Meteorological Fields Research Articles

Model Behavior Regarding In- and Below-Cloud 137Cs Wet Deposition Following the Fukushima Accident: A Multiprocess Analysis Using 1-km-Resolution Meteorological Field Data

Model Behavior Regarding In- and Below-Cloud 137Cs Wet Deposition Following the Fukushima Accident: A Multiprocess Analysis Using 1-km-Resolution Meteorological Field Data

Impact of land surface processes on the simulation of sea breeze circulation and tritium dispersion over the Kaiga complex terrain region near west coast of India using the Weather Research and Forecasting (WRF) model

Kaiga, located on the west coast of Karnataka, India is a complex terrain region with dense forest cover often influenced by land-sea breeze flows during warm summer season. In this study, dispersion of tritium released from Kaiga Nuclear Power Plant (NPP) under the influence of land-sea breeze circulations is simulated using FLEXPART-WRF models. Sensitivity experiments with Weather Research and Forecasting (WRF) are conducted with two Land Surface Models (LSMs), Noah and Noah-Multi Physics (Noah-MP) to study their impact on the wind flow and meteorological characteristics. Mini Boundary Layer Masts (MBLMs) and Doppler SODAR observations are used for evaluating the model performance. WRF simulations showed large sensitivity of simulated sea breeze characteristics to the model land surface physics. It has been found that the Noah-MP better represented the onset time, inland extent, intensity and duration of the sea breeze circulation over the Kaiga complex terrain region with lesser error statistics compared to Noah. The improvements with Noah-MP over Noah are due to the better representation of vegetation and soil hydrological processes over the dense forest region, realistic simulation of surface energy and momentum fluxes and reduction in the land-sea temperature contrast by including a separate canopy physics. FLEXPART simulation of tritium releases from the Kaiga NPPs using WRF predicted meteorological fields showed large diurnal variability in the dispersion pattern and air concentrations around the Kaiga valley. Tritium plume is transported to the northwest sector during early morning and morning time by the land-breeze and to the east-southeast sector by the sea breeze during daytime. Predicted tritium concentrations are found to be higher during the morning under stable atmospheric conditions and lower during sea breeze hours due to stronger winds and deep mixed layers. FLEXPART predicted tritium activity concentrations at different measurement locations using WRF-Noah-MP meteorological data are in better agreement with measurements with higher correlation coefficient and index of agreement. • Tritium dispersion over a coastal complex terrain site is modeled by using the coupled WRF-FLEXPART model. • Dispersion of tritium released from a Nuclear Power Plant under mesoscale sea breeze and land breeze circulations is presented. • Sensitivity of Land Surface Models, Noah and Noah-MP in WRF on the simulation of sea breeze characteristics and tritium dispersion are assessed. • Explicit treatment of forest canopy in the Noah-MP LSM over a model domain with dense forest improved the model results.

Permitted speed decision of single-unit trucks with emergency braking maneuver on horizontal curves under rainy weather.

Under adverse weather conditions, visibility and the available pavement friction are reduced. The improper selection of speed on curved road sections leads to an unreasonable distribution of longitudinal and lateral friction, which is likely to cause rear-end collisions and lateral instability accidents. This study considers the combined braking and turning maneuvers to obtain the permitted vehicle speed under rainy conditions. First, a braking distance computation model was established by simplifying the relationship curve between brake pedal force, vehicle braking deceleration, and braking time. Different from the visibility commonly used in the meteorological field, this paper defines "driver’s sight distance based on real road scenarios" as a threshold to measure the longitudinal safety of the vehicle. Furthermore, the lateral friction and rollover margin is defined to characterize the vehicle’s lateral stability. The corresponding relationship between rainfall intensity-water film thickness-road friction is established to better predict the safe speed based on the information issued by the weather station. It should be noted that since the road friction factor of the wet pavement not only determined the safe vehicle speed but also be determined by the vehicle speed, so we adopt Ferrari’s method to solve the quartic equation about permitted vehicle speed. Finally, the braking and turning maneuvers are considered comprehensively based on the principle of friction ellipse. The results of the TruckSim simulation show that for a single-unit truck, running at the computed permitted speed, both lateral and longitudinal stability meet the requirements. The proposed permitted vehicle speed model on horizontal curves can provide driving guidance for drivers on curves under rainy weather or as a decision-making basis for road managers.

