scholarly journals Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

2020 ◽  
Author(s):  
Kathryn I. Wheeler ◽  
Michael C. Dietze
Keyword(s):  
Climate Change ◽  
Temporal Resolution ◽  
Ecosystem Processes ◽  
Western Hemisphere ◽  
High Temporal Resolution ◽  
P Values ◽  
Modis Ndvi ◽  
Remotely Sense ◽  
Limited Coverage ◽  
Lower Temporal Resolution

Abstract. Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 ± 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values 

scholarly journals Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

2021 ◽  
Vol 18 (6) ◽  
pp. 1971-1985
Author(s):  
Kathryn I. Wheeler ◽  
Michael C. Dietze
Keyword(s):  
Temporal Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Transition Period ◽  
Western Hemisphere ◽  
P Value ◽  
High Temporal Resolution ◽  
P Values ◽  
Modis Ndvi ◽  
Modis Evi

Abstract. Monitoring leaf phenology tracks the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam network, provide valuable information on phenological state at high temporal resolution, but they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p values ≤0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than those from MODIS NDVI for the estimates of the beginning and middle of spring. GOES will improve the monitoring of phenology at large spatial coverages and provides real-time indicators of phenological change even when the entire spring transition period occurs within the 16 d resolution of these MODIS products.

Climate change during the past 1000 years: a high-temporal-resolution multiproxy record from a mire in northern Finland

2012 ◽  
Vol 28 (2) ◽  
pp. 152-164 ◽  
Author(s):  
Walter Finsinger ◽  
Kristian Schoning ◽  
Sheila Hicks ◽  
Andreas Lücke ◽  
Tomasz Goslar ◽  
...  
Keyword(s):  
Climate Change ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
The Past ◽  
Past 1000 Years

scholarly journals Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

2021 ◽  
Vol 25 (6) ◽  
pp. 3207-3225
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

scholarly journals Rainfall intensity bursts and the erosion of soils: an analysis highlighting the need for high temporal resolution rainfall data for research under current and future climates

2019 ◽  
Author(s):  
David L. Dunkerley
Keyword(s):  
Climate Change ◽  
Temporal Resolution ◽  
Rainfall Intensity ◽  
Explanatory Power ◽  
Tropical Climate ◽  
Rainfall Data ◽  
High Temporal Resolution ◽  
Hourly Data ◽  
Rainfall Extremes ◽  
Period Data

Abstract. Many landsurface processes, including splash dislodgment and downslope transport of soil materials, are influenced strongly by short-lived peaks in rainfall intensity but are less well accounted for by longer-term average rates. Specifically, rainfall intensities reached over periods of 10–30 minutes appear to have more explanatory power than hourly or longer-period data. However, most analyses of rainfall, and particularly scenarios of possible future rainfall extremes under climate change, rely on hourly data. Using two Australian pluviograph records with 1 second resolution, one from an arid and one from a wet tropical climate, the nature of short-lived intensity bursts is analysed from the raw inter-tip times of the tipping bucket gauges. Hourly apparent rainfall intensities average just 1.43 mm h−1 at the wet tropical site, and 2.12 mm h−1 at the arid site. At the wet tropical site, intensity bursts of extreme intensity occur frequently, those exceeding 30 mm h−1 occurring on average at intervals of  60 mm h−1 occurring on average at intervals of

scholarly journals QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks

2018 ◽  
Vol 15 (7) ◽  
pp. 2251-2269 ◽  
Author(s):  
Camille Minaudo ◽  
Florence Curie ◽  
Yann Jullian ◽  
Nathalie Gassama ◽  
Florentina Moatar
Keyword(s):  
Climate Change ◽  
Temporal Resolution ◽  
Regional Scale ◽  
High Temporal Resolution ◽  
P Availability ◽  
Model Quality ◽  
Deterministic Models ◽  
Bacterial Dynamics ◽  
Complex Interactions ◽  
Loire River

Abstract. To allow climate change impact assessment of water quality in river systems, the scientific community lacks efficient deterministic models able to simulate hydrological and biogeochemical processes in drainage networks at the regional scale, with high temporal resolution and water temperature explicitly determined. The model QUALity-NETwork (QUAL-NET) was developed and tested on the Middle Loire River Corridor, a sub-catchment of the Loire River in France, prone to eutrophication. Hourly variations computed efficiently by the model helped disentangle the complex interactions existing between hydrological and biological processes across different timescales. Phosphorus (P) availability was the most constraining factor for phytoplankton development in the Loire River, but simulating bacterial dynamics in QUAL-NET surprisingly evidenced large amounts of organic matter recycled within the water column through the microbial loop, which delivered significant fluxes of available P and enhanced phytoplankton growth. This explained why severe blooms still occur in the Loire River despite large P input reductions since 1990. QUAL-NET could be used to study past evolutions or predict future trajectories under climate change and land use scenarios.

Reply to “Comments on ‘Flash Flood Verification: Pondering Precipitation Proxies’”

2021 ◽  
Vol 22 (3) ◽  
pp. 749-752
Author(s):  
Russ S. Schumacher ◽  
Gregory R. Herman
Keyword(s):  
Temporal Resolution ◽  
Flash Flood ◽  
Hydrologic Model ◽  
High Temporal Resolution ◽  
Model Output ◽  
Primary Reason ◽  
Flash Flooding ◽  
Temporal Sampling ◽  
The Relationship ◽  
Lower Temporal Resolution

AbstractWe applaud Gourley and Vergara for their thorough investigation of the relationship between precipitation and flash flood reports, as well as their inclusion of information from advanced hydrologic model output. We conducted some additional analysis to identify the reasons for the substantial differences between their findings and ours. The primary reason for the differences was found to be temporal sampling. The high temporal resolution of the MRMS dataset, as well as their use of “rolling” accumulation periods, explains most of the discrepancies. For guidance related to real-time warning decisions for flash flooding, Gourley and Vergara’s analyses provide an important new guide and we recommend the use of their results for this purpose. For other applications, including model postprocessing and for precipitation datasets with lower temporal resolution, our results will continue to prove useful.

scholarly journals Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

2020 ◽  
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Multidetector-row Computed Tomography in the Assessment of Coronary Artery Disease – New Techniques and Insights

2010 ◽  
Vol 6 (2) ◽  
pp. 43 ◽  
Author(s):  
Andreas H Mahnken ◽  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Data Acquisition ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Multidetector Row ◽  
Ct Scanners ◽  
Artery Disease ◽  
Cardiac Mdct

Over the last decade, cardiac computed tomography (CT) technology has experienced revolutionary changes and gained broad clinical acceptance in the work-up of patients suffering from coronary artery disease (CAD). Since cardiac multidetector-row CT (MDCT) was introduced in 1998, acquisition time, number of detector rows and spatial and temporal resolution have improved tremendously. Current developments in cardiac CT are focusing on low-dose cardiac scanning at ultra-high temporal resolution. Technically, there are two major approaches to achieving these goals: rapid data acquisition using dual-source CT scanners with high temporal resolution or volumetric data acquisition with 256/320-slice CT scanners. While each approach has specific advantages and disadvantages, both technologies foster the extension of cardiac MDCT beyond morphological imaging towards the functional assessment of CAD. This article examines current trends in the development of cardiac MDCT.

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