Comparative Evaluation of Different Post Processing Methods for Numerical Prediction of Temperature Forecasts over Iran

Vashani, S.; Azadi, Mehdi; Hajjam, S

doi:10.3923/rjes.2010.305.316

Cited by 14 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, many previous studies that implemented various statistical techniques (i.e., MOS and KF) to correct the NWP model temperature forecasts compared the models by performing bias corrections for each station, which cannot produce the spatial distribution maps of air temperature (Isaksson, 2018; Vashani et al, 2010). However, machine learning methods that use all stations can produce the spatial distribution of air temperature.…”

Section: Resultsmentioning

confidence: 99%

“…Among various machine learning classifiers, Artificial Neural Network (ANN) has been the most popular technique for air temperature forecasting in the literature (Isaksson, 2018; Marzban, 2003; Vashani et al, 2010; Zjavka, 2016). Marzban (2003) used ANN for post‐processing of the Advanced Regional Prediction System (ARPS) model's hourly temperature outputs, getting an average 40% reduction in the mean squared error for all validated weather stations.…”

Section: Introductionmentioning

confidence: 99%

“…Marzban (2003) used ANN for post‐processing of the Advanced Regional Prediction System (ARPS) model's hourly temperature outputs, getting an average 40% reduction in the mean squared error for all validated weather stations. Vashani et al (2010) found that the ANN and KF methods demonstrate better bias correction performance than the other methods for the summing accuracy of 30 weather stations in Iran, and ANN produced slightly higher accuracy than KF for longer forecast ranges (i.e., 2 to 5 days ahead forecasts). Zjavka (2016) reported that a polynomial neural network could successfully bias‐correct the National Oceanic and Atmospheric Administration (NOAA) meso‐scale model to forecast hourly air temperature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

124

View full text Add to dashboard Cite

Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

124

View full text Add to dashboard Cite

show abstract

“…Generally speaking, the updating procedure consists of three methods: input updating, parameter/state updating and output/error updating (Romanowicz et al 2008). Note that the aforementioned updating procedures developed based on the statistical correction techniques are commonly regarded as the post-processing method (e.g., Vashani et al 2010;Liu et al 2012). Pagano et al (2011) indicated that the post-processing method can effectively reduce errors by eliminating systematic bias and/or by reducing transient errors.…”

Section: Introductionmentioning

confidence: 99%

Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method

Shen

Chang

et al. 2015

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

This study modifies a real-time correction method for water stage forecasts (named the RTEC_TS&KF model) using the time series method developed by Wu et al. (Stoch Environ Res Risk Assess 26:519-531, 2012) (named the RTEC_TS model), by incorporating the Kalman filter (KF) model. The RTEC_TS&KF model adjusts the corrected water stage forecasts resulting from the RTEC_TS model by taking into account the uncertainties in the model structure/inputs as well as the measurement bias. In detail, the water stage forecasts are corrected by separately adding the forecasted errors by the times series model and KF method into the stage forecasts. As compared to the results from the RTEC_TS model using the forecasted and observed water stages for Typhoons Morakot (2009), Saola (2012) and Soulik (2013), the RTEC_TS&KF model not only effectively lessens the uncertainties in regard to the water stage forecasts, but also consistently presents high correction performance of water level forecasts for various rainstorm events. This reveals that the RTEC_TS&KF model is superior to the RTEC_TS model in the correction of water stage forecasts. In the future, the RTEC_TS&KF model will be applied in the real-time corrections of other hydrological variates, such as the outflow of a reservoir, in the case of observation being provided on time.

show abstract

“…Anadranistakis et al (2004) compared post-processing by a Kalman filter with that of an empirical post-processing method on 2-m temperature and humidity forecasts from a fine-resolution model, with focus on an agricultural site in Greece. Vashani et al (2010) compared several methods for post-processing maximum and minimum surface temperatures, also focusing on agricultural sites, but used an intermediate-resolution model. As stated above, we have chosen to compare different variants of the Kalman filter and the MA since these do not require a long training period.…”

Section: Introductionmentioning

confidence: 99%

Local temperature forecasts based on statistical post‐processing of numerical weather prediction data

Alerskans

Kaas

2021

Meteorological Applications

View full text Add to dashboard Cite

Six adaptive, short-term post-processing methods for correcting systematic errors in numerical weather prediction (NWP) forecasts of near-surface air temperatures using local meteorological observations are assessed and compared. The methods tested are based on the simple moving average and the more advanced Kalman filter. Forecasts from the rather coarse-resolution global NWP model Global Forecast System (GFS) and the regional highresolution NWP model HARMONIE are post-processed, and the results are evaluated for 100 private weather stations in Denmark. The performance of the post-processing methods differs depending on the NWP model. Overall, the combined moving average and a so-called lead time Kalman filter performs the best. The moving average was shown to be superior to a diurnal bias correction Kalman filter at removing the longer-term systematic errors for HAR-MONIE forecast data and comparable for GFS forecast data. Subsequent application of the lead time Kalman filter corrects for the short-term errors using the real-time forecast error. The post-processing method is adaptive and there is no need for a long record of observations or a historical archive of forecasts.

show abstract

Comparative Evaluation of Different Post Processing Methods for Numerical Prediction of Temperature Forecasts over Iran

Cited by 14 publications

References 1 publication

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method

Local temperature forecasts based on statistical post‐processing of numerical weather prediction data

Contact Info

Product

Resources

About