This study focuses on retrieving and validating Nitrogen dioxide (NO2) trace gas vertical column densities (VCDs) using an experimental Portable Differential Optical Absorption Spectroscopy (PDOAS) instrument deployed at Kaohsiung during the National Aeronautics and Space Administration (NASA) Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) field campaign in the spring of 2024. The PDOAS spectral measurements at multiple viewing elevation angles between 1 and 90 deg were processed with the widely recognized QDOAS software package to obtain Differential Slant Column Densities (DSCD) for NO2 and O4. Subsequently, the QDOAS results were further analyzed using the Retrieval of Atmospheric Parameters from Spectroscopic Observations using DOAS Instruments (RAPSODI) software to determine the vertical column densities (VCDs) of NO2. As an initial evaluation of the NO2 VCD retrievals from PDOAS measurements, the PDOAS retrievals are compared with data from the Geostationary Environment Monitoring Spectrometer (GEMS). After excluding measurements contaminated by clouds, 10 effective measurements collected over four days were compared. A high correlation coefficient of 78.93% was found between our PDOAS-retrieved NO2 VCDs and those from GEMS. This correlation coefficient is comparable to correlations between long-term daily mean NO2 retrievals from GEMS and in-situ station observations in Kaohsiung. The compact and portable design of PDOAS allows for flexible and mobile air quality measurements. Additionally, the instrument and methodologies developed for PDOAS can be extended beyond NO2 analysis, making them suitable for examining aerosols and other trace gases as well.
This paper compares three dust detection algorithms over land that were developed for operational, near-real-time processing using the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite instrument. The three algorithm approaches use different spectral bands, namely deep blue bands, infrared (IR)-visible bands, and IR bands, and are applied for dust observed over dark as well as bright surfaces. The evaluations are performed both using case studies and AERONET matchup data over western CONUS-Mexico region and North Africa-Arabian Peninsula region. The deep blue-based algorithm is found to have the most false detections and its detection performance depends on the Sun-satellite geometries. Simulation analysis shows that there are three causes of this problem: surface reflectance, air mass factors, and phase functions in different geometries. The algorithm based on IR-visible bands has much less false detection than the deep blue bands-based algorithm and has better true positive detection than the IR-based algorithm. The IR bands-based algorithm performs well in the case studies over CONUS–Mexico region, but it fails to detect most of the dust cases over North Africa–Arabian Peninsula region. The results suggest that the IR-visible algorithm is the most suitable for the dust detection of the three algorithms with a small modification. Because the IR-visible algorithm is not able to detect all the dust pixels, detections from the deep blue algorithm only and those from the IR-visible algorithm with relaxed criteria are also provided but are distinguished with a lower quality.
The NOAA GOES-R Advanced Baseline Imager (ABI) will have nearly the same capabilities as NASA's Moderate
Resolution Imaging Spectroradiometer (MODIS) to generate multi-wavelength retrievals of aerosol optical depth (AOD)
with high temporal and spatial resolution, which can be used as a surrogate of surface particulate measurements such as
PM2.5 (particulate matter with diameter less than 2.5 μm). To prepare for the launch of GOES-R and its application in
the air quality forecasting, we have transferred and enhanced the Infusing satellite Data into Environmental Applications
(IDEA) product from University of Wisconsin to NOAA NESDIS. IDEA was created through a NASA/EPA/NOAA
cooperative effort. The enhanced IDEA product provides near-real-time imagery of AOD derived from multiple satellite
sensors including MODIS Terra, MODIS Aqua, GOES EAST and GOES WEST imager. Air quality forecast guidance
is produced through a trajectory model initiated at locations with high AOD retrievals and/or high aerosol index (AI)
from OMI (Ozone Monitoring Instrument). The product is currently running at
http://www.star.nesdis.noaa.gov/smcd/spb/aq/. The IDEA system will be tested using the GOES-R ABI proxy dataset,
and will be ready to operate with GOES-R aerosol data when GOES-R is launched.
Environmental satellite data provides a unique capability to monitor large areas of the globe for the occurrence of fires
and the smoke that they generate which can cause considerable degradation of air quality on a regional basis. The Hazard
Mapping System (HMS) incorporates seven polar and geostationary satellites into a single workstation environment.
While individual satellite platforms can provide important information that can be used in air quality models, integrating
several platforms allows for the combined strengths of various spacecraft instruments to overcome their individual
limitations. The HMS was specifically designed as an interactive tool to identify fires and the smoke emissions they
produce over North America in an operational environment. Automated fire detection algorithms are employed for each
of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are
deemed to be false and adding hotspots that the algorithms have not detected via examination of the satellite imagery.
Areas of smoke are outlined by the analyst using animated visible channel imagery. An estimate of the smoke
concentration is assigned to each plume outlined. The automated Geostationary Operational Environmental Satellite
(GOES) Aerosol and Smoke Product (GASP) is used as an aid in providing smoke concentrations and identifying areas
of smoke.
