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.

scholarly journals Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

2019 ◽  
Vol 20 (7) ◽  
pp. 1379-1398 ◽  
Author(s):  
Shasha Han ◽  
Paulin Coulibaly
Keyword(s):  
Bias Correction ◽  
Short Range ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Hydrologic Uncertainty ◽  
Ensemble Weather Forecasts ◽  
Range Forecasts

Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.

BIAS CORRECTION METHOD FOR d4PDF TYPHOON TRACK DATASET AND FUTURE CHANGES IN TYPHOON CENTRAL PRESSURE

2021 ◽  
Vol 77 (2) ◽  
pp. I_973-I_978
Author(s):  
Junichi ARIMURA ◽  
Zhongrui QIU ◽  
Tetsuya OKAYASU ◽  
Koutarou CHICHIBU ◽  
Kunihiro WATANABE ◽  
...  
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Central Pressure ◽  
Bias Correction Method ◽  
Typhoon Track

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

Ensemble weather forecast of precipitation with a stochastic weather generator based on analogues circulation

2020 ◽  
Author(s):  
Meriem Krouma ◽  
Pascal Yiou ◽  
Céline Déandréis ◽  
Soulivanh Thao
Keyword(s):  
Model Development ◽  
Weather Generator ◽  
Lead Times ◽  
The European Union ◽  
Ensemble Forecasts ◽  
Analogue Model ◽  
Data Set ◽  
Stochastic Weather Generator ◽  
Horizon 2020 ◽  
Skill Scores

<p><strong>Abstract</strong></p><p>The aim of this study is to assess the skills of a stochastic weather generator (SWG) to forecast precipitation in Europe. The SWG is based on the random sampling of circulation analogues, which is a simple form of machine learning simulation. The SWG was developed and tested by Yiou and Déandréis (2019) to forecast daily average temperature and the NAO index. Ensemble forecasts with lead times from 5 to 80 days were evaluated with CRPSS scores against climatology and persistence forecasts. Reasonable scores were obtained up to 20 days.  In this study, we adapt the parameters of the analogue SWG to optimize the simulation of European precipitations. We then analyze the performance of this SWG for lead times of 2 to 20 days, with the forecast skill scores used by Yiou and Déandréis (2019). To achieve this objective, the SWG will use ECA&D precipitation data (Haylock. 2002), and the analogues of circulation will be computed from sea-level pressure (SLP) or geopotential heights (Z500) from the NCEP reanalysis. This provides 100-member ensemble forecasts on a daily time increment. We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence to weather regimes. Comparisons with “real” medium range forecasts from the ECMWF will be performed.</p><p><strong>References</strong></p><p>Yiou, P., and Céline D.. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development 12, 2 (2019): 723‑34.</p><p>Haylock, M. R. et al.. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. - Atmospheres 113, D20 (2008): doi:10.1029/2008JD010201.</p><p> </p><p><strong>A</strong><strong>cknowledge</strong></p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

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