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I have gone through the website of TRMM that explains how the TRMM satellite gather precipitation data- http://pmm.nasa.gov/node/158. During the Indian Summer Monsoon (ISM), we have synoptic systems that deliver rainfall during the months of June through September. These systems are spread over days and often provide continuous rainfall i.e. they are continuous atmospheric processes. How does the TRMM satellite, which I do not believe is a geostationary satellite, 'continuously' measure the rainfall over a wide region of space?

Now one way they could overcome that is they mention they have a dozen such satellites that measure the precipitation but do those dozen satellites coordinate their work as in a A-train satellite constellation? If they do not coordinate their work they probably return to the same location every few hours to make a measurement.

If that is true what sort of assumptions are made about what happens between these observations from a algorithms perspective to derive precipitation values?

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The Tropical Rainfall Measuring Mission (TRMM) satellite is a single satellite, carrying multiple instruments on board, as described here. It makes a total of 16 orbits/day at a low altitude of 400 km, with different swath widths for each instrument. Thus, the measurement product that you get is not a "continuous" measurement of a single atmospheric phenomenon, but a composite of multiple orbital passes and instruments, where the density of coverage will depend on the time period over which the composite is made.

If you take a look at TRMM data access page, there are 3 levels of data, each level depending on the amount of processing and quality control:

  • Level 1: Precipitation radar (PR) reflectivity

  • Level 2: Combined rainfall profile - PR + Microwave interferometer (TMI)

  • Level 3: 3-hourly composite of the above + additional data from other instruments (satellites, ground measurements, ground radars), with extended range to 60S - 60N.

Level 1 and 2 data are nearly raw, and are not composites - they give you data from all 16 orbits/day, and each swath cross-section along the orbit will have a timestamp associated with it. Level 3 data will be the most spatially "continuous" of the three, but one must be aware that this is a composite of multiple orbital swaths and multiple instruments, so it is more suitable for slowly evolving (larger scale) systems.

Related: Is there a database of global rainfall time series?

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  • $\begingroup$ as a corollary the best way to observe a continuous atmospheric process is a geostationary satellite especially for synoptic systems ? $\endgroup$
    – user1066
    Commented Feb 13, 2015 at 5:39
  • $\begingroup$ The limited swath width of the instruments on board would not make them feasible (cost effective) to mount on a geostationary satellite. The microwave radiometer swath is 880 km; infrared radiometer is 830 km; precipitation radar swath is 250 km. You see that for a continuous in time and global coverage, you would need a dense network of geostationary satellites. Synoptic systems are slowly evolving, so polar-orbiting satellite like TRMM is more suitable for observing such systems, rather than smaller scale ones. $\endgroup$
    – milancurcic
    Commented Feb 13, 2015 at 15:55
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I am going to have a different perspective on this question. In relation to this incident - Kedarnath Floods and from this paper - Cloud Burst in Kedarnath no TRMM PR passes were available for the 16th of June and only two passes were available for the 15th - 17th June period over Uttarakhand. This raises the question whether TRMM can be indeed be used to study synoptic systems as large spatial and temporal variations in rainfall require hourly satellite data to measure the impact of heavy rainfall. Hence the authors are using an indirect estimate from IR radiance derived from Meteosat-7 geostationary satellite observations. The broad gist of these findings is that colder clouds in IR images are more likely to precipitate than warmer clouds as the cold clouds have higher cloud tops than warmer ones.

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