Top Of Atmosphere Research Articles

A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment.

Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China

Two distinct algorithms, namely Deep Blue (DB) and Multiangle Implementation of Atmospheric Correction (MAIAC), based on the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide aerosol optical depth (AOD) products over bright surfaces. Recently, the newest MAIAC Collection 6.1 (C6.1) product was available, which corrected many known deficiencies of C6. Due to the significant impact of high-reflectivity surfaces on the accuracy of aerosol retrieval, long-term validation and comprehensive assessment of AOD over bright surfaces are required at a regional scale. Firstly, this study conducted a systematic evaluation of three products against ground-based Sunphotometer data obtained from 21 sites of the China Aerosol Remote Sensing Network (CARSNET) and 2 sites of the Aerosol Robotic Network (AERONET) from the year 2002 to 2014. The validation results showed a significant underestimation in DB and MAIAC AOD retrievals as indicated by the large percent of retrievals below the expected error (EE) envelope, and significant negative biases. In comparison, both versions of MAIAC products performed better than DB product, which was also revealed by the site scale validation. Besides, the performance of the C6.1 MAIAC was slightly improved over the study region compared to the C6 MAIAC. Finally, this study made an in-depth investigation of the underestimation of AOD products affected by various factors, i.e., aerosol loading, land type, NDVI, surface albedo, and scattering angle. The results indicated that the large negative bias of AOD retrievals for the three products tended to occur in the case of large aerosol loadings and high bright surfaces, which could be attributed to the inaccurate assumption of aerosol models and the insensitivity of top of atmosphere (TOA) reflectance to AOD variations caused by the high reflectivity of the underlying surface. The findings of this study are expected to provide some reference for the improvement of aerosol retrieval algorithms over bright surfaces.

Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network.

This paper introduces a novel back propagation (BP) neural network method to accurately characterize optical properties of liquid cloud droplets, including black carbon. The model establishes relationships between black carbon volume fraction, wavelength, cloud effective radius, and optical properties. Evaluated on a test set, the value of the root mean square error (RMSE) of the asymmetry factor, extinction coefficient, single-scattering albedo, and the first 4 moments of the Legendre expansion of the phase function are less than 0.003, with the maximum mean relative error (MRE) reaching 0.2%, which are all better than the traditional method that only uses polynomials to fit the relationship between the effective radius and optical properties. Notably, the BP neural network significantly compresses the optical property database size by 37,800 times. Radiative transfer simulations indicate that mixing black carbon particles in water clouds reduces the top-of-atmosphere (TOA) reflectance and heats the atmosphere. However, if the volume fraction of black carbon is less than 10-6, the black carbon mixed in the water cloud has a tiny effect on the simulated TOA reflectance.

Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm

Efficient land management and farming practices are critical to maintaining agricultural production, especially in Europe with limited arable land. It is very time consuming to rely on a manual field inspection of cultivated land to archive farm crops. But with the help of satellite monitoring data on the earth’s surface, it is a new vision to classify farmland based on deep learning. This article has studied the Sentinel 2 (S2) data, which are top-of-atmosphere (TOA) reflectance values at the processing level-1C (L1C) observed from some areas of Germany and France. Aiming at the problem that the interference of atmosphere and cloud coverage weakens the recognition accuracy of subsequent algorithms, a method of combining feature expansion and feature importance analysis is proposed to optimize the raw S2 data. Specifically, the new 13 spectral features are expanded based on the linear and nonlinear combination of the raw 13 spectral bands of S2. The random forest (RF) algorithm is used to score the importance of features, and the important features of each time series are selected to form a new dataset. Then, an end-to-end deep learning model has been used for training. The structure of the model is a two-layer unidirectional recurrent neural network with long short-term memory (LSTM) as the backbone. And two linear layers as the output, which form two decision-making heads, respectively, representing output classification probability and the stop decision. The results show that adding features and selecting features is beneficial for the model to improve classification accuracy and predict the classification without all of the input data. This end-to-end classification pattern with early prediction would support intelligent monitoring of farm crops with a great advantage to the implementation of various agricultural policies.

