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Messages - Pankaj Dey

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TITLE: "An Extreme Event Attribution Study of the Heat Wave of Summer 2015 and Chennai Rain Event of December 2015"




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Announcements / ENSO initiation
« on: May 05, 2017, 09:22:51 AM »

This news item discusses a potential enso initiation pathway.
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If you are dealing with huge datasets that are saved in .mat format, loading them to workspace eats lot of RAM. So, matlab has an option to read part of such files without loading them to memory. Have a look at this link:
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Land-use and land cover change (LULCC) can have significant biophysical impacts on regional precipitation, including monsoon rainfall. Using global simulations with and without LULCC from 5 general circulation models (GCM), under the Representative Concentration Pathway (RCP) 8.5 scenario, we find that future LULCC significantly reduce monsoon precipitation in at least 4 (out of 8) monsoon regions. While monsoon rainfalls are likely to intensify under future global warming, we estimate that biophysical effects of LULCC substantially weaken future projections of [UTF-8?]monsoons’ rainfall by 9% (Indian region), 12% (East Asian), 32% (South African) and 41% (North African) relative to the changes in RCP8.5, with an average of ~30% for projections across the global monsoon regions. A similar strong contribution is found for biophysical effects of past LULCC to monsoon rainfall changes since the preindustrial period. Rather than remote effects, local land-atmosphere interactions, implying a decrease in evapotranspiration, soil-moisture and clouds along with more anticyclonic conditions, could explain this future reduction in monsoon rainfall.

(published recently in Geophysical Research Letters: )
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CAOS PhD Thesis Defence

Title: Temporal Persistence and Spatial Coherence of Tropical Rainfall

Speaker: Ram Ratan

Date: December 30, 2016 (Friday)

Time: 4:00PM

Venue: CAOS Seminar Hall


This work focuses on documenting the multiscale nature of the temporal
persistence and spatial coherence of tropical rainfall.  The first two
parts utilise satellite-retrieved rainfall  at multiple space and time
resolutions,   and   the   third   part  assesses   the   ability   of
state-of-the-art  coupled models  to  reproduce some  of the  observed

In  the  first part  of  the  study,  which  focuses on  the  temporal
persistence  of rain,  we  analyze the  Tropical Rainfall  Measurement
Mission (TRMM)  satellite-based observations  to compare  and contrast
wet and dry spell characteristics over the tropics (30S-30N).  Defining
a wet (dry) spell as the  number of consecutive rainy (nonrainy) days,
we find  that the distributions  of wet spells (independent  of spatial
resolution) exhibit  universality in the following  sense.  While both
ocean and land regions with high seasonal rainfall accumulation (humid
regions) show a predominance of 2-4 day wet spells, those regions with
low seasonal rainfall accumulation (arid  regions) exhibit a wet spell
duration distribution that is  essentially exponential in nature, with
a peak  at 1  day. The behaviour  that we observed  for wet  spells is
reversed for  dry spell distributions. The  total rainfall accumulated
in each wet  spell has also been  analyzed, and we find that the major
contribution to  seasonal rainfall  for arid  regions comes  from very
short length wet spells; however, for humid regions, this contribution
comes from wet spells of duration as  long as 30 days. We also explore
the role  of chance  in determining the  2-4 day mode  as well  as the
influence of organized convection in separating reality from chance.

The second part deals with the spatial coherence of tropical rain.  We
take two different  approaches, namely, a global and  local view.  The
global view attempts to quantify  the conventional view of rain, i.e.,
the dominance of the intertropical  convergence zone (ITCZ), while the
local view tries to  answer the question: if it rains,  how far is the
influence  felt   in  zonal   and  meridional  directions?    In  both
approaches, the  classical e-folding length for  spatial decorrelation
is used as  a measure of spatial coherence.  The  major finding in the
global  view approach  is that,  at short  timescales of  accumulation
(daily to  pentad to even  monthly), rain  over the Equator  shows the
most  dominant  zonal scale.   It  is  only  at larger  timescales  of
accumulation (seasonal or annual) that the dominance of ITCZ around 7N
is evident.  The local view quantifies  the dominance of the zonal
scale  in the  tropical ocean  convergence zones,  with an  anisotropy
value (ratio of zonal to meridional scales) of 3-4.  Over land, on the
other  hand,  the  zonal  and  meridional  scales  are  comparable  in
magnitude,  suggesting that  rain tends  to be  mostly isotropic  over
continental regions.  This latter  finding holds true, irrespective of
the  spatial  and temporal  resolutions  at  which rain  is  observed.
Interestingly, the anisotropy over ocean, while invariant with spatial
resolution, is found  to be a function of temporal  resolution: from a
value  of 3-4  at daily  timescale, it  decreases to  around 1.5-2  at
3-hourly resolution.

