Pre-estimation assessment tool development

1. IDL code enhancement: 
The IDL code will take in one run that you're interested at and 
generates all the graphics for assessement. 

1. clear-sky assessment: 
   . map plot:
      - tb 
      - tbf 
      - tb - tbf (diff)
   . scatter plot
      - tb VS tbf 
      - tb - tbf VS latitude (all orbits)
      - tb - tbf VS latitude (ascending orbit)
      - tb - tbf VS latitude (descending orbit)
   -------------------
   Filter to remove : 
   -------------------
      CLW > 0.05
      rwp > 0.01
      gwp > 0.01

   Implementation order: 
   a) use NWP to filter data
   b) use EDR to filter data

2. Cloudy sky (non-precipitation cloud)
   . map plot:
      - tb 
      - tbf 
      - tb - tbf (diff)
   . scatter plot:
      - tb VS tbf 
      - tb - tbf VS CLW 
   ------------------
   Filter to remove: 
   ------------------
      RWP gt 0.01
      GWP gt 0.01
   ------------------
   Filter to keep:
   ------------------
   a) 0.05 < CLW < 0.3 
   b) 0.05 < CLW 

   Implementation order: 
   a) use NWP to filter data
   b) use EDR to filter data

3. Precipitation
   . map plot: 
      - tb 
      - tbf 
      - tb - tbf (diff)
   . scatter plot:
      - tb VS tbf
      - tb - tbf VS RWP
      - tb - tbf VS GWP
      - tb - tbf VS CLW > 0.3
   ------------------
   Filter to keep:
   ------------------
      RWP > 0.01  
      GWP > 0.01
      possible CLW > 0.3 when using NWP 
      
   Implementation order: 
   a) use NWP to filter data
   b) use EDR to filter data


2. Bias calculation (fortran code) 
1. clear-sky bias (all points) 
   . statistical mean bias 
   . historgram 

1. clear-sky bias (ascending mode)
   . statistical mean bias 
   . historgram 

2. clear-sky bias (descending mode)
   . statistical mean bias 
   . historgram 

3. clear-sky bias (ascending mode)
   . statistical mean bias 
   . historgram 

---------------------
 Clear sky filtering 
---------------------
a) based on NWP CLW/GWP (focused on this)
b) based on EDR CLW/RWP/GWP
c) based on NWP,EDR CLW/RWP/GWP

Path: mirs_trunk/src/testbed/biasGenericAndMonit/
File: Calib_generic_rad.f90
It generates two files: 
   a) mean bias from HIST
      biasCorrec_f18_2013_01_20.dat_gfs
   b) standard deviation
      ModelErrFile_f18_2013_01_20.dat_gfs

Implementation process order: 

1. Bias generation

/data/home001/dxu/mirs_trunk/src/testbed/biasGenerAndMonit
Calib_generic_rad.f90  (f90 version)
Calib_generic_rad.pro  (idl version)

Input:  

  ???

Output: 
  biasCorrec_f18_2013_01_20.dat_gfs
  ModelErrFile_f18_2013_01_20.dat_gfs

2. Bias and standard deviation location
/data/home001/dxu/mirs_trunk/data/SemiStaticData/biasCorrec
biasCorrec_f18.dat  (static bias) 
biasCorrec_f18_2013_01_20.dat_gfs
ModelErrFile_f18_2013_01_20.dat_gfs

3. Plot the bias as a function of channel and scan position. 

/net/orbit232l/home/pub/kgarrett/mirs_utilities/src/idl/monitorBias
plotBiasAndAvg.pro 

Here is what Sid suggested to do: 
1. After applying filters, we can reduce standard deviation. 
2. Use that filters above, we look at bias to optimize 
   observation error.

File location:  

1. Scene file: 
location: /data/home001/dxu/mirs_trunk/src/testbed/grid
file: nongridMirs_edr.pro
purpose: to plot scene data

location: /data/home001/dxu/mirs_trunk/src/lib_idl
file: io_scene.pro

location: /data/home001/dxu/mirs_trunk/src/lib/io
file: IO_Scene.f90

2. Bias file: 
location: /data/home001/dxu/mirs_trunk/src/testbed/biasGenerAndMonit
file: Calib_generic_rad.f90
file: Calib_generic_rad.pro

location: /data/home001/dxu/mirs_trunk/data/SemiStaticData/biasCorrec
file: biasCorrec_f18.dat  (static bias) 
file: biasCorrec_f18_2013_01_20.dat_gfs
file: ModelErrFile_f18_2013_01_20.dat_gfs

location: /net/orbit232l/home/pub/kgarrett/mirs_utilities/src/idl/monitorBias
file: plotBiasAndAvg.pro

Input and Output data location:

Input:
   a) ssmis input data (what format?)
   /data/home001/dxu/archive_data/mirs_test/f18_ssmis/2013-01-20
   31M NPR.TDRN.SC.D13020.S1452.E1637.B1680102.NS

   b) "gfs 6-hr fcst" valid at
   /data/home001/dxu/archive_data/mirs_test/gfs
   3.3M gfs_sfc2013-01-20.t06
    11M gfs_atm2013-01-20.t06

   c) ecmwf analysis
   /data/home001/dxu/archive_data/mirs_test/ecmwf
    24M ecmwf_sfc2013-08-01.t06
    1.8G ecmwf_atm2013-08-01.t06

Output:
   a) Interpolated from GFS 6-hrs fcst valid at
   /data/home001/dxu/mirs_trunk/data/TestbedData/DynamicData/nwp_analys/f18_ssmis/2013-01-20
   53M NWP_GFSN.SC.D13020.S0423.E0604.B1679596.NS.UAS

   b) obs radiance
   /data/home001/dxu/mirs_trunk/data/TestbedData/DynamicData/fmsdr/f18_ssmis/2013-01-20
   5.0M FMSDR_SN.SC.D13020.S0100.E0248.B1679394.NS.UAS

   c) sim radiance
   /data/home001/dxu/mirs_trunk/data/TestbedData/DynamicData/fwd_analys/f18_ssmis/2013-01-20
   4.5M FWD_GFSN.SC.D13020.S0100.E0248.B1679394.NS.UAS

   d) reg data
   /data/home001/dxu/mirs_trunk/data/TestbedData/DynamicData/regress_retr/f18_ssmis/2013-01-20
   57M REGRESSN.SC.D13020.S0100.E0248.B1679394.NS.UAS

   e) bias data
   /data/home001/dxu/mirs_trunk/data/SemiStaticData/biasCorrec
   27K biasCorrec_f18_2013_01_20.dat_gfs
   572 ModelErrFile_f18_2013_01_20.dat_gfs

   f) 1-dvar output
   /data/home001/dxu/mirs_trunk/data/TestbedData/Outputs/edr/f18_ssmis/2013-01-20
   63M EDR_SN.SC.D13020.S0241.E0427.B1679495.NS.UAS.ORB