Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Data assimilation and numerical modelling of atmospheric composition

Atmospheric chemistry and transport models are used for a wide range of applications which include predicting dispersion of a hazardous pollutants, forecasting regional air quality, and modelling global distribution of aerosols and reactive gases. However, any such prediction is uncertain due to inaccuracies in input data, model parametrisations and lack of resolution. This thesis studies methods for integrating remote sensing and in-situ observations into atmospheric chemistry models with the aim of improving the predictions. Techniques of data assimilation, originally developed for numerical weather prediction, are evaluated for improving regional-scale predictions in two forecast experiments, one targeting the photochemical pollutants ozone (O3) and nitrogen dioxide (NO2), the other targeting sulphur dioxide (SO2). In both cases, assimilation of surface-based air quality monitoring data is found to initially improve the forecast when assessed on monitoring stations not used in assimilation. However, as the forecast length increased, the forecast converged towards the reference simulations where no data assimilation was used. The relaxation time was 6-12 hours for SO2 and NO2 and about 24 hours for O3. An alternative assimilation scheme was tested for SO2. In addition to the initial state of the forecast, the scheme adjusted the gridded emission fluxes based on the observations within the last 24 hours. The improvements due to adjustment of emissions were generally small but, where observed, the improvements persisted throughout the 48 hour forecast. The assimilation scheme was further adapted for estimating emission fluxes in volcanic eruptions. Assimilating retrievals of the Infrared Atmospheric Sounding Interferometer (IASI) instrument allowed reconstructing both the vertical and horizontal profile of SO2 emissions during the 2010 eruption of Eyjafjallaj¨okull in Iceland. As a novel feature, retrievals of plume height were assimilated in addition to the commonly used column density retrievals. The results for Eyjafjallaj¨okull show that the plume height retrievals provide a useful additional constraint in conditions where the vertical distribution would otherwise remain ambiguous. Finally, the thesis presents a rigorous description and evaluation of a numerical scheme for solving the advection equation. The scheme conserves tracer mass and non-negativity, and is therefore suitable for regional and global atmospheric chemistry models. The scheme is particularly adapted for handling discontinuous solutions; for smooth solutions, the scheme is nevertheless found to perform comparably to other state-of-art schemes used in atmospheric models

13 th International Workshop on Greenhouse Gas Measurements from Space : Book of Abstracts

The 13th International Workshop on Greenhouse Gas Measurements from Space (IWGGMS) will be held on 6-8 June, 2017, at the University of Helsinki in Helsinki, Finland. The workshop is organised by the Finnish Meteorological Institute with support from the University of Helsinki. The workshop gathers together more than 160 scientists from the EU, USA, Japan, China, Australia, Canada, and Russia. This report is the official abstract book of the workshop. Background. Success in space-based global measurement of greenhouse gases, such as carbon dioxide and methane, is critical for advancing the understanding of carbon cycle. The recent developments in observations and in interpreting the data are very promising. Space-based greenhouse gas measurement, however, poses a wide array of challenges, many of which are complex and thus demand close international cooperation. The goal of the workshop is to review the state of the art in remote sensing of CO 2 , CH 4 , and other greenhouse gases from space including the current satellite missions, missions to be launched in the near future, emission hot spots on regional and global scales, process studies and interactions of carbon cycle and climate, pre-flight and on-orbit instrument calibration techniques, retrieval algorithms and uncertainty quantification, validation methods and instrumentation, related ground-based, shipboard, and airborne measurements, and flux inversion from space based measurements. The workshop is part of the programme for the centenary of Finland's independence in 2017. The workshop is also one of the activities arranged by the Finnish Meteorological Institute to support Finland's chairmanship of the Arctic Council, 2017 - 2019. The workshop is sponsored by the Finnish Meteorological Institute, the University of Helsinki, the European Space Agency, the City of Helsinki, the Federation of Finnish Learned Societies, and ABB Inc

Significant wintertime PM_(2.5) mitigation in the Yangtze River Delta, China, from 2016 to 2019: observational constraints on anthropogenic emission controls

Ambient fine particulate matter (PM_(2.5)) mitigation relies strongly on anthropogenic emission control measures, the actual effectiveness of which is challenging to pinpoint owing to the complex synergies between anthropogenic emissions and meteorology. Here, observational constraints on model simulations allow us to derive not only reliable PM_(2.5) evolution but also accurate meteorological fields. On this basis, we isolate meteorological factors to achieve reliable estimates of surface PM_(2.5) responses to both long-term and emergency emission control measures from 2016 to 2019 over the Yangtze River Delta (YRD), China. The results show that long-term emission control strategies play a crucial role in curbing PM_(2.5) levels, especially in the megacities and other areas with abundant anthropogenic emissions. The G20 summit hosted in Hangzhou in 2016 provides a unique and ideal opportunity involving the most stringent, even unsustainable, emergency emission control measures. These emergency measures lead to the largest decrease (∼ 35 µg m⁻³, ∼ 59 %) in PM_(2.5) concentrations in Hangzhou. The hotspots also emerge in megacities, especially in Shanghai (32 µg m⁻³, 51 %), Nanjing (27 µg m⁻³, 55 %), and Hefei (24 µg m⁻³, 44 %) because of the emergency measures. Compared to the long-term policies from 2016 to 2019, the emergency emission control measures implemented during the G20 Summit achieve more significant decreases in PM_(2.5) concentrations (17 µg m⁻³ and 41 %) over most of the whole domain, especially in Hangzhou (24 µg m⁻³, 48 %) and Shanghai (21 µg m⁻³, 45 %). By extrapolation, we derive insight into the magnitude and spatial distribution of PM_(2.5) mitigation potential across the YRD, revealing significantly additional room for curbing PM_(2.5) levels