Cloudy Scenes Research Articles

An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

Vertical information of CO from TROPOMI total column measurements in context of the CAMS-IFS data assimilation scheme

Abstract. Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) on board ESA's Copernicus Sentinel-5 satellite (S5-P) has provided the operational carbon monoxide (CO) data product with daily global coverage on a spatial resolution of 5.5×7 km2. The European Centre for Medium-Range Weather Forecasts (ECMWF) plans to assimilate the retrieved total columns and the corresponding vertical sensitivities in the Copernicus Atmosphere Monitoring Service Integrated Forecasting System (CAMS-IFS) to improve forecasts of the atmospheric chemical composition. The TROPOMI data will primarily constrain the vertical integrated CO field of CAMS-IFS but to a lesser extent also its vertical CO distribution. For clear-sky conditions, the vertical sensitivity of the TROPOMI CO data product is useful throughout the atmosphere, but for cloudy scenes it varies due to cloud shielding and light scattering. To assess the profile information, we deploy an a posteriori profile retrieval that combines an ensemble of TROPOMI CO column retrievals with different vertical sensitivities to obtain a vertical CO profile that is then a representative average for the chosen spatial and temporal domain. We demonstrate the approach on three CO pollution cases. For the so-called “Rabbit Foot Fire” in Idaho on 12 August 2018, we estimate a CO profile showing the pollution at an altitude of about 5 km in good agreement with airborne in situ measurements of the Biomass Burning Flux Measurements of Trace Gases and Aerosol (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft (length =212 km, width =34 km), decoupled from the ground, is sensed by TROPOMI but is not present in the CAMS-IFS model. For a large-scale event, we analyzed the CO pollution from Siberian wildfires that took place from 14 to 17 August 2018. The TROPOMI data estimate the height of the pollution plume over Canada at 7 km in agreement with CAMS-IFS. However, CAMS-IFS underestimates the enhanced CO vertical column densities sensed by TROPOMI within the plume by more than 100 ppb. Finally, we study the seasonal biomass burning in the Amazon. During the burning season the CO profile retrieved from the TROPOMI measurements (1–15 August 2019) agrees well with the one of CAMS-IFS with a similar vertical shape between ground and 14 km altitude. Hence, our results indicate that assimilating TROPOMI CO retrieval with different vertical sensitivities (e.g., under clear-sky and cloudy conditions) provides information about the vertical distribution of CO.

Simulated Clear-Sky Water Vapor and Temperature Retrievals from PREFIRE Measurements

Abstract The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will measure Earth’s emission at wavelengths ranging from 3 to 54 μm. The prelaunch clear-sky retrieval algorithm, evaluated with simulated test data, indicates that PREFIRE measurements will be valuable for retrieving atmospheric water vapor and temperature profiles. Far infrared measurements provide unique retrieval information, indicated by the high ranking of select FIR channels as primary contributors to the total degrees of freedom for signal (DFS). In utilizing all the PREFIRE channels, the average total DFS of 4 test regions ranges from 1.90 to 4.71. The information content increases with higher column water vapor and in the presence of near-surface temperature inversions. Using the DFS profiles for guidance, the retrieval concentrates information into 7 distinct layers to reduce the retrieval uncertainty per layer. Sensitivity tests indicate forward model error due to surface emissivity uncertainty results in about a 9% increase in column water vapor uncertainty. The clear-sky retrieval is sensitive to the presence of undetected ice clouds, especially those with optical depths larger than 0.2. Hence, in addition to a separate PREFIRE cloud mask, optimal estimation retrieval metrics are explored as possible indicators of cloudy scenes.

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts

Abstract. NOAA's Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing capabilities similar to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, as well as OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear-sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear-sky and partially cloudy scenes, for which instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause a maximum retrieval error of 0.25×1015 molec. cm−2 for a PS of 5 %. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth's atmosphere.

