Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE

Filetype[PDF-2.89 MB]


Details You May Also Like

Details:

  • Journal Title:
    Water Resources Research
  • Personal Author:
  • NOAA Program & Office:
    CIRES (Cooperative Institute for Research in Environmental Sciences)
  • Description:
    Snow depth observations and modeled snow density can be combined to calculate snow water equivalent (SWE). In this approach, SWE uncertainty is dominated by snow density uncertainty, which depends on meteorological data quality and process representation (e.g., compaction) in models. We test whether assimilating snow depth observations with the particle filter can improve modeled snow density, thus improving SWE estimated from intermittent depth observations. We model snowpack at Mammoth Mountain (California) over water years 2013–2016, assuming monthly snow depth data (e.g., sampling intervals relevant to lidar or manual surveys) for assimilation, and validate against observed SWE and density. The particle filter reduced density and SWE root‐mean‐square error by 27% and 28% relative to open loop simulations when using high‐quality, point location forcing. Assimilation gains were greater (35% and 51% reduction in density and SWE root‐mean‐square error) when using coarse‐resolution North American Land Data Assimilation System phase 2 meteorology. Ensembles created with both meteorological and compaction perturbations led to the greatest model improvements. Because modeled depth and density were both generally lower than observations, assimilation favored particles with higher precipitation and thus more overburden compaction. This moved depth and density (therefore SWE) closer to observations. In contrast, ensemble generation that varied only compaction parameters degraded performance. These results were supported by synthetic experiments with prescribed error sources. Thus, assimilation of snow depth data from lidar or other techniques can likely improve snow density and SWE derived at the basin scale. However, supplementary in situ observations are valuable to identify primary error sources in simulated snow depth and density.
  • Keywords:
  • Source:
    Water Resources Research, 55(2): 1296–1311
  • DOI:
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files

More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26