River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.

A retrieval of total column water vapour (TCWV) from MODIS (Moderate-resolution Imaging Spectroradiometer) measurements is presented. The algorithm is adapted from a retrieval for MERIS (Medium Resolution Imaging Spectrometer) from Lindstrot et al. (2012). It obtains the TCWV for cloud-free scenes above land at a spatial resolution of 1 km × 1 km and provides uncertainties on a pixel-by-pixel basis. The algorithm has been extended by introducing empirical correction coefficients for the transmittance calculation within the forward operator. With that, a wet bias of the MODIS algorithm against ARM microwave radiometer data has been eliminated. The validation against other ground-based measurements (GNSS water vapour stations, GUAN radiosondes, and AERONET sun photometers) on a global scale reveals a bias between −0.8 and −1.6 mm and root mean square deviations between 0.9 and 2 mm. This is an improvement in comparison to the operational TCWV Level 2 product (bias between −1.9 and −3.2 mm and root mean square deviations between 1.9 and 3.4 mm). The comparison to MERIS TCWV for an example overpass exposes a systematic dry bias.

The Earth Explorer mission Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) will not only provide profile information on aerosols but also deliver a horizontal context to it through measurements by its Multi-Spectral Imager (MSI). The columnar aerosol product relying on these passive signals is called M-AOT (MSI-Aerosol Optical Thickness). Its main parameters are aerosol optical thickness (AOT) at 670 nm over ocean and valid land pixels and at 865 nm over ocean. Here, the algorithm and assumptions behind it are presented. Further, first examples of product parameters are given based on applying the algorithm to simulated EarthCARE test data and Moderate Resolution Imaging Spectroradiometer (MODIS) Level-1 data. Comparisons to input fields used for simulations, to the official MODIS aerosol product, to AErosol RObotic NETwork (AERONET) and to Maritime Aerosol Network (MAN) show an overall reasonable agreement. Over ocean, correlations are 0.98 (simulated scenes), 0.96 (compared to MYD04) and 0.9 (compared to MAN). Over land, correlations are 0.62 (simulated scenes), 0.87 (compared to MYD04) and 0.77 (compared to AERONET). A concluding discussion will focus on future improvements that are necessary and envisioned to enhance the product.

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements.

The aerosol layer height (ALH) is an important parameter that characterizes aerosol interaction with the environment. An estimation of the vertical distribution of aerosol is necessary for studies of those interactions, their effect on radiance and for aerosol transport models. ALH can be retrieved from satellite-based radiance measurements within the oxygen absorption band between 760 and 770 nm ($O_2$A band). The oxygen absorption is reduced when light is scattered by an elevated aerosol layer. The Ocean and Land Colour Imager (OLCI) has three bands within the oxygen absorption band. We show a congruent sensitivity study with respect to ALH for dust and smoke cases over oceans. Furthermore, we developed a retrieval of the ALH for those cases and an uncertainty estimation by applying linear uncertainty propagation and a bootstrap method. The sensitivity study and the uncertainty estimation are based on radiative transfer simulations. The impact of ALH, aerosol optical thickness (AOT), the surface roughness (wind speed) and the central wavelength on the top of atmosphere (TOA) radiance is discussed. The OLCI bands are sufficiently sensitive to ALH for cases with AOTs larger than 0.5 under the assumption of a known aerosol type. With an accurate spectral characterization of the OLCI $O_2$A bands better than 0.1 nm, ALH can be retrieved with an uncertainty of a few hundred meters. The retrieval of ALH was applied successfully on an OLCI dust and smoke scene. The found ALH is similar to parallel measurements by the Tropospheric Monitoring Instrument (TROPOMI). OLCI’s high spatial resolution and coverage allow a detailed overview of the vertical aerosol distribution over oceans.

Complex terrain features—in particular, environmental conditions, high population density and potential socio-economic damage—make the Trans-Mexican Volcanic Belt (TMVB) of particular interest regarding the study of deep convection and related severe weather. In this research, 10 years of Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud observations are combined with Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall data to characterize the spatio-temporal distribution of deep convective clouds (DCCs) and their relationship to extreme precipitation. From monthly distributions, wet and dry phases are identified for cloud fraction, deep convective cloud frequency and convective precipitation. For both DCC and extreme precipitation events, the highest frequencies align just over the higher elevations of the TMVB. A clear relationship between DCCs and terrain features, indicating the important role of orography in the development of convective systems, is noticed. For three sub-regions, the observed distributions of deep convective cloud and extreme precipitation events are assessed in more detail. Each sub-region exhibits different local conditions, including terrain features, and are known to be influenced differently by emerging moisture fluxes from the Gulf of Mexico and the Pacific Ocean. The observed distinct spatio-temporal variabilities provide the first insights into the physical processes that control the convective development in the study area. A signal of the midsummer drought in Mexico (i.e., “canícula”) is recognized using MODIS monthly mean cloud observations.