EarthCARE and its payload

Gelsthorpe, R. V.; Hélière, Arnaud; Lefebvre, Alain; Lemanczyk, J.; Mateu, E.; Wallace, Kotska

doi:10.1117/12.804914

Cited by 6 publications

(6 citation statements)

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“…The GCRM we evaluate here is the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) [ Satoh et al ., ]. The satellite signals are simulated using the Joint Simulator for Satellite Sensors (Joint‐Simulator), which is developed under the joint European Space Agency/Japan Aerospace Exploration Agency (JAXA) Earth Clouds and Radiation Explorer (EarthCARE) mission [ Gelsthorpe et al ., ; Kimura et al ., ]. The symbols and acronyms used in this paper are listed in Table .…”

Section: Introductionmentioning

confidence: 99%

Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO

et al. 2013

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[1] We describe a method to evaluate cloud microphysics simulated with a global cloud-resolving model against CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite data. Output from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) is run through a satellite-sensor simulator (Joint Simulator for Satellite Sensors), then directly compared to the radar and lidar signals from CloudSat and CALIPSO. The forward approach allows for consistency in cloud microphysical assumption involved in the evaluation. To investigate the dependence of the signals on the temperature, we use temperature extensively as the vertical coordinate. The global statistical analysis of the radar reflectivity shows that the simulation overestimates all the percentiles above À50°C and that snow category contributes significantly to low reflectivity values between À80 and À40°C. The simulated lidar signals have two modes associated with cloud ice and snow categories, though the observations have only one mode. The synergetic use of radar reflectivity and lidar backscatter enables us to determine the relative magnitudes of ice/liquid water contents and effective radii without use of retrievals. The radar-and-lidar diagnosis for cloud tops shows that, due to snow category, NICAM overestimates the mass-equivalent effective radius and underestimates ice water content. Also, the diagnosis was shown to be useful to investigate sensitivities of the parameters of bulk microphysical schemes on the water contents and sizes. The nonspherical scattering of ice particles was shown to affect the above radar-and-lidar diagnosis for large reflectivity ranges but not to alter most of the other diagnoses for this simulation.

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Section: Introductionmentioning

confidence: 99%

Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO

et al. 2013

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“…Better understanding of the various feedbacks, in particular those involving clouds, requires spatial resolution adequate to resolve cloud field heterogeneity, improved methods for measuring and taking into account real-time anisotropic reflectance, better time sampling, and of course still better radiometric accuracy. Some of these questions are well or better addressed by satellite missions with less than global coverage, as, for example, ERBS and TRMM in the past, Meteosat with GERB, and coming missions such as Megha-Tropiques (Desbois et al 2007) and EarthCARE (Gelsthorpe et al 2008). The question remains: Can we measure and understand the changes in Earth's energy and water fluxes faster than human activities change them?…”

Section: Concluding Remarks: Feedbacks and The Futurementioning

confidence: 97%

“…The broadband SW data from the Geostationary ERB (GERB) instrument on board Meteosat (Harries et al 2005), although imperfect because of the multiple reflections, improve monitoring of rapid albedo changes. In the future, the Broad-Band Radiometer (BBR) on board the projected EarthCARE mission (Kandel et al 2003;Capderou and Viollier 2006;Gelsthorpe et al 2008) is to provide three nearly simultaneous views of each pixel along the satellite track, so as to reduce reliance on statistically based bidirectional reflectance distribution functions and to attain 10 W m -2 accuracy in deriving instantaneous reflected SW flux from the SW radiance measurements.…”

Section: Albedo Forcingmentioning

confidence: 99%

Understanding and Measuring Earth’s Energy Budget: From Fourier, Humboldt, and Tyndall to CERES and Beyond

Kandel

2012

Surv Geophys

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This historical perspective on the determination of Earth's energy fluxes, beginning with the classical description of climate, outlines the establishment of the basic physics of the Earth climate system in the nineteenth century. After recalling the early twentieth century ground-based attempts to determine the Earth's energy budget, I review the growing contributions of observations from space to quantifying these exchanges. In particular, space observations have shown that variations of solar luminosity have been extremely small (of order 0.1%) over past decades and probably past centuries and that they play practically no role in present-day climate variations or variations that may be expected in coming decades. Overall geographical structure, diurnal and seasonal cycles, and some of the interannual and interdecadal variations of Earth's energy exchanges with the Sun and space are now quite well determined, but much remains to be done regarding, on the one hand, fluxes at the surface and, on the other hand, variations of clouds. Improvements are essential if scientific assessment of anthropogenic climate change risk is to keep up with the changes themselves.Keywords Earth's energy flows Á Satellite observations Á Historical background Historical PerspectiveThe Earth's energy budget is at the heart of physical climatology, and the principles of this science have been well established for over a century and a half, well before the development of any understanding of the physical structure of the Sun and stars, the nature of the physical processes accounting for their luminosities, and indeed the details of the transfer of radiation in the atmospheres of stars and planets. Only celestial mechanics is an older science.

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“…This paper advances the approach by using a cloud particle type retrieval product to clarify the dependence of CREs on the phase of hydrometeors from the space for the first time and attempts to link the biases in CREs to those in cloud microphysics. The joint ESA/JAXA Earth Clouds and Radiation Explorer (EarthCARE) mission [ Gelsthorpe et al, ; Kimura et al, ; Illingworth et al, ] will have a cloud radar with Doppler measurement, a high spectral resolution lidar, a multispectral imager, and a broadband radiometer, which is expected to take over the observation of CloudSat and CALIPSO. The approach proposed in this study can be directly applied to observations of EarthCARE satellite.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Arctic cloud radiative effects simulated by NICAM with A‐train

et al. 2016

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Evaluation of cloud radiative effects (CREs) in global atmospheric models is of vital importance to reduce uncertainties in weather forecasting and future climate projection. In this paper, we describe an effective way to evaluate CREs from a 3.5 km mesh global nonhydrostatic model by comparing it against A‐train satellite data. The model is the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and its output is run through a satellite‐sensor simulator (Joint Simulator for satellite sensors) to produce the equivalent CloudSat radar, CALIPSO lidar, and Aqua Clouds and the Earth's Radiant Energy System (CERES) data. These simulated observations are then compared to real observations from the satellites. We focus on the Arctic, which is a region experiencing rapid climate change over various surface types. The NICAM simulation significantly overestimates the shortwave CREs at top of atmosphere and surface as large as 24 W m−2 for the month of June. The CREs were decomposed into cloud fractions and footprint CREs of cloud types that are defined based on the CloudSat‐CALIPSO cloud top temperature and maximum radar reflectivity. It turned out that the simulation underestimates the cloud fraction and optical thickness of mixed‐phase clouds due to predicting too little supercooled liquid and predicting overly large snow particles with too little mass content. This bias was partially offset by predicting too many optically thin high clouds. Offline sensitivity experiments, where cloud microphysical parameters, surface albedo, and single scattering parameters are varied, support the diagnosis. Aerosol radiative effects and nonspherical single scattering of ice particles should be introduced into the NICAM broadband calculation for further improvement.

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EarthCARE and its payload

Cited by 6 publications

References 0 publications

Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO

Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO

Understanding and Measuring Earth’s Energy Budget: From Fourier, Humboldt, and Tyndall to CERES and Beyond

Evaluating Arctic cloud radiative effects simulated by NICAM with A‐train

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