Note: The links in the steps below are links to the processing example.
| Step [1]: | Use master to create a file that defines an area of interest and map projection. | |
| [Alternative methods for creating a master: TeraMaster (tmaster), master2, master4.] | ||
| Step [2]: | Create TeraScan OLS and special sensor datasets from raw, decrypted RTD data using the function rtdin. | |
| Note: If you are planning to display both OLS and special sensor data together in the same TeraVision window, process the whole pass; do not specify the use of a master. However, if you are interested only in the OLS data, or plan to view the special sensor data separately from the OLS data, limit the size of the dataset by specifying the master file created in Step 1 to cut out an area of interest. | ||
| OLS Processing: | ||
| Step [3]: | Navigate the OLS data: | |
| a. | Use navbox2 to create the boxes to be used to navigate the image. | |
| b. | Use nav2 to navigate the image using the boxes created. | |
| c. | If you are unable to navigate automatically, use xvu to navigate the image manually. | |
| Step [4]: | Run fastreg on the OLS dataset to register the data to the master created in Step 1. | |
| Note: Because the OLS has a variable scan, which means that the distance between adjacent pixels on a scan line is the same near the nadir as it is at the ends of the scan line, the resolution of the pixels is the same across the full sensor swath. Thus, registration of OLS data is not absolutely necessary. However, if you plan to display OLS data with special sensor data, you'll need to register all to the same master. | ||
| SSM/I Processing: | ||
| Step [5]: | Run miedr to create geophysical parameters from SSM/I data output by rtdin. | |
| Step [6]: | Run fastreg on the SSM/I dataset to register the data to the master created in Step 1. | |
| Note: Data from SSM/I, a conical scanning sensor, must be registered. | ||
| SSM/T1 Processing: | ||
| Step [7]: | Run t1edr to create geophysical parameters from SSM/T1 data output by rtdin. | |
| Note: SSM/T1 data does not requre remapping. However, because the pixels are coarser at the limbs of the scan line, the data looks better if remapped. | ||
| SSM/T2 Processing: | ||
| Step [8]: | Run t2edr to create geophysical parameters from SSM/T2 data output by rtdin. | |
| Note: SSM/T1 data does not requre remapping. However, because the pixels are coarser at the limbs of the scan line, the data looks better if remapped. | ||
|
To limit the size of the final output dataset,
you may want to cut out an
area of interest from the data. To do this, first create a master that you will use to
limit the data processed to a specific area of interest. pixel_width
and pixel_height are set to .55, which is the resolution of the
high-resolution channel.
Here, we are using the function master to create an area of interest of the Hudson Bay area. In the example, the final number of lines and samples cut out by the master will be approximately 1000 by 1000. |
trainer% master output file : char(255) ? [Master] MHudson.55 projection : char( 13) ? stereo center_lat : char( 15) ? 55n center_lon : char( 15) ? 80w num_lines : int ? 1000 num_samples : int ? 1000 pixel_width : real ? [1.1132] .55 pixel_height : real ? [1.1132] .55 rotate_angle : real ? [0] move_center : char( 3) ? [no]
|
Use the function rtdin to ingest RTD telemetry. (For this example, we are using this f-11 pass because it contains OLS data as well as data from all the special sensors.) The output data is calibrated and earth-located. Notes:
|
trainer% rtdin output file(s) : char(255) ? [.] dmsp_types : char( 19) ? [all] on_pass_disk : char( 3) ? [yes] pass_number : int ( 15) ? [13] 14 ols_res : char( 3) ? [hi] (see Note 1) temp_units : char( 10) ? [celsius] use_master : char( 3) ? [yes] n (see Note 2) start_time : char( 15) ? [00:00:00] fine_lines : int ? [2000] 10000 start_sample : int ? [1] fine_samples : int ? [7340] ss_calib : char( 12) ? [bright_temp] Processing pass 14 on pass disk. File 1, pass 14, satellite f-11, date 1994/01/11, time 21:37:48 9582 fine OLS lines; about 427 SSMI, 25 SSMT1, 101 SSMT2, and 811 SSJ4 lines.
Good data starts at OLS line 528 Operator given pass start time 21:37:48 Satellite given pass start time 21:38:35.914 Difference in pass start time 00:00:47.914
OLS thermal data is high resolution, visible data is low resolution.
