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The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellite (GOES-16) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potentials comparing to the conventional segmentation technique used in PERSIANN-CCS to improve rain detection and estimation skills with an accuracy rate of up to 98% in identifying cloud regions.

Tensile and low-cycle fatigue tests of high-strength compacted graphite cast iron (CGI, RuT450) were carried out at 25 °C, 400 °C, and 500 °C, respectively. The results show that with the increase in temperature, the tensile strength decreases slowly and then decreases rapidly. The fatigue life decreases, and the life reduction increases at high temperature and high strain amplitude. The oxide layer appears around the graphite and cracks at high temperature, and the dependence of crack propagation on ferrite gradually decreases. With the increase in strain amplitude, the initial cyclic stress of compacted graphite cast iron increases at three temperatures, and the cyclic hardening phenomenon is obvious. The fatigue life prediction method based on the energy method and damage mechanism for compacted graphite cast iron is summarized and proposed after comparing and analyzing a large amount of fatigue data. [ABSTRACT FROM AUTHOR]

Modern computer weather models rely heavily on satellite observations. Operational weather forecasting centers, such as NOAA's National Centers for Environmental Prediction, use statistical techniques called data assimilation to combine previous [...]

The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44 m s−1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente's intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow. [ABSTRACT FROM AUTHOR]