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Messages - Pankaj Dey

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We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.


Link to paper: https://www.nature.com/articles/sdata2017191
Link to data: http://doi.org/10.7923/G43J3B0R
The following users thanked this post: Chandan Banerjee, Diwan

2
Interesting information / PICKING A COLOUR SCALE FOR SCIENTIFIC GRAPHICS
« on: January 10, 2018, 11:01:52 AM »
Here are some recommendations for making scientific graphics which help your audience understand your data as easily as possible. Your graphics should be striking, readily understandable, should avoid distorting the data (unless you really mean to), and be safe for those who are colourblind. Remember, there are no really “right” or “wrong” palettes (OK, maybe a few wrong ones), but studying a few simple rules and examples will help you communicate only what you intend.


Please find the link to the blog: https://betterfigures.org/2015/06/23/picking-a-colour-scale-for-scientific-graphics/
The following users thanked this post: Sat Kumar Tomer, Agilan

3
Models / Google Earth Engine
« on: November 13, 2017, 05:55:12 PM »
Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users.
Earth Engine stores satellite imagery, organizes it, and makes it available for the first time for global-scale data mining. The public data archive includes historical earth imagery going back more than forty years, and new imagery is collected every day. Earth Engine also provides APIs in JavaScript and Python, as well as other tools, to enable the analysis of large datasets.


link: https://earthengine.google.com/
The following users thanked this post: Agilan, gowri

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Announcements / The Future of Water Cycle Earth Observing Systems
« on: November 10, 2017, 06:15:00 PM »
Union Symposium at the 2011 General Assembly of the European Geosciences Union. (Credit: EGU/CNTV.at)


Link to the video: https://www.youtube.com/watch?v=7VLeGa7v-lU&feature=em-uploademail
The following users thanked this post: prashantbhagawati

5
AbstractInspired by the work of Newton, Darwin, and Wegener, this paper tracks the drivers and dynamics that have shaped the growth of hydrological understanding over the last century. On the basis of an interpretation of this history, the paper then speculates about what kind of future is in store for hydrology and how we can better prepare for it. The historical narrative underpinning this analysis indicates that progress in hydrological understanding is brought about by changing societal needs and technological opportunities: new ideas are generated by hydrologists through addressing societal needs with the technologies of their time. We suggest that progress in hydrological understanding over the last century has expressed itself through repeated cycles of euphoria and disenchantment, which have served as stimuli for the progress. The progress, for it to happen, also needed inspirational leaders as well as a supportive scientific community that provided the backdrop to major advances in the field. The paper concludes that, in a similar way to how Newton, Darwin, and Wegener conducted their research, hydrology too can benefit from synthesis activities aimed at “connecting the dots.”


Link: http://onlinelibrary.wiley.com/doi/10.1002/2017WR021396/abstract
The following users thanked this post: Hemant Kumar

6
Data / CCI Toolbox
« on: November 07, 2017, 12:10:46 PM »
In 2009, ESA, the European Space Agency, has launched the Climate Change Initiative (CCI), a programme to respond the need for climate-quality satellite data as expressed by GCOS, the Global Climate Observing System that supports the UNFCCC, the United Nations Framework Convention on Climate Change.
In the ESA CCI programme 14 Essential Climate Variables (ECV) are produced by individual expert teams, and cross-cutting activities provide coordination, harmonisation and support. The CCI Toolbox and the CCI Open Data Portal are the two main technical support projects within the programme. The CCI Open Data Portal will provide a single point of harmonised access to a subset of mature and validated ECV-related data products. The CCI Toolbox will provide tools that support visualisation, analysis and processing across CCI and other climate data products.
Please follow the links for more information:
1. https://cci-tools.github.io/
2. http://cate.readthedocs.io/en/latest/index.html
The following users thanked this post: Agilan

7
The Variable Infiltration Capacity (VIC) hydrologic and river routing model simulates the water and energy fluxes that occur near the land surface and provides useful information regarding the quantity and timing of available water within a watershed system. However, despite its popularity, wider adoption is hampered by the considerable effort required to prepare model inputs and calibrate the model parameters. This study presents a user-friendly software package, named VIC-Automated Setup Toolkit (VIC-ASSIST), accessible through an intuitive MATLAB graphical user interface. VIC-ASSIST enables users to navigate the model building process through prompts and automation, with the intention to promote the use of the model for practical, educational, and research purposes. The automated processes include watershed delineation, climate and geographical input set-up, model parameter calibration, sensitivity analysis, and graphical output generation. We demonstrate the package's utilities in various case studies.


