Sensor Description
The Special Sensor Microwave Imager/Sounder (SSMIS) is a conically scanning passive microwave radiometer with a 53.1 degree Earth incidence angle sensing upwelling microwave radiation at 24 channels covering a wide range of frequencies from 19 – 183 GHz. Data is collected along an active scan of 144 degrees across track producing a swath width on the ground of 1707 km. The first of five sensors was launched on board DMSP F16 on October 18, 2003. The SSMIS is a joint US Air Force/Navy multi-channel passive microwave sensor that combines and extends the imaging and sounding capabilities of the previous DMSP microwave sensors SSM/T, SSM/T2 and SSM/I. It was built by Northrup-Grumman Electronic Systems.
SSMIS channel characteristics of the channels which are intercalibrated in FCDR:
| Center Frequencies (GHz) |
19.35 |
19.35 |
22.235 |
37.0 |
37.0 |
91.655 |
91.655 |
|---|
| Channel Number |
13 |
12 |
14 |
16 |
15 |
17 |
18 |
| Polarization |
V |
H |
V |
V |
H |
V |
H |
| Bandwidth (MHz) |
400 |
400 |
450 |
1500 |
1500 |
1500 |
1500 |
| NEDT (K) |
0.7 |
0.7 |
0.7 |
0.5 |
0.5 |
0.9 |
0.9 |
| EFOV (km x km) * |
74 x 45 |
74 x 45 |
74 x 45 |
45 x 28 |
45 x 28 |
16 x 13 |
16 x 13 |
| Sampling Interval (km x km) * |
12.5 x 25 |
12.5 x 25 |
12.5 x 25 |
12.5 x 25 |
12.5 x 25 |
12.5 x 12.5 |
12.5 x 12.5 |
SSMIS channel characteristics of the moisture sounding channels which are NOT intercalibrated in FCDR:
| Center Frequencies (GHz) |
150.0 |
183.31±1 |
183.31±3 |
183.31±7 |
|---|
| Channel Number |
8 |
11 |
10 |
9 |
| Polarization |
H |
H |
H |
H |
| Bandwidth (MHz) |
1500 |
500 |
1000 |
1500 |
| NEDT (K) |
0.88 |
1.25 |
1.0 |
1.2 |
| EFOV (km x km)* |
16 x 13 |
16 x 13 |
16 x 13 |
16 x 13 |
| Sampling Interval (km x km)* |
12.5 x 12.5 |
12.5 x 12.5 |
12.5 x 12.5 |
12.5 x 12.5 |
SSMIS channel characteristics of the temperature sounding channels which are NOT intercalibrated in FCDR:
| Center Frequencies (GHz) |
50.3 |
52.8 |
53.596 |
54.40 |
55.50 |
57.29 |
59.4 |
63.283 |
60.793 |
60.793 |
60.793 |
60.793 |
60.793 |
|---|
| Channel Number |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
19 |
20 |
21 |
22 |
23 |
24 |
| Polarization |
H |
H |
H |
H |
H |
RC |
RC |
RC |
RC |
RC |
RC |
RC |
RC |
| Bandwidth (MHz) |
400 |
400 |
400 |
400 |
400 |
350 |
250 |
1.5 |
1.5 |
1.5 |
3.0 |
8.0 |
30.0 |
| NEDT (K) |
0.4 |
0.4 |
0.4 |
0.4 |
0.4 |
0.5 |
0.6 |
2.4 |
2.4 |
1.8 |
1.0 |
0.6 |
0.7 |
| EFOV (km x km)* |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
27 x 18 |
| Sampling Interval (km x km)* |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 37.5 |
12.5 x 75 |
12.5 x 75 |
12.5 x 75 |
12.5 x 75 |
12.5 x 75 |
12.5 x 75 |
* EFOV and Sampling Interval values are km along track x km across track.
Channel characteristics shown above are from the Algorithm and Data User Manual for SSMIS, Table 1 (Bandwidth), Table 3 (EFOV, Sampling Interval), and Kunkee et al. 2006 (Sensitivity).
