New and improved data from the Pandonia Global Network for satellite validation

2021 ◽  
Author(s):  
Alexander Cede ◽  
Martin Tiefengraber ◽  
Manuel Gebetsberger ◽  
Michel Van Roozendael ◽  
Henk Eskes ◽  
...  
Keyword(s):  
Environmental Protection Agency ◽  
Program Planning ◽  
Global Network ◽  
High Temporal Resolution ◽  
Validation Data ◽  
Total Uncertainty ◽  
Satellite Validation ◽  
Initial Comparison ◽  
New Feature ◽  
Planning Group

<p>The worldwide operating Pandonia Global Network (PGN) is measuring atmospheric trace gases at high temporal resolution with the purpose of air quality monitoring and satellite validation. It is an activity carried out jointly by NASA and ESA as part of their “Joint Program Planning Group Subgroup” on calibration and validation and field activities, with additional collaboration from other institutions, most notably a strongly growing participation of the US Environmental Protection Agency (EPA). The more than 50 official PGN instruments are homogeneously calibrated and their data are centrally processed in real-time. Since 2019, total NO2 column amounts from the PGN are uploaded daily to the ESA Atmospheric Validation Data Centre (EVDC), where they are used for operational validation of Sentinel 5P (S5P) retrievals. During 2020, a new processor version 1.8 has been developed, which produces improved total NO2 column amounts and also the following new PGN products: total columns of O3, SO2 and HCHO based on direct sun observations and tropospheric columns, surface concentrations and tropospheric profiles of NO2 and HCHO based on sky observations. In this presentation we show some first examples of comparisons of the new PGN products with S5P data. Compared to the total NO2 columns from the previous processor version 1.7, the 1.8 data use better estimations for the effective NO2 temperature and the air mass factor. The effect of this improvement on the comparison with S5P retrievals is shown for some remote and high-altitude PGN sites. The new PGN total O3 column algorithm also retrieves the effective O3 temperature, which is a rather unique feature for ground-based direct sun retrievals. This allows us to analyze whether potential differences to satellite O3 columns might be influenced by the O3 temperature. Including the O3 temperature in the spectral fitting has also allowed the retrieval of accurate total SO2 columns. This PGN data product is of particular interest for satellite validation, as ground-based total SO2 column amounts are hardly measured by other instrumentation. An initial comparison of the PGN SO2 columns with S5P retrievals at selected PGN sites around the world is shown. PGN total HCHO columns from direct sun measurements are now possible for those PGN instruments, where the hardware parts made of Delrin, which outgasses HCHO, have been replaced by Nylon pieces. An initial comparison to HCHO retrievals from S5P is shown for locations with these upgraded instruments. Another new feature in the 1.8 PGN data is that they come with comprehensive uncertainty estimations, separated in the output files as independent, structured, common and total uncertainty.</p>

Operational satellite validation with data from the Pandonia Global Network (PGN)

2020 ◽  
Author(s):  
Alexander Cede ◽  
Martin Tiefengraber ◽  
Angelika Dehn ◽  
Barry Lefer ◽  
Jonas von Bismarck ◽  
...  
Keyword(s):  
Program Planning ◽  
Low Earth Orbit ◽  
Global Network ◽  
High Temporal Resolution ◽  
Vertical Column ◽  
Validation Data ◽  
Passive Remote Sensing ◽  
Satellite Validation ◽  
Data Products ◽  
Planning Group

<p>The Pandonia Global Network (PGN) is a worldwide operating network of passive remote sensing spectrometer systems named “Pandora”. PGN is measuring atmospheric trace gases at high temporal resolution with the purpose of air quality monitoring and satellite validation. PGN is an activity carried out jointly by NASA, through the Pandora project at Goddard Space Flight Center, and ESA, through the Austrian contractor LuftBlick, as part of their Joint Program Planning Group Subgroup on calibration and validation and field activities. Many of the more than 50 actual PGN instruments are directly owned by NASA or ESA, another part belongs to other collaborating governmental and academic institutions. A major objective of the PGN is to support the validation and verification of more than a dozen low-earth orbit and geostationary orbit based UV-visible sensors, most notably Sentinel 5P, TEMPO, GEMS and Sentinel 4. PGN instruments are homogeneously calibrated and their data are centrally processed in real-time. Starting in June 2019, the PGN team has made more and more network locations “official PGN sites”, which means all required technical and logistical steps for this purpose have been performed. At the end of 2019 there are 18 such official network sites, where quality assured data are uploaded daily to EVDC (ESA Atmospheric Validation Data Centre), where they are used for operational validation of Sentinel 5P retrievals (see e.g. http://mpc-vdaf-server.tropomi.eu/no2/no2-offl-pandora). The current official PGN data products are total vertical column amounts of NO2 and O3 from direct sun observations. Research data products include total vertical columns amounts of SO2 and HCHO from direct sun observations as well as surface concentrations, tropospheric columns amounts, and vertical profiles for NO2 and HCHO from sky observations. These named research products are planned to become official over the course of the year 2020.</p>

scholarly journals Open-path, quantum cascade-laser-based sensor for high-resolution atmospheric ammonia measurements

