Changes between Version 164 and Version 165 of doc/app/particle_parameters

Timestamp:

Jul 14, 2017 4:40:53 PM (7 years ago)

Author:

hoffmann

Comment:

--

doc/app/particle_parameters

@@ -109 +109 @@
 }}}
 |----------------
-{{{#!td style="vertical-align:top"
-[=#collision_algorithm '''collision_algorithm''']
-}}}
-{{{#!td style="vertical-align:top"
-C*15
-}}}
-{{{#!td style="vertical-align:top"
-'all_or_nothing'
-}}}
-{{{#!td
-Parameter to steer the algorithm for cloud droplet growth by collision.\\\\
-
-By default, the collision algorithm is set to 'all_or_nothing'. The user can choose between the following algorithms:\\\\
-
-'' 'all_or_nothing' ''
-      Probabilistic collision algorithm based on the ideas of Shima et al. (2009) and Sölch and Kärcher (2010).  Each particles represented by one superdroplet grows by the collection of one particle of another superdroplet if the probability for this event if larger than a random number.
-
-'' 'average_impact' ''
-      Original PALM collision algorithm (Riechelmann et al, 2012), in which the average grow of every superdroplet is calculated. In contrast to the  'all_or_nothing' algorithm, the number of collected particles is equally distributed over the collecting particles, i.e., a particle might grow by collecting a certain fraction of particles.  
-
-}}}
-|----------------
 {{{#!td style="vertical-align:top;width: 150px"
 [=#curvature_solution_effects '''curvature_solution_effects''']
@@ -141 +119 @@
 }}}
 {{{#!td
-Parameter to consider solution and curvature effects on the equilibrium vapor pressure of cloud droplets. For the initialization of the corresponding dry aerosol spectrum, see [#init_aerosol_probabilistic init_aerosol_probabilistic]. In this case, the radius growth equation is a stiff o.d.e, which is integrated in time using a Rosenbrock method (see Numerical Recipes in FORTRAN, 2nd Edition, p.731). '''curvature_solution_effects''' = ''.T.'' may significantly increase CPU time of jobs.
+Parameter to consider solution and curvature effects on the equilibrium vapor pressure of cloud droplets. For the initialization of the corresponding dry aerosol spectrum, see [#aero_type aero_type]. In this case, the radius growth equation is integrated in time using a Rosenbrock method. 
 }}}
 |----------------
@@ -319 +297 @@
 |----------------
 {{{#!td style="vertical-align:top"
-[=#init_aerosol_probabilistic '''init_aerosol_probabilistic''']
-}}}
-{{{#!td style="vertical-align:top"
-L
-}}}
-{{{#!td style="vertical-align:top"
-.FALSE.
-}}}
-{{{#!td
-A logical which steers the initialization of the aerosol spectrum (only necessary if [#curvature_solution_effects curvature_solution_effects] are activated). Up to 3 log-normal distributions can be predefined to initialize the aerosol spectrum via [#n1 n1], [#n2 n2], [#n3 n3], [#rm1 rm1], [#rm2 rm2], [#rm3 rm3], [#s1 s1], [#s2 s2], [#s3 s3] in the dry aerosol radius range from 0.01 to 1.0 microns. In a subsaturated environment, the initial radius of the haze particle is computed by a parametrization, i.e., the parameter [#radius radius] does not affect the initial radius of the particles. If no aerosol spectrum is desired, see [#monodisperse_aerosols monodisperse_aerosols].
-
-Options:
-* .TRUE.: The aerosol dry radius is initialized by a random number generator. The weighting factor is not changed by this initialization.
-* .FALSE.: The aerosol spectrum is divided in logarithmically-spaced bins (the number of bins equals the number of super-droplets per grid box). The dry aerosol radius of the super-droplet is set to the mean dry aerosol radius of the super-droplet's bin. The weighting factor is adjusted to be proportional to the number of aerosols in the bin, but the mean weighting factors still matches the [#initial_weighting_factor initial_weighting_factor].
-}}}
-|----------------
-{{{#!td style="vertical-align:top"
 [=#max_number_particles_per_gridbox '''max_number_particles_per_gridbox''']
 }}}
@@ -381 +342 @@
 '''Remark:'''\\
 In case you are using larger number of gridpoints and if you release particles in a limited area of the total domain only, you may think about reducing the default value to a small number, e.g. 2. For example, if you release particles only near the surface, you may never need to allocate memory for particles well above the boundary layer. Furthermore, please be aware that you may run out of memory if you are using the default value of '''min_nr_particle''' for a larger number of grid points.