Sensitivities of simulated global aerosol optical depth and aerosol-radiation interactions to different horizontal resolutions in CAS-FGOALS-f3

The variations of horizontal grid spacing in aerosol climate models lead to discrepancies in simulated aerosol optical properties and radiation effects. To quantitatively investigate the effects of model horizontal resolution on global atmospheric aerosol simulation, the aerosol optical depths (AODs) and aerosol-radiation interactions (ARIs) for 10 years (2005–2014) are simulated by the Chinese Academy of Sciences Flexible Global Ocean Atmosphere Land System (CAS-FGOALS-f3) with a high resolution (HRM, ∼25 km) and a low resolution (LRM, ∼100 km). The experiments are performed with the same model parameterizations except the time step in HRM and LRM to maintain the stability of the dynamical core. The results show that the AODs simulated in HRM are more comparable to the multi-satellites and ground-based retrievals than those in LRM. Upon investigating the aerosol components, the differences in the carbonaceous and sulfate AODs between the HRM and LRM are more than 30%. The resolution sensitivities of hygroscopic aerosol simulations are mainly attributed to the simulated relative humidity (RH) and aerosol wet scavenging. The aerosol transports by wind fields alter the distributions of aerosol. The more reasonable simulations of the meteorological fields in the CAS-FGOALS-f3 with high resolution are favorable for aerosol simulations. The HRM-simulated ARI (−2.35 W m−2) is generally lower than the LRM-simulated one (−3.40 W m−2) at top of the atmosphere, and the relative difference is about 30%, which is similar to the relative difference of AOD.

Application of CCM SOCOL-AERv2-BE to cosmogenic beryllium isotopes: description and validation for polar regions

Abstract. The short-living cosmogenic isotope 7Be, which is produced by cosmic rays in the atmosphere, is often used as a tracer for atmospheric dynamics, with precise and high-resolution measurements covering the recent decades. The long-living isotope 10Be, as measured in polar ice cores with an annual resolution, is a proxy for long-term cosmic-ray variability, whose signal can, however, be distorted by atmospheric transport and deposition that need to be properly modeled to be accounted for. While transport of 7Be can be modeled with high accuracy using the known meteorological fields, atmospheric transport of 10Be was typically modeled using case-study-specific simulations or simplified box models based on parameterizations. Thus, there is a need for a realistic model able to simulate atmospheric transport and deposition of beryllium with a focus on polar regions and (inter)annual timescales that is potentially able to operate in a self-consistent mode without the prescribed meteorology. Since measurements of 10Be are extremely laborious and hence scarce, it is difficult to compare model results directly with measurement data. On the other hand, the two beryllium isotopes are believed to have similar transport and deposition properties, being different only in production and lifetime, and thus the results of 7Be transport can be generally applied to 10Be. Here we present a new model, called CCM SOCOL-AERv2-BE, to trace isotopes of 7Be and 10Be in the atmosphere based on the chemistry–climate model (CCM) SOCOL (SOlar Climate Ozone Links), which has been improved by including modules for the production, deposition, and transport of 7Be and 10Be. Production of the isotopes was modeled for both galactic and solar cosmic rays by applying the CRAC (Cosmic Ray Atmospheric Cascade) model. Transport of 7Be was modeled without additional gravitational settling due to the submicron size of the background aerosol particles. An interactive deposition scheme was applied including both wet and dry deposition. Modeling was performed using a full nudging to the meteorological fields for the period of 2002–2008 with a spin-up period of 1996–2001. The modeled concentrations of 7Be in near-ground air were compared with the measured ones at a weekly time resolution in four nearly antipodal high-latitude locations: two in the Northern (Finland and Canada) and two in the Southern (Chile and the Kerguelen Islands) Hemisphere. The model results agree with the measurements in the absolute level within error bars, implying that the production, decay, and lateral deposition are correctly reproduced. The model also correctly reproduces the temporal variability of 7Be concentrations on annual and sub-annual scales, including the presence and absence of the annual cycle in the Northern and Southern Hemisphere, respectively. We also modeled the production and transport of 7Be for a major solar energetic particle event (SPE) on 20 January 2005, which appears insufficient to produce a measurable signal but may serve as a reference event for historically known extreme SPEs. Thus, a new full 3D time-dependent model, based on CCM SOCOL, of 7Be and 10Be atmospheric production, transport, and deposition has been developed. Comparison with real data on the 7Be concentration in the near-ground air validates the model and its accuracy.

Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19

Asian dust can be transported at least one full circuit around the globe. During the transportation, dust can interact with local air-borne dust and pollutants, and has a profound impact on the environment. A novel coronavirus (COVID-19) has been affecting human activities worldwide since early 2020. The Chinese government has implemented emergency control measures. Since April 2020, control measures to reduce anthropogenic emissions have been gradually reduced. The optical properties of aerosols during the dust transport were affected by meteorological conditions, local environmental conditions and human activities. Therefore, two dust weather processes in March 2018 and March 2020 were screened under similar meteorological fields and transportation paths, which were mainly affected by human activities. Based on lidar data, in East China, compared with 2018, the average aerosol optical depth (AOD) of all types of aerosols at 0–4 km in 2020 decreased by 55.48%, while the AOD of dust aerosols decreased by 43.59%. The average particle depolarization ratio and color ratio decreased by 40.33 and 10.56% respectively. Due to the reduction of anthropogenic emissions in China (detected by lidar), the concentration of surface PM2.5 decreased by 57.47%. This indicated that due to the decrease in human activities caused by COVID-19 control measures, the optical properties of aerosols were significantly reduced during dust weather process in eastern China. However, in the Pacific region, compared with 2018, the AOD values of 0–1 km layer and 1–6 km layer in 2020 increased by 56.4% and decreased by 29.2% respectively. The difference between the two contributions of dust aerosols was very small. Meanwhile, compared with 2018, China’s near surface pollutants decreased significantly in 2020, indicating that the near surface AOD of the Pacific in 2020 was mainly contributed by local pollutants. This study was of great significance to the study of long-range and cross regional transport of pollutants.

Diurnal cycle of surface energy fluxes in high mountain terrain: High‐resolution fully coupled atmosphere‐hydrology modelling and impact of lateral flow

AbstractWater and energy fluxes are inextricably interlinked within the interface of the land surface and the atmosphere. In the regional earth system models, the lower boundary parameterization of land surface neglects lateral hydrological processes, which may inadequately depict the surface water and energy fluxes variations, thus affecting the simulated atmospheric system through land‐atmosphere feedbacks. Therefore, the main objective of this study is to evaluate the hydrologically enhanced regional climate modelling in order to represent the diurnal cycle of surface energy fluxes in high spatial and temporal resolution. In this study, the Weather Research and Forecasting model (WRF) and coupled WRF Hydrological modelling system (WRF‐Hydro) are applied in a high alpine catchment in Northeastern Tibetan Plateau, the headwater area of the Heihe River. By evaluating and intercomparing model results by both models, the role of lateral flow processes on the surface energy fluxes dynamics is investigated. The model evaluations suggest that both WRF and coupled WRF‐Hydro reasonably represent the diurnal variations of the near‐surface meteorological fields, surface energy fluxes and hourly partitioning of available energy. By incorporating additional lateral flow processes, the coupled WRF‐Hydro simulates higher surface soil moisture over the mountainous area, resulting in increased latent heat flux and decreased sensible heat flux of around 20–50 W/m2 in their diurnal peak values during summertime, although the net radiation and ground heat fluxes remain almost unchanged. The simulation results show that the diurnal cycle of surface energy fluxes follows the local terrain and vegetation features. This highlights the importance of consideration of lateral flow processes over areas with heterogeneous terrain and land surfaces.

