Comparison of seasonal evapotranspiration of temperate coniferous forests with Copernicus Sentinel-1 time series

2021 ◽  
Author(s):  
Marlin Markus Mueller ◽  
Clémence Dubois ◽  
Thomas Jagdhuber ◽  
Carsten Pathe ◽  
Christiane Schmullius
Keyword(s):  
Time Series ◽  
Extreme Weather Events ◽  
Coniferous Forests ◽  
Weather Data ◽  
Complex Nature ◽  
Southeastern Part ◽  
Backscatter Signal ◽  
Multiple Observations ◽  
Depth Analysis

<p>A changing climate accompanied by an increasing number of extreme weather events puts pressure on ecosystems around the globe. Evapotranspiration is one of the key metrics for understanding vegetation dynamics and changes in an ecosystem. Due to its complex nature, evapotranspiration is difficult to determine on a larger scale.<br>Existing approaches to correlate evapotranspiration measurements and radar backscatter signals were completed in boreal forests using ground-based scatterometers for short time series (several months) with much higher temporal resolution (multiple observations per hour) for small test sites. Our goal is to build upon this research to establish a broader understanding on the influences of evapotranspiration on the signal of the widely used Copernicus Sentinel-1 C-Band SAR for managed temperate coniferous forests. Variations of the observed backscatter signals (VV, VH) over several growing seasons and years (2016-2020) are investigated.<br>Besides wind, temperature or precipitation as some of the influencing parameters on the C-band SAR signal, we focus our analyses on the influence of evapotranspiration on the Sentinel-1 C-band signal. Therefore, we recorded long time series of Sentinel-1 data to investigate and estimate the correlation between forest evapotranspiration dynamics and SAR signal variations. For this purpose, Sentinel-1 and weather data from July 2016 to December 2020 were obtained for forested areas in the southeastern part of the Free State of Thuringia, central Germany.<br>We use four different weather station datasets with daily measurements to calculate evapotranspiration values following the Penman-Monteith approach and apply regression analyses to gain a better understanding about the influence on the SAR signal. To obtain regions with speckle-suppressed backscatter for in situ comparison, forest areas in a radius of five kilometers around the four weather stations are considered. For the analysis, radar datasets are differentiated in co- and cross-polarized data as well as descending and ascending flight directions. It seems also important to distinguish between frozen and no-frozen conditions as we discover strong changes in the C-band SAR signal but only minor changes in evapotranspiration values for temperatures below freezing level. Excluding frozen conditions, in situ evapotranspiration measurements and the SAR backscatter variations over four years directly correlate with R2-values up to 0.48 without any parameterization or calibration on both sides (SAR & in situ). Currently we are investigating statistical methods for in-depth analysis of the correlation between the two datasets. As the SAR backscatter signal at C-band is not a direct and sole function of evapotranspiration, future work will combine the modelling of the different influence parameters of the environment on the SAR backscatter signal and aim at quantifying their respective influence on the signal to better understand the seasonal signal variations.</p>

Using a novel combination of autonomous vehicles for air-sea interaction studies: Results from the Eurec4a campaign 

2021 ◽  
Author(s):  
Elizabeth Siddle ◽  
Karen J. Heywood ◽  
Ben Webber ◽  
Peter Bromley
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Heat Budget ◽  
Heat Content ◽  
Flux Measurement ◽  
Extreme Weather Events ◽  
Upper Ocean ◽  
Momentum Exchange ◽  
Tropical North Atlantic

