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Inland lake temperature initialization via coupled cycling with atmospheric data assimilation

CSS Publication Number
Full Publication Date
September 5, 2022

Application of lake models coupled within earth-system prediction models, especially for predictions from days to weeks, requires accurate initialization of lake temperatures. Commonly used methods to initialize lake temperatures include interpolation of global sea-surface temperature (SST) analyses to inland lakes, daily satellite-based observations, or model-based reanalyses. However, each of these methods have limitations in capturing the temporal characteristics of lake temperatures (e.g., effects of anomalously warm or cold weather) for all lakes within a geographic region and/or during extended cloudy periods. An alternative lake-initialization method was developed which uses two-way-coupled cycling of a small-lake model within an hourly data assimilation system of a weather prediction model. The lake model simulated lake temperatures were compared with other estimates from satellite and in situ observations and interpolated-SST data for a multi-month period in 2021. The lake cycling initialization, now applied to two operational US NOAA weather models, was found to decrease errors in lake surface temperature from as much as 5–10 K vs. interpolated-SST data to about 1–2 K compared to available in situ and satellite observations.

Stanley G. Benjamin
Tatiana G. Smirnova
Eric P. James
Eric J. Anderson
Ayumi Fujisaki-Manome
John G. W. Kelley
Greg E. Mann
Philip Chu
Sean G. T. Kelley
Research Areas
Water Resources
Framework, Methods & Tools

Coupled cycling

Inland lake temperature

Publication Type
Journal Article
Digital Object Identifier
Full Citation

Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, Sean G. T. Kelley Inland lake temperature initialization via coupled cycling with atmospheric data assimilation Geoscientific Model Development, Volume 15, Issue 17, 6659-6676, 2022. CSS22-38