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This paper describes a new global dataset of cloud properties observed by MODIS relying on the current (collection 6.1) processing of MODIS data and produced to facilitate comparison with results from the MODIS observational proxy used in climate models. The dataset merges observations from the two MODIS instruments into a single netCDF file. Statistics (mean, standard deviation, and number of observations) are accumulated over daily and monthly timescales on an equal-angle grid for viewing and illumination geometry, cloud detection, cloud-top pressure, and cloud properties (optical thickness, effective particle size, and water path) partitioned by thermodynamic phase and an assessment as to whether the underlying observations come from fully or partly cloudy pixels. Similarly partitioned joint histograms are available for (1) optical thickness and cloud-top pressure, (2) optical thickness and particle size, and (3) cloud water path and particle size. Differences with standard data products, caveats for data use, and guidelines for comparison to the MODIS simulator are described. Data are available on daily (https://doi.org/10.5067/MODIS/MCD06COSP_D3_MODIS.062; NASA, 2022b) and monthly (https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062; NASA, 2022a) timescales.

Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

Current production processes are frequently dependent on global supply chains for raw materials and prefabricated inputs. With rising political and global risks, these supply networks are threatened, which leads to a reduction of supply chain resilience. At the same time, urban areas are currently one of the main consumers of products and waste material generators. The raw material sourcing for this consumption commonly takes place in globally connected supply chains due to economy of scale effects. Therefore, cities are especially vulnerable to supply chain disruptions. A recent development which could reduce this vulnerability is the installation of urban factories among other urban production concepts, which can be symbiotically embedded into the urban metabolism to utilize the locally available (waste) materials. This, however, is hampered by the smaller production scale of decentralized urban production facilities, limited knowledge and challenges about the urban material flows and their characteristics. Against this background, we introduce a new factory type which is placed between the primary and secondary industrial sector: An urban secondary raw material factory which utilizes local waste material and other urban material flows for the extraction and refinement of secondary raw materials to supply production sites in its surrounding environment. To enable this small-to medium-scale factory type, the application of new production technologies plays a crucial role. Therefore, this paper proposes an approach for matching relevant potential waste streams to different technologies for waste-to-resource refinement. The applicability of the method for identification and evaluation of suitable technologies regarding their potential to be located in urban environments is demonstrated for plastic and metallic materials. Subsequently, key challenges and characteristics of the new factory type are summarized. With the introduction of this new factory type, the lack of scale effects in urban symbiotic networks is expected to be reduced. In conclusion, challenges such as the data-based management of symbiotic relationships among manufacturing companies are highlighted as still relevant in decentral value chains.

Marine low clouds display rich mesoscale morphological types and distinct spatial patterns of cloud fields. Being able to differentiate low-cloud morphology offers a tool for the research community to go one step beyond bulk cloud statistics such as cloud fraction and advance the understanding of low clouds. Here we report the progress of our project that aims to create an observational record of low-cloud mesoscale morphology at a near-global (60∘ S–60∘ N) scale. First, a training set is created by our team members manually labeling thousands of mesoscale (128×128) MODIS scenes into six different categories: stratus, closed cellular convection, disorganized convection, open cellular convection, clustered cumulus convection, and suppressed cumulus convection. Then we train a deep convolutional neural network model using this training set to classify individual MODIS scenes at 128×128 resolution and test it on a test set. The trained model achieves a cross-type average precision of about 93 %. We apply the trained model to 16 years of data over the southeastern Pacific. The resulting climatological distribution of low-cloud morphology types shows both expected and unexpected features and suggests promising potential for low-cloud studies as a data product.

Simulations are performed for the period 2000–2015 by two different regional climate models, ALADIN and RegCM, to quantify the direct and semi-direct radiative effects of biomass-burning aerosols (BBAs) in the southeast Atlantic (SEA) region. Different simulations have been performed using strongly absorbing BBAs in accordance with recent in situ observations over the SEA. For the July–August–September (JAS) season, the single scattering albedo (SSA) and total aerosol optical depth (AOD) simulated by the ALADIN and RegCM models are consistent with the MACv2 climatology and MERRA-2 and CAMS-RA reanalyses near the biomass-burning emission sources. However, the above-cloud AOD is slightly underestimated compared to satellite (MODIS and POLDER) data during the transport over the SEA. The direct radiative effect exerted at the continental and oceanic surfaces by BBAs is significant in both models and the radiative effects at the top of the atmosphere indicate a remarkable regional contrast over SEA (in all-sky conditions), with a cooling (warming) north (south) of 10 ∘S, which is in agreement with the recent MACv2 climatology. In addition, the two models indicate that BBAs are responsible for an important shortwave radiative heating of ∼0.5–1 K per day over SEA during JAS with maxima between 2 and 4 km a.m.s.l. (above mean sea level). At these altitudes, BBAs increase air temperature by ∼0.2–0.5 K, with the highest values being co-located with low stratocumulus clouds. Vertical changes in air temperature limit the subsidence of air mass over SEA, creating a cyclonic anomaly. The opposite effect is simulated over the continent due to the increase in lower troposphere stability. The BBA semi-direct effect on the lower troposphere circulation is found to be consistent between the two models. Changes in the cloud fraction are moderate in response to the presence of smoke, and the models differ over the Gulf of Guinea. Finally, the results indicate an important sensitivity of the direct and semi-direct effects to the absorbing properties of BBAs. Over the stratocumulus (Sc) region, DRE varies from +0.94 W m−2 (scattering BBAs) to +3.93 W m−2 (most absorbing BBAs).

Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses five Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland, grassland, snow and ice, barren desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models was compared against collocated CALIOP products from 2017. It is shown that, when using a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison to the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top three algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, particularly for pixels over snow and ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

The southeast Atlantic (SEA) region is host to a climatologically significant biomass burning aerosol layer overlying marine stratocumulus. We present the first results of the directly measured above-cloud aerosol optical depth (ACAOD) from the recent ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) airborne field campaign during August and September 2016. In our analysis, we use data from the Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research (4STAR) instrument and found an average ACAOD of 0.32 at 501 nm (range of 0.02 to 1.04), with an average Ångström exponent (AE) above clouds of 1.71. The AE is much lower at 1.25 for the full column (including below-cloud-level aerosol, with an average of 0.36 at 501 nm and a range of 0.02 to 0.74), indicating the presence of large aerosol particles, likely marine aerosol, in the lower atmospheric column. The ACAOD is observed from 4STAR to be highest near the coast at about 12∘ S, whereas its variability is largest at the southern edge of the average aerosol plume, as indicated by 12 years of MODIS observations. In comparison to MODIS-derived ACAOD and long-term fine-mode plume-average AOD along a diagonal routine track extending out from the coast of Namibia, the directly measured ACAOD from 4STAR is slightly lower than the ACAOD product from MODIS. The peak ACAOD expected from MODIS AOD retrievals averaged over a long term along the routine diagonal flight track (peak of 0.5) was measured to be closer to coast in 2016 at about 1.5–4∘ E, with 4STAR ACAOD averages showing a peak of 0.42. When considering the full observation set over the SEA, by spatially binning each sampled AOD, we obtain a geographically representative mean ACAOD of 0.37. Vertical profiles of AOD showcase the variability in the altitude of the aerosol plume and its separation from the cloud top. We measured larger AOD at a high altitude near the coast than farther from the coast, while generally observing a larger vertical gap farther from the coast. Changes in AOD with altitude are correlated with carbon monoxide, a gas tracer of the biomass burning aerosol plume. Vertical extent of gaps between aerosol and cloud show a wide distribution, with a near-zero gap being most frequent. The gap distribution with longitude is observed to be largest at about 7∘ E, farther from coast than expected from previous studies.

Determining the direct aerosol radiative effect (DARE) of absorbing aerosols above clouds from satellite observations alone is a challenging task, in part because the radiative signal of the aerosol layer is not easily untangled from that of the clouds below. In this study, we use aircraft measurements from the NASA ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES) project in the southeastern Atlantic to derive it with as few assumptions as possible. This is accomplished by using spectral irradiance measurements (Solar Spectral Flux Radiometer, SSFR) and aerosol optical depth (AOD) retrievals (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research, 4STAR) during vertical profiles (spirals) that minimize the albedo variability of the underlying cloud field – thus isolating aerosol radiative effects from those of the cloud field below. For two representative cases, we retrieve spectral aerosol single scattering albedo (SSA) and the asymmetry parameter (g) from these profile measurements and calculate DARE given the albedo range measured by SSFR on horizontal legs above clouds. For mid-visible wavelengths, we find SSA values from 0.80 to 0.85 and a significant spectral dependence of g. As the cloud albedo increases, the aerosol increasingly warms the column. The transition from a cooling to a warming top-of-aerosol radiative effect occurs at an albedo value (critical albedo) just above 0.2 in the mid-visible wavelength range. In a companion paper, we use the techniques introduced here to generalize our findings to all 2016 and 2017 measurements and parameterize aerosol radiative effects.

Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent (∼0.7 over continental sources and ∼0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m−2 over the SAO (20∘ S–10∘ N and 10∘ W–20∘ E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes (−25 W m−2) and surface land temperature (−1.5 ∘C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ∼200 m.

This paper presents the physical basis of the Earth Polychromatic Imaging Camera (EPIC) cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-infrared. A suite of algorithms has been developed to generate the standard EPIC Level 2 cloud products that include cloud mask, cloud effective pressure/height, and cloud optical thickness. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 and 764 nm) and B-band (680 and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single-channel approach in which the 780 and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input.

Abstract Background In Kawasaki disease (KD), a vasculitis of unknown etiology, the most serious complication is the development of coronary artery aneurysm (CAA). To date, the exact pathomechanism of KD is unknown. Both environmental and genetic factors seem to be associated with the development of the disease. Methods Data on KD patients recruited from the population-based German Pediatric Surveillance Study during 2012–2014 were used to evaluate the impact of various factors from the perinatal and infancy period on the development of KD. The study design was a matched case-control study with respect to age, sex and place of residence (n = 308 KD cases, n = 326 controls). All KD patients were individually re-evaluated; all fulfilled the international diagnostic KD criteria. A standardized questionnaire was used to review breastfeeding practices, vitamin D supplementation and birth characteristics. Logistic regression analyses were performed to obtain odds ratios (OR) for various risk factors among the case-control pairs. Simple measures of association were used to assess the impact of these factors on the clinical course. Results There was no difference in lengths of gestation, birth weight or parturition between KD patients and controls, but independently from each other vitamin D supplementation and breastfeeding were negatively associated with KD, even when adjusted for age, place of residence and sex. The duration of vitamin D was significantly shorter among children with KD than among children without KD (p = 0.039, OR = 0.964, 95% CI: 0.931–0.998), as was the duration of breastfeeding (p = 0.013, OR = 0.471, 95% CI: 0.260–0.853). Comparing KD patients with and without breastfeeding and/or vitamin D supplementation, there were no differences regarding developing CAA, being refractory to intravenous immunoglobulin treatment, age at onset of the disease and levels of inflammatory laboratory values. Conclusion Our findings indicate breastfeeding and vitamin D supplementation to have protective effects in association with KD in our study population; however, these seem not to influence the natural course of the disease. Although the overall effects were relatively small, they nevertheless underline the overall benefit of both interventions. Trial registration Clinical Trial Registration: German clinical trial registration, http://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00010071. Date of registration was 26. February 2016. The trial was registered retrospectively.

