[R-sig-Geo] Climate data in R

Wed Aug 3 01:07:33 CEST 2016

Another for the list:

Daily tmax/tmin/tavg going back a very long way for the globe at 1degx1deg:
http://berkeleyearth.org/data/.

On Aug 2, 2016 3:52 PM, "Bacou, Melanie" <mel at mbacou.com> wrote:

> I also recommended browsing through NOAA PSD curated catalog. I believe
> it's up-to-date and provide useful metadata and links to original sources:
>
> http://www.esrl.noaa.gov/psd/data/
>
> --Mel.
>
>
> On 8/2/2016 5:34 PM, Tom Philippi wrote:
>
>> Without knowing what part of the globe you need climate data for, nor what
>> data you need (30 year "normals", bioclim parameters, monthly values,
>> daily, extremes?), I can't give a useful specific answer.
>>
>> What follows is an initial draft of a web page on climate data in R.  It's
>> somewhat US and North America centric.
>>
>> The number of APIs providing climate data is growing rapidly.  The
>> downside
>> is that most data providers are region/continent specific.  NOAA NDCD,
>> ACIS, Mesowest, PRISM, SNOTEL, SNODAS, and others are North America or
>> CONUS, there are different APIs and packages for EU, UK, NZ, and Canadian
>> climate.  The few worldwide services tend to have lower station densities
>> than region-specific services, especially for older dates.
>>
>> Some APIs and overviews:
>> ACIS (NOAA):  http://www.rcc-acis.org/docs_webservices.html
>> NOAA Co-ops: http://tidesandcurrents.noaa.gov/api/
>> PRISM http://www.prism.oregonstate.edu/index.phtml
>> MesoWest: http://www.rcc-acis.org/docs_webservices.html
>> http://synopticlabs.org/api/
>> http://www.esrl.noaa.gov/psd/data/gridded/
>>
>> https://climatedataguide.ucar.edu/climate-data/global-temperature-data-sets-overview-comparison-table
>>
>> http://www.cgiar-csi.org/data/uea-cru-ts-v3-10-01-historic-climate-database
>>
>> All of those APIs can be hit from R; some have R packages available to
>> make
>> it easier.  [And, more importantly for my use cases, those packages
>> isolate
>> my code that hits the package functions from changes in the underlying
>> API.  Instead of each of us having to keep up with changing APIs and
>> fixing
>> our broken code every 6-12 months, package maintainers put out new
>> versions
>> that work with the new APIs but use the same parameters and return the
>> same
>> objects.  If my code from last year fails to fetch data this year, I
>> usually can simply update the package.  Thanks, maintainers!]
>>
>> For R packages, start with https://github.com/ropensci/opendata#climate
>> and
>> http://ropensci.org/packages/
>>
>> Most non-raster APIs have 2 steps: a station finder that you query by name
>> or location, then a data fetcher pulling data from the found stations.
>>
>> Raster datasets tend to be huge, and tend to be organized as 1 file per
>> date.  There's a PRISM API that lets you select locations and retrieve
>> data
>> for a range of dates for requested rectangular AOAs.
>>
>>
>> My 2 lessons learned as an ecologist using climate data (not a real
>> climatologist) are:
>>
>> 1: Spatial interpolation is non-trivial.  PRISM uses not just distances
>> from stations of record, but also topography: elevation for temperatures
>> and precipitation and side of the mountain (very important for
>> precipitation).  Don't just use the nearest station or inverse-distance
>> krige unless you are in a broad flat plain or steppe.  [Sorry, Ed!]  There
>> are a couple of raster products for daily data (not just the monthly
>> PRISM), but they tend to be available a few weeks after the fact.
>>
>> 2: Station records are instrumentation values, not actual temperatures &
>> precipitation amounts.  Almost all climate data providers QA and filter
>> the
>> values, because sensors drift and fail.  However, the appropriate amount
>> of
>> filtering is dependent on your use.  Some blips in the raw data are
>> instrumentation errors, some are short extreme events; some are of a
>> magnitude & duration where it can't be determined which of those they are.
>> The appropriate or optimal amount of filtering for uses such as crop
>> production models is much greater than the appropriate amount for some
>> ecological applications where short intense events can be important.  Even
