Satellite Retrievals Research Articles

Anthropogenic, biogenic, and photochemical influences on surface formaldehyde and its significant decadal (2006–2017) decrease in the Lewiston-Clarkston valley of the northwestern United States

Formaldehyde (HCHO) is a key carcinogen and plays an important role in atmospheric chemistry. Both field measurements and Positive Matrix Factorization (PMF) modeling have been employed to investigate the concentrations and sources of HCHO in the Lewiston-Clarkston (LC) valley of the mountainous northwestern U.S. Different instruments were deployed to measure surface formaldehyde and other related compounds in July of 2016 and 2017. The measurements reveal that the average HCHO concentrations have significantly decreased to 2–5 ppb in the LC valley in comparison to its levels (10–20 ppb) observed in July 2006. This discovery with surface measurements deserves attention given that satellite retrievals showed an increasing long-term trend from 2005 to 2014 in total vertical column density of HCHO in the region, suggesting that satellite instruments may not adequately resolve small valleys in the mountainous region. Our PMF modeling identified four major sources of HCHO in the valley: (1) emissions from a local paper mill, (2) secondary formation and background, (3) biogenic sources, and (4) traffic. This study reveals that the emissions from the paper mill cause high HCHO spikes (6–19 ppb) in the early morning. It is found that biogenic volatile organic compounds (VOCs) in the area are influenced by national forests surrounding the region (e.g., Nez Perce-Clearwater, Umatilla, Wallowa-Whitman, and Idaho Panhandle National Forests). The results provide useful information for developing strategies to control HCHO levels and have implications for future HCHO studies in atmospheric chemistry, which affects secondary aerosols and ozone formation.

Aerosol characteristics in CMIP6 models' global simulations and their evaluation with the satellite measurements

AbstractGlobal and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971–2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging Spectro Radiometer (MISR) AOD for the 2001–2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI‐ESM2‐0 and MPI‐ESM‐1‐2‐HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine‐ and coarse‐mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI‐ESM‐1‐2‐HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550–870 nm). On the other hand, MRI‐ESM2‐0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations.

Assimilating Aeolus Satellite Wind Data on a Regional Level: Application in a Mediterranean Cyclone Using the WRF Model

This study uses a limited area model to improve the understanding of assimilating Aeolus Level 2B wind profiles on a regional level under severe weather conditions. Aeolus wind profile measurements have offered new insights into weather analysis and applications. The assimilation of Aeolus Level 2B winds has enhanced the observed state of the atmosphere spatially and temporally in global modeling systems. This work is focused on the development and evolution of a Mediterranean tropical-like cyclone that occurred between 27–30 September 2018. Aeolus coverage had a good spatial and temporal alignment with the broader area and time periods during which the cyclone originated and developed, affording the opportunity to explore the direct influence of Aeolus satellite retrievals in model initialization processes. Using the WRF 3DVar modeling system, model results showcase the effects stemming from Aeolus data ingestion, with the main differences presenting after the first 24 h of simulation. Smaller or larger deviations in the runs with and without the Aeolus wind data assimilation are evident in most cyclonic characteristics, extending vertically up to the mid-troposphere. The absence of a consistent trend in cyclone intensification or weakening underlines the unique impact of the Aeolus dataset in each case.

Modeling Water Isotopes Using a Global Non‐Hydrostatic Model With an Explicit Convection: Comparison With Gridded Data Sets and Site Observations

