scholarly journals KrigR – A tool for downloading and statistically downscaling climate reanalysis data

2022 ◽  
Author(s):  
Erik Kusch ◽  
Richard Davy
Keyword(s):  
Temporal Resolution ◽  
R Package ◽  
Reanalysis Data ◽  
Climate Variables ◽  
Climate Data ◽  
Time Period ◽  
Wide Range ◽  
Bioclimatic Variables ◽  
Data Products ◽  
Temporal And Spatial

Abstract Advances in climate science have rendered obsolete the gridded observation data widely used in downstream applications. Novel climate reanalysis products outperform legacy data products in accuracy, temporal resolution, and provision of uncertainty metrics. Consequently, there is an urgent need to develop a workflow through which to integrate these improved data into biological analyses. The ERA5 product family (ERA5 and ERA5-Land) are the latest and most advanced global reanalysis products created by the European Center for Medium-range Weather Forecasting (ECMWF). These data products offer up to 83 essential climate variables (ECVs) at hourly intervals for the time-period of 1981 to today with preliminary back-extensions being available for 1950-1981. Spatial resolutions range from 30x30km (ERA5) to 11x11km (ERA5-Land) and can be statistically downscaled to study-requirements at finer spatial resolutions. Kriging is one such method to interpolate data to finer resolutions and has the advantages that one can leverage additional covariate information and obtain the uncertainty associated with the downscaling. The KrigR R-package enables users to (1) download ERA5(-Land) climate reanalysis data for a user-specified region, and time-period, (2) aggregate these climate products to desired temporal resolutions and metrics, (3) acquire topographical co-variates, and (4) statistically downscale spatial data to a user-specified resolution using co-variate data via kriging. KrigR can execute all these tasks in a single function call, thus enabling the user to obtain any of 83 (ERA5) / 50 (ERA5-Land) climate variables at high spatial and temporal resolution with a single R-command. Additionally, KrigR contains functionality for computation of bioclimatic variables and aggregate metrics from the variables offered by ERA5(-Land). This R-package provides an easy-to-implement workflow for implementation of state-of-the-art climate data while avoiding issues of storage limitations at high temporal and spatial resolutions by providing data according to user-needs rather than in global data sets. Consequently, KrigR provides a toolbox to obtain a wide range of tailored climate data at unprecedented combinations of high temporal and spatial resolutions thus enabling the use of world-leading climate data through the R-interface and beyond.

scholarly journals Reconciling high resolution climate datasets using KrigR

2021 ◽  
Author(s):  
Richard Davy ◽  
Erik Kusch
Keyword(s):  
High Resolution ◽  
Temporal Resolution ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
R Package ◽  
Reanalysis Data ◽  
Climate Data ◽  
Community Of Researchers ◽  
The Difference

Abstract There is an increasing need for high spatial and temporal resolution climate data for the wide community of researchers interested in climate change and its consequences. Currently, there is a large mismatch between the spatial resolutions of global climate model and reanalysis datasets (at best around 0.25o and 0.1o respectively) and the resolutions needed by many end-users of these datasets, which are typically on the scale of 30 arcseconds (~900m). This need for improved spatial resolution in climate datasets has motivated several groups to statistically downscale various combinations of observational or reanalysis datasets. However, the variety of downscaling methods and inputs used makes it difficult to reconcile the resultant differences between these high-resolution datasets. Here we make use of the KrigR R-package to statistically downscale the world-leading ERA5(-Land) reanalysis data using kriging. We show that kriging can accurately recover spatial heterogeneity of climate data given strong relationships with co-variates; that by preserving the uncertainty associated with the statistical downscaling, one can investigate and account for confidence in high-resolution climate data; and that the statistical uncertainty provided by KrigR can explain much of the difference between widely used high resolution climate datasets (CHELSA, TerraClimate, and WorldClim2) depending on variable, timescale, and region. This demonstrates the advantages of using KrigR to generate customized high spatial and/or temporal resolution climate data.

scholarly journals Mapping the climate: guidance on appropriate techniques to map climate variables and their uncertainty

2012 ◽  
Vol 5 (1) ◽  
pp. 245-256 ◽  
Author(s):  
N. R. Kaye ◽  
A. Hartley ◽  
D. Hemming
Keyword(s):  
Climate Change ◽  
Data Use ◽  
Lessons Learned ◽  
Background Information ◽  
Mapping Technique ◽  
Climate Variables ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Mapping Approach ◽  
Wide Range

