Atmospheric Radiative Transfer Research Articles

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

Assessment of Black Carbon, optical properties and aerosol radiative forcing at Pranmati basin Himalayan critical zone observatory

The study aimed to understand the optical properties of Black Carbon (BC) and radiative forcing over a data deficient Himalayan region focusing on critical zone observatory employing ground-based measurements by Aethalometer for BC and satellite retrieval techniques for optical properties during mid-May-June 2022 and January–May 2023.BC mass concentration ranged from 0.18 to 4.43 μgm−3, exhibit a mean of 1.47 ± 0.83 μgm−3 with higher summer concentration (1.51 ± 0.94 μgm−3) than winter (1.39 ± 0.61 μgm−3). The average Absorption Ångström Exponent observed to be significantly higher than unity (1.77 ± 0.31) over the studied high-altitude Himalayan region, suggesting the dominance of biomass-burning aerosol. Higher aethalometer derived compensation parameter (K) in winter suggesting locally originated BC while, lower K value in summer suggesting aged BC transported from Indo-Gangetic Plains.Optical properties calculated from “Optical Properties of Aerosol and Cloud” (OPAC) model are used in the “Santa Barbara DISORT Atmospheric Radiative Transfer” (SBDART) model to calculate the aerosol Direct Radiative Force (DRF). The entire studied period is characterized by the predominance of absorbing aerosols, particularly BC, increasing Aerosol Optical Depth, Asymmetric Parameters and decreasing Single Scattering Albedo, leading to a considerable increase in atmospheric radiative forcing (+0.9 Wm−2, top of atmosphere) and Heating Rate (0.36 KDay−1). The mean radiative forcing within atmosphere during summer was higher (+14.29 Wm−2) relative to the winter (+12.00 Wm−2), emphasizing the impact of absorbing aerosols on regional warming and potential glacier melting in the Himalayas at a faster rate. Urgent policy consideration for the reduction of absorbing aerosols is highlighted, recognizing the critical roles of Black Carbon in the changing behaviour of Critical Zone observatory. The study's data serve as a valuable resource to understanding and addressing uncertainties in climate models, aiding effective policy implementation for Black Carbon reduction.

Surface and Atmospheric Heating Responses to Spectrally Resolved Albedo of Frozen and Liquid Water Surfaces

AbstractMultiple Earth system models (ESMs) discretize surface albedo into two semi‐broadbands comprising the UV/visible and near‐infrared (NIR) wavelengths. Here, we use an offline single‐column radiative transfer model to investigate the radiative effects of spectrally resolving the surface albedo. We use the Snow, Ice, and Aerosol Radiative model, extended to simulate liquid water, to calculate snow, ice, and liquid water albedo. We flux‐weight the hyperspectral albedo into the coarser spectral bands used by the atmospheric shortwave radiative transfer model. We establish representative atmospheric profiles for the three surface types and compare their shortwave fluxes and atmospheric warming rates with the spectrally resolved albedo to those calculated with the semi‐broadband approximation. Spectrally resolved surface albedo over snow and ice reduces atmospheric warming by darkening the albedo of NIR bands, correcting the too‐strong surface absorption in visible bands, and too‐weak surface absorption in shortwave infrared bands caused by the semi‐broadband approximation. We explore the effects on surface and atmospheric warming rates of varying solar zenith angle, cloud cover, relative humidity, and snow grain/air bubble radii. The semi‐broadband albedo biases can exceed 10% and 2% for the surface and atmospheric net flux respectively, being particularly strong under conditions which alter the distributions of surface insolation (i.e., cloud cover or increased atmospheric water vapor). These results show that transmitting a higher resolution spectral radiation field between the atmosphere and surface reduces biases in surface absorption and atmospheric heating present in ESMs that currently use the semi‐broadband approximation.

An improved BRDF hotspot model and its use in VLIDORT for studying the impact of atmospheric scattering on hotspot directional signatures in the atmosphere

Abstract. The term “hotspot” refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature.

Interactions between aerosols and surface ozone in arid and semi-arid regions of China.