Evaluating Satellite Sounders for Monitoring the Tropical Cyclone Environment in Operational Forecasting

Tropical cyclones can form over open ocean where in situ observations are limited, so forecasters rely on satellite observations to monitor their development and track. We explore the utility of an operational satellite sounding product for tropical forecasting by characterizing the products retrieval skill during research flights. Scientists from both the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) research team and tropical cyclone communities collaborated to target relevant tropical cyclones during the campaign. This effort produced 130 dropsondes that are well-timed with satellite sounder overpasses over three different tropical cyclones and one Saharan Air Layer outbreak. For the combined infrared and microwave retrieval, the NUCAPS temperature has a root mean square error (RMSE) of 1.2 K near the surface (1000–600 mb) and 0.8 K in the mid-troposphere (600–300 mb), which is in line with global product requirements. The water vapor mixing ratio RMSE was 26% near the surface and 46% in the mid-troposphere. NUCAPS microwave-only retrievals can also be useful for cloudy scenes, with surface RMSE values of 4 K (temperature) and 23% (water vapor). Using information content analysis, we estimated that the vertical resolution near the surface was 1.7 km for the temperature retrievals and 2.2 km for the water vapor retrievals in this study. We discuss the feasibility of implementing NUCAPS in an operational forecasting setting, which requires rapid data delivery to forecaster software tools.

A spatial-spectral adaptive thin-cloud removal method based on slow feature analysis

ABSTRACT Clouds cover corrupts the spatial and spectral information of optical remote sensing (RS) images, which seriously affects the use of RS data. To solve the problem of missing information, a spatial-spectral adaptive method based on slow feature analysis (SFA) is proposed to restore cloudy scenes in this letter. SFA converts the sequence signal into slowly varying signal signatures and the clouds will be located in the first component. We propose spatial and spectral adaptive correction methods to reduce interference from highlighted pixels and jointly constrain the cloud coefficients in each band according to reflectance and gradient. The effectiveness of our method is verified on Landsat-8 OLI simulated and real cloudy datas, the restored results are visually rich in texture detail and moderately corrected. The average PSNR of the four real scenes is 42.9738 dB and coefficient of determination (R 2) is 0.8203, and many indicators have proved that our method is better than the existing methods.

Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

Retrieving H<sub>2</sub>O/HDO columns over cloudy and clear-sky scenes from the Tropospheric Monitoring Instrument (TROPOMI)

Abstract. This paper presents an extended scientific HDO/H2O total column data product from short-wave infrared (SWIR) measurements by the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes. The retrieval employs a forward model which accounts for scattering, and the algorithm infers the trace gas column information, surface properties, and effective cloud parameters from the observations. Compared to the previous clear-sky-only data product, coverage is greatly enhanced by including scenes over low clouds, particularly enabling data over oceans as the albedo of water in the SWIR spectral range is too low to retrieve under cloud-free conditions. The new dataset is validated against co-located ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON). The median bias for clear-sky scenes is 1.4×1021 molec cm−2 (2.9 %) in H2O columns and 1.1×1017 molec cm−2 (−0.3 %) in HDO columns, which corresponds to −17 ‰ (9.9 %) in a posteriori δD. The bias for cloudy scenes is 4.9×1021 molec cm−2 (11 %) in H2O, 1.1×1018 molec cm−2 (7.9 %) in HDO, and −20 ‰ (9.7 %) in a posteriori δD. At low-altitude stations, the bias is small at low and middle latitudes and has a larger value at high latitudes. At high-altitude stations, an altitude correction is required to compensate for different partial columns seen by the station and the satellite. The bias in a posteriori δD after altitude correction depends on sensitivity due to shielding by clouds and on realistic a priori profile shapes for both isotopologues. Cloudy scenes generally involve low sensitivity below the clouds, and since the information is filled up by the prior, a realistic shape of the prior is important for realistic total column estimation in these cases. Over oceans, aircraft measurements with the Water Isotope System for Precipitation and Entrainment Research (WISPER) instrument from a field campaign in 2018 are used for validation, yielding biases of −3.9 % in H2O and −3 ‰ in δD over clouds. To demonstrate the added value of the new dataset, a short case study of a cold air outbreak over the Atlantic Ocean in January 2020 is presented, showing the daily evolution of the event with single-overpass results.

