Sinograin (China)

Global storm-resolving simulations at kilometer scales (1–5 km) provide new opportunities to represent convective processes, yet they remain in the gray zone of deep convection, where cumulus parameterization choices can strongly affect model performance. Using global kilometer-scale simulations from the Digital Earth Global Hackathon 2025, this study applies a new vortex–mesoscale convective system (MCS) tracking and matching algorithm to examine how two convection parameterization configurations—turning off deep convection (IFS-deepoff) and deep convection with reduced cloud-base mass flux (IFS-rcbmf)—influence the simulation of MCSs, vortices, and precipitation during the 2020 Meiyu season over East Asia. Results show that IFS-deepoff outperforms IFS-rcbmf in reproducing the total amount and spatial distribution of precipitation, although both schemes overestimate MCS frequency and their contribution to rainfall over the Sichuan Basin and the middle–lower Yangtze River. Importantly, precipitation biases are not governed by MCS frequency alone, but depend strongly on the coupling between MCSs and vortices. Precipitation in both schemes is highly sensitive to vortex simulation, with IFS-deepoff producing stronger extremes due to enhanced moisture convergence associated with boundary-layer vortices and increased convective available potential energy (CAPE). These findings highlight vortex–MCS coupling as a critical control on precipitation in the kilometer-scale gray zone, demonstrating that convection parameterization influences rainfall primarily through its modulation of multiscale dynamical interactions. This study provides new insight for improving convection treatment in next-generation global storm-resolving models.

The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for visual tracking problem. The VBI-based KF algorithm works to estimate the unknown measurement noise covariance matrix (MNCM) and process noise covariance matrix (PNCM), and the MCKF works to deal with non-Gaussian process noise and measurement noise. In order to cater for the visual tracking target scenario, where the MNCM and PNCM are unknown and non-Gaussian, we modify the VBI-based KF algorithm to work for suddenly occurring outliers and modify the MCKF work for small bandwidth. Simulations show that the proposed VBI-MCKF algorithm performs well when both covariance matrices of Gaussian process noise and non-Gaussian measurement noise are unknown or inaccurate.

ABSTRACT Controlling grain temperatures is the main approach employed in large commercial warehouses to maintain quality and prevent infestation by insects and fungi. Large commercial warehouses in northeast China have been utilizing inner cyclic cooling aeration (ICCA), a newly developed method for managing summertime temperatures, haphazardly and without tight standards. We utilized a homemade detector to track the temperature, humidity, and CO2 concentrations of maize that was held for four years under ICCA treatment. The monitoring occurred every week, specifically from July 1st to September 30th. In addition, we conducted weekly tests to determine the moisture content (MC) and free fatty acids (FFA) of the stored maize samples. Without the use of ICCA, the average temperature of the stored maize above a depth of 3.5 m ranged from 20°C to 28°C, and the relative humidity (RH) in the headspace was typically higher than 75%. However, when ICCA was applied, the average temperature could be maintained at 22°C, and the RH was below 65% for most of the time. In the first year, the MC of the stored maize at a depth of 1.0 m experienced a loss of approximately 0.5%. However, for one year, there was no significant loss in the MC of the maize stored at the same depth. Furthermore, the FFA in the stored maize at a depth of 1 m increased by 1–5 mg KOH/100 g. The use of ICCA effectively controls the temperature and RH in the stored maize, reducing the risk of insect and fungal reproduction.

In order to overcome the problem that traditional phase shifting interferometry requires hardware equipment with high stability, a phase extraction method for grating projection interferograms with two-step random phase shifting based on the improved Gram–Schmidt orthonormalization technology is proposed. Firstly, the background of the two-frame grating projection interferograms obtained by random phase shifting is filtered by Gaussian high-pass filter. Then, the phase shift value is obtained by the relation between the norms of two vectors. The phase distribution of the interferogram is extracted by a two-step phase shifting algorithm based on the arctangent function. In addition, the grating projection measurement system is designed, and the real experiments results show that compared with GS algorithm, the proposed method can obtain the phase shift value, and has obvious advantages in accuracy and calculation speed.