AbstractIn this study, we developed a global cloud system‐resolving model (GCSRM) incorporating stable water isotopes (NICAM‐WISO). Using a single‐moment cloud microphysics scheme, we applied the new model to conduct a current climate simulation at a horizontal resolution of 56 km. NICAM‐WISO simulated the seasonal means of the atmospheric hydrological cycle, as well as water isotopic ratios of precipitation and vapor. The model captured the general features of precipitation isotope effects and its spatial correlations were comparable to those of other isotope‐incorporated global atmospheric models. The model showed better spatial correlation between simulated and observed values for a fine‐horizontal‐resolution (14 km) simulation compared to coarse‐horizontal‐resolution (56 km) simulation. However, the model had isotopic biases in tropical mid‐troposphere, ocean, and cold continental regions. A comparison of stable water isotopes between the simulation and observations offered clues for improving the model. For example, in the tropical mid‐troposphere, we found a negative bias in the mixing ratio and isotopic ratio of water vapor. Our analysis using satellite retrievals revealed that these underestimations were caused by weak mixing with the boundary layer vapor and low raindrop evaporation with a small evaporation fraction. The underestimations indicated weak shallow convective mixing in the model, inducing negative bias in the mixing ratio and isotopic ratio of the mid‐tropospheric vapor. These biases were also seen in the fine horizontal‐resolution simulation. Furthermore, we conducted several km‐scale atmospheric isotope circulation simulations using NICAM‐WISO. We expect that global‐scale fine‐horizontal‐resolution simulations using isotope‐incorporated GCSRMs will improve our understanding of the atmospheric hydrological cycle.

Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan

Aerosol Optical Depth (AOD) is critical to air quality research and has a major impact on the Earth's energy budget. Recently, satellite retrieval has been a common data source for large-scale AOD information, for which the new-generation geostationary Himawari-8 satellite, which monitors East Asia and the Pacific region every 10 min, provides an unprecedented opportunity to track its dynamics. However, in the previous evaluation research, the Himawari-8 L2 AOD product has a large deviation from the ground observation. In this study, we proposed a deep neural network (DNN) model to correct the existing Himawari-8 L2 AOD using the synchronous information recorded in the Himawari-8 spectral bands in Japan. The results show that the accuracy has been dramatically improved, as indicated by the random 10-fold cross-validation (CV) (R2: 0.76 vs. 0.24; and RMSE: 0.068 vs. 0.194). Furthermore, many more samples fall in the error lines of the Global Climate Observation System (improved from 24.7% to 52.0% in 10-fold CV). In addition, we also obtained better performance when evaluated using the leave-one-station-out CV (R2 = 0.49 and RMSE = 0.101). The Shapley Additive exPlanations values (SHAP) based feature importance result showed that the Day of Year (DOY) contributed the most to the AOD retrievals, followed by the Himawari-8 L2 AOD. The performance decreased when either DOY or Himawari-8 L2 AOD was removed from the model. Overall, the results of this study will provide a methodology to obtain the high temporal resolution AOD with improved accuracy and thus will help to quantify the dynamics of aerosols in a synchronous manner.

Impacts of irregular and strategic lockdown on air quality over Indo-Pak Subcontinent: Pre-to-post COVID-19 analysis

The COVID-19 pandemic has received enormous attention worldwide due to its environmental, and societal impacts. After the discovery of this novel virus in December 2019, the governmental bodies have suspended the social and economic activities and declare an unprecedented lockdown emergency. The present work sought to investigate the impact of COVID-19 containment measures on air quality in the region of Indo-Pak subcontinent (IPS). The satellite retrievals of aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and Ozone (O3) was used to estimate the spatial-temporal extent of major air pollutants in the region during lock down period (March–May 2020) in comparison to the pre-lockdown period (March–May 2015–2019) and post-lockdown (March–May 2021) across the IPS. From the results, a considerable reduction in air pollutants concentration has been observed during the lockdown phase that in turns significantly improved the regional air quality. Moreover, we have also done comparative analysis among meteorological parameters and ambient air pollutants and observed that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. In addition, backward trajectory analysis has been done through the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to find the path and direction of air particles parcels as large-scale atmospheric circulation is a critical factor in driving pollutant. Following the implementation of strict lockdown, reduction in anthropogenic activities led to a substantial decline in AOD, SO2 and NO2 whereas, a considerable increase in tropospheric O3 has been observed and well supported by other regional studies. Overall, the findings of this study are believed to be a useful addition to the regulatory authorities and will help the policymakers to formulate the air pollution mitigation strategies in a long-term perspective.

Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

A Comprehensive Assessment of Anthropogenic and Natural Sources and Sinks of Australasia's Carbon Budget

AbstractRegional carbon budget assessments attribute and track changes in carbon sources and sinks and support the development and monitoring the efficacy of climate policies. We present a comprehensive assessment of the natural and anthropogenic carbon (C‐CO2) fluxes for Australasia as a whole, as well as for Australia and New Zealand individually, for the period from 2010 to 2019, using two approaches: bottom‐up methods that integrate flux estimates from land‐surface models, data‐driven models, and inventory estimates; and top‐down atmospheric inversions based on satellite and in situ measurements. Our bottom‐up decadal assessment suggests that Australasia's net carbon balance was close to carbon neutral (−0.4 ± 77.0 TgC yr−1). However, substantial uncertainties remain in this estimate, primarily driven by the large spread between our regional terrestrial biosphere simulations and predictions from global ecosystem models. Within Australasia, Australia was a net source of 38.2 ± 75.8 TgC yr−1, and New Zealand was a net CO2 sink of −38.6 ± 13.4 TgC yr−1. The top‐down approach using atmospheric CO2 inversions indicates that fluxes derived from the latest satellite retrievals are consistent within the range of uncertainties with Australia's bottom‐up budget. For New Zealand, the best agreement was found with a national scale flux inversion estimate based on in situ measurements, which provide better constrained of fluxes than satellite flux inversions. This study marks an important step toward a more comprehensive understanding of the net CO2 balance in both countries, facilitating the improvement of carbon accounting approaches and strategies to reduce emissions.

Signature of the stratosphere–troposphere coupling on recent record-breaking Antarctic sea-ice anomalies

Abstract. In February 2023, the sea-ice extent around Antarctica dropped to 1.79×106 km2, setting a satellite-era record low for the second straight year. Recent records stress the need for further research into the factors behind record-breaking Antarctic sea-ice anomalies. By influencing the circumpolar westerly winds, the stratospheric polar vortex has played a major role in the Antarctic surface climate in recent decades. However, the footprint of the polar vortex variability in the year-to-year changes in the Antarctic sea-ice cover remains obscured. Here, we use satellite retrievals and reanalysis data to study the response of the sea-ice extent around Antarctica to changes in the polar vortex strength. We focus on the last 2 decades that saw sharp changes in the stratospheric zonal flow, the tropospheric westerly winds and the sea-ice cover (the latter climbed to record highs in 2013 and 2014 before dropping to record lows in 2017, 2022 and 2023). Our results suggest that this unprecedented interannual variability is noticeably influenced by the polar vortex dynamics. The signature of the stratosphere–troposphere coupling is apparent in recent records (highs and lows) in the sea-ice extent around Antarctica.

Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

Evaluation of spatial downscaling for satellite retrieval of evapotranspiration from the nonparametric approach in an arid area

Evapotranspiration (ET) data is critical for monitoring the limited hydrological resources in arid areas. At present, the spatial resolution of ET derived by the thermal infrared images from satellite data is coarse (always more than 500 m). The low-resolution data cannot meet the needs of the high-precision mapping of ET. Therefore, it is important to undertake a downscaling procedure to improve the spatial resolution of ET. This paper selects the oasis region and surrounding deserts of Zhangye City of China as study area and compares with two downscaling procedures that include input and output downscaling procedure. The ET is estimated with the Remote Sensing-Non Parametric (RS-NP) model. The two downscaling procedures use Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to improve the spatial resolution of ET from 1-km to 100 m. Compared with the result derived from the output downscaling procedure, the high-resolution ET derived from the input downscaling procedure has a slightly better performance with R2 of 0.92 and RMSE of 41.8 W/m2, respectively. Neglectingthe downscaling of land surface temperature and broadband reflectance (BBR) result in larger errors of ET estimation with bias values of 73.0 W/m2 and 23.7 W/m2, respectively. Those values account for almost 1/3 and 1/10 of ET values, respectively. In non-vegetated areas, those proportions increase further around 90.6 % and 21.3 %, respectively. Furthermore, the influence of the neglect of the downscaling of the near-infrared reflectance on the ET estimation dominates that of BBR with a proportion of 42 %. The input downscaling procedure is suitable for generating the high-precision map of ET, especially in vegetated areas and during northern hemisphere summer months. Our study is helpful to obtain ET maps in fine spatial resolutions and contribute to the comprehensive understanding of the importance of the downscaling of each kind of input for reliable ET estimations.

Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals

Abstract. The formation of ice in clouds is an important process in mixed-phase clouds, and the radiative properties and dynamical developments of clouds strongly depend on their partitioning between the liquid and ice phases. In this study, we investigated the sensitivities of the cloud phase to the ice-nucleating particle (INP) concentration and thermodynamics. Moreover, passive satellite retrieval algorithms and cloud products were evaluated to identify whether they could detect cloud microphysical and thermodynamical perturbations. Experiments were conducted using the ICOsahedral Nonhydrostatic (ICON) model at the convection-permitting resolution of about 1.2 km on a domain covering significant parts of central Europe, and they were compared to two different retrieval products based on Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements. We selected a day with multiple isolated deep convective clouds, reaching a homogeneous freezing temperature at the cloud top. The simulated cloud liquid pixel fractions were found to decrease with increasing INP concentration, both within clouds and at the cloud top. The decrease in the cloud liquid pixel fraction was not monotonic and was stronger in high-INP cases. Cloud-top glaciation temperatures shifted toward warmer temperatures with an increasing INP concentration by as much as 8 ∘C. Moreover, the impact of the INP concentration on cloud-phase partitioning was more pronounced at the cloud top than within the cloud. Furthermore, initial and lateral boundary temperature fields were perturbed with increasing and decreasing temperature increments from 0 to ±3 and ±5 K between 3 and 12 km, respectively. Perturbing the initial thermodynamic state was also found to systematically affect the cloud-phase distribution. However, the simulated cloud-top liquid pixel fraction, diagnosed using radiative transfer simulations as input to a satellite forward operator and two different satellite remote-sensing retrieval algorithms, deviated from one of the satellite products regardless of perturbations in the INP concentration or the initial thermodynamic state for warmer subzero temperatures while agreeing with the other retrieval scheme much better, in particular for the high-INP and high-CAPE (convective available potential energy) scenarios. Perturbing the initial thermodynamic state, which artificially increases the instability of the mid- and upper-troposphere, brought the simulated cloud-top liquid pixel fraction closer to the satellite observations, especially in the warmer mixed-phase temperature range.

Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting

Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.

Use of a new Tibetan Plateau network for permafrost to characterize satellite-based products errors: An application to soil moisture and freeze/thaw

