Search Results for author:
Found 5 papers, 0 papers with code
On the modern deep learning approaches for precipitation downscaling
To test the efficacy of different DL approaches, we apply four different methods of downscaling and evaluate their performance.
Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction
We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times.
Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia
This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied.
Deep learning for improved global precipitation in numerical weather prediction systems
This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts.
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region
In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season.
