scholarly journals The CAMS reanalysis of atmospheric composition

2019 ◽  
Vol 19 (6) ◽  
pp. 3515-3556 ◽  
Author(s):  
Antje Inness ◽  
Melanie Ades ◽  
Anna Agustí-Panareda ◽  
Jérôme Barré ◽  
Anna Benedictow ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Chemical Species ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Reanalysis Dataset ◽  
Weather Forecasts ◽  
Forecasting System ◽  
Total Column

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

scholarly journals The CAMS reanalysis of atmospheric composition

2018 ◽  
Author(s):  
Antje Inness ◽  
Melanie Ades ◽  
Anna Agusti-Panareda ◽  
Jérôme Barré ◽  
Anna Benedictow ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Chemical Species ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Data Set ◽  
Forecasting System ◽  
One Year ◽  
Total Column

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis data set of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of 3-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding one year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO, tropospheric column NO2, aerosol optical depth and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF’s Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared to independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

scholarly journals Added Value of Aerosol-Cloud Interactions for Representing Aerosol Optical Depth in an Online Coupled Climate-Chemistry Model over Europe

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 360 ◽  
Author(s):  
Laura Palacios-Peña ◽  
Juan P. Montávez ◽  
José M. López-Romero ◽  
Sonia Jerez ◽  
Juan J. Gómez-Navarro ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Chemical Species ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Added Value ◽  
Computational Time ◽  
Climate Modelling ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Aerosol-cloud interactions (ACI) represent one of the most important sources of uncertainties in climate modelling. In this sense, realistic simulations of ACI are needed for a better understanding of the complex interactions between air pollution and the climate system. This work quantifies the added value of including ACI in an online coupled climate/chemistry model (WRF-Chem, 0.44 ∘ horizontal resolution, years 2003 to 2010) in order to assess whether there is an improvement in the representation of aerosol optical depth (AOD). Modelling results for each species have been evaluated against the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, and AOD at 675 nm has been compared to AERONET data. Results indicate that the improvements of the monthly biases are around 8% for total AOD550 when including ACI, reaching 20% for the monthly bias in AOD550 coming from dust. Moreover, the temporal representation of AOD550 largely improves (increase in the Pearson time correlation coefficients), ranging from 6% to 20% depending on the chemical species considered. The benefits from this improvement overcome the problems derived from the high computational time required in ACI simulations (eight times higher with respect to simulations not including aerosol-cloud interactions).

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation using the ECMWF C-IFS and TOMCAT/SLIMCAT 3-D model

2017 ◽  
Vol 17 (11) ◽  
pp. 6663-6678 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agustí-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
Forecasting System ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

scholarly journals A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

Fire activity and Aerosol Optical Depth over PEEX area for the last two decades

2020 ◽  
Author(s):  
Larisa Sogacheva ◽  
Anu-Maija Sundström ◽  
Gerrit de Leeuw ◽  
Antti Arola ◽  
Tuukka Petäjä ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Composition ◽  
Individual Species ◽  
Observation System ◽  
Northern Eurasia ◽  
Modeling Tools ◽  
Burned Areas ◽  
Fire Activity

<p><span>The <span>Pan-Eurasian Experiment Program (PEEX) is an interdisciplinary scientific program bringing together ground-based in situ and remote sensing observations, satellite measurements and modeling tools aiming to improve the understanding of land-water-atmosphere interactions, feedback mechanisms and their effects on the ecosystem, climate and society in northern Eurasia, Russia and China. One of the </span>pillars of the PEEX program is the ground-based observation system with new stations being established across the whole PEEX domain complementing existing infrastructure. However, in view of the large area covering thousands of kilometres, large gaps will remain where no or little observational information will be available. The gap can partly be filled by satellite remote sensing of relevant parameters as regards atmospheric composition, land and water surface properties including snow and ice, and vegetation.</span></p><p><span>Forest fires and corresponding emissions to the atmosphere dramatically change the atmospheric composition in case of long-lasting fire events, which might cover extended areas. In the burned areas, CO2 exchange, as well as emissions of different compounds are getting to higher levels, which might contribute to climate change by changing the radiative budget through the aerosol-cloud interaction and cloud formation. In the boreal forest, after CO2, CO and CH4, the largest emission factors for individual species were formaldehyde, followed by methanol and NO2 (Simpson et al., ACP, 2011). The emitted long-life components, e.g., black carbon, might further be transported to the distant areas and measured at the surface far from the burned areas.</span></p><p><span> During the last few decades, several burning episodes have been observed over PEEX area by satellites (as fire counts), specifically over Siberia and central Russia. Fire activity can also be seen in increasing Aerosol Optical depth (AOD) retrieved from satellites, as well as fire radiative power (FRP) calculated using the satellite data. In the current work, we study the time series of the fire activity, FRP and AOD over PEEX area and specifically over selected cities.</span></p>

scholarly journals Two global climatologies of daily fire emission injection heights since 2003