Attention mechanism-based generative adversarial networks for cloud removal in Landsat images

The existence of clouds affects the quality of optical remote sensing images. Cloud removal is an important preprocessing procedure to effectively improve the utilization of optical remote sensing images. Thin clouds partly obscure the land surfaces beneath them, making it possible to correct the cloudy scenes according to the available information. In this research, we introduce the attention mechanism-based generative adversarial networks for cloud removal (AMGAN-CR) method for Landsat images. First, attention maps of the input cloudy images are generated to extract the cloud distributions and features through an attentive recurrent network. Second, clouds are removed by an attentive residual network under the guidance of the attention maps. Finally, the generated feature maps are fed to a reconstruction network to restore the final cloud-free images. The networks are trained by cloudy and cloud-free Landsat image pairs, and the cloudy images are tested to validate the effectiveness of AMGAN-CR. Both simulated and real cloud experimental results show that the proposed method is more outstanding than the other five state-of-the-art traditional and deep learning methods in removing cloud.

Cloudy Image Arithmetic: A Cloudy Scene Synthesis Paradigm With an Application to Deep-Learning-Based Thin Cloud Removal

We present a cloudy scene synthesis paradigm that produces cloudy images for arbitrary optical cloud-free observations. The synthesis paradigm consists of two fundamental operations, i.e., 1) cloud self-subtraction and 2) cloud addition-to-scene. Cloud self-subtraction extracts cloud ingredient images from cloudy images of weak texture regions (typically sea areas). The cloud ingredient images exhibit clouds in more realistic forms than simulated clouds. Cloud addition-to-scene incorporates the cloud ingredient images into arbitrary cloud-free land images, synthesizing cloudy scenes. It provides a means of constructing data pairs of cloud-free scene images and cloudy scene images, which are highly needed but considerably insufficient in the remote sensing literature. We refer to the overall paradigm consisting of the two fundamental operations as cloudy image arithmetic. We explore the use of the cloudy image arithmetic for the purpose of thin cloud removal. To this end, we develop a multi scale generative adversarial net (MSGAN) that removes thin clouds from cloudy scenes. We use the cloudy image arithmetic to construct a comprehensive training dataset for the MSGAN. Experimental evaluations validate that the cloudy image arithmetic synthesizes good cloudy scenes and the MSGAN with aid of the cloudy image arithmetic gives effective results in thin cloud removal.

Intercomparison of CO measurements from TROPOMI, ACE-FTS, and a high-Arctic ground-based Fourier transform spectrometer

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially resolved (initially 7×7 km2, upgraded to 7×5.6 km2 in August 2019) global dataset of CO columns; however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite intercomparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier transform spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05∘ N, 86.42∘ W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 28 November 2017 to 31 May 2020 displays excellent agreement between the two datasets (R=0.93) and a small relative bias of -0.83±0.26% (bias ± standard error of the mean). Additional comparisons were performed within five latitude bands: the north polar region (60 to 90∘ N), northern mid-latitudes (20 to 60∘ N), the equatorial region (20∘ S to 20∘ N), southern mid-latitudes (60 to 20∘ S), and the south polar region (90 to 60∘ S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R=0.93 (southern mid-latitudes) to R=0.86 (equatorial region) between the CO products but display a dependence of the mean differences on latitude. Positive mean biases of 7.93±0.61 % and 7.21±0.52 % were found in the northern and southern polar regions, respectively, while a negative bias of -9.41±0.55% was observed in the equatorial region. To investigate whether these differences are introduced by cloud contamination, which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, as well as for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear+cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite datasets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R=0.88); however, a positive mean bias of 14.7±0.16 % was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R=0.79) and a mean positive bias of 7.89±0.21 % in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all intercomparisons in this work, as well as with recent ground-based validation efforts, suggesting that the current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15 %, providing further confirmation that the data quality in these remote high-latitude regions meets this specification.