In a context of global climate change, the understanding of the radiative role of clouds is crucial. On average, ice clouds such as cirrus have a significant positive radiative effect, but under some conditions the effect may be negative. However, many uncertainties remain regarding the role of ice clouds on Earth's radiative budget and in a changing climate. Global satellite observations are particularly well suited to monitoring clouds, retrieving their characteristics and inferring their radiative impact. To retrieve ice cloud properties (optical thickness and ice crystal effective size), current operational algorithms assume that each pixel of the observed scene is plane-parallel and homogeneous, and that there is no radiative connection between neighboring pixels. Yet these retrieval assumptions are far from accurate, as real radiative transfer is 3-D. This leads to the plane-parallel and homogeneous bias (PPHB) plus the independent pixel approximation bias (IPAB), which impacts both the estimation of top-of-the-atmosphere (TOA) radiation and the retrievals. An important factor that determines the impact of these assumptions is the sensor spatial resolution. High-spatial-resolution pixels can better represent cloud variability (low PPHB), but the radiative path through the cloud can involve many pixels (high IPAB). In contrast, low-spatial-resolution pixels poorly represent the cloud variability (high PPHB), but the radiation is better contained within the pixel field of view (low IPAB). In addition, the solar and viewing geometry (as well as cloud optical properties) can modulate the magnitude of the PPHB and IPAB. In this, Part II of our study, we simulate TOA 0.86 and 2.13 µm solar reflectances over a cirrus uncinus scene produced by the 3DCLOUD model. Then, 3-D radiative transfer simulations are performed with the 3DMCPOL code at spatial resolutions ranging from 50 m to 10 km, for 12 viewing geometries and nine solar geometries. It is found that, for simulated nadir observations taken at resolution higher than 2.5 km, horizontal radiation transport (HRT) dominates biases between 3-D and 1-D reflectance calculations, but these biases are mitigated by the side illumination and shadowing effects for off-zenith solar geometries. At resolutions coarser than 2.5 km, PPHB dominates. For off-nadir observations at resolutions higher than 2.5 km, the effect that we call THEAB (tilted and homogeneous extinction approximation bias) due to the oblique line of sight passing through many cloud columns contributes to a large increase of the reflectances, but 3-D radiative effects such as shadowing and side illumination for oblique Sun are also important. At resolutions coarser than 2.5 km, the PPHB is again the dominant effect. The magnitude and resolution dependence of PPHB and IPAB is very different for visible, near-infrared and shortwave infrared channels compared with the thermal infrared channels discussed in Part I of this study. The contrast of 3-D radiative effects between solar and thermal infrared channels may be a significant issue for retrieval techniques that simultaneously use radiative measurements across a wide range of solar reflectance and infrared wavelengths.

This paper presents a study on the impact of cirrus cloud heterogeneities on MODIS simulated thermal infrared (TIR) brightness temperatures (BTs) at the top of the atmosphere (TOA) as a function of spatial resolution from 50 m to 10 km. A realistic 3-D cirrus field is generated by the 3DCLOUD model (average optical thickness of 1.4, cloud-top and base altitudes at 10 and 12 km, respectively, consisting of aggregate column crystals of Deff = 20 µm), and 3-D thermal infrared radiative transfer (RT) is simulated with the 3DMCPOL code. According to previous studies, differences between 3-D BT computed from a heterogenous pixel and 1-D RT computed from a homogeneous pixel are considered dependent at nadir on two effects: (i) the optical thickness horizontal heterogeneity leading to the plane-parallel homogeneous bias (PPHB) and the (ii) horizontal radiative transport (HRT) leading to the independent pixel approximation error (IPAE). A single but realistic cirrus case is simulated and, as expected, the PPHB mainly impacts the low-spatial-resolution results (above ∼ 250 m) with averaged values of up to 5–7 K, while the IPAE mainly impacts the high-spatial-resolution results (below ∼ 250 m) with average values of up to 1–2 K. A sensitivity study has been performed in order to extend these results to various cirrus optical thicknesses and heterogeneities by sampling the cirrus in several ranges of parameters. For four optical thickness classes and four optical heterogeneity classes, we have found that, for nadir observations, the spatial resolution at which the combination of PPHB and HRT effects is the smallest, falls between 100 and 250 m. These spatial resolutions thus appear to be the best choice to retrieve cirrus optical properties with the smallest cloud heterogeneity-related total bias in the thermal infrared. For off-nadir observations, the average total effect is increased and the minimum is shifted to coarser spatial resolutions.