>> within larger agencies like NOAA, different regional climatologists filter
>> the data to different extents.  To do valid science, if extreme events
>> matter for your use, you need to know the preprocessing on your
>> datastream,
>> lest you get fooled into thinking that region-specific filtering is
>> climatological signal.
>>
>> If anyone has comments, additions, or corrections to the above, please
>> send
>> them to me!
>>
>> If the OP has a specific data type in a specific region, I might have a
>> specific suggestion.
>>
>> Tom 2
>>
>> Tom Philippi
>> Quantitative Ecologist & Data Therapist
>> Inventory and Monitoring Program
>> US National Park Service
>>
>>
>> On Mon, Aug 1, 2016 at 12:49 PM, Isaque Daniel <
>> isaquedanielre at hotmail.com>
>> wrote:
>>
>> Hi,
>>>
>>> You can follow the example in:
>>>
>>> http://stackoverflow.com/questions/34619218/extract-raster-values-from-stack-to-points-in-for-loop
>>>
>>>
>>> Is really simple, using coordinates and extract functions of raster
>>> package.
>>>
>>>
>>> Best
>>>
>>> Isaque
>>>
>>>
>>>
>>>
>>> ------------------------------------------------------------------------------------------------------------------
>>> Eng. Agr. Isaque Daniel Rocha Eberhardt
>>> Mestre em Sensoriamento Remoto - Instituto Nacional de Pesquisas
>>> Espaciais
>>> (INPE)
>>> Doutorando em Transportes - Universidade de Bras??lia (UNB)
>>> Mobile: +55 (061) 99015658
>>>
>>>
>>> ------------------------------------------------------------------------------------------------------------------
>>> Agronomist engineer
>>> Master in Remote Sensing - National  Institute for Space Research (INPE)
>>> -
>>> Brazil
>>> PHD Student in Transport - Bras??lia University (UNB)
>>>
>>>
>>> ________________________________
>>> De: R-sig-Geo <r-sig-geo-bounces at r-project.org> em nome de Kenny Bell <
>>> kmb56 at berkeley.edu>
>>> Enviado: segunda-feira, 1 de agosto de 2016 19:47
>>> Para: M. Edward (Ed) Borasky
>>> Cc: R-sig-geo mailing list
>>> Assunto: Re: [R-sig-Geo] Climate data in R
>>>
>>> See https://cran.r-project.org/web/packages/prism/index.html for
>>> CRAN - Package prism<
>>> https://cran.r-project.org/web/packages/prism/index.html>
>>> cran.r-project.org
>>> prism: Access Data from the Oregon State Prism Climate Project. Allows
>>> users to access the Oregon State Prism climate data. Using the web
>>> service
>>> API data can easily ...
>>>
>>>
>>> interpolated US weather data
>>>
>>> On Mon, Aug 1, 2016 at 12:43 PM, M. Edward (Ed) Borasky <znmeb at znmeb.net
>>> >
>>> wrote:
>>>
>>> I have a similar question. Assume I've downloaded the climate data for
>>>>
>>> all
>>>
>>>> the stations in the US state of Oregon, including their latitude,
>>>>
>>> longitude
>>>
>>>> and elevation, using rnoaa. How do I *interpolate* climate values for an
>>>> arbitrary latitude / longitude / elevation inside a triangle defined by
>>>>
>>> the
>>>
>>>> nearest stations?
>>>>
>>>> On Mon, Aug 1, 2016 at 3:24 AM Miluji Sb <milujisb at gmail.com> wrote:
>>>>
>>>> Dear all,
>>>>>
>>>>> I have a set of coordinates. Is it possible to extract climate data
>>>>> (temperature and precipitation) by coordinates using the R packages
>>>>>
>>>> such
>>>
>>>> as
>>>>
>>>>> rnoaa?
>>>>>
>>>>> For example;
>>>>>
>>>>> out <- ncdc(datasetid='ANNUAL', stationid='GHCND:USW00014895',
>>>>> datatypeid='TEMP')
>>>>>
>>>>> But instead of stationid can I pass a list of coordinates through it?
>>>>> Thanks a lot!
>>>>>
>>>>> Sincerely,
>>>>>
>>>>> Milu
>>>>>
>>>>>          [[alternative HTML version deleted]]
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-Geo mailing list
>>>>> R-sig-Geo at r-project.org
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>>>
>>>>> --
>>>> How many people can stand on the shoulders of a giant before the giant
>>>> collapses?
>>>>
>>>>          [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-Geo mailing list
>>>> R-sig-Geo at r-project.org
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>>
>>>>
>>>
>>> --
>>> Kendon Bell
>>> Email: kmb56 at berkeley.edu
>>> Phone: (510) 612-3375
>>>
>>> Ph.D. Candidate
>>> Department of Agricultural & Resource Economics
>>> University of California, Berkeley
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>

	[[alternative HTML version deleted]]