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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Frequency and site selection criteria for MST radars, part 5.1A

The majority of mesosphere-stratosphere-troposphere (MST) and ST radars are located in or near mountainous terrain. When measuring horizontal velocities, the terrain is a small factor, but when measuring vertical velocities, the meteorological noise induced by rough terrain can severely limit the usefulness of the observations. When the variance of the vertical velocity is too large, it is not possible to suitably filter the data to detect the small synoptic-scale signal with reasonable statistical confidence. The variance of vertical velocity at all tropospheric levels is directly related to the low level wind speed during flow over rough terrain. It is suggested that the synoptic-scale vertical velocity can be measured by ST radars where the terrain is smooth. The large-scale vertical velocity cannot always be reliably determined from MST radar data when the underlying terrain is rough. The vertical velocity is potentially on of future radar site selections, taking into account the desired meteorological applications of the data and engineering design factors. If the synoptic-scale vertical velocity is a desired variable, the radar should not be located near mountains.

Nastrom, G. D.↗
NASA NTRS2008

Differences and Similarities in MaCWAVE Summer and Winter Temperatures and Winds

Small meteorological rockets released inflatable falling spheres during the MaCWAVE Campaign. The Mountain and Convective Waves Ascending Vertically Experiment (MaCWAVE) was carried out in two parts, a summer sequence from Andoya Rocket Range (69N) during July 2002 to examine convective initiation of gravity waves and a winter sequence from ESRANGE (68N) during January 2003 to examine mountain-terrain initiated gravity waves. The sphere-tracked data provided significant information about the variation of temperature and wind from 70 km and above. The changes observed may be considered akin to tidal motion; unfortunately the launch activity was restricted to 12-hour periods, thus the observation of a full diurnal cycle was not possible. During summer, temperature variation was smaller than that observed during winter when peak to null differences reached 15-20 K at 80-85 km. Variation in the zonal winds varied up to 100+mps in summer and winter. Examination of the times of peak wind vs altitude showed that the peak zonal wind occurred approximately two hours ahead of the peak meridional wind. We provide details about the measurements and observed variations.

Schmidlin, F. J.↗
DOE OSTI2023

Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed

Evaluating the interactions between above- and below-ground processes is important to understand and quantify how ecosystems respond differently to atmospheric forcings and/or perturbations and how this depends on their intrinsic characteristics and heterogeneity. Improving such understanding is particularly needed in snow-impacted mountainous systems where the complexity in water and carbon storage and release arises from strong heterogeneity in meteorological forcing and terrain, vegetation and soil characteristics. This study investigates spatial and temporal interactions between terrain, soil moisture, and plant seasonal dynamics at the intra- and inter-annual scale along a 160 m long mountainous, non-forested hillslope-to-floodplain system in the upper East River Watershed in the upper Colorado River Basin. To this end, repeated UAV-based multi-spectral aerial imaging, ground-based soil electrical resistivity imaging, and soil moisture sensors were used to quantify the interactions between above and below-ground compartments. Results reveal significant soil-plant co-dynamics. The spatial variation and dynamics of soil water content and electrical conductivity, driven by topographic and soil intrinsic characteristics, correspond to distinct plant types, with highest plant productivity in convergent areas. Plant productivity in heavy snow years benefited from more water infiltration as well as a shallow groundwater table depth. In comparison, low snowpack years with an early first bare-ground date, which are linked to an early increase in plant greenness, imply a short period of saturated conditions that leads to lower average and maximum greenness values during the growing season. Overall, these results emphasize the strong impact of snowpack dynamics, and terrain and subsurface characteristics on the heterogeneity in plant type and seasonal dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
NASA NTRS2018

Quantifying the Early Snowmelt Event of 2015 in the Cascade Mountains, USA by Developing and Validating MODIS-Based Snowmelt Timing Maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from "snow-covered" to "no snow" during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 meter MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001-2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001-2015 average, with 25 percent of its land area melting more than 65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

snowmelt timing↗
NASA NTRS2010

Space-Frame Lunar Lander

The space-frame lunar lander was originally intended to (1) land on rough lunar terrain, (2) deform itself to conform to the terrain so as to be able to remain there in a stable position and orientation, and (3) if required, further deform itself to perform various functions. In principle, the space-frame lunar lander could be used in the same way on Earth, as might be required, for example, to place meteorological sensors or a radio-communication relay station on an otherwise inaccessible mountain peak. the space-frame lunar lander would include a truss-like structure consisting mostly of a tetrahedral mesh of nodes connected by variable-length struts, the lengths of which would be altered in coordination to impart the desired overall size and shape to the structure. Thrusters (that is, small rocket engines), propellant tanks, a control system, and instrumentation would be mounted in and on the structure (see figure). Once it had landed and deformed itself to the terrain through coordinated variations in the lengths of the struts, the structure could be further deformed into another space-frame structure

Curtis, Steven A.↗
NASA NTRS2017

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.

Lake Ontario↗
DOE OSTI2026

GPM IMERG V07B and V06B: Evaluation Using Ground-Based Radar Observations and Application in Global Mesoscale Convective System Tracking

This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.

Zhang, Sihan↗
NASA NTRS1989

The use of DFDR information in the analysis of a turbulence incident over Greenland

Digital flight data recorder (DFDR) tapes from commercial aircraft can provide useful information about the mesoscale environment of severe turbulence incidents. Air motion computations from these data and their errors are briefly described. An example of mesoscale meteorological information available from DFDR tapes is presented for a case of turbulence in mountain waves over the Greenland icecap.

Lester, Peter F.↗
NASA NTRS1982

Power spectral measurements of clear-air turbulence to long wavelengths for altitudes up to 14,000 meters

Measurements of three components of clear air atmospheric turbulence were made with an airplane incorporating a special instrumentation system to provide accurate data resolution to wavelengths of approximately 12,500 m (40,000 ft). Flight samplings covered an altitude range from approximately 500 to 14,000 m (1500 to 46,000 ft) in various meteorological conditions. Individual autocorrelation functions and power spectra for the three turbulence components from 43 data runs taken primarily from mountain wave and jet stream encounters are presented. The flight location (Eastern or Western United States), date, time, run length, intensity level (standard deviation), and values of statistical degrees of freedom for each run are provided in tabular form. The data presented should provide adequate information for detailed meteorological correlations. Some time histories which contain predominant low frequency wave motion are also presented.

Murrow, H. N.↗
NASA NTRS1990

Lidar observations of ozone changes induced by subpolar air mass motion over Table Mountain, California (34.4 deg N)

Between October 15 and November 8, 1988, the Goddard Space Flight Center mobile stratospheric lidar was in place at the (JPL) Table Mountain Facility (located at 34.4 deg N, 117.7 deg W) for the purpose of intercomparing with the JPL lidar permanently stationed at the observatory. During the course of the intercomparison both lidar systems detected a significant change in the vertical profile of ozone lasting for several days. An analysis of meteorological data available from the National Meteorological Center has shown this change to be dynamical in origin due to the transport of subpolar air over Table Mountain.

Mcgee, Thomas J.↗
NASA NTRS1983

A long-lived mesoscale convective complex. I - The mountain-generated component

The South Park Area Cumulus Experiment (SPACE) was conducted during the summer of 1977, and takes into account meteorological developments in a broad, elevated basin called South Park, which is located northeast of Colorado Springs, Colorado. The experiment was designed to investigate the organization of the mesoscale and storm-scale systems in detail over South Park. Many of the convective storms which were observed in the developing stages over South Park participated in the genesis of mesoscale convective complexes (MCCs) on the meso-alpha-scale. The present investigation is concerned with the evolution of the second MCC of this episode, which matured on the evening of August 4, 1977. Attention is given to the events from the preconvective stage early on August 4 to the incipient MCC stage in the early evening.

Cotton, W. R.↗
NASA NTRS1973

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

The author has identified the following significant results. The percentage of snow cover area on specific drainage basins was measured from ERTS-1 imagery by video density slicing with a repeatability of 4 percent of the snow covered area. Data from ERTS-1 images of the melt season snow cover in the Thunder Creek drainage basin in the North Cascades were combined with existing hydrologic and meteorologic observations to enable calculations of the time distribution of the water stored in this mountain snowpack. Similar data could be used for frequent updating of expected inflow to reservoirs. Equivalent snowline altitudes were determined from area measurements. Snowline altitudes were also determined by combining enlarged ERTS-1 images with maps. ERTS-1 imagery was also successfully used to measure glacier accumulation area ratios for a small test basin.

Meier, M. F.↗
NASA NTRS1973

Evaluation of ERTS imagery for mapping and detection of changes of snowcover land and on glaciers

The percentage of snowcover area on specific drainage basins was measured from ERTS imagery by video density slicing with a repeatability of 4 percent of the snowcovered area. Data from ERTS images of the melt season snowcover in the Thunder Creek drainage basin in the North Cascades were combined with existing hydrologic and meteorologic observations to enable calculation of the time distribution of the water stored in this mountain snowpack. Similar data could be used for frequent updating of expected inflow to reservoirs. Equivalent snowline altitudes were determined from area measurements. Snowline altitudes were also determined by combining enlarged ERTS images with maps with an accuracy of about 60 m under favorable conditions. Ability to map snowcover or to determine snowline altitude depends primarily on cloud cover and vegetation and secondarily on slope, terrain roughness, sun angle, radiometric fidelity, and amount of spectral information available.

Meier, M. F.↗
NASA NTRS1980

The 1979 clear air turbulence flight test program

A clear air turbulence (CAT) flight test to evaluate and test four different sensors in the detection and measuring of CAT and other meteorological targets that relate to turbulence is discussed. The primary types of CAT investigated were mountain wave CAT, jetstream CAT, CAT in cirrus clouds, and CAT in frontal wind shears, troughs, and ridges. The sensors included the CO2 pulsed Doppler lidar and three radiometers. One of the radiometers, at a frequency of 55.5 GHz, looked at atmospheric temperature structure. Another, at a frequency of 180.1 GHz, looked at atmospheric water vapor and investigated the feasibility of measuring at the microwave frequency the turbulence features seen in the infrared (IR) frequencies. An IR radiometer at 27 to 33 microns was the fourth sensor. This last device and the temperature structure radiometer worked well at all flight levels.

Weaver, E. A.↗
DOE OSTI2020

Assessment of Spatiotemporal Variability of Evapotranspiration and Its Governing Factors in a Mountainous Watershed at the East River, Colorado, Water: Dataset

This data package contains simulation results of daily evapotranspiration for 17 locations of meteorological stations with different precipitation, air temperature and vegetation within the East River Watershed, Colorado (CLMResult.csv). Daily precipitation data is included for reference. These are the main results of the paper Tran, 2019. Meteorological stations names, latitude and longitude coordinates of meteorological stations, types of lands from the National Land Cover Database, and a comparison with the community land model (CLM) data are also included (Met_LC.csv). The purpose of this data is to analyze the spatio-temporal variations of evapotranspiration at the East River Watershed and understand the factors that control these variations. More information regarding the study, methods, and findings can be found in the Related Reference paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