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.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources

ABSTRACT The proper radiometric calibration of the INSAT-3D and −3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and −3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coefficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and −3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and −3DR IMAGER data for weather forecasting and monitoring applications.

Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat’s calibrated Earth observations. This study comprehensively assesses the continuity of reflectance and the Normalized Difference Vegetation Index (NDVI) between Landsat-8 and Landsat-9 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact reflectance and NDVI consistency. Although Landsat-9’s top-of-atmosphere (TOA) reflectance is slightly lower than that of Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR and SWIR bands (and larger RMSE for NDVI at 0.0422) fall within acceptable ranges for Earth observation applications. Applying atmospheric corrections markedly enhances reflectance uniformity and brings regression slopes closer to unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, ensuring measurement reliability, and the NDVI maintains robust consistency across various reflectance types, time series, and land cover classes. These findings affirm Landsat-9’s success in achieving data continuity within the Landsat program, allowing interchangeable use of Landsat-8 and Landsat-9 OLI data for diverse Earth observation purposes. Future research may explore specific sensor correlations across different vegetation types and seasons while integrating data from complementary platforms, such as Sentinel-2, to enhance the understanding of data continuity factors.

Spectral aerosol optical depth from SI-traceable spectral solar irradiance measurements

Abstract. Spectroradiometric measurements of direct solar irradiance traceable to the SI were performed by three spectroradiometer systems during a 3-week campaign in September 2022 at the Izaña Atmospheric Observatory (IZO) located on the island of Tenerife, Canary Islands, Spain. The spectroradiometers provided direct spectral irradiance measurements in the spectral ranges 300 to 550 nm (QASUME), 550 to 1700 nm (QASUME-IR), 300 to 2150 nm (BiTec Sensor, BTS), and 316 to 1030 nm (Precision Solar Spectroradiometer, PSR), with relative standard uncertainties of 0.7 %, 0.9 %, and 1 % for QASUME/QASUME-IR, the PSR, and the BTS respectively. The calibration of QASUME and QASUME-IR was validated prior to this campaign at Physikalisch-Technische Bundesanstalt (PTB) by measuring the spectral irradiance from two spectral irradiance sources, the high-temperature blackbody BB3200pg as a national primary standard and the tuneable laser facility TULIP. The top-of-atmosphere (ToA) solar irradiance spectra from the spectroradiometers were retrieved from direct solar irradiance measurements using zero-air-mass extrapolation during cloud-free conditions, which were then compared to the TSIS-1 Hybrid Solar Reference Spectrum (HSRS). These ToA solar spectra agreed to within 1 % for spectral ranges longer than 400 nm (for QASUME also at shorter wavelengths) in the spectral regions free of significant trace gas absorption and were well within the combined uncertainties over the full investigated spectral range. Using the results from the comparison with QASUME, the relative standard uncertainty of the TSIS-1 HSRS ToA solar spectrum in the spectral range 308 to 400 nm could be reduced from its nominal 1.3 % to 0.8 %, representing the relative standard uncertainty of the QASUME ToA solar spectrum in this spectral range. The spectral aerosol optical depth (AOD) retrieved from the solar irradiance measurements of these spectroradiometers using the TSIS-1 HSRS as the reference ToA solar spectrum agreed to within 0.01 in optical depth in nearly all common spectral channels of two narrowband filter radiometers belonging to the Global Atmosphere Watch Precision Filter Radiometer (GAW-PFR) network and Aerosol Robotic Network (AERONET). This study shows that it is now possible to retrieve spectral AOD over the extended spectral range from 300 to 1700 nm using solar irradiance measurements traceable to the SI using laboratory-calibrated spectroradiometers with similar quality to that from traditional Langley-based calibrated instruments. The main improvement to previous investigations is the recent availability of the high-spectral-resolution TSIS-1 HSRS with very low uncertainties, which provides the top-of-atmosphere reference for the spectral atmospheric transmission measurements obtained from ground-based solar irradiance measurements.

