Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

2015 ◽  
Vol 33 (2) ◽  
pp. 175-183 ◽  
Author(s):  
Yongku Kim ◽  
Balaji Rajagopalan ◽  
GyuWon Lee
Keyword(s):  
Statistical Downscaling ◽  
Weather Generator ◽  
Stochastic Weather Generator ◽  
Precipitation And Temperature

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.

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.

Impacts of climate change on the frequency and severity of floods in the Châteauguay River basin, Canada

2007 ◽  
Vol 34 (9) ◽  
pp. 1048-1060 ◽  
Author(s):  
Arnaud Mareuil ◽  
Robert Leconte ◽  
François Brissette ◽  
Marie Minville
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Weather Generator ◽  
Flood Frequency Analysis ◽  
Monthly Precipitation ◽  
Climate Scenarios ◽  
Stochastic Weather Generator ◽  
Peak Flows ◽  
Precipitation And Temperature ◽  
Impacts Of Climate Change

This study aims at evaluating the hydrologic impacts of climate change on the Châteauguay River basin in the province of Quebec, Canada. Three global climate models (GCMs) covering a range of climate sensitivities were selected, and their output was employed to adjust the parameters of a stochastic weather generator using simple transformation rules for precipitation and temperature. Values of monthly precipitation and temperature were extracted from the GCMs for the current (1960–1990) and future (2040–2060) climate. The International Panel on Climate Change emission scenario known as B2 was selected. It represents an average scenario and corresponds approximately to a doubling of the atmospheric CO2 concentration. Resorting to stochastically generated climate scenarios allowed assessing whether the modelled effects of climate change on flows were statistically significant. Results indicate that spring and summer–fall peak flows were reduced on average by 30% and 12%, respectively, using the Echam4 model derived scenarios. The Hadcm3 model produced a weaker signal that was not statistically significant. The CGCM2 model produced a statistically significant reduction in spring peak flows of 8% on average, whereas the simulated reduction in summer flows was not statistically significant for many of the return periods considered. Many sources of uncertainties were partially considered in this study. One is the downscaling of the GCM climatology at the watershed scale. The approach employed to generate the future climate scenarios changed the precipitation variability through an adjustment of the parameters of the Gamma distribution function used to model precipitation amounts. Whether this approach is truly typical of climate change effect remains to be ascertained. Using more physically based hydrological models would help reduce uncertainties in climate change impacts studies.Key words: climate change, weather generator, flood, frequency analysis, hydrological modelling.

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

2015 ◽  
Vol 12 (10) ◽  
pp. 10067-10108 ◽  
Author(s):  
B. Grouillet ◽  
D. Ruelland ◽  
P. V. Ayar ◽  
M. Vrac
Keyword(s):  
High Resolution ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Hydrological Model ◽  
Flow Distribution ◽  
Western Mediterranean ◽  
Climatic Conditions ◽  
Cumulative Distribution ◽  
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 (1986–2005) to capture different climatic conditions in the basins. Streamflow was simulated using the GR4j conceptual model. 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 datasets 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 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 of 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.

scholarly journals Postprocessing of Ensemble Weather Forecasts Using a Stochastic Weather Generator

2014 ◽  
Vol 142 (3) ◽  
pp. 1106-1124 ◽  
Author(s):  
Jie Chen ◽  
François P. Brissette ◽  
Zhi Li
Keyword(s):  
Bias Correction ◽  
Weather Forecast ◽  
Correction Method ◽  
Weather Generator ◽  
Ensemble Forecasts ◽  
Stochastic Weather Generator ◽  
Weather Forecasts ◽  
Bias Correction Method ◽  
Ensemble Weather Forecasts ◽  
Precipitation And Temperature

Abstract This study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator. Key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature, allowing the generation of an infinite number of daily times series that are fully coherent with the ensemble weather forecast. This method was verified through postprocessing reforecast datasets derived from the Global Forecast System (GFS) for forecast leads ranging between 1 and 7 days over two Canadian watersheds in the Province of Quebec. The calibration of the ensemble weather forecasts was based on a cross-validation approach that leaves one year out for validation and uses the remaining years for training the model. The proposed method was compared with a simple bias correction method for ensemble precipitation and temperature forecasts using a set of deterministic and probabilistic metrics. The results show underdispersion and biases for the raw GFS ensemble weather forecasts, which indicated that they were poorly calibrated. The proposed method significantly increased the predictive power of ensemble weather forecasts for forecast leads ranging between 1 and 7 days, and was consistently better than the bias correction method. The ability to generate discrete, autocorrelated daily time series leads to ensemble weather forecasts’ straightforward use in forecasting models commonly used in the fields of hydrology or agriculture. This study further indicates that the calibration of ensemble forecasts for a period up to one week is reasonable for precipitation, and for temperature it could be reasonable for another week.

Sign in / Sign up

Export Citation Format

Share Document