Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties

Abstract

Abstract. We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems

“…Cloud optical (thickness, effective radius, water path) and thermal (cloud top temperature and pressure) properties are retrieved from ORAC using an optimal estimation-based approach. These retrievals and reanalysis profiles of temperature, humidity and ozone are then ingested into BUGSrad, a two-stream correlated-k broadband flux algorithm (Stephens et al, 2001) that outputs the fluxes at the top and bottom of the atmosphere and is shown to have excellent agreement when applied to both active (CloudSat) and passive (Advanced Along Track Scanning Radiometer) satellite sensors compared to the Clouds and the Earth's Radiant Energy System (Henderson et al, 2013;Stengel et al, 2020). In addition, offline radiative transfer sensitivity tests using vertical profiles from our model were conducted with BUGSrad to identify the source of the differences in fluxes between clean and polluted conditions.…”

Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors

“…For AVHRR datasets retrieving liquid while CALIOP retrieving ice, CLARA-A2 revealed more cases than Cloud_cci v2 and v3 for temperatures below −30°C and down to −41°C; For this reason, CLARA-A2 shows SLF around 7% between −40°C and −41°C using the collocated data ( Figure 1a) and around 17% using the noncollocated data (Figure 1b). A quantitative analysis of the differences between CALIOP and Cloud_cci v2 and v3 can be found in Stengel et al (2020): While any phase bias of Cloud_cci v2 and v3 with respect to CALIOP has nearly vanished for COTs of ∼0.15 into the clouds, there is still a significant bias at COT = 1 for the Cloud Top Height (CTH) of ice clouds, to which CTT is linked. As a consequence, CTH is usually retrieved from levels below the levels used to retrieve the phase, so that the retrieved CTT can be warmer than the effective temperature of the assigned cloud top phase, agreeing to our results.…”

“…The datasets we analyze are Cloud_cci AVHRR‐PMv2 (Stengel et al., 2017), Cloud_cci AVHRR‐PMv3 (Stengel et al., 2020), CLARA‐A2 (Karlsson et al., 2017), and CALIOP V4 (Liu et al., 2019). While the first three are based on the polar‐orbiting passive satellite sensor AVHRR onboard NOAA satellites, CALIOP is an active sensor onboard the polar‐orbiting CALIPSO satellite and is part of the NASA A‐Train.…”

Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors

“…Thus, in general, the Naïve Bayesian method produces reasonably good results, but some defects are unavoidable. Consequently, future developments should aim to introduce true Bayesian approaches (e.g., as suggested in [36,37]) or more advanced machine learning methods (e.g., as suggested in [10,38]). However, it is imperative to use the principles from this study to ensure that the different conditions for cloud detection over different Earth surfaces are taken into account for such approaches as well.…”

“…Examples of this are given in [1][2][3][4][5], providing climate monitoring applications based on real-time or near-real-time cloud screening methods. Some schemes introduce increased flexibility [6][7][8][9][10], but no method focuses exclusively on the climate monitoring task. In this paper, we address the disadvantages associated with using similar methods in both real-time and climate monitoring applications.…”