2014 ◽  
Vol 7 (1) ◽  
pp. 81-93 ◽  
Author(s):  
D. J. Miller ◽  
K. Sun ◽  
L. Tao ◽  
M. A. Khan ◽  
M. A. Zondlo
Keyword(s):  
Gas Phase ◽  
Quantum Cascade Laser ◽  
High Temporal Resolution ◽  
Reduced Pressure ◽  
Total Uncertainty ◽  
Atmospheric Ammonia ◽  
Quantum Cascade ◽  
Open Path ◽  
Time Calibration

Abstract. We demonstrate a compact, open-path, quantum cascade-laser-based atmospheric ammonia sensor operating at 9.06 μm for high-sensitivity, high temporal resolution, ground-based measurements. Atmospheric ammonia (NH3) is a gas-phase precursor to fine particulate matter, with implications for air quality and climate change. Currently, NH3 sensing challenges have led to a lack of widespread in situ measurements. Our open-path sensor configuration minimizes sampling artifacts associated with NH3 surface adsorption onto inlet tubing and reduced pressure sampling cells, as well as condensed-phase partitioning ambiguities. Multi-harmonic wavelength modulation spectroscopy allows for selective and sensitive detection of atmospheric pressure-broadened absorption features. An in-line ethylene reference cell provides real-time calibration (±20% accuracy) and normalization for instrument drift under rapidly changing field conditions. The sensor has a sensitivity and noise-equivalent limit (1σ) of 0.15 ppbv NH3 at 10 Hz, a mass of ~ 5 kg and consumes ~ 50 W of electrical power. The total uncertainty in NH3 measurements is 0.20 ppbv NH3 ± 10%, based on a spectroscopic calibration method. Field performance of this open-path NH3 sensor is demonstrated, with 10 Hz time resolution and a large dynamic response for in situ NH3 measurements. This sensor provides the capabilities for improved in situ gas-phase NH3 sensing relevant for emission source characterization and flux measurements.

scholarly journals A Machine Learning Approach to Predict Air Quality in California

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Mauro Castelli ◽  
Fabiana Martins Clemente ◽  
Aleš Popovič ◽  
Sara Silva ◽  
Leonardo Vanneschi
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Urban Areas ◽  
Principal Component ◽  
Ground Level ◽  
Support Vector ◽  
Validation Data ◽  
Pollutant Concentrations ◽  
Rbf Kernel

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.

Quality assessment of two years of Sentinel-5p TROPOMI NO2 data

2020 ◽  
Author(s):  
Tijl Verhoelst ◽  
Steven Compernolle ◽  
José Granville ◽  
Arno Keppens ◽  
Gaia Pinardi ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Quality Indicators ◽  
Scattered Light ◽  
Global Network ◽  
Visible Range ◽  
Network Data ◽  
Random Errors ◽  
Validation Data ◽  
Comparison Results ◽  
Data Products

<p>For more than two years now the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve operationally the total, tropospheric and stratospheric  column abundance of atmospheric NO<sub>2</sub>.  In support of these routine operations, the S5P Mission Performance Centre (MPC) performs continuous QA/QC of these data products and produces key Quality Indicators enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in monitoring mode in the S5P Automated Validation Server (AVS) and globally in more complex in-depth analyses.  Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons.  After two years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO<sub>2</sub> data products delivered publicly.</p><p>S5P NO2 data are compared routinely to ground-based network measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Direct-sun measurements from the Pandonia Global Network (PGN) serve as a reference for total NO<sub>2</sub> validation, Multi-Axis DOAS network data for tropospheric  NO<sub>2</sub> validation, and NDACC zenith-scattered-light DOAS network data for stratospheric NO<sub>2</sub> validation.  Comparison methods are optimized to limit spatial and temporal mismatch to a minimum (information-based spatial co-location strategy, photochemical adjustment to account for local time measurement difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and irreducible mismatch errors, and (3) the dependence of bias and dispersion on key influence quantities (surface albedo, cloud cover…)</p><p>Intercomparison of S5P products (NRTI vs. OFFL) and comparison to other satellite data, including a similar processing of OMI measurements, complement the ground-based validation with relative biases and spatio-temporal patterns/artefacts related to instrumental issues (e.g. striping) and to the sensitivity to geophysical features (e.g. clouds and sea/ice albedo contrast).  </p><p>Overall, the MPC quality assessment of S5P NO<sub>2</sub> data concludes to an excellent performance for the stratospheric column data (bias2 vs. ground-based data. This dispersion larger than the mission requirement on data precision can partly be attributed to comparisons errors such as those due to differences in horizontal resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm<sup>2</sup>), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ in specific cases of direct comparisons.       </p>

scholarly journals Ultrasound functional neuroimaging reveals propagation of task-related brain activity in behaving primates

