Atmospheric Science Data Center Research Articles

Preface

Welcome to the Proceedings of the 8th International Scientific Conference of College of Science in Atmospheric Sciences (ISCCSAS-2023) May 3 – 4 2023, Mustansiriyah University, Baghdad, Iraq under the slogan “Sustainable lifestyle Development”. This is the second conference specializing in Atmospheric Sciences in Iraq. It aims to build scientific capabilities in atmospheric sciences, provide a scientific forum for researchers and specialists in the field of atmospheric sciences, and review the latest developments and research in this field. The Department of Atmospheric Sciences is the only unique department of this specialization among Iraqi universities. It has achieved many goals and contributed to the field of sustainable development and environmental systems; through the completion of the environmental park project, participation in afforestation campaigns, cultivation of green spaces, and the establishment of an educational weather observation station to monitor weather elements and an astronomical observatory followed by the Atmospheric Science Data Center (ASDC) powered by solar energy and provides data on many weather elements and aerosols for the city of Baghdad, and the introduction of higher diploma studies in climate change, and work is continuing to achieve many strategic plans and to cooperate with government institutions such as the Ministry of Water Resources / National Water Center, the Ministry of Environment, the Ministry of Transport and the Municipality of Baghdad. Today, the Department of Atmospheric Sciences concludes its achievements by holding its second specialized conference with the participation of many researchers from many Universities and research centers from inside and abroad in Iraq. All accepted papers will be published in the journal “IOP Conference Series: Earth and Environmental Science” which lies within the Scopus classification leading to improving the scientific reputation of the University. Because the conference included many participants, speakers, and researchers, from different countries of the world, some of the conference sessions were held online (virtual) using the google meet application and offline inside the halls of the Faculty of Science and the Department of Atmospheric Sciences. The conference shed light on some topics of atmospheric sciences to share views and experiences in the field of air pollution and weather events that occur all over the world due to climate change and to investigate the causes and how to adapt and mitigate them. The conference was also concerned with the use of geographic information systems (GIS) in atmospheric sciences for remote sensing, weather forecasts, and renewable energy. In conclusion, we are grateful for all the supporting, sponsoring, and supervising efforts of this conference, also we extend our gratitude to the preparatory, scientific, and electronic management committees and all the supporting committees that worked for a long time to success this conference a. Finally, we would like to thank all the researchers participated in this conference from inside and outside Iraq, wishing them success and for the conference to come up with recommendations that serve our society and country. May Allah grant everyone, peace, and Allah’s mercy and blessings be upon you. List of Editors Keynote Speakers, Scientific Committee, Organizing Committee are available in this pdf.

A Collection of Airborne Measurements and Analyses of Trace Gases Emitted From Multiple Fires in California

AbstractBiomass burning is an important source of trace gases and particles, and can influence air quality on local, regional, and global scales. With the threat of wildfire events increasing due to changes in land use, increasing population, and climate change, the importance of characterizing wildfire emissions is vital. In this work we characterize trace gas emissions from 12 wildfires and 1 prescribed fire in California between 2013 and 2017, in some cases with multiple measurements performed during different burn periods of a specific fire. Airborne measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor, temperature, and three‐dimensional winds were made by the Alpha Jet Atmospheric eXperiment and have been published at NASA's Atmospheric Science Data Center (https://doi.org/10.5067/ASDC/AJAX/wildfire, Iraci et al., 2021). The majority of these measurements were made as close as possible to each fire and represent fresh emissions from known fire sources. This set of observations from 13 different fires offers the opportunity to explore trace gas emissions over a range of meteorology, fire conditions, and to a lesser extent, vegetation type and drought, and adds to the body of knowledge collected by other investigators and field campaigns.

NASA’s Multi-Angle Imager for Aerosols (MAIA): Applications of NASA Satellite Data to Airborne Particulate Matter Exposure and Human Health

The upcoming NASA Multi-Angle Imager for Aerosols (MAIA) investigation seeks to extend NASA’s capabilities to study the impact of different size and compositional mixtures of particulate matter (PM) on adverse health outcomes. The MAIA satellite instrument, in development at the Jet Propulsion Laboratory and planned for launch in mid-2022, will collect multiangular, multispectral, and polarimetric measurements over a set of globally distributed target areas. Retrievals of aerosol properties will be combined with ground-based monitor data and chemical transport modeling to produce 1-km gridded data products of daily-averaged PM10 and PM2.5 mass, as well as the fractional abundances of sulfate, nitrate, organic carbon, elemental carbon, and dust making up the retrieved PM2.5 mixtures. Epidemiologists on the MAIA Science Team and their collaborators will use these data in studies aimed at associating health risk with particle types. The MAIA Early Adopters Program is designed to engage the wider air quality and public health communities and assist them in using MAIA data products, which will be publicly available, free of charge, from NASA’s Atmospheric Science Data Center. This presentation will cover the plans for operational MAIA data products as well as test data products that will be available prior to launch, and opportunities for Early Adopters to become involved in the program.

