Search NASA⌕ Search

SEARCH · Search NASA

Results for “meteorological variables”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Model Simulations of CO2 Transport Using Assimilated Meteorological Fields

The numerical simulation of CO2 transport (and other tracers such as CO, CH4, and biomass burning tracers) in the atmosphere is required to determine the fate of anthropogenic source gases. Estimation of the CO2 exchange between the ocean surface, the terrestrial biosphere, and the atmosphere is of first-order importance to understanding the global carbon cycle and the processes that are most crucial in determining the atmospheric CO2 concentration. Forward transport simulations have been conducted using two-dimensional, time-dependent grids of average surface fluxes (from TRANSCOM) and three-dimensional wind data from a prototype data assimilation system (FV-DAS) run by the Goddard Data Assimilation Office. The objective is to better understand the contribution of meteorological variability to changes in CO2 and other constituents, By accurately accounting for meteorological variability, through use of assimilated winds, we hope to better characterize the distribution of surface sources and sinks (and chemistry where applicable). With assimilated meteorology such chemistry/transport runs provide the basic framework to analyze existing (and proposed) measurement data on a point-by-point, real-time basis. We compare with measured CO2 concentration gradients on a daily, seasonal, regional, and interhemispheric basis to examine the consistency of sources, sinks, and transport formulation. We will also examine the inter-annual variability of atmospheric CO2 due to atmospheric circulation changes using longer runs with assimilated winds.

Kaea, S. R.↗
NASA NTRS1991

Climatological perspectives, oceanographic and meteorological, on variability in the subtropical convergence zone in the northwestern Atlantic

The large-scale climatological environment of the Frontal Air-Sea Interaction Experiment (FASINEX) is described, with emphasis on the largest scales. Both long-term and annual sea surface temperature (SST) variability is discussed; a climatology of the west-central North Atlantic, derived from various sets of data obtained during the intensive phases of January-March, is presented, and the meteorological and oceanographic context for FASINEX is thus established. Surface marine observations and SST variability are discussed, and the marine meteorology of the FASINEX area is examined in terms of the surface pressure and winds, the sea-air temperature difference, and the cloud cover. Near 28 deg N in February is found to be a favorable time and place to observe large mean temperature gradients and downward Ekman pumping. The large-scale processes that set up the favorable environment for frontal activity are not limited to the winter months.

Hanson, Howard P.↗
NASA NTRS2017

Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

The recent change in the Environmental Protection Agency's surface ozone regulation, lowering the surface ozone daily maximum 8-h average (MDA8) exceedance threshold from 75 to 70 ppbv, poses significant challenges to U.S. air quality (AQ) forecasters responsible for ozone MDA8 forecasts. The forecasters, supplied by only a few AQ model products, end up relying heavily on self-developed tools. To help U.S. AQ forecasters, this study explores a surface ozone MDA8 forecasting tool that is based solely on statistical methods and standard meteorological variables from the numerical weather prediction (NWP) models. The model combines the self-organizing map (SOM), which is a clustering technique, with a step wise weighted quadratic regression using meteorological variables as predictors for ozone MDA8. The SOM method identifies different weather regimes, to distinguish between various modes of ozone variability, and groups them according to similarity. In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights that are based on their similarity to this specific regime. This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases. To produce probabilistic MDA8 ozone forecasts, REGiS weighs and combines all of the developed regression models on the basis of the weather patterns predicted by an NWP model. REGiS is evaluated over the San Joaquin Valley in California and the northeastern plains of Colorado. The results suggest that the model performs best when trained and adjusted separately for an individual AQ station and its corresponding meteorological site.

Ozone variables↗
NASA NTRS2015

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.

