# Monte Carlo Simulations in Aviation Contrail Study: A Review
## 5 Challenges and Perspectives
(1999) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (Eds.) _Aviation and the Global Atmosphere: A Special Report of the Intergovernmental Panel on Climate Change_; Cambridge University Press: Cambridge, UK, 1999. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] [PERSON] Statistical analysis of contrail lifetimes from a satellite perspective. _Meteorol. Z._**2018**, _27_, 183-193. [CrossRef]
* [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Forecast and tracking the evolution of cloud clusters using satellite infrared imagery: Methodology and validation. _Wearthe Forecast_**2008**, _23_, 233-245. [CrossRef]
* [PERSON] and [PERSON] (2010) [PERSON]; [PERSON]; [PERSON] An automatic contrail tracking algorithm. _Atmos. Meas. Tech._**2010**, \(3\), 1089-1101. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON] [PERSON]; [PERSON] Contrail life cycle and properties from 1 year of MSG/SEVIRI rapid scan images. _Atmos. Chem. Phys._**2015**, _15_, 8739-8749. [CrossRef]
* [PERSON] (1951) [PERSON] A statistical distribution function of wide applicability. _ASME_, _J. Appl. Mech._**1951**, _18_, 293-297. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Can we reliably assess climate mitigation options for air traffic scenarios despite large uncertainties in atmospheric processes? _Transp. Res. Part D Transp. Environ._**2016**, _46_, 40-55. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] [PERSON] Contrail cirrus radiative forcing for future air traffic. _Atmos. Chem. Phys._**2019**, _19_, 8163-8174. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Mitigating the climate forcing of aircraft contrils by small-scale diversions and technology adoption. _Environ. Sci. Technol._**2020**, _54_, 2941-2950. [CrossRef]
* [PERSON] et al. (2003) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. _The Atmospheric General Circulation Model ECHAM 5. PART I: Model Description_; Max-Planck-Institut fur Meteorologie: Hamburg, Germany, 2003. * [PERSON] (2002) [PERSON] A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. _J. Atmos. Sci._**2002**, _59_, 1917-1942. [CrossRef]
* [PERSON] (2016) [PERSON]; [PERSON] The temporal evolution of a long-lived contrail cirrus cluster: Simulations with a global climate model. _J. Geophys. Res. Atmos._**2016**, _121_, 3548-3565. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Reassessing properties and radiative forcing of contrail cirrus using a climate model. _J. Geophys. Res. Atmos._**2016**, _121_, 9717-9736. [CrossRef]
* [PERSON] et al. (1989) [PERSON]; [PERSON]; [PERSON] Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. _Mon. Weather Rev._**1989**, _117_, 1641-1657. [CrossRef]
* [PERSON] (2008) [PERSON]; [PERSON] AirClim: An efficient climate impact assessment tool. _Atmos. Chem. Phys._**2008**, \(8\), 4621-4639. [CrossRef]* [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Estimating the effective radiative forcing of contrail cirrus. _J. Clim._**2020**, _33_, 1991-2005. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]. Impact of biofuels on contrail warming. _Environ. Res. Lett._**2017**, _12_, 114013. [CrossRef]
* [PERSON] (2012) [PERSON] A contrail cirrus prediction model. _Geosci. Model Dev._**2012**, \(5\), 543-580. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Lagrangian condensation microphysics with Twomey CCN activation. _Geosci. Model Dev._**2018**, _11_, 103-120. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling of cloud microphysics: Can we do better? _Bull. Am. Meteorol. Soc._**2019**, _100_, 655-672.