Satellite Observations Research Articles

Assessing CESM2 Clouds and Their Response to Climate Change Using Cloud Regimes

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

Unexpected HCHO transnational transport: influence on the temporal and spatial distribution of HCHO in Tibet from 2013 to 2021 based on satellite

Formaldehyde (HCHO) is a serious hazardous air pollutant and crucial precursor of PM2.5 and ozone compound pollution. There has been a dearth of HCHO research in Tibet where pressing need to protect the unique ecosystem. Therefore, this study aims to investigate the spatial-temporal distribution of HCHO from 2013 to 2021 and identify its influencing factors using satellite observations. Our findings reveal a noteworthy annual growth rate of 2.25% yr−1 in HCHO vertical column density (VCD) in Tibet. This rate is comparable to that in India and even surpasses levels observed in many other regions worldwide, including eastern China. Furthermore, unlike other areas, the eastern region of Tibet exhibits no discernible seasonal pattern in HCHO VCD. The anomalous variation in HCHO concentrations in Tibet can primarily be attributed to long-distance transnational transport originating from incomplete combustion in India Assam. Our research underscores the urgent need for enhanced atmospheric environmental management in Tibet.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Sea ice-melt amount estimated from spring hydrography in the Sea of Okhotsk: spatial and interannual variabilities

AbstractThis study provides the first estimation of sea ice-melt amount in the Sea of Okhotsk based on spring hydrographic data accumulated for nearly a hundred years. Just after sea ice melts completely, a low-salinity layer appears on the ocean surface, overlying the layer of Winter Water at the freezing point. The integration of the salinity decrease from Winter Water should correspond to the total ice-melt amount. We developed an algorithm to extract the profiles that clearly show the salinity deficit and converted the salinity deficit to the ice-melt amount from all available data. The climatological map shows that ice-melt amount decreases toward the ice edge and exhibits large values around the northern Sakhalin Island, reflecting the ice thickness distribution. In the southern area (south of 48°N), where sea ice is transported from the north, the average ice-melt amount is estimated to be ~ 71 cm in thickness. It is clearly shown that the ice-melt amount has decreased by ~ 30% in the southern area since the 1990s. These changes possibly affect the regional climate through the decreased latent heat of sea ice and potentially affect biological production through weakened stratification caused by decreased ice melt. We also suggested that ice-melt amount did not show a significant trend during the 1930s–1970s, implying that our methodology could extract information on sea ice before the era of satellite observations.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Modeling Climate Characteristics of Qinghai Lake Ice in 1979–2017 by a Quasi-Steady Model

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons.

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe

North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m2) and within the atmosphere (from −3.3 to −2.4 W/m2). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2).

Satellite group infall into the Milky Way: Exploring the Crater-Leo case with new HST proper motions

Within Lambda cold dark matter (Lambda CDM) simulations, Milky Way-like galaxies accrete some of their satellite galaxies in groups of 3-5 members rather than individually, and this has been suggested as a possible mechanism driving the formation of satellite planes. Objects accreted in groups are expected to share similar specific total energy and angular momentum, and to also have identical orbital planes and directions. Looking at observations of Milky Way satellites, the dwarf galaxies Leo II, IV, V, and Crater II, and the star cluster Crater 1 were proposed to be a vestige of group infall. The suggested `Crater-Leo group' shows a coherent distance gradient and all these objects align along a great circle on the sky. We used proper motion data to investigate whether the phase-space distribution of the members of the proposed group are indeed consistent with group infall. To further investigate this possibility, we used Gaia Data Release 3 (DR3) and new Hubble Space Telescope (HST) proper motions -- namely, $( alpha* 0.0523)$ mas $ for Leo IV and $( alpha* 0.1704)$ mas $ for Leo V -- to derive accurate orbital properties for the proposed group objects. In addition, we explored other possible members of this putative association. par Leo II, Leo IV, and Crater 1 show orbital properties consistent with those we predict from assuming group infall. However, our results suggest that Crater II was not accreted with the rest of the objects. If confirmed with increasingly accurate proper motions in the future, the Crater-Leo objects would appear to constitute the first identified case of a cosmologically expected, typical group infall event, as opposed to the highly hierarchical Magellanic Cloud system.

