scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogs

2019 ◽  
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Long Time ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorology records used to characterize the large-scale atmospheric configuration of the generation day. To overcome those limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days in the 20th century to generate a 1000-year sequence of new atmospheric trajectories and (2) a stochastic downscaling model in a second step, applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analog-based weather generators.

scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

2020 ◽  
Vol 24 (9) ◽  
pp. 4339-4352
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Weather Variables ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

Simulation of Spatial Temperature-Precipitation Compound Events with Circulation-Conditioned Weather Generator

2020 ◽  
Author(s):  
Martin Dubrovsky ◽  
Ondrej Lhotka ◽  
Jiri Miksovsky
Keyword(s):  
Weather Generator ◽  
European Regions ◽  
Time Step ◽  
Temporal Scales ◽  
Stochastic Weather Generator ◽  
Fire Weather Index ◽  
Spatial And Temporal Scales ◽  
Temperature And Precipitation ◽  
Surface Weather ◽  
Spatial Temperature

<p>GRIMASA project aims to develop a spatial (not only, but especially a gridded version) stochastic weather generator (WG) applicable at various spatial and temporal scales, for both present and future climates. The multi-purpose SPAGETTA generator (Dubrovsky et al, 2019, Theoretical and Applied Climatology) being developed within this project is based on a parametric approach suggested by Wilks (1998, 2009). It was presented already at EGU-2017 and EGU-2018 conferences. It is run mainly at daily time step and allows to produce multivariate weather series for up to 100 (approximately) grid-points. In developing and validating the generator, we employ also various compound weather indices defined by multiple weather variables, which allows to account for the inter-variable correlations in the validation process. In our first experiments, the WG was run at 100 km resolution (50 km EOBS data were used for calibrating the WG) for eight European regions, and its performance was compared with RCMs (CORDEX simulations for EUR-44 domain). In our EGU-2019 contribution, our WG was validated in terms of characteristics of spatial temperature-precipitation compound spells (including dry-hot spells). Most recently, after implementing wind speed and humidity into the generator, the WG was run at much finer resolution (using data from irregularly distributed weather stations in Czechia and Sardinia) and validated in terms of spatial spells of wildfire-prone weather (using Fire Weather Index) (results were presented at AGU-2019).</p><p> </p><p>Present project activities aim mainly at (A) going into finer spatial and temporal scales, and (B) conditioning the surface weather generator on larger scale circulation simulated by circulation weather generator run at much coarser resolution. The development of the circulation generator (CIRCULATOR) has started in 2019. It is based on the first-order multivariate autoregressive model (similar to the one used in SPAGETTA), and the set of generator’s variables consists of larger scale characteristics of atmospheric circulation (derived from the NCEP/NCAR reanalysis), temperature and precipitation defined for a 2.5 degree grid. In our contribution, we will show results related to these two activities, focusing on (i) WG’s ability to reproduce spatial temperature-precipitation spells at various spatial scales (down to EUR-11 resolution) for eight European regions, (ii) validation of the circulation generator in terms of its ability to reproduce frequencies of circulation patterns and larger-scale temperature and precipitation characteristics for the 8 regions, and (iii) assessing an effect of using the circulation generator to drive the surface weather generator on its ability to reproduce the compound spells.</p><p> </p><p>Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).</p>

scholarly journals Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

2020 ◽  
Vol 245 ◽  
pp. 07001
Author(s):  
Laura Sargsyan ◽  
Filipe Martins
Keyword(s):  
Time Series ◽  
Control System ◽  
Distribution System ◽  
Large Scale ◽  
High Energy Physics ◽  
Time Series Data ◽  
High Energy ◽  
Series Data ◽  
Test Version ◽  
Long Time

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

scholarly journals Winterization of Texan power system infrastructure is profitable but risky

2021 ◽  
Author(s):  
Katharina Gruber ◽  
Tobias Gauster ◽  
Gregor Laaha ◽  
Peter Regner ◽  
Johannes Schmidt
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Power Plants ◽  
Large Scale ◽  
Low Frequency ◽  
Cold Spell ◽  
Climate Data ◽  
Temperature Dependent ◽  
Long Time ◽  
Regulatory Measures