Atmospheric aerosols affect surface ozone concentrations by influencing radiation, but the mechanism and dominant factors are unclear. Therefore, this paper analyses the changes in aerosol-radiative-surface ozone in China's arid and semi-arid regions with the help of the Atmospheric Radiative Transfer (SBDART) model. The results suggest that Aerosol Optical Depth (AOD) and coarse Particulate Matter (PM10) have the same trend, with high values in spring and winter and low values in summer and autumn. Surface ozone is high in spring and summer and low in autumn and winter. Surface ozone is higher in spring and summer and lower in autumn and winter. In winter, mainly secondary pollutants are dominated by high pollution levels. In the rest of the seasons, a mixture of dust, motor vehicle exhaust, and soot is dominated by low pollution levels. Surface ozone is positively correlated with fine particles and negatively correlated with coarse particles. Temperature is positively correlated with surface ozone in all seasons and negatively correlated with PM10 in summer, autumn, and winter. Precipitation negatively correlates with PM10 each season and surface ozone in winter and spring. Analysis of surface ozone and PM10 sources in the more polluted city of Hohhot based on the back-line trajectory model showed that airflow trajectories mainly transported surface ozone and PM10 pollution from northwestern Inner Mongolia and western Mongolia. During dusty solid weather, the decrease in radiation reaching the Earth's surface and the cooling effect of aerosols lead to lower temperatures, which slows down the rate of chemical reactions of precursors of surface ozone, resulting in lower ozone concentrations at the surface. This study can provide a theoretical reference for aerosol and surface ozone control in arid and semi-arid areas of China.

PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations

Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

Predicting the rotational dependence of line broadening using machine learning

Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for the many exotic molecules expected in exoplanetary atmospheres. Here we explore modern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base. To this end, state-of-the-art machine learning models are used to fit to existing, empirical air-broadening data from the HITRAN database. A computationally cheap method for large-scale production of pressure broadening parameters is developed, which is shown to be reasonably (69%) accurate for unseen active molecules. This method has been used to augment the previously insufficient ExoMol line broadening diet, providing air-broadening data for all ExoMol molecules, so that the ExoMol database has a full and more accurate treatment of line broadening. Suggestions are made for improved air-broadening parameters for species present in atmospheric databases.

Molecular analysis of secondary organic aerosol and brown carbon from the oxidation of indole

Abstract. Indole (ind) is a nitrogen-containing heterocyclic volatile organic compound commonly emitted from animal husbandry and from different plants like maize with global emissions of 0.1 Tg yr−1. The chemical composition and optical properties of indole secondary organic aerosol (SOA) and brown carbon (BrC) are still not well understood. To address this, environmental chamber experiments were conducted to investigate the oxidation of indole at atmospherically relevant concentrations of selected oxidants (OH radicals and O3) with or without NO2. In the presence of NO2, the SOA yields decreased by more than a factor of 2, but the mass absorption coefficient at 365 nm (MAC365) of ind-SOA was 4.3 ± 0.4 m2 g−1, which was 5 times higher than that in experiments without NO2. In the presence of NO2, C8H6N2O2 (identified as 3-nitroindole) contributed 76 % to all organic compounds detected by a chemical ionization mass spectrometer, contributing ∼ 50 % of the light absorption at 365 nm (Abs365). In the absence of NO2, the dominating chromophore was C8H7O3N, contributing to 20 %–30 % of Abs365. Indole contributes substantially to the formation of secondary BrC and its potential impact on the atmospheric radiative transfer is further enhanced in the presence of NO2, as it significantly increases the specific light absorption of ind-SOA by facilitating the formation of 3-nitroindole. This work provides new insights into an important process of brown carbon formation by interaction of two pollutants, NO2 and indole, mainly emitted by anthropogenic activities.