2018 ◽  
Author(s):  
Alexandre Dizeux ◽  
Marc Gesnik ◽  
Harry Ahnine ◽  
Kevin Blaize ◽  
Fabrice Arcizet ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Anterior Cingulate ◽  
Global Network ◽  
High Temporal Resolution ◽  
Wide Field ◽  
Spatiotemporal Resolution ◽  
Macro Scale ◽  
The Brain

ABSTRACTIn recent decades, neuroimaging has played an invaluable role in improving the fundamental understanding of the brain. At the macro scale, neuroimaging modalities such as MRI, EEG, and MEG, exploit a wide field of view to explore the brain as a global network of interacting regions. However, this comes at the price of either limited spatiotemporal resolution or limited sensitivity. At the micro scale, electrophysiology is used to explore the dynamic aspects of neuronal activity with a very high temporal resolution. However, this modality requires a statistical averaging of several tens of single task responses. A large-scale neuroimaging modality of sufficient spatial and temporal resolution and sensitivity to study brain region activation dynamically would open new territories of possibility in neuroscienceWe show that neurofunctional ultrasound imaging (fUS) is both able to assess brain activation during single cognitive tasks within superficial and deeper areas of the frontal cortex areas, and image the directional propagation of information within and between these regions. Equipped with an fUS device, two macaque rhesus monkeys were instructed before a stimulus appeared to rest (fixation) or to look towards (saccade) or away (antisaccade) from a stimulus. Our results identified an abrupt transient change in activity for all acquisitions in the supplementary eye field (SEF) when the animals were required to change a rule regarding the task cued by a stimulus. Simultaneous imaging in the anterior cingulate cortex and SEF revealed a time delay in the directional functional connectivity of 0.27 ± 0.07 s and 0.9 ± 0.2 s for animals S and Y, respectively. These results provide initial evidence that recording cerebral hemodynamics over large brain areas at a high spatiotemporal resolution and sensitivity with neurofunctional ultrasound can reveal instantaneous monitoring of endogenous brain signals and behavior.

scholarly journals Development of time-varying global gridded <i>T</i><sub>s</sub>–<i>T</i><sub>m</sub> model for precise GPS–PWV retrieval

2019 ◽  
Vol 12 (2) ◽  
pp. 1233-1249 ◽  
Author(s):  
Peng Jiang ◽  
Shirong Ye ◽  
Yinhao Lu ◽  
Yanyan Liu ◽  
Dezhong Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Pressure ◽  
The Other ◽  
Radiosonde Data ◽  
High Temporal Resolution ◽  
Time Varying ◽  
Total Uncertainty ◽  
Weighted Mean Temperature ◽  
Small Uncertainty ◽  
Gpt2w Model

Abstract. Water-vapor-weighted mean temperature, Tm, is the key variable for estimating the mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). For the near-real-time GPS–PWV retrieval, estimating Tm from surface air temperature Ts is a widely used method because of its high temporal resolution and fair degree of accuracy. Based on the estimations of Tm and Ts at each reanalysis grid node of the ERA-Interim data, we analyzed the relationship between Tm and Ts without data smoothing. The analyses demonstrate that the Ts–Tm relationship has significant spatial and temporal variations. Static and time-varying global gridded Ts–Tm models were established and evaluated by comparisons with the radiosonde data at 723 radiosonde stations in the Integrated Global Radiosonde Archive (IGRA). Results show that our global gridded Ts–Tm equations have prominent advantages over the other globally applied models. At over 17 % of the stations, errors larger than 5 K exist in the Bevis equation (Bevis et al., 1992) and in the latitude-related linear model (Y. B. Yao et al., 2014), while these large errors are removed in our time-varying Ts–Tm models. Multiple statistical tests at the 5 % significance level show that the time-varying global gridded model is superior to the other models at 60.03 % of the radiosonde sites. The second-best model is the 1∘ × 1∘ GPT2w model, which is superior at only 12.86 % of the sites. More accurate Tm can reduce the contribution of the uncertainty associated with Tm to the total uncertainty in GPS–PWV, and the reduction augments with the growth of GPS–PWV. Our theoretical analyses with high PWV and small uncertainty in surface pressure indicate that the uncertainty associated with Tm can contribute more than 50 % of the total GPS–PWV uncertainty when using the Bevis equation, and it can decline to less than 25 % when using our time-varying Ts–Tm model. However, the uncertainty associated with surface pressure dominates the error budget of PWV (more than 75 %) when the surface pressure has an error larger than 5 hPa. GPS–PWV retrievals using different Tm estimates were compared at 74 International GNSS Service (IGS) stations. At 74.32 % of the IGS sites, the relative differences of GPS–PWV are within 1 % by applying the static or the time-varying global gridded Ts–Tm equations, while the Bevis model, the latitude-related model and the GPT2w model perform the same at 37.84 %, 41.89 % and 29.73 % of the sites. Compared with the radiosonde PWV, the error reduction in the GPS–PWV retrieval can be around 1–2 mm when using a more accurate Tm parameterization, which accounts for around 30 % of the total GPS–PWV error.