All-sky surface and top-of-atmosphere shortwave radiation components estimation: Surface shortwave radiation, PAR, UV radiation, and TOA albedo

Surface and top-of-atmosphere (TOA) shortwave radiation components are key parameters in the energy budget of the Earth-atmosphere system, which influence the global climate, ecology, hydrology, etc. However, except for total surface shortwave downward radiation (SWDR), only a few satellite missions consistently release other radiation components such as solar direct/diffuse radiation, photosynthetically active radiation (PAR), ultraviolet radiation-A/B (UVA/UVB), as well as TOA albedo, although they are indispensable for many land and ocean applications. In this study, a unified framework by combining multi-band LUT inversion and classification of the atmospheric condition is proposed, which enables the simultaneous derivation of nine surface and TOA radiation variables and emphasizes the separation of direct and diffuse components. The estimated radiation variables include total and direct components of SWDR, PAR, UVA, UVB, as well as TOA albedo. The proposed method is easy-to-use with only a few inputs and works well with reasonable accuracy. Using Moderate Resolution Imaging Spectroradiometer (MODIS) raw resolution images as test data, the surface radiation variables are validated by 80 global sites from BSRN, SURFRAD, and FLUXNET, and the instantaneous RMSEs (biases) of SWDR, SWDRdir, PAR, PARdiff, and UVB are 103.6 (0.1), 114 (−1.7), 47.8 (6.8), 32.9 (5.7), and 0.14 (0.003) W/m2, respectively, demonstrating comparable or even better accuracy compared with existing products. In particular, the estimated SWDR behaves more accurate than CERES in polar regions. Due to the lack of in-situ measurements, TOA albedo is compared with CERES TOA products and shows good agreement with R2 of 0.9 and RMSE (bias) of 0.055 (−0.003). The unified framework reveals obvious advantages over existing studies in generating almost all physically consistent shortwave components in the same manner with simple inputs, implying the great potentials in globally mapping spatio-temporally continuous multiple components of shortwave radiation with a unified scheme.

Broadband radiative quantities for the EarthCARE mission: the ACM-COM and ACM-RT products

Abstract. The EarthCARE satellite mission's objective is to retrieve profiles of aerosol and water cloud physical properties from measurements made by its cloud-profiling radar, backscattering lidar, and passive multi-spectral imager. These retrievals, together with other geophysical properties, are input into broadband (BB) radiative transfer (RT) models that predict radiances and fluxes commensurate with measurements made and inferred from EarthCARE's BB radiometer (BBR). The scientific goal is that modelled and “observed” BB top-of-atmosphere (TOA) fluxes differ, on average, by less than ±10 W m−2. When sound synergistic retrievals from the ACM-CAP process (ACM: ATLID – backscattering lidar, CPR – cloud-profiling radar, and MSI – multi-spectral imager; CAP: clouds, aerosols, and precipitation) are available, they are acted on by the RT models. When they are not available, the RT models act on “composite” profiles of properties retrieved from measurements made by individual sensors. Compositing is performed in the ACM-COM (COM: composite) process. The majority of this report describes the RT models – and their products – that make up EarthCARE's ACM-RT process. Profiles of BB shortwave (SW) and longwave (LW) fluxes and heating rates (HRs) are computed by 1D RT models for each ∼ 1 km nadir column of inferred properties. Three-dimensional RT models compute radiances for the BBR's three viewing directions, with the SW model also computing flux and HR profiles; the 3D LW model produces upwelling flux at just one level. All 3D RT products are averages over 5×21 km “assessment domains” that are constructed using MSI data. Some of ACM-RT's products are passed forward to the “radiative closure assessment” process that quantifies, for each assessment domain, the likelihood that EarthCARE's goal has been achieved. As EarthCARE represents the first mission to make “operational” use of 3D RT models, emphasis is placed on differences between 1D and 3D RT results. For upwelling SW flux at 20 km altitude, 1D and 3D values can be expected to differ by more than EarthCARE's scientific goal of ±10 W m−2 at least 50 % of the time.

