An assessment of the quality of aerosol retrievals over the Red Sea and evaluation of the climatological cloud-free dust direct radiative effect in the region

Ground-based and satellite observations are used in conjunction with the Rapid Radiative Transfer Model (RRTM) to assess climatological aerosol loading and the associated cloud-free aerosol direct radiative effect (DRE) over the Red Sea. Aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments are first evaluated via comparison with ship-based observations. Correlations are typically better than 0.9 with very small root-mean-square and bias differences. Calculations of the DRE along the ship cruises using RRTM also show good agreement with colocated estimates from the Geostationary Earth Radiation Budget instrument if the aerosol asymmetry parameter is adjusted to account for the presence of large particles. A monthly climatology of AOD over the Red Sea is then created from 5 years of SEVIRI retrievals. This shows enhanced aerosol loading and a distinct north to south gradient across the basin in the summer relative to the winter months. The climatology is used with RRTM to estimate the DRE at the top and bottom of the atmosphere and the atmospheric absorption due to dust aerosol. These climatological estimates indicate that although longwave effects can reach tens of W m−2, shortwave cooling typically dominates the net radiative effect over the Sea, being particularly pronounced in the summer, reaching 60 W m−2 at the surface. The spatial gradient in summertime AOD is reflected in the radiative effect at the surface and in associated differential heating by aerosol within the atmosphere above the Sea. This asymmetric effect is expected to exert a significant influence on the regional atmospheric and oceanic circulation

Cirrus cloud identification from airborne far-infrared and mid-infrared spectra

Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far-and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm's performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid-and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA's 9th Earth Explorer

A test of the ability of current bulk optical models to represent the radiative properties of cirrus cloud across the mid-and far-infrared

Measurements of mid- to far-infrared nadir radiances obtained from the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft during the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) are used to assess the performance of various ice cloud bulk optical (single-scattering) property models. Through use of a minimisation approach, we find that the simulations can reproduce the observed spectra in the mid-infrared to within measurement uncertainty but are unable to simultaneously match the observations over the far-infrared frequency range. When both mid and far-infrared observations are used to minimise residuals, first order estimates of the flux differences between the best performing simulations and observations indicate a strong compensation effect between the mid and far infrared such that the absolute broadband difference is < 0.7 W m−2. However, simply matching the spectra using the mid-infrared observations in isolation leads to substantially larger discrepancies, with absolute differences reaching ~ 1.8 W m−2. These results highlight the benefit of far infrared observations for better constraining retrievals of cirrus cloud properties and their radiative impact, and provide guidance for the development of more realistic ice cloud optical models

Cirrus cloud identification from airborne far-infrared and mid-infrared spectra

Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer

FORUM: Unique Far-Infrared Satellite Observations to Better Understand How Earth Radiates Energy to Space

International audienc

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Systme Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations