An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation Forecasts

2021 ◽  
Vol 27 (4) ◽  
pp. 428-436
Author(s):  
LIU Jin-Qing ◽  
DAI Guang-Feng ◽  
OU Xiao-Feng
Keyword(s):  
Bias Correction ◽  
Quantitative Precipitation Forecasts

Improving Ensemble-Based Quantitative Precipitation Forecasts for Topography-Enhanced Typhoon Heavy Rainfall over Taiwan with a Modified Probability-Matching Technique

2013 ◽  
Vol 141 (11) ◽  
pp. 3908-3932 ◽  
Author(s):  
Xingqin Fang ◽  
Ying-Hwa Kuo
Keyword(s):  
Bias Correction ◽  
Heavy Rainfall ◽  
Rainfall Pattern ◽  
Systematic Bias ◽  
Probability Matching ◽  
Ensemble Forecasts ◽  
Rainfall Prediction ◽  
Matching Technique ◽  
Quantitative Precipitation Forecasts ◽  
Matching Procedure

Abstract In this paper, a modified probability-matching technique is developed for ensemble-based quantitative precipitation forecasts (QPFs) associated with landfalling typhoons over Taiwan. The main features of this technique include a resampling of the ensemble realizations, a rainfall pattern adjustment, and a bias correction. Using this technique, a synthetic ensemble is created for the purpose of rainfall prediction from a large-size (32 members), low-resolution (36 km) ensemble and a small-size (8 members), high-resolution (4 km) ensemble. The rainfall pattern is adjusted based on the precipitation distribution of the 36- and 4-km ensembles. A bias-correction scheme is then applied to remove the known systematic bias from the resampled 4-km ensemble realizations as part of the probability-matching procedure. The modified probability-matching scheme is shown to substantially reduce or eliminate the intrinsic model rainfall bias and to provide better QPF guidance. The encouraging results suggest that this modified probability-matching technique is a useful tool for the QPF of the topography-enhanced typhoon heavy rainfall over Taiwan using ensemble forecasts at dual resolutions.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

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