PM2.5↗
NASA NTRS1994

Turbulent transport and production/destruction of ozone in a boundary layer over complex terrain

The first Intensive Observation Period (IOP) of the Swiss air pollution experiment POLLUMET took place in 1990 in the Aare River Valley between Bern and Zurich. During the IOP, fast response measurements of meteorological variables and ozone concentration were made within the boundary layer aboard a motorglider. In addition, mean values of meteorological variables and the concentrations of ozone and other trace species were measured using other aircraft, pilot balloons, tethersondes, and ground stations. Turbulent flux profiles of latent and sensible heat and ozone are calculated from the fast response data. Terms in the ozone mean concentration budget (time rate of change of mean concentration, horizontal advection, and flux divergence) are calculated for stationary time periods both before and after the passage of a cold front. The source/sink term is calculated as a residual in the budget, and its sign and magnitude are related to the measured concentrations of reactive trace species within the boundary layer. Relationships between concentration ratios of trace species and ozone concentration are determined in order to understand the influence of complex terrain on the processes that produce and destroy ozone.

Greenhut, Gary K.↗
NASA NTRSDate not supplied

Evaluating Subgrid-Scale Properties of Low Clouds over the Beaufort Sea in Arctic System Reanalysis using ARISE Airborne In Situ Observations

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. To better understand the magnitude and possible causes of this problem, we analyze the disagreements between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. Previous analysis of grid scale-averaged meteorological variables revealed a lack of cloud water produced in ASR, related to warm/dry biases, a failure to produce sufficient cloud water in conditions that are observed to be favorable for large cloud water values, and sampling issues with the ARISE flight paths. However, ARISE collected meteorological observations at one second intervals, offering spatial data resolution comparable to the subgrid scale of ASR. Subgrid scale properties and processes must be parameterized in conventional atmospheric models and reanalyses such as ASR, and often rely on assumed distributions of meteorological variables about the grid box mean state. To enable an examination of the realism of ASR’s subgrid scale properties, we use high resolution data from ARISE determine the “subgrid scale” distributions of cloud water and thermodynamic variables in the real atmosphere. Preliminary results show that the subgrid scale distribution of thermodynamic variables about the grid scale mean resembles the triangular distribution assumed in many bulk microphysical parameterization schemes, but also with tails resembling Gaussian distributions. The main goal is to understand the partitioning of the total available atmospheric water into water vapor and cloud water. Models typically covert some percentage of total water in a grid box to cloud water based on the estimated amount of supersaturation from the assumed vapor distribution. The ARISE measurements can reveal the relationship between subgrid scale supersaturation and the partition between cloud water and water vapor.

Jason Brant Dodson↗
NASA NTRS2021

Effect of volcanic emissions on clouds during the 2008 and 2018 Kilauea degassing events

Volcanic eruptions in otherwise clean environments are “natural experiments” wherein the effects of aerosol emissions on clouds and climate can be partitioned from meteorological variability and anthropogenic activities. In this work, we combined satellite retrievals, reanalysis products, and atmospheric modeling to analyze the mechanisms of aerosol–cloud interactions during two degassing events at the Kilauea volcano in 2008 and 2018. The eruptive nature of the 2008 and 2018 degassing events was distinct from long-term volcanic activity for Kilauea. Although previous studies assessed the modulation of cloud properties from the 2008 event, this is the first time such an analysis has been reported for the 2018 event and that multiple degassing events have been analyzed and compared at this location. Both events resulted in significant changes in cloud effective radius and cloud droplet number concentration that were decoupled from local meteorology and in line with an enhanced cloud albedo. However, it is likely that the effects of volcanic emissions on liquid water path and cloud fraction were largely offset by meteorological variability. Comparison of cloud anomalies between the two events suggested a threshold response of aerosol–cloud interactions to overcome meteorological effects, largely controlled by aerosol loading. In both events, the ingestion of aerosols within convective parcels enhanced the detrainment of condensate in the upper troposphere, resulting in deeper clouds than observed under pristine conditions. Accounting for ice nucleation on ash particles led to enhanced ice crystal concentrations at cirrus levels and a slight decrease in ice water content, improving the correlation of the model results with the satellite retrievals. Overall, aerosol loading, plume characteristics, and meteorology contributed to changes in cloud properties during the Kilauea degassing events.