The final part  analyses a few models from the  suite of Coupled Model
Intercomparison Project  (CMIP5) models, to evaluate  their ability to
reproduce some of  these aforementioned features.  For  all the strong
biases that  models are known to  have, some of the  observed features
(e.g., 2-4  day mode  in wet  spells, isotropic  rain over  ocean) are
remarkably well reproduced.
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Data / High resolution irrigation maps for India : 2000-2015
« on: December 28, 2016, 01:05:54 PM »
Annual irrigated area maps at a resolution of 250 metres for the period 2000-2015 covering all the agroecological zones of India were developed and published by Prof. Vimal Mishra from IIT-Gandhinagar and his team.

Link to the article :

The maps can be downloaded @ :
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Interesting paper. Thanks for sharing.

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Post your question/information / Atmospheric Rivers
« on: December 23, 2016, 09:15:26 PM »
Atmospheric Rivers can be defined as narrow corridors of concentrated moisture suspended in the atmosphere. It is also known as rivers in the sky. Scientists found that it was responsible for the mysterious mass die-off of wild Olympia oysters in San Francisco Bay in 2011.

For more information, read the article below.

Title :

Atmospheric rivers and the mass mortality of wild oysters: insight into an extreme future?
Abstract :
Climate change is predicted to increase the frequency and severity of extreme events. However, the biological consequences of extremes remain poorly resolved owing to their unpredictable nature and difficulty in quantifying their mechanisms and impacts. One key feature delivering precipitation extremes is an atmospheric river (AR), a long and narrow filament of enhanced water vapour transport. Despite recent attention, the biological impacts of ARs remain undocumented. Here, we use biological data coupled with remotely sensed and in situ environmental data to describe the role of ARs in the near 100% mass mortality of wild oysters in northern San Francisco Bay. In March 2011, a series of ARs made landfall within California, contributing an estimated 69.3% of the precipitation within the watershed and driving an extreme freshwater discharge into San Francisco Bay. This discharge caused sustained low salinities (less than 6.3) that almost perfectly matched the known oyster critical salinity tolerance and was coincident with a mass mortality of one of the most abundant populations throughout this species' range. This is a concern, because wild oysters remain a fraction of their historical abundance and have yet to recover. This study highlights a novel mechanism by which precipitation extremes may affect natural systems and the persistence of sensitive species in the face of environmental change.

Link to the article :
Media report:
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MATLAB has a function called 'MEX function - Matlab EXecutable function' which is used to compile your source codes written in C or C++ or Fortran and invoke them from MATLAB. It is a facility through which you can create a C/C++/Fortran wrapper code (called the MEX function in this context) which in turn is used to pass data from MATLAB to source codes which does the computations and pass the outputs back to MATLAB.

MATLAB----> Prepare your data in MATLAB ----> Pass data to MEX function ----> MEX function passes data to C/C++/Fortran codes which does computation and produces output ----> Read output in MATLAB

Effectively, once you compile the MEX function, it just acts as any other MATLAB function, although it's written in C/C++/Fortran language.

The biggest advantage of this procedure is that the computations are extremely fast because of precise memory allocations that you carry out in your C/C++/Fortran codes.

So, you can write core operations of your bulky MATLAB codes in these machine languages and call them using MEX function.

You can find further information in
MATLAB Documentation: 1) 2)

Some tutorial:

I would like to illustrate an example of this function where the task is to add two matrices. So, I write the following MEX function in C language (code's name is addmat.c) to carry out the task.

