Benchmarking the Raw Model-Generated Background Forecast in Rapidly Updated Surface Temperature Analyses. Part I: Stations
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

Benchmarking the Raw Model-Generated Background Forecast in Rapidly Updated Surface Temperature Analyses. Part I: Stations

  • 2020

  • By Hamill, T.M.
  • Source: Monthly Weather Review, 148(2), 689-700.
Filetype[PDF-3.42 MB]


Details Related Documents You May Also Like

Details:

  • Journal Title:
    Monthly Weather Review
  • Personal Author:
    Hamill, T.M.
  • NOAA Program & Office:
  • Description:
    High-quality, high-resolution, hourly unbiased surface (2 m) temperature analyses are needed for many applications, including training and validation of statistical postprocessing applications. These temperature analyses are often generated through data assimilation procedures, whereby a background short-range gridded forecast is adjusted to newly available observations. Even with frequent updates to newly available observations, surface-temperature analysis errors and biases can be comparatively large relative to errors and biases of midtropospheric variables, especially over land, despite more near-surface in situ observations. Larger near-surface errors may have several causes, including biased background forecasts and the spatial heterogeneity of surface temperatures that results from subgrid-scale surface, vegetation, land-use, and terrain variations. Are biased raw background forecasts the predominant cause of surface temperature analysis errors? Part I of this two-part series describes a simple benchmark for evaluating the error characteristics of short-term (1 h) raw model background surface temperature forecasts. For stations with a relatively complete time series of data, it is possible to generate an hourly, diurnally, and seasonally dependent observation climatology at a station. The deviation of the current hour’s temperature observation with respect to this hour’s and Julian day’s climatology is added to the climatology for the next hour. For contiguous U.S. stations in July 2015, the station benchmark was lower in error than interpolated 1-h high-resolution numerical predictions of surface temperature from NOAA’s High-Resolution Rapid Refresh (HRRR) system, although not including full postprocessing. For August 2018, 1-h HRRR forecasts were much improved when tested against the station benchmark.
  • Keywords:
  • Source:
    Monthly Weather Review, 148(2), 689-700.
  • DOI:
    https://doi.org/10.1175/MWR-D-19-0027.1
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
  • File Type:

Supporting Files

  • No Additional Files

More +

Related Documents

You May Also Like