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NASA NTRS2022

Chapter 10 - Remote Sensing Measurements of Aerosol Properties

Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.

aerosol properties↗

NASA NTRS2009

The Long, Hard Journey: Expanding the Use of NASA Data and Models for Sustainable Development Planning Around the World

In 2007, the National Research Council's committee on Extending Observations and Research Results to Practical Applications recommended that NASA's Applied Science Program (ASP) directly engage with a broader community of users - not just federal agencies. Soon afterwards, scientists at the NASA Marshall Space Flight Center began discussions on a collaborative research project with EMBARQ - the World Resource Institute's Center for Sustainable Transport. The discussions initially focused on how best to utilize satellite observations and atmospheric models for assessing the impact of a proposed transportation project on land use and air quality. Discussions exposed the participants to a broad spectrum of science and policy challenges that these diverse organizations face on a routine basis. It brought into clear focus the need for an observation-modeling system that will allow a proactive approach towards development planning, and the fact that satellite systems do not always provide the spatial and temporal resolution useful for urban-scale applications, underscoring the need for earth system models to bridge this gap. Realizing the significant risk that unplanned urbanization and climate change pose to the social and functional stability of large cities, both organizations decided to expand the scope of their preliminary discussion to include water resources and agriculture. A pilot project, funded by NASA ASP, EMBARQ and Istanbul Technical University focused on quantifying the magnitude and extent of urbanization in Istanbul, and analyzed the combined effect of urbanization and projected climate change on local climate, air quality, and its consequent effects on agricultural productivity. Preliminary results show that Istanbul has undergone a significant amount of Land Use/Land Cover change over the past two decades. While some forested areas have been lost to urban-landscapes, urbanization has mostly occurred over former croplands due to the fact that in contrast to forested areas, croplands have flat terrain, making them a more attractive target for conversion to urban land. The reduction in land area for agricultural and increase in temperature has reduced agricultural production in the vicinity of the city. The United Nations expects the world population to reach 9.2 billion by 2050. Most of this increase will be absorbed by countries that are least able to sustain it. Rapid economic growth is likely to continue and will require massive infrastructure investments. If not properly managed, the magnitude and extent of these initiatives will overwhelm sensitive ecosystems around the world. Successful relationships such as the one described here are critically important if we are to stabilize the threatened ecosystems on which human systems ultimately rely. The challenges, needs, culture, and operating environment of development planning organizations are fundamentally different from a research organization. An appreciation of these differences is a prerequisite for any successful collaboration.

Khan, Maudood↗

NASA NTRS1998

Water Isotopes in Precipitation: Data/Model Comparison for Present-Day and Past Climates

Variations of HDO and H2O-18 concentrations are observed in precipitation both on a geographical and on a temporal basis. These variations, resulting from successive isotopic fractionation processes at each phase change of water during its atmospheric cycle, are well documented through the IAEA/WMO network and other sources. Isotope concentrations are, in middle and high latitudes, linearly related to the annual mean temperature at the precipitation site. Paleoclimatologists have used this relationship to infer paleotemperatures from isotope paleodata extractable from ice cores, deep groundwater and other such sources. For this application to be valid, however, the spatial relationship must also hold in time at a given location as the location undergoes a series of climatic changes. Progress in water isotope modeling aimed at examining and evaluating this assumption has been recently reviewed with a focus on polar regions and, more specifically, on Greenland. This article was largely based on the results obtained using the isotopic version of the NASA/GISS Atmospheric General Circulation Model (AGCM) fitted with isotope tracer diagnostics. We extend this review in comparing the results of two different isotopic AGCMs (NASA/GISS and ECHAM) and in examining, with a more global perspective, the validity of the above assumption, i.e. the equivalence of the spatial and temporal isotope-temperature relationship. We also examine recent progress made in modeling the relationship between the conditions prevailing in moisture source regions for precipitation and the deuterium-excess of that precipitation.

