scholarly journals Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density

2018 ◽  
Vol 15 ◽  
pp. 184-202 ◽  
Author(s):  
C. Berndt ◽  
U. Haberlandt
Keyword(s):  
Temporal Resolution ◽  
Spatial Interpolation ◽  
Network Density ◽  
Climate Variables ◽  
Northern Germany

Mapping day-of-burning with coarse-resolution satellite fire-detection data

2014 ◽  
Vol 23 (2) ◽  
pp. 215 ◽  
Author(s):  
Sean A. Parks
Keyword(s):  
Temporal Resolution ◽  
Spatial Interpolation ◽  
Fire Detection ◽  
Objective Evaluation ◽  
Daily Basis ◽  
Poor Quality ◽  
High Temporal Resolution ◽  
Daily Weather ◽  
Coarse Spatial Resolution ◽  
In Fire

Evaluating the influence of observed daily weather on observed fire-related effects (e.g. smoke production, carbon emissions and burn severity) often involves knowing exactly what day any given area has burned. As such, several studies have used fire progression maps – in which the perimeter of an actively burning fire is mapped at a fairly high temporal resolution – or MODIS satellite data to determine the day-of-burning, thereby allowing an evaluation of the influence of daily weather. However, fire progression maps have many caveats, the most substantial being that they are rarely mapped on a daily basis and may not be available in remote locations. Although MODIS fire detection data provide an alternative due to its global coverage and high temporal resolution, its coarse spatial resolution (1km2) often requires that it be downscaled. An objective evaluation of how to best downscale, or interpolate, MODIS fire detection data is necessary. I evaluated 10 spatial interpolation techniques on 21 fires by comparing the day-of-burning as estimated with spatial interpolation of MODIS fire detection data to the day-of-burning that was recorded in fire progression maps. The day-of-burning maps generated with the best performing interpolation technique showed reasonably high quantitative and qualitative agreement with fire progression maps. Consequently, the methods described in this paper provide a viable option for producing day-of-burning data where fire progression maps are of poor quality or unavailable.

scholarly journals Reference crop evapotranspiration database in Spain (1961–2014)

2019 ◽  
Author(s):  
Miquel Tomas-Burguera ◽  
Sergio M. Vicente-Serrano ◽  
Santiago Beguería ◽  
Fergus Reig ◽  
Borja Latorre
Keyword(s):  
Spatial Interpolation ◽  
Climate Models ◽  
Drought Indices ◽  
Watershed Scale ◽  
Climate Variables ◽  
Evaporative Demand ◽  
Reference Crop Evapotranspiration ◽  
Climate Studies ◽  
Reference Crop ◽  
Impacts Of Climate Change

Abstract. Obtaining climate grids for distinct variables is of high importance to develop better climate studies, but also to offer usable products for other researchers and to end users. As a measure of atmospheric evaporative demand (AED), reference evapotranspiration (ETo) is a key variable for understanding both water and energy terrestrial balances, being important for climatology, hydrology and agronomy. In spite of its importance, the calculation of ETo is not very common, mainly because data of a high number of climate variables are required, and some of them are not commonly available. To solve this problem, a strategy based on the spatial interpolation of climate variables previous to calculation of ETo using FAO-56 Penman-Monteith was followed to obtain an ETo database for Continental Spain and Balearic Islands covering the 1961–2014 period at a spatial resolution of 1.1 km and at weekly temporal resolution. In this database, values for the radiative and aerodynamic components as well as the estimated uncertainty related with ETo are also provided. This database is available to download in Network Common Data Form (netcdf) format at https://doi.org/10.20350/digitalCSIC/8615 (Tomas-Burguera et al., 2019), and a map visualization tool (http://speto.csic.es) is also available to help users to download data of one specific point in comma-separated values (csv) format. A relevant number of research ares could take advantage of this database. Providing only some examples: i) the study of budyko curve, which relates rainfall data with evapotranspiration and AED at watershed scale; ii) the calculation of drought indices using AED data, such as SPEI or PDSI; iii) agroclimatic studies related with irrigation requirement; iv) validation of Climate Models water and energy balance; v) the study of the impacts of climate change in AED.

