Parametric estimation of non-crossing quantile functions

2021 ◽  
pp. 1471082X2110365
Author(s):  
Gianluca Sottile ◽  
Paolo Frumento
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Standard Method ◽  
R Package ◽  
Parametric Estimation ◽  
The Arctic ◽  
Parametric Functions ◽  
Quantile Functions ◽  
Standing Problem ◽  
Analysis Of Time Series

Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated the crossing problem, but did not eliminate it completely. Finally, we exploited the imposed parametric structure to formulate a constrained optimization algorithm that enforced monotonicity. The proposed example showed how the relatively unexplored field of parametric quantile functions could offer new solutions to the long-standing problem of quantile crossing. Our approach is particularly convenient in situations, like the analysis of time series, in which the fitted model may be used to predict extreme quantiles or to perform extrapolation. The described estimator has been implemented in the R package qrcm.

scholarly journals Time-Series of Cloud-Free Sentinel-2 NDVI Data Used in Mapping the Onset of Growth of Central Spitsbergen, Svalbard

2021 ◽  
Vol 13 (15) ◽  
pp. 3031
Author(s):  
Stein Rune Karlsen ◽  
Laura Stendardi ◽  
Hans Tømmervik ◽  
Lennart Nilsen ◽  
Ingar Arntzen ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Cloud Cover ◽  
The Arctic ◽  
Vegetation Types ◽  
Threshold Method ◽  
High Increase ◽  
Temperature Changes ◽  
Sentinel 2 ◽  
Phenological Phases

The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, the study area was the central part of Spitsbergen, Svalbard, located between 77.28°N and 78.44°N. The goals of this study were: (1) to prepare, analyze and present a cloud-free time-series of daily Sentinel-2 NDVI datasets for the 2016 to 2019 seasons, and (2) to demonstrate the use of the dataset in mapping the onset of growth. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud-free Sentinel-2 data were used. The onset of growth was then mapped by a NDVI threshold method, which showed significant correlation (r2 = 0.47, n = 38, p < 0.0001) with ground-based phenocam observation of the onset of growth in seven vegetation types. However, large bias was found between the Sentinel-2 NDVI-based mapped onset of growth and the phenocam-based onset of growth in a moss tundra, which indicates that the data in these vegetation types must be interpreted with care. In 2018, the onset of growth was about 10 days earlier compared to 2017.

scholarly journals Consistent variability but different spatial patterns between observed and reanalysed sea-ice thickness

2019 ◽  
Author(s):  
Joula Siponen ◽  
Petteri Uotila ◽  
Eero Rinne ◽  
Steffen Tietsche
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Sea Ice ◽  
Satellite Altimetry ◽  
Winter Season ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Large Variability ◽  
Ice Volume

Abstract. Changes in sea-ice thickness are one of the most visible signs of climate change. However, to gain a comprehensive understanding of mechanisms involved, long time series are needed. Importantly, the development of more accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is here compared to the ocean reanalysis ORAS5 by ECMWF for the first time. The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017 and continuing. Time series of sea-ice volume for the CCI coverage reveal years of extremely low volume as well as recovery during the winter season. The 15-year trends in sea-ice volume are clearly negative over the time series and despite large variability between years statistically significant. The 15-year ORAS5 trends have larger interannual variability than the CCI trends and are therefore not statistically significant despite of a good match in terms of year-to-year variability. The observed negative trends result from changes in both atmospheric and oceanic forcing. The CCI product performs well in the validation of the ORAS5 reanalysis: overall root-mean-square difference (RMSD) between sea-ice thickness from CCI and ORAS5 is below 1 m. However, seasonal and interannual RMSD variations during the time series are large, from 0.5 m to 1.3 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

A MODELING SYSTEM FOR CLIMATE CHANGE RISK ASSESSMENT, MANAGEMENT AND HEDGING IN COASTAL AREAS

2017 ◽  
Author(s):  
Sergei Soldatenko ◽  
Sergei Soldatenko ◽  
Genrikh Alekseev ◽  
Genrikh Alekseev ◽  
Alexander Danilov ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Coastal Areas ◽  
Mitigation Strategies ◽  
System Structure ◽  
The Arctic ◽  
Risk Modeling ◽  
Wide Range ◽  
Adverse Weather ◽  
The Impact

Every aspect of human operations faces a wide range of risks, some of which can cause serious consequences. By the start of 21st century, mankind has recognized a new class of risks posed by climate change. It is obvious, that the global climate is changing, and will continue to change, in ways that affect the planning and day to day operations of businesses, government agencies and other organizations and institutions. The manifestations of climate change include but not limited to rising sea levels, increasing temperature, flooding, melting polar sea ice, adverse weather events (e.g. heatwaves, drought, and storms) and a rise in related problems (e.g. health and environmental). Assessing and managing climate risks represent one of the most challenging issues of today and for the future. The purpose of the risk modeling system discussed in this paper is to provide a framework and methodology to quantify risks caused by climate change, to facilitate estimates of the impact of climate change on various spheres of human activities and to compare eventual adaptation and risk mitigation strategies. The system integrates both physical climate system and economic models together with knowledge-based subsystem, which can help support proactive risk management. System structure and its main components are considered. Special attention is paid to climate risk assessment, management and hedging in the Arctic coastal areas.

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