Combining POLDER-3 satellite observations and WRF-Chem numerical simulations to derive biomass burning aerosol properties over the southeast Atlantic region

Abstract. Aerosol absorption is a key property to assess the radiative impacts of aerosols on climate at both global and regional scales. The aerosol physico-chemical and optical properties remain not sufficiently constrained in climate models, with difficulties to properly represent both the aerosol load and their absorption properties in clear and cloudy scenes, especially for absorbing biomass burning aerosols (BBA). In this study we focus on biomass burning (BB) particle plumes transported above clouds over the southeast Atlantic (SEA) region off the southwest coast of Africa, in order to improve the representation of their physico-chemical and absorption properties. The methodology is based on aerosol regional numerical simulations from the WRF-Chem coupled meteorology–chemistry model combined with a detailed inventory of BB emissions and various sets of innovative aerosol remote sensing observations, both in clear and cloudy skies from the POLDER-3/PARASOL space sensor. Current literature indicates that some organic aerosol compounds (OC), called brown carbon (BrOC), primarily emitted by biomass combustion absorb the ultraviolet-blue radiation more efficiently than pure black carbon (BC). We exploit this specificity by comparing the spectral dependence of the aerosol single scattering albedo (SSA) derived from the POLDER-3 satellite observations in the 443–1020 nm wavelength range with the SSA simulated for different proportions of BC, OC and BrOC at the source level, considering the homogeneous internal mixing state of particles. These numerical simulation experiments are based on two main constraints: maintaining a realistic aerosol optical depth both in clear and above cloudy scenes and a realistic BC/OC mass ratio. Modelling experiments are presented and discussed to link the chemical composition with the absorption properties of BBA and to provide estimates of the relative proportions of black, organic and brown carbon in the African BBA plumes transported over the SEA region for July 2008. The absorbing fraction of organic aerosols in the BBA plumes, i.e. BrOC, is estimated at 2 % to 3 %. The simulated mean SSA are 0.81 (565 nm) and 0.84 (550 nm) in clear and above cloudy scenes respectively, in good agreement with those retrieved by POLDER-3 (0.85±0.05 at 565 nm in clear sky and at 550 nm above clouds) for the studied period.

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

Examination of all‐sky infrared radiance simulation of Himawari‐8 for global data assimilation and model verification

AbstractThe systematic difference between observations and simulation from weather forecast model hampers effective data assimilation and model improvement. The purpose of this study is to identify the characteristics and cause of the systematic difference or observation‐minus‐background (O − B) bias for all‐sky infrared radiances of the Himawari‐8 satellite, and propose data assimilation preprocessings and model verification. The O − B bias in cloudy scenes showed substantial negative values because of the shortage of high‐altitude clouds generated in the forecast model. Additionally, a positive bias appeared for thin ice clouds because of the excessive absorption of radiative transfer models (RTMs). These biases were traced based on a bottom‐up approach investigating individual uncertainty of RTMs, observation calibration, and the forecast model using two RTMs, reference hyperspectral sounders and synergetic measurements of CloudSat and CALIPSO. Based on these findings, data assimilation preprocessing such as quality‐control procedures excluding samples that models poorly reproduced was developed. Although the quality controls reduced the number of biased samples, non‐negligible O − B biases remained. Possible problems and treatments for the biases were discussed, including bias correction, observation error inflation, and correction of the cloud effect parameter. The O–B statistics also suggested insufficient representation of the diurnal variation in the cloud fraction in the tropics. Modified physical processes in the forecast model to increase ice clouds were tested to help improve the model bias and develop data assimilation. This trial indicated the difficulty in improving both O − B bias and variance and the necessity of adjusting the cloud effect parameters in data assimilation.

Impacts of MOPITT cloud detection revisions on observation frequency and mapping of highly polluted scenes

Measurements made by the MOPITT (“Measurements of Pollution in the Troposphere”) instrument on the NASA Terra polar-orbiting platform enable the retrieval of tropospheric carbon monoxide (CO) concentrations. As determined by the Terra orbit and MOPITT swath width, the frequency of MOPITT observations at a specific location, or measurement sampling frequency, is typically about once every three to four days. However, because the MOPITT retrieval algorithm is only applicable to clear-sky scenes, MOPITT retrieval sampling frequency strongly depends on regional cloudiness and can be much smaller than the measurement sampling frequency. Moreover, highly polluted scenes, characterized by high aerosol optical depths, can be confused with cloudy scenes and thus be discarded unnecessarily by the MOPITT cloud detection algorithm. Herein are described revisions to this algorithm which substantially increase retrieval sampling over land in varying pollution conditions. The performance of the revised cloud detection algorithm is evaluated through validation, case studies, and continental-scale maps of retrieval sampling frequency. Presented case studies illustrate (1) why the current operational MOPITT cloud detection algorithm excludes extended areas of potentially valuable cloud-free MOPITT observations and (2) how, for the same scenes, improved retrieval coverage benefits analyses of regional CO variability. Maps of retrieval sampling frequency for South America and Asia exhibit well-defined improvements, especially in regions with poor sampling frequency in the current product.

