scholarly journals Validation of Satellite (TMPA and IMERG) Rainfall Products with the IMD Gridded Data Sets over Monsoon Core Region of India

2019 ◽  
Author(s):  
Tumuluru Venkata Lakshmi Kumar ◽  
Humberto Alves Barbosa ◽  
Manoj Kumar Thakur ◽  
Franklin Paredes-Trejo
Keyword(s):  
Core Region ◽  
Data Sets ◽  
Gridded Data

scholarly journals Correction: Brodzik, M.J., et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS International Journal of Geo-Information 2012, 1, 32–45

2014 ◽  
Vol 3 (3) ◽  
pp. 1154-1156 ◽  
Author(s):  
Mary Brodzik ◽  
Brendan Billingsley ◽  
Terry Haran ◽  
Bruce Raup ◽  
Matthew Savoie
Keyword(s):  
Data Sets ◽  
Gridded Data

Visualizing sparse gridded data sets

2000 ◽  
Vol 20 (5) ◽  
pp. 52-57 ◽  
Author(s):  
S. Djurcilov ◽  
A. Pang
Keyword(s):  
Data Sets ◽  
Gridded Data

scholarly journals A novel hydrographic gridded data set for the northern Antarctic Peninsula

2021 ◽  
Vol 13 (2) ◽  
pp. 671-696
Author(s):  
Tiago S. Dotto ◽  
Mauricio M. Mata ◽  
Rodrigo Kerr ◽  
Carlos A. E. Garcia
Keyword(s):  
Southern Ocean ◽  
Antarctic Peninsula ◽  
Initial Conditions ◽  
Transitional Zone ◽  
Water Masses ◽  
Easy Access ◽  
Data Sets ◽  
Gridded Data ◽  
Data Set ◽  
Early 21St Century

Abstract. The northern Antarctic Peninsula (NAP) is a highly dynamic transitional zone between the subpolar-polar and oceanic-coastal environments, and it is located in an area affected by intense climate change, including intensification and spatial shifts of the westerlies as well as atmospheric and oceanic warming. In the NAP area, the water masses originate mainly from the Bellingshausen and Weddell seas, which create a marked regional dichotomy thermohaline characteristic. Although the NAP area has relatively easy access when compared to other Southern Ocean environments, our understanding of the water masses' distribution and the dynamical processes affecting the variability of the region is still limited. That limitation is closely linked to the sparse data coverage, as is commonly the case in most Southern Ocean environments. This work provides a novel seasonal three-dimensional high-resolution hydrographic gridded data set for the NAP (version 1), namely the NAPv1.0. Hydrographic measurements from 1990 to 2019 comprising data collected by conductivity, temperature, depth (CTD) casts; sensors from the Marine Mammals Exploring the Oceans Pole to Pole (MEOP) consortium; and Argo floats have been optimally interpolated to produce maps of in situ temperature, practical salinity, and dissolved oxygen at ∼ 10 km spatial resolution and 90 depth levels. The water masses and oceanographic features in this regional gridded product are more accurate than other climatologies and state estimate products currently available. The data sets are available in netCDF format at https://doi.org/10.5281/zenodo.4420006 (Dotto et al., 2021). The novel and comprehensive data sets presented here for the NAPv1.0 product are a valuable tool to be used in studies addressing climatological changes in the unique NAP region since they provide accurate initial conditions for ocean models and improve the end of the 20th- and early 21st-century ocean mean-state representation for that area.

scholarly journals Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability

2015 ◽  
Vol 36 (9) ◽  
pp. 3413-3422 ◽  
Author(s):  
Santiago Beguería ◽  
Sergio M. Vicente-Serrano ◽  
Miquel Tomás-Burguera ◽  
Marco Maneta
Keyword(s):  
Climate Variability ◽  
Data Sets ◽  
Gridded Data

Environmental Electronic Sensing Systems

Geography ◽  
2021 ◽  
Keyword(s):  
Mathematical Models ◽  
Remote Sensing Data ◽  
Sensor Calibration ◽  
Scaling Analysis ◽  
Data Sets ◽  
Gridded Data ◽  
Environmental Processes ◽  
Sensing Systems ◽  
Electronic Sensing ◽  
Selection Of

Although environmental measurement instrumentation has been utilized by human civilizations for thousands of years, the use of electronics to conduct measurements closely parallels the development of electrical theory from the 19th century to the present. Environmental electronic sensing systems have been created to automate measurement tasks that are difficult for humans to repeat in a precise and synchronous fashion or to measure phenomena that cannot be manually observed at scales ranging from the microscopic to the planetary. The collection and recording of data at regular timesteps enable inputs to mathematical models that provide predictions and forecasts of environmental processes; moreover, these models can be used to better understand planetary systems. Data measurements conducted at different scales can be subjected to statistical or scaling analysis to provide gridded data sets for application of mathematical models. Point measurements made at a single geographic location provide calibration or validation for satellite remote sensing data products. Measurements made by different sensors can be utilized along with sensor fusion algorithms to calculate indexes or gridded data sets. The sources in this article have been selected to provide an overview of the sensors and associated sensing systems that measure components of the environment on or near the surface of the Earth. Each first-level heading demarcates different environmental components. The final section of the article provides a selection of references pertaining to the engineering of sensor networks that are used to obtain areal measurements of environmental processes. Each section contains a series of subsections that divide the literature according to the type of sensor or measurement. An emphasis is placed on the selection of references that provide insight into the measurement physics of the sensor and the environmental physics of the phenomena being measured. Moreover, references are selected that provide schematic diagrams and engineering design considerations suitable for replication and development of new sensors. Papers on sensor calibration and error analysis as well as case studies are included for operational use and field deployment applications. Due to the numerous papers that have been published on environmental sensing systems, it is not possible to cite all available literature pertaining to a certain type of sensor. To close gaps in the literature and to provide ideas for students, instrument developers, engineers, and environmental scientists, overview papers are also provided in this article. These overview papers often present ideas in a succinct fashion and the associated sensor mathematics, design, and signal processing are provided in a manner to enhance pedagogical value.

scholarly journals Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets

2014 ◽  
Vol 50 (11) ◽  
pp. 8714-8735 ◽  
Author(s):  
Stephan Thober ◽  
Juliane Mai ◽  
Matthias Zink ◽  
Luis Samaniego
Keyword(s):  
Data Sets ◽  
Monthly Precipitation ◽  
Gridded Data ◽  
Temporal Disaggregation

scholarly journals Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

2017 ◽  
Vol 11 (4) ◽  
pp. 1625-1645 ◽  
Author(s):  
Silvia Terzago ◽  
Jost von Hardenberg ◽  
Elisa Palazzi ◽  
Antonello Provenzale
Keyword(s):  
Remote Sensing ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Gridded Data ◽  
Mountain Areas ◽  
Ensemble Mean ◽  
Snow Water

Abstract. The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

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