We previously developed the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm for the Ocean Color Instrument (OCI) on the new NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Here, we apply CHROMA to observations from the Ocean Land Colour Instrument (OLCI) to guide expectations for PACE, as it will take some time to obtain large-scale validation data for OCI. We use cloud top height (CTH), phase, and (for liquid clouds) cloud optical thickness (COT) data from the ground-based Atmospheric Radiation Measurement (ARM) network to evaluate the OLCI retrievals. We found that OLCI and Moderate Resolution Imaging Spectroradiometer (MODIS) CTH compare similarly well to the ARM reference. OLCI has a tendency to underestimate CTH as CTH increases, and algorithm assumptions about cloud geometric thickness may contribute to this. ARM COT from multifilter shadowband radiometers (MFRSR) and Sun photometers are well-correlated with one another, albeit with a roughly 30 % offset on average; OLCI and MODIS COT agree more closely with the MFRSR data. OLCI retrieval uncertainty estimates show skill at telling low-uncertainty cases from high-uncertainty ones, although CTH uncertainties are underestimated. Additionally, we compare the OLCI data to satellite retrievals based on thermal infrared measurements from MODIS and Sea and Land Surface Temperature Radiometer (SLSTR) data. Differences are broadly consistent with physical expectations based on the A-band vs. thermal techniques, although one key challenge in such aggregated comparisons is different cloud masking sensitivities and algorithm failure rates meaning additional sampling differences are introduced. We conclude by discussing the transition to and possible enhancements for PACE OCI.

This study aimed to develop field emission factors of gaseous pollutants, PM2.5, OC/EC, water-soluble ions, and trace elements and to estimate the total gas and particulate emissions from peatland fires. This study was con-ducted in Palangka Raya, Central Kalimantan, in September 2019 during the main fire episode. Gas samples were collected using the LGR-ICOS analyzer at a flow rate of 0.5 L/min. In comparison, particulate pollutant samples were collected using two Airmetrics Mini Volume Portable Air Samplers at a flow rate of 5 L/min with Quartz fiber and PTFE filters. For further analysis, quartz fiber filters were used for carbon aerosols and water-soluble inorganic ions, while PTFE filters were used for trace element analysis. The gaseous emission factors determined in this study for CO2, CO, and CH4 were 1589.5 +/- 41.8 g/kg, 236.5 +/- 25.7 g/kg, and 5.4 +/- 1.3 g/kg, respec-tively. Meanwhile, the emission factors for PM2.5, OC, EC, water-soluble inorganic ions, and elements were 27.8 +/- 41.8 g/kg, 18.1 +/- 8.9 g/kg, 0.25 +/- 0.06 g/kg, 1.7 +/- 0.7 g/kg, and 62.5 +/- 32.3 mg/kg, respectively. With peat burned density of 0.11 g/cm3, a burned depth of 37 cm, and a total area burned of 317,749 ha, peatland fires in Central Kalimantan in 2019 had emitted CO2, CO, PM2.5, total carbon aerosol, water-soluble ions, and elements about 34.9 +/- 0.9 Tg, 5.3 +/- 0.6 Tg, 0.6 +/- 0.3 Tg, 0.4 +/- 0.2 Tg, 36.7 +/- 15.9 Gg, and 1.4 +/- 0.7 Gg, respectively.

Abstract. We describe a technique using singular vector decomposition (SVD), that can identify the spatial patterns that best describe the temporal variability of a global satellite dataset. These patterns, and their temporal evolution, are then correlated with established climate indices. We apply this technique to datasets of cloud properties over three decades, derived from five visible/IR imagers ((A)ATSR, SLSTR-A/-B and MODIS and jointly from the IR and microwave sounders on MetOp (IASI, MHS,AMSU-A), but it can be more generically used to extract the pattern of variability of any regular gridded dataset such as different parameters from satellite products and models. The leading singular vector for these three independent global data sets, on both cloud fraction and cloud-top height, from these polar orbiting satellites covering different time periods, is found to be strongly correlated with the ENSO index. The SVD approach could potentially offer a new tool for using global satellite observations in assessing global climate model (GCM) performance.

The ESA-funded Aerosol Radiance Assimilation Study (ARAS) has provided ground-breaking research in using visible radiance data from satellite to estimate the concentration of aerosols. Satellite observations in the infrared and microwave parts of the spectrum have long been assimilated into forecasting systems to help estimate the best possible initial conditions for global weather predictions. Assimilating radiances in the visible part of the spectrum, on the other hand, continues to pose many challenges.The reason lies in the complex interactions of cloud and aerosol particles with radiation at those wavelengths and in the complex characteristics of the surface as a reflector of visible light. These factors make it difficult to develop an observation operator, which converts model values into satellite observation equivalents. One of the key achievements of ARAS is to have developed an observation operator for aerosol reflectances in the visible part of the spectrum. This operator was comprised of two elements: a fast radiative transfer code based on a Look-Up-Table approach developed by RAL Space for aerosol retrievals (Thomas et al, 2009) and adapted to the ECMWF’s Integrated Forecast System as well as a surface reflectance model for ocean and land. This enabled the first-ever experimental assimilation of reflectances into the 4D-Var assimilation system of ECMWF’s Integrated Forecasting System (IFS) to help estimate aerosol concentrations. The assimilation experiments were very successful. The performance was remarkable considering that this was a new development rolled out over the course of just two years. The observations used in the ARAS project were cloud-cleated aerosol reflectances from the MODIS instrument on board the Aqua and Terra satellites. Experiments were carried out to compare the impact of assimilating these observations with the impact of assimilating traditional satellite-derived AOD observations. The results show that the performance of reflectance assimilation is broadly comparable to that of satellite AOD assimilation. However, it varies depending on the metrics used and the period analysed. While assimilating aerosol reflectances is still experimental, the results show great potential for future operational implementation in atmospheric composition forecasts. Such forecasts are routinely produced by the EU‐funded Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF. However, the scope for future applications is much wider than that. Many of the tools developed in ARAS for aerosol visible reflectance assimilation could be adapted to clouds. This could open the way towards a fuller exploitation of visible radiances to improve numerical weather prediction. References Thomas G.E., Carboni E., Sayer A.M., Poulsen C.A., Siddans R., Grainger R.G. (2009) Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers. In: Kokhanovsky A.A., de Leeuw G. (eds) Satellite Aerosol Remote Sensing over Land. Springer Praxis Books. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69397-0_7

