CoChat
Sign Up
Cited by 111Precipitation Measurement and AnalysisHydrology and Watershed Management StudiesHydrology and Drought Analysis

The DOI matches its Crossref record.

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo, C., Brunetti, M., Maugeri, M., Nanni, T.·2010·International Journal of Climatology
DOI 10.1002/joc.1992
Open this paper in CoChat — free account

Abstract

This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long‐term statistics, are preserved. The completion method is based on a two‐step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet‐classified days is estimated by a modified multi‐linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression‐based approaches. Copyright © 2009 Royal Meteorological Society

Overview

Integrity signals

Registry

Crossref · 10.1002/joc.1992

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Self-contained infilling only performs well for variables with high temporal autocorrelation such as temperature and is problematic for daily precipitation and in covering lengthy data gaps.

    “Self-contained infilling, however, only performs well for variables with high temporal autocorrelation such as temperature and is problematic for daily precipitation (Simolo et al., 2010; Teegavarapu and Chandramouli, 2005) and in covering lengthy data gaps.”

    From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Simolo et al., 2010

  • Quantile mapping performs well in estimating missing station data.

    “Quantile mapping (QM) is widely used to correct biases in meteorological data (Maraun, 2013; Cannon et al., 2015), and it performs well in estimating missing station data (Simolo et al., 2010; Newman et al., 2015, 2019; Devi et al., 2019).”

    From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Simolo et al., 2010

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 47

  1. 10.5194/hess-10-197-2006 · DOI
  2. 10.1029/2005JD006290 · DOI · Paper page
  3. 10.1016/S0168-1923(96)02358-1 · DOI
  4. 10.1111/j.1752-1688.1989.tb05410.x · DOI
  5. 10.1016/S0022-1694(96)03076-4 · DOI
  6. 10.1007/s00382-006-0184-y · DOI
  7. 10.1126/science.237.4811.171 · DOI
  8. 10.1175/1520-0493(1972)100<0607:ROPPEF>2.3.CO;2 · DOI
  9. 10.1002/joc.660 · DOI
  10. 10.1002/joc.613 · DOI
  11. 10.1029/2003JD004296 · DOI
  12. 10.1016/j.jhydrol.2007.04.020 · DOI
  13. 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2 · DOI
  14. 10.1029/WR018i002p00413 · DOI
  15. 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2 · DOI
  16. 10.1175/1520-0450-34.2.371 · DOI
  17. 10.1002/joc.1063 · DOI
  18. 10.1175/1520-0450(1995)034<2787:TQCOLT>2.0.CO;2 · DOI
  19. 10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2 · DOI
  20. 10.1080/02626660009492388 · DOI
  21. 10.1080/02626660009492389 · DOI
  22. 10.1029/2006WR005788 · DOI
  23. 10.1023/A:1005432803188 · DOI
  24. 10.1057/jors.1996.21 · DOI
  25. 10.1016/0016-7061(94)90027-2 · DOI
  26. 10.1175/3238.1 · DOI
  27. 10.1038/377217a0 · DOI
  28. 10.1175/1520-0477(1998)079<0231:STOPAF>2.0.CO;2 · DOI
  29. 10.1175/1520-0450(1983)022<1587:EMDMAM>2.0.CO;2 · DOI
  30. 10.1016/S0022-1694(00)00332-2 · DOI
  31. 10.1016/j.jhydrol.2004.10.026 · DOI
  32. 10.1023/A:1005495627778 · DOI
  33. 10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2 · DOI
  34. 10.1002/joc.1657 · DOI · Paper page
  35. 10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2 · DOI
  36. ShepardD.1968.A two‐dimensional interpolation function for irregularly spaced data.Proceeding of the Twenty‐Third National Conference of the Association for Computing Machinery: Washington DC;517–524. · DOI
  37. Shepard D, 1984, Computer Mapping: The SYMAP Interpolation Algorithm, in Spatial Statistics and Models, 133145
  38. 10.2307/2981736 · DOI
  39. Suhaila J, 2008, Revised spatial weighting methods for estimation of missing rainfall data, Asia‐Pacific Journal of Atmospheric Sciences, 44, 93
  40. 10.1111/j.1752-1688.1985.tb00147.x · DOI
  41. 10.1016/0169-8095(95)00067-4 · DOI
  42. 10.1016/j.jhydrol.2005.02.015 · DOI · Paper page
  43. 10.1016/S0168-1923(00)00210-0 · DOI
  44. 10.1175/1520-0493(1992)120<2561:ATWMFI>2.0.CO;2 · DOI
  45. 10.1029/91WR00977 · DOI
  46. Wilks DS, 1995, Statistical Methods in the Atmospheric Sciences, 464
  47. 10.1559/152304085783914686 · DOI

Cited by

Sorted by citations

Metadata

Authors
Simolo, C., Brunetti, M., Maugeri, M., & Nanni, T.
Affiliations
  • Institute of Atmospheric Sciences and Climate (IT)
  • University of Milan (IT)
Corresponding author
Brunetti, M.
ORCID
Venue
International Journal of Climatology
Publisher
Wiley
Published
2010-07-21
Volume
30
Issue
10
Pages
1564-1576
ISSN
  • 0899-8418 (ISSN-L)
  • 1097-0088
DOI
10.1002/joc.1992
OpenAlex
W2025560597
Cited by
111
References
0
Citations by year
2026: 4 · 2025: 4 · 2024: 3 · 2023: 5 · 2022: 10 · 2021: 16 · 2020: 11 · 2019: 11 · 2018: 3 · 2017: 12 · 2016: 8 · 2015: 9 · 2014: 4 · 2013: 3 · 2012: 2
FWCI
1.65
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
International Journal of Climatology (publishedVersion)
Funders
Centro Euro - Mediterraneo per i Cambiamenti Climatici
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Keywords
missing data estimation, daily precipitation, multi-linear regression

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review