This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.

The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950, Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953, Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954, Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158, Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945, Zhuge 2023e).

We discuss the recently presented Belle results on the decays $\mathrm{$\Upsilon${}}(5S)$\rightarrow${}$\pi${}$\pi${}$\pi${}{$\chi${}}_{bJ}(1P)$. The data indicate that in addition to the $$\omega${}$ emission, $\mathrm{$\Upsilon${}}(5S)$\rightarrow${}$\omega${}{$\chi${}}_{bJ}$, there is a significant nonresonant background in the three pion spectrum. We suggest that a sizable fraction of this background may be associated with the cascade process $\mathrm{$\Upsilon${}}(5S)$\rightarrow${}$\pi${}{Z}_{b}$\rightarrow${}$\pi${}$\rho${}{$\chi${}}_{b}$ involving the ${Z}_{b}(10610)$ and ${Z}_{b}(10650)$ resonances. If confirmed by the data, this would be the first observation of transition from the ${Z}_{b}$ resonances to lower bottomonium with emission of a light meson state different from a single pion, which may provide a new input in understanding of the internal dynamics of these resonances.

With the development of refined numerical forecasts, problems such as score distortion due to the division of precipitation thresholds in both traditional and improved scoring methods for precipitation forecasts and the increasing subjective risk arising from the scale setting of the neighborhood spatial verification method have become increasingly prominent. To address these issues, a general comprehensive evaluation method (GCEM) is developed for cross-scale precipitation forecasts by directly analyzing the proximity of precipitation forecasts and observations in this study. In addition to the core indicator of the precipitation accuracy score (PAS), the GCEM system also includes score indices for insufficient precipitation forecasts, excessive precipitation forecasts, precipitation forecast biases, and clear/rainy forecasts. The PAS does not distinguish the magnitude of precipitation and does not delimit the area of influence; it constitutes a fair scoring formula with objective performance and can be suitable for evaluating rainfall events such as general and extreme precipitation. The PAS can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts, enabling the quantitative evaluation of the comprehensive capability of various refined precipitation forecasting products. Based on the GCEM, comparative experiments between the PAS and threat score (TS) are conducted for two typical precipitation weather processes. The results show that relative to the TS, the PAS better aligns with subjective expectations, indicating that the PAS is more reasonable than the TS. In the case of an extreme-precipitation event in Henan, China, two high-resolution models were evaluated using the PAS, TS, and fraction skill score (FSS), verifying the evaluation ability of PAS scoring for predicting extreme-precipitation events. In addition, other indices of the GCEM are utilized to analyze the range and extent of both insufficient and excessive forecasts of precipitation, as well as the precipitation forecasting ability for different weather processes. These indices not only provide overall scores similar to those of the TS for individual cases but also support two-dimensional score distribution plots which can comprehensively reflect the performance and characteristics of precipitation forecasts. Both theoretical and practical applications demonstrate that the GCEM exhibits distinct advantages and potential promotion and application value compared to the various mainstream precipitation forecast verification methods.