A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedi...

To improve precipitation estimation accuracy, new methods, which are able to merge different measurement modalities, necessary. In this study, we propose a deep learning method rain gauge measurements with ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs multiscale analysis the t...

The present study attempts to introduce a method of statistical downscaling with a synoptic view. The precipitation data of Golestan Province has been used for the years 1971 to 2010. Employing multivariable regression, this study models the precipitation gauges in the station scale, by making use of 26 predicting components of model HadCM3, on the basis of two A2 and B2 scenarios. However, the...

We compared the protein-A-bearing Staphylococcus aureus immunoadsorbent to the double-antibody precipitation method for separating bound and free radiolabeled antigen in a radioimmunoassay. With human albumin (antigen) and rabbit anti-human albumin (antibody) as a model, our results indicate that formalin-fixed, heat-killed S. aureus cells could be substituted for the double-antibody precipitat...