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 Signal Stations: Newly Digitized Historical Climate Data of the German Bight and the Southern Baltic Sea Coast

2016 ◽  
Vol 33 (12) ◽  
pp. 2735-2741 ◽  
Author(s):  
Dörte Wagner ◽  
Birger Tinz ◽  
Hans von Storch
Keyword(s):  
Baltic Sea ◽  
Spatial Data ◽  
German Bight ◽  
Observation Data ◽  
Climate Data ◽  
Pressure Data ◽  
Southern Baltic Sea ◽  
Southern Baltic ◽  
Sea Coast

AbstractAt the German Meteorological Service in Hamburg, handwritten journals of meteorological observation data of 164 signal stations exist that were digitized. These data contain long-term time series of up to 125 years for the period 1877–1999 and allow for studies of regional meteorological conditions with greatly improved spatial resolution. Wind and air pressure data of selected signal stations along the German Bight and the southern Baltic Sea coast show a spatial data homogeneity that allows for an improved description of two historical storms, in 1906 and 1913. This is the first presentation of signal station data.

scholarly journals SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF

2021 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Jörg Trentmann ◽  
Rainer Hollmann
Keyword(s):  
Water Cycle ◽  
Sunshine Duration ◽  
Global Radiation ◽  
Surface Radiation ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Time Period ◽  
Data Record ◽  
Climate Data Record

<p>The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.</p><p>In fall 2021, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.</p><p>In this presentation, an overview of the SARAH climate data record and their applications will be provided. A focus will be on the SARAH-3 developments and improvements (i.e. improved consideration of snow-covered surfaces). First validation results of the new Climate Data Record using surface reference observations will be presented. Further, SARAH-3 will be used for the analysis of the climate variability in Europe during the last decades.</p><p>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</p>

scholarly journals The Calibration of Evaporation Models against the Penman–Monteith Equation on Lake Most

2020 ◽  
Vol 13 (1) ◽  
pp. 313
Author(s):  
Viktor Dubovský ◽  
Dagmar Dlouhá ◽  
Lukáš Pospíšil
Keyword(s):  
Water Cycle ◽  
State Of The Art ◽  
Free Water ◽  
Climate Data ◽  
Area Of Interest ◽  
Measurement Devices ◽  
Main Components ◽  
Evaporation Models ◽  
Monteith Equation

Evaporation is one of the main components of the water cycle in nature. Our interest in free water surface evaporation is due to the needs of ongoing hydric recultivation of the former Ležáky–Most quarry, i.e., Lake Most, and also other planned hydric recultivations in the region. One of the key components of hydric reclamation planning is the securitization of long-term sustainability, which is based on the capability to keep the final water level at a stable level. In our work, we are interested in the evaporation estimation in the area of Lake Most (Czech Republic, Europe). This lake has been artificially created only a few years ago, and nowadays we are looking for a simple evaporation model, based on which we will be able to decide which measurement devices have to be installed at the location to provide more localized data to the model. In this paper, we calibrate state-of-the-art simplified evaporation models against the Penman–Monteith equation based on the Nash–Sutcliffe efficiency maximization. We discuss the suitability of this approach using real-world climate data from the weather station located one km from the area of interest.

scholarly journals Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 188
Author(s):  
Naman Bansal ◽  
Maurice Defo ◽  
Michael A. Lacasse
Keyword(s):  
Relative Humidity ◽  
Full Range ◽  
Support Vector ◽  
Data Series ◽  
Climate Variables ◽  
Climate Data ◽  
Moisture Performance ◽  
Wood Frame ◽  
Massive Timber

The objective of this study was to explore the potential of a machine learning algorithm, the Support Vector Machine Regression (SVR), to forecast long-term hygrothermal responses and the moisture performance of light wood frame and massive timber walls. Hygrothermal simulations were performed using a 31-year long series of climate data in three cities across Canada. Then, the first 5 years of the series were used in each case to train the model, which was then used to forecast the hygrothermal responses (temperature and relative humidity) and moisture performance indicator (mold growth index) for the remaining years of the series. The location of interest was the exterior layer of the OSB and cross-laminated timber in the case of the wood frame wall and massive timber wall, respectively. A sliding window approach was used to incorporate the dependence of the hygrothermal response on the past climatic conditions, which allowed SVR to capture time, implicitly. The variable selection was performed using the Least Absolute Shrinkage and Selection Operator, which revealed wind-driven rain, relative humidity, temperature, and direct radiation as the most contributing climate variables. The results show that SVR can be effectively used to forecast hygrothermal responses and moisture performance on a long climate data series for most of the cases studied. In some cases, discrepancies were observed due to the lack of capturing the full range of variability of climate variables during the first 5 years.

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 Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

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