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.

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.

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 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.

scholarly journals Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

2018 ◽  
Vol 45 ◽  
pp. 147-153 ◽  
Author(s):  
Hristos Tyralis ◽  
Georgia A. Papacharalampous
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Temperature Time Series ◽  
Monthly Precipitation ◽  
Time Step ◽  
Large Dataset ◽  
Additional Information ◽  
Large Scale Assessment ◽  
Monthly Streamflow ◽  
Temperature Time

Abstract. We assess the performance of the recently introduced Prophet model in multi-step ahead forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results derived through two different approaches. The first approach uses past information about the time series to be forecasted only (standard approach), while the second approach uses exogenous predictor variables alongside with the use of the endogenous ones. The additional information used in the fitting and forecasting processes includes monthly precipitation and/or temperature time series, and their forecasts respectively. Specifically, the exploited exogenous (observed or forecasted) information considered at each time step exclusively concerns the time of interest. The algorithms based on the Prophet model are in total four. Their forecasts are also compared with those obtained using two classical algorithms and two benchmarks. The comparison is performed in terms of four metrics. The findings suggest that the compared approaches are equally useful.

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

2014 ◽  
Vol 7 (2) ◽  
pp. 531-543 ◽  
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.

scholarly journals Applications of the MVWG Multivariable Stochastic Weather Generator

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Nándor Fodor ◽  
Ildikó Dobi ◽  
János Mika ◽  
László Szeidl
Keyword(s):  
Weather Generator ◽  
Weather Data ◽  
Data Series ◽  
Series Length ◽  
Stochastic Weather Generator ◽  
Crop Models ◽  
Weather Generators ◽  
Interpolation Technique ◽  
Meteorological Observations ◽  
Homogeneous Regions

Weather generators (WG) became significant modules of crop models and decision support systems in the past decade. Using a large meteorological database from North America; two basic problems, related to the applicability of WGs in case of short or lacking data series, were investigated in the framework of the Multivariable weather generator (MVWG). First, the minimum data series length, required for adequate parameterization of the WG, was determined. Our results suggest that 15 years of observed data are enough for adequate parameterization of the MVWG. We then investigated a possibility of spatial interpolation of WG parameters using the outputs of the WG for sites with no meteorological observations. Coupled with the presented interpolation technique, MVWG was able to generate realistic weather data for sites with no measurements situated in climatically and geographically homogeneous regions.

scholarly journals Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

2016 ◽  
Vol 20 (3) ◽  
pp. 1031-1047 ◽  
Author(s):  
Benjamin Grouillet ◽  
Denis Ruelland ◽  
Pradeebane Vaittinada Ayar ◽  
Mathieu Vrac
Keyword(s):  
High Resolution ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Hydrological Model ◽  
General Circulation Models ◽  
Western Mediterranean ◽  
Cumulative Distribution ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Precipitation And Temperature

Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986&ndsh;2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function–transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.

Sign in / Sign up

Export Citation Format

Share Document