From CloudSat‐CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar‐lidar observations

Ceccaldi, M.; Delanoë, Julien; Hogan, Robin J.; Pounder, Nicola L.; Protat, Alain; Pelon, Jacques

doi:10.1002/jgrd.50579

Cited by 111 publications

(153 citation statements)

References 38 publications

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“…First all targets detected by radar are classified as containing ice due to the sensitivity of that instrument to the largest particles. Then liquid and ice as detected by lidar are distinguished based on the vertical gradient of lidar backscatter, which is higher in liquid cloud (Ceccaldi et al, 2013); this method of distinguishing liquid cloud is consistent with the method of Yoshida et al (2010) using the lidar depolarization ratio. Where radar detects ice and lidar detects liquid, mixed-phase cloud is diagnosed.…”

Section: Target Classificationsupporting

confidence: 58%

“…We exploit the instruments' complementary sensitivities to different classes of hydrometeors to infer the presence of liquid cloud, rain and drizzle, and ice. This approach to radar-lidar target classification is similar to that described for CloudSat-CALIPSO in Ceccaldi et al (2013); however, the categories are simplified in this analysis.…”

Section: Target Classificationmentioning

confidence: 99%

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Improved rain rate and drop size retrievals from airborne Doppler radar

2017

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Abstract. Satellite remote sensing of rain is important for quantifying the hydrological cycle, atmospheric energy budget, and cloud and precipitation processes; however, radar retrievals of rain rate are sensitive to assumptions about the raindrop size distribution. The upcoming EarthCARE satellite will feature a 94 GHz Doppler radar alongside lidar and radiometer instruments, presenting opportunities for enhanced retrievals of the raindrop size distribution.We demonstrate the capability to retrieve rain rate as a function of drop size and drop number concentration from airborne 94 GHz Doppler radar measurements using CAP-TIVATE, the variational retrieval algorithm developed for EarthCARE. For a range of rain regimes observed during the Tropical Composition, Cloud and Climate Coupling field campaign, we explore the contributions of mean Doppler velocity and path-integrated attenuation (PIA) measurements to the retrieval of rain rate, and the retrievals are evaluated against independent measurements from an independent 9.6 GHz Doppler radar. The retrieved drop number concentrations vary over 5 orders of magnitude between very light rain from melting ice and warm rain from liquid clouds. In light rain conditions mean Doppler velocity facilitates estimates of rain rate without PIA, suggesting the possibility of EarthCARE rain rate estimates over land; in moderate warm rain, drop number concentration can be retrieved without mean Doppler velocity, with possible applications to CloudSat.

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Section: Target Classificationsupporting

confidence: 58%

Section: Target Classificationmentioning

confidence: 99%

Improved rain rate and drop size retrievals from airborne Doppler radar

2017

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“…DARDAR contains two products: DARDAR-MASK (Delanoë and Hogan, 2010;Ceccaldi et al, 2013) and DARDAR-CLOUD Hogan, 2008, 2010), which are both available through the ICARE Thematic Center (http://www.icare.univ-lille1.fr/archive, last access: 1 July 2017). DARDAR-MASK provides the vertical cloud classification and a range of additional categorisations (e.g.…”

Section: Dardarmentioning

confidence: 99%

Ice cloud microphysical trends observed by the Atmospheric Infrared Sounder

et al. 2018

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Abstract. We use the Atmospheric Infrared Sounder (AIRS) version 6 ice cloud property and thermodynamic phase retrievals to quantify variability and 14-year trends in ice cloud frequency, ice cloud top temperature (T ci ), ice optical thickness (τ i ) and ice effective radius (r ei ). The trends in ice cloud properties are shown to be independent of trends in information content and χ 2 . Statistically significant decreases in ice frequency, τ i , and ice water path (IWP) are found in the SH and NH extratropics, but trends are of much smaller magnitude and statistically insignificant in the tropics. However, statistically significant increases in r ei are found in all three latitude bands. Perturbation experiments consistent with estimates of AIRS radiometric stability fall significantly short of explaining the observed trends in ice properties, averaging kernels, and χ 2 trends. Values of r ei are larger at the tops of opaque clouds and exhibit dependence on surface wind speed, column water vapour (CWV) and surface temperature (T sfc ) with changes up to 4-5 µm but are only 1.9 % of all ice clouds. Non-opaque clouds exhibit a much smaller change in r ei with respect to CWV and T sfc . Comparisons between DARDAR and AIRS suggest that r ei is smallest for singlelayer cirrus, larger for cirrus above weak convection, and largest for cirrus above strong convection at the same cloud top temperature. This behaviour is consistent with enhanced particle growth from radiative cooling above convection or large particle lofting from strong convection.

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“…the synergistic retrievals for the IIR thermal camera and CALIOP aboard CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) by Garnier et al, 2012Garnier et al, , 2013Garnier et al, , 2015 or fly in a satellite constellation like the Atrain (e.g. the synergistic retrievals for CALIOP and CPR or CALIOP, CPR and MODIS by Donovan and van Lammeren, 2001;Deng et al, 2010;Ceccaldi et al, 2013;Hogan, 2008, 2010).…”

mentioning

confidence: 99%

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate largescale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary.In this paper the CiPS (Cirrus Properties from SE-VIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m −2 . For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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From CloudSat‐CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar‐lidar observations

Cited by 111 publications

References 38 publications

Improved rain rate and drop size retrievals from airborne Doppler radar

Improved rain rate and drop size retrievals from airborne Doppler radar

Ice cloud microphysical trends observed by the Atmospheric Infrared Sounder

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

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