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#monodisperse_aerosols '''monodisperse_aerosols''']
-}}}
-{{{#!td style="vertical-align:top"
-L
-}}}
-{{{#!td style="vertical-align:top"
-.FALSE.
-}}}
-{{{#!td
-* .TRUE.: Initializes a monodisperse aerosol spectrum, i.e., the dry aerosol radius is 0.1 micron for each super-droplet. 
-* .FALSE.: A log-nomal distributed aerosol spectrum is initialized (see [#init_aerosol_probabilistic init_aerosol_probabilistic]).
 }}} 
 |----------------
@@ -442 +389 @@
 |----------------
 {{{#!td style="vertical-align:top"
-[=#n1 '''n1''']
-}}}
-{{{#!td style="vertical-align:top"
-R
+[=#na '''na''']
+}}}
+{{{#!td style="vertical-align:top"
+R(3)
 }}}
 {{{#!td style="vertical-align:top"
@@ -451 +398 @@
 }}}
 {{{#!td
-Number concentration of the first log-normal distribution steering the initial dry aerosol spectrum. See [#init_aerosol_probabilistic init_aerosol_probabilistic] for more details. 
-
-'''n1''' can be given in arbitrary units, since the final number concentration is still steered via [#initial_weighting_factor initial_weighting_factor].
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#n2 '''n2''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-0.0
-}}}
-{{{#!td
-Number concentration of the second log-normal distribution steering the initial dry aerosol spectrum. See [#n1 n1].
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#n3 '''n3''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-0.0
-}}}
-{{{#!td
-Number concentration of the third log-normal distribution steering the initial dry aerosol spectrum. See [#n1 n1].
+Number concentration (in m^-3^) of the log-normal distribution steering the initial dry aerosol spectrum. See [#aero_type aero_type] for more details. Up to three values of '''na''' can be prescribed.
 }}} 
 |----------------
@@ -733 +652 @@
 |----------------
 {{{#!td style="vertical-align:top"
-[=#rm1 '''rm1''']
-}}}
-{{{#!td style="vertical-align:top"
-R
+[=#rm '''rm''']
+}}}
+{{{#!td style="vertical-align:top"
+R(3)
 }}}
 {{{#!td style="vertical-align:top"
@@ -742 +661 @@
 }}}
 {{{#!td
-Mode radius of the first log-normal distribution steering the initial dry aerosol spectrum. See [#init_aerosol_probabilistic init_aerosol_probabilistic] for more details. 
-
-'''rm1''' should be entered in meters.
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#rm2 '''rm2''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-0.05E-6
-}}}
-{{{#!td
-Mode radius of the second log-normal distribution steering the initial dry aerosol spectrum. See [#rm1 rm1].
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#rm3 '''rm3''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-0.05E-6
-}}}
-{{{#!td
-Mode radius of the third log-normal distribution steering the initial dry aerosol spectrum. See [#rm1 rm1].
+Mode radius (in m) of the log-normal distribution steering the initial dry aerosol spectrum. See [#aero_type aero_type] for more details. Up to three values of '''rm''' can be prescribed.
 }}} 
 |----------------
@@ -891 +782 @@
 |----------------
 {{{#!td style="vertical-align:top"
-[=#s1 '''s1''']
-}}}
-{{{#!td style="vertical-align:top"
-R
+[=#log_sigma '''log_sigma''']
+}}}
+{{{#!td style="vertical-align:top"
+R(3)
 }}}
 {{{#!td style="vertical-align:top"
@@ -900 +791 @@
 }}}
 {{{#!td
-Geometric standard deviation of the first log-normal distribution steering the initial dry aerosol spectrum. See [#init_aerosol_probabilistic init_aerosol_probabilistic] for more details. 
-
-'''s1''' has no units.
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#s2 '''s2''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-2.0
-}}}
-{{{#!td
-Geometric standard deviation of the second log-normal distribution steering the initial dry aerosol spectrum. See [#s1 s1].
-}}} 
-|----------------
-{{{#!td style="vertical-align:top"
-[=#s3 '''s3''']
-}}}
-{{{#!td style="vertical-align:top"
-R
-}}}
-{{{#!td style="vertical-align:top"
-2.0
-}}}
-{{{#!td
-Geometric standard deviation of the third log-normal distribution steering the initial dry aerosol spectrum. See [#s1 s1].
+Logarithm (log10) of geometric standard deviation of the log-normal distribution steering the initial dry aerosol spectrum. See [#aero_type aero_type] for more details. Up to three values of '''log_sigma''' can be prescribed.
 }}} 
 |----------------