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua–MODIS (Moderate Resolution Imaging Spectroradiometer) collections 5.1 (C5.1) and 6 (C6) cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS cloud product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above-cloud emission, cloud-top pressure (CTP), and pixel registration. Global results show a neutral to positive (> 50 cm−3) change for C6-derived CDNC relative to C5.1 for the 1.6 and 2.1 µm channel retrievals, corresponding to a neutral to −2 µm difference in droplet effective radius (re). For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning −25 to +50 cm−3 related to a +2.5 to −1 µm transition in effective radius. Cloud optical thickness (τ) differences were small relative to effective radius and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant inter-collection differences in aggregated values of effective radius due to changes to the precomputed retrieval lookup tables (LUTs) for ocean scenes, changes to retrieved cloud-top pressure, solar irradiance, or above-cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS-derived climatologies and validation studies of effective radius and CDNC.

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTER-specific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in [Zhao and Di Girolamo(2006)]. To validate and evaluate the cloud optical thickness (τ) and cloud effective radius (reff) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τaA and reff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between reff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.

Previous bi-spectral imager retrievals of cloud optical thickness (COT) and effective particle radius (CER) based on the Nakajima and King (1990) approach, such as those of the operational MODIS cloud optical property retrieval product (MOD06), have typically paired a non-absorbing visible or near-infrared wavelength, sensitive to COT, with an absorbing shortwave or mid-wave infrared wavelength sensitive to CER. However, in practice it is only necessary to select two spectral channels that exhibit a strong contrast in cloud particle absorption. Here it is shown, using eMAS observations obtained during NASA's SEAC4RS field campaign, that selecting two absorbing wavelength channels within the broader 1.88 µm water vapor absorption band, namely the 1.83 and 1.93 µm channels that have sufficient differences in ice crystal single scattering albedo, can yield COT and CER retrievals for thin to moderately thick single-layer cirrus that are reasonably consistent with other solar and IR imager-based and lidar-based retrievals. A distinct advantage of this channel selection for cirrus cloud retrievals is that the below-cloud water vapor absorption minimizes the surface contribution to measured cloudy top-of-atmosphere reflectance, in particular compared to the solar window channels used in heritage retrievals such as MOD06. This reduces retrieval uncertainty resulting from errors in the surface reflectance assumption and reduces the frequency of retrieval failures for thin cirrus clouds.

This paper presents an investigation of the expected uncertainties of a single-channel cloud optical thickness (COT) retrieval technique, as well as a simple cloud-temperature-threshold-based thermodynamic phase approach, in support of the Deep Space Climate Observatory (DSCOVR) mission. DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations in the ultraviolet and visible spectra. Since EPIC is not equipped with a spectral channel in the shortwave or mid-wave infrared that is sensitive to cloud effective radius (CER), COT will be inferred from a single visible channel with the assumption of appropriate CER values for liquid and ice phase clouds. One month of Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) daytime granules from April 2005 is selected for investigating cloud phase sensitivity, and a subset of these granules that has similar EPIC Sun-view geometry is selected for investigating COT uncertainties. EPIC COT retrievals are simulated with the same algorithm as the operational MODIS cloud products (MOD06), except using fixed phase-dependent CER values. Uncertainty estimates are derived by comparing the single-channel COT retrievals with the baseline bi-spectral MODIS retrievals. Results show that a single-channel COT retrieval is feasible for EPIC. For ice clouds, single-channel retrieval errors are minimal (