scholarly journals Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

Data ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 126
Author(s):  
Renato Okabayashi Miyaji ◽  
Felipe Valencia de Almeida ◽  
Lucas de Oliveira Bauer ◽  
Victor Madureira Ferrari ◽  
Pedro Luiz Pizzigatti Corrêa ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Data Science ◽  
Original Data ◽  
Air Pollutant ◽  
Amazon Rainforest ◽  
Meteorological Variables ◽  
Climate Variables ◽  
Training Models ◽  
Central Amazonia ◽  
Atmospheric Radiation Measurement

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

Spatial Interpolation of Climate Variables in Nebraska

2010 ◽  
Vol 53 (6) ◽  
pp. 1759-1771 ◽  
Author(s):  
A. Irmak ◽  
P. K. Ranade ◽  
D. Marx ◽  
S. Irmak ◽  
K. G. Hubbard ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Climate Variables

scholarly journals Influence of spatial interpolation methods for climate variables on the simulation of discharge and nitrate fate with SWAT

2010 ◽  
Vol 27 ◽  
pp. 91-98 ◽  
Author(s):  
S. van der Heijden ◽  
U. Haberlandt
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Data Sets ◽  
Climate Variables ◽  
Climate Data ◽  
Data Set ◽  
Interpolation Methods ◽  
Automated Calibration ◽  
Kriging With External Drift ◽  
External Drift

Abstract. For ecohydrological modeling climate variables are needed on subbasin basis. Since they usually originate from point measurements spatial interpolation is required during preprocessing. Different interpolation methods yield data of varying quality, which can strongly influence modeling results. Four interpolation methods to be compared were selected: nearest neighbour, inverse distance, ordinary kriging, and kriging with external drift (Goovaerts, 1997). This study presents three strategies to evaluate the influence of the interpolation method on the modeling results of discharge and nitrate load in the river in a mesoscale river catchment (~1000 km2) using the Soil and Water Assessment Tool (SWAT, Neitsch et al., 2005) model: I. Automated calibration of the model with a mixed climate data set and consecutive application of the four interpolated data sets. II. Consecutive automated calibration of the model with each of the four climate data sets. III. Random generation of 1000 model parameter sets and consecutive application of the four interpolated climate data sets on each of the 1000 realisations, evaluating the number of realisations above a certain quality criterion threshold. Results show that strategies I and II are not suitable for evaluation of the quality of the interpolated data. Strategy III however proves a significant influence of the interpolation method on nitrate modeling. A rank order from the simplest to the most sophisticated method is visible, with kriging with external drift (KED) outperforming all others. Responsible for this behaviour is the variable temperature, which benefits most from more sophisticated methods and at the same time is the main driving force for the nitrate cycle. The missing influence of the interpolation methods on discharge modeling is explained by a much higher measuring network density for precipitation than for all other climate variables.

scholarly journals Small shards and long distances – three cryptotephra layers from the Nahe palaeolake including the first discovery of Laacher See Tephra in Schleswig-Holstein (Germany)

2020 ◽  
Author(s):  
Sascha Krüger ◽  
Christel van den Bogaard
Keyword(s):  
High Resolution ◽  
Lake Sediments ◽  
Temporal Resolution ◽  
Early Holocene ◽  
Northern Germany ◽  
North West ◽  
Pollen Stratigraphy ◽  
The North ◽  
Laacher See Tephra

Investigations of Lateglacial to Early Holocene lake sediments from the Nahe palaeolake (northern Germany) provided a high-resolution palynological record. To increase the temporal resolution of the record a targeted search for cryptotephra was carried out on the basis of pollen stratigraphy. Three cryptotephra horizons were detected and geochemically identified as Saksunarvatn Ash, Vedde Ash and Laacher See Tephra. Here we present the first geochemically confirmed finding of the ash from the Laacher See Eruption in Schleswig-Holstein – extending the so far detected fallout fan of the Lateglacial eruption further to the North-West. These finds enable direct stratigraphical correlations and underline the potential of the site for further investigations.

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.

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