We deliver the first analysis of the 2021 cold spell in Texas which combines temperature dependent load estimates with temperature dependent estimates of power plant outages to understand the frequency of loss of load events, using a 71 year long time series of climate data. The expected avoided loss from full winterization is 11.74bn\$ over a 30 years investment period. We find that large-scale winterization, in particular of gas infrastructure and gas power plants, would be profitable, as related costs for winterization are substantially lower. At the same moment, the necessary investments involve risk due to the low-frequency of events – the 2021 event was the largest and we observe only 8 other similar ones in the set of 71 simulated years. Regulatory measures may therefore be necessary to enforce winterization.

scholarly journals The construction of a Central Netherlands temperature

2010 ◽  
Vol 6 (6) ◽  
pp. 2517-2555 ◽  
Author(s):  
G. van der Schrier ◽  
A. van Ulden ◽  
G. J. van Oldenborgh
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Large Scale ◽  
Climate Models ◽  
Temperature Series ◽  
Homogeneity Analysis ◽  
Temperature Changes ◽  
Long Time Series ◽  
Scale Temperature ◽  
Long Time

Abstract. The Central Netherlands Temperature (CNT) is a monthly daily mean temperature series constructed from homogenised time series from the centre of the Netherlands. The purpose of this series is to offer a homogeneous time series representative of a larger area to study large-scale temperature changes. It will also facilitate a comparison with climate models, which resolve similar scales. From 1906 onwards, temperature measurements in the Netherlands have been sufficiently standardised to construct a high-quality series. Long time series have been constructed by merging nearby stations, using the overlap to calibrate the differences. These long time series and a few time series of only a few decades in length, have been subjected to a homogeneity analysis in which significant breaks and artificial trends have been corrected. Many of the detected breaks correspond to changes in the observations that are documented in the station metadata. This version of the CNT, to which we attach the version number 1.1, is constructed as the unweighted average of four stations (De Bilt, Winterswijk/Hupsel, Oudenbosch/Gilze-Rijen and Gemert/Volkel) with the stations Eindhoven and Deelen added from 1951 and 1958 respectively onwards.

scholarly journals AnaWEGE: a weather generator based on analogues of atmospheric circulation

2013 ◽  
Vol 6 (3) ◽  
pp. 4745-4774
Author(s):  
P. Yiou
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Weather Generator ◽  
Climate Variable ◽  
Stochastic Weather Generator ◽  
Sea Level Pressure ◽  
The North ◽  
The North Atlantic ◽  
Meteorological Observations ◽  
Stochastic Rules

Abstract. This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

Studies of crustal deformation due to hydrological loading on GPS height estimates

2011 ◽  
Vol 60 (2) ◽  
pp. 135-144 ◽  
Author(s):  
Marcin Rajner ◽  
Tomasz Liwosz
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Large Scale ◽  
Water Cycle ◽  
Single Point ◽  
Surface Loading ◽  
Height Estimates ◽  
Long Time ◽  
Positioning Technique ◽  
Point Positioning

Studies of crustal deformation due to hydrological loading on GPS height estimates The paper deals with large-scale crustal deformation due to hydrological surface loads and its influence on seasonal variation of GPS estimated heights. The research was concentrated on the area of Poland. The deformation caused by continental water storage has been computed on the basis of WaterGAP Hydrological Model data by applying convolution of water masses with appropriate Green's function. Obtained site displacements were compared with height changes estimated from GPS observations using the Precise Point Positioning (PPP) method. Long time series of the solutions for 4 stations were used for evaluation of surface loading phenomena. Good agreement both in amplitude and phase was found, however some discrepancies remain which are assigned to single point positioning technique deficiencies. Annual repeatability of water cycle and demanding procedure for computing site displacements for each site, allowed to develop a simple model for Poland which could be applied to remove (or highly reduce) seasonal hydrological signal from time series of GPS solutions.

Sign in / Sign up

Export Citation Format

Share Document