2016 ◽  
Author(s):  
S. Rémy ◽  
A. Veira ◽  
R. Paugam ◽  
M. Sofiev ◽  
J. W. Kaiser ◽  
...  
Keyword(s):  
Case Studies ◽  
Biomass Burning ◽  
Atmospheric Composition ◽  
Positive Trend ◽  
Global Database ◽  
Weather Forecasts ◽  
Radiative Power ◽  
Forecasting System ◽  
Monitoring Service ◽  
Semi Empirical

Abstract. The Global Fire Assimilation System (GFAS) assimilates Fire Radiative Power (FRP) observations from satellite-based sensors to produce daily estimates of biomass burning emissions. It has been extended to include information about injection heights provided by two distinct algorithms, which also use meteorological information from the operational weather forecasts of ECMWF. Injection heights are provided by the semi-empirical IS4FIRES parameterization and an analytical one-dimension Plume Rise Model (PRM). The two algorithms provide estimates for injection heights for each satellite pixel. Similarly to how FRP observations are processed in GFAS, these estimates are then gridded, averaged and assimilated, using a simple observation operator, so as to fill the observational gaps. A global database of daily biomass burning emissions and injection heights at 0.1° resolution has been produced for 2003–2015. The database is being extended in near-real-time with the operational GFAS service of the Copernicus Atmospheric Monitoring Service (CAMS). The two injection height datasets were compared against a new dataset of satellite-based plume height observations. The IS4FIRES parameterization showed a better overall agreement against observations, while the PRM was better at capturing the variability of injection heights and at estimating the injection heights of large fires. The results from both also show a differentiation depending on the type of vegetation. A positive trend with time in median injection heights from the PRM was noted, less marked from the IS4FIRES parameterization. This is provoked by a negative trend in number of small fires, especially in regions such as South America. The use of biomass burning emission heights from GFAS in atmospheric composition forecasts was assessed in two case studies: the South AMerican Biomass Burning Analysis (SAMBBA) campaign which took place in September 2012 in Brazil, and a series of large fire events in the Western U.S. in August 2013. For these case studies, forecasts of biomass burning aerosol species by the Composition-Integrated Forecasting System (C-IFS) of CAMS were found to better reproduce the observed vertical distribution when using PRM injection heights from GFAS.

scholarly journals Simulation of trace gases and aerosols over the Indian domain: evaluation of the WRF-Chem Model

2013 ◽  
Vol 13 (5) ◽  
pp. 12287-12336 ◽  
Author(s):  
M. Michael ◽  
A. Yadav ◽  
S. N. Tripathi ◽  
V. P. Kanawade ◽  
A. Gaur ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Black Carbon ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Indian Subcontinent ◽  
Trace Gases ◽  
Atmospheric Composition ◽  
Emission Rates ◽  
Vertical Profiles ◽  
Monsoon Period

Abstract. The "online" meteorological and chemical transport Weather Research and Forecasting/Chemistry (WRF-Chem) model has been implemented over the Indian subcontinent for three consecutive summers in 2008, 2009 and 2010 to study the aerosol properties over the domain. The initial and boundary conditions are obtained from NCAR reanalysis data. The emission rates of sulfur dioxide, black carbon, organic carbon and PM2.5, which are developed over India at a grid resolution of 0.25° × 0.25° have been used in the present study. The remaining emissions are obtained from global inventories (RETRO and EDGAR). The model simulated the meteorological parameters, trace gases and particulate matter. Predicted mixing ratios of trace gases (Ozone, carbon monoxide and sulfur dioxide) are compared with ground based observations over Kanpur. Simulated aerosol optical depth are compared with those observed at nine Aerosol Robotic Network stations (AERONET). The simulations show that the aerosol optical depth of the less polluted regions is better simulated compared to that of the locations where the aerosol loading is very high. The vertical profiles of extinction coefficient observed at Kanpur Micropulse Lidar Network (MPLNET) station is in agreement with the simulated values for altitudes greater than 1.5 km and qualitatively simulate the elevated layers of aerosols. The simulated mass concentration of black carbon shows very good correlation with observations, due to the better local emission inventory used. The vertical profiles of black carbon at various locations have also been compared with observations from aircraft campaign held during pre-monsoon period of 2008 and 2009 resulting in good agreement. This study shows that WRF-Chem model captures many important features of the observations and therefore can be used for understanding and predicting regional atmospheric composition over Indian subcontinent.

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