Fig.7: Global distribution of averaged OWS from MSMR
empirical algorithm (upper panel), SSM/I (middle panel) and
MSMR-operational algorithm (lower panel) for Sept. 1999.
Figure 8 shows monthly map of SST from MSMR empirical
algorithm (upper panel) and operational algorithm (lower panel).
The two figures are in good match.
MSMR - Seo Surface Jengeralure (Kk)
September 199
NSMR iSperationdd - Ses Surface Temperature (K)
eptember 1999
4 2 2-30 32
Fig.4: Global distribution of averaged SST from MSMR
empirical algorithm (upper panel) and MSMR-operational
algorithm (lower panel) for Sep. 1999.
5. CONCLUSION
In this paper a simple technique for retrieval of GPs from IRS-P4
MSMR is presented. The technique has its awn advantages and
disadvantages as discussed in section 2. The technique has shown
very promising results, especially for the retrieval of CLW, which
is found much closer to SSM/I derived values as compared to the
CLW values from MSMR operational algorithm. All the GPs
from empirical algorithm are found to have good match with
those from SSM/I, more qualitatively. The IWV values from
empirical algorithm are also found very encouraging. They are in
good agreement with those from SSM/I and from MSMR
operational algorithm. The OWS seems to be underestimated by
MSMR empirical algorithm. The IWV and SST are also found
marginally underestimated by empirical algorithm. The
underestimation of CLW from MSMR empirical algorithm is
possibly due to high spatial and temporal variability of CLW
associated with nature of the satellite orbit. SSM/I GPs used in
this study are from the SSM/I onboard DMSP-F13, which is
having equator crossing close to the known diurnal maximum of
the cloudiness in the equatorial region. The other reason which
may account for underestimation of all the GPs is possibly due to
classification of valid range for all the parameters and CLW. based
screening of valid values for IWV, OWS and SST (with CLW <
14 mg cm”) in our algorithm. A third reason could be due to
lacking of complete representatives of all possible atmospheric
and oceanic situations of the data set used for the development of
empirical algorithm defined by equation 1. A forth reason could
be the due to difference in TMI and SSM/I derived values of
geophysical parameters, where the former is used for
development of the algorithm and later is used for comparison.
Varma et al. (2002 a) presented comparison of TMI and SSM/I
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002
derived common GPs. They found that IWV and OWS are
marginally over estimated and CLW is underestimated by SSM/.
In short, a better agreement between our MSMR empirically
derived geophysical parameters and SSM/I is possible, if the
screening criteria and valid range is properly worked out using
appropriate sensitivity study, and development of algorithm is
carried out with a larger data set comprising of all possible
atmospheric and oceanic conditions. A more detailed study in
underway and will be reported elsewhere.
6. ACENOWLEDGMENT
The authors are thankful to Dr. A.K.S. Gopalan, Director and Dr.
M.S. Narayanan of Space Applications Centre for their consistent
encouragement for this study. The authors are thankful to GHRC-
NASA for providing TMI and SSM/I data.
7. REFERENCES
Gairola, R.M., Varma, A.K., Gohil, B.S., and Agarwal, V.K.,
2000, Assessment of TRMM-TMI, DMSP-SSM/I and IRS-
P4-MSMR observations over Indian oceanic region, Proc.
of 5" Pacific Ocean Remote Sensing Conference, 5-8 Dec,
2000 at NIO, Goa, India, pp 217-220.
Gairola, R.M., Viltard, C., and Moreau, E., 2002, Microwave
radar and radiometric measurements of precipitation from
TRMM sensors using multiple regression and Neural
Network approach, Ist Science Conf. on TRMM, Honolulu,
Hawaii, 20-25 July, 2002.
Gohil, B.S., Mathur, A.K., and Varma, A.K., 2000, Geophysical
parameter retrieval over global oceans from IRS-
P4/MSMR, Proc. of Pacific Ocean Remote Sensing
Conference, 5-8 December 2000, NIO, Goa, India, pp 207-
211.
Mishra, T. Jha, A.M., Putrevu, D., Rao, J., Dave, D.B., and Rana,
S.S., 2002, Ground calibration of Multifrequency Scanning
Microwave Radiometer (MSMR), IEEE Transactions on
Geosciences and Remote Sensing, 40 (2), pp 504-508.
Samir, P., Varma, A K., Gairola, R.M., and Agarwal, V.K., 2002,
Distribution and Intra-seasonal variability of rain over
Indian oceanic region from IRS-P4 MSMR, Jour. Geophys.
Res., submitted.
Sharma R., Babu, K.N., Mathur, A.K., and Ali, M.M., 2002,
Identification of large scale atmospheric and oceanic
features from IRS-P4 MSMR: Preliminary results, Jour.
Atmos. Oceanic Tech., 19, pp 1127-1134.
Varma, A.K., Gairola, RM, Mathur, A.K., Gohil, B.S., and
Agarwal, V.K., 2002 a, Intercomparison of IRS-P4-MSMR
derived geophysical products with DMSP-SSM/I and TRMM.-
TMI finished products, Proc. of Ind. Acad. Sci. — Earth and
Planet. Sci., 111(3), pp 247-256..
Varma, A.K., Gairola, R.M., Pokhrel S., Gohil, B.S., Mathur,
A.K., and Agarwal, V.K., 2002 b, Rain Rate Measurements
over Global Oceans from IRS-P4 MSMR, Proc. Ind. Acad.
Sci. — Earth and Planet. Sci., 111 (3), pp 257-266.
Varma, A.K., Gairola, R.M., Pokhrel S., and Agarwal, V.K., 2002
c, An Empirical Algorithm for Cloud Liquid Water from
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to IEEE Trans. Geosci. And Rem. Sensing.
Wentz, F.J., 1997, A well calibrated ocean algorithm for Special
Sensor Microwave Imager, Jour. Geophys. Res., 102 (C4), pp
8703-8718.
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