Estimating PM2.5 Concentrations in Contiguous Eastern Coastal Zone of China Using MODIS AOD and a Two-Stage Random Forest Model

Abstract The coarse Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta–Fujian–Pearl River delta (YRD–FJ–PRD) region of China. A first-stage random forest–integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest–derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD–FJ–PRD region, and we achieved a site-based cross validation (CV) R2 of 0.87 and a time-based CV R2 of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 μg m−3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas.

Trends and Variability of Ozone Pollution over the Mountain-Basin Areas in Sichuan Province during 2013–2020: Synoptic Impacts and Formation Regimes

Sichuan Province, the most industrialized and populated region in southwestern China, has been experiencing severe ozone pollution in the boreal warm season (April–September). With a surface ozone monitoring network and reanalysis dataset, we find that nearly all cities in Sichuan Province showed positive increasing trends in the warm-season ozone levels. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.0 ppb (4.8%) year−1 as a whole, with slightly larger trends in some sites such as a site in Zigong (5.2 ppb year−1). Seasonally, the monthly ozone level in Sichuan peaks from May to August (varies with year). The predominant warm-season synoptic patterns were objectively identified based on concurrent hourly meteorological fields from ERA5. High-pressure systems promote ozone production and result in high ozone concentrations, due to strong solar radiation as well as hot and dry atmospheric conditions. The increased occurrence of high-pressure patterns probably drives the ozone increase in Sichuan. When ozone pollution is relatively weak (with MDA8 ozone around 170 μg m−3), the air quality standard could be achieved in the short term by a 25% reduction of NOx and VOCs emissions. Strengthened emission control is needed when ozone pollution is more severe. Our study provides implications for effective emission control of ozone pollution in Sichuan.

Meteorological parameters and water balance components of Priyadarshini Lake at the Schirmacher Oasis, East Antarctica

The meteorological parameters and water balance components of Priyadarshini Lake at Schirmacher Oasis, East Antarctica, were studied using meteorological data procured from the India Meteorological Department (IMD) weather station, Maitri and field observations in summer seasons of 2017–2018 and 2018–2019. Meteorological data was analysed (daily temporal and diurnal variation) for 2018, and the water balance components were estimated for January–February of 2018. The present work revealed the status of meteorological parameters (air temperature, relative humidity, wind speed, and snowfall) at Schirmacher Oasis compared to long-term historical trends at the east and west regions of Antarctica. As Priyadarshini Lake is non-endorheic, it transferred freshwater to the sea at the rate of ∼335 × 103 m3/month during summer season of 2018. The water consumption by Maitri station (term W) is very minimal compared to other terms and does not play a substantial role in the lake's water balance. The limited availability of topographic and historical hydro-meteorological data limits the application of physically based hydrological models to simulate snow melt surface runoff at the inflow and assess the impact of climate change on hydrological regimes of the lake. Thus, long term monitoring of the lake with a permanent gauging network for inflows and outflow will pronounce our understanding and enable the option to assess linkage with changing climate.

Synergistic effects of air pollutants on meteorological fields at the junction of the Loess Plateau, Mongolian Plateau and Tibetan Plateau over Northwest China during summer