The Uncertainty Analysis of the Entrance Pupil Irradiance for a Moon-Based Earth Radiation Observation Instrument

Moon-Based Earth Radiation Observation (MERO) is expected to improve and enrich the current Earth radiation budget (ERB). For the design of MERO’s instrument and the interpretation of Moon-based data, evaluating the uncertainty of the instrument’s Entrance Pupil Irradiance (EPI) is an important part. In this work, by analyzing the effect of the Angular Distribution Models (ADMs), Earth’s Top of Atmosphere (TOA) flux, and the Earth–Moon distance on the EPI, the uncertainty of EPI is finally studied with the help of the theory of errors. Results show that the ADMs have a stronger influence on the Short-Wave (SW) EPI than those from the Long-Wave (LW). For the change of TOA flux, the SW EPI could keep the attribute of varying hourly time scales, but the LW EPI will lose its hourly-scale variability. The variation in EPI caused by the hourly change of the Moon–Earth distance does not exceed 0.13 mW∙m−2 (1σ). The maximum hourly combined uncertainty reveals that the SW and LW combined uncertainties are about 5.18 and 1.08 mW∙m−2 (1σ), respectively. The linear trend extraction of the EPI demonstrates that the Moon-based data can effectively capture the overall linear change trend of Earth’s SW and LW outgoing radiation, and the uncertainty does not change the linear trend of data. The variation of SW and LW EPIs in the long term are 0.16 mW∙m−2 (SW) and 0.23 mW∙m−2 (LW) per decade, respectively. Based on the constraint of the uncertainty, a simplified dynamic response model is built for the cavity radiometer, a kind of MERO instrument, and the results illuminate that the Cassegrain optical system and electrical substitution principle can realize the detection of Earth’s outing radiation with the sensitivity design goal 1 mW∙m−2.

Estimation of Aerosol Layer Height from OLCI Measurements in the O2A-Absorption Band over Oceans

The aerosol layer height (ALH) is an important parameter that characterizes aerosol interaction with the environment. An estimation of the vertical distribution of aerosol is necessary for studies of those interactions, their effect on radiance and for aerosol transport models. ALH can be retrieved from satellite-based radiance measurements within the oxygen absorption band between 760 and 770 nm (O2A band). The oxygen absorption is reduced when light is scattered by an elevated aerosol layer. The Ocean and Land Colour Imager (OLCI) has three bands within the oxygen absorption band. We show a congruent sensitivity study with respect to ALH for dust and smoke cases over oceans. Furthermore, we developed a retrieval of the ALH for those cases and an uncertainty estimation by applying linear uncertainty propagation and a bootstrap method. The sensitivity study and the uncertainty estimation are based on radiative transfer simulations. The impact of ALH, aerosol optical thickness (AOT), the surface roughness (wind speed) and the central wavelength on the top of atmosphere (TOA) radiance is discussed. The OLCI bands are sufficiently sensitive to ALH for cases with AOTs larger than 0.5 under the assumption of a known aerosol type. With an accurate spectral characterization of the OLCI O2A bands better than 0.1 nm, ALH can be retrieved with an uncertainty of a few hundred meters. The retrieval of ALH was applied successfully on an OLCI dust and smoke scene. The found ALH is similar to parallel measurements by the Tropospheric Monitoring Instrument (TROPOMI). OLCI’s high spatial resolution and coverage allow a detailed overview of the vertical aerosol distribution over oceans.

Leveraging Landsat-8/-9 underfly observations to evaluate consistency in reflectance products over aquatic environments

With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11–17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρt) and atmospherically corrected reflectance (aquatic reflectance; ρwAR) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρwAR products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρw product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρwAR, ρwac, and ρwpol, respectively. Overall, we found the OLI2-OLI ρt products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ∼0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ∼2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρw (ρwAR) were estimated to be ∼2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7–30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρw from 0.3 to 5.3% (e.g., ∆ρwAR443nmΔVZAΔRAA=ρwAR,OLI443+6.310−ρwAR,OLI2443+3.9−13<0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρwac are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.

Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product

Abstract. The EarthCARE satellite mission's objective is to retrieve profiles of aerosol and cloud physical and optical properties using the combination of cloud-profiling radar (CPR), high-spectral-resolution UV lidar (ATLID) and passive multi-spectral imager (MSI) data. Based on synergistic retrievals using data from these instruments, the 3D atmospheric cloud–aerosol state is estimated and then used to model the top-of-atmosphere (TOA) broadband radiances, which may then be compared to co-incident EarthCARE broadband radiometer (BBR) measurements. A high-spectral-resolution lidar enables the independent retrieval of extinction and backscatter but, being space based, suffers from relatively low signal-to-noise ratio (SNR) levels. The ATLID FeatureMask (A-FM) product provides a feature detection mask for the existence of atmospheric features within the lidar profiles based on a number of (statistical) image reconstruction techniques. Next to this, it also identifies those regions where the lidar beam has been fully attenuated and where the surface backscatter has impacted the measured lidar backscatter signals directly above the surface. From the pixels assigned as clear sky (with no features present above), the clear-sky-averaged profiles for the three ATLID channels, the co-polar Mie channel, the total cross channel and the co-polar Rayleigh channel are created. These feature-free or clear-sky profiles are useful for e.g., assessing the quality of the ATLID Level-1 (L1) attenuated backscatters. An important goal of the A-FM product is to guide smoothing strategies within downstream processors e.g., the ATLID profile retrieval (A-PRO) algorithm which directly follows A-FM within the EarthCARE Level-2 (L2) processing chain. Within the A-PRO algorithm, profiles of extinction, backscatter and linear depolarization ratio are retrieved. However, smoothing of the ATLID L1 attenuated backscatter is necessary since the SNR levels present at the ATLID native resolution are generally not sufficient for meaningful retrievals to be conducted. At the same time, to prevent biased retrievals, any smoothing procedure must respect the cloud–aerosol structure and avoid mixing strong features, e.g., clouds, and weak features, e.g., aerosol regions, together. The A-FM product provides the A-PRO algorithm with important information that is used to guide various smoothing procedures. To enable the processing of the large datasets from observation up to L2 retrievals, each EarthCARE orbit is separated into eight frames, divided at latitudes of 22.5∘ N and 22.5∘ S and 62.5∘ N and 62.5∘ S. As a secondary product, A-FM outputs can be used to conduct a frame-by-frame evaluation of the ATLID L1 cross-talk calibration, where an EarthCARE frame is one-eighth of a full orbit. This evaluation can be performed by comparing the retrieved clear-sky profiles to the expected channel profiles. The A-FM product has been applied to both synthetic data from the EarthCARE end-to-end simulator (ECSIM) and the L1 data from the Aeolus wind lidar mission. Comparisons against the ECSIM model truth indicate that A-FM has a percentage correctness &gt; 90 % and is capable of reliably detecting aerosol and cloud regions within extinctions (&gt; 10−5 m−1).

Radiometric Cross-Calibration of Wide-Field-of-View Cameras Based on Gaofen-1/6 Satellite Synergistic Observations Using Landsat-8 Operational Land Imager Images: A Solution for Off-Nadir Wide-Field-of-View Associated Problems

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (&gt;20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference.

