MSMR operational products are provided with a statistical 
technique using radiative transfer simulations (Gohil et al., 2000). 
Difference in MSMR operational GPDs with TMI or SSM/I may 
arise due to difference in their operating frequencies, noise figures 
and algorithms. The basic features of retrieval algorithm for TMI 
and MSMR are shown in table 1, which shows the difference in 
basic approach as well as the channels used. Table 2 shows the 
theoretical accuracy of MSMR operational retrieval algorithms. 
Table 1: Salient features of retrieval algorithms 
  
Features MSMR Algorithm Wentz Algorithm 
  
  
1. Type Statistical technique Minimization 
using Radiative approach between 
Transfer Simulations | measured and 
(Gohil et al., 2000) simulated brightness 
temperatures (Wentz, 
2. Channels 1997). 
for- 
IWV | 18,21 (H & V) 22 (V), 37(V & H) 
OWS | 6.1018, 21 (H & V) 22 (V), 37(V & H) 
10,18, 21(V &FD ] ^  -——— 
CLW | 18,21 (V & H) 
SST 1.6,10,13, 21(VR IH) |  —— 
  
  
  
3. Other Climate SST and Average SST and 
Inputs in incidence angle incidence angle 
Retrieval 
Algorithms 
  
Table-2: Theoretical Retrieval Accuracy of MSMR GPDs (Gohil 
et al., 2000) 
  
  
  
  
  
  
  
  
Parameter Tropics Midlat. Polar 
SST (K) (grid: 150 km) 1.52 1.92 1.90 
OWS (ms™) (grid: 150 km) 1.63 1.59 1.51 
OWS (ms™) (grid: 75 km) 2.10 2.00 1.91 
IWV (g cm™)' (grid: any) 0.20 0.18 0.15 
CLW (mg cm” ) (grid: any) 13.0 11.0 9.0 
  
  
  
  
  
  
   
Table 3: Comparison of MSMR GPDs with insitu observations 
(Sharma et al., 2002) 
  
      
  
  
  
  
   
  
      
  
  
  
  
  
  
Para- | No. of Rmsd Bias Rmsd after 
meter | Points removal of 
bias 
SST 153 1.49 K 0.98 K 1.13 K 
OWS | 162 1.62 ms” 1.62 ms” 1.80 ms” 
IWV 16 053gcm^ | 039gcm? | 032g cm? 
  
    
    
  
Table 4: Intercomparison of colocated MSMR and TMI GPDs 
within 1 hr. of temporal difference (Varma et al., 2002a) 
  
        
    
  
       
        
     
  
      
      
   
  
  
         
  
Para- | Grid No.of | R bias S.D. rms 
meter | (km) pts. of diff. | diff. 
50 27543 | 0.96 | -0.23 0.40 0.46 
IWS 1:75 26626 | 0.96 | -0.22 0.40 0.46 
150 10884 | 0.96 | -0.21 0.37 0.43 
OWS | 75 24953 | 0.66 | -1.81 2.31 2.93 
150 10254 | 0.73 | -1.78 2.01 2.68 
50 25923 | 0.52 | 4.42 12.91 13.64 
CLW | 75 25077 | 0.48 | 4.57 13.82 14.56 
150 10219 | 0.44 | 5.39 10.60 11.89 
  
  
  
  
  
  
  
  
       
IAPRS & SIS, Vol.34, Part 7, "Resource and Environmental Monitoring", Hyderabad, India,2002 
Sharma et al. (2002) and Varma et al. (2002a) presented the 
validation and intercomparison of operationally available 
geophysical parameters with insitu and other satellites. They 
found a very close agreement of IWV from MSMR with insitu 
and other satellites (TMI & SSM/I). The OWS and SST from 
MSMR compare reasonably well. The comparison of CLW from 
MSMR was not found in a reasonable agreement with other 
satellites. Comparison of MSMR GPs with insitu is shown in 
Table 3 and that with TMI is shown in Table 4. 
Furthermore, Varma et al. (2002b and 2002c) and Gairola et al. 
(2000) presented sensitivity of MSMR channels for rainfall, and 
presented an algorithm for the retrieval of rainfall. Pokhrel et al. 
(2002) used the rainfall from MSMR to study variability of S-W 
monsoon over Indian oceanic region. 
In general, there are many approaches for the retrieval of GPs 
from satellite measurements. One of them is empirical approach 
in which a statistical relationship is established between 
concurrent satellite measurements and insitu observations. This 
approach is not general in nature and is specific to a sensor, 
satellite, region and period. However, it takes care of all sensor 
related errors. It cannot be applied in the initial phase of satellite 
operations. On the other hand, simulation techniques are more 
robust and are independent of these aspects except that they need 
fine-tuning to account for satellite, sensor and other errors. In 
either of these retrieval techniques, statistical inversion is an 
essential component where a few approaches, like statistical 
regression technique (Gohil et al., 2000), neural network (Gairola 
et al, 2002), principal component analysis and iterative 
minimization could be used 
3. DATA AND ANALYSIS 
In the present study, near concurrent observations of MSMR and 
TMI of July 1999 are used and statistical relationships are 
established between MSMR brightness temperatures and TMI 
derived GPDs. As the north-south coverage of TMI is restricted to 
X40? latitudes, the algorithms thus obtained will best represent 
this range of latitudes. Due to high variability of CLW over space 
and time, to establish empirical relation for CLW, we have 
selected colocated observations from two sensors (MSMR & 
TMI) in very close proximity of within 10 km and 5 minutes. 
However, the other parameters which show less variability, in 
order to allow larger number of colocated valid points for more 
stable statistical relationship, while maintaining the spatial 
difference of 5 km, the maximum temporal difference was relaxed 
to 10 minutes. As presence of CLW and rain effects the retrieval 
of IWV, OWS and SST, for retrieval of these GPs, we have 
selected colocated observations with low amount of CLW in the 
atmosphere. For, development of retrieval algorithm for IWV and 
OWS, the CLW values up to 15 mg cm” are allowed, whereas for 
retrieval of SST, CLW values up to 10 mg cm” are allowed. Out 
of various forms selected for multiple regression between 
colocated MSMR brightness temperatures and TMI GPDs, 
following general form is selected and used for retrieval of all 
GPs for their best statistical results. 
3 
3 3 
Bra Ya: infas0 - ny). b, in (20 - ni). Xue infaso > (v E nu) 
1 i + = 1 I 1 
= 
i=1 i=1 1 
— (1) 
  
    
    
    
   
   
    
  
   
    
      
   
    
   
    
     
     
     
     
    
   
   
      
   
   
  
   
  
  
   
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