scholarly journals Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product

2018 ◽  
Author(s):  
Paul Bodesheim ◽  
Martin Jung ◽  
Fabian Gans ◽  
Miguel D. Mahecha ◽  
Markus Reichstein
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Predictor Variables ◽  
Energy Fluxes ◽  
Diurnal Cycles ◽  
Monthly Average ◽  
Hourly Data ◽  
Data Products

Abstract. Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role for understanding biogeochemical processes on ecosystem level or global scale. However, the fluxes can only be measured at individual sites by eddy covariance towers and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, eight-day, or daily average values and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily scales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5◦ spatial resolution, which allows for analyzing diurnal cycles globally. The dataset contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half- hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still enables to study the important characteristics of diurnal courses globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short- term fluctuations on subdaily scales compared to the plain half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.

scholarly journals Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product

2018 ◽  
Vol 10 (3) ◽  
pp. 1327-1365 ◽  
Author(s):  
Paul Bodesheim ◽  
Martin Jung ◽  
Fabian Gans ◽  
Miguel D. Mahecha ◽  
Markus Reichstein
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Gross Primary Production ◽  
Predictor Variables ◽  
Energy Fluxes ◽  
Data Set ◽  
Diurnal Cycles ◽  
Monthly Average ◽  
Hourly Data ◽  
Data Products

Abstract. Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role in understanding biogeochemical processes on an ecosystem level or global scale. However, the fluxes can only be measured at individual sites, e.g., by eddy covariance towers, and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, 8-day, or daily average values, and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily timescales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5° spatial resolution, which allows for analyzing diurnal cycles globally. The data set contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half-hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still allows studying the important characteristics of diurnal patterns globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short-term fluctuations on subdaily timescales compared to the full half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.

Research and Design of the Distributed Mass Small File Storage System Based on WCF

2013 ◽  
Vol 765-767 ◽  
pp. 1087-1091
Author(s):  
Hong Lin ◽  
Shou Gang Chen ◽  
Bao Hui Wang
Keyword(s):  
Fault Tolerance ◽  
Distributed Computing ◽  
Data Storage ◽  
Large Scale ◽  
Storage System ◽  
Hardware Platform ◽  
File Storage ◽  
Computing Framework ◽  
Research And Design ◽  
Small File

Recently, with the development of Internet and the coming of new application modes, data storage has some new characters and new requirements. In this paper, a Distributed Computing Framework Mass Small File storage System (For short:Dnet FS) based on Windows Communication Foundation in .Net platform is presented, which is lightweight, good-expansibility, running in cheap hardware platform, supporting Large-scale concurrent access, and having certain fault-tolerance. The framework of this system is analyzed and the performance of this system is tested and compared. All of these prove this system meet requirements.

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

scholarly journals Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

2019 ◽  
pp. 201900278 ◽  
Author(s):  
Troy S. Magney ◽  
David R. Bowling ◽  
Barry A. Logan ◽  
Katja Grossmann ◽  
Jochen Stutz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Spatial Scales ◽  
Gross Primary Production ◽  
Canopy Structure ◽  
Operating Efficiency ◽  
Conifer Forest ◽  
Spatiotemporal Resolution ◽  
Evergreen Forests ◽  
Spectrometer System

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

scholarly journals 3D DNA structural barcode copying and random access

2020 ◽  
Author(s):  
Filip Bošković ◽  
Alexander Ohmann ◽  
Ulrich F. Keyser ◽  
Kaikai Chen
Keyword(s):  
Data Storage ◽  
Single Molecule ◽  
Self Assembly ◽  
Large Scale ◽  
Three Dimensional ◽  
Random Access ◽  
Scale Production ◽  
Digital Information ◽  
Dna Nanostructures ◽  
Large Scale Production

AbstractThree-dimensional (3D) DNA nanostructures built via DNA self-assembly have established recent applications in multiplexed biosensing and storing digital information. However, a key challenge is that 3D DNA structures are not easily copied which is of vital importance for their large-scale production and for access to desired molecules by target-specific amplification. Here, we build 3D DNA structural barcodes and demonstrate the copying and random access of the barcodes from a library of molecules using a modified polymerase chain reaction (PCR). The 3D barcodes were assembled by annealing a single-stranded DNA scaffold with complementary short oligonucleotides containing 3D protrusions at defined locations. DNA nicks in these structures are ligated to facilitate barcode copying using PCR. To randomly access a target from a library of barcodes, we employ a non-complementary end in the DNA construct that serves as a barcode-specific primer template. Readout of the 3D DNA structural barcodes was performed with nanopore measurements. Our study provides a roadmap for convenient production of large quantities of self-assembled 3D DNA nanostructures. In addition, this strategy offers access to specific targets, a crucial capability for multiplexed single-molecule sensing and for DNA data storage.

scholarly journals The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

2019 ◽  
Vol 11 (3) ◽  
pp. 1385-1409 ◽  
Author(s):  
Stefan Leyk ◽  
Andrea E. Gaughan ◽  
Susana B. Adamo ◽  
Alex de Sherbinin ◽  
Deborah Balk ◽  
...  
Keyword(s):  
Large Scale ◽  
Population Data ◽  
Disaster Risk ◽  
Spatial Allocation ◽  
Quality Aspects ◽  
Data Products ◽  
Methodological Approaches ◽  
Different Characteristics ◽  
Population Counts ◽  
Relative Quality

Abstract. Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

scholarly journals Evaluation of modeled global carbon dynamics: analysis based on global carbon flux and above-ground biomass data

2016 ◽  
Author(s):  
Bao-Lin Xue ◽  
Qinghua Guo ◽  
Tianyu Hu ◽  
Yongcai Wang ◽  
Shengli Tao ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Carbon Flux ◽  
Large Scale ◽  
Gross Primary Production ◽  
Carbon Dynamics ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Carbon Cycles ◽  
Ground Biomass ◽  
Global Carbon

Abstract. Dynamic global vegetation models are useful tools for the simulation of carbon dynamics on regional and global scales. However, even the most validated models are usually hampered by the poor availability of global biomass data in the model validation, especially on regional/global scales. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential above-ground biomass (AGB), on the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Independent validation showed that IBIS could reproduce GPP and evapotranspiration with acceptable accuracy at site and global levels. On the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed spatial patterns of AGB for Amazonian forests. The differences among the AGB spatial patterns were mainly caused by the single-parameter set of the model used. This study showed that different meteorological inputs can also introduce substantial differences in AGB on the global scale. Further analysis showed that this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. The research also shows that to simulate large-scale carbon dynamics, both carbon flux and AGB data are necessary to constrain the model. The main conclusions of our research will help to improve model simulations of global carbon cycles.

scholarly journals Hallucinating Optical Flow Features for Video Classification

2019 ◽  
Author(s):  
Yongyi Tang ◽  
Lin Ma ◽  
Lianqiang Zhou
Keyword(s):  
Optical Flow ◽  
Data Storage ◽  
Large Scale ◽  
State Of The Art ◽  
Video Content ◽  
Video Classification ◽  
Temporal Relationships ◽  
Optical Flow Computation ◽  
Flow Features ◽  
Optical Flow Features

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features

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