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Five global monthly top-of-atmosphere (TOA) outgoing longwave radiation (OLR) products are evaluated in this study, including the products derived from the High-Resolution Infrared Radiation Sounder (HIRS), Clouds and the Earth’s Radiant Energy System (CERES), Advanced Very High Resolution Radiometer (AVHRR), the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data (CLARA), and the Global Energy and Water Cycle EXchanges (GEWEX) project. Results show that overall there is good consistency among these five products. Larger differences are found between GEWEX and CERES (HIRS) after (before) 2000 (RMSE ~ 5 W/m2), particularly in the tropical regions. In terms of global mean values, GEWEX shows large differences with the other products from the year 1992 to 2002, and CLARA shows large differences from the year 1979 to 1981, which are more obvious in the global ocean values. Large discrepancies among these products exist at low latitudinal bands, particularly before the year 2000. Australia and Asia (mid–low latitude part) are two typical regions in which larger differences are found.

Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5)

Abstract. Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate change and variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks for global warming. As these assessments are largely focused on the top of atmosphere (TOA) radiation budget, less attention has been paid to the surface radiation budget or the associated surface radiative sensitivity kernels. Based on the fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at https://doi.org/10.17632/vmg3s67568 (Huang and Huang, 2023). By comparing these with other published radiative kernels, we find that the TOA kernels are generally in agreement in terms of global mean radiative sensitivity and analyzed overall feedback strength. The unexplained residual in the radiation closure tests is found to be generally within 10 % of the total feedback, no matter which kernel dataset is used. The uncertainty in the TOA feedbacks caused by inter-kernel differences, as measured by the standard deviation of the global mean feedback parameter value, is much smaller than the inter-climate model spread of the feedback values. However, we find relatively larger discrepancies in the surface kernels. The newly generated ERA5 kernel outperforms many other datasets in closing the surface energy budget, achieving a radiation closure comparable to the TOA feedback decomposition, which confirms the validity of the kernel method for the surface radiation budget analysis. In addition, by investigating the ERA5 kernel values computed from the atmospheric states of different years, we notice some apparent interannual differences, which demonstrates the dependence of radiative sensitivities on the mean climate state and partly explains the inter-dataset kernel value differences. In this paper, we provide a detailed description of how ERA5 kernels are generated and considerations to ensure proper use of them in feedback quantifications.

Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?

Abstract. To date the climate intervention (CI) proposal of cirrus cloud thinning (CCT) was only assessed in general circulation models (GCMs) using a globally uniform distribution of artificial ice nucleating particles (INPs). In this study, we made the first attempt using the ECHAM–HAM (Hamburg Aerosol Module) GCM to simulate CCT using a fully prognostic cirrus seeding aerosol species. Seeding particles were assumed to be made of bismuth triiodide and were emitted into the atmosphere following aircraft emissions of black carbon (soot). This new approach drastically reduced the number concentration of seeding particles available as INPs in our cirrus ice nucleation sub-model compared to the globally uniform approach. As a result, we found that in order to achieve a significant signal we needed to reduce the assumed radius of emitted seeding particles by an order of magnitude to 0.01 µm and scale the mass emissions of seeding particles by at least a factor of 100 or 1000. This latter scaling factor led to a large net top-of-atmosphere (TOA) warming effect of 5.9 W m−2. This warming effect was a clear response to overseeding with a large concentration of seeding particles (&gt;105 L−1 in the Northern Hemisphere) that was most evident in the tropics. Due to this undesired effect, in a second series of simulations we avoided seeding the tropics by restricting emissions to only the Northern Hemisphere (NH) during winter. We also found a small and insignificant effect, or overseeding, which for the extreme case was reduced compared to the global aircraft emission scenario (2.2 W m−2). Ice crystal radius anomalies were not what we expected, with the largest reduction in size found for the case with a mass scaling factor of 10 instead of the extreme, ×1000, scenario. We attributed this peculiar behavior to the differences in the competition between different seeding particle concentrations and background particles. Finally, we also found that seeding with such large concentrations increased the albedo effect of mixed-phase clouds in the NH due to less efficient cloud droplet consumption, consistent with previous findings from our model. Overall, however, based on this study it is recommended to pause further modeling efforts of CCT unless more observational-based evidence of aerosol–ice-cloud interactions indicates favorable conditions for producing the desired outcome of this CI proposal.