Intensive Field Campaign Research Articles

Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM2.5, O3, and NO2, respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea.

Influence of anthropogenic activities on metals, sugars and PAHs in PM10 in the city of Fez, Morocco: Implications on air quality

Particulate matter (PM) is an important component in the atmosphere, affecting air quality, health, radiation balance, and global climate. To assess regional air quality in the city of Fez, an intensive field campaign was carried out in the autumn of 2019 in the Middle Atlas region of Morocco. Aerosol sampling was performed simultaneously at two urban sites in the city of Fez: (1) Fez University (FU), a sub-urban site, and (2) Fez Parc (FP), an urban site located in the city center of Fez, using PM10 collectors. Various laboratory analyses were carried out, including PM mass, trace metals, inorganic ions, OC/EC, sugar compounds, and PAHs. The results indicate that the PM10 mass (61 ng m−3) was comparable at both sites, with a 37–107 ng m−3 range. Most of the 19 investigated PAHs at the FU site (10.2 ± 6.2 ng m−3) were low-molecular-weight PAHs, while the most abundant PAHs at the FP site (6.9 ± 3.8 ng m−3) were mainly higher-molecular-weight PAHs. A diagnostic ratio analysis at both sites indicated that PAHs originated from fossil fuel combustion and traffic emissions from diesel engines, with Ant/(Ant + Phe) and Flu/(Flu + Pyr) ratios averaging 0.22 and 0.84, respectively. PMF analysis identified traffic emissions as a major source (30%), with secondary inorganic aerosols (20%) and biomass burning (14%). Polar plots highlight the dominance of local anthropogenic activities in PM pollution, with vehicular emissions, industrial activities, and biomass burning. This study shows that local sources and combustion processes significantly contribute to PM10 sources in Morocco, providing insights into air pollution mitigation in North Africa.

Microphysical mechanisms of wintertime postfrontal precipitation enhancement over the Australian Snowy Mountains

AbstractA heavy orographic precipitation event associated with the postfrontal sector of a midlatitude cyclone over the Australian Snowy Mountains (ASM) was analyzed using field observations and numerical simulations. This event, observed during a 2018 intensive field campaign, was of particular interest as three distinct precipitation episodes were identified within a prolonged postfrontal period. Deep mixed‐phase clouds (MPCs) characterized by cold cloud‐top temperatures (colder than −30°C) and the presence of updrafts extending 3.5–4.5 km above the boundary‐layer height, produced the three enhanced precipitation events over the windward slopes of the ASM. The presence of conditional instabilities and deep updrafts were also found in the sounding and Doppler velocity observations respectively, while the cloud radar observations show the deep MPCs with cloud tops reaching to 6–7 km a.s.l. Orographic convection invigoration was found to be the main mechanism producing the precipitation enhancement over the windward slopes and higher terrain. Using the Weather Research/Forecasting model, we analyzed the rates of microphysical processes to explicitly account for the enhancement of precipitation formation processes in these MPCs. This analysis showed that the precipitation formation processes were further enhanced through depositional and riming growth of ice‐phase hydrometeors during the three precipitation events. Deposition is simulated at higher levels (above the −15°C level) and most likely enabled by deep convective updrafts through the midtroposphere, whereas riming is stronger at lower levels (below −10°C level) due to the persistent production of feeder supercooled liquid water clouds sustained by the orographic lifting.

Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC.

Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations

Abstract. Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top–down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO2 (XCO2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO2 enhancement (ΔXCO2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of ∼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO2 emissions and biogenic fluxes. Although some of the diurnal variation of ΔXCO2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO2 simulation results from the posterior CO2 fluxes were improved (mean bias, −0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO2 d−1 at the 95 % confidence level, respectively. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO2 emissions.

Chemical Composition and Source of PM2.5 during Winter Heating Period in Guanzhong Basin

