scholarly journals Processing of high-resolution temporal climate data for daily simulations of a complex agro-ecosystem.

2021 ◽  
pp. 202-219
Author(s):  
Maria Catarina Paz ◽  
Sónia A.P.-Santos ◽  
Raquel Barreira
Keyword(s):  
Ecosystem Services ◽  
High Resolution ◽  
Data Series ◽  
Similar Data ◽  
Olive Grove ◽  
Climate Data ◽  
R Language ◽  
Dynamic Landscape ◽  
Natural Pest Control ◽  
Climate Series

Ecosystem services, such as natural pest control, are essential tools to be incorporated in future agricultural methodologies. In this paper we focus on the processing of climate data series that feed to a system of computer models simulating daily interactions of a pest and its predator, in a dynamic landscape, the olive grove. We filled hourly climate data series and converted them to daily climate series using R language. The methodology used produces acceptable climate data series for the system to run and allows to segregate specific periods of the day while maintaining daily temporal resolution. We expect this paper can be helpful when dealing with similar data and purpose.

Vulnerability and Importance of Arctic Wetlands as large-scale nature-based solutions for Sustainability in a Changing Climate

2021 ◽  
Author(s):  
Elisie Kåresdotter ◽  
Zahra Kalantari
Keyword(s):  
Ecosystem Services ◽  
High Resolution ◽  
Large Scale ◽  
Natural Capital ◽  
Environmental Benefits ◽  
Indigenous Populations ◽  
The Arctic ◽  
Current Status ◽  
Human Beings ◽  
Arctic Wetlands

<p>Wetlands as large-scale nature-based solutions (NBS) provide multiple ecosystem services of local, regional, and global importance. Knowledge concerning location and vulnerability of wetlands, specifically in the Arctic, is vital to understand and assess the current status and future potential changes in the Arctic. Using available high-resolution wetland databases together with datasets on soil wetness and soil types, we created the first high-resolution map with full coverage of Arctic wetlands. Arctic wetlands' vulnerability is assessed for the years 2050, 2075, and 2100 by utilizing datasets of permafrost extent and projected mean annual average temperature from HadGEM2-ES climate model outputs for three change scenarios (RCP2.6, 4.5, and 8.5). With approximately 25% of Arctic landmass covered with wetlands and 99% being in permafrost areas, Arctic wetlands are highly vulnerable to changes in all scenarios, apart from RCP2.6 where wetlands remain largely stable. Climate change threatens Arctic wetlands and can impact wetland functions and services. These changes can adversely affect the multiple services this sort of NBS can provide in terms of great social, economic, and environmental benefits to human beings. Consequently, negative changes in Arctic wetland ecosystems can escalate land-use conflicts resulting from natural capital exploitation when new areas become more accessible for use. Limiting changes to Arctic wetlands can help maintain their ecosystem services and limit societal challenges arising from thawing permafrost wetlands, especially for indigenous populations dependent on their ecosystem services. This study highlights areas subject to changes and provides useful information to better plan for a sustainable and social-ecological resilient Arctic.</p><p>Keywords: Arctic wetlands, permafrost thaw, regime shift vulnerability, climate projection</p>

scholarly journals Cluster-based analysis of multi-model climate ensembles

2018 ◽  
Vol 11 (6) ◽  
pp. 2033-2048 ◽  
Author(s):  
Richard Hyde ◽  
Ryan Hossaini ◽  
Amber A. Leeson
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Models ◽  
Climate Model ◽  
Complex Data ◽  
Similar Data ◽  
Climate Data ◽  
Efficient Manner ◽  
Clustering Techniques ◽  
Insight Into

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

Synergy of very high resolution optical and radar data for object-based olive grove mapping

2011 ◽  
Vol 25 (6) ◽  
pp. 971-989 ◽  
Author(s):  
Jan Peters ◽  
Frieke Van Coillie ◽  
Toon Westra ◽  
Robert De Wulf
Keyword(s):  
High Resolution ◽  
Radar Data ◽  
Olive Grove ◽  
Object Based ◽  
Very High

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

scholarly journals A 40-year global dataset of visible channel remote sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Author(s):  
Benjamin R. Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Climate Data Record ◽  
Very High

Abstract. A consistently calibrated 40-year length dataset of visible channel remote sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time-series. The dataset uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record (CDR) for top-of-atmosphere (TOA) visible channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote sensing reflectance. The resulting dataset is produced at daily, and archived at monthly, resolution, on a 0.1° × 0.1° grid at https://doi.pangaea.de/10.1594/PANGAEA.892175. The primary aim of deriving this dataset is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania Huxleyi over the past 40 years.

scholarly journals Noise performance of microwave humidity sounders over their life time

2017 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Viju O. John ◽  
Jonathan Mittaz ◽  
Stefan A. Buehler
Keyword(s):  
Uncertainty Propagation ◽  
Life Time ◽  
Noise Spectrum ◽  
Data Series ◽  
Allan Deviation ◽  
Noise Performance ◽  
Climate Data ◽  
Time Data ◽  
Accessible Information ◽  
Long Time

Abstract. The microwave humidity sounders Special Sensor Microwave Water Vapour Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) to date have been providing data records for 25 years. So far, the data records lack uncertainty information essential for constructing consistent long time data series. In this study, we assess the quality of the recorded data with respect to the uncertainty caused by noise. We calculate the noise on the raw calibration counts from the deep space view (DSV) of the instrument and the Noise Equivalent Differential Temperature (NEΔT) as a measure for the radiometer sensitivity. For this purpose, we use the Allan Deviation that is not biased from an underlying varying mean of the data and that has been suggested only recently for application in atmospheric remote sensing. Moreover, we use the bias function related to the Allan Deviation to infer the underlying spectrum of the noise. As examples, we investigate the noise spectrum in flight for some instruments. For the assessment of the noise evolution in time, we provide a descriptive and graphical overview of the calculated NEΔT over the life span of each instrument and channel. This overview can serve as an easily accessible information for users interested in the noise performance of a specific instrument, channel and time. Within the time evolution of the noise, we identify periods of instrumental degradation, which manifest themselves in an increasing NEΔT, and periods of erratic behaviour, which show sudden increases of NEΔT interrupting the overall smooth evolution of the noise. From this assessment and subsequent exclusion of the aforementioned periods, we present a chart showing available data records with NEΔT < K. Due to overlapping life spans of the instruments, these reduced data records still cover without gaps the time since 1994 and may therefore serve as first step for constructing long time series. Our method for count noise estimation, that has been used in this study, will be used in the data processing to provide input values for the uncertainty propagation in the generation of a new set of Fundamental Climate Data Records (FCDR) that are currently produced in the project Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO).

scholarly journals Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

2018 ◽  
Vol 10 (1) ◽  
pp. 181-196 ◽  
Author(s):  
Mehdi Bahrami ◽  
Samira Bazrkar ◽  
Abdol Rassoul Zarei
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Occurrence Frequency ◽  
Natural Phenomenon ◽  
Drought Severity ◽  
Data Series ◽  
Climate Data ◽  
Seasonal Drought

Abstract Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

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