Katherine H. Breen↗
NASA NTRS1992

Conservative-coordinate transformations for atmospheric measurements

This lecture describes a technique by which atmospheric measurements of trace species with medium to long lifetimes can be 'coincidentally' compared and validated even though measurements are taken at different locations and different times In other words, the method, under suitable counditions, can remove a large amount of the natural meteorological variability. The technique involves the use of quasi-Lagrangian or conservative coordinates - air parcel tags which are invariant or nearly invariant under the motion of the parcel. The technique is called 'reconstruction', since measurements taken at one location can be reconstructed at different locations. In order to do this, the data are transformed into the conservative coordinates and accumulated. Within the conservative reference frame, much of the meteorological variability is removed. Once enough data are obtained within the system, the observations can be tranformed back into physical space at any location and compared with other measurements. The method by which the trace species data are obtained makes no difference; satellite, balloon, rocket, aircraft and ground-based data all become equivalent and can be intercompared. The conservative-coordinate system not only allows for intercomparison of data, but shows how data can be taken in such a way as to maximize the physical scope of the information. In other words, the method automatically suggests when conditions might be suitable to obtain information with different environmental situations. It also allows for the forecast of constituent fields using only the meteorological forecasts and limited observational data.

Schoeberl, M. R.↗
NASA NTRS2021

Intercomparison of Middle Atmospheric Meteorological Analyses for the Northern Hemisphere Winter 2009-2010

Detailed meteorological analyses based on observations extending through the middle atmosphere (~15 to 100 km altitude) can provide key information to whole atmosphere modelling systems regarding the physical mechanisms linking day-to-day changes in ionospheric electron density to meteorological variability near the Earth’s surface. However, the extent to which independent middle atmosphere analyses differ in their representation of wave-induced coupling to the ionosphere is unclear. To begin to address this issue, we present the first intercomparison among four such analyses, JAGUAR-DAS, MERRA-2, NAVGEM-HA, and WACCMX+DART, focusing on the Northern Hemisphere (NH) 2009-2010 winter, which includes a major sudden stratospheric warming (SSW). This intercomparison examines the altitude, latitude, and time dependences of zonal mean zonal winds and temperatures among these four analyses over the 1 December 2009 – 31 March 2010 period, as well as latitude and altitude dependences of monthly mean amplitudes of the diurnal and semidiurnal migrating solar tides, the eastward propagating diurnal zonal wave number 3 nonmigrating tide, and traveling planetary waves associated with the quasi-5-day and quasi-2-day Rossby modes. Our results show generally good agreement among the four analyses up to the stratopause (~50 km altitude). Large discrepancies begin to emerge in the mesosphere and lower thermosphere owing to (1) differences in the types of satellite data assimilated by each system and (2) differences in the details of the global atmospheric models used by each analysis system. The results of this intercomparison provide initial estimates of uncertainty in analyses commonly used to constrain middle atmospheric meteorological variability in whole atmosphere model simulations.

John P Mccormack↗
NASA NTRS2018

Ground and Satellite Based Observation Datasets for the Lower Mekong River Basin

In ‘Satellite observations and modeling to understand the Lower Mekong River Basin streamflow variability’ [1] hydrological fluxes, meteorological variables, land cover land use maps, and soil characteristics and parameters data were compiled and processed for the Lower Mekong River Basin. In this work, daily streamflow time series data at nine gauges located at five different countries in the Mekong region (Thailand, Laos People׳s Democratic Republic (PDR), Myanmar, Cambodia, and Viet Nam) is presented. Satellite-based daily precipitation and air temperature (minimum & maximum) data is processed and provided over the entire basin as part of the dataset provided in this work. Moreover, land cover land use raster data that contains 18 classes that cover agriculture, urban, range and forests land cover land use classes for the basin is offered. In addition, a soil data that contains physical and chemical characteristics needed by physically based hydrological models to simulate the cycling of water and air is also provided.

