<p>It depends on your data, if the rainfall data is in a grid with temporal and spatial dimension, you can use a NetCDF format to analyse the data via R, matlab, IDL or Python. NetCDF is Network Common Data Form, which is a very common format of grided and temporal data. In Python, you can simply do several 10-year average map to see the long term decadal trend. Or you could plot a transient period of a smaller region with average rainfall to see monthly or seasonally change. To find out the intensity of distribution you could plot a map with a logarithmic scale of colour. Use a threshold to identify extreme rainfall event that is above the threshold. I use a Python package called iris which can easily plot graphs using NetCDF data. You can look at some examples here <a href="http://scitools.org.uk/iris/docs/latest/gallery.html" rel="nofollow noreferrer">http://scitools.org.uk/iris/docs/latest/gallery.html</a></p>

<p>For getting the rainfall data, you can download the rainfall data from the CRU-NCEP database <a href="https://crudata.uea.ac.uk/cru/data/hrg/" rel="nofollow noreferrer">https://crudata.uea.ac.uk/cru/data/hrg/</a> CRU stand for Climate Research Unit and it has the most accurate historical data available to the public. They are reanalyzed data from meteorological stations and ships. The highest resolution available is 0.5 latitude * 0.5 longitude degree grid. And for temporal resolution they have 6-hourly and daily. The data is in NetCDF format which is a format most map processing data use. If you don't know how to handle ncdf you could use Google Earth as well <a href="https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/ge/" rel="nofollow noreferrer">https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/ge/</a> .</p>

<p>To know more how the data is derived you can read this paper. Hope it helps. <a href="http://onlinelibrary.wiley.com/doi/10.1002/joc.3711/abstract" rel="nofollow noreferrer">http://onlinelibrary.wiley.com/doi/10.1002/joc.3711/abstract</a></p>

CC BY-SA 3.0
2017-03-03T12:05:09.450Z
2017-03-03T12:05:09.450Z
Felix Leung
<p>What statistical tools are best analysing rainfall data (overall trends, decadal trends, intensity distribution, extremes, etc.)?</p>

CC BY-SA 3.0
2017-02-26T05:17:33.553Z
For what region?  It may affect what options are available.
JeopardyTempest
CC BY-SA 3.0
2017-02-26T05:21:12.860Z
Are you asking where to get the data or how to analyze it?  I don't know of any sites/tools that do both.  I'm tending to think you might have to download the data (hourly if you're looking for intensities as usually thought of) for each observation site, then throw it into Excel/R/etc to do some of the analysis, but I could be wrong, that there is something out there I haven't come across.
JeopardyTempest
CC BY-SA 3.0
2017-02-26T11:17:50.123Z
My question is how analyze it, if I have daily rainfall data for, say, 30 years over Indian region. What statistical methods are apt (regression, pdf etc) for analysing it.
ajilesh
CC BY-SA 3.0
2017-02-26T22:21:52.530Z
Daily rainfall data like measurements from one or more stations or like gridded data?
FuzzyLeapfrog
CC BY-SA 3.0
2017-02-27T02:25:45.443Z
Gridded data 0.25 degree resolution
ajilesh
CC BY-SA 3.0
2017-02-27T10:18:46.797Z
I know this paper, where they created such a gridded data set. Additionally, they analyzed the data set and compared it with other existing data sets. Maybe this will give you some ideas: https://dx.doi.org/10.1127/0941-2948/2013/0436
FuzzyLeapfrog
CC BY-SA 3.0
2017-03-01T00:22:42.180Z
One consideration is that rainfall data are frequently non-normal, so you cannot apply methods that assume normality (e.g. most regression methods) without some type of transformation (e.g. logit).
El Niño
<meteorology><rainfall><statistics><data-analysis>
What statistical methods are best for analyzing long-term rainfall data?
ajilesh
# What statistical methods are best for analyzing long-term rainfall data?

(Asked by: username_0 on 2017-02-25)

What statistical tools are best analysing rainfall data (overall trends, decadal trends, intensity distribution, extremes, etc.)?

**Tags:** meteorology, rainfall, statistics, data-analysis

> **Comments:**
> **username_1**: For what region?  It may affect what options are available.  
> **username_1**: Are you asking where to get the data or how to analyze it?  I don't know of any sites/tools that do both.  I'm tending to think you might have to download the data (hourly if you're looking for intensities as usually thought of) for each observation site, then throw it into Excel/R/etc to do some of the analysis, but I could be wrong, that there is something out there I haven't come across.  
> **username_0**: My question is how analyze it, if I have daily rainfall data for, say, 30 years over Indian region. What statistical methods are apt (regression, pdf etc) for analysing it.  
> **username_2**: Daily rainfall data like measurements from one or more stations or like gridded data?  
> **username_0**: Gridded data 0.25 degree resolution  
> **username_2**: I know this paper, where they created such a gridded data set. Additionally, they analyzed the data set and compared it with other existing data sets. Maybe this will give you some ideas: https://dx.doi.org/10.1127/0941-2948/2013/0436  
> **username_3**: One consideration is that rainfall data are frequently non-normal, so you cannot apply methods that assume normality (e.g. most regression methods) without some type of transformation (e.g. logit).  

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## 1 answers

### Answer by username_4 on 2017-03-03. Score: 2 | Accepted answer

It depends on your data, if the rainfall data is in a grid with temporal and spatial dimension, you can use a NetCDF format to analyse the data via R, matlab, IDL or Python. NetCDF is Network Common Data Form, which is a very common format of grided and temporal data. In Python, you can simply do several 10-year average map to see the long term decadal trend. Or you could plot a transient period of a smaller region with average rainfall to see monthly or seasonally change. To find out the intensity of distribution you could plot a map with a logarithmic scale of colour. Use a threshold to identify extreme rainfall event that is above the threshold. I use a Python package called iris which can easily plot graphs using NetCDF data. You can look at some examples here <http://scitools.org.uk/iris/docs/latest/gallery.html>

For getting the rainfall data, you can download the rainfall data from the CRU-NCEP database <https://crudata.uea.ac.uk/cru/data/hrg/> CRU stand for Climate Research Unit and it has the most accurate historical data available to the public. They are reanalyzed data from meteorological stations and ships. The highest resolution available is 0.5 latitude \* 0.5 longitude degree grid. And for temporal resolution they have 6-hourly and daily. The data is in NetCDF format which is a format most map processing data use. If you don't know how to handle ncdf you could use Google Earth as well <https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/ge/> .

To know more how the data is derived you can read this paper. Hope it helps. <http://onlinelibrary.wiley.com/doi/10.1002/joc.3711/abstract>


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