Extreme fires in the peatlands of South East (SE) Asia are arguably the world’s greatest biomass burning events, resulting in some of the worst ambient air pollution ever recorded (PM10 > 3000 µg·m−3). The worst of these fires coincide with El Niño related droughts, and include huge areas of smouldering combustion that can persist for months. However, areas of flaming surface vegetation combustion atop peat are also seen, and we show that the largest of these latter fires appear to be the most radiant and intensely smoke-emitting areas of combustion present in such extreme fire episodes. Fire emissions inventories and early warning of the air quality impacts of landscape fire are increasingly based on the fire radiative power (FRP) approach to fire emissions estimation, including for these SE Asia peatland fires. “Top-down” methods estimate total particulate matter emissions directly from FRP observations using so-called “smoke emission coefficients” [Ce; g·MJ−1], but currently no discrimination is made between fire types during such calculations. We show that for a subset of some of the most thermally radiant peatland fires seen during the 2015 El Niño, the most appropriate Ce is around a factor of three lower than currently assumed (~16.8 ± 1.6 g·MJ−1 vs. 52.4 g·MJ−1). Analysis indicates that this difference stems from these highly radiant fires containing areas of substantial flaming combustion, which changes the amount of particulate matter emitted per unit of observable fire radiative heat release in comparison to more smouldering dominated events. We also show that even a single one of these most radiant fires is responsible for almost 10% of the overall particulate matter released during the 2015 fire event, highlighting the importance of this fire type to overall emission totals. Discriminating these different fires types in ways demonstrated herein should thus ultimately improve the accuracy of SE Asian fire emissions estimates derived using the FRP approach, and the air quality modelling which they support.

Abstract. We present version 3 (V3) of the Cloud_cci ATSR-2/AATSR dataset. The dataset was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) program. The cloud properties were retrieved from the second Along- Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the Advanced ATSR (AATSR) on board Envisat, which spanned 2002–2012. The data comprises a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius and optical thickness alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top and bottom-of-atmosphere short- and long-wave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with collocated CALIPSO data. The cloud masking has improved significantly, particularly the ability to detect clear pixels The Kuiper Skill score has increased from .49 to .66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulous cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments, the Correlation with MAC-LWP increase from .4 to over .8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth’s Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new dataset is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol-cloud interactions, and providing contextual information for collocated ATSR-2/AATSR surface temperature and aerosol products. For the Cloud_cci ATSR-2/AATSRv3 dataset a new digital identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 Poulsen et al. (2019).

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1∘×1∘ daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1∘×1∘ grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

The Aerosol Radiance Assimilation Study (ARAS) has created a new approach for the assimilation of visible/near-IR radiances into the ECMWF’s Integrated Forecast System (IFS) for the constraining aerosol properties within the model. The capability is based on a new observation operator, based on the forward model used in the Optimal Retrieval of Aerosol and Cloud (ORAC) retrieval scheme, which predicts top-of-atmosphere radiances based on the model's aerosol field with sufficient accuracy while being computationally efficient enough to run in a operational analysis system such as that run at ECMWF. The system has been tested in the full IFS assimilation system, replacing the currently operational assimilation of MODIS AOD products, using MODIS radiances.This presentation will give an overview of the new operator, show example results of its impact on the model output and discuss its merits and disadvantages compared to the AOD assimilation.

Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02∘. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.For each dataset a digital object identifier has been issued:Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).

Radiative forcing by clouds, aerosols, and their interactions constitute some of the largest sources of uncertainties in the climate system (Chapter 7 IPCC, 2013). It is essential to understand the past through examination of long-term satellite observation records to provide insight into the uncertainty characteristics of these radiative forcers. As part of the ESA CCI (Climate Change Initiative) we have recently implemented a broadband radiative flux algorithm (known as BUGSrad) into the Optimal Retrieval for Aerosol and Cloud (ORAC) scheme. ORAC achieves radiative consistency of its aerosol and cloud products through an optimal estimation scheme and is highly versatile, enabling retrievals for numerous satellite sensors: ATSR, MODIS, VIIRS, AVHRR, SLSTR, SEVIRI, and AHI. An analysis of the 17-year well-calibrated Along Track Scanning Radiometer (ATSR) data is used to quantify trends in cloud and aerosol …

In this paper we evaluate the impact on the cloud parameter retrievals of the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm following the inclusion of stereo-derived cloud top heights as a priori information. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use such independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The Along-Track Scanning Radiometer (AATSR) instrument has two views and three thermal channels, so it is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact of the stereo a priori information on the microphysical cloud properties of cloud optical thickness (COT) and effective radius (RE) was evaluated and generally found to be very small for single-layer clouds conditions over open water (mean RE differences of 2.2 (±5.9) microns and mean COD differences of 0.5 (±1.8) for single-layer ice clouds over open water at elevations of above 9 km, which are most strongly affected by the inclusion of the a priori).