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

In this paper, we studied the frequency of occurrence and shortwave direct radiative effects (DREs) of above-cloud aerosols (ACAs) over global oceans using 8 years (2007–2014) of collocated CALIOP and MODIS observations. Similar to previous work, we found high ACA occurrence in four regions: southeastern (SE) Atlantic region, where ACAs are mostly light-absorbing aerosols, i.e., smoke and polluted dust according to CALIOP classification, originating from biomass burning over the African Savanna; tropical northeastern (TNE) Atlantic and the Arabian Sea, where ACAs are predominantly windblown dust from the Sahara and Arabian deserts, respectively; and the northwestern (NW) Pacific, where ACAs are mostly transported smoke and polluted dusts from Asian. From radiative transfer simulations based on CALIOP–MODIS observations and a set of the preselected aerosol optical models, we found the DREs of ACAs at the top of atmosphere (TOA) to be positive (i.e., warming) in the SE Atlantic and NW Pacific regions, but negative (i.e., cooling) in the TNE Atlantic Ocean and the Arabian Sea. The cancellation of positive and negative regional DREs results in a global ocean annual mean diurnally averaged cloudy-sky DRE of 0.015 W m−2 (range of −0.03 to 0.06 W m−2) at TOA. The DREs at surface and within the atmosphere are −0.15 W m−2 (range of −0.09 to −0.21 W m−2), and 0.17 W m−2 (range of 0.11 to 0.24 W m−2), respectively. The regional and seasonal mean DREs are much stronger. For example, in the SE Atlantic region, the JJA (July–August) seasonal mean cloudy-sky DRE is about 0.7 W m−2 (range of 0.2 to 1.2 W m−2) at TOA. All our DRE computations are publicly available1. The uncertainty in our DRE computations is mainly caused by the uncertainties in the aerosol optical properties, in particular aerosol absorption, the uncertainties in the CALIOP operational aerosol optical thickness retrieval, and the ignorance of cloud and potential aerosol diurnal cycle. In situ and remotely sensed measurements of ACA from future field campaigns and satellite missions and improved lidar retrieval algorithm, in particular vertical feature masking, would help reduce the uncertainty. 1 https://drive.google.com/folderview?id=0B6gKx4dgNY0GMVYzcEd0bkZmRmc&usp=sharing

This paper presents a study, based on simulations, of the impact of cirrus cloud heterogeneities on the retrieval of cloud parameters (optical thickness and effective diameter) for the Imaging Infrared Radiometer (IIR) on board CALIPSO. Cirrus clouds are generated by the stochastic model 3DCLOUD for two different cloud fields and for several averaged cloud parameters. One cloud field is obtained from a cirrus observed on 25 May 2007 during the airborne campaign CIRCLE-2 and the other is a cirrus uncinus. The radiative transfer is simulated with the 3DMCPOL code. To assess the errors due to cloud heterogeneities, two related retrieval algorithms are used: (i) the split-window technique to retrieve the ice crystal effective diameter and (ii) an algorithm similar to the IIR operational algorithm to retrieve the effective emissivity and the effective optical thickness. Differences between input parameters and retrieved parameters are compared as a function of different cloud properties such as the mean optical thickness, the heterogeneity parameter and the effective diameter. The optical thickness heterogeneity for each 1 km × 1 km observation pixel is represented by the optical thickness standard deviation computed using 100 m × 100 m subpixels. We show that optical thickness heterogeneity may have a strong impact on the retrieved parameters, mainly due to the plane-parallel approximation (PPA assumption). In particular, for cirrus clouds with ice crystal diameter of approximately 10 μm, the averaged error on the retrieved effective diameter and optical thickness is about 2.5 μm (~ 25%) and −0.20 (~ 12%), respectively. Then, these biases decrease with increasing effective size due to a decrease of the cloud absorption and, thus, the PPA bias. Cloud horizontal heterogeneity effects are greater than other possible sources of retrieval errors such as those due to cloud vertical heterogeneity impact, surface temperature or atmospheric temperature profile uncertainty and IIR retrieval uncertainty. Cloud horizontal heterogeneity effects are larger than the IIR retrieval uncertainty if the standard deviation of the optical thickness, inside the observation pixel, is greater than 1.