Link: http://www.sciencedirect.com/science/article/pii/S1364815216308131

The following users thanked this post: Diwan

8
Programming / EcoHydRology: A R Package
« on: September 08, 2017, 05:59:57 PM »
This package provides a flexible foundation for scientists, engineers, and policy makers to base teaching exercises as well as for more applied use to model complex eco-hydrological interactions.
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Link: https://cran.r-project.org/web/packages/EcoHydRology/EcoHydRology.pdf
The following users thanked this post: Sonali, Himanshu Mishra

9
Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of ~10+% when focusing on extremes and values above (below) high (low) thresholds. Moreover, the effects of the data processing method on trend estimation are generally small, even though the use of the daily minimum and maximum readings reduces the power of trend detection (~5-10% fewer trends detected in comparison with the reference data).


Reference: http://www.sciencedirect.com/science/article/pii/S0169809517300765
The following users thanked this post: Sat Kumar Tomer, Alok Pandey

10
Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing e statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package ”musica” which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.


LINK: https://cran.r-project.org/web/packages/musica/musica.pdf
The following users thanked this post: Alok Pandey, Diwan

11
Data / Relevant Datasets and their sources
« on: August 16, 2017, 05:42:38 PM »
Please find the attached document for datasets and their online links.


To read the "State of the Climate 2016" by American Meteorological Society, follow the link:


https://www.ametsoc.org/ams/index.cfm/publications/bulletin-of-the-american-meteorological-society-bams/state-of-the-climate/


Thank you,
The following users thanked this post: Sonali, P KABBILAWSH

12
Study material / Lattice: Multivariate Data Visualization with R (Book)
« on: August 12, 2017, 06:04:34 PM »
Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed.

The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

Deepayan Sarkar won the 2004 John M. Chambers Statistical Software Award for writing lattice while he was a graduate student in Statistics at the University of Wisconsin-Madison. He is currently doing postdoctoral research in the Computational Biology program at the Fred Hutchinson Cancer Research Center, a member of the R Core Team, and an active participant on the R mailing lists.


link:  http://lmdvr.rforge.r-project.org
The following users thanked this post: Sat Kumar Tomer

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Programming / rworldmap: A New R package for Mapping Global Data
« on: August 12, 2017, 05:48:30 PM »
rworldmap is a relatively new package available on CRAN for the mapping and visualisation of global data. The vision is to make the display of global data easier, to facilitate understanding and communication. The initial focus is on data referenced by country or grid due to the frequency of use of such data in global assessments. Tools to link data referenced by country (either name or code) to a map, and then to display the map are provided as are functions to map global gridded data. Country and gridded functions accept the same arguments to specify the nature of categories and colour and how legends are formatted. This package builds on the functionality of existing packages, particularly sp, maptools and fields. Example code is provided to produce maps, to link with the packages classInt, RColorBrewer and ncdf, and to plot examples of publicly available country and gridded data.


link: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_South.pdf
The following users thanked this post: Karthikeyan L

14
Programming / Re: Exporting High Resolution figures from 'R'
« on: July 06, 2017, 01:19:13 PM »
Hi Shailza,


This might be helpful:


1. R Bloggers: https://www.r-bloggers.com/high-resolution-figures-in-r/
2. Particularly for .tiff file: https://stackoverflow.com/questions/38907514/saving-a-high-resolution-image-in-r
3. Use of some packages: http://gforge.se/2013/02/exporting-nice-plots-in-r/

The following users thanked this post: shailza

15
Data includes monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations.


link to paper: http://onlinelibrary.wiley.com/doi/10.1002/joc.5086/epdf
The following users thanked this post: Agilan

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