<div> <p>The Tropical North Atlantic region is a key driver of climate variability and extreme weather events, driven largely by heat and momentum exchanges across the air-sea boundary. Observations of these fluxes by satellites and vessels are limited in their spatial resolution and length of time series respectively. In-situ samples across long time periods are needed, which can be obtained through developing a network of in-situ flux measurement platforms. UEA and AutoNaut have worked to address this challenge with the deployment of <em>Caravela</em> - an AutoNaut uncrewed surface vessel. <em>Caravela</em> is a wave and solar powered autonomous vessel, equipped with meteorological and oceanographic sensors and the ability to transport a Seaglider. <em>Caravela</em> successfully completed its first scientific deployment as part of the Eurec<sup>4</sup>a campaign. </p> </div><div> <p>Eurec<sup>4</sup>a ran from January—March 2020 from Barbados, investigating climate change feedback in the Tropical North Atlantic and the role of cloud systems. <em>Caravela</em> spent 11 days of her 33-day deployment occupying a 10 km square, co-located with other Eurec<sup>4</sup>a platforms to gather in-situ surface data on heat and momentum exchange. Preliminary results from <em>Caravela</em> give us an insight into heat exchange at the surface, downwelling radiation and wind conditions during deployment. There is an identifiable diurnal cycle during the deployment, particularly visible in temperature data, which will feed into our understanding of changes in fluxes at a local scale. Profiling ocean gliders at the study site allow us to determine a time series of upper ocean heat content changes. These data, alongside that collected by other platforms during Eurec<sup>4</sup>a, should enable an upper ocean heat budget to be calculated at <em>Caravela’s</em> study site. </p> </div>

New determinations of tides on the north-western Ross Ice Shelf

2020 ◽  
pp. 1-14
Author(s):  
Richard D. Ray ◽  
Kristine M. Larson ◽  
Bruce J. Haines
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
High Rate ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
The North ◽  
Global Positioning ◽  
North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

scholarly journals Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

2021 ◽  
Vol 18 (5) ◽  
pp. 2442
Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely
Keyword(s):  
Relative Risk ◽  
The United States ◽  
Extreme Weather ◽  
South Florida ◽  
Heat Index ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Steady Increase ◽  
Daily Weather Data ◽  
The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Strong equatorial plasma bubble associated with prominent TEC enhancement observed at mid-latitude ionosphere under the quiescent condition

2021 ◽  
Author(s):  
Fuqing Huang ◽  
Jiuhou Lei ◽  
Chao Xiong
Keyword(s):  
Electron Density ◽  
Electron Content ◽  
Radio Waves ◽  
Plasma Bubble ◽  
Total Electron ◽  
Middle Latitudes ◽  
Multiple Observations ◽  
Equatorial Plasma Bubbles ◽  
Low Latitudes

<p>Equatorial plasma bubbles (EPBs) are typically ionospheric irregularities that frequently occur at the low latitudes and equatorial regions, which can significantly affect the propagation of radio waves. In this study, we reported a unique strong EPB that happened at middle latitudes over the Asian sector during the quiescent period. The multiple observations including total electron content (TEC) from Beidou geostationary satellites and GPS, ionosondes, in-situ electron density from SWARM and meteor radar are used to explore the characteristic and mechanism of the observed EPB. The unique strong EPB was associated with great nighttime TEC/electron density enhancement at the middle latitudes, which moves toward eastward. The potential physical processes of the observed EPB are also discussed.</p>

Documenting the Ikpana interrogative system

2021 ◽  
Vol 42 (1) ◽  
pp. 63-100
Author(s):  
Jason Kandybowicz ◽  
Bertille Baron Obi ◽  
Philip T. Duncan ◽  
Hironori Katsuda
Keyword(s):  
Comprehensive Treatment ◽  
Left Periphery ◽  
Long Distance ◽  
Southeastern Part ◽  
Endangered Language ◽  
Research Questions ◽  
Volta Region ◽  
Movement Asymmetry ◽  
Syntactic Properties

Abstract This article provides a comprehensive treatment of the interrogative system of Ikpana (ISO 639-3: lgq), an endangered language spoken in the southeastern part of Ghana’s Volta region. The article features a description and analysis of both the morphosyntax and intonation of questions in the language. Polar questions in Ikpana are associated with dedicated prosodic patterns and may be segmentally marked. As for wh- interrogatives, Ikpana allows for optional wh- movement. Interrogative expressions may appear clause-internally in their base-generated positions or in the left periphery followed by one of two optionally droppable particles with distinct syntactic properties. In this way, wh- movement structures are either focus-marked constructions or cleft structures depending on the accompanying particle. We identify an interesting wh- movement asymmetry – unlike all other wh- movement structures, ‘how’ questions may not be formed via the focus-marked or cleft strategy. We document a number of other attested wh- structures in the language, including long-distance wh- movement, partial wh- movement, long-distance wh- in-situ, and multiple wh- questions. We argue that by allowing our documentation efforts to be shaped and guided by theoretically driven research questions, we reach deeper levels of description than would have been possible if approached from a purely descriptive-documentary perspective.

