ALBERT

Description: Shortwave (SW) fluxes estimated from broadband radiometry rely on empirically gathered and hemispherically resolved fields of outgoing top-of-atmosphere (TOA) radiances. This study aims to provide more accurate and precise fields of TOA SW radiances reflected from clouds over ocean by introducing a novel semiphysical model predicting radiances per narrow sun-observer geometry. This model was statistically trained using CERES-measured radiances paired with MODIS-retrieved cloud parameters as well as reanalysis-based geophysical parameters. By using radiative transfer approximations as a framework to ingest the above parameters, the new approach incorporates cloud-top effective radius and above-cloud water vapor in addition to traditionally used cloud optical depth, cloud fraction, cloud phase, and surface wind speed. A two-stream cloud albedo – serving to statistically incorporate cloud optical thickness and cloud-top effective radius – and Cox–Munk ocean reflectance were used to describe an albedo over each CERES footprint. Effective-radius-dependent asymmetry parameters were obtained empirically and separately for each viewing-illumination geometry. A simple equation of radiative transfer, with this albedo and attenuating above-cloud water vapor as inputs, was used in its log-linear form to allow for statistical optimization. We identified the two-stream functional form that minimized radiance residuals calculated against CERES observations and outperformed the state-of-the-art approach for most observer geometries outside the sun-glint and solar zenith angles between 20 and 70∘, reducing the median SD of radiance residuals per solar geometry by up to 13.2 % for liquid clouds, 1.9 % for ice clouds, and 35.8 % for footprints containing both cloud phases. Geometries affected by sun glint (constituting between 10 % and 1 % of the discretized upward hemisphere for solar zenith angles of 20 and 70∘, respectively), however, often showed weaker performance when handled with the new approach and had increased residuals by as much as 60 % compared to the state-of-the-art approach. Overall, uncertainties were reduced for liquid-phase and mixed-phase footprints by 5.76 % and 10.81 %, respectively, while uncertainties for ice-phase footprints increased by 0.34 %. Tested for a variety of scenes, we further demonstrated the plausibility of scene-wise predicted radiance fields. This new approach may prove useful when employed in angular distribution models and may result in improved flux estimates, in particular dealing with clouds characterized by small or large droplet/crystal sizes.

Description: The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).

Description: 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. Together with the ensured spectral consistency and rigorous uncertainty propagation though all processing levels, the Cloud_cci datasets approach new benchmarks for climate data records of cloud properties based on passive imaging sensors. 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/V002 Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

Description: 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

Description: A total of 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data are used to quantify possible changes in the cloud vertical distribution over the tropical Atlantic. For the analysis multiple linear regression techniques are used. For the investigated time period significant linear changes were found in the domain-averaged cloud-top height (CTH) (−178 m per decade), the high-cloud fraction (HCF) (−0.0006 per decade), and the low-cloud amount (0.001 per decade). The interannual variability of the time series (especially CTH and HCF) is highly influenced by the El Niño–Southern Oscillation (ENSO). Separating the time series into two phases, we quantified the linear change associated with the transition from more La Niña-like conditions to a phase with El Niño conditions (Phase 2) and vice versa (Phase 1). The transition from negative to positive ENSO conditions was related to a decrease in total cloud fraction (TCF) (−0.018 per decade; not significant) due to a reduction in the high-cloud amount (−0.024 per decade; significant). Observed anomalies in the mean CTH were found to be mainly caused by changes in HCF rather than by anomalies in the height of cloud tops themselves. Using the large-scale vertical motion ω at 500 hPa (from ERA-Interim ECMWF reanalysis data), the observed anomalies were linked to ENSO-induced changes in the atmospheric large-scale dynamics. The most significant and largest changes were found in regions with strong large-scale upward movements near the Equator. Despite the fact that with passive imagers such as MODIS it is not possible to vertically resolve clouds, this study shows the great potential for large-scale analysis of possible changes in the cloud vertical distribution due to the changing climate by using vertically resolved cloud cover and linking those changes to large-scale dynamics using other observations or model data.