An intensive field campaign was carried out from December 2022 to March 2023 at six different sites across five major cities (Xi’an, Baoji, Xianyang, Weinan, and Hancheng) in the Guanzhong Basin, China, covering most of the heating period there, which is characterized by high PM2.5 pollution levels. During the campaign, the mean PM2.5 concentrations at these sites exceeded the 24 h PM2.5 standard (75 μg m−3), except the site at Hancheng, with mean PM2.5 concentrations of 57.8 ± 32.3 μg m−3. The source apportionment of PM2.5 varied significantly across sites, with vehicle exhaust being the dominant source at urban sites located in Xi’an and Baoji, coal combustion at suburban sites in Hancheng, and comparable contribution from coal combustion and industrial emissions at suburban sites in Xianyang and Weinan. Compared with clean condition, the contribution of vehicle exhaust and secondary inorganic sources (SIs) were largely enhanced during heavy PM2.5 pollution periods, while the contribution from biomass burning (BB) and dust decreased significantly at all sites. Combined with an analysis of meteorological parameters, the study further found that higher contributions of SIs and heavy PM2.5 pollution were generally associated with higher relative humidity (RH). In addition, higher PM2.5 concentrations at suburban sites were related to lower wind speeds, which could be explained by the stagnant condition favoring the accumulation of local emissions as well as the formation of secondary pollutants. In contrast, at urban sites (e.g., Xianyang), higher PM2.5 concentrations were more associated with the strong influence of vehicle exhaust at slightly higher wind speeds.

Very high particulate pollution over northwest India captured by a high-density in situ sensor network

Exposure to particulate matter less than 2.5 µm in diameter (PM2.5) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM2.5 Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM2.5 in the region increased gradually from < 60 µg m−3 in 6–10 October to up to 500 µg m−3 on 5–9 November, which subsequently decreased to about 100 µg m−3 in 20–30 November. Two distinct plumes of PM2.5 over 500 µg m−3 are tracked from crop residue burning in Punjab to Delhi NCR on 2–3 November and 10–11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street

AbstractAerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement‐oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global‐scale satellite and surface‐based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi‐variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co‐variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface‐based) program aimed at systematically quantifying the cloud‐scale microphysics, cloud optical properties, and cloud‐related vertical velocities associated with aerosol‐cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.

Surf, Turf, and Above the Earth: Unmet Needs for Coastal Air Quality Science in the Planetary Boundary Layer (PBL)

AbstractCoastal areas are some of the most densely populated and economically important regions in the world. As such, protecting the health of the human population and ecosystems at the coastal interface and understanding the impacts of environmental stressors such as air pollutants provides wide‐ranging benefits. Air quality (AQ) processes within coastal regions have been studied using ground and space‐based platforms, with intensive field campaigns focused on addressing key science questions that are typically partitioned into either direct atmospheric effects (e.g., anthropogenic emissions creating air pollution) or indirect processes and feedback loops (e.g., terrestrial/marine biogenic processes modifying atmospheric properties). The atmospheric planetary boundary layer (PBL) and its depth (or height) connect land, air, and the water surface via many pathways, especially with transport and exchange processes tied to the complexities of the coastal interface. We still cannot accurately characterize—through field, aircraft, or space‐based observations—the spatial and temporal PBL variability and processes within the PBL that couple together coastal dynamics and air quality. Several upcoming geostationary and polar‐orbiting satellite missions are likely to make significant progress in characterizing these air/land/water interactions over the next decade. Here, we present a framework of the current understanding of the PBL's role in coastal regions, primarily regarding air quality and atmospheric deposition, to motivate future concerted efforts from ground‐ and space‐based platforms to achieve a holistic understanding of the coastal interface.

Simulation of organic aerosol, its precursors, and related oxidants in the Landes pine forest in southwestern France: accounting for domain-specific land use and physical conditions

Abstract. Organic aerosol (OA) still remains one of the most difficult components of the atmospheric aerosols to simulate, given the multitude of its precursors, the uncertainty in its formation pathways, and the lack of measurements of its detailed composition. The LANDEX (LANDes Experiment) project, during its intensive field campaign in summer 2017, gives us the opportunity to compare biogenic secondary OA (BSOA) and its precursors and oxidants obtained within and above the Landes forest canopy to simulations performed with CHIMERE, a state-of-the-art regional chemistry transport model. The Landes forest is situated in the southwestern part of France and is one of the largest anthropized forests in Europe (1×106 ha). The majority of the forest is comprised of maritime pine trees, which are strong terpenoid emitters, providing a large potential for BSOA formation. In order to simulate OA buildup in this area, a specific model configuration setup adapted to the local peculiarities was necessary. As the forest is nonhomogeneous, with interstitial agricultural fields, high-resolution 1 km simulations over the forest area were performed. Biogenic volatile organic compound (BVOC) emissions were predicted by MEGAN, but specific land cover information needed to be used and was thus chosen from the comparison of several high-resolution land cover databases. Moreover, the tree species distribution needed to be updated for the specific conditions of the Landes forest. In order to understand the canopy effect in the forest, canopy effects on vertical diffusivity, winds, and radiation were implemented in the model in a simplified way. The refined simulations show a redistribution of BVOCs with a decrease in isoprene and an increase in terpenoid emissions with respect to the standard case, both of which are in line with observations. Corresponding changes to simulated BSOA sources are tracked. Very low nighttime ozone, sometimes near zero, remains overestimated in all simulations. This has implications for the nighttime oxidant budget, including NO3. Despite careful treatment of physical conditions, simulated BSOA is overestimated in the most refined simulation. Simulations are also compared to air quality sites surrounding the Landes forest, reporting a more realistic simulation in these stations in the most refined test case. Finally, the importance of the sea breeze system, which also impacts species concentrations inside the forest, is made evident.

