ECSIM: the simulator framework for EarthCARE

Voors, Robert; Donovan, D. P.; Acarreta, J. R.; Eisinger, Michael; Franco, Raffaella; Lajas, D.; Moyano, Ricardo; Pirondini, Fabrizio; Ramos, José Luis Martínez; Wehr, T.

doi:10.1117/12.737738

Cited by 31 publications

(29 citation statements)

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“…More recently, several studies have followed this approach to exploit data from the active sensors (lidar and radar) (Haynes et al 2007;Bodas-Salcedo et al 2008;Chepfer et al 2008;Wilkinson et al 2008;Marchand et al 2009;Zhang et al 2010) and from the Multiangle Imaging Spectroradiometer (MISR) (Marchand and Ackerman 2010). The development of simulators is also an active area of research in the observational community (e.g., Voors et al 2007;Masunaga et al 2010), and it may also help address the question as to whether cloud-resolving models (CRMs) used to develop parameterizations are adequate for doing so. The main drawback of the simulator approach is that interpreting results in terms of physical processes may sometimes be problematic because the comparison variables are not trivially related to any single geophysical quantity.…”

Section: Cospmentioning

confidence: 99%

COSP: Satellite simulation software for model assessment

Bodas‐Salcedo

Webb

Bony

et al. 2011

Bulletin of the American Meteorological Society

563

451

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By simulating the observations of multiple satellite instruments, COSP enables quantitative evaluation of clouds, humidity, and precipitation processes in diverse numerical models.G eneral circulation models (GCMs) of the atmosphere, including those used for numerical weather prediction (NWP) and climate projections, operate with resolutions from a few kilometers to hundreds of kilometers. Many atmospheric processes, such as turbulence and microphysical processes within clouds, operate at smaller scales and hence cannot be resolved by current model resolutions. These processes are included by means of parameterizations, which are semiempirical or statistical models that relate gridbox mean variables to these subgrid processes. For instance, some cloud parameterizations diagnose the amount of cloud condensate and the fraction of the grid box that a cloud occupies (cloud area fraction) as a function of the relative humidity (RH) of the grid box (Slingo 1980;Smith 1990). The formulation of these parameterizations is very important for the model evolution because they modify the three-dimensional structure of temperature and humidity directly (e.g., condensation/evaporation) or indirectly by interacting with other parameterizations (e.g., radiation) and the large-scale dynamics. Therefore, the evaluation of these parameterizations is crucial to improving our weather forecasts or increasing our confidence in climate projections.Satellites have proven to be very helpful tools for this purpose because they provide global or nearglobal coverage, thereby giving a representative sample of all meteorological conditions. However, satellites do not measure directly those geophysical quantities of interest, such as the amount or phase of cloud condensate. They measure the intensity of radiation coming from a particular area and direction in a particular wavelength range (radiances). The range of wavelengths covered by past and current systems spans several orders of magnitude, from COSP

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Section: Cospmentioning

confidence: 99%

COSP: Satellite simulation software for model assessment

Bodas‐Salcedo

Webb

Bony

et al. 2011

Bulletin of the American Meteorological Society

563

451

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“…Example results of MC calculations applied to idealized cirrus cloud are shown in Fig The MC calculations were performed using the lidar component [4] of the EarthCARE simulator (ECSIM) [5]. ATLID has three channels, a copolar `Mie' channel, a co-polar Rayleigh channel and a cross-polar Rayleigh+Mie channel.…”

Section: Multiple Scattering In Ice Cloudsmentioning

confidence: 99%

The Expected Impact of Multiple Scattering on ATLID Signals

Donovan

2016

EPJ Web of Conferences

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ATLID stands for "ATmospheric LIDar" and is the lidar to be flown on the Earth Clouds and Radiation Explorer (EarthCARE) platform in 2018. ATLID is a High-Spectral Resolution (HSRL) system operating at 355nm with a narrower field-of-view and lower orbit than the CALIPSO lidar. In spite of the smaller footprint multiple-scattering (MS) will have an important impact on ATLID cloud signals and, in some aspects, the accurate treatment of MS will be more important for ATLID than CALIPSO. On the other hand, the relationship between integrated backscatter and integrated MS induced depolarization in water clouds will be similar between ATLID and CALIPSO indicating that a CALIPSO-like strategy for cloud-phase identification can be successfully applied to ATLID.

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“…Such simulator packages [e.g., COSP (http:// cfmip.metoffice.com/COSP.html), CRTM (www.star. nesdis.noaa.gov/smcd/spb/CRTM), ECSIM (Voors et al 2007), RTTOV (Matricardi et al 2004;Bauer et al 2006), ISSARS (under development, Tanelli 2009, and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in Table 1.…”

mentioning

confidence: 99%