Streamflow↗
NASA NTRS2019

Understanding the Impact of Meteorological Spatiotemporal Variability on Distant Focusing Overpressure Risk

Since the 1950s, the amplification of blast energy from explosions has been understood to be a significant hazard to public safety at launch ranges. Historically, the risk assessment of the Distant Focusing Overpressure (DFO) hazard started with a single temperature and wind profile (e.g., a radiosonde launch) as input to acoustic 1-D ray tracing models. By analyzing rays propagation and performing ray tracing, population centers under high DFO risk can be identified. Although this method is useful, less is known about how the blast waves can be focused when the spatiotemporal variability of the input profiles are considered. In summary, this work aims to consider how realistic atmospheric boundary layer variability (e.g., turbulence, land-surface contrasts) may affect blast waves propagation and focusing and, as a result, DFO risk assessment results.

Blumberg, W. Greg↗
NASA NTRS1989

The Southern Hemisphere lower stratosphere during August and September 1987 - Analyses based on the United Kingdom Meteorological Office Global Model

This paper presents a series of meteorological analyses used to aid the interpretation of the in situ Airborne Antarctic Ozone Experiment (AAOE) observations obtained aboard the ER-2 and DC-8 aircraft and examines the basis and accuracy of the analytical procedure. Maps and sections of meteorological variables derived from the UK Meteorological Office Global Model are presented for ER-2 and DC-8 flight days. It is found that analyzed temperatures and winds are generally in good agreement with AAOE observations at all levels; minor discrepancies were evident only at DC-8 altitudes. Maps of potential vorticity presented on the 428-K potential temperature surface show that the vortex is essentially circumpolar, although there are periods when major distortions are apparent.

Mckenna, D. S.↗
NASA NTRS2000

Interannual Variability in the Equatorial Stratosphere: Comparison of 2D Model Simulations with Satellite Constituent Data

Satellite observations from UARS and TOMS have provided unique data sets for studying seasonal and interannual stratospheric constituent variability during the 1990's. The corresponding meteorological variability is also well represented by the partly satellite-based UKMO assimilation winds and temperatures. In this study, we use the UKMO meteorological fields to derive zonal mean transport components (residual circulation, horizontal eddy diffusion) for each year, 1992-1998. These fields are then used in our GSFC two-dimensional chemistry and transport model to simulate the year to year changes in ozone and long lived tracers in the equatorial stratosphere due to interannual dynamical variability. The resulting model simulation of ozone and CH4 compares reasonably well with UARS data in reproducing a dominant QBO signal in the lower stratosphere, and a dominant semiannual signature in the upper stratosphere which appears to be modulated by the lower stratospheric QBO. The model also compares well with TOMS data in simulating the phase of the QBO in total ozone, although the model QBO amplitude is somewhat underestimated relative to TOMS. These comparisons illustrate that the general zonal mean transport characteristics in the equatorial stratosphere can be reasonably well simulated using an empirically-based residual circulation.

Fleming, Eric L.↗
NASA NTRS1980

Test and evaluation of water vapor radiometers

The accuracy of very long baseline interferometry in geodetic and astrometric applications is primarily limited by the propagation delays through the troposphere. The part of this delay that is most difficult to predict is due to atmospheric water vapor which can contribute up to about 40 cm of excess propagation path at microwave frequencies. The water vapor content in the atmosphere is variable and is not well correlated with surface meteorological variables. The brightness temperature measured near the transition of water vapor at 22.2 GHz and the propagation delay due to water vapor, or wet path length, are well correlated. This correlation is not perfect because the absorption coefficient and index of refraction do not have the same dependence on temperature and pressure.

