scholarly journals Gridded climate data products are an alternative to instrumental measurements as inputs to rainfall-runoff models

2017 ◽  
Vol 31 (18) ◽  
pp. 3283-3293 ◽  
Author(s):  
José L.J. Ledesma ◽  
Martyn N. Futter
Keyword(s):  
Rainfall Runoff ◽  
Climate Data ◽  
Data Products ◽  
Gridded Climate Data

Listening to Inuit and Naskapi peoples in the eastern Canadian Subarctic: a quantitative comparison of local observations with gridded climate data

2017 ◽  
Vol 18 (1) ◽  
pp. 189-203 ◽  
Author(s):  
Michel Rapinski ◽  
◽  
Fanny Payette ◽  
Oliver Sonnentag ◽  
Thora Martina Herrmann ◽  
...  
Keyword(s):  
Quantitative Comparison ◽  
Climate Data ◽  
Gridded Climate Data

Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling

2022 ◽  
Author(s):  
Janaína Cassiano dos Santos ◽  
Gustavo Bastos Lyra ◽  
Marcel Carvalho Abreu ◽  
José Francisco de Oliveira-Júnior ◽  
Leonardo Bohn ◽  
...  
Keyword(s):  
Methodological Approach ◽  
Climate Data ◽  
Cartographic Modeling ◽  
Gridded Climate Data ◽  
Aridity Indices

Streamlining Oceanic Biogeochemical Dataset Assembly in Support of Global Data Products

2020 ◽  
Author(s):  
Eugene Burger ◽  
Benjamin Pfeil ◽  
Kevin O'Brien ◽  
Linus Kamb ◽  
Steve Jones ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Integration ◽  
Scientific Data ◽  
Climate Data ◽  
Science Data ◽  
Metadata Record ◽  
Data Products ◽  
Analysis And Synthesis ◽  
Data Integration System ◽  
Global Data

<p>Data assembly in support of global data products, such as GLODAP, and submission of data to national data centers to support long-term preservation, demands significant effort. This is in addition to the effort required to perform quality control on the data prior to submission. Delays in data assembly can negatively affect the timely production of scientific indicators that are dependent upon these datasets, including products such as GLODAP. What if data submission, metadata assembly and quality control can all be rolled into a single application? To support more streamlined data management processes in the NOAA Ocean Acidification Program (OAP) we are developing such an application.This application has the potential for application towards a broader community.</p><p>This application addresses the need that data contributing to analysis and synthesis products are high quality, well documented, and accessible from the applications scientists prefer to use. The Scientific Data Integration System (SDIS) application developed by the PMEL Science Data Integration Group, allows scientists to submit their data in a number of formats. Submitted data are checked for common errors. Metadata are extracted from the data that can then be complemented with a complete metadata record using the integrated metadata entry tool that collects rich metadata that meets the Carbon science community requirements. Still being developed, quality control for standard biogeochemical parameters will be integrated into the application. The quality control routines will be implemented in close collaboration with colleagues from the Bjerknes Climate Data Centre (BCDC) within the Bjerknes Centre for Climate Research (BCCR).  This presentation will highlight the capabilities that are now available as well as the implementation of the archive automation workflow, and it’s potential use in support of GLODAP data assembly efforts.</p>

scholarly journals Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

2018 ◽  
Author(s):  
Joanne Nightingale ◽  
Folkert Boersma ◽  
Jan-Peter Muller ◽  
Steven Compernolle ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Climate Model ◽  
Earth Observation ◽  
Evidence Based ◽  
The European Union ◽  
Climate Data ◽  
Area Index ◽  
Data Products ◽  
Reference Measurements

Data from Earth Observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts and manage natural resources. Policy makers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products as well as the reference measurements used to calibrate, validate or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service, the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite derived datasets by prototyping a robust, generic system for the implementation and evaluation of Quality Assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface Albedo, Leaf Area Index, FAPAR, NO2, HCHO and CO. Provision of standardized QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products their specific applications.

scholarly journals Calculating Human Health Risks with General Weather Data

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Sarah Derouin
Keyword(s):  
Human Health ◽  
Health Risks ◽  
Mortality Risk ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Climate Data ◽  
Human Mortality ◽  
Human Health Risks ◽  
Gridded Climate Data

Gridded climate data sets are just as effective as weather station data at assessing human mortality risk related to heat and cold, researchers suggest.

scholarly journals Analysis of climate variability and droughts in East Africa using high-resolution climate data products

2020 ◽  
Vol 186 ◽  
pp. 103130 ◽  
Author(s):  
Solomon H. Gebrechorkos ◽  
Stephan Hülsmann ◽  
Christian Bernhofer
Keyword(s):  
High Resolution ◽  
Climate Variability ◽  
East Africa ◽  
Climate Data ◽  
Data Products

scholarly journals Ten Priority Science Gaps in Assessing Climate Data Record Quality

2019 ◽  
Vol 11 (8) ◽  
pp. 986 ◽  
Author(s):  
Joanne Nightingale ◽  
Jonathan P.D. Mittaz ◽  
Sarah Douglas ◽  
Dick Dee ◽  
James Ryder ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Data Sets ◽  
Climate Data ◽  
Science Knowledge ◽  
Knowledge Gaps ◽  
Quality Aspects ◽  
Data Products ◽  
In Situ Observations

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union’s Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons. This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described. The development of the EQC framework has highlighted cross-domain as well as ECV specific science knowledge gaps in relation to addressing the quality of climate data sets derived from satellite and in situ observations. We discuss 10 common priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and provide users with the range of information necessary to confidently select relevant products for their specific application.

scholarly journals Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications

2020 ◽  
Vol 13 (2) ◽  
pp. 789-819 ◽  
Author(s):  
Maximilian Reuter ◽  
Michael Buchwitz ◽  
Oliver Schneising ◽  
Stefan Noël ◽  
Heinrich Bovensmann ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Data Sets ◽  
Climate Data ◽  
Single Observation ◽  
Dry Air ◽  
Data Products ◽  
Level 3 ◽  
Global Bias ◽  
Level 2

Abstract. Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).

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