Abstract. Maps are a crucial asset in communicating climate science to a diverse audience, and there is a wealth of software available to analyse and visualise climate information. However, this availability makes it easy to create poor maps as users often lack an underlying cartographic knowledge. Unlike traditional cartography, where many known standards allow maps to be interpreted easily, there is no standard mapping approach used to represent uncertainty (in climate or other information). Consequently, a wide range of techniques have been applied for this purpose, and users may spend unnecessary time trying to understand the mapping approach rather than interpreting the information presented. Furthermore, communicating and visualising uncertainties in climate data and climate change projections, using for example ensemble based approaches, presents additional challenges for mapping that require careful consideration. The aim of this paper is to provide background information and guidance on suitable techniques for mapping climate variables, including uncertainty. We assess a range of existing and novel techniques for mapping variables and uncertainties, comparing "intrinsic" approaches that use colour in much the same way as conventional thematic maps with "extrinsic" approaches that incorporate additional geometry such as points or features. Using cartographic knowledge and lessons learned from mapping in different disciplines we propose the following 6 general mapping guidelines to develop a suitable mapping technique that represents both magnitude and uncertainty in climate data: – use a sensible sequential or diverging colour scheme; – use appropriate colour symbolism if it is applicable; – ensure the map is usable by colour blind people; – use a data classification scheme that does not misrepresent the data; – use a map projection that does not distort the data – attempt to be visually intuitive to understand. Using these guidelines, we suggest an approach to map climate variables with associated uncertainty, that can be easily replicated for a wide range of climate mapping applications. It is proposed this technique would provide a consistent approach suitable for mapping information for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).

scholarly journals The CM SAF R Toolbox—A Tool for the Easy Usage of Satellite-Based Climate Data in NetCDF Format

2019 ◽  
Vol 8 (3) ◽  
pp. 109 ◽  
Author(s):  
Steffen Kothe ◽  
Rainer Hollmann ◽  
Uwe Pfeifroth ◽  
Christine Träger-Chatterjee ◽  
Jörg Trentmann
Keyword(s):  
Graphical User Interface ◽  
Spatial Data ◽  
Water Cycle ◽  
Sunshine Duration ◽  
R Package ◽  
Climate Variables ◽  
Climate Data ◽  
Time Dimension ◽  
Visual Appearance

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) provides satellite-based climate data records of essential climate variables of the energy budget and water cycle. The data records are generally distributed in NetCDF format. To simplify the preparation, analysis, and visualization of the data, CM SAF provides the so-called CM SAF R Toolbox. This is a collection of R-based tools, which are optimized for spatial data with longitude, latitude, and time dimension. For analysis and manipulation of spatial NetCDF-formatted data, the functionality of the cmsaf R-package is implemented. This R-package provides more than 60 operators. The visualization of the data, its properties, and corresponding statistics can be done with an interactive plotting tool with a graphical user interface, which is part of the CM SAF R Toolbox. The handling, functionality, and visual appearance are demonstrated here based on the analysis of sunshine duration in Europe for the year 2018. Sunshine duration in Scandinavia and Central Europe was extraordinary in 2018 compared to the long-term average.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

scholarly journals An Overview of Flood Risk Analysis Methods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 474
Author(s):  
Daniel Constantin Diaconu ◽  
Romulus Costache ◽  
Mihnea Cristian Popa
Keyword(s):  
Core Collection ◽  
Web Of Science ◽  
Research Directions ◽  
Economic Implications ◽  
Forecasting Methods ◽  
Wide Range ◽  
Scientific Papers ◽  
Recent Developments ◽  
Flood Analysis ◽  
Temporal And Spatial

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

scholarly journals GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

2019 ◽  
Vol 12 (11) ◽  
pp. 4661-4679 ◽  
Author(s):  
Bin Cao ◽  
Xiaojing Quan ◽  
Nicholas Brown ◽  
Emilie Stewart-Jones ◽  
Stephan Gruber
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Ground Surface ◽  
Reanalysis Data ◽  
Meteorological Variables ◽  
Ensemble Simulations ◽  
Driving Time ◽  
Wide Range ◽  
Global Coverage ◽  
Uncertain Input

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

Basin-Wide Connections of Upper-Ocean Temperature Variability in the Equatorial Indian Ocean

2021 ◽  
pp. 1-50
Author(s):  
Ge Song ◽  
Bohua Huang ◽  
Rongcai Ren ◽  
Zeng-Zhen Hu
Keyword(s):  
Indian Ocean ◽  
Equatorial Indian Ocean ◽  
Reanalysis Data ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Ocean Reanalysis ◽  
Near Surface ◽  
Surface Warming ◽  
Transition Mechanism ◽  
Wide Range

AbstractIn this paper, the interannual variability of upper-ocean temperature in the equatorial Indian Ocean (IO) and its basin-wide connections are investigated using 58-year (1958-2015) comprehensive monthly mean ocean reanalysis data. Three leading modes of an empirical orthogonal function (EOF) analysis dominate the variability of upper-ocean temperature in the equatorial IO in a wide range of timescales. A coherent interannual band within the first two EOF modes identifies an oscillation between the zonally tilting thermocline across the equatorial IO in its peak phases and basin-wide displacement of the equatorial thermocline in its transitional phases. Consistent with the recharge oscillation paradigm, this oscillation is inherent of the equatorial IO with a quasi-periodicity around 15 months, in which the wind-induced off-equatorial Rossby waves near 5°S-10°S provide the phase-transition mechanism. This intrinsic IO oscillation provides the biennial component in the observed IOD variations. The third leading mode shows a nonlinear long-term trend of the upper-ocean temperature, including the near-surface warming along the equatorial Indian Ocean, accompanied by cooling trend in the lower thermocline originating further south. Such vertical contrary trends may lead to an enhanced stratification in the equatorial IO.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

<p>Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.</p><p>We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application.  </p><p>We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.</p><p>The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.</p>

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