Chloride (HCl ∕ Cl−) dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter: modeling and comparison with observations

Abstract. The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain (IGP) during the winter of 2017–2018. Here, we report the first comparison in South Asia of high-temporal-resolution simulation of ammonia (NH3) along with ammonium (NH4+) and total NHx (i.e., NH3+ NH4+) using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and measurements made using the Monitor for AeRosols and Gases in Ambient Air (MARGA) at the WiFEX research site. In the present study, we incorporated the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme into WRF-Chem. Despite simulated total NHx values and variability often agreeing well with the observations, the model frequently simulated higher NH3 and lower NH4+ concentrations than the observations. Under the winter conditions of high relative humidity (RH) in Delhi, hydrogen chloride (HCl) was found to promote the increase in the particle fraction of NH4+ (which accounted for 49.5 % of the resolved aerosol in equivalent units), with chloride (Cl−) (29.7 %) as the primary anion. By contrast, the absence of chloride (HCl / Cl−) chemistry in the standard WRF-Chem model results in the prediction of sulfate (SO42-) as the dominant inorganic aerosol anion. To understand the mismatch associated with the fraction of NHx in the particulate phase (NH4+ / NHx), we added HCl / Cl− to the model and evaluated the influence of its chemistry by conducting three sensitivity experiments using the model: no HCl, base case HCl (using a published waste burning inventory), and 3 × base HCl run. We found that 3 × base HCl increased the simulated average NH4+ by 13.1 µg m−3 and NHx by 9.8 µg m−3 concentration while reducing the average NH3 by 3.2 µg m−3, which is more in accord with the measurements. Thus HCl / Cl− chemistry in the model increases total NHx concentration, which was further demonstrated by reducing NH3 emissions by a factor of 3 (−3 × NH3_EMI) in the 3 × base HCl simulation. Reducing NH3 emissions in the 3 × base HCl simulation successfully addressed the discrepancy between measured and modeled total NHx. We conclude that modeling the fate of NH3 in Delhi requires a correct chemistry mechanism accounting for chloride dynamics with accurate inventories of both NH3 and HCl emissions.

Simulation of Heavy Precipitation and the Production of Graupel Related to the Passage of a Cold Front over the Australian Snowy Mountains

Abstract The case study of a heavy precipitation event associated with the passage of cold front over the Australian Snowy Mountains (ASM) on 3 August 2018 has been examined using the observational data from an intensive field campaign and high-resolution (1 km) Weather Research and Forecasting (WRF) simulation. We divided this event into prefrontal, cold front, and postfrontal periods. The cold front and postfrontal periods were characterized by higher production of graupel, while relatively low graupel was produced in the prefrontal period. Overall, aggregation along with deposition are likely the main growth mechanisms of snow in the prefrontal clouds, while heavy rain was produced below the melting level over windward slopes of the ASM. The simulated melting level is lower compared to the observations, which is consistent with model cold bias. Stronger orographic uplift and frontal forcing were mainly responsible for the enhanced supercooled liquid water (SLW) production over the ASM in the cold front period. A drop in elevation of the freezing level and increase in low-level relative humidity further enhanced the SLW production. The production of graupel through riming processes was highly efficient in the cold front period given the high concentration of ice-phase hydrometeors in the frontal clouds and the development of clouds comprising supercooled liquid water. The orographic updrafts and embedded convection were the main dynamical processes generating postfrontal SLW clouds and graupel. Ice initiation processes were activated once SLW cloud tops reached −15°C level followed by graupel production through riming processes.

Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

AbstractThe Earth's surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale‐dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower‐based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso‐β, meso‐γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid‐latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface‐atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller‐scale turbulent and larger‐scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single‐point tower measurements through traditional time‐domain half‐hourly Reynolds decomposition. We report scale‐resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface‐atmospheric interactions over unstructured heterogeneities and can help inform multi‐scale model‐data integration of weather and climate models at a sub‐grid scale.