Improving the usability of the Multi-angle Imaging SpectroRadiometer (MISR) L1B2 Georectified Radiance Product (2000–present) in land surface applications

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform has been acquiring global measurements of the spectrodirectional reflectance of the Earth since 24 February 2000 and is still operational as of this writing. The primary radiometric data product generated by this instrument is known as the Level 1B2 (L1B2) Georectified Radiance Product (GRP): it contains the 36 radiometric measurements acquired by the instrument's nine cameras, each observing the planet in four spectral bands. The product version described here is projected on a digital elevation model and is available from the NASA Langley Atmospheric Science Data Center (ASDC; http://doi.org/10.5067/Terra/MISR/MI1B2T_L1.003; Jovanovic et al., 1999). The MISR instrument is highly reliable. Nevertheless, its onboard computer occasionally becomes overwhelmed by the number of raw observations coming from the cameras' focal planes, especially when switching into or out of Local Mode acquisitions that are often requested in conjunction with field campaigns. Whenever this occurs, one or more lines of data are dropped while the computer resets and readies itself for accepting new data. Although this type of data loss is minuscule compared to the total number of measurements acquired and is marginal for atmospheric studies dealing with large areas and long periods of time, this outcome can be crippling for land surface studies that focus on the detailed analysis of particular scenes at specific times. This paper describes the problem, reports on the prevalence of missing data, proposes a practical solution to optimally estimate the values of the missing data and provides evidence of the performance of the algorithm through specific examples in southern Africa. The software to process MISR L1B2 GRP data products as described here is openly available to the community from the GitHub website (https://github.com/mmverstraete or https://doi.org/10.5281/zenodo.3519988). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020, https://doi.org/10.5880/fidgeo.2020.012) includes five items: (A1) a compressed archive (L1B2_Out.zip) containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual (L1B2_Out.pdf) describing how to install, uncompress and explore those files; (A3) an additional compressed archive (L1B2_Suppl.zip) containing a similar set of results, only for eight other sites, spanning a much wider range of geographical, climatic and ecological conditions; (A4) a companion user manual (L1B2_Suppl.pdf) describing how to install, uncompress and explore those additional files; and (A5) a separate input MISR data archive (L1B2_input_68050.zip) for Path 168, Orbit 68050. This latter archive is usable with the second set (B; Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.011), which includes (B1) a stand-alone, self-contained, executable version of the L1B2 correction codes (L1B2_Soft_Win.zip) that uses the IDL Virtual Machine technology and does not require a paid IDL license as well as (B2) a user manual (L1B2_Soft_Win.pdf) that explains how to install, uncompress and use this software.

Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The level 2 (swath) product, which is reported on a 4.4 km spatial grid, is designated as version 23 (V23) and contains retrieved aerosol optical depth (AOD) and aerosol particle property information derived from MISR's multi-angle observations over both land and water. The changes from the previous version of the algorithm (V22) have significant impacts on the data product and its interpretation. The V23 data set is created from two separate retrieval algorithms that are applied over dark water and land surfaces, respectively. Besides increasing the horizontal resolution to 4.4 km compared with the coarser 17.6 m resolution in V22 and streamlining the format and content, the V23 product has added geolocation information, pixel-level uncertainty estimates, and improved cloud screening. MISR data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://eosweb.larc.nasa.gov/project/misr/misr_table (last access: 11 October 2019). The version number for the V23 level 2 aerosol product is F13_0023. The level 3 (gridded) aerosol product is still reported at 0.5∘×0.5∘ spatial resolution with results aggregated from the higher-resolution level 2 data. The format and content at level 3 have also been updated to reflect the changes made at level 2. The level 3 product associated with the V23 level 2 product version is designated as F15_0032. Both the level 2 and level 3 products are now provided in NetCDF format.

Assessment of Solar Energy Source Distribution and Potential in Zambia

Zambia is vastly endowed with a wide range of energy resources. Yet, to date, Zambia has not fully exploited its potential in solar energy utilisation for electricity generation due to various reasons such as lack of understanding of the distribution of solar energy potential in the country and limitation of access to solar energy resource information. This paper assess the solar energy distribution and potential in Zambia. Nine provinces with different geographical and climatic regions that makes up Zambia were assesed. The distribution of solar radiation within the country were assessed using spatial analysis in ArcGIS Software. The 22year period solar datasets were considered for assessment. These datasets were obtained from NASA Atmospheric Science Data Center using Surface Meteorology and Solar Energy. The analyzed results indicate that Zambia has approximately 20,442TWh/year technical solar energy potential and receives 2109.97kWh/m2 of solar energy per year with 4403.12hours of sunshine. The country has 186,121km2 available and suitable land area for RETs implementations. This study is important as it present an overview of the technical solar energy potential for Zambia which is vital for decision making, energy mix and sustainable deployment of solar energy technologies in the country

Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land

The Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite has demonstrated the capability to retrieve aerosol optical depths, surface bidirectional reflectance factors, and hemispherical reflectances over a wide variety of land surface types. In particular, its multiangular imaging design has enabled the application of algorithms that minimize sensitivity of the aerosol retrievals to the brightness of the underlying surface. The novel aerosol algorithm that was developed prior to launch has had notable quantitative success. Over certain scene types, however, the approach contained obvious spatial artifacts, so a postlaunch refinement to the algorithm was implemented. It constrains the retrieved aerosol models and optical depths such that the implied angular shape of the surface hemispherical–directional reflectance factor (HDRF) is similar among all of the MISR wavelengths. This upgrade has resulted in three tangible benefits: (1) the occurrence of outliers has been dramatically reduced, (2) correlations with AERosol RObotic NETwork (AERONET) aerosol sunphotometer data are quantitatively improved, and (3) the quality of surface products is markedly enhanced. MISR Level 2 aerosol and surface products are archived at the NASA Langley Atmospheric Sciences Data Center. Those having version numbers v0012 and higher incorporate this upgrade in the data processing software.

Reply to comment by N. D. Marsh and H. Svensmark on “Solar influences on cosmic rays and cloud formation: A reassessment”

[1] Svensmark and Friis-Christensen [1997] (hereinafter referred to as SFC97) proposed a ‘‘galactic cosmic ray (GCR), clouds, and climate’’ hypothesis that cosmic ray flux, modulated by solar activity, may modify global total cloud cover (TCC) and thus global surface temperature by changing the number of ions in the atmosphere and thus the cloud droplet formation. This GCR-TCC hypothesis has been questioned by many authors who examined correlations of GCR with various satellite cloud properties and over different time intervals. We note that the SFC97 hypothesis differs from the one proposed by Marsh and Svensmark [2000] (hereinafter referred to as MS00), who postulated that the GCR influence on cloudiness is restricted to low cloud cover (LCC). [2] All the articles in this debate were based on satellite data and limited to the ocean. By contrast, Sun and Bradley [2002] (hereinafter SB02) combined short-period satellite data (from the International Satellite Cloud Climatology Project, ISCCP) with long-term visual total cloud observations to reassess the GCR-TCC relationship over the ocean and the land as well. The SB02 study was initialized in early 2000, focusing on the GCR-TCC hypothesis. In the early spring of 2001 we added to our revision several comments regarding the GCR-LCC hypothesis. We concluded in our analysis that there is no solid evidence for the existence of a GCR-cloud correlation (either total cloud or low cloud cover). [3] Marsh and Svensmark [2004] (hereinafter referred to as MS04) raised five comments about our analysis and finally claimed that ‘‘On the basis of satellite observations, there continue to be strong indications of a globally distributed correlation between cosmic rays and low-cloud cover.’’ We will demonstrate in this reply that their comments are either unfounded or illogical. All of their comments are dealt with in section 2, but issues relevant to the GCR-LCC hypothesis will be separately addressed in section 3. New evidence will be present in section 3 to indicate that the quality of the ISCCP infrared (IR) LCC data set, which MS00 and Marsh and Svensmark [2003] (hereinafter referred to as MS03) used to create the GCR-LCC hypothesis, is highly questionable. This reply confirms the view we expressed on the work of SB02 that there is no solid GCR-cloud relationship.

Performance of the MISR LAI and FPAR algorithm: a case study in Africa

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is designed to provide global imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. The MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). These parameters are being routinely processed from MISR data at the Langley Atmospheric Sciences Data Center (ASDC) since October 2002. This paper describes the research basis for transitioning the MISR LAI/FPAR product from beta to provisional status. The quality and spatial coverage of MISR land surface reflectances that are input to the algorithm determine the quality and spatial coverage of the LAI and FPAR products. Therefore, considerable efforts have been expended to analyze the performance of the algorithm as a function of uncertainties of MISR surface reflectances and to establish the convergence property of the MISR LAI/FPAR algorithm, namely, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. An additional objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types—information that is usually an input to remote sensing algorithms that use single-angle observations. An upper limit of uncertainties of MISR surface reflectances that allows discrimination between biomes, minimizes the impact of biome misidentification on LAI retrievals, and maximizes the spatial coverage of retrievals was estimated. Algorithm performance evaluated on a limited set of MISR data from Africa suggests valid LAI retrievals and correct biome identification in about 20% of the pixels, on an average, given the current level of uncertainties in the MISR surface reflectance data. The other 80% of the LAI values are retrieved using incorrect information about the type of biome. However, the use of multi-angle data minimizes the impact of biome misidentification on LAI retrievals; that is, with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. We also discuss in depth the parameters that characterize LAI/FPAR product quality—such as quality assessment (QA) that is available to the users along with the product. The analysis of the MISR LAI/FPAR product presented here demonstrates the physical basis of the radiative transfer algorithm used in the retrievals and, importantly, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. Further improvements in the quality of MISR surface reflectances are therefore expected to lead to LAI and FPAR retrievals of increasing quality.