Jouzel, J.↗

NASA NTRS1995

Ground-based grasslands data to support remote sensing and ecosystem modeling of terrestrial primary production

Estimating terrestrial net primary production (NPP) using remote-sensing tools and ecosystem models requires adequate ground-based measurements for calibration, parameterization, and validation. These data needs were strongly endorsed at a recent meeting of ecosystem modelers organized by the International Geosphere-Biosphere Program's (IGBP's) Data and Information System (DIS) and its Global Analysis, Interpretation, and Modelling (GAIM) Task Force. To meet these needs, a multinational, multiagency project is being coordinated by the IGBP DIS to compile existing NPP data from field sites and to regionalize NPP point estimates to various-sized grid cells. Progress at Oak Ridge National Laboratory (ORNL) on compiling NPP data for grasslands as part of the IGBP DIS data initiative is described. Site data and associated documentation from diverse field studies are being acquired for selected grasslands and are being reviewed for completeness, consistency, and adequacy of documentation, including a description of sampling methods. Data are being compiled in a database with spatial, temporal, and thematic characteristics relevant to remote sensing and global modeling. NPP data are available from the ORNL Distributed Active Archive Center (DAAC) for biogeochemical dynamics. The ORNL DAAC is part of the Earth Observing System Data and Information System, of the US National Aeronautics and Space Administration.

Olson, R. J.↗

NASA NTRS2012

MAGIC: A Tool for Combining, Interpolating, and Processing Magnetograms

Transients in the solar coronal magnetic field are ultimately the source of space weather. Models which seek to track the evolution of the coronal field require magnetogram images to be used as boundary conditions. These magnetograms are obtained by numerous instruments with different cadences and resolutions. A tool is required which allows modelers to fmd all available data and use them to craft accurate and physically consistent boundary conditions for their models. We have developed a software tool, MAGIC (MAGnetogram Interpolation and Composition), to perform exactly this function. MAGIC can manage the acquisition of magneto gram data, cast it into a source-independent format, and then perform the necessary spatial and temporal interpolation to provide magnetic field values as requested onto model-defined grids. MAGIC has the ability to patch magneto grams from different sources together providing a more complete picture of the Sun's field than is possible from single magneto grams. In doing this, care must be taken so as not to introduce nonphysical current densities along the seam between magnetograms. We have designed a method which minimizes these spurious current densities. MAGIC also includes a number of post-processing tools which can provide additional information to models. For example, MAGIC includes an interface to the DA VE4VM tool which derives surface flow velocities from the time evolution of surface magnetic field. MAGIC has been developed as an application of the KAMELEON data formatting toolkit which has been developed by the CCMC.

Allred, Joel↗

NASA NTRS2016

Evaluation of UTLS Carbon Monoxide Simulations in GMI and GEOS-Chem Chemical Transport Models using Aura MLS Observations

This study evaluates the distribution and variation of carbon monoxide (CO) in the upper troposphere and lower stratosphere (UTLS) during 2004-2012 as simulated by two chemical transport models, using the latest version of Aura Microwave Limb Sounder (MLS) observations. The simulated spatial distributions, temporal variations and vertical transport of CO in the UTLS region are compared with those observed by MLS. We also investigate the impact of surface emissions and deep convection on CO concentrations in the UTLS over different regions, using both model simulations and MLS observations. Global Modeling Initiative (GMI) and GEOS-Chem simulations of UTLS CO both show similar spatial distributions to observations. The global mean CO values simulated by both models agree with MLS observations at 215 and 147 hPa, but are significantly underestimated by more than 40% at 100 hPa. In addition, the models underestimate the peak CO values by up to 70% at 100 hPa, 60% at 147 hPa and 40% at 215 hPa, with GEOS-Chem generally simulating more CO at 100 hPa and less CO at 215 hPa than GMI. The seasonal distributions of CO simulated by both models are in better agreement with MLS in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH), with disagreements between model and observations over enhanced CO regions such as southern Africa. The simulated vertical transport of CO shows better agreement with MLS in the tropics and the SH subtropics than the NH subtropics. We also examine regional variations in the relationships among surface CO emission, convection and UTLS CO concentrations. The two models exhibit emission-convection- CO relationships similar to those observed by MLS over the tropics and some regions with enhanced UTLS CO.