Radiative effect of atmospheric Brown clouds over the Indo-Gangetic plain

Atmospheric brown clouds (ABCs) are widespread hazy accumulation of particles and gases dominant with absorbing nature impacting the air quality, hydrology and climate. The Indo-Gangetic plain (IGP) being an ABCs hotspots is prone to extreme pollution episodes due to anthropogenic activities. However, there is a scarcity of studies that specifically compare episodes of absorbing aerosols (ABCs) and comparatively non-absorbing aerosols (non-ABCs) along with their respective radiative impacts over the IGP. Here, we have used ground-based remote-sensors’ retrieved datasets from AErosol RObotic NETwork (AERONET) to study spatio-temporal variation of ABCs’ frequency and associated radiative properties across the IGP region during 2001–2019. The shortwave aerosol radiative effect (ARE) during ABC and non-ABC episodes is estimated using Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. Our results show significant spatial variability in the frequency of ABC episodes across the IGP stations with ∼17 ± 5%, ∼19 ± 11% and ∼18 ± 13% average occurrence during winter, pre-monsoon and post-monsoon seasons, respectively. ARE estimations show enhanced surface dimming (ARESRF) and atmospheric warming (AREATM) during ABC episodes as compared to non-ABC episodes. A general increasing trend for ARE is observed as we move from west to east across the IGP due to the meteorological and geographical variations in the region. Our results show day-time averaged ARESRF varying between −142.46 ± 25.13 and −56.66 ± 6.88 W/m2 while AREATM ranging from 58.07 ± 2.58 to 132.69 ± 7.43 W/m2 for ABC episodes. Though ABC episodes show lower aerosol loading as compared to non-ABC episodes in our study, we found higher atmospheric heating during ABC episodes mainly due to distinct chemical composition (lower single scattering albedo) of absorbing aerosols. The upper bound of the difference between atmospheric heating rate during ABC and non-ABC episodes is found to be 0.48 ± 0.27 K/day.

Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Prediction of Atmospheric Carbon Dioxide Radiative Transfer Model based on Machine Learning

Global warming is caused by the increasing amount of greenhouse gases in the atmosphere. The Kyoto Protocol to the United Nations Framework Convention on Climate Change defines carbon dioxide (CO), methane (CH:), nitrous oxide (N0) fluorocarbons (HFC), holocarbon (PFC), and sulfur hexafluoride (SF.) Six gases are divided into the main greenhouse gases, of which CO, the largest proportion of emissions, is an important man-made greenhouse gas." As the main gas affecting the global greenhouse effect (75%), the amount of CO in the atmosphere has increased from 315 ppm in 1958 to 417 ppm in 2022, and global annual CO emissions have increased from 27 Pg to 49 Pg% over the past 40 years. Therefore, under the background of deep reinforcement learning, it is a problem that all countries pay more attention to predict the concentration emission of carbon dioxide to cope with the severe situation of climate warming. In order to obtain the radiative forcing value under the influence of carbon dioxide concentration, a simplified net radiative flux model is established. Based on the radiative transfer equation, this model can calculate the net radiative flux and radiative forcing of each layer of the atmosphere due to changes in carbon dioxide concentration. The results are compared with the RRTMG-LW long-wave radiative transfer model of American Center for Atmospheric and Environmental Research (AER) under the same factor, and the error is less than 1%.

Cloud Overlap Features from Multi-Year Cloud Radar Observations at the SACOL Site and Comparison with Satellites

Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

UNCERTAINTY MODELING AND ANALYSIS OF SPACEBORNE INFRARED HYPERSPECTRAL IMAGES OVER RUGGED LAND SURFACE

Abstract. Infrared hyperspectral imaging is an important technical means to obtain the emissivity spectra and temperature of land surface target, and is an important development direction of spaceborne optical remote sensing in the future. Under the natural rugged land surface condition, the quality of infrared hyperspectral imaging data is affected by terrain condition, atmospheric condition and instrument performance. Therefore, the instrument signal-to-noise ratio and the calibration accuracy could not directly describe the measurement accuracy of the hyperspectral characteristics of target. An uncertainty prediction model of spaceborne infrared hyperspectral images over rugged land surface is established, in this paper. This model could simulate the surface scene, the atmospheric radiation transfer over rugged land surface, and the imaging process of the spaceborne spectrometer. At the same time, the uncertainty transfer from the surface signal to the restored radiance data product could be realized. The generated uncertainty results include the fluctuation of the surface signal, the error of the atmospheric transmission model, the influence of topographic relief, the response characteristics of the imaging spectrometer and the calibration uncertainty. Based on this model, we can also realize the ranking of the uncertainty contribution of the above links, which could help to identify the weak link in the remote sensing measurement chain. The random simulation experiments over a rugged desert scene were conducted to verified the model. It is indicated that more than 99.9% of the stochastic simulation radiance spectra are in the range of the predicted uncertainty.