HMS analysts provide estimates on the size, initiation and duration of smoke emitting fires that are used as input to
NOAA's national air quality forecast capability. This system is currently providing 48 hour smoke forecast guidance for
air quality forecasters and utilizes the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model.
Biomass burning releases a significant amount of trace gases and aerosol emissions into the
atmosphere. If unaccounted for in the modeling of climate, carbon cycle, and air quality, it leads
to large uncertainties. The amount of biomass burning emissions depends significantly on burned
areas. This study estimates near-real time burned areas from multiple satellite-based active fires
in Hazard Mapping System (HMS) developed in NOAA, which capitalizes automated fire
detections from Geostationary Operational Environmental Satellite (GOES) Imager, Advanced
Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer
(MODIS). The HMS fire counts are compared with a set of Landsat ETM+ burn scars for various
ecosystems to investigate the rate of burned area in a fire count. The fire size and fire duration
derived from multiple satellites are then used to calculate burned area every half hour. The
estimated burned areas are evaluated using national inventory of burned area across the United
States for 2005.
Various methods to generate satellite-based biomass burning emission estimates
have recently been developed for their use in air quality models. Each method has different
assumptions, data sources, and algorithms. This paper compares three different satellitebased
biomass burning emission estimates against a control case of no biomass burning and
ground-based biomass estimate in an air quality model. We have chosen August 2002 for
comparison, since all data sets were readily available. In addition, there was significant
wildfire activity during this month. Our results suggest that there is large uncertainty in the
emission estimates which results in both under-prediction and over-prediction of PM2.5
concentration fields.
We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates, thereby enhancing environmental monitoring and prediction capabilities.
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as an interactive tool to identify fires and the smoke emissions they produce over North America in an operational environment. The system utilizes 2 geostationary and 5 polar orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery.
Areas of smoke are outlined by the analyst using animated visible channel imagery. A quantitative assessment of the smoke concentration is not performed at this time. However, integration of automated aerosol and smoke products into the HMS, such as the Geostationary Operational Environmental Satellite (GOES) Aerosol and Smoke Product (GASP) and the MODIS aerosol product in early 2006 and the aerosol product from the Ozone Monitoring Instrument (OMI) in late 2006 are expected to aid in providing smoke concentrations and identifying areas of smoke.
HMS analysts also denote fires that are producing smoke emissions detected in satellite imagery as well as the start and end times of the emissions. These fire locations are used as input to the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. HYSPLIT utilizes a dynamic emissions rate for these fires as specified by the Blue Skies framework.
India and the United States of America (U.S.A.) held a joint conference from June 21-25, 2004 in
Bangalore, India to strengthen and expand cooperation in the area of space science, applications, and
commerce. Following the recommendations in the joint vision statement released at the end of the
conference, the National Oceanic and Atmospheric Administration (NOAA) and the Indian Space and
Reconnaissance Organization (ISRO) initiated several joint science projects in the area of satellite product
development and applications. This is an extraordinary step since it concentrates on improvements in the
data and scientific exchange between India and the United States, consistent with a Memorandum of
Understanding (MOU) signed by the two nations in 1997. With the relationship between both countries
strengthening with President Bush's visit in early 2006 and new program announcements between the two
countries, there is a renewed commitment at ISRO and other Indian agencies and at NOAA in the U.S. to
fulfill the agreements reached on the joint science projects. The collaboration is underway with several
science projects that started in 2005 providing initial results.
NOAA and ISRO agreed that the projects must promote scientific understanding of the satellite
data and lead to a satellite-based decision support systems for disaster and public health warnings. The
projects target the following areas:
--supporting a drought monitoring system for India
--improving precipitation estimates over India from Kalpana-1
--increasing aerosol optical depth measurements and products over India
--developing early indicators of malaria and other vector borne diseases via satellite monitoring of
environmental conditions and linking them to predictive models
--monitoring sea surface temperature (SST) from INSAT-3D to support improved forecasting of
regional storms, monsoon onset and cyclones.
The research collaborations and results from these projects will be presented and discussed in the
context of India-US cooperation and the Global Earth Observation System of Systems (GEOSS) concept.
In 2006, we began a three-year project funded by the NASA Integrated Decisions Support program to develop a three-dimensional air quality system (3D-AQS). The focus of 3D-AQS is on the integration of aerosol-related NASA Earth Science Data into key air quality decision support systems used for air quality management, forecasting, and public health tracking. These will include the U.S. Environmental Protection Agency (EPA)'s Air Quality System/AirQuest and AIRNow, Infusing satellite Data into Environmental Applications (IDEA) product, U.S. Air Quality weblog (Smog Blog) and the Regional East Atmospheric Lidar Mesonet (REALM). The project will result in greater accessibility of satellite and lidar datasets that, when used in conjunction with the ground-based particulate matter monitors, will enable monitoring across horizontal and vertical dimensions. Monitoring in multiple dimensions will enhance the air quality community's ability to monitor and forecast the geospatial extent and transboundary transport of air pollutants, particularly fine particulate matter. This paper describes the concept of this multisensor system and gives current examples of the types of products that will result from it.
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