Description: 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data is analyzed to identify possible changes of the cloud vertical distribution over the Tropical Atlantic Ocean (TAO). For the analysis multiple linear regression techniques are used. Within the investigated period, no significant trend in the domain-averaged cloud vertical distribution was found. In terms of linear changes, two major phases (before and after November 2011) in the time-series of the TAO domain-average Cloud Top Height (CTH) and High Cloud Fraction (HCF) can be distinguished. While phase 1 is dominated by a significant linear increase, phase 2 is characterized by a strong, significant linear decrease. The observed trends were mainly caused by the El Niño Southern Oscillation (ENSO). The increase in CTH and HCF in phase 1, was attributed to the transition from El Niño (2002) to La Niña (2011) conditions. The strong decrease in phase 2, was caused by the opposite transition from a La Niña (2011) to a major El Niño event (2016). A comparison with the large scale vertical motion ω at 500 hPa obtained from ERA-Interim ECMWF Re-Analyses and the Nino3.4-Index indicates that the changes in HCF are induced by ENSO linked changes in the large scale vertical upward movements over regions with strong large scale ascent. A first comparison with the DARDAR data set, which combines CloudSat radar and CALIPSO lidar measurements, shows qualitatively good agreements for the interannual variability of the high cloud amount and its linear decrease in phase 2.

Description: The remote sensing of Total Column Water Vapour (TCWV) from polar orbiting, sun-synchronous satellite spectrometers such as the Medium Resolution Imaging Spectrometer (MERIS) on board of ENVISAT and the Moderate Imaging Spectroradiometer (MODIS) on board of Aqua and Terra enables observations on a high spatial resolution and a high accuracy over land surfaces. The observations serve studies about small scale variations of water vapour as well as the detection of local and global trends. However, depending on the swath width of the sensor, the temporal sampling is low and the observations of TCWV are limited to cloud-free land scenes. This study quantifies the representativeness of a single TCWV observation at the time of the satellite overpass under cloud-free conditions by investigating the diurnal cycle of TCWV using 9 years of a 2-hourly TCWV data set from global GNSS (Global Navigation Satellite Systems) stations. It turns out, that the TCWV observed at 10:30 local time (LT) is generally lower than the daily mean TCWV by 0.65 mm (4 %) on average for cloud-free cases. Averaging over all GNSS stations, the monthly mean TCWV at 10:30 LT, constrained to cases that are cloud-free, is by 5 mm (25 %) lower than the monthly mean TCWV at 10:30 LT of all cases. Additionally, the diurnal variability of TCWV is assessed. For the majority of GNSS stations, the amplitude of the averaged diurnal cycle ranges between 1 % and 5 % of the daily mean with a minimum between 6 LT and 10 LT and maximum between 16 LT and 20 LT. However, a high variability of TCWV on an individual day is detected. On average, the TCWV varies by 15 % around the daily mean.

Description: The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude/latitude grid of 2° × 2°. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988-2008, as well as seven specific humidity and seven temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): doi:10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterisation of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions on systematic differences in TCWV between the classes.

Description: The remote-sensing reflectance (Rrs) is in someway an artificial unit, that is constructed in order to contain the spectral colour information of the water body, but to be hardly influenced by the atmosphere above. In ocean colour remotesensing it is the measure to define the optical properties of the water/water constituents. Rrs is the ratio of water-leaving radiance and down-welling irradiance. It is derived from top-of-atmosphere radiance/reflectance measurements through atmospheric correction. A database with Rrs from radiative 5 transfer simulations is capable to serve as a forward model for the retrieval of water constituents. For the present database the Rrs is simulated in dependency of inherent optical properties (IOPs) representing pure water with different salinities and 5 water constituents (Chlorophyll-a-pigment, Detritus, CDOM (coloured dissolved organic matter), a "big" and a "small" scatterer) in a global range of concentrations. The interpolation points for each IOP were chosen in order to reproduce the entire functional relationship between this particular IOP and the corresponding Rrs. The IOPs are varied independently. The data is available for 9 solar, 9 viewing zenith and 25 azimuth angles. The spectral resolution of the data is 1nm, which allows the convolution to any ocean colour sensors’ spectral response function. The data is produced with the radiative transfer code MOMO (Matrix Operator Model), which simulates the full radiative transfer in atmosphere and ocean. The code is hosted at the institute of space sciences at Freie Universität Berlin and is not publicly available. The look-up table (LUT) is available at: doi:10.1594/WDCC/LUT_for_WDC_I (Kritten et al., 2017).

Description: The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude–latitude grid of 2∘ × 2∘. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988–2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes.