Revise Title: Rainfall analysis for Indian monsoon region from merged
dense rain
gauge observations and satellite estimates: Evaluation of monsoon rainfall features
(DS0601)
Point wise reply to comments of Referee 1
Comment 1:It is surprising that the authors have not chosen to compare their ‘product’
with the TRMM data, say 3B42, which is 3 hourly and 0.25 deg, Granted that TRMM
observations over the Western Ghats are not adequate to resolve the variability caused
by orography. What about the rest of India ?
Reply 1: In the present work TRMM data is not used for comparison. The TRMM data
has got some limitations over the land areas due to factors like topography, meso scale
convection etc. The new analysis is compared with the standard global product of
GPCC.
Comment 2: Importance of using INSAT data is not completely clear to me. It is used for
filling gaps over mostly sea and neighboring countries.
All the data over land in India
appears to come from their mapping procedure, Could a merging procedure over land
have helped extract the advantages of both sets of observations, rather than be limited
by the choice only one data source ?. The impression one gets from the title, the
abstract and conclusion is that gauge data and INSAT data have been combined (which
have been more fantastic) and that, from main body of the manuscript is not adequate.
Reply 2: INSAT data is used for filling gaps over the sea and other data gap areas. The
main purpose of this study is to generate daily rainfall analysis which could be used for
real time real-time operational application as well as for validation of NWP models over
Indian monsoon region.
Previous studies suggest (references are
given in the
manuscript) that the performance of INSAT QPE over the oceanic areas are reasonably
good but performance deteriorates over the land areas. In order to extract
the
advantage of raingauge data over the land areas and satellite estimates over the ocean,
the gridded raingauge data over the land areas are merged with the QPE (at the same
resolution) over the ocean and over the raingauge gap areas. In section 2 of the revised
1
version, a detailed discussion on the objective analysis procedure is added and very
clearly described how gridded rainfall data is merged with the satellite estimates.
To avoid confusion regarding merged or combined, the title of the paper is accordingly
changed. Abstract and other relevant portions of the manuscript are also revised.
Comment 3: Given that the mapping of data from rain gauges to a grid remains a open
problem, more space needs to be allocated to discussing the use of the weighting
function, namely to address issues such as: (i) What other method have the author
tried? (ii) why this particular weighting function ? (iii) What are the error bars for their
estimation/mapping procedure ? are they more along/across the mountains and less
along the flats ? Surely the density of stations (fig 1) plays an important role in governing
the error. The choice of R=200 km seems to on higher side for mappings on to I deg
box. True that there is a reference provided, but a brief description of this choice would
be helpful for readers, and perhaps make the manuscript more self sufficient. On these
aspects of (and several other related to) point to area mapping the presentation is less
than satisfactory.
Reply 3: The methodology (section 2) is revised addressing the concerns of the
referee. Reference of other works on rainfall analysis is suitably cited in section 1 and
2.
In a very recent study, Roy Bhowmik et al. (2005) applied the technique of
correlation co-efficient (CC) as the function of distance for objectively determination of
radius of influence of rainfall over Indian monsoon region. The method is tested for
different synoptic situation as well as for the different sectors of the country to investigate
its geographical variation. The study showed that, in general, the radius of influence for
Indian monsoon region is 200 km. The result of this study is referred in the revised
version (in section 2)
Comment 4: It would have been great to have seen the analysis for more years. Just
one year (and that too only about 90 days) is too small a sample to make any objective
assessment about the reported comparison. Perhaps this persistent thought in my mind
of wishing to see the analysis and comparison made over a large sample, made section
5 appear a tad bit forced.
2
Reply 4: During monsoon 2002, INSAT 1D was defunct and no INSAT QPE data was
available. The analysis for monsoon
2003 with the INSAT (KALPANA-1) QPE
is
included in the revised version (in section 4). A new section (section 3) is added where
characteristics of monsoon 2001 and 2003 are briefly described.
Comment 5: As a minor comment, the author say that the analysis was performed over
a large area, but most of their inferences are about the Western Ghats.
Reply 5: The discussion covered Western Ghats (orographic rainfall ) as well as rainfall
associated with other monsoon synoptic systems. The discussion on orographic rainfall
has got more importance in the manuscript due to the fact that significant improvement
is noticed for orographic rainfall in the new analysis.
Point wise reply to comments of Referee 2
Comment 1: The description of the methodology is very poor. I am not clear how the
satellite observation is brought into the analysis. The estimated rainfall value at a grid
pint is the weighted average of observation at N sites as given by the first equation. The
satellite data is available on a 1 deg grid, how is this included in the analysis ? Are the
satellite observations are also considered as one of the N site observation. This neds
clear description. Methodology
Reply 1: The methodology (section 2) is revised where a detailed discussion on the
objective analysis procedure is added and very clearly described how gridded rainfall
data is merged with the satellite estimates.
Comment 2: The radius of influence is fixed as 200 km. How is this determined? And
more importantly how sensitive are the final estimates to the choice of this radius? And
are their objective method to obtain this?
3
Reply 2: In a very recent study, Roy Bhowmik et al. (2005) applied the technique of
correlation co-efficient (CC) as the function of distance for objectively determination of
radius of influence of rainfall over Indian monsoon region and found that radius of
influence for Indian monsoon region is 200 km. The results of above study is referred in
the revised version (in section 2).
Comment 3: The comparison of the proposed method with other products is done
visually. While this is good, I think to make a case the author have
to provide
quantitative measures of comparison, e.g., RMSE. This is one of the shortcomings of the
[paper.
Reply 3: In a recent study Rajeevan et al. (2005) made an quantitative assessment of
the high resolution ( 1
o
x1
o
lat/long ) daily gridded rainfall data developed for Indian
region. The study considered daily land station observations for the period from 1951 to
2003 covering the domain from 6.5
o
N to 37.5
o
N, 67.5
o
E to 101.5o E. The study
reported that the difference between the IMD and GPCP data are positive along the west
coast implying that GPCP data set underestimates the heavy rainfall amounts along the
west coast. Similarly over the eastern parts of central India, GPCP data underestimates
the heavy rainfall amounts. Over the most parts of the country the differences are of the
order of 50 mm. The correlations between GPCP and IMD rainfall data exceeds 0.4 over
central and northwest India. In the revised version, section 1 is modified referring the
work of Rajeevan (2005). No further quantitative assessment of these data set is made
in the present work. In order to extract the advantage of station observations and to
cover the data gap over the
sea area,
attempt has been made in this paper to
generate daily rainfall analysis for the domain from 0 o N to 40 o N, 60 o E to 100o E over
Indian monsoon region merging satellite estimates and land station observations. This
is
the
monsoon domain for which validation of operational
NWP models is very
important. In this paper, we subjectively examine the potential of this merged gridded
dataset to capture characteristic features (large scale as well as mesoscale) of Indian
summer monsoon.
For the validation purpose, maps of monthly rainfall analysis) are also compared with
the station rainfall plots.
4
Comment 4: In the process of merging, what is the contribution of satellite data? In other
words, if one performs the analysis using the weighted average equation based on only
on the observation data and only on the satellite data how different wil they be ? This will
provide a sense of relative importance of satellite observations.
Reply 4: This issue was addressed in our very recent study (Roy Bhowmik and Sen
Roy, 2006) where an quantitative assessment of QPE in relation to gridded rainfall
(based on raingauge observation only ) over the land areas has been documented. The
result of this study is referred in the revised version (in section 2).
Comment 5: The Methodology proposed is a nonparametric ‘local’ functional estimation
method.
Reply 5: This point has been discussed in section 2 referring the results of our previous
study (Roy Bhowmik et al., 2005)
regarding computation of radius of influence.
Reference of other works on rainfall analysis is also suitably cited in section 1 and 2.
The manuscript is
revised in-corporating comments and suggestions
of
both the
referees. The language is further improved. Title of the manuscript is suitably changed
as
“Rainfall analysis for Indian monsoon region from merged dense rain gauge
observations and satellite estimates: Evaluation of monsoon rainfall features”.
5