Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/american-meteorological-society/sensitivity-analysis-of-quality-assurance-using-the-spatial-regression-wqIiVCgcGV
References (15)
-
K. Hubbard, A. Degaetano, K. Robbins (2004)
SERVICES: A Modern Applied Climate Information SystemBulletin of the American Meteorological Society, 85
-
N. Guttman, R. Quayle (1990)
A Review of Cooperative Temperature Data ValidationJournal of Atmospheric and Oceanic Technology, 7
-
(2001)
Multiple station quality control procedures. Automated Weather Stations for Applications in Agriculture and Water Resources -
M. Katz (2006)
Validation of models -
(2001)
Automated Weather Stations for Applications in Agriculture and Water Resources Management -
C. Wade (1987)
A Quality Control Program for Surface Mesometeorological DataJournal of Atmospheric and Oceanic Technology, 4
-
J. Eischeid, C. Baker, T. Karl, H. Diaz (1995)
The Quality Control of Long-Term Climatological Data Using Objective Data AnalysisJournal of Applied Meteorology, 34
-
Multiple station quality control procedures -
N. Guttman, C. Karl, T. Reek, V. Shuler (1988)
Measuring the Performance of Data ValidatorsBulletin of the American Meteorological Society, 69
-
J. Nash, J. Sutcliffe (1970)
River flow forecasting through conceptual models part I — A discussion of principles☆Journal of Hydrology, 10
-
D. Meek, J. Hatfield (1994)
Data quality checking for single station meteorological databasesAgricultural and Forest Meteorology, 69
-
K. Hubbard, S. Goddard, W. Sorensen, N. Wells, T. Osugi (2005)
Performance of Quality Assurance Procedures for an Applied Climate Information SystemJournal of Atmospheric and Oceanic Technology, 22
-
J. Eischeid, P. Pasteris, H. Diaz, M. Plantico, N. Lott (2000)
Creating a Serially Complete, National Daily Time Series of Temperature and Precipitation for the Western United StatesJournal of Applied Meteorology, 39
-
L. Gandin (1988)
Complex Quality Control of Meteorological ObservationsMonthly Weather Review, 116
-
A. Mandeville, P. O'connell, J. Sutcliffe, J. Nash (1970)
River flow forecasting through conceptual models part III - The Ray catchment at Grendon UnderwoodJournal of Hydrology, 11
- Publisher
- American Meteorological Society
- Copyright
- Copyright © 2004 American Meteorological Society
- ISSN
- 1520-0426
- DOI
- 10.1175/JTECH1790.1
- Publisher site
- See Article on Publisher Site
Abstract
Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature ( T max ) and the minimum air temperature ( T min ) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.
Journal
Journal of Atmospheric and Oceanic Technology – American Meteorological Society
Published: Oct 8, 2004