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. It addresses the overlap of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure while also accounting for subgrid-scale variations of aerosols. The method is computationally efficient because of its use of grid-level cloud and aerosol statistics, instead of pixel-level products, and a precomputed look-up table based on radiative transfer calculations. We verify that for smoke and polluted dust over the southeastern Atlantic Ocean the method yields a seasonal mean instantaneous (approximately 13:30 local time) shortwave DRE of above-cloud aerosol (ACA) that generally agrees with a more rigorous pixel-level computation within 4%. We also estimate the impact of potential CALIOP aerosol optical depth (AOD) retrieval bias of ACA on DRE. We find that the regional and seasonal mean instantaneous DRE of ACA over southeastern Atlantic Ocean would increase, from the original value of 6.4 W m−2 based on operational CALIOP AOD to 9.6 W m−2 if CALIOP AOD retrievals are biased low by a factor of 1.5 (Meyer et al., 2013) and further to 30.9 W m−2 if CALIOP AOD retrievals are biased low by a factor of 5 as suggested in Jethva et al. (2014). In contrast, the instantaneous ACA radiative forcing efficiency (RFE) remains relatively invariant in all cases at about 53 W m−2 AOD−1, suggesting a near-linear relation between the instantaneous RFE and AOD. We also compute the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global oceans based on 4 years of CALIOP and MODIS data. We find that given an above-cloud aerosol type the optical depth of the underlying clouds plays a larger role than above-cloud AOD in the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol. While we demonstrate our method using CALIOP and MODIS data, it can also be extended to other satellite data sets.

We investigated the dynamics of denitrification and nitrous oxide (N2O) accumulation in 4 nitrate (NO−3) contaminated denitrifying sand and gravel aquifers of northern Germany (Fuhrberg, Sulingen, Thülsfelde and Göttingen) to quantify their potential N2O emission and to evaluate existing concepts of N2O emission factors. Excess N2 – N2 produced by denitrification – was determined by using the argon (Ar) concentration in groundwater as a natural inert tracer, assuming that this noble gas functions as a stable component and does not change during denitrification. Furthermore, initial NO−3 concentrations (NO−3 that enters the groundwater) were derived from excess N2 and actual NO−3 concentrations in groundwater in order to determine potential indirect N2O emissions as a function of the N input. Median concentrations of N2O and excess N2 ranged from 3 to 89 μg N L−1 and from 3 to 10 mg N L−1, respectively. Reaction progress (RP) of denitrification was determined as the ratio between products (N2O-N + excess N2) and starting material (initial NO−3 concentration) of the process, characterizing the different stages of denitrification. N2O concentrations were lowest at RP close to 0 and RP close to 1 but relatively high at a RP between 0.2 and 0.6. For the first time, we report groundwater N2O emission factors consisting of the ratio between N2O-N and initial NO−3-N concentrations (EF1). In addition, we determined a groundwater emission factor (EF2) using a previous concept consisting of the ratio between N2O-N and actual NO−3-N concentrations. Depending on RP, EF(1) resulted in smaller values compared to EF(2), demonstrating (i) the relevance of NO−3 consumption and consequently (ii) the need to take initial NO−3-N concentrations into account. In general, both evaluated emission factors were highly variable within and among the aquifers. The site medians ranged between 0.00043–0.00438 for EF(1) and 0.00092–0.01801 for EF(2), respectively. For the aquifers of Fuhrberg and Sulingen, we found EF(1) median values which are close to the 2006 IPCC default value of 0.0025. In contrast, we determined significant lower EF values for the aquifers of Thülsfelde and Göttingen. Summing the results up, our study supports the substantial downward revision of the IPCC default EF5-g from 0.015 (1997) to 0.0025 (2006).