“How’s the weather?” is probably the most oft uttered question in the history of mankind. And with the recent epic hurricane devastating Houston, Texas, the weather is literally on everyone’s minds these days.
Weather has been the bane of humans for as long as we have been around. Everyone thinks that the Inuik have the most words for snow (between 40-50 depending on how you count) but in reality, it is the Scots who claim the most snow-related words, 421 to be precise. Who knew? Flindrikin means a light snow shower, at least if you are in Scotland where people apparently take their weather seriously. Weather is also a serious topic for the researchers tackling Arctic climate at the University of Alaska.
Uma S. Bhatt, Professor of Atmospheric Sciences, Geophysical Institute, University of Alaska, probably knows more words for snow than most. She is seeking a better understanding of the Arctic earth system with respect to the need for long-term climate information (e.g., air temperature, precipitation, wind speeds). The challenge is, most of these data (e.g., atmospheric re-analyses, climate models) are available at spatial resolutions on the order of 100s of kilometers, which is not nearly at a high enough resolution needed to support process studies and to assess local impacts. To address this need, high resolution climate information has been created at a 20-km resolution through a process called dynamical downscaling.
|European Center Reanalysis (ERA-Interim) daily average temperature for 4 July 1979 (left) and dynamically downscaled maximum temperature (Tmax) from the Weather Research Forecast (WRF) model at ~20km resolution. Units are ˚C.|
Downscaling is particularly successful in improving climate information from lower resolution models in areas of complex topography by producing more realistic precipitation and temperature gradients. Capturing the local temperature variations is only possible through downscaling of climate information. This downscaling activity at the University of Alaska is supported by the Alaska Climate Science Center through the Department of Interior, and is possible only because of the locally available Mellanox HPC computing resources. A key advantage of dynamical downscaling is that a full suite of atmospheric model variables are available, which provides a rich data source for understanding the underlying mechanisms of Arctic climate change. Variables at the surface include precipitation, snow water equivalent, soil moisture and temperature, solar radiation, terrestrial radiation, and sensible latent heat fluxes. Variables at multiple levels in the atmosphere include temperature, moisture, winds, and geopotential height. No wonder your local weather person gets it wrong so often. Investigations of these data will help advance the world’s understanding of climate drivers of various parts of the Earth system. Beyond scientific endeavors, the research team is generously making available this downscaled data to glaciologists, hydrologists, ocean wave modelers, wildlife biologists and others for use in other scientific investigations. These collaborations help everyone better understand data as additional scientists are evaluating this data in the context of their part of the Earth system. This rich data set is also being used to ask questions about glacier mass balance in southern Alaska, rain-on-snow events relevant for caribou mortality, wildland fire susceptibility, and numerous other topics relevant for Alaska and other parts of the world.
According to Ms. Bhatt, the computational demands for dynamical downscaling are quite daunting. Just the data storage requirements can be 3.3 TB (that’s terabyte) and that’s just for one year of raw model output and which reduces to about 300 GB of post-processed data when only the most used variables are extracted and saved as daily values.
So, just much is 1 terabyte these days? Assuming that the average size photo is 500K, then a 1TB hard drive would hold some 2 million photos.
Augmenting HPC resources at UAF in January 2017 by adding Mellanox InfiniBand solutions across multiple racks to form their HPC system, has allowed the team the chance to downscale additional models and different climate scenarios in order to reduce the uncertainty in future projections for Alaska. And sharing this valuable data space with other researchers is key to spirit and generosity of the University of Alaska and their mission to innovate in all areas of research; physics and aeronomy; atmospheric sciences; snow, ice, and permafrost; seismology; volcanology; remote sensing; and tectonics and sedimentation. Along with the University of Alaska, Mellanox is proud to be part of this journey, to be helping with this quest for knowledge and a deeper understanding of our planet and the universe beyond.