Measurement-based modeling of bromine chemistry in the boundary layer: 1. Bromine chemistry at the Dead Sea

International audienceThe Dead Sea is an excellent natural laboratory for the investigation of Reactive Bromine Species (RBS) chemistry, due to the high RBS levels observed in this area, combined with anthropogenic air pollutants up to several ppb. The present study investigated the basic chemical mechanism of RBS at the Dead Sea using a numerical one-dimensional chemical model. Simulations were based on data obtained from comprehensive measurements performed at sites along the Dead Sea. The simulations showed that the high BrO levels measured frequently at the Dead Sea could only partially be attributed to the highly concentrated Br? present in the Dead Sea water. Furthermore, the RBS activity at the Dead Sea cannot solely be explained by a pure gas phase mechanism. This paper presents a chemical mechanism which can account for the observed chemical activity at the Dead Sea, with the addition of only two heterogeneous processes: the "Bromine Explosion" mechanism and the heterogeneous decomposition of BrONO2. Ozone frequently dropped below a threshold value of ~1 to 2 ppbv at the Dead Sea evaporation ponds, and in such cases, O3 became a limiting factor for the production of BrOx (BrO+Br). The entrainment of O3 fluxes into the evaporation ponds was found to be essential for the continuation of RBS activity, and to be the main reason for the jagged diurnal pattern of BrO observed in the Dead Sea area, and for the positive correlation observed between BrO and O3 at low O3 concentrations. The present study has shown that the heterogeneous decomposition of BrONO2 has a great potential to affect the RBS activity in areas influenced by anthropogenic emissions, mainly due to the positive correlation between the rate of this process and the levels of NO2. Further investigation of the influence of the decomposition of BrONO2 may be especially important in understanding the RBS activity at mid-latitudes

Utility of Geostationary Lightning Mapper-derived lightning NO emission estimates in air quality modeling studies

Lightning is one of the primary natural sources of nitric oxide (NO), and the influence of lightning-induced NO (LNO) emission on air quality has been investigated in the past few decades. In the current study an LNO emissions model, which derives LNO emission estimates from satellite-observed lightning optical energy, is introduced. The estimated LNO emission is employed in an air quality modeling system to investigate the potential influence of LNO on tropospheric ozone. Results show that lightning produced 0.174 Tg N of nitrogen oxides (NOx = NO + NO2) over the contiguous US (CONUS) domain between June and September 2019, which accounts for 11.4 % of the total NOx emission. In August 2019, LNO emission increased ozone concentration within the troposphere by an average of 1 %–2 % (or 0.3–1.5 ppbv), depending on the altitude; the enhancement is maximum at ∼ 4 km above ground level and minimum near the surface. The southeastern US has the most significant ground-level ozone increase, with up to 1 ppbv (or 2 % of the mean observed value) difference for the maximum daily 8 h average (MDA8) ozone. These numbers are near the lower bound of the uncertainty range given in previous studies. The decreasing trend in anthropogenic NOx emissions over the past 2 decades increases the relative contribution of LNO emissions to total NOx emissions, suggesting that the LNO production rate used in this study may need to be increased. Corrections for the sensor flash detection efficiency may also be helpful. Moreover, the episodic impact of LNO on tropospheric ozone can be considerable. Performing backward trajectory analyses revealed two main reasons for significant ozone increases: long-distance chemical transport and lightning activity in the upwind direction shortly before the event.</p

TEMPO Applications to Air-Quality and Health

No abstract availabl

Ozone Lidar Observations for Air Quality Studies

Tropospheric ozone lidars are well suited to measuring the high spatio-temporal variability of this important trace gas. Furthermore, lidar measurements in conjunction with balloon soundings, aircraft, and satellite observations provide substantial information about a variety of atmospheric chemical and physical processes. Examples of processes elucidated by ozone-lidar measurements are presented, and modeling studies using WRF-Chem, RAQMS, and DALES/LES models illustrate our current understanding and shortcomings of these processes

Satellite Data Assimilation into Meteorological/Air Quality

A report directed at improving the specification of surface parameters such as insolation, soil moisture, and surface heat capacity

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active&ndash;passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented

Incorporating GOES Satellite Photosynthetically Active Radiation (PAR) Retrievals to Improve Biogenic Emission Estimates in Texas

This study examines the influence of insolation and cloud retrieval products from the Geostationary Operational Environmental Satellite (GOES) system on biogenic emission estimates and ozone simulations in Texas. Compared to surface pyranometer observations, satellite‐retrieved insolation and photosynthetically active radiation (PAR) values tend to systematically correct the overestimation of downwelling shortwave radiation in the Weather Research and Forecasting (WRF) model. The correlation coefficient increases from 0.93 to 0.97, and the normalized mean error decreases from 36% to 21%. The isoprene and monoterpene emissions estimated by the Model of Emissions of Gases and Aerosols from Nature are on average 20% and 5% less, respectively, when PAR from the direct satellite retrieval is used rather than the control WRF run. The reduction in biogenic emission rates using satellite PAR reduced the predicted maximum daily 8 h ozone concentration by up to 5.3 ppbV over the Dallas‐Fort Worth (DFW) region on some days. However, episode average ozone response is less sensitive, with a 0.6 ppbV decrease near DFW and 0.3 ppbV increase over East Texas. The systematic overestimation of isoprene concentrations in a WRF control case is partially corrected by using satellite PAR, which observes more clouds than are simulated by WRF. Further, assimilation of GOES‐derived cloud fields in WRF improved CAMx model performance for ground‐level ozone over Texas. Additionally, it was found that using satellite PAR improved the model's ability to replicate the spatial pattern of satellite‐derived formaldehyde columns and aircraft‐observed vertical profiles of isoprene

Relevance of Photolysis Frequencies Calculation Aspects to the Ozone Concentration Simulation

For the simulation of photochemically created pollutants like ozone it is essential to correctly consider reaction rates induced by short-wave radiation. In atmospheric chemistry transport models this is achieved by the use of either off- or online calculated photolysis frequencies. In this study the effect of different input parameters of a radiation model on the calculated photolysis frequencies have been investigated. In the second step an atmospheric chemistry transport model was used to assess the impact of changed photolysis frequencies on the simulation of ozone concentrations. The impact of changed radiation model input parameters on the calculated photolysis frequencies vary not only with regard to the changed parameter but also with regard to the to the species to be dissociated. Furthermore the impact of different sets of photolysis rates employed in a chemical transport simulation on the modelled concentrations is differed and likely to be less important than other aspects of the simulation like the resolution of the grid and the emissions used. Apart from major surface albedo changes (grass to snow) and extreme changes in total ozone column content for JO3 clouds are the dominating factor in modifying the photolysis frequencies especially as they feature a highly temporal and special variation. The results show that simulated maximum ozone concentrations in areas with clouds are reduced