The synergistic effects of various air pollutants on meteorological fields have not yet been widely studied and fully understood, especially at the junction of the Loess Plateau, Mongolian Plateau and Tibetan Plateau. Two experiments were carried out with and without the incorporation of air pollutants to explore the synergistic effects using the Weather Research and Forecasting (WRF) model and WRF model coupled with chemistry (WRF-Chem) at the junction of the above three plateaus. The incorporation of air pollutants led to negative and positive variation patterns of meteorological fields and improved the simulation accuracy of meteorological factors to some extent. The negative variation pattern was caused by the direct absorption and scattering of solar radiation by air pollutants, and the positive variation pattern was driven by the evaporation of clouds due to the synergistic semidirect effects of air pollutants, which was more significant in this region. In the positive variation pattern, the solar radiation flux reaching the ground and the outgoing longwave radiation at the top of the atmosphere were increased by approximately 2–8% and 1–4%, respectively, the surface was warmed by 0.2–0.6 K, planetary boundary layer height (PBLH) increased by 3–12%, and relative humidity (RH) decreased by 1–4%. Moreover, the change in wind speed was approximately −0.3–0.3 m/s in the region. Furthermore, the correlation of conventional air pollutants with changes in meteorological factors indicated that there were not simple linear effects of atmospheric pollutants on meteorological fields and that considering a single pollutant cannot reflect the synergistic effects of various air pollutants on meteorological fields. This study quantifies the synergistic effects of various air pollutants on meteorological fields, which is a key issue that cannot be ignored in the modelling of meteorological fields and in the exploration of the interactions between air pollutants and synoptic weather.

Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019)

AbstractBetter understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

Source apportionment modelling of PM2.5 using CMAQ-ISAM over a tropical coastal-urban area

This study aims to explain the role of local emission sources to PM2.5 mass concentration in a tropical coastal-urban area, highly influenced by industrial and urban emissions, located in the Southeast of Brazil. The Integrated Source Apportionment Method (ISAM) tool was coupled with the chemistry and transport Community Multiscale Air Quality (CMAQ) model (CMAQ-ISAM) to quantify the contribution of ten emission sectors of PM2.5. The simulations were performed over five months between spring 2019 and summer 2020 using a local inventory, which was processed by the Sparse Matrix Operator Kernel Emission (SMOKE). The meteorological fields were provided by the Weather Research and Forecasting (WRF-Urban) model. The boundary and initial conditions to the CMAQ-ISAM were performed by the GEOS-Chem model. The simulations results show that the road dust resuspension (36%) and point (17%) emissions sources were the major contributors to PM2.5 mass in the Metropolitan Region of Vitória (MRV). The boundary conditions (BCON), representing the transport contribution from sources outside the domain, were also a dominant contributor in the MRV (20% on average). Furthermore, the primary atmospheric pollutants emitted by the point (14%) and shipping (7%) sectors in the MRV also affected the cities located in the south region of the domain, strengthened by the wind fields that mostly come from the northeast direction.

The Resolvable Scales of Regional‐Scale CO2 Transport in the Context of Imperfect Meteorology: The Predictability of CO2 in a Limited‐Area Model

AbstractTransport model error is an important source of uncertainty when estimating surface CO2 fluxes via an atmospheric model inversion. In this study, the transport error due to uncertainty of meteorological fields is investigated with a high resolution, limited‐area model. We characterize the extent to which errors in meteorological initial conditions (ICs) and lateral boundary conditions (LBCs) impact the quality of atmospheric CO2 transport across spatial scales. A series of experiments is conducted using different meteorological ICs and LBCs that possess varying levels of accuracy. We find that the transport error of CO2 is more sensitive to errors in meteorology at smaller scales O(10 km) than at larger scales O(1,000 km), and that surface CO2 fluxes can explain the predictability of CO2 at the largest scales. We also determine the spatial scales resolvable in the context of uncertain meteorology. These findings have implications for the development of regional‐scale inverse modeling systems. When assimilating CO2 observations near the surface, using accurate meteorological ICs is important for resolving fine‐scale spatial variability of CO2 because CO2 transport at lower levels is more sensitive to meteorological ICs and surface CO2 fluxes than to meteorological LBCs. However, when assimilating aircraft CO2 measurements or XCO2 satellite retrievals which contain information at higher altitudes, using accurate meteorological LBCs is also important. Improvement in meteorological inputs through a data assimilation system could be helpful in further resolving finer spatial scales of CO2 at regional scales.

