Java listener explained in a simple way

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hit summary

1 Main scripts
East Pacific:  

trackep2014_ep.sh
trackep2013_ep.sh
trackep2012_ep.sh
trackep2011_ep.sh

Atlantic: 

trackat2013_at.sh
trackat2012_at.sh
trackat2011_at.sh
trackat2014_at.sh

Track mean for both East Pacific and Atlantic:

track_mean.sh

 2. File needed for a storm
 For instance: storm  Amanda
 "trackep2014_ep.sh"
  Amanda)         code1=ep012014.dat; DATEST=20140522; DATEND=20140529;;

ep012014.dat is used to find two files aep012014.dat and bep012014.dat within hit package's 
archive data location: hit_pkg/hit/tpctrack

Note: 
a) use NHC's website "http://www.nhc.noaa.gov/archive/2014/" to verify storm ID and storm base. 
b) If you don't set it right "  Amanda)         code1=ep012014.dat; DATEST=20140522; DATEND=20140529;;" in script such as  "trackep2014_ep.sh", then everything will go wrong. 

ep012014.dat : storm #1 in East Pacific  
aep012014.dat  : A file contains all the forecast information from various storm forecast centers.
bep012014.dat : A file contains the BEST track/intensity information for the storm. 

3. Input file
Users' input file such as "atcfunix.gdas.2011102406" will be appended to 
aep012014.dat to make a new forecast information file, which contains forecast result of your model plus other models that are already in. 

4. To be continued .... 

 

3D diagram of sun, earth and moon position relationship

 

GSI info

vsdb summary 2

1. for loop layers in script "run_scorecard.sh"

for stat in $statlist ; do            #Loop over cor, rms, bias
   for vnam in $vnamlist ; do  #Loop over HGT, T, U, V, WIND
      for reg in $reglist ; do       #Loop over G2, NH, SH
         for dd in $day ; do         #Loop over day 1,3,5,6,8,10

             file1=${scoredir}/score_${stat}_${namedaily}_${mdnamec1}_day${dd}.txt
             file2=${scoredir}/score_${stat}_${namedaily}_${mdnamec2}_day${dd}.txt
             file3=${scoredir}/score_${stat}_conflimit_${namedaily}_${mdnamec2}_day${dd}.txt
            # if any of three files missing, exit 88, no good!
            # I change it so it continues. 
             if [[ ! -s "$file1" || ! -s "$file2" || ! -s "$file3" ]] ; then
                   #dxu  exit 88
                   continue         # dxu: skip that day, and move on to the next ....
              fi
          done
      done
   done
done

2. Two ways to run script "run_scorecard.sh" 
 

Grid models vs. spectral models

Grid models vs. spectral models

The three dimensions of space can be accounted for in various ways in numerical weather or climate prediction models. Most models are grid models, in which variables are computed at discrete grid points in the horizontal and vertical directions. The model resolution refers to the (horizontal) spacing between gridpoints. The grid spacing is not necessarily equidistant. For instance, some models use a longitude difference as zonal grid spacing, so near the poles the zonal grid spacing becomes zero. In the vertical direction the spacing is usually variable, the model resolution typically is highest just above sea level.

Other models, in particular those whose domain is global, are spectral models (Note 15.H): these transform the variation of some variable (e.g. temperature) with latitude and longitude into a series of waves; the highest wave number retained in the Fourier transform is a measure of the model resolution.

Numerical prediction models are based on the equations of motion (Note 15.G), and these involve many partial derivatives in space. Partial derivatives of wave fields (as used in spectral models) can be calculated exactly, rather than by means of a finite difference approach (used in grid models). This is the main advantage of spectral models. Of course the wave form is converted back into a spatial form after the calculations, in order to analyze the forecasts.