. . . Time count indicated 1.00 second gap. Missing SSMT2 data at or near lines 86 through 86. End of file encountered. Available data is being processed. TDF dataset ./f11.94011.2138.mi_s created with 368 lines. TDF dataset ./f11.94011.2138.t1_s created with 20 lines. TDF dataset ./f11.94011.2138.t2_s created with 87 lines. TDF dataset ./f11.94011.2138.j4_s created with 711 lines. TDF dataset ./f11.94011.2138.dmdm created with 2048 DMDM characters. TDF dataset ./f11.94011.2138.ols created with 9054 fine and 2000 smooth lines.
|
Note 1: The OLS sensor data is composed of two channels: a visible and a thermal channel. The image data from each of these channels has approximately the same spatial resolution. However, in the RTD telemetry, the data from one of these channels (the low-resolution channel) is averaged in the scan direction (every five pixels) and output with a sampling resolution that is lower in the scan direction by a factor of five. Note 2: To be able to display OLS and special sensor data in the same TeraVision window, ingest the whole pass. Do not specify a master or a new start time; if you do, the time of the data in the OLS file will be different than the time of the data in the special sensor files. When you specify a start time, the OLS data ingestion starts with the first line having a time greater than the time specified. However, the special sensor data start at the first valid start time for the pass (thus, the entire pass is processed). |
|
Now, look at the contents of the dataset using the contents function. Note that the data is calibrated. Also note the fine_data and telemetry attributes. Output from rtdin indicates that for this dataset, "OLS thermal data is high resolution, visible data is low resolution." |
trainer% contents f11.94011.2138.ols printout : char( 3) ? [no] Contents of File: f11.94011.2138.ols Page 1
Dimension Size Coord Scale Offset Variable smooth_line 2000 y 5 2 smooth_sample 1468 x 5 2 fine_line 9054 y 1 0 fine_sample 7340 x 1 0
Attribute Type Units Value fine_data string8 thermal telemetry string3 rtd projection long 0 et_affine double 1 0 0 1 0 -45 projection_name string16 sensor_scan satellite string12 f-11 sensor long 3 sensor_name string12 ols pass_date long std_date 1994/01/11 start_time double std_time 21:38:36.914 . . .
Variable Type Units ols_visible byte ols_infrared byte temp_deg_c
Variable Dimension Size ols_visible smooth_line 2000 ols_visible smooth_sample 1468 ols_infrared fine_line 9054 ols_infrared fine_sample 7340
| Now let's navigate the OLS data to correlate the image data
with land/water boundaries. For this example data, we will need to navigate
manually using xvu. Click here
for the steps to follow for manually navigating your data if you are unable to
navigate automatically.
Click here for an example of navigating automatically with navbox2 and nav2. |
|
Use fastreg to remap the OLS data to the master you created in Step 1. The image will be oriented exactly as the master, and will have exactly the same size as the master [see "Using a Master at Registration"]. |
trainer% fastreg f11.94011.2138.ols f11.94011.2138.ols.r master_file : char(255) ? [Master] MHudson.55 include_vars : char(255) ? [] poly_size : real ? [100] f11.94011.2138.ols.r: ols_infrared: [ 1, 1000] X [ 1, 1000] f11.94011.2138.ols.r: ols_visible: [ 1, 1000] X [ 1, 1000]
|
Then look at the contents of the dataset, noting the dimensions. Note also that the data is no longer in sensor scan. View the image in TeraVision and apply a lat/lon grid to help you see the difference from unregistered data. |
trainer% contents f11.94011.2138.ols.r printout : char( 3) ? [no] Contents of File: f11.94011.2138.ols.r Page 1
Dimension Size Coord Scale Offset Variable line 1000 y 1 0 sample 1000 x 1 0
Attribute Type Units Value
fine_data string8 thermal
telemetry string3 rtd
projection long 1
et_affine double -1.81818181818 0 0 1.81818181818
500.5 500.5
projection_name string16 stereographic
.
.
.