An archive of Lake Superior temperature and current measurements, 2005–2020

AbstractSince 2005, investigators at the University of Minnesota, Duluth's Large Lakes Observatory have been maintaining subsurface moorings and surface buoys in Lake Superior to study thermal structure and currents throughout the water column and throughout the year. A single site has been continuously occupied for over 17 years as of the writing of this manuscript, another 10 sites have been occupied for multiple years, and for 3 months in summer 2017 an intensive field campaign occupied 12 sites simultaneously in western Lake Superior. All of these data are available on a publicly accessible archival site hosted by the University of Minnesota.

A New Algorithm of Atmospheric Boundary Layer Height Determined from Polarization Lidar

Accurately determining the atmospheric boundary layer height (ABLH) is needed when one is addressing the air quality-related issues in highly urbanized areas, as well as when one is investigating issues that are related to the emission and transport of dust aerosols over the source region. In this study, we propose a new ABLH retrieval method, which is named ADEILP (ABLH that is determined by polarization lidar); it is based on the short-term polarized lidar observation that took place during the intensive field campaign in July 2021 in Tazhong, the hinterland of Taklimakan Desert. Furthermore, we conducted comparisons between the ABLH that was identified using a radiosonde (ABLHsonde), the ABLH that was identified by ERA5 (ABLHERA5) and the ABHL that was identified by ADELIP (ABLHADELIP), and we discussed the implications of the dust events. The ADELIP method boasts remarkable advancements in two parts: (1) the lidar volume linear depolarization ratio (VLDR) that represented the aerosol type was adopted, which is very effective in distinguishing between the different types of boundary layers (e.g., mixing layer and residual layer); (2) the idea of breaking up the entire layer into sub-layers was applied on the basis of the continues wavelet transform (CWT) method, which is favorable when one is considering the effect of fine stratification in an aerosol layer. By combining the appropriate height limitations, these factors ensured that there was good robustness of the ADELIP method, thereby enabling it to deal with complex boundary layer structures. The comparisons revealed that ABLHADELIP shows good consistency with ABLHsonde and ABLHERA5 for non-dust events. Nevertheless, the ADELIP method overestimated the stable boundary layer and underestimated the heights of the mixing layer. The dust events seem to be a possible reason for the great difference between ABLHERA5 and ABLHsonde. Thus, it is worth suggesting that the influence that is caused by the differences of the vertical profile in the ERA5 product should be carefully considered when the issues on dust events are involved. Overall, these findings support the climatological analysis of the atmosphere boundary layer and the vertical distribution characteristics of aerosols over typical climatic zones.

Experimental study on the evolution of droplet size distribution during the fog life cycle

Abstract. The evolution of the droplet size distribution (DSD) during the fog life cycle remains poorly understood and progress is required to reduce the uncertainty of fog forecasts. To gain insights into the physical processes driving the microphysical properties, intensive field campaigns were conducted during the winters of 2010–2013 at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) in a semi-urban environment southwest of Paris city center to monitor the simultaneous variations in droplet microphysical properties and their potential interactions at the different evolutionary stages of the fog events. Liquid water content (LWC), fog droplet number concentration (Nd) and effective diameter (Deff) show large variations among the 42 fog events observed during the campaign and for individual events. Our findings indicate that the variability of these parameters results from the interaction between microphysical, dynamical and radiative processes. During the formation and development phases, activation of aerosols into fog droplets and condensational growth were the dominant processes. When vertical development of radiation fog occurred under the influence of increasing wind speed and subsequent turbulent motion, additional condensational growth of fog droplets was observed. The DSDs with single mode (around 11 µm) and double mode (around 11 and 22 µm) were observed during the field campaign. During the development phase of fog with two droplet size modes, a mass transfer occurred from the smaller droplets into the larger ones through collision–coalescence or Ostwald ripening processes. During the mature phase, evaporation due to surface warming induced by infrared radiation emitted by fog was the dominant process. Additional droplet removal through sedimentation is observed during this phase for fog with two droplet size modes. Because of differences in the physical processes involved, the relationship between LWC and Nd is largely driven by the DSD. Although a positive relationship is found in most of the events due to continuous activation of aerosol into fog droplets, LWC varies at a constant Nd in fog with large Deff (&gt;17 µm) due to additional collision–coalescence and Ostwald ripening processes. This work illustrates the need to accurately estimate the supersaturation for simulating the continuous activation of aerosols into droplets during the fog life cycle and to include advanced parameterizations of relevant microphysical processes such as collision–coalescence and Ostwald ripening processes, among others, in numerical models.