Satellite Calibration/Validation and Related Activities Carried out through NASA/ESA Joint Program Planning Group Subgroup

2021 ◽  
Author(s):  
Jack Kaye ◽  
Malcolm Davidson
Keyword(s):  
Program Planning ◽  
Field Work ◽  
Us Studies ◽  
Joint Program ◽  
The Status ◽  
Satellite Missions ◽  
Planning Group ◽  
Future Plans

&lt;p&gt;The NASA/ESA Joint Program Planning Group (JPPG) subgroup on satellite calibration/validation was created to facilitate coordinated efforts between ESA, NASA, and their respective investigator communities to enhance calibration and/or validation activities for current and/or future satellite missions. The cooperation enabled through this activity includes airborne campaigns, use of surface-based measurements, and satellite-to-satellite intercomparisons. Numerous examples of such activities exist over the ten years of the JPPG. In this talk, examples of calibration/validation focused activities, accomplishments, and future plans will be presented. A particular focus will be on how the COVID-19 pandemic has affected field work planned for 2020 and 2021.&amp;#160; The JPPG subgroup also includes joint European-US studies of satellite results that integrate the results of both parties&amp;#8217; observational capabilities, and the status of those activities will be presented as well.&lt;/p&gt;

Pi-MEP Salinity – an ESA-NASA Platform for sustained satellite surface salinity validation 

2021 ◽  
Author(s):  
Roberto Sabia ◽  
Sebastien Guimbard ◽  
Nicolas Reul ◽  
Tony Lee ◽  
Julian Schanze ◽  
...  
Keyword(s):  
Program Planning ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Data ◽  
Sea Level Anomalies ◽  
Ocean Salinity ◽  
Salinity Data ◽  
Planning Group

&lt;p&gt;The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.&lt;/p&gt;&lt;p&gt;Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA&amp;#160;Joint Program Planning Group (JPPG).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature).&amp;#160;&lt;/p&gt;&lt;p&gt;Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The Platform is also meant to monitor salinity in selected oceanographic &amp;#8220;case studies&amp;#8221;, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.&lt;/p&gt;

scholarly journals Uncertainties in Air Toxics Calculated by the Dispersion Models AERMOD and ISCST3 in the Houston Ship Channel Area

2007 ◽  
Vol 46 (9) ◽  
pp. 1372-1382 ◽  
Author(s):  
Steven R. Hanna ◽  
Robert Paine ◽  
David Heinold ◽  
Elizabeth Kintigh ◽  
Dan Baker
Keyword(s):  
Wind Speed ◽  
Environmental Protection Agency ◽  
Dispersion Model ◽  
Model Parameters ◽  
Air Toxics ◽  
Population Census ◽  
Total Uncertainty ◽  
Census Tracts ◽  
Probabilistic Uncertainty ◽  
American Meteorological Society

Abstract The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used U.S. air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological Society–Environmental Protection Agency Model (AERMOD). The effects of uncertainties in emissions input, meteorological input, and dispersion model parameters are investigated using Monte Carlo probabilistic uncertainty methods, which involve simultaneous random and independent perturbations of all inputs. The focus is on a 15 km × 15 km domain in the Houston, Texas, ship channel area. Concentrations are calculated at hypothetical receptors located at the centroids of population census tracts. The model outputs that are analyzed are the maximum annually averaged maximum concentration at any single census tract or monitor as well as the annually averaged concentration averaged over the census tracts. The input emissions uncertainties are estimated to be about a factor of 3 (i.e., covering the 95% range) for each of several major categories. The uncertainties in meteorological inputs (such as wind speed) and dispersion model parameters (such as the vertical dispersion coefficient σz) also are estimated. The results show that the 95% range in predicted annually averaged concentrations is about a factor of 2–3 for the air toxics, with little variation by model. The input variables whose variations have the strongest effect on the predicted concentrations are on-road mobile sources and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and σz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than do the uncertainties of the meteorological/dispersion input group.

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