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New on the blog: we're discussing our experience using #Kerchunk to improve access times to short-range streamflow predictions generated by NOAA's #NationalWaterModel Predictions Dataset.

https://www.element84.com/software-engineering/using-kerchunk-to-make-noaas-national-water-model-dataset-more-accessible/

Using Kerchunk to make NOAA’s National Water Model Dataset more accessible

In this blog, we discuss our experience using Kerchunk to improve access times to short-range streamflow predictions generated by NOAA’s National Water Model Predictions Dataset, achieving a speedup of 4 times, using 16 times less memory.

Did you know Azavea is working with #NOAA to store large amounts of #NationalWaterModel data using cloud-optimized formats like #Parquet and #Zarr?

Learn more in our our blog focused on benchmarking: https://www.azavea.com/blog/2022/09/22/benchmarking-zarr-and-parquet-data-retrieval-using-the-national-water-model-nwm-in-a-cloud-native-environment/

Benchmarking Zarr and Parquet Data Retrieval using the National Water Model (NWM) in a Cloud-native environment | Azavea

In order to benchmark efficiency, we take a deep dive into Zarr and Parquet data retrieval to compare performance on various time scales. 

Azavea