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X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme. [ABSTRACT FROM AUTHOR]

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In the dynamic ensemble selection algorithm, the region of competence of the test sample was composed of fixed samples, which would affect the classifier selection. Therefore, the paper proposed the DES-DCR-CIER algorithm based on dynamic region of competence strategy. Firstly, this algorithm used the heterogeneous classifier to generate the base classifier pool to deal with the problem that the difference between the homogeneous ensemble classifiers was little and the number of heterogeneous ensemble classifiers was small. Next, it applied three steps including the mutual K nearest neighbor with adaptive distance algorithm, approaching the distance center of the sample set and removing the class edge samples to determine the dynamic region of competence of the test sample and it used overall complementarity index to select a set of classifiers. Finally, the classifiers were synthesized by the ER rule to get integration. Experiments on the diagnosis data of breast mass from 8 sonographers in a tertiary hospital in Hefei, Anhui Province and American Wisconsin Breast Cancer Diagnostic data set showed that the diagnostic model based on the DES-DCR-CIER algorithm had better accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI. [ABSTRACT FROM AUTHOR]

Large volumes of data have been accumulated in identical or very similar contexts of decision making. To generate accurate and explanatory decision recommendations by using these data, this paper proposes a data-driven multi-criteria decision making (MCDM) method based on machine learning (ML) algorithms and the evidential reasoning (ER) approach. In the method, based on the assessments of all historical alternatives, a comparison framework is designed to determine the most appropriate ML algorithm with the highest predictive accuracy. An optimization model is then constructed to connect the appropriate ML algorithm with the ER approach. Through the optimization model, the difference between overall assessments derived from the ER approach and the predicted results derived from the appropriate ML algorithm is minimized to learn criterion weights. The learned criterion weights are used to generate accurate and explanatory decisions. Such a combination takes advantage of high predictability of ML algorithms and favorable interpretability of the ER approach simultaneously. To demonstrate the validity and applicability of the proposed method, it is used to aid the diagnosis of thyroid nodules for a tertiary hospital located in Hefei, Anhui, China. Its merits are further highlighted by its comparison with two traditional ER approaches and the appropriate ML algorithm. • Data-driven decision making based on ER approach and ML algorithms is proposed. • ML algorithms are compared based on their linkage with ER approach. • An optimization model is constructed to make explanatory decisions based on ML. • The proposed method is used to help radiologists diagnose thyroid nodules. • The proposed method is compared with decision making learning from accuracy and AUC. [ABSTRACT FROM AUTHOR]