Two basic properties of soil moisture, namely water content and its phase status (freeze/thaw, referred to as F/T), strongly influence the pattern and efficiency of water and heat exchanges at the land-atmosphere interface. Therefore, accurate soil moisture and F/T retrievals are crucial to explore the impact of soil moisture and temperature on multi-sphere interactions, especially in the permafrost region of the third pole (Tibetan Plateau, TP), which is highly sensitive to climate changes. However, given the difficulty of installing and maintaining ground stations in harsh environments, dense ground networks are scarce over the TP with an average altitude of 4000 m. In this study, we reported a new soil moisture and temperature wireless sensor network consisting of 10 stations, which were established over the permafrost in the WuDaoLiang region (referred to as SMN-WDL) of the TP in 2019. In situ measurements from the SMN-WDL were used to simultaneously assess satellite-based surface soil moisture (SSM) and F/T products. The reliability of categorical triple collocation (CTC) method was investigated by comparing CTC-derived and ground-based relative performance rankings. The results reveal that the low-frequency (L-band) passive microwave SSM and F/T products outperformed both high-frequency (C/X/Ka-bands) passive and active microwave products, especially SMAP (Soil Moisture Active Passive) SSM and F/T products displayed the best statistical metrics to SMN-WDL ground-based measurements. The existing satellite F/T products had varying degrees of earlier freezing in autumn and later thawing in spring (mainly in ASCAT F/T) within SMN-WDL, which resulted in unsatisfactory thaw accuracy (< 85% in most cases) and overall accuracy (33.16% to 94.92%). In SMN-WDL, using existing F/T products as a masking criterion to filter out frozen soil for SSM validation studies would incorrectly mask out too many data samples and, lead to a decrease of the performance of SSM products. The overestimation of F/T thresholds may be one of the main reasons for the above misclassification of satellite F/T products. Compared to existing F/T products, the overall accuracy of threshold-optimal F/T retrievals can be increased by >10% during nighttime after optimizing thresholds based on ground-based measurements. CTC can provide consistent performance ranking with ground-based results in most cases, but the risk of CTC performing incorrect rankings was higher within the triplet where two products strongly covariate or one of the products has a significant error. This study is the first to use dense ground-based measurements in the permafrost region (SMN-WDL) to characterize the errors of four satellite SSM and F/T products. These results are expected to provide a reference with smaller representativeness errors for the further refinement of satellite retrievals in the permafrost region of the third pole.

Questioning whether there was a short-term interaction between the 6 February 2023 earthquakes and air quality parameters in Türkiye

On 6 February 2023, Türkiye was hit by two earthquakes with 7.5 (Eq2) and 7.8 (Eq1) magnitudes. In this study, the hourly ground-level air pollutant concentrations measured in the earthquake areas were examined in detail. In addition, the remote sensing retrievals of CO, NO2, SO2 and CH4 from TROPOMI instrument were also processed and investigated. The hourly mean PM2.5 and CO increased at four stations close to the epicentre several hours prior to the earthquake. The abrupt increase in CO is likely, but PM2.5 is probably related to the degassing activities in the faults. At the day of the earthquake, a significant TROPOMI NO2 increase and a different spatial distribution over the region are observed. This increase coincides with the Eq2 (20 min prior) and a distinctive spatial distribution was observed on that day, indicating this signal can be associated with the earthquake or another process affected by the earthquake. On the other hand, TROPOMI CO indicated a significantly different pattern from NO2 with lowest levels around or prior to the earthquakes, and increased levels afterwards. Limited data from TROPOMI was available for CH4 and SO2 due to meteorological conditions and data quality over the region to draw any conclusive findings. According to the results, it may be helpful to perform special Earth-based observations of NO2, CO and counts of nanoparticles in the ground-based atmosphere near fault lines in seismically active belts. In addition, anomalies can also be monitored via available satellite retrievals, especially in regions with active fault lines.

Effect of the boundary layer low-level jet on fast fog spatial propagation

Abstract. The spatiotemporal variation of fog reflects the complex interactions among fog, boundary layer thermodynamics and synoptic systems. Previous studies revealed that fog can present a fast spatial propagation feature and attribute it to the boundary layer low-level jet (BLLJ), but the effect of the BLLJ on fog propagation is not quantitatively understood. Here we analyze a large-scale fog event in Jiangsu, China, from 20 to 21 January 2020. Satellite retrievals show that fog propagates from the southeast coastal area to the northwest inland area with a speed of 9.6 m s−1, which is 3 times larger than the ground wind speeds. The ground meteorologies are insufficient to explain the fast fog propagation, which is further investigated by Weather Research and Forecasting model (WRF) simulations. The fast fog propagation could be attributed to the BLLJ occurring between 50 and 500 m, because the wind speeds (10 m s−1) and directions (southeast) of the BLLJ core are consistent with fog propagation. Through sensitive experiments and process analysis, three possible mechanisms of the BLLJ are revealed: (1) the abundant oceanic moisture is transported inland, increasing the humidity of the boundary layer and promoting condensation; (2) the oceanic warm air is transported inland, enhancing the inversion layer and favoring moisture accumulation; and (3) the moisture advection probably promotes low-stratus formation, and later it subsides to become ground fog by turbulent mixing of fog droplets. The fog propagation speed would decrease notably by 6.4 m s−1 (66 %) in the model if the BLLJ-related moisture and warm advections were turned off.

