Host: Linjiong Zhou

Clouds and precipitation prediction is one of the most challenging tasks for all numerical models. We have seen significant biases in cloud fraction, cloud height, precipitation rate, and precipitation diurnal cycle predictions in the globally 13-km System for High-resolution prediction on Earth-to-Local Domains (SHiELD) developed here at GFDL. Given the high potential for improving convective-scale prediction in a model with a horizontal grid space smaller than 10 km, we are moving the horizontal resolution of the SHiELD prediction system towards 6.5 km and 3.25 km. In this project, we will analyze the global clouds and precipitation prediction of our sub-10-km SHiELD compared with observations. These analyses will help to identify the biases and find possible solutions to improve the predictions. The intern will learn how clouds and precipitation are predicted in numerical models, including the basic concept of cloud fraction, cloud height, precipitation rate, precipitation type, diurnal cycles, etc. This position requires an intern with experience in using common programming and scripting languages, e.g., Python, R, MATLAB, or NCL, to analyze and visualize model data and experience working on a Linux operating system.