A new measurement approach for validating satellite-based above-cloud aerosol optical depth

Abstract. The retrieval of aerosol parameters from passive satellite instruments in cloudy scenes is challenging, partly because clouds and cloud-related processes may significantly modify aerosol optical depth (AOD) and particle size, a problem that is further compounded by 3D radiative processes. Recent advances in retrieval algorithms such as the “color ratio” method, which utilizes the measurements at a shorter (470 nm) and a longer (860 nm) wavelength, have demonstrated the simultaneous derivation of AOD and cloud optical depth (COD) for scenes in which absorbing aerosols are found to overlay low-level cloud decks. This study shows simultaneous retrievals of above-cloud aerosol optical depth (ACAOD) and aerosol-corrected cloud optical depth (COD) from airborne measurements of cloud-reflected and sky radiances using the color ratio method. These airborne measurements were taken over marine stratocumulus clouds with NASA's Cloud Absorption Radiometer (CAR) during the SAFARI 2000 field campaign offshore of Namibia. The ACAOD is partitioned between the AOD below-aircraft (AOD_cloudtop) and above-aircraft AOD (AOD_sky). The results show good agreement between AOD_sky and sun-photometer measurements of the above-aircraft AOD. The results also show that the use of aircraft-based sun-photometer measurements to validate satellite retrievals of the ACAOD is complicated by the lack of information on AOD below aircraft. Specifically, the CAR-retrieved AOD_cloudtop captures this “missing” aerosol layer caught between the aircraft and cloud top, which is required to quantify above-cloud aerosol loading and effectively validate satellite retrievals. In addition, the study finds a strong anticorrelation between the AOD_cloudtop and COD for cases in which COD < 10 and a weaker anticorrelation for COD > 10, which may be associated with the uncertainties in the color ratio method at lower AODs and CODs. The influence of 3D radiative effects on the retrievals is examined, and the results show that at cloud troughs, 3D effects increase retrieved ACAOD by about 3 %–11 % and retrieved COD by about 25 %. The results show that the color ratio method has little sensitivity to 3D effects at overcast stratocumulus cloud decks. These results demonstrate a novel airborne measurement approach for assessing satellite retrievals of aerosols above clouds, thereby filling a major gap in global aerosol observations.

Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm

Ice clouds play a critical role in the balance of the earth–atmosphere radiation system, but there are some limitations in the existing remote sensing methods for ice clouds. Terahertz wave is expected to be the best waveband for retrieving ice clouds, with terahertz wavelengths in the order of the size of typical ice cloud particles. An inversion method for the remote sensing of ice clouds at terahertz wavelengths based on genetic algorithm is proposed in this paper. First, suitable channel sets in the terahertz band, which are mainly a combination of absorption lines and window regions, are determined. Then, to improve the efficiency of the generation of the retrieval database, based on the brightness temperature simulated by the atmospheric radiative transfer simulator (ARTS) for different cloud parameters, a fast forward operator is constructed using three-dimensional interpolation to simulate the brightness temperature difference between clear sky and a cloudy scene. Finally, an inversion model to retrieve the ice cloud base height, the effective particle diameter and the ice water path is established based on the genetic algorithm, and an analysis of the inversion errors is performed. The results show that the forward operator, constructed by the nearest interpolation, can accurately calculate the brightness temperature difference at a high speed. The proposed inversion method at terahertz wavelengths based on the genetic algorithm can achieve the expected scientific requirement. The absolute error of the cloud height is around 0.2 km, and the absolute error of the low ice water path (below 20 g/m2) is small, while the relative error of the high ice water path is generally maintained at around 10%, and the absolute error of the effective particle diameter is mostly around 4 μm.

