Sub-pixel Cloud Research Articles

An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud

ABSTRACT Following the success of MODIS, several widely used algorithms have been developed for different satellite sensors to provide global aerosol optical depth (AOD) products. Despite the progress made in improving the accuracy of satellite-derived AOD products, the presence of sub-pixel clouds and the corresponding cloud shadows still significantly degrade AOD products. This is due to the difficulty in identifying sub-pixel clouds, as they are hardly identified, which inevitably leads to the overestimation of AOD. To overcome these conundrums, we proposed an improved deep learning network for retrieving AOD from remote sensing imagery focusing on sub-pixel clouds especially and we call it the Sub-Pixel AOD network (SPAODnet). Two specific improvements considering sub-pixel clouds have been made; a spatial adaptive bilateral filter is applied to top-of-atmosphere (TOA) reflectance images for removing the noise induced by sub-pixel clouds and the corresponding shadows at the first place and channel attention mechanism is added into the convolutional neural network to further emphasize the relationship between the uncontaminated pixels and the ground measured AOD from AERONET sites. In addition, a compositive loss function, Huber loss, is used to further improve the accuracy of retrieved AOD. The SPAODnet model is trained by using ten AERONET sites within Beijing-Tianjin-Hebei (BTH) region in China, along with their corresponding MODIS images from 2011 to 2020; Subsequently, the trained network is applied over the whole BTH region and the AOD images over the BTH region from 2011 ~ 2020 are retrieved. Based on a comprehensive validation with ground measurements, the MODIS products, and the AOD retrieved from the other neural network, the proposed network does significantly improve the overall accuracy, the spatial resolution, and the spatial coverage of the AOD, especially for cases with sub-pixel clouds and cloud shadows.

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 2: Impact on NO2 retrieval and mitigation strategies

Abstract. Operational retrievals of tropospheric trace gases from space-borne spectrometers are based on one-dimensional radiative transfer models. To minimize cloud effects, trace gas retrievals generally implement a simple cloud model based on radiometric cloud fraction estimates and photon path length corrections. The latter relies on measurements of the oxygen collision pair (O2–O2) absorption at 477 nm or on the oxygen A-band around 760 nm to determine an effective cloud height. In reality however, the impact of clouds is much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighbouring pixels, and cloud shadow effects, such that unresolved three-dimensional effects due to clouds may introduce significant biases in trace gas retrievals. Although clouds have significant effects on trace gas retrievals, the current cloud correction schemes are based on a simple cloud model, and the retrieved cloud parameters must be interpreted as effective values. Consequently, it is difficult to assess the accuracy of the cloud correction only based on analysis of the accuracy of the cloud retrievals, and this study focuses solely on the impact of the 3D cloud structures on the trace gas retrievals. In order to quantify this impact, we study NO2 as a trace gas example and apply standard retrieval methods including approximate cloud corrections to synthetic data generated by the state-of-the-art three-dimensional Monte Carlo radiative transfer model MYSTIC. A sensitivity study is performed for simulations including a box cloud, and the dependency on various parameters is investigated. The most significant bias is found for cloud shadow effects under polluted conditions. Biases depend strongly on cloud shadow fraction, NO2 profile, cloud optical thickness, solar zenith angle, and surface albedo. Several approaches to correct NO2 retrievals under cloud shadow conditions are explored. We find that air mass factors calculated using fitted surface albedo or corrected using the O2–O2 slant column density can partly mitigate cloud shadow effects. However, these approaches are limited to cloud-free pixels affected by surrounding clouds. A parameterization approach is presented based on relationships derived from the sensitivity study. This allows measurements to be identified for which the standard NO2 retrieval produces a significant bias and therefore provides a way to improve the current data flagging approach.

Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)

Abstract. The Aqua, SNPP (Suomi National Polar-orbiting Partnership), and JPSS (Joint Polar Satellite System) satellites carry a combination of hyperspectral infrared sounders (AIRS, Atmospheric Infrared Sounder, and CrIS, Cross-track Infrared Sounder) and high-spatial-resolution narrowband imagers (MODIS, Moderate Resolution Imaging Spectroradiometer, and VIIRS, Visible Infrared Imaging Radiometer Suite). They provide an opportunity to acquire high-quality, long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at the pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS version 7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) version 2 for sounders and the standard Aqua MODIS collection 6.1 and the NASA MODIS–VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at the sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found to be highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at the sounder FOV shows good agreement between the standard and continuity products from different satellites. However, the impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA's progress toward building a continuous cloud data record with sufficient quality to investigate clouds' role in global environmental change.

Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

Spaceborne Middle‐ and Far‐Infrared Observations Improving Nighttime Ice Cloud Property Retrievals

AbstractTwo upcoming missions are scheduled to provide novel spaceborne observations of upwelling far‐infrared spectra. In this study, the accuracy of ice cloud property retrievals using spaceborne middle‐to‐far‐infrared (MIR‐FIR) measurements is examined toward a better understanding of retrieval biases and uncertainties. Theoretical sensitivity studies demonstrate that the MIR‐FIR spectra are sensitive to ice cloud properties, thereby providing a robust means for retrieving cloud properties under nighttime conditions. However, the temperature dependence of the ice refractive index and relevant ice particle shape models need to be incorporated into the retrieval procedure to avoid systematic biases in inferring cloud optical thickness and effective particle radius. Furthermore, prior information of subpixel cloud fractions is essential to mitigation of substantial systematic retrieval biases due to inconsistent subpixel cloud fractions.

Cloud phase characteristics over Southeast Asia from A-Train satellite observations

Abstract. This study examines the climatology of cloud phase over Southeast Asia (SEA) based on A-Train satellite observations. Using the combined CloudSat–CALIPSO (CC) data, five main cloud groups are investigated: ice-only, ice-above-liquid, liquid-only, ice-above-mixed, and mixed-only clouds that have annual mean frequencies of 28.6 %, 20.1 %, 16.0 %, 9.3 %, and 6.7 %, respectively. Liquid-only clouds tend to occur in relatively cold, dry, and stable lower troposphere. The other four cloud groups appear more frequently in relatively warm, humid, and unstable conditions, and their seasonal distributions move with the Asian monsoon and the Intertropical Convergence Zone (ITCZ). Liquid clouds are found to be highly inhomogeneous based on the heterogeneity index (Hσ) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), while ice-only and mixed-only clouds are often very smooth. Ice-above-liquid clouds are more heterogeneous than ice-only clouds owing to ice clouds being optically thin. We demonstrate that the distribution of clear-sky Hσ has a long tail towards heterogeneous values that are caused by undetected subpixel cloud within both CC and MODIS datasets. The reflectance at 0.645 µm (R0.645) and brightness temperature at 11 µm (BT11) of CC ice-only, liquid-only, and ice-above-liquid clouds show peak frequencies near that of clear sky (R0.645∼0.02; BT11∼294 K), which explains why up to 30 % of these CC cloud groups are classified as clear by MODIS. In contrast, mixed-only clouds are thick (average top ∼13 km), bright (average R0.645∼0.6), and cold (average BT11 ∼234 K). Cloud phase comparison between CC and MODIS reveals only modest agreement, with the best agreement (73 %) occurring between CC ice-above-mixed and MODIS ice clouds. The intraseasonal and interannual behaviors of the all-sky Hσ and spectral signatures follow that of cloud phase and vary with the Madden–Julian oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) phases.

A comparative evaluation of Aura-OMI and SKYNET near-UV single-scattering albedo products

Abstract. The aerosol single-scattering albedo (SSA) retrieved by the near-UV algorithm applied to the Aura Ozone Monitoring Instrument (OMI) measurements (OMAERUV) is compared with an independent inversion product derived from the sky radiometer network SKYNET – a ground-based radiation observation network with sites in Asia and Europe. The present work continues previous efforts to evaluate the consistency between the retrieved SSA from satellite and ground sensors. The automated spectral measurements of direct downwelling solar flux and sky radiances made by the SKYNET Sun-sky radiometer are used as input to an inversion algorithm that derives spectral aerosol optical depth (AOD) and single-scattering albedo (SSA) in the near-UV to near-IR spectral range. The availability of SKYNET SSA measurements in the ultraviolet region of the spectrum allows, for the first time, a direct comparison with OMI SSA retrievals eliminating the need of extrapolating the satellite retrievals to the visible wavelengths as is the case in the evaluation against the Aerosol Robotic Network (AERONET). An analysis of the collocated retrievals from over 25 SKYNET sites reveals that about 61 % (84 %) of OMI–SKYNET matchups agree within the absolute difference of ±0.03 (±0.05) for carbonaceous aerosols, 50 % (72 %) for dust aerosols, and 45 % (75 %) for urban–industrial aerosol types. Regionally, the agreement between the two inversion products is robust over several sites in Japan influenced by carbonaceous and urban–industrial aerosols; at the biomass burning site Phimai in Thailand; and the polluted urban site in New Delhi, India. The collocated dataset yields fewer matchups identified as dust aerosols mostly over the site Dunhuang with more than half of the matchup points confined to within ±0.03 limits. Altogether, the OMI–SKYNET retrievals agree within ±0.03 when SKYNET AOD (388 or 400 nm) is larger than 0.5 and the OMI UV Aerosol Index is larger than 0.2. The remaining uncertainties in both inversion products can be attributed to specific assumptions made in the retrieval algorithms, i.e., the uncertain calibration constant, assumption of spectral surface albedo and particle shape, and subpixel cloud contamination. The assumption of fixed and spectrally neutral surface albedo (0.1) in the SKYNET inversion appears to be unrealistic, leading to underestimated SSA, especially under lower aerosol load conditions. At higher AOD values for carbonaceous and dust aerosols, however, retrieved SSA values by the two independent inversion methods are generally consistent in spite of the differences in retrieval approaches.

Cloud Heterogeneity in the Marine Midlatitudes: Dependence on Large‐Scale Meteorology and Implications for General Circulation Models

AbstractWe examine the sensitivity of cloud heterogeneity to large‐scale meteorology in the marine midlatitudes using satellite observations from the Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer instruments aboard the Terra satellite and nudged simulations from the UK Met Office's Global Atmosphere 7.0 (GA7) for the year 2007. Using Multiangle Imaging Spectroradiometer observations, we quantify several sources of observational uncertainty due to cloud heterogeneity such as finite‐resolution cloud fraction biases and subpixel heterogeneity. With a simple measure of cloud geometry, we show that these sources of observational uncertainty are maximized in post–cold‐frontal conditions, where scattered subpixel clouds are frequent, and are minimized in the warm sector, where subpixel clouds are infrequent. These results demonstrate the greater difficulty in interpreting remote sensing measurements in post–cold‐frontal conditions compared to quiescent or warm‐sector conditions. We show that the neglect of this observational uncertainty can qualitatively alter the interpretation of how cloud properties respond to large‐scale meteorology in GA7 and general circulation models in general. However, conservative application of the satellite data still allows robust evaluation of the simulated clouds. For overcast domains, Moderate Resolution Imaging Spectroradiometer observations show that overcast cloud heterogeneity is independent of large‐scale meteorology. However, heterogeneity increases when moving from warm sector to post‐cold‐frontal conditions in GA7. GA7 overestimates the heterogeneity of the low‐topped clouds that populate these regimes. For overcast domains, this bias is equivalent to a 15% underestimation in the mean cloud optical depth. These results suggest a systematic error in the subgrid‐scale variability parameterization, which, when corrected, will improve the simulation of the midlatitudes in GA7.

