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Abstract
In support of the Geostationary Operational Environmental Satellite R series (GOES-R) program, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison is generating high quality simulated Advanced Baseline Imager (ABI) radiances and derived products in real time over the continental United States. These data are mainly used for testing data-handling systems, evaluating ABI-derived products, and providing training material for forecasters participating in GOES-R Proving Ground test bed activities. The modeling system used to generate these datasets consists of advanced regional and global numerical weather prediction models in addition to state-of-the-art radiative transfer models, retrieval algorithms, and land surface datasets. The system and its generated products are evaluated for the 2014 Pacific Northwest wildfires; the 2013 Moore, Oklahoma, tornado; and Hurricane Sandy. Simulated aerosol optical depth over the Front Range of Colorado during the Pacific Northwest wildfires was validated using high-density Aerosol Robotic Network (AERONET) measurements. The aerosol, cloud, and meteorological modeling system used to generate ABI radiances was found to capture the transport of smoke from the Pacific wildfires into the Front Range of Colorado and true-color imagery created from these simulated radiances provided visualization of the smoke plumes. Evaluation of selected simulated ABI-derived products for the Moore tornado and Hurricane Sandy cases was done using real-time GOES sounder/imager products produced at CIMSS. Results show that simulated ABI moisture and atmospheric stability products, cloud products, and red–green–blue (RGB) airmass composite imagery are well suited as proxy ABI data for user preparedness.
Abstract
In support of the Geostationary Operational Environmental Satellite R series (GOES-R) program, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison is generating high quality simulated Advanced Baseline Imager (ABI) radiances and derived products in real time over the continental United States. These data are mainly used for testing data-handling systems, evaluating ABI-derived products, and providing training material for forecasters participating in GOES-R Proving Ground test bed activities. The modeling system used to generate these datasets consists of advanced regional and global numerical weather prediction models in addition to state-of-the-art radiative transfer models, retrieval algorithms, and land surface datasets. The system and its generated products are evaluated for the 2014 Pacific Northwest wildfires; the 2013 Moore, Oklahoma, tornado; and Hurricane Sandy. Simulated aerosol optical depth over the Front Range of Colorado during the Pacific Northwest wildfires was validated using high-density Aerosol Robotic Network (AERONET) measurements. The aerosol, cloud, and meteorological modeling system used to generate ABI radiances was found to capture the transport of smoke from the Pacific wildfires into the Front Range of Colorado and true-color imagery created from these simulated radiances provided visualization of the smoke plumes. Evaluation of selected simulated ABI-derived products for the Moore tornado and Hurricane Sandy cases was done using real-time GOES sounder/imager products produced at CIMSS. Results show that simulated ABI moisture and atmospheric stability products, cloud products, and red–green–blue (RGB) airmass composite imagery are well suited as proxy ABI data for user preparedness.
Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.
Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.
We review the progress of tropospheric trace gas observations and address the need for additional measurement capabilities as recommended by the National Research Council. Tropospheric measurements show pollution in the Northern Hemisphere as a result of fossil fuel burning and a strong seasonal dependence with the largest amounts of carbon monoxide and nitrogen dioxide in the winter and spring. In the summer, when photochemistry is most intense, photochemically generated ozone is found in large concentrations over and downwind from where anthropogenic sources are largest, such as the eastern United States and eastern China. In the tropics and the subtropics, where photon flux is strong throughout the year, trace gas concentrations are driven by the abundance of the emissions. The largest single tropical source of pollution is biomass burning, as can be seen readily in carbon monoxide measurements, but lightning and biogenic trace gases may also contribute to trace gas variability. Although substantive progress has been achieved in seasonal and global mapping of a few tropospheric trace gases, satellite trace gas observations with considerably better temporal and spatial resolution are essential to forecasting air quality at the spatial and temporal scales required by policy makers. The concurrent use of atmospheric composition measurements for both scientific and operational purposes is a new paradigm for the atmospheric chemistry community. The examples presented illustrate both the promise and challenge of merging satellite information with in situ observations in state-of-the-art data assimilation models.
We review the progress of tropospheric trace gas observations and address the need for additional measurement capabilities as recommended by the National Research Council. Tropospheric measurements show pollution in the Northern Hemisphere as a result of fossil fuel burning and a strong seasonal dependence with the largest amounts of carbon monoxide and nitrogen dioxide in the winter and spring. In the summer, when photochemistry is most intense, photochemically generated ozone is found in large concentrations over and downwind from where anthropogenic sources are largest, such as the eastern United States and eastern China. In the tropics and the subtropics, where photon flux is strong throughout the year, trace gas concentrations are driven by the abundance of the emissions. The largest single tropical source of pollution is biomass burning, as can be seen readily in carbon monoxide measurements, but lightning and biogenic trace gases may also contribute to trace gas variability. Although substantive progress has been achieved in seasonal and global mapping of a few tropospheric trace gases, satellite trace gas observations with considerably better temporal and spatial resolution are essential to forecasting air quality at the spatial and temporal scales required by policy makers. The concurrent use of atmospheric composition measurements for both scientific and operational purposes is a new paradigm for the atmospheric chemistry community. The examples presented illustrate both the promise and challenge of merging satellite information with in situ observations in state-of-the-art data assimilation models.
The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.
Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.
This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.
These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.
The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.
Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.
This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.
These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.
Abstract
The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NO x = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.
Abstract
The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NO x = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.