Moran, J.↗
NASA NTRSDate not supplied

Decoupling the Effects of Anthropogenic Emission Reductions from the Meteorology and Natural Emissions in TROPOMI NO 2 Retrievals During the 2020 COVID-19 Lockdowns

Satellite measurements during the COVID-19 lockdowns that began in 2020 revealed unprecedented reductions in NO 2 tropospheric vertical column densities (VCD). These reductions have largely been attributed to reduced anthropogenic emissions associated with abrupt decreases in road traffic and other power consuming business activities. Although decreased emissions tended to be the main contributor to the observed NO 2 VCD reduction, meteorological variability also played a role. Whereas the observed VCD changes were predominantly negative in places where public health policies were strictly enforced, meteorology had both positive and negative effects over short time intervals. Here, we present results from a global study of the NO 2 reductions aimed at disentangling the meteorological and natural emission variability from the anthropogenic emissions over the world’s most populated megacities. For this study, NASA’s TROPOMI NO 2 algorithm was used in conjunction with the Global Modeling Initiative (GMI) chemical transport model to separate the contributions due emissions and meteorology. A priori NO 2 profiles were generated from two GMI simulations performed for 2020 at a resolution of 0.25° longitude x 0.25° latitude. The first simulation used updated, COVID-impacted NOX emissions based on Forster et al. (2020), while the second simulation used the 2019 emissions with the 2020 meteorology, referred to here as 2020BAU. The 2020BAU data set allowed for the decoupling of the emission component from the meteorology. When compared to the same period in 2019, NO 2 column amounts during the lockdowns were reduced in 35 out of 36 cities. While reduced emissions contributed most to the observed total change in NO 2 during the lockdowns, the effects of meteorology were significant, ranging between 40% (Chennai) and 15% (Beijing). In China, an increase in NO 2 levels due to meteorology were observed in five out of the seven cities considered in the study. Use of different a-priori NO 2 profiles from the two simulations in our TROPOMI retrievals allowed for a determination of retrieval errors that ranged from -1.7% to -11.0%. We used the quality assurance flag > 0.75 to select the highest quality scenes in the study period. Using the GMI, we estimated the sampling biases to be in the range -11% to 10% of the total change for the cities in our study.

Brad Fisher↗
NASA NTRSDate not supplied

Going With the Flow: Using a Particle Dispersion Model to Reveal Links Between Ocean Ecosystems and Marine Aerosols

Marine biogenic particle contributions to atmospheric aerosol concentrations are not well understood though they are important for determining cloud optical and cloud nucleating properties. Here we examine the relationship between marine aerosol measurements with satellite and model fields of ocean biology and meteorological variables during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES consisted of four field campaigns between November 2015 and April 2018 that aligned with the four major phases of the annual phytoplankton bloom cycle. The FLEXPART Lagrangian particle dispersion model is used to connect these variables spatiotemporally to ship-based aerosol. We find that correlations between some aerosol measurements with satellite measured and modelled variables increase with increasing trajectory length. This indicates biological and meteorological processes over the air mass history are influential to measured particle properties. For example, results show organic aerosol mass is driven by biogenic volatile organic compound (VOC) emissions from photosynthesis by phytoplankton stocks during advection into the region. In contrast, primary marine aerosol (PMA) concentrations showed better correlations to variables weighted with shorter air mass residence times, which reflects their localized origin as primary emissions. Evidence also suggest variability in PMA concentrations likely play an important role in VOC emission removal. The removal of VOCs lessen their impact on particle number and mass concentrations. The sensitivity of marine aerosols to the main ecosystem-atmosphere drivers and its dependence on air mass history are to be further discussed.

Kevin J Sanchez↗
NASA NTRS1973

Joint Conference on Sensing of Environmental Pollutants, 2nd, Washington, D.C., December 10-12, 1973, Proceedings

Subjects considered are related to the remote passive sensing of atmosphere pollutants, the extension of laboratory measurement techniques for field use, instrument quality and measurement standardization, the remote active sensing of atmospheric pollutants, stationary source sensing, and air quality standards and measurement accuracy. Aspects of radiological, electromagnetic, and acoustic pollution monitoring are discussed together with new methods in particulate analysis, the measurement of meteorological variables that impact on atmospheric pollutants, and the impact of meteorological parameters on pollution analysis. The in-situ sensing of acoustic chemical and biological pollutants is reported along with global scale pollution monitoring and the remote sensing of water pollutants. Individual items are announced in this issue.

Source record↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