Exploring Sources of Surface Bias in HRRR Using New York State Mesonet

AbstractIn recent years, there has been increasing demand for applications of short‐term forecasting of renewable energy potential and assessments of the likelihood of extreme weather events using the High‐Resolution Rapid Refresh (HRRR) model. Examining the biases in the newest version of HRRR is necessary to promote further model development. Using data from one of the most comprehensive and dense monitoring networks, New York State Mesonet (NYSM), we evaluate the HRRR version 3 meteorological fields for an entire year. In this work, the land‐atmosphere‐cloud coupling system is evaluated as an integrated whole. We investigate the physical processes influencing the soil hydrological balance and the thermodynamic interactions, from surface fluxes up to the level of boundary layer convection from both temporal (seasonal and diurnal) and spatial perspectives. Results show that the model 2 m temperature and humidity biases are seasonally dependent, with warm and dry bias present during the warm season, and an extreme nocturnal cold bias in winter. The summer warm bias includes both a land‐surface‐induced bias and a cloud‐induced bias. Inaccurate representation of energy partition and soil hydrological process across different land use types as well as a hydrological bias in describing spring snowmelt are identified as the main source of the land‐surface‐induced bias. A feedback loop linking cloud presence, flux changes, and temperature contributes to the cloud‐induced bias. The positive solar radiation bias increases from clear sky to overcast sky conditions. The most significant bias occurs during overcast and thick cloud conditions associated with frontal passage and thunderstorms.

Evaluating the impact of storage-and-release on aircraft-based mass-balance methodology using a regional air-quality model

Abstract. We investigate the potential for aircraft-based top-down emission rate retrieval over- and under-estimation using a regional chemical transport model, the Global Environmental Multiscale-Modeling Air-Quality and CHemistry (GEM-MACH). In our investigations we consider the application of the mass-balance approach in the Top-down Emission Rate Retrieval Algorithm (TERRA). Aircraft-based mass-balance retrieval methodologies such as TERRA require relatively constant meteorological conditions and source emission rates to reliably estimate emission rates from aircraft observations. Avoiding cases where meteorology and emission rates change significantly is one means of reducing emissions retrieval uncertainty, and quantitative metrics that may be used for retrieval accuracy estimation are therefore desirable. Using these metrics has the potential to greatly improve emission rate retrieval accuracy. Here, we investigate the impact of meteorological variability on mass-balance emission rate retrieval accuracy by using model-simulated fields as a proxy for real-world chemical and meteorological fields, in which virtual aircraft sampling of the GEM-MACH output was used for top-down mass balance estimates. We also explore the impact of upwind emissions from nearby sources on the accuracy of the retrieved emission rates. This approach allows the state of the atmosphere used for top-down estimates to be characterized in time and 3D space; the input meteorology and emissions are “known”, and thus potential means for improving emission rate retrievals and determining the factors affecting retrieval accuracy may be investigated. We found that emissions retrieval accuracy is correlated with three key quantitative criteria, evaluated a priori from forecasts and/or from observations during the sampling period: (1) changes to the atmospheric stability (described as the change in gradient Richardson number), (2) variations in the direction of transport, as a result of plume vertical motion and in the presence of vertical wind shear, and (3) the combined effect of the upwind-to-downwind concentration ratio and the upwind-to-downwind concentration standard deviations. We show here that cases where these criteria indicate high temporal variability and/or high upwind emissions can result in “storage-and-release” events within the sampled region (control volume), which decrease emission rate retrieval accuracy. Storage-and-release events may contribute the bulk of mass-balance emission rate retrieval under- and over-estimates, ranging in the tests carried out here from −25 % to 24 % of the known (input) emissions, with a median of −2 %. Our analysis also includes two cases with unsuitable meteorological conditions and/or significant upwind emissions to demonstrate conditions which may result in severe storage, which in turn cause emission rate under-estimates by the mass-balance approach. We also introduce a sampling strategy whereby the emission rate retrieval under- and over-estimates associated with storage-and-release are greatly reduced (to −14 % to +5 %, respectively, relative to the magnitude of the known emissions). We recommend repeat flights over a given facility and/or time-consecutive upwind and downwind (remote) vertical profiling of relevant fields (e.g., tracer concentrations) in order to measure and account for the factors associated with storage-and-release events, estimate the temporal trends in the evolution of the system during the flight/sampling time, and partially correct for the effects of meteorological variability and upwind emissions.

