anchor.diff:      call geo_LUT(training_obs, training_atm)
       call geo_LUT(training_obs, predictions)
       
  -    call io_write("obs.nc","data",training_obs%variables(1)%data)
  -    call io_write("training.nc","data",training_atm%variables(1)%data)
  -    call io_write("predictor.nc","data",predictions%variables(1)%data)
  +    if (options%debug) then
  +        print*, ""
  +        print*, "Witing "
  +        do i=1,size(training_obs%variables)
  +            call io_write("obs_"//trim(training_obs%variables(i)%name)//".nc","data", &
  +                            training_obs%variables(i)%data)
  +        enddo
  +        do i=1,size(training_atm%variables)
  +            call io_write("training_"//trim(training_atm%variables(i)%name)//".nc","data", &
  +                            training_atm%variables(i)%data)
  +            call io_write("predictor_"//trim(predictions%variables(i)%name)//".nc","data", &
  +                            predictions%variables(i)%data)
  +        enddo
  +    endif
       
       print*, ""
       print*, "=========================================="
  
positive.code:     print*, "=========================================="
      print*, "Reading obs"
      training_obs = read_obs(options%obs)
      
      ! read in the atmospheric predictor data (e.g. GCM or GEFS)
      print*, ""
      print*, "=========================================="
      print*, "Reading predictor"
      predictions  = read_atm(options%prediction)
      
      print*, ""
      print*, "=========================================="
  
negative.code:     
      call model_init(options)
      
      ! read in the atmospheric predictor data (e.g. GCM or GEFS)
      print*, ""
      print*, "=========================================="
      print*, "Reading predictor"
      predictions  = read_atm(options%prediction)
      
      ! read in the training atmospheric data (e.g. reanalysis or GEFS)
      print*, ""
      print*, "=========================================="
  