The Influence of Validation Colocation on XCO2 Satellite–Terrestrial Joint Observations

Comparing satellite retrieval with high-precision ground observations is an essential component for the validation of CO2 satellite products. The initial stage of assessing the bias in retrieval products from satellite and ground sources involves establishing a geographical connection between observations that are temporally and spatially proximate. The primary aim of this paper is to evaluate the influence of variations in neighborhood definitions and colocation methods on the assessment of satellite products and provide quantitative references. To achieve this, a series of experiments were conducted involving the Global Total Column Carbon Observation Network (TCCON) and the OCO-2 satellite. Various spatial-temporal neighborhoods and colocation methods were considered in these experiments. The results indicate that spatial neighborhoods exert a more substantial influence on bias compared to temporal neighborhoods. In the mid-latitudes of the Northern Hemisphere, there is an observed linear increase trend between the difference of OCO-2 and TCCON observations and the spatial neighborhood, with an average increase of 0.32 ppm as the neighborhood size changes from 1° to 10°. Regarding colocation methods, the simple spatiotemporal geographic constraints tend to overlook changes in the atmospheric state to a certain extent. The target geographic constraint method reduces the bias by 2% to 5% by increasing the proportion of OCO-2 observations targeting TCCON while the method of introducing T700 potential temperature reduces by 2% to 13% by screening the gradient of CO2 concentration change. Moreover, an evident correlation exists between the bias and their corresponding latitudes, with a 0.20 ppm increase in bias observed for every 10° increment in latitudes in the Northern Hemisphere. The bias of TCCON and OCO-2 shows a pronounced seasonal regularity, with the highest in summer. The study also discusses the selection of spatiotemporal matching with low satellite coverage, the bias distribution, and the attribution of bias to the natural wind field.

Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions

Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019–2021, the maximum daily 8-h average ozone concentrations in eastern China (110–122.5°E, 26–42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m3, which is much larger than 4.8 μg/m3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m3 in eastern China and an ozone increase by 2.0 (2.2) μg/m3 in ROC, which is primarily induced by the changes in NOx emissions. The surface measurements and satellite retrievals also indicate that the reduction in NOx emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019–2021. Our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.

Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals

Abstract. The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in the DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model–observation discrepancies. The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated, but simulated liquid water path (LWP) is lower than observed and layer mean droplet concentration (Nd) comparisons are highly variable depending on the Nd retrieval technique. E3SMv1's cloud albedo is greater than observed for given LWP and Nd values due to a lower cloud effective radius than observed. However, the simulated albedo response to Nd is suppressed due to a correlation between the solar zenith angle (SZA) and Nd created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1's COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1's adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in top-of-atmosphere (TOA) retrievals narrows the retrieved ln Nd distribution, which increases the cloud albedo sensitivity to ln Nd to match the TOA sensitivity. The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of Nd to CCN is greater than observed for TOA-retrieved Nd, and once model–observation cloud adiabaticity differences are removed, this is also true for surface-retrieved Nd. The LWP response to Nd in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP–Nd relationship, observed clouds become much more adiabatic as Nd increases, while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP–Nd relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP–Nd relationship non-causal. Nd retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model–observation comparisons, though enough consistency exists to suggest that aerosol activation, drizzle, and entrainment processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol–cloud interactions in E3SM.

Irradiance and cloud optical properties from solar photovoltaic systems

Abstract. Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.