Multi-Site Stochastic Weather Generator for Daily Precipitation and Temperature

2014 ◽  
pp. 1375-1391 ◽  
Keyword(s):  
Daily Precipitation ◽  
Weather Generator ◽  
Stochastic Weather Generator ◽  
Precipitation And Temperature

A Versatile Weather Generator for Daily Precipitation and Temperature

2012 ◽  
Vol 55 (3) ◽  
pp. 895-906 ◽  
Author(s):  
J. Chen ◽  
F. P. Brissette ◽  
R. Leconte ◽  
A. Caron
Keyword(s):  
Daily Precipitation ◽  
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 Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN

2020 ◽  
Author(s):  
Andrew T. Fullhart ◽  
Mark A. Nearing ◽  
Gerardo Armendariz ◽  
Mark A. Weltz
Keyword(s):  
Land Surface ◽  
Daily Precipitation ◽  
Weather Generator ◽  
Stochastic Weather Generator ◽  
Spatial Coverage ◽  
Surface Models ◽  
Input Parameters ◽  
International Climate ◽  
Data Commons ◽  
Selection Of

Abstract. This dataset contains input parameters for 12,703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-year, 20-year, and 10-year minimum record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the U.S., which were compared to a selection of stations from an existing U.S. CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications, and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020c).

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 A Constrained Stochastic Weather Generator for Daily Mean Air Temperature and Precipitation

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Feifei Pan ◽  
Lisa Nagaoka ◽  
Steve Wolverton ◽  
Samuel F. Atkinson ◽  
Timothy A. Kohler ◽  
...  
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Weather Conditions ◽  
Weather Generator ◽  
Total Precipitation ◽  
Sinusoidal Wave ◽  
Stochastic Weather Generator ◽  
Temperature And Precipitation ◽  
Unique Method

A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

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.

Sign in / Sign up

Export Citation Format

Share Document