Improving Cloud Optical Property Retrievals for Partly Cloudy Pixels Using Coincident Higher‐Resolution Single Band Measurements: A Feasibility Study Using ASTER Observations

AbstractClear‐sky contamination is a challenging and long‐lasting problem for cloud optical thickness (τ) and effective droplet radius (reff) retrievals using passive satellite sensors. This study explores the feasibility of improving both τ and reff retrievals for partly cloudy (PCL) pixels by using available subpixel samples in a visible to near‐infrared band, which many satellite sensors offer. Data are provided by high‐resolution reflectance (R) observations and cloud property retrievals by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at horizontal resolutions between 30‐960m. For partly cloudy 960‐m observations, the clear‐sky component of the pixels induces significant underestimations of up to 58% for τ, while overestimations in reff can exceed 41%. This yields underestimations in the derived liquid water path and cloud droplet number concentration of up to 68% and 72%, respectively. By means of three different assumptions it is shown that subpixel R observations in the visible to near‐infrared band can be used to estimate higher‐resolution R for the second band in the retrieval scheme, as well as the subpixel cloud cover. The estimated values compare well to actually observed ASTER results and are used to retrieve cloud properties, which are unbiased by the clear‐sky component of PCL pixels. While the presented retrieval approach is only evaluated for marine boundary layer clouds, it is computationally efficient and can be easily applied to observations from different imagers. As an example, the PCL retrieval scheme is applied to data by the Moderate Resolution Imaging Spectroradiometer, where similar biases for PCL pixels are observed.

Cirrus Horizontal Heterogeneity and 3‐D Radiative Effects on Cloud Optical Property Retrievals From MODIS Near to Thermal Infrared Channels as a Function of Spatial Resolution

AbstractTo retrieve cloud optical properties, current satellite operational imager algorithms simplify the forward radiative transfer problem by assuming that cloudy pixels are horizontally homogeneous and radiatively independent. This study investigates the effects of cirrus horizontal heterogeneity and 3‐D radiative effects on cloud optical thickness (COT) and ice crystal effective radius (CER) retrievals obtained using simulated nadir near‐infrared/shortwave‐infrared (NIR/SWIR) reflectances at 0.86 and 2.13 μm and thermal infrared (TIR) radiances at 8.5, 11.0, and 12.0 μm, first separately and next using the five wavelengths together. Synthetic cirrus radiation fields are generated using a cirrus 3‐D cloud generator and a 3‐D radiative transfer code. When both cloud 3‐D and heterogeneity effects are considered, the solar reflectance‐based retrievals have the largest errors (up to 10% for COT and 80% for CER, depending on solar angles) for spatial resolutions less than 500–1,000 m, while the TIR‐based retrievals have the largest errors (up to 30% for COT and 50% for CER) above this resolution due to parallel homogeneous approximation bias. Therefore, TIR radiance‐based retrievals are preferable for spatial resolutions equal or higher than ~500 m to 1 km, while NIR/SWIR reflectance‐based retrievals are preferable for coarser spatial resolutions. The combination of NIR/SWIR and TIR measurements performed better together than individually for CER retrieval only for resolutions coarser than 2.5 km because 3‐D effects are negligible at this scale. Thus, the spectral dependence of subpixel cloud horizontal heterogeneity and 3‐D radiative effects has strong consequences when simultaneously using different channels for retrieving cirrus properties.

