scholarly journals Validation of the ENSEMBLES global climate ­models over southwestern Europe using probability density functions, from a downscaling perspective

2011 ◽  
Vol 48 (2) ◽  
pp. 145-161 ◽  
Author(s):  
S Brands ◽  
S Herrera ◽  
D San-Martín ◽  
JM Gutiérrez
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Global Climate ◽  
Probability Density Functions ◽  
Global Climate Models ◽  
Density Functions

scholarly journals A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

2018 ◽  
Vol 18 (7) ◽  
pp. 5089-5113 ◽  
Author(s):  
Reinhold Spang ◽  
Lars Hoffmann ◽  
Rolf Müller ◽  
Jens-Uwe Grooß ◽  
Ines Tritscher ◽  
...  
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Michelson Interferometer ◽  
European Space Agency ◽  
Equilibrium Temperature ◽  
Probability Density Functions ◽  
Solid Particles ◽  
Ratio Method ◽  
Orthogonal Polarization ◽  
Density Functions

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models.

scholarly journals Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

2007 ◽  
Vol 20 (17) ◽  
pp. 4356-4376 ◽  
Author(s):  
S. E. Perkins ◽  
A. J. Pitman ◽  
N. J. Holbrook ◽  
J. McAneney
Keyword(s):  
Probability Density ◽  
Minimum Temperature ◽  
Climate Models ◽  
Quantitative Measure ◽  
Probability Density Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Density Functions ◽  
Coupled Climate Models

Abstract The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

scholarly journals Multi-variate probability density functions with dynamics for cloud droplet activation in large-scale models: single column tests

2010 ◽  
Vol 3 (2) ◽  
pp. 475-486 ◽  
Author(s):  
H. Guo ◽  
J.-C. Golaz ◽  
L. J. Donner ◽  
V. E. Larson ◽  
D. P. Schanen ◽  
...  
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Cloud Droplet ◽  
Probability Density Functions ◽  
Aerosol Concentration ◽  
Small Scale ◽  
Density Functions ◽  
Droplet Concentration ◽  
Single Column ◽  
Aerosol Activation

Abstract. Successful simulation of cloud-aerosol interactions (indirect aerosol effects) in climate models requires relating grid-scale aerosol, dynamic, and thermodynamic fields to small-scale processes like aerosol activation. A turbulence and cloud parameterization, based on multi-variate probability density functions of sub-grid vertical velocity, temperature, and moisture, has been extended to treat aerosol activation. Multi-variate probability density functions with dynamics (MVD PDFs) offer a solution to the problem of the gap between the resolution of climate models and the scales relevant for aerosol activation and a means to overcome the limitations of diagnostic estimates of cloud droplet number concentration based only on aerosol concentration. Incorporated into the single-column version of GFDL AM3, the MVD PDFs successfully simulate cloud properties including precipitation for cumulus, stratocumulus, and cumulus-under-stratocumulus. The extension to treat aerosol activation predicts droplet number concentrations in good agreement with large eddy simulations (LES). The droplet number concentrations from the MVD PDFs match LES results more closely than diagnostic relationships between aerosol concentration and droplet concentration. In the single-column model simulations, as aerosol concentration increases, droplet concentration increases, precipitation decreases, but liquid water path can increase or decrease.

scholarly journals A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

2017 ◽  
Author(s):  
Reinhold Spang ◽  
Lars Hoffmann ◽  
Rolf Müller ◽  
Jens-Uwe Grooß ◽  
Ines Tritscher ◽  
...  
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Michelson Interferometer ◽  
Equilibrium Temperature ◽  
Probability Density Functions ◽  
Solid Particles ◽  
Ratio Method ◽  
Orthogonal Polarization ◽  
Density Functions ◽  
Night Time

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument onboard the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in IR limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), liquid droplets of super-cooled ternary solution (STS) as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day and night time climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on ECMWF reanalysis temperature data show the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry climate models.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

2021 ◽  
Vol 13 (12) ◽  
pp. 2307
Author(s):  
J. Javier Gorgoso-Varela ◽  
Rafael Alonso Ponce ◽  
Francisco Rodríguez-Puerta
Keyword(s):  
Probability Density ◽  
Linear Models ◽  
Pinus Halepensis ◽  
Beta Function ◽  
Probability Density Functions ◽  
Lidar Data ◽  
Density Functions ◽  
Minimum Diameter ◽  
Location Parameters ◽  
Diameter Distributions

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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