This paper presents a stand-alone cloud detection algorithm over land (CDL) for Microwave Humidity Sounder -2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning (ML) algorithm Gradient Boosting Decision Tree (GBDT), which achieves the state-of-the-art performance on tabular data, with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and hum-algorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave (MW) radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.

Calreticulin (CRT) is widely expressed in eukaryotes. As a molecular chaperone and a Ca²⁺ binding protein, CRT is involved in many biological pathways such as the regulation of calcium homeostasis, calcium-dependent signaling, endoplasmic reticulum quality control, plant growth and development, immunity and response to stress. However, the response of CRT of sugarcane (Saccharum spp. hybrid) challenged by Sugarcane mosaic virus (SCMV) has not been reported. In this study, a CRT gene was cloned from the noble cane cultivar Badila (S. officinarum) and designed as ScCRT1. ScCRT1 had an open reading frame (ORF) length of 1281 bp and encoded 426 amino acids. Bioinformatics analysis showed that ScCRT1 was a stable hydrophilic protein and possesses a signal peptide at the N-terminal, a typical transmembrane domain, and a typical endoplasmic reticulum location signal at the C-terminal. The secondary structure of ScCRT1 was composed of mostly random coils. Phylogenetic tree analysis indicated that ScCRT1 belonged to the CRT1/CRT2 subtype and was divergent between monocotyledons and dicotyledons. Subcellular location assays showed that ScCRT1 was mainly located in the endoplasmic reticulum. Real-time quantitative PCR analysis showed that ScCRT1 gene was extensively expressed in different tissues of sugarcane, with the highest expression in leaf roll and the lowest expression in the 8th internode. ScCRT1 gene was up regulated in the early stage of SCMV infection, but down regulated with time going. ScCRT1 interacted with the 6K2 from SCMV as confirmed by yeast two hybrid and bimolecular fluorescence complementation assays. Based on these foundlings, we speculated SCMV interfered the calcium homeostasis by the interaction of 6K2 with ScCRT1, thereby facilitating viral infection of sugarcane.

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).