Code: [Select]
/********* addmat.c ************/
#include "mex.h"
void mexFunction( int nlhs, mxArray *plhs[],
                  int nrhs, const mxArray *prhs[])
    /* Declarations */
    double *A, *B, *C; /* A, B, C are pointers */
    int m,n,i,j;
    /************** INPUT AND OUTPUT INITIALIZATIONS *********************************/
    /* Initialization - 'mxGetPr' is used to pass data; 'mxGetM' & 'mxGetN' are used to estimate the size of matrix */
    A = mxGetPr(prhs[0]); /* Passes first input matrix using prhs[0] which is a pointer to input */
    B = mxGetPr(prhs[1]); /* Passes first second matrix using prhs[1] which is a pointer to input */
    m = mxGetM(prhs[0]);
    n = mxGetN(prhs[0]);
   /* Similar to prhs, 'plhs' are array of pointers which pass expected output to MATLAB */
   /* In the step below pointer to output matrix is being created, which is of consistent dimensions with that of A and B */
    plhs[0] = mxCreateDoubleMatrix(m, n, mxREAL);

    C = mxGetPr(plhs[0]); /* This line indicates initialization of plhs[0] pointer to that of C, this step ensures that what ever modifications we do to C will be reflected in output, i.e., we ultimately get C as the output */


    /************** CODE TO ADD TWO MATRICES *********************************/
    for (i=0;i<m;i++)
        for (j=0;j<n;j++)

Once you write this C code, its MEX function can be created by running line >> mex addmat.c in MATLAB and you get following outcome.
Code: [Select]
>> mex addmat.c
Building with 'gcc'.
MEX completed successfully.

Once MEX file is successfully compiled you can observe a file addmat.mexa64 created in your working directory. That's it! Now you can use your mex code in MATLAB the following way to add two matrices.
Code: [Select]
>> a=[1 2;3 4];b=[7 -2;6 -10];
>> a

a =

     1     2
     3     4

>> b

b =

     7    -2
     6   -10

>> c=addmat(a,b)

c =

     8     0
     9    -6

One can write much complicated C/C++/Fortran codes and invoke them from MATLAB.

Writing MEX function is the most efficient way of tackling with cumbersome algorithms. Such a facility exists in R ( and Python ( too and the concept is something very similar to that of MATLAB.
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Book Title :
Observed Climate Variability and Change over the Indian Region

Editors :  Madhavan Rajeevan Nair & Shailesh Nayak

Brief Introduction :
The objective of the book is to make a comprehensive documentation of the observed variability and change of the regional climate system over the Indian region using the past observed data. The book addresses all the important parameters of regional climate system so that a physically consistent view of the changes of the climate system is documented. The book contains 16 chapters written by the subject experts from different academic and research institutes in India. The book addresses all important components/parameters of the climate system, like rainfall, temperature, humidity, clouds, moisture, sea surface temperature and ocean heat content, sea level, glaciers and snow cover, tropical cyclones and monsoon depressions, extreme rainfall and rainstorms, heat waves and cold waves, meteorological droughts, aerosols, atmospheric aerosols, ozone and trace gases and atmospheric radiative fluxes. One chapter deals with the past monsoon using monsoon proxy data. The last chapter deals with the future climate change projections over the Indian region (rainfall and temperature) made using coupled climate models.

Most of the analyses (especially on rainfall, temperature, extreme rainfall, sea surface temperature, meteorological droughts) are based on the data for a longer period of 110 years, 1901–2010. For some other parameters like moisture, clouds, heat waves and cold waves, atmospheric aerosols, ozone and trace gases and radiative fluxes, data of shorter period have been used. The articles documented inter-annual and decadal variability in addition to documenting long term trends of different parameters. The trends have been tested for statistical significance using standard techniques.

It is expected that the present book will be an excellent reference material for researchers as well as for policy makers. These results will be useful in interpreting future climate change scenarios over the region being projected using coupled climate models. Further analysis of these results is required for attributing the observed variability and change to natural and anthropogenic activities.

Source of the information :
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Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. (
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Study material / Geo-Spatial Tutorials
« on: July 29, 2016, 11:19:04 PM »
Geo-Spatial Tutorials for beginners and advanced learners.
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Data / Global 1-km Cloud Cover
« on: July 11, 2016, 09:40:07 PM »
The datasets integrate 15 years of twice-daily remote sensing-derived cloud observations at 1-km resolution.
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