Variable Dimension Size ols_infrared line 1000 ols_infrared sample 1000 ols_visible line 1000 ols_visible sample 1000
|
Now, let's process the SSM/I data. First, look at the contents of the .mi_s dataset output by rtdin. |
trainer% contents f11.94011.2138.mi_s printout : char( 3) ? [no] Contents of File: f11.94011.2138.mi_s Page 1
Dimension Size Coord Scale Offset Variable miline_hi 368 y 1 0 misamp_hi 128 x 1 0 miline_lo 184 y 2 0 misamp_lo 64 x 2 0
Variable Type Units mi_85v short kelvin mi_85h short kelvin mi_19v short kelvin mi_19h short kelvin mi_37v short kelvin mi_37h short kelvin mi_22v short kelvin
Variable Dimension Size mi_85v miline_hi 368 mi_85v misamp_hi 128 mi_85h miline_hi 368 mi_85h misamp_hi 128 mi_19v miline_lo 184 mi_19v misamp_lo 64 mi_19h miline_lo 184 mi_19h misamp_lo 64 mi_37v miline_lo 184 mi_37v misamp_lo 64 mi_37h miline_lo 184 mi_37h misamp_lo 64 mi_22v miline_lo 184 mi_22v misamp_lo 64
|
Then, run the miedr function to create geophysical parameters such as sea ice concentration, total columnar liquid water, rain rate, etc. from the SSM/I data. The miedr function implements the Air Force Global Weather Center D-matrix algorithm. (An additional function, geoph, derives similar parameters based on published papers.) |
trainer% miedr input file(s) : char(255) ? f11.94011.2138.mi_s
Processing f11.94011.2138.mi_s SSM/I data starts at 21:38:40.73 GMT on 1994/01/11. TDF data set f11.94011.2138.mi_e created.
|
Now, look at the contents of the .mi_e SSM/I dataset output by miedr and compare with the contents of the .mi_s dataset. |
trainer% contents f11.94011.2138.mi_e printout : char( 3) ? [no] Contents of File: f11.94011.2138.mi_e Page 1
Dimension Size Coord Scale Offset Variable miline_lo 184 y 1 0 misamp_lo 64 x 1 0
Variable Type Units surface_type byte wind_speed short m/s cloud_water short kg/m2 water_vapor short kg/m2 rain_rate short mm/hr rain_flag byte soil_moisture short millimeters surf_temp short kelvin snow_depth short millimeters ice_concen short percent ice_age byte edge byte
trainer% fastreg in/out files : char(255) ? f11.94011.2138.mi_e f11.94011.2138.mi_e.r master_file : char(255) ? [Master] MHudson.55 include_vars : char(255) ? [] poly_size : real ? [100] f11.94011.2138.mi_e.r: cloud_water: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: edge: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: ice_age: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: ice_concen: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: rain_flag: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: rain_rate: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: snow_depth: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: soil_moisture: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: surf_temp: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: surface_type: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: water_vapor: [ 1, 1000] X [ 1, 1000] f11.94011.2138.mi_e.r: wind_speed: [ 1, 1000] X [ 1, 1000]
|
Now, let's process the SSM/T1 data. First, look at the contents of the .t1_s dataset output by rtdin. |
trainer% contents f11.94011.2138.t1_s printout : char( 3) ? [no] Contents of File: f11.94011.2138.t1_s Page 1
Dimension Size Coord Scale Offset Variable mt1line 20 y 1 0 mt1samp 7 x 1 0 mt1chans 7 ? 1 0
Variable Type Units mt1_1 short kelvin mt1_2 short kelvin mt1_3 short kelvin mt1_4 short kelvin mt1_5 short kelvin mt1_6 short kelvin mt1_7 short kelvin
|
Then, run the t1edr function to estimate the temperature, pressure height, and wind vector profiles of the atmosphere from the SSM/T1 data. The t1edr function implements the Air Force Global Weather Center D-matrix algorithm. |
trainer% t1edr input file(s) : char(255) ? f11.94011.2138.t1_s af_elevation : char( 3) ? [yes] 1000mb_height : char( 8) ? [constant] constant_height: real ? [110] wind : char( 3) ? [no]
Processing f11.94011.2138.t1_s SSM/T1 data starts at 21:39:34.914 GMT on 1994/01/11. TDF data set f11.94011.2138.t1_e created.
