Modelling the tail behaviour of precipitation aggregates using conditional spatial extremes.

2021 ◽  
Author(s):  
Jordan Richards ◽  
Jonathan Tawn ◽  
Simon Brown
Keyword(s):  
Forecast Model ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Analysis Framework ◽  
Value Analysis ◽  
Single Location ◽  
Catchment Areas ◽  
Tail Behaviour ◽  
Spatial Domains ◽  
Precipitation Events

<p>Fluvial flooding is not caused by high intensity rainfall at a single location, rather it is caused by the extremes of precipitation events aggregated over spatial catchment areas. Accurate modelling of the tail behaviour of such events can help to mitigate the financial aspects associated with floods, especially if river defences are built within specification to withstand an <em>n</em>-year event of this kind. Within an extreme value analysis framework, univariate methods for estimating the size of these <em>n</em>-year events are well studied and cemented in asymptotic theory.</p><p> To complement these techniques, we develop a high-resolution spatial model for extreme precipitation by providing a fully spatial extension of the conditional approach for modelling multivariate extremes. We simulate realistic precipitation fields from this model and use univariate techniques to make inference about the extremal behaviour of aggregates over specified spatial domains. The challenge of zero precipitation data is overcome and further applications of the model are discussed. The model is fit to data from a convection permitting forecast model within the 2018 UK Climate Projections (UKCP18).</p>

Future European changes of extreme precipitation : What can we learn from inter-model cross-validation?

2020 ◽  
Author(s):  
Torben Schmith ◽  
Peter Thejll ◽  
Fredrik Boberg ◽  
Peter Berg ◽  
Ole Bøssing Christensen ◽  
...  
Keyword(s):  
Error Correction ◽  
Cross Validation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Return Levels ◽  
Hourly Data ◽  
Calibration Techniques ◽  
Precipitation Events

<p>Severe precipitation events occur rarely and are often localized in space and of short duration, but are important for societal managing of infrastructure such as sewage systems, metros etc. Therefore, there is a demand for estimating expected future changes in the statistics of these rare events. These are usually projected using RCM scenario runs combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to RCM imperfections, the modelled climate for the present-day usually has errors relative to observations. Therefore, the RCM results are ‘error corrected‘ to match observations more closely in order to increase reliability of results.</p><p>In the present work we evaluate different error correction techniques and compare with non-corrected projections. This is done in an inter-model cross-validation setup, in which each model in turn plays the role of observations, against which the remaining error-corrected models are validated. The study uses hourly data (historical & RCP8.5 late 21<sup>st</sup> century) from 13 models covering the EURO-CORDEX ensemble at 0.11 degree resolution (about 12.5 km), from which fields of selected return levels are extracted for 1 h and 24 h duration. The error correction techniques applied to the return levels are based on extreme value analysis and include analytical quantile-quantile matching together with a simpler climate factor approach.</p><p>The study identifies regions where the error correction techniques perform differently, and therefore contributes to guidelines on how and where to apply calibration techniques when projecting extreme return levels.</p>

scholarly journals Extreme Value Analysis of Dynamical Wave Climate Projections in the Mediterranean Sea

2015 ◽  
Vol 66 ◽  
pp. 210-219 ◽  
Author(s):  
Zacharias G. Kapelonis ◽  
Panagiotis N. Gavriliadis ◽  
Gerassimos A. Athanassoulis
Keyword(s):  
Mediterranean Sea ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Value Analysis ◽  
The Mediterranean

Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates

2022 ◽  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Warm Season ◽  
Dew Point ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Pool Data ◽  
Value Analysis ◽  
Point Temperature ◽  
Dew Point Temperature

Abstract This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Uncertainties in Extreme Value Analysis of Wave Climate Data and Wave Climate Projections

2015 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Data Sets ◽  
Climate Data ◽  
Value Analysis ◽  
Significant Wave

The extreme values of climate data are of interest in design of marine structures and the return values of certain met-ocean parameters such as significant wave height is of particular importance. However, there are various ways of analyzing the extremes and estimating the required return values, which introduce additional uncertainties. These are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that will be considered is the initial distribution approach, the block maxima approach, the peak over threshold (POT) approach and the average conditional exceedance rate method (ACER). Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate can clearly be discerned in the particular datasets that have been analysed.

Projected Changes in Precipitation Extremes for Western Canada based on High-Resolution Regional Climate Simulations

2016 ◽  
Vol 29 (24) ◽  
pp. 8841-8863 ◽  
Author(s):  
Andre R. Erler ◽  
W. Richard Peltier
Keyword(s):  
High Resolution ◽  
Regional Climate ◽  
Summer Precipitation ◽  
Precipitation Extremes ◽  
Convective Precipitation ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Western Canada ◽  
Value Analysis ◽  
Combining Data

Abstract An analysis of changes in precipitation extremes in western Canada is presented, based upon an ensemble of high-resolution regional climate projections. The ensemble is composed of four independent, identically configured Community Earth System Model (CESM) integrations that were dynamically downscaled to 10-km resolution, using the WRF Model in two different configurations. Only the representative concentration pathway 8.5 (RCP8.5) scenario is considered. Changes in extremes are found to generally follow changes in the (seasonal) mean, but changes in mean and extreme precipitation differ strongly between seasons and regions (where extremes are defined as the seasonal maximum of daily precipitation). At the end of the twenty-first century, the highest projected increase in precipitation extremes is approximately 30% in winter away from the coast and in fall at the coast. Changes in winter are consistent between models; however, changes in summer are not: CESM is characterized by a decrease in summer precipitation (and extremes), while one WRF configuration shows a significant increase and another no statistically significant change. Nevertheless, the fraction of convective precipitation (extremes) in summer increases by 20%–30% in all models. There is also evidence that the climate change signal in summer is sensitive to the choice of the convection scheme. A comparison of CESM and WRF shows that higher resolution clearly improves the representation of winter precipitation (extremes), while summer precipitation does not appear to be sensitive to resolution (convection is parameterized in both models). To increase the statistical power of the extreme value analysis that has been performed, a novel method for combining data from climatologically similar stations was employed.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

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