A comparison of Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: Pixel‐to‐pixel comparisons

AbstractWe compare differences in ice and liquid water cloud physical and optical properties between Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 (C6) and collection 5.1 (C51). The C6 cloud products changed significantly due to improved calibration, improvements based on comparisons with the Cloud‐Aerosol Lidar with Orthogonal Polarization, treatment of subpixel liquid water clouds, introduction of a roughened ice habit for C6 rather than the use of smooth ice particles in C51, and more. The MODIS cloud products form a long‐term data set for analysis, modeling, and various purposes. Thus, it is important to understand the impact of the changes. Two cases are considered for C6 to C51 comparisons. Case 1 considers pixels with valid cloud retrievals in both C6 and C51, while case 2 compares all valid cloud retrievals in each collection. One year (2012) of level‐2 MODIS cloud products are examined, including cloud effective radius (CER), optical thickness (COT), water path, cloud top pressure (CTP), cloud top temperature, and cloud fraction. Large C6–C51 differences are found in the ice CER (regionally, as large as 15 μm) and COT (decrease in annual average by approximately 25%). Liquid water clouds have higher CTP in marine stratocumulus regions in C6 but lower CTP globally (−5 hPa), and there are 66% more valid pixels in C6 (case 2) due to the treatment of pixels with subpixel clouds. Simulated total cloud radiative signatures from C51 and C6 are compared to Clouds and the Earth's Radiant Energy System Energy Balanced And Filled (EBAF) product. The C6 CREs compare more closely with the EBAF than the C51 counterparts.

Reprocessing of HIRS Satellite Measurements from 1980 to 2015: Development toward a Consistent Decadal Cloud Record

AbstractThis paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (εf, or cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.

A framework based on 2-D Taylor expansion for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method.

The bi-spectral method retrieves cloud optical thickness (τ) and cloud droplet effective radius (r e ) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved τ and r e . In the literature, the retrievals of τ and r e are often assumed to be independent and considered separately when investigating the impact of sub-pixel cloud reflectance variations on the bi-spectral method. As a result, the impact on τ is contributed only by the sub-pixel variation of VIS/NIR band reflectance and the impact on r e only by the sub-pixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the τ and r e retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the τ and r e retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the r e retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in r e retrievals, leading to a potential contribution of positive bias to the r e retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from MODIS. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used to estimate the retrieval uncertainty from sub-pixel reflectance variations in operational satellite cloud products and to help understand the differences in τ and r e retrievals between two instruments.

Global assessment of OMI aerosol single‐scattering albedo using ground‐based AERONET inversion

AbstractWe compare the aerosol single‐scattering albedo (SSA) retrieved by the near‐UV two‐channel algorithm (OMAERUV) applied to the Aura/Ozone Monitoring Instrument (OMI) measurements with an equivalent inversion made by the ground‐based Aerosol Robotic Network (AERONET). A recent upgrade of the OMAERUV algorithm incorporates a modified carbonaceous aerosol model, a Cloud‐Aerosol Lidar with Orthogonal Polarization‐based aerosol height climatology, and a robust aerosol‐type identification. This paper is the first comprehensive effort to globally compare the OMI‐retrieved SSA with that of AERONET using all available sites spanning the regions of biomass burning, dust, and urban pollution. An analysis of the colocated retrievals over 269 sites reveals that about 46% (69%) of OMI‐AERONET matchups agree within the absolute difference of ±0.03 (±0.05) for all aerosol types. The comparison improves to 52% (77%) when only “smoke” and “dust” aerosol types were identified by the OMAERUV algorithm. Regionally, the agreement between the two inversions was robust over the biomass burning sites of South America, Sahel, Indian subcontinent, and oceanic/coastal sites followed by a reasonable agreement over Northeast Asia. Over the desert regions, OMI tends to retrieve higher SSA, particularly over the Arabian Peninsula. Globally, the OMI‐AERONET matchups agree mostly within ±0.03 for the aerosol optical depth (440 nm) and UV‐aerosol index larger than 0.4 and 1.0, respectively. Possible sources of uncertainty in the OMI retrieval can be the subpixel cloud contamination, assumptions of the surface albedo, and spectral aerosol absorption. We expect further refinement in the OMAERUV algorithm which stands uniquely in characterizing aerosol absorption from space.

Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

AbstractAccurate cloud detection is very important for infrared (IR) radiance assimilation; improved cloud detection could reduce cloud contamination and hence improve the assimilation. Although operational numerical weather prediction (NWP) centers are using IR sounder radiance data for cloud detection, collocated high spatial resolution imager data could help sounder subpixel cloud detection and characterization. IR sounder radiances with improved cloud detection using Atmospheric Infrared Sounder (AIRS)/Moderate Resolution Imaging Spectroradiometer (MODIS) were assimilated for Hurricane Sandy (2012). Forecast experiments were run with Weather Research and Forecasting (WRF) as the forecast model and the Three‐Dimensional Variational Assimilation (3DVAR)‐based Gridpoint Statistical Interpolation (GSI) as the analysis system. Results indicate that forecasts of both hurricane track and intensity are substantially improved when the collocated high spatial resolution MODIS cloud mask is used for AIRS subpixel cloud detection for assimilating radiances. This methodology can be applied to process Crosstrack Infrared Sounder (CRIS)/Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi‐NPOESS Preparatory Project (NPP)/Joint Polar Satellite System (JPSS) and Infrared Atmospheric Sounding Interferometer (IASI)/Advanced Very High Resolution Radiometer (AVHRR) onboard the Metop series for improved radiance assimilation in NWP.

A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

Abstract. A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

A global survey of the effect of cloud contamination on the aerosol optical thickness and its long‐term trend derived from operational AVHRR satellite observations

Subpixel cloud contamination is one of the major issues plaguing passive satellite aerosol remote sensing. Its impact on the aerosol optical thickness (AOT) retrieval has been analyzed/evaluated by many studies. However, the question of how it influences the AOT trend remains to be answered. In this paper, four long‐term advanced very high resolution radiometer (AVHRR) AOT data sets from 1981 to 2009 over global oceans for four different definitions of clear sky, respectively, are produced by applying a two‐channel aerosol retrieval algorithm to the AVHRR clear‐sky reflectances derived by combining NOAA Pathfinder Atmosphere's Extended AVHRR climate data record level‐2b all‐sky reflectances with the cloud probability parameter determined from the Bayesian probabilistic cloud detection technique. A global analysis of the effect of cloud contamination on the AVHRR AOT retrieval as well as on its long‐term trend is then performed by comparing the results from the four data sets. It was found that cloud contamination imposes not only a positive bias on AOT values but also a positive bias on its long‐term trend such that negative trends become less negative and positive trends become more positive. A cloud probability value of ≤1% has been identified as an optimal criterion for clear‐sky definition to minimize the cloud contamination in the AVHRR aerosol retrieval while still retaining strong aerosol signals. In order for a satellite aerosol product to be useful and reliable in aerosol trend detection, the cloud contamination effect on aerosol trends needs to be studied/evaluated carefully along with the effects of calibration error, surface disturbance, and aerosol model assumptions.

An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS

Abstract. In this study, a substantially improved version of the Meteosat cirrus detection algorithm (MeCiDA2) will be presented, which now allows application to the full earth disc visible by the Meteosat satellite. As cirrus clouds have an influence on the radiation budget of the earth, their optical properties and their global coverage has to be monitored at the global scale using instruments aboard geostationary satellites. Since MeCiDA was optimised for the area of Europe only, various changes were necessary to handle the variable conditions found over the full Meteosat disc. Required changes include the consideration of the viewing angle dependency and of the sensitivity of the 9.7 μm channel to the ozone column. To this end, a correction is implemented that minimises the influence of the variability of the stratospheric ozone. The evaluation of the proposed improvements is carried out by using MeCiDA applied to MODIS (moderate resolution imaging spectrometer) data to address viewing angle-dependent cirrus detection, and by additionally comparing it to the cloud optical properties MOD06 cirrus product. The new MeCiDA version detects less cirrus than the original one for latitudes larger than 40°, but almost the same amount elsewhere. MeCiDA's version for MODIS is more sensitive than that for SEVIRI (spinning enhanced visible and infrared imager) with cirrus occurrences higher by 10%, and the new MeCiDA provides almost the same cirrus coverage (±0.1) as given by the cloud phase optical properties from MODIS for latitudes smaller than 50°. Finally, the influence of sub-pixel clouds on the SEVIRI cirrus detection has been examined: more than 60% of the undetected SEVIRI cirrus pixels have a cirrus coverage smaller than 0.5.