|
Now, look at the contents of the .t1_e SSM/T1 dataset output by t1edr. Note that both 2-dimensional and 3-dimensional variables (at standard levels and standard layers) are created. |
trainer% contents f11.94011.2138.t1_e printout : char( 3) ? [no] Contents of File: f11.94011.2138.t1_e Page 1
Dimension Size Coord Scale Offset Variable mt1line 20 y 1 0 mt1samp 7 x 1 0 standard_levels 15 z 1 0 press_levels standard_layers 14 z 1 0 press_midlevels mt1line_1 19 y 1 0.5 mt1samp_1 6 x 1 0.5
Variable Type Units press_levels float millibars press_midlevels float millibars temp_profile short kelvin press_heights float meters layer_thick float meters trop_temp short kelvin trop_press float millibars rain_flag byte zone byte season byte geography_type byte elevation short meters
Variable Dimension Size press_levels standard_levels 15 press_midlevels standard_layers 14 temp_profile mt1line 20 temp_profile mt1samp 7 temp_profile standard_levels 15 press_heights mt1line 20 press_heights mt1samp 7 press_heights standard_levels 15 layer_thick mt1line 20 layer_thick mt1samp 7 layer_thick standard_layers 14 trop_temp mt1line 20 trop_temp mt1samp 7 trop_press mt1line 20 trop_press mt1samp 7 rain_flag mt1line 20 rain_flag mt1samp 7 zone mt1line 20 zone mt1samp 7 season mt1line 20 season mt1samp 7 geography_type mt1line 20 geography_type mt1samp 7 elevation mt1line 20 elevation mt1samp 7
|
Now, let's process the SSM/T2 data. First, look at the contents of the .t2_s dataset output by rtdin. |
trainer% contents f11.94011.2138.t2_s printout : char( 3) ? [no] Contents of File: f11.94011.2138.t2_s Page 1
Variable Type Units mt2_1 short kelvin mt2_2 short kelvin mt2_3 short kelvin mt2_4 short kelvin mt2_5 short kelvin
|
Then, run the t2edr function to derive relative humidity, specific humidity, and dew-point temperature from the SSM/T2 data. The t2edr function implements the Air Force Global Weather Center D-matrix algorithm. [Note that the 150 GHz channel (channel 5) of SSM/T2 on f-11 is bad.] |
trainer% t2edr input file(s) : char(255) ? f11.94011.2138.t2_s af_elevation : char( 3) ? [yes] dew_point : char( 3) ? [no]
Processing f11.94011.2138.t2_s SSM/T2 data starts at 21:38:41.463 GMT on 1994/01/11.
150 GHz Channel is bad, will be computed using regression on other channels. TDF data set f11.94011.2138.t2_e created.
|
Now, look at the contents of the .t2_e SSM/T2 dataset output by t2edr. Note that both 2-dimensional and 3-dimensional variables (at standard levels and standard layers) are created. |
trainer% contents f11.94011.2138.t2_e printout : char( 3) ? [no] Contents of File: f11.94011.2138.t2_e Page 1
Dimension Size Coord Scale Offset Variable mt2line 87 y 1 0 mt2samp 28 x 1 0 standard_levels 6 ? 1 0 press_levels standard_layers 7 ? 1 0 press_midlevels
Variable Type Units press_levels float millibars press_midlevels float millibars rel_humidity short percent spec_humidity short gm/kg water_vapor short kg/m2 geography_type byte quality_flag byte atmos_type byte elevation short meters
Variable Dimension Size press_levels standard_levels 6 press_midlevels standard_layers 7 rel_humidity mt2line 87 rel_humidity mt2samp 28 rel_humidity standard_levels 6 spec_humidity mt2line 87 spec_humidity mt2samp 28 spec_humidity standard_levels 6 water_vapor mt2line 87 water_vapor mt2samp 28 water_vapor standard_layers 7 geography_type mt2line 87 geography_type mt2samp 28 quality_flag mt2line 87 quality_flag mt2samp 28 atmos_type mt2line 87 atmos_type mt2samp 28 elevation mt2line 87 elevation mt2samp 28
Last Update: $Date: 2001/10/05 00:32:58 $