Bias-Correction Fuzzy C-Regressions Algorithm

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.

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The Application of Firth Bias Correction in Variance Components Estimation of Clustered Random Intercept Binary Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1863/1/012028 ◽

2021 ◽

Vol 1863 (1) ◽

pp. 012028

Author(s):

R Arisanti ◽

I M Sumertajaya ◽

K A Notodiputro ◽

Indahwati

Keyword(s):

Bias Correction ◽

Variance Components ◽

Random Intercept ◽

Binary Model ◽

Variance Components Estimation

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

Agronomy ◽

10.3390/agronomy11061207 ◽

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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Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer

Diagnostics ◽

10.3390/diagnostics11010085 ◽

2021 ◽

Vol 11 (1) ◽

pp. 85

Author(s):

Yu Shi ◽

Ethan Wahle ◽

Qian Du ◽

Luke Krajewski ◽

Xiaoying Liang ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

Bias Correction ◽

Cross Validation ◽

Prostate Cancer Risk ◽

Tissue Level ◽

Peripheral Region ◽

The Novel ◽

Omega 3 ◽

Field Bias

Prostate cancer is the most common noncutaneous cancer and the second leading cause of cancer deaths among American men. Statins and omega-3 are two medications recently found to correlate with prostate cancer risk and aggressiveness, but the observed associations are complex and controversial. We therefore explore the novel application of radiomics in studying statin and omega-3 usage in prostate cancer patients. On MRIs of 91 prostate cancer patients, two regions of interest (ROIs), the whole prostate and the peripheral region of the prostate, were manually segmented. From each ROI, 944 radiomic features were extracted after field bias correction and normalization. Heatmaps were generated to study the radiomic feature patterns against statin or omega-3 usage. Radiomics models were trained on selected features and evaluated with 500-round threefold cross-validation for each drug/ROI combination. On the 1500 validation datasets, the radiomics model achieved average AUCs of 0.70, 0.74, 0.78, and 0.72 for omega-3/prostate, omega-3/peripheral, statin/prostate, and statin/peripheral, respectively. As the first study to analyze radiomics in relation to statin and omega-3 uses in prostate cancer patients, our study preliminarily established the existence of imaging-identifiable tissue-level changes in the prostate and illustrated the potential usefulness of radiomics for further exploring these medications’ effects and mechanisms in prostate cancer.

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