A study of two young multipolar planetary nebulae: Hen 2-73 and Hen 2-96

We perform an infrared (IR) spectral and visible morphological study of two young planetary nebulae (YPNe) Hen 2-73 and Hen 2-96 using archival Spitzer Space Telescope and Hubble Space Telescope (HST) observations to understand their dust properties and nebular structures. High-resolution HST images of these nebulae show several bipolar lobes and ionised tori in the central regions of both objects. The presence of these multi-lobe structures suggests that the formation process of these nebulae is complex. To search for a possible link between the central sources and multipolar appearances of these objects, the Transiting Exoplanet Survey Satellite (TESS) observations are used to examine whether their central stars (CSs) exhibit periodic photometric variations. In the TESS observations, the CS light curve of Hen 2-96 shows a photometric variation with a period of 2.23 hr. The IR spectra of these two YPNe suggest that the nebulae have mixed dust environments, which are associated with the presence of dense tori created by central binary interactions in these objects. Two three-dimensional models are constructed to study the complex nebular structures of the YPNe. These simulations suggest that the number of multipolar YPNe may be larger than observed. In addition, we analyse the spectral energy distributions of these nebulae to study their gas, dust, and photospheric components.

Characteristics and Seasonal Variations of Cirrus Clouds from Ground-Based Lidar and Satellite Observations Over Shouxian Area, China

Characteristics and Seasonal Variations of Cirrus Clouds from Ground-Based Lidar and Satellite Observations Over Shouxian Area, China

Ozone Production over Arid Regions: Insights into Meteorological and Chemical Drivers

Abstract Arid urban areas are pivotal in the global landscape, and their air quality issues are highlighted by the complexities of tropospheric ozone production. Here, we use recent satellite observations from TROPOMI and a longer record of data from OMI to investigate the levels of ozone precursors (NO2 and CH2O) in 12 major cities in arid regions. Using a space-based CH2O/NO2 indicator, we identified the dominant chemical regime influencing ozone formation, revealing a clear temporal trend that aligns with previously reported economic trajectories as well as variation in emission control strategies implemented in these cities. Our results show that, NO2 concentrations decreased in cities with proactive regulatory policies, such as Madrid and Los Angeles in semi-arid and arid regions. A contrasting increase was observed in rapidly developing cities within arid and hyper-arid regions, such as Tehran and Cairo, where emission controls are less strict. An increase in CH2O levels was also apparent, requiring more attention to VOCs control. Furthermore, our analysis clearly shows that the interactions between ozone production and climatic factors such as temperature exhibit a nonlinear relationship, especially in arid climates. These findings highlight the importance of emission reduction strategies that consider the meteorological and chemical drivers of dry regions, particularly in light of the rising global aridity.

Disaggregating National Statistical Data to Assess the Sub-National SDG 6.4.2 Water Stress Indicator by Integrating Satellite Observations and Model Data

Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach.

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Scenarios for the Altamira cave CO2 concentration from 1950 to 2100

A data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO2 in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO2 inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO2 were reached during the period 1950–1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO2 in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO2 will exceed the levels reached during the 1980s and the 1990s when the CO2 introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments.

