The IAGOS Research Infrastructure for monitoring atmospheric composition and air quality using commercial aircraft

2021 ◽  
Author(s):  
Hannah Clark ◽  
Iagos Team
Keyword(s):  
Air Quality ◽  
Research Infrastructure ◽  
Atmospheric Composition ◽  
Infrastructure Development ◽  
Commercial Aircraft ◽  
Air Quality Prediction ◽  
Major Interest ◽  
Automated Instruments ◽  
Monitoring Service

<p>IAGOS (In-service Aircraft for a Global Observing System) is a European Research Infrastructure for global observations of atmospheric composition using commercial aircraft. Commercial aircraft are ideal platforms for providing long-term in-situ measurements with high vertical and temporal resolution, particularly at cruise altitude (between 9 and 13 km) in the climate-sensitive region of the atmosphere known as the upper troposphere-lower stratosphere (UTLS). IAGOS also provides landing and take-off profiles at almost 300 airports throughout the world which are of major interest for air quality issues. Fully automated instruments are permanently installed on Airbus A330 aircraft operated by different airlines. Data are collected on about 500 flights per aircraft per year. All the aircraft measure the abundances of many essential climate variables, chiefly ozone and the precursor carbon monoxide, water vapour, clouds and meteorological parameters. Additional instruments can be installed to measure nitrogen oxides, aerosols, or the greenhouse gases carbon dioxide and methane. The data are transmitted in near to real real time to weather services and are freely available for the scientific community, national air quality prediction centres and the Copernicus Atmosphere Monitoring Service (CAMS). We describe the importance of these measurements in the monitoring of global atmospheric composition and air quality. I<span>n particular, we show examples from the Copernicus Atmosphere Monitoring Service (CAMS) where IAGOS data are used in the evaluation and improvement of forecasts of air quality over Europe, and discuss how the development of the IAGOS data transmission and instrumentation may fertilize infrastructure development for other airborne platforms.</span></p>

IAGOS Research Infrastructure for Global-Scale Atmosphere Monitoring by Instrumented Passenger Aircraft

2021 ◽  
Author(s):  
Andreas Petzold ◽  
Valerie Thouret ◽  
Christoph Gerbig ◽  
Andreas Zahn ◽  
Martin Gallagher ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Relative Humidity ◽  
Water Vapour ◽  
Research Infrastructure ◽  
Good Resolution ◽  
European Research ◽  
Commercial Aircraft ◽  
High Quality

<p>IAGOS (www.iagos.org) is a European Research Infrastructure using commercial aircraft (Airbus A340, A330, and soon A350) for automatic and routine measurements of atmospheric composition including reactive gases (ozone, carbon monoxide, nitrogen oxides, volatile organic compounds), greenhouse gases (water vapour, carbon dioxide, methane), aerosols and cloud particles along with essential thermodynamic parameters. The main objective of IAGOS is to provide the most complete set of high-quality essential climate variables (ECV) covering several decades for the long-term monitoring of climate and air quality. The observations are stored in the IAGOS data centre along with added-value products to facilitate the scientific interpretation of the data. IAGOS began as two European projects, MOZAIC and CARIBIC, in the early 1990s. These projects demonstrated that commercial aircraft are ideal platforms for routine atmospheric measurements. IAGOS then evolved as a European Research Infrastructure offering a mature and sustainable organization for the benefits of the scientific community and for the operational services in charge of air quality and climate change issues such as the Copernicus Atmosphere Monitoring Services (CAMS) and the Copernicus Climate Change Service (C3S). IAGOS is also a contributing network of the World Meteorological Organization (WMO).</p> <p>IAGOS provides measurements of numerous chemical compounds which are recorded simultaneously in the critical region of the upper troposphere – lower stratosphere (UTLS) and geographical regions such as Africa and the mid-Pacific which are poorly sampled by other means. The data are used by hundreds of groups worldwide performing data analysis for climatology and trend studies, model evaluation, satellite validation and the study of detailed chemical and physical processes around the tropopause. IAGOS data also play an important role in the re-assessment of the climate impact of aviation.</p> <p>Most important in the context of weather-related research, IAGOS and its predecessor programmes provide long-term observations of water vapour and relative humidity with respect to ice in the UTLS as well as throughout the tropospheric column during climb-out and descending phases around airports, now for more than 25 years. The high quality and very good resolution of IAGOS observations of relative humidity over ice are used to better understand the role of water vapour and of ice-supersaturated air masses in the tropopause region and to improve their representation in numerical weather and climate forecasting models. Furthermore, CAMS is using the water vapour vertical profiles in near real time for the continuous validation of the CAMS atmospheric models. </p>