Assessing biases in atmospheric parameters for radiative effects estimation in tropical regions

Many studies highlight the importance of water vapor in tropical atmospheric radiative transfer calculations. However, limited research focuses on understanding the effects of biases in different atmospheric factors, such as pressure, temperature, water vapor, and ozone densities. These biases stem from various sources, including satellites, reanalysis data, and the standard TROPICAL atmosphere. This study explores how these biases influence estimates of two key things: atmospheric radiative forcing (ARF) and heating rate (HR) profiles over the tropical region. The study is undertaken at Gadanki, a rural background site in the tropics. It considers different situations - clear skies and those with aerosols - across different parts of the electromagnetic spectrum and throughout the seasons. Additionally, this study investigates how sensitive ARF and HR profiles are to biases in each of these atmospheric factors within specific spectral ranges. The study results show that the standard TROPICAL atmosphere often differs significantly from real-time measurements, leading to varied effects on radiation. Among the various factors, biases in water vapor density have the most substantial impact on ARF and HR, followed by biases in temperature. Moreover, longwave radiation is more sensitive to water vapor and temperature changes. This study emphasizes the importance of considering the exact state of the atmosphere, especially in tropical regions, when estimating ARF and HR. To improve estimates when real-time data isn't available, the study suggests combining data from the Microwave Limb Sounder (MLS) and the Atmospheric Infrared Sounder (AIRS), and using reanalysis data for broader assessments. It's also important to be cautious when using the TROPICAL atmosphere to analyze the radiative effects of aerosols.

Evaluation of MAX-DOAS Profile Retrievals under Different Vertical Resolutions of Aerosol and NO2 Profiles and Elevation Angles

In the Multi-Axis Differential Absorption Spectroscopy (MAX-DOAS) trace gas and aerosol profile inversion algorithm, the vertical resolution and the observation information obtained through a series of continuous observations with multiple elevation angles (EAs) can affect the accuracy of an aerosol profile, thus further affecting the results of the gas profile. Therefore, this study examined the effect of the vertical resolution of an aerosol profile and EAs on the NO2 profile retrieval by combining simulations and measurements. Aerosol profiles were retrieved from MAX-DOAS observations and co-observed using light detection and ranging (Lidar). Three aerosol profile shapes (Boltzmann, Gaussian, and exponential) with vertical resolutions of 100 and 200 m were used in the atmospheric radiative transfer model. Firstly, the effect of the vertical resolution of the input aerosol profile on the retrieved aerosol profile with a resolution of 200 m was studied. The retrieved aerosol profiles from the two vertical resolution aerosol profiles as input were similar. The aerosol profile retrieved from a 100 m resolution profile as input was slightly overestimated compared to the input value, whereas that from a 200 m resolution input was slightly underestimated. The relative deviation of the aerosol profile retrieved from the 100 m resolution as input was higher than that of the 200 m. MAX-DOAS observations in Hefei city on 4 September 2020 were selected to verify the simulation results. The aerosol profiles retrieved from the oxygen collision complex (O4) differential slant column density derived from MAX-DOAS observations and Lidar simulation were compared with the input Lidar aerosol profiles. The correlation between the retrieved and input aerosol profiles was high, with a correlation coefficient R > 0.99. The aerosol profiles retrieved from the Lidar profile at 100 and 200 m resolutions as input closely matched the Lidar aerosol profiles, consistent with the simulation result. However, aerosol profiles retrieved from MAX-DOAS measurements differed from the Lidar profiles due to the influence of the averaging kernel matrix smoothing, the different location and viewing geometry, and uncertainties associated with the Lidar profiles. Next, NO2 profiles of different vertical resolutions were used as input profiles to retrieve the NO2 profiles under a single aerosol profile scenario. The effect of the vertical resolution on the retrieval of NO2 profiles was found to be less significant compared to aerosol retrievals. Using the Lidar aerosol profile as the a priori aerosol information had little effect on NO2 profile retrieval. Additionally, the retrieved aerosol profiles and aerosol optical depths varied under different EAs. Ten EAs (i.e., 1, 2, 3, 4, 5, 6, 8, 15, 30, and 90°) were found to obtain more information from observations.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