3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning

Abstract. Upper tropospheric (UT) cloud systems constructed from Atmospheric Infrared Sounder (AIRS) cloud data provide a horizontal emissivity structure, allowing the convective core to be linked to anvil properties. By using machine learning techniques, we composed a horizontally complete picture of the radiative heating rates deduced from CALIPSO lidar and CloudSat radar measurements, which are only available along narrow nadir tracks. To train the artificial neural networks, we combined the simultaneous AIRS, CALIPSO and CloudSat data with ERA-Interim meteorological reanalysis data in the tropics over a period of 4 years. The resulting non-linear regression models estimate the radiative heating rates as a function of about 40 cloud, atmospheric and surface properties, with a column-integrated mean absolute error (MAE) of 0.8 K d−1 (0.5 K d−1) for cloudy scenes and 0.4 K d−1 (0.3 K d−1) for clear sky in the longwave (shortwave) spectral domain. Developing separate models for (i) high opaque clouds, (ii) cirrus, (iii) mid- and low-level clouds and (iv) clear sky, independently over ocean and over land, leads to a small improvement, when considering the profiles. These models were applied to the whole AIRS cloud dataset, combined with ERA-Interim, to build 3D radiative heating rate fields. Over the deep tropics, UT clouds have a net radiative heating effect of about 0.3 K d−1 throughout the troposphere from 250 hPa downward. This radiative heating enhances the column-integrated latent heating by about 22±3 %. While in warmer regions the net radiative heating profile is nearly completely driven by deep convective cloud systems, it is also influenced by low-level clouds in the cooler regions. The heating rates of the convective systems in both regions also differ: in the warm regions the net radiative heating by the thicker cirrus anvils is vertically more extended, and their surrounding thin cirrus heat the entire troposphere by about 0.5 K d−1. The 15-year time series reveal a slight increase of the vertical heating in the upper and middle troposphere by convective systems with tropical surface temperature warming, which can be linked to deeper systems. In addition, the layer near the tropopause is slightly more heated by increased thin cirrus during periods of surface warming. While the relative coverage of convective systems is relatively stable with surface warming, their depth increases, measured by a decrease of their near-top temperature of -3.4±0.2 K K−1. Finally, the data reveal a connection of the mesoscale convective system (MCS) heating in the upper and middle troposphere and the (low-level) cloud cooling in the lower atmosphere in the cool regions, with a correlation coefficient equal to 0.72, which consolidates the hypothesis of an energetic connection between the convective regions and the subsidence regions.

A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.

Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy

Optical and microphysical cloud properties are retrieved from measurements acquired in 2013 and 2014 at the Concordia base station in the Antarctic Plateau. Two sensors are used synergistically: a Fourier transform spectroradiometer named REFIR-PAD (Radiation Explorer in Far Infrared-Prototype for Applications and Developments) and a backscattering-depolarization LiDAR. First, in order to identify the cloudy scenes and assess the cloud thermodynamic phase, the REFIR-PAD spectral radiances are ingested by a machine learning algorithm called Cloud Identification and Classification (CIC). For each of the identified cloudy scenes, the nearest (in time) LiDAR backscattering profile is processed by the Polar Threshold (PT) algorithm that allows derivation of the cloud top and bottom heights. Subsequently, using the CIC and PT results as external constraints, the Simultaneous Atmospheric and Clouds Retrieval (SACR) code is applied to the REFIR-PAD spectral radiances. SACR simultaneously retrieves cloud optical depth and effective dimensions and atmospheric vertical profiles of water vapor and temperature. The analysis determines an average effective diameter of 28 μm with an optical depth of 0.76 for the ice clouds. Water clouds are only detected during the austral Summer, and the retrieved properties provide an average droplet diameter of 9 μm and average optical depth equal to four. The estimated retrieval error is about 1% for the ice crystal/droplet size and 2% for the cloud optical depth. The sensitivity of the retrieved parameters to the assumed crystal shape is also assessed. New parametrizations of the optical depth and the longwave downwelling forcing for Antarctic ice and water clouds, as a function of the ice/liquid water path, are presented. The longwave downwelling flux, computed from the top of the atmosphere to the surface, ranges between 70 and 220 W/m2. The estimated cloud longwave forcing at the surface is (31 ± 7) W/m2 and (29 ± 6) W/m2 for ice clouds and (64 ± 12) and (62 ± 11) W/m2 for water clouds, in 2013 and 2014, respectively. The total average cloud forcing for the two years investigated is (46 ± 9) W/m2.