Evolution of the CAMS global air quality forecasting system

2020 ◽  
Author(s):  
Zak Kipling ◽  
Melanie Ades ◽  
Anna Agusti-Panareda ◽  
Jérôme Barré ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Additional Species ◽  
Meteorological Model ◽  
Forecasting System ◽  
Long Term Trends ◽  
Phase Chemistry ◽  
Monitoring Service ◽  
The Impact

<p>As part of the Copernicus Atmosphere Monitoring Service (CAMS), operated by ECMWF on behalf of the European Commission, global analyses and forecasts of atmospheric composition have been produced operationally since 2015. These were built on many years of previous work under the GEMS and MACC projects, which began producing regular forecasts in 2007.</p><p>Since the transition to an operational service, there have continued to be many new developments and improvements to the system in five major upgrades, including increased horizontal and vertical resolution, updated emissions and paramterisations, additional species such as nitrate aerosol, as well as updates to the underlying meteorological model and data assimilation. The components of this system (aerosols, gas-phase chemistry, meteorology and the ocean) are also now coupled more tightly via active feedbacks then ever before.</p><p>In this interactive presentation, we will demonstrate the impact of a number of these developments on the performance of the resulting global air quality forecasts, alongside the continuing evolution of our approaches to assessing model improvement against independent in-situ and remote-sensing observations from a variety of platforms.</p><p>Because the continuing evolution of an operational system can make the analysis of long-term trends problematic, we will also contrast this with the CAMS global reanalysis product, which (while not using the very latest version of the model) do provide a consistent long-term dataset from 2003 onwards.</p>

Ex-hurricane Ophelia and air quality impacts over Europe in CAMS forecast systems

2020 ◽  
Author(s):  
Dimitris Akritidis ◽  
Eleni Katragkou ◽  
Aristeidis K. Georgoulias ◽  
Prodromos Zanis ◽  
Stergios Kartsios ◽  
...  
Keyword(s):  
Air Quality ◽  
Cold Front ◽  
Distinct Pattern ◽  
Saharan Dust ◽  
Atmospheric Composition ◽  
Regional Systems ◽  
Fire Smoke ◽  
Satellite Retrievals ◽  
Regional Air Quality ◽  
Monitoring Service

<p>Within the framework of the Copernicus Atmosphere Monitoring Service (CAMS) element CAMS-84 (Global and regional a posteriori evaluation and quality assurance), we analyze and evaluate the performance of CAMS forecast systems during the passage of ex-hurricane Ophelia in mid-October 2017, carrying Saharan dust and Iberian fire smoke over several Western European regions. To this end, day-1 forecasts from CAMS-global (ECMWF Integrated Forecast System; IFS) and CAMS-regional (ensemble of seven regional air quality models) products are compared against satellite retrievals (MODIS/Terra and Aqua, CALIPSO) and ground-based measurements. The analysis indicates that dust and smoke are injected into the warm sector of Ophelia, lying in the vicinity of the warm and cold front, respectively, gradually affecting the air quality and atmospheric composition over France, the Netherlands and Great Britain. The distinct pattern of enhanced aerosol optical depth (AOD) over Western coastal Europe seen in satellite retrievals is well reproduced by the CAMS near-real time forecast. The observed implications for air quality (PM10 and PM2.5) are satisfactorily forecasted in qualitative terms by both CAMS-global and CAMS-regional systems, while in quantitative terms, the CAMS-regional system exhibits a better performance in predicting surface PM concentrations (higher correlation and lower bias) compared to the global.</p>

scholarly journals Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 669 ◽  
Author(s):  
Adrienn Varga-Balogh ◽  
Ádám Leelőssy ◽  
István Lagzi ◽  
Róbert Mészáros
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Prediction ◽  
Heating Season ◽  
Air Quality Prediction ◽  
Consistent Model ◽  
Urban Monitoring ◽  
Overall Performance ◽  
Monitoring Service ◽  
The City