Characterization of the atmospheric circulation near the Empty Quarter Desert during major weather events

In this study, we characterize the meteorological conditions and planetary boundary layer dynamics in western United Arab Emirates based on 2012–2020 in-situ measurements. This multi-year analysis is then complemented by an intensive field-campaign in winter 2021 from which we address the main patterns of the atmospheric circulation and the boundary layer structure during typical weather events identified in the long-term analysis. We found that, 10-m and 60-m wind speeds are generally below 5 m s−1 and 8 m s−1, respectively, blowing predominantly from a northerly direction. They peak in intensity at around 12–18 Local Time (LT) and 02–09 LT in association with the sea-breeze and the downward mixing of momentum from the nighttime low-level jet, respectively. The wind is stronger in the cold season, varying mostly in response to mid-latitude baroclinic systems, while the proximity of the site to the core of the Arabian Heat Low leads to more quiescent conditions in the summer. Radiation fog is a regular occurrence mostly from December to February owing to the colder nights and weaker wind speeds, with a peak fog occurrence around local sunrise. Sea fog, which develops when the hotter desert air is transported over the cooler Arabian Gulf waters, is advected to the site in the summer. Deep and very deep convective clouds are more common in March-April in association with organized convective systems, with generally reduced cloud cover from May to October. The region of interest exhibits a monthly-mean aerosol optical depth ranging from ∼0.3 in December-January to ∼1.2 in July due to its increased exposure to dust storms in the summer season. Dust activity is also found to peak during winter and spring associated with the intrusion of cold fronts from mid-latitudes.

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3.

Validation of geostationary operational environmental satellite-16 advanced baseline imager radiometric calibration with airborne field campaign data and reanalysis of north-south scan data

The advanced baseline imager (ABI) on board geostationary operational environmental satellite-16 (GOES-16) provides high quality reflective solar band (RSB) and thermal emission band (TEB) image data. Intensive field campaigns for postlaunch validation of the ABI Level-1B spectral radiance observations were carried out during March to May of 2017 to ensure the Système International traceability of the ABI. Radiometric calibrations of the RSBs and TEBs of the ABI were evaluated by comparison with the spectral measurements of the airborne visible infrared imaging spectrometer-next generation (AVIRIS-NG) and scanning high-resolution interferometer sounder (S-HIS) on board the high-altitude aircraft ER2, respectively. The comparison between ABI RSB mesoscale (MESO) data and AVIRIS-NG measurements showed that the mean biases are within 2% with uncertainty <2.5 % for ABI CH01, CH03, CH05, and CH06. The brightness temperature differences between GOES-16 ABI and S-HIS measurements were shown to be within 0.45 K with uncertainty <0.35 K at the 300 K scene equivalent for ABI CH08 to CH10 and CH13 to CH15. The ABI uses large focal plane arrays with the detector number varying from hundreds to more than a thousand per channel, which makes the evaluation of detector uniformity a challenge. Reanalysis of the GOES-16 ABI north-south scan data from the field campaign and lunar observation was performed for ABI CH01 to CH03 with two versions of calibration coefficients. Significant improvements in detector uniformity with the updated solar calibration algorithm were verified.

Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring

Managed grasslands cover about one third of the European utilized agricultural area. Appropriate grassland management is key for balancing trade-offs between provisioning and regulating ecosystem services. The timing and frequency of mowing events are major factors of grassland management. Recent studies have shown the feasibility of detecting mowing events using remote sensing time series from optical and radar satellites. In this study, we present a new method combining the regular observations of Sentinel-1 (S1) and the better accuracy of Sentinel-2 (S2) grassland mowing detection algorithms. This multi-source approach for grassland monitoring was assessed over large areas and in various contexts. The method was first validated in six European countries, based on Planet image interpretation. Its performances and sensitivity were then thoroughly assessed in an independent study area using a more precise and complete reference dataset based on an intensive field campaign. Results showed the robustness of the method across all study areas and different types of grasslands. The method reached a F1-score of 79% for detecting mowing events on hay meadows. Furthermore, the detection of mowing events along the growing season allows to classify mowing practices with an overall accuracy of 69%. This is promising for differentiating grasslands in terms of management intensity. The method could therefore be used for large-scale grassland monitoring to support agri-environmental schemes in Europe.