The Development of METAL-WRF Regional Model for the Description of Dust Mineralogy in the Atmosphere

The mineralogical composition of airborne dust particles is an important but often neglected parameter for several physiochemical processes, such as atmospheric radiative transfer and ocean biochemistry. We present the development of the METAL-WRF module for the simulation of the composition of desert dust minerals in atmospheric aerosols. The new development is based on the GOCART-AFWA dust module of WRF-Chem. A new wet deposition scheme has been implemented in the dust module alongside the existing dry deposition scheme. The new model includes separate prognostic fields for nine (9) minerals: illite, kaolinite, smectite, calcite, quartz, feldspar, hematite, gypsum, and phosphorus, derived from the GMINER30 database and also iron derived from the FERRUM30 database. Two regional model sensitivity studies are presented for dust events that occurred in August and December 2017, which include a comparison of the model versus elemental dust composition measurements performed in the North Atlantic (at Izaña Observatory, Tenerife Island) and in the eastern Mediterranean (at Agia Marina Xyliatos station, Cyprus Island). The results indicate the important role of dust minerals, as dominant aerosols, for the greater region of North Africa, South Europe, the North Atlantic, and the Middle East, including the dry and wet depositions away from desert sources. Overall, METAL-WRF was found to be capable of reproducing the relative abundances of the different dust minerals in the atmosphere. In particular, the concentration of iron (Fe), which is an important element for ocean biochemistry and solar absorption, was modeled in good agreement with the corresponding measurements at Izaña Observatory (22% overestimation) and at Agia Marina Xyliatos site (4% overestimation). Further model developments, including the implementation of newer surface mineralogical datasets, e.g., from the NASA-EMIT satellite mission, can be implemented in the model to improve its accuracy.

Simulation of Thermal Infrared Brightness Temperatures from an Ocean Color and Temperature Scanner Onboard a New Generation Chinese Ocean Color Observation Satellite

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms.

Optical Properties of Individual Tar Balls in the Free Troposphere.

Tar balls are brown carbonaceous particles that are highly viscous, spherical, amorphous, and light absorbing. They are believed to form in biomass burning smoke plumes during transport in the troposphere. Tar balls are also believed to have a significant impact on the Earth's radiative balance, but due to poorly characterized optical properties, this impact is highly uncertain. Here, we used two nighttime samples to investigate the chemical composition and optical properties of individual tar balls transported in the free troposphere to the Climate Observatory "Ottavio Vittori" on Mt. Cimone, Italy, using multimodal microspectroscopy. In our two samples, tar balls contributed 50% of carbonaceous particles by number. Of those tar balls, 16% were inhomogeneously mixed with other constituents. Using electron energy loss spectroscopy, we retrieved the complex refractive index (RI) for a wavelength range from 200 to 1200 nm for both inhomogeneously and homogeneously mixed tar balls. We found no significant difference in the average RI of inhomogeneously and homogeneously mixed tar balls (1.40-0.03i and 1.36-0.03i at 550 nm, respectively). Furthermore, we estimated the top of the atmosphere radiative forcing using the Santa Barbara DISORT Atmospheric Radiative Transfer model and found that a layer of only tar balls with an optical depth of 0.1 above vegetation would exert a positive radiative forcing ranging from 2.8 W m-2 (on a clear sky day) to 9.5 W m-2 (when clouds are below the aerosol layer). Understanding the optical properties of tar balls can help reduce uncertainties associated with the contribution of biomass-burning aerosol in current climate models.