Chalmers Cloud Ice Climatology

The Rain over Africa (RoA) dataset consists of spaceborn estimates of precipitation of Rain over Africa using only geostationary imagery and obtained through a convolutional and quantile regression neural network. The dataset also contains some uncertainty estimates.

This dataset provides several global fields describing the state of atmosphere, ocean, land and ice from a high-resolution (0.1o for the ocean/ice models 0.25o for the land/atmosphere models) numerical earth system model, the Community Earth System Model (CESM, https://www.cesm.ucar.edu/). Texas A&M University (TAMU) and National Center for Atmospheric Research together with international partners collaboratively carried out a large set of high-resolution climate simulations, including a 500-year long preindustrial control simulation (PI-CTRL) described here. The CESM uses dynamic equations with a climatological (observations, long-term averaged) initial state of the earth system and the preindustrial greenhouse gas forcing (kept constant at the 1850 conditions) for making this PI-CTRL. Compared to standard low-resolution (1o) CMIP-class simulations, this high-resolution simulation dataset enables researchers to explore the contribution from processes at a wide range of spatial sc[...]

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. Read our doc on how to get AWS Credentials[...]

UPDATE TO GHCN PREFIXES - The NODD team is working on improving performance and access to the GHCNd data and will be implementing an updated prefix structure. For more information on the prefix changes, please see the "READ ME on the NODD Github". If you have questions, comments, or feedback, please reach out to nodd@noaa.gov with GHCN in the subject line. Global Historical Climatology Network - Daily is a dataset from NOAA that contains daily observations over global land areas. It contains station-based measurements from land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. It is a composite of climate records from numerous sources that were merged together and subjected to a com[...]