Local observing times:
-
The DMSP Local Observing Times are shown over the 25+ year record of the nine
satellites in the FCDR data record. Note that because the DMSP spacecraft are not maintained in a specific orbit, the local
observing times change as the orbit decays over time due to atmospheric drag. This results in changes to both the Earth
incidence angle or view angle as well as the impact of diurnal cycle variability. If not properly accounted for by the
geophysical retrieval algorithms, both of these factors can lead to time-dependent biases that appear as climate trends. The
plot shows the time of the am or morning pass, which is the ascending pass for all of the sensors except for F08. The pm or
evening pass occurs at the time shown plus 12 hours.
Geolocation
The original SSMIS pixel geolocation is based on predicted spacecraft ephemeris. For the FCDR data we recompute the spacecraft ephemeris using orbital element information contained in two-line element (TLE) files produced by the North American Aerospace Defense Command (NORAD). Using this updated spacecraft ephemeris along with software to compute the pixel geoleocation based on the geometry of the sensor, we perform an examination of geolocation errors and the subsequent impact on the view angle, or Earth incidence angle (EIA). Given that the observed Tb are highly sensitive to the view angle, properly accounting for changes in EIA across an orbit and over time is an important step that must be addressed before attempting to intercalibrate the sensors. Using a previously developed coastline analysis technique, estimates of changes in the spacecraft attitude including deviations in roll, pitch, and yaw have been computed for the life of each of the SSMIS sensors. Applying these corrections results in an improved pixel geolocation, but more importantly provides accurate estimates of the EIA across the scan and throughout each orbit. An analysis of uncertainties in the calculation of EIA shows mean errors within 0.1 degrees, which translates to errors in the calibration of less than 0.2 K for all channels. Equally important, is the availability of improved geolocation and EIA estimates for algorithm developers using the SSMIS data, which is critical for producing unbiased estimates of geophysical parameters for use in climate applications.
Additional Information on Geolocation
Intercalibration
As with the SSM/I sensors, for the SSMIS sensors (including F16, F17, F18 and F19) multiple intecalibration approaches were implemented. Ultimately, however, the final intercalibration was obtained based on the results from a double difference approach using coincident overpasses with TRMM TMI and geophysical parameters from the ECMWF Interim reanalysis. Unlike the SSM/I sensors where a simple calibration offset was applied independent of scene temperature, with the SSMIS sensors there are significant variations in the calibration differences relative to F13 as a function of the scene tempeature. As a result, a temperature-dependent calibration was applied. Given the lack of an absolute calibration target for the microwave frequencies employed on SSM/I and SSMIS, using multiple approaches provides confidence in the results and an estimate of the residual uncertainty in the intercalibration. The techniques that have currently been used include the following.
- Coincident overpasses with TRMM TMI: A double difference approach that uses difference between coincident observations from TMI to eliminate diurnal variability along with differences between simulated TBs to account for differences in the channel characteristics. The simulated TBs are computed using both retrieved geophysical parameters based on an optimal estimation algorithm as well as model analyses.
- Coincident overpasses over polar regions: Direct comparisons of TBs from sensors operating concurrently are made over the polar regions, which is the only region that the sun-synchronous DMSP spacecraft view the same scenes simultaneously.
- Model double differences: Geophysical parameters from both global model analysese including MERRA and ECMWF Interim are used to compute simulated TBs from a pair of sensors operating concurrently. Differences between the observed and simulated TBs are then used to compute intercalibration differences. This approach relies on the model to account for diurnal variability, which avoids the need for a non-sunsynchronous satellite like TRMM, but relies on the model's diurnal cycle. Using multiple models helps to reduce/quantify this error, however.
- Vicarious cold calibration: This approach employs an analysis of the cumulative histogram of observed TBs to compute a vicarious cold reference TB, which represents a statistical lower bound on the observed TB. A comparison of the vicarious cold temperatures from different sensors provides a calibration difference for the coldest TBs. Simulations based on model analysis are subsequently used to account for slight variations in the viewing angle of the sensor.
- Vicarious warm calibration: This approach is similar to the vicarious cold calibration, but it uses highly vegetated (i.e. warm) scenes over the Amazon and other jungles/forests with high surface emissivities. A retrieval is used to account for diurnal temperature variations etc.
Additional Information on Intercalibration