The impact of data assimilation into the meteorological WRF model on birch pollen modelling

We analyse the impact of ground-based data assimilation to the Weather Research and Forecasting (WRF) meteorological model on parameters relevant for birch pollen emission calculations. Then, we use two different emission databases (BASE – no data assimilation, OBSNUD – data assimilation for the meteorological model) in the chemical transport model and evaluate birch pollen concentrations. Finally, we apply a scaling factor for the emissions (BASE and OBSNUD), based on the ratio between simulated and observed seasonal pollen integral (SPIn) to analyse its impact on birch concentrations over Central Europe. Assimilation of observational data significantly reduces model overestimation of air temperature, which is the main parameter responsible for the start of pollen emission and amount of released pollen. The results also show that a relatively small bias in air temperature from the model can lead to significant differences in heating degree days (HDD) value. This may cause the HDD threshold to be attained several days earlier/later than indicated from observational data which has further impact on the start of pollen emission. Even though the bias for air temperature was reduced for OBSNUD, the model indicates a start for the birch pollen season that is too early compared to observations. The start date of the season was improved at two of the 11 stations in Poland. Data assimilation does not have a significant impact on the season's end or SPIn value. The application of the SPIn factor for the emissions results in a much closer birch pollen concentration level to observations even though the factor does not improve the start or end of the pollen season. The post-processing of modelled meteorological fields, such as the application of bias correction, can be considered as a way to further improve the pollen emission modelling.

A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R2 of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 <sup>106</sup>Ru event

Abstract. Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as error covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to better quantify these distributions, and therefore to get a better representation of the uncertainties. First, we propose a method based on ensemble forecasting: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for physical model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Second, once the choice of the statistical likelihood is shown to alter the nuclear source assessment, we suggest several suitable distributions for the errors. Finally, we propose two specific designs of the covariance matrix associated with the observation error. These methods are applied to the source term reconstruction of the 106Ru of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, and to quantify the uncertainties associated with the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.

Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios

The potential effects of 21st century climate change on ozone (O3) concentrations in the United States are investigated using global climate simulations to drive higher-resolution regional meteorological and chemical transport models. Community Earth System Model (CESM) and Coupled Model version 3 (CM3) simulations of the Representative Concentration Pathway 8.5 scenario are dynamically downscaled using the Weather Research and Forecasting model, and the resulting meteorological fields are used to drive the Community Multiscale Air Quality model. Air quality is modeled for five 11-year periods using both a 2011 air pollutant emission inventory and a future projection accounting for full implementation of promulgated regulatory controls. Across the U.S., CESM projects daily maximum temperatures during summer to increase 1–4 °C by 2050 and 2–7 °C by 2095, while CM3 projects warming of 2–7 °C by 2050 and 4–11 °C by 2095. The meteorological changes have geographically varying impacts on O3 concentrations. Using the 2011 emissions dataset, O3 increases 1–5 ppb in the central Great Plains and Midwest by 2050 and more than 10 ppb by 2095, but it remains unchanged or even decreases in the Gulf Coast, Maine, and parts of the Southwest. Using the projected emissions, modeled increases are attenuated while decreases are amplified, indicating that planned air pollution control measures ameliorate the ozone climate penalty. The relationships between changes in maximum temperature and changes in O3 concentrations are examined spatially and quantified to explore the potential for developing an efficient approach for estimating air quality impacts of other future climate scenarios. Implications Statement: The effects of climate change on ozone air quality in the United States are investigated using two global climate model simulations of a high warming scenario for five decadal periods in the 21st century. Warming summer temperatures simulated under both models lead to higher ozone concentrations in some regions, with the magnitude of the change increasing with temperature over the century. The magnitude and spatial extent of the increases are attenuated under a future emissions projection that accounts for regulatory controls. Regional linear regression relationships are developed as a first step toward development of a reduced form model for efficient estimation of the health impacts attributable to changes in air quality resulting from a climate change scenario.