A complete robust control network based on skewed temporal logic

2021 ◽  
pp. 1-14
Author(s):  
Liuxing Li
Keyword(s):  
Time Series ◽  
Robust Control ◽  
Temporal Logic ◽  
Sufficient Conditions ◽  
Control Flow ◽  
Skewed Distribution ◽  
Linear Matrix ◽  
Distribution Data ◽  
Control Network ◽  
Depth Analysis

The robust control network for nonlinear large-scale systems with parametric uncertainties also considers the uncertain robust stabilization problem for controlled networks. In heterogeneous populations, hybrid regression models are the most important statistical analysis tools. To aim of the study is to conduct a more in-depth analysis of the existing completive robust control networks relying on biased temporal logic. Compared with the symmetric distribution, the skewed distribution can obtain accurate and effective information. Therefore, a time-series logic model under skewed distribution is proposed. The temporal logic under skew state is applied to describe the normative language of fuzzy systems. Firstly, the mixed nonlinear regression model under skewed distribution data is introduced to test whether the temporal logic formula can be realized under the skew state. Secondly, through the method of reduction, the control flow interval logic CFITL is studied, and the time series logic sequence is used to describe the measurement output loss. The sufficient conditions for the control network system to satisfy the exponential stability and H ∞ performance index are given. The linear matrix inequality obtains the completeness control network to be designed, and the effectiveness of the proposed method is verified by stochastic simulation experiments. Finally, the method is verified to be practical and feasible based on actual data. The maximum recognition rates of nearest neighbor classification, nearest subspace classification and biased distribution temporal logic classification reached 0.9019, 0.9622 and 0.9304, respectively.

Brazilian aviation nowcasting system: current stage and some results

2021 ◽  
Author(s):  
Vinícius Almeida ◽  
Gutemberg França ◽  
Francisco Albuquerque Neto ◽  
Haroldo Campos Velho ◽  
Manoel Almeida ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Weather Data ◽  
Vertical Acceleration ◽  
In Situ Data ◽  
Forecasting System ◽  
Under Construction ◽  
Convective Weather

<p>Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.</p>

scholarly journals Acoustic and optical methods to infer water transparency at Time Series Station Spiekeroog, Wadden Sea

Ocean Science ◽  
2016 ◽  
Vol 12 (6) ◽  
pp. 1155-1163 ◽  
Author(s):  
Anne-Christin Schulz ◽  
Thomas H. Badewien ◽  
Shungudzemwoyo P. Garaba ◽  
Oliver Zielinski
Keyword(s):  
Time Series ◽  
Wadden Sea ◽  
Acoustic Doppler Current Profiler ◽  
Water Transparency ◽  
Optical Observations ◽  
Optical Methods ◽  
Acoustic Measurements ◽  
Data Set ◽  
Backscatter Signal ◽  
Current Profiler

Abstract. Water transparency is a primary indicator of optical water quality that is driven by suspended particulate and dissolved material. A data set from the operational Time Series Station Spiekeroog located at a tidal inlet of the Wadden Sea was used to perform (i) an inter-comparison of observations related to water transparency, (ii) correlation tests among these measured parameters, and (iii) to explore the utility of both acoustic and optical tools in monitoring water transparency. An Acoustic Doppler Current Profiler was used to derive the backscatter signal in the water column. Optical observations were collected using above-water hyperspectral radiometers and a submerged turbidity metre. Bio-fouling on the turbidity sensors optical windows resulted in measurement drift and abnormal values during quality control steps. We observed significant correlations between turbidity collected by the submerged metre and that derived from above-water radiometer observations. Turbidity from these sensors was also associated with the backscatter signal derived from the acoustic measurements. These findings suggest that both optical and acoustic measurements can be reasonable proxies of water transparency with the potential to mitigate gaps and increase data quality in long-time observation of marine environments.

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