Rechunking National Water Model v3.0 retrospective simulations to more (cloud-)approachable chunks in Zarr format.

Authors: James McCreight (NCAR), Ishita Srivastava (NCAR), Rich Signell (USGS), and Yongxin Zhang (NCAR)

Overview

The following table describes the simulation time period, domain and frequency of the inputs for the National Water Model (NWM) version 3.0 retrospective simulation:

DOMAIN	Simulation Time Period	Data Frequency
CONUS	Feb 1979 - Feb 2023	Inputs are hourly and outputs are provided at hourly or 3-hourly resolution
Alaska	Jan 1981 - Dec 2019	Inputs are hourly and outputs are provided at hourly or 3-hourly resolution
Hawaii	Jan 1994 - Jan 2014	Inputs are hourly and outputs are provided at hourly, 15-min, 45-min resolution
Puerto Rico	Jan 2008 - June 2023	Inputs are hourly and outputs are provided at hourly or 3-hourly resolution

Additional details are provided below and in this retrospective overview document.

The model writes separate files at each output time. Within those individual files the data are not chunked in space. In the use case of opening a full timeseries at a single point or a sub-region, the user would be required to read in the entire data set: a very inefficient data access pattern for a very common use case.

Enter rechunking. The goal of rechunking this model dataset is to provide chunks (data pieces partitioning the dimnensions of of the data) that support efficient data access for most use cases. When a specific, intensive use case would benefit from a different chunk scheme than that provided, the provided datasets can be rechunked to accomodate that pattern. Examples of use cases are supplied, including re-rechunking.

Data overview

Following zarr stores have been created, corresponding closely to the model output files. The time resolution is noted for each product.

CONUS

Data Type	Description	Frequency	Size
lakeout	Output from the lake model	Hourly	5.0GB
gwout	Output from the groundwater model	Hourly	317GB
chrtout	Output from the streamflow model	Hourly	2.9TB
precip	Input precipitation fields from the OWP AORC forcing	Hourly	1.3TB
lwdown	Input lwdown fields from the OWP AORC forcing	Hourly	7.7TB
psfc	Input psfc fields from the OWP AORC forcing	Hourly	8.0TB
q2d	Input q2d fields from the OWP AORC forcing	Hourly	4.6TB
swdown	Input swdown fields from the OWP AORC forcing	Hourly	3.9TB
t2d	Input t2d fields from the OWP AORC forcing	Hourly	4.6TB
u2d	Input u2d fields from the OWP AORC forcing	Hourly	4.2TB
v2d	Input v2d fields from the OWP AORC forcing	Hourly	4.2TB
ldasout	Output from the NoahMP land surface model	3-hourly	14TB
rtout	Output from the overland and subsurface terrain	3-hourly	3.5TB

Alaska

Data Type	Description	Frequency	Size
lakeout	Output from the lake model	Hourly	5.5GB
gwout	Output from the groundwater model	Hourly	1.7TB
chrtout	Output from the streamflow model	Hourly	346GB
forcing	Input fields from the OWP AORC forcing	Hourly	2.4TB
ldasout	Output from the NoahMP land surface model	3-hourly	548GB
rtout	Output from the overland and subsurface terrain	3-hourly	46GB

Hawaii

Data Type	Description	Frequency	Size
lakeout	Output from the lake model	Hourly	31MB
gwout	Output from the groundwater model	Hourly	15GB
chrtout	Output from the streamflow model	Hourly	36GB
forcing	Input fields from the OWP AORC forcing	Hourly	444GB
ldasout	Output from the NoahMP land surface model	3-hourly	132GB
rtout	Output from the overland and subsurface terrain	3-hourly	64GB

Puerto Rico

Data Type	Description	Frequency	Size
lakeout	Output from the lake model	Hourly	26MB
gwout	Output from the groundwater model	Hourly	4.3GB
chrtout	Output from the streamflow model	Hourly	9.6GB
forcing	Input fields from the OWP AORC forcing	Hourly	37GB
ldasout	Output from the NoahMP land surface model	3-hourly	28GB
rtout	Output from the overland and subsurface terrain	3-hourly	25GB

Additonal detail on these stores (variables contained and space-time information) is provided in the data description section below and via accompanying notebooks.

Data Description

Data as accessed by xarray.open_zarr can be found in the accompanying notebook (html) (jupyter_notebook). This includes metadata, chunking schemes, and data types for all variables and coordinates.

Further details in this accompanying notebook (html) (jupyter notebook) including the xarray dataset reports and also xarray and Zarr details for each variable showing storage data types, levels of compression and other information. Note that the difference in the data types between xarray and zarr result from the use of scale_factor and add_offset metadata in the underlying Zarr data set which xarray uses to recover floating point variables from the stored integers.

Use Cases

• Example of retrieving and plotting a single timeserires from the chrtout store (html) (jupyter notebook)

• Example of subsetting and rechunking the store to optimize data access pattern: selecting only streamflow gages from chrtout (html) (jupyter notebook)

Code overview

An overview of the code used can be found in README_code.md.