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

A Long-Term Re-Analysis of Atmospheric Composition and Air Quality

2017 ◽  
pp. 55-59 ◽  
Author(s):  
M. Sofiev ◽  
R. Kouznetsov ◽  
M. Prank ◽  
J. Soares ◽  
J. Vira ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition

scholarly journals Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 214 ◽  
Author(s):  
Xue-Bo Jin ◽  
Nian-Xiang Yang ◽  
Xiao-Yi Wang ◽  
Yu-Ting Bai ◽  
Ting-Li Su ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Prevention ◽  
Fixed Number ◽  
Frequency Characteristics ◽  
Air Quality Prediction ◽  
Mode Decomposition ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

Update on European Regional Air Quality Forecast in the Copernicus Atmosphere Monitoring Service (CAMS)

2020 ◽  
Author(s):  
Augustin Colette ◽  
Gaelle Collin ◽  
Jérôme Barré
Keyword(s):  
Air Quality ◽  
Model Development ◽  
Atmospheric Composition ◽  
Air Quality Forecast ◽  
Regional Air Quality ◽  
Long Timescales ◽  
Monitoring Service ◽  
Distributed Production ◽  
European Regional ◽  
Future Plans

<p> </p><p>The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wealth of information on atmospheric composition change over short to long timescales. One of the core products of CAMS regards short term air quality forecasts with a three days lead time as well as reanalyses over the past years for the European region.</p><p>This service is covered by the CAMS_50 project which is now operational since 2015. It relies on a distributed production of 9 individual air quality models, consolidated by a centralised regional production unit at Météo-France before delivery to the European Centre on Medium Range Meteorological Forecasts, which implements the CAMS service.</p><p>Each model is operated by its own development team across Europe, all of them deliver air quality forecasts covering the whole continent at 10km resolution. The modelling team currently operational are at present: CHIMERE (France), DEHM (Denmark), EMEP/MSC-W (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MOCAGE (France), SILAM (Finland). Two additional models are now applying to join the ensemble: MINNI (Italy), and MONARCH (Spain).</p><p>Such an ensemble of different models offers excellent complementarity in model capabilities as demonstrated by the performances of the ENSEMBLE product. It also leads to substantial challenges in coordinated model development. We will present the main recent achievements, status, and future plans for the validation and development of models underlying the service.</p><p> </p>

scholarly journals Observing requirements for geostationary satellites to enable ozone air quality prediction

2011 ◽  
Vol 11 (7) ◽  
pp. 19291-19355 ◽  
Author(s):  
P. D. Hamer ◽  
K. W. Bowman ◽  
D. K. Henze
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Regime Changes ◽  
Ozone Prediction ◽  
Air Quality Prediction ◽  
Model Setup ◽  
Primary Focus ◽  
Observing System Simulation Experiment ◽  
Assimilation System

Abstract. We conduct a variety of analyses to support mission planning for geostationary satellite measurements of atmospheric composition. We carry out a simplified observing system simulation experiment (OSSE) using a photochemical box model and its adjoint integrated with a Lagrangian 4-D-variational data assimilation system. Using this framework in conjunction with pseudo observational constraints we estimate surface emissions and assess the improvement in ozone air quality forecasting and prediction. We use an analytical model as our principle method of conducting uncertainty analyses, which is the primary focus of this work. We investigate the impacts of changing the observed species (e.g., ozone, carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO)), observation frequency and quality upon the ability to predict the magnitude of summertime peak ozone events, characterize the uncertainties of those predictions, and the performance of the assimilation system. We use three observed species scenarios: CO and NO2; ozone, CO, and NO2; and HCHO, CO and NO2. These scenarios are designed to test the effects of adding observations of either ozone or HCHO to an existing CO and NO2 observing system. The studies were conducted using the photochemical model setup to simulate a range of summertime polluted environments spanning NOx limited to volatile organic compound (VOC) limited conditions. As the photochemical regime changes the relative importance of trace gas observations to constrain emission estimates and subsequent ozone forecasts varies. For example, adding ozone observations to an NO2 and CO observing system is found to decrease ozone prediction error under NOx and VOC limited regimes, and complimenting the NO2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC limited conditions.

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