spatial distribution↗

NASA NTRS2021

Statistical Error Analysis on White-Light Filter Ratio Experiments to Measure Electron Parameters

The Filter ratio technique to remotely measure electron temperature and speed using four color filters in visible light and a polarization camera was described in detail in four articles by Reginald et al., Solar physics, (2018, 2019a, 2019b, 2020). In these four articles we quantified the systematic error associated with using models of symmetric corona to interpret results from asymmetric corona. We also showed the criteria applied to select the bandwidths of filters, and the pros and cons of replacing the traditional linear polarizer with a polarization camera to measure pB. What started in 1990s as ground experiments conducted during total solar eclipses that lasted for a few minutes lead to a balloon borne experiment lasting eight hours in 2019 and will blossom into a space experiment on the International Space Station in 2023. Due to constraints on the bandwidths of the four filters, a successful mission requires quantification of the statistical error using Monte Carlo simulation to generate two feasibility profiles, which are unique to the design parameters of the coronagraph, to comprehend the feasibility to measure temperature and speed within the desired temporal and spatial resolutions. For the statistical error analysis, we use modeled K and F corona profiles, representative theoretical diffraction, scattering, and vignetting profiles, assumed efficiencies for lenses, mirrors, and polarizers, assumed detector properties on quantum efficiency, full well depth, dark noise, and read noise, and assumed instrument properties on aperture diameter, solid angle, and pixel resolution. We hope future white-light coronagraphs will exhibit capabilities to measure electron density, temperature, and speed.

Nelson Reginald↗

NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗

NASA NTRS1975

Signal dispersion within a hippocampal neural network

A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

Horowitz, J. M.↗

NASA NTRS1990

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

Crane, Robert G.↗

NASA NTRS1996

Intraseasonal and Interannual Variability of Mars Present Climate

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

Hollingsworth, Jeffery L.↗

NASA NTRS2019

Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

Raut, Bipin↗

NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗

NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

Carl Malings↗

NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Neural Networks↗

NASA NTRS2016

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

remote sensing↗

NASA NTRS1991

Laboratory evaluation and application of microwave absorption properties under simulated conditions for planetary atmospheres

Laboratory measurements of microwave and millimeter wave properties of the simulated atmosphere of the outer planets and their satellites has continued. One of the focuses is on the development of a radiative transfer model of the Jovian atmosphere at wavelengths from 1 mm to 10 cm. This modeling effort led to laboratory measurements of the millimeter wave opacity of hydrogen sulfide (H2S) under simulated Jovian conditions. Descriptions of the modeling effort, the Laboratory experiment, and the observations are presented. Correlative studies of measurements with Pioneer-Venus radio occultation measurements with longer wavelength emission measurements have provided new ways for characterizing temporal and spatial variations in the abundance of both gases H2SO4 and SO2, and for modeling their roles in the subcloud atmosphere. Laboratory measurements were conducted on 1.35 cm (and 13 cm) opacity of gaseous SO2 and absorptivity of gaseous SO2 at the 3.2 mm wavelength under simulated Venus conditions. Laboratory measurements were completed on millimeter wave dielectric properties of liquid H2SO4, in order to model the effects of the opacity of the clouds of Venus onto millimeter wave emission spectrum.

Steffes, Paul G.↗

NASA NTRS1993

Chemistry and transport in a multi-dimensional model

The focus of our research program is the achievement of a quantitative understanding of the spatial distribution and temporal variation of chemical species in the terrestrial middle atmosphere, with emphasis on ozone. Although not directed at assessments of anthropogenic impacts, our activities contribute to a refinement of model descriptions of chemical and dynamical processes that are needed for assessment tasks. A unique feature of our research effort is a close interaction with the chemical kinetics group at JPL and the field measurement groups at JPL. The principal tools used in our investigations are l-D and 2-D photochemical models. The advective component of the meridional transport in these models is approximated by the diabatic circulation, which is derived diagnostically from the net radiative heating rates. We are developing accurate radiative transfer algorithms to find the net heating rates and the diabatic circulation for use in the 2-D model.

Yung, Yuk L.↗