A better understanding of cloud optical thickness derived from the passive sensors MODIS/AQUA and POLDER/PARASOL in the A-Train constellation

Abstract. Cloud optical thickness (COT) is one of the most important parameter for the characterization of cloud in the Earth radiative budget. Its retrieval strongly depends on instrument characteristics and on many cloud and environment factors. Using coincident observations from POLDER/PARASOL and MODIS/AQUA in the A-Train constellation, geographical distributions and seasonal changes of COT are presented, in good agreement with general cloud climatology characteristics. Retrieval uncertainties mainly associated to sensor spatial resolution, cloud inhomogeneity and microphysical assumptions are discussed. Comparisons of COT derived from POLDER and MODIS illustrate that as the primary factor, the sensor spatial resolution impacts COT retrievals and statistics through both cloud detection and sub-pixel cloud inhomogeneity sensitivity. The uncertainties associated to cloud microphysics assumptions, namely cloud phase, particle size and shape, also impact significantly COT retrievals. For clouds with unambiguous cloud phase, strong correlations exist between the two COTs, with MODIS values comparable to POLDER ones for liquid clouds and MODIS values larger than POLDER ones for ice clouds. The large differences observed in ice phase cases are due to the use of different microphysical models in the two retrieval schemes. In cases when the two sensors disagree on cloud phase decision, COT retrieved assuming liquid phase is systematically larger. The angular biases related to specific observation geometries are also quantified and discussed in particular based on POLDER observations. Those exhibit a clear increase of COT with decreasing sun elevation and a decrease of COT in forward scattering directions due to sub-pixel inhomogeneities and shadowing effects, this especially for lower sun. It also demonstrates unrealistic COT variations in the cloudbow and backward directions due to inappropriate cloud optical properties representation and an important increase of COT in the sun-glint directions in case of broken cloud.

A fast SEVIRI simulator for quantifying retrieval uncertainties in the CM SAF cloud physical property algorithm

Abstract. The uncertainties in the cloud physical properties derived from satellite observations make it difficult to interpret model evaluation studies. In this paper, the uncertainties in the cloud water path (CWP) retrievals derived with the cloud physical properties retrieval algorithm (CPP) of the climate monitoring satellite application facility (CM SAF) are investigated. To this end, a numerical simulator of MSG-SEVIRI observations has been developed that calculates the reflectances at 0.64 and 1.63 μm for a wide range of cloud parameter values, satellite viewing geometries and surface albedos using a plane-parallel radiative transfer model. The reflectances thus obtained are used as input to CPP, and the retrieved values of CWP are compared to the original input of the simulator. Cloud parameters considered in this paper refer to e.g. sub-pixel broken clouds and the simultaneous occurrence of ice and liquid water clouds within one pixel. These configurations are not represented in the CPP algorithm and as such the associated retrieval uncertainties are potentially substantial. It is shown that the CWP retrievals are very sensitive to the assumptions made in the CPP code. The CWP retrieval errors are generally small for unbroken single-layer clouds with COT > 10, with retrieval errors of ~3% for liquid water clouds to ~10% for ice clouds. In a multi-layer cloud, when both liquid water and ice clouds are present in a pixel, the CWP retrieval errors increase dramatically; depending on the cloud, this can lead to uncertainties of 40–80%. CWP retrievals also become more uncertain when the cloud does not cover the entire pixel, leading to errors of ~50% for cloud fractions of 0.75 and even larger errors for smaller cloud fractions. Thus, the satellite retrieval of cloud physical properties of broken clouds as well as multi-layer clouds is complicated by inherent difficulties, and the proper interpretation of such retrievals requires extra care.