MET Online Tutorial for METv8.0 | MODE-Time-Domain > Run

Next, we'll run MTD on the command line using the following command:

mtd \
-fcst $MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_03.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_06.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_09.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_12.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_15.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_18.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_21.tm00_G212 \
$MET_TUTORIAL_DATA/input/sample_fcst/2005080700/wrfprs_ruc13_24.tm00_G212 \
-obs $MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080703V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080706V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080709V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080712V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080715V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080718V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080721V_03A.nc \
$MET_TUTORIAL_DATA/input/sample_obs/ST2ml_3h/sample_obs_2005080724V_03A.nc \
-outdir $MET_TUTORIAL_DATA/output/mtd \
-config $MET_TUTORIAL_DATA/config/MTDConfig_tutorial \
-v 2

Just as with MODE, MTD applies a convolution operation to smooth the data. However, there are two important differences. In MODE, the convolution shape is a circle (radius = conv_radius). In MTD, the convolution shape is a square (width = 2*conv_radius+1) and for time t, the values in that square are averaged for times t-1, t, and t+1. Convolving in space plus time enables MTD to identify more continuous space-time objects.

If your data has high enough time frequency that the features at one timestep overlap those at the next timestep, it may be well-suited for MTD.