urban areas are aimed at determining potential applications of TIMS data for classifying and assessing land use and surface temperatures. Land use centers on surface impermeability studies for storm runoff assessment and pollution control. These studies focus on determining the areal abundance of urban vegetation and undeveloped soil. Highly experimental applications include assessment and monitoring of pavement condition. Temperature studies focus on determining swimming pool area and temperature for use in monitoring evaporating and urban water consumption. These activities are of particular relevance as the Phoenix metropolitan area undergoes major expansion into the surrounding desert area.

Christensen, Philip R.↗