Increased summertime wildfire as a major driver of the clear-sky dimming in the Siberian Arctic from 2000 to 2020

A warming Arctic is expected to exacerbate wildfires in Siberia, potentially creating a critical feedback to the Arctic climate change. However, our understanding of these fire-climate interactions remains limited. This study investigated changes in East Siberian wildfires and their influence on fire emissions, aerosol optical depth (AOD), and the surface clear-sky insolation across the Siberian Arctic from 2000 to 2020 using satellite observations. Our analysis reveals a substantial increase in wildfires, with fire counts doubling (a 114% increase) and fire radiative power surging by 8.4 × 106 MW compared to the early 21st century. Over 93% of this increase occurred during the boreal summer months. These intensified wildfires led to a significant rise in aerosol emission (organic carbon, PM2.5, and black carbon) exceeding 75% in East Siberia. Consequently, fire-season AOD in the Siberian Arctic increased by 33% (6.0 × 10−2), with 85% (5.1 × 10−2) attributable to wildfire changes. The wildfire-associated increase in AOD resulted in enhanced clear-sky dimming of 4.1 ± 3.2 W m−2 across the Siberian Arctic from 2000 to 2020. These findings suggest a critical feedback mechanism: a warming Arctic drives increased wildfires in Siberia, which in turn significantly impact the Arctic surface radiative budget through enhanced clear-sky dimming. Future simulations and projections for the Arctic should prioritize incorporating the feedback effects of intensifying wildfires.

An iterative algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using CALIOP

Abstract. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite has been widely used in climate and environment studies to obtain the vertical profiles of atmospheric aerosols. To retrieve the vertical profile of aerosol extinction, the CALIOP algorithm assumes column-averaged lidar ratios based on a clustering of aerosol optical properties measured at surface stations. On one hand, these lidar ratio assumptions may not be appropriate or representative at certain locations. One the other hand, the two-wavelength design of CALIOP has the potential to constrain aerosol size information, which has not been considered in the operational algorithm. In this study, we present a modified inversion algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using two-wavelength elastic lidars such as CALIOP. Specifically, a lookup table is built to relate the lidar ratio with the Ångström exponent calculated using aerosol extinction at the two wavelengths, and the lidar ratio is then determined iteratively without a priori assumptions. The retrieved two-wavelength extinction at each layer is then converted to the particle effective radius assuming a lognormal distribution. The algorithm is tested on synthetic data, Raman lidar measurements and then finally the real CALIOP backscatter measurements. Results show improvements over the CALIPSO operational algorithm by comparing with ground-based Raman lidar profiles.

Winter “warm Arctic-cold Eurasia” pattern and its statistical linkages to oceanic precursors during the era of satellite observations

AbstractA striking recurrent feature of winter climate variability is the “warm Arctic-cold Eurasia” (WACE) pattern of opposite sign anomalies of surface air temperature (SAT) in the Barents Sea region and midlatitude Eurasia. Its origins and mechanisms are hotly debated, and its predictability remains unknown. This study investigates statistical relationships of the winter WACE dipole with concurrent anomalies of atmospheric circulation and oceanic precursors during the era of satellite observations. The results reveal a high potential for seasonal predictability of not only the WACE dipole but also several related indicators of winter climate variability, including the Arctic and Eurasian SAT anomalies. During subperiods of extreme covariability between the Arctic and Eurasian SATs around the early 1980s and late 2000s, most of the WACE variability is explained by ocean temperature and surface turbulent heat flux anomalies in the Barents Sea region during the preceding months. Anomalies in summer Atlantic water temperature (AWT) and autumnal sea surface temperature (SST) in this region explain about 70–80% of the variance of the following winter WACE variability during all events of strong Arctic-Eurasian SAT covariability. Analysis of SST variability in the Arctic-North Atlantic region suggests that the winter WACE link to the summer AWT anomalies reflects an atmospheric response to a large-scale surface reemergence of ocean temperature anomalies. However, this linkage had been robust only until the early 2000s. Since then, the winter WACE variability has been strongly related to autumnal SST anomalies in the Barents Sea region and the North Pacific.

Reviews and Responses for AI-Driven Identification of Contrail Sources: Integrating Satellite Observations and Air Traffic Data

See detailed reviews and responses in the PDF file. DOI for the original paper: https://doi.org/10.59490/joas.2023.7209