### Abstract

Earth science data consists of a strong seasonality component as indicated by the cycles of repeated patterns in climate variables such as air pressure, temperature and precipitation. The seasonality forms the strongest signals in this data and in order to find other patterns, the seasonality is removed by subtracting the monthly mean values of the raw data for each month. However since the raw data like air temperature, pressure, etc. are constantly being generated with the help of satellite observations, the climate scientists usually use a moving reference base interval of some years of raw data to calculate the mean in order to generate the anomaly time series and study the changes with respect to that. In this paper, we evaluate different measures for base computation and show how an arbitrary choice of base can skew the results and lead to a favorable outcome which might not necessarily be true. We perform a detailed study of different base selection criterion and base periods to highlight that the outcome of data mining can be sensitive to choice of the base. We present a case study of the dipole in the Sahel region to highlight the bias creeping into the results due to the choice of the base. Finally, we propose a generalized model for base selection which uses Monte-Carlo based methods to minimize the expected variance in the anomaly time-series of the underlying datasets. Our research can be instructive for climate scientists and researchers in temporal domain to enable them to choose the right base which would not bias the outcome of the results.

Original language | English (US) |
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Pages | 189-203 |

Number of pages | 15 |

State | Published - Dec 1 2011 |

Event | NASA Conference on Intelligent Data Understanding, CIDU 2011 - Mountain View, CA, United States Duration: Oct 19 2011 → Oct 21 2011 |

### Other

Other | NASA Conference on Intelligent Data Understanding, CIDU 2011 |
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Country | United States |

City | Mountain View, CA |

Period | 10/19/11 → 10/21/11 |

### Fingerprint

### Cite this

*Anomaly construction in climate data: Issues and challenges*. 189-203. Paper presented at NASA Conference on Intelligent Data Understanding, CIDU 2011, Mountain View, CA, United States.

**Anomaly construction in climate data : Issues and challenges.** / Kawale, Jaya; Chatterjee, Singdhansu B; Kumar, Arjun; Liess, Stefan; Steinbach, Michael S; Kumar, Vipin.

Research output: Contribution to conference › Paper

}

TY - CONF

T1 - Anomaly construction in climate data

T2 - Issues and challenges

AU - Kawale, Jaya

AU - Chatterjee, Singdhansu B

AU - Kumar, Arjun

AU - Liess, Stefan

AU - Steinbach, Michael S

AU - Kumar, Vipin

PY - 2011/12/1

Y1 - 2011/12/1

N2 - Earth science data consists of a strong seasonality component as indicated by the cycles of repeated patterns in climate variables such as air pressure, temperature and precipitation. The seasonality forms the strongest signals in this data and in order to find other patterns, the seasonality is removed by subtracting the monthly mean values of the raw data for each month. However since the raw data like air temperature, pressure, etc. are constantly being generated with the help of satellite observations, the climate scientists usually use a moving reference base interval of some years of raw data to calculate the mean in order to generate the anomaly time series and study the changes with respect to that. In this paper, we evaluate different measures for base computation and show how an arbitrary choice of base can skew the results and lead to a favorable outcome which might not necessarily be true. We perform a detailed study of different base selection criterion and base periods to highlight that the outcome of data mining can be sensitive to choice of the base. We present a case study of the dipole in the Sahel region to highlight the bias creeping into the results due to the choice of the base. Finally, we propose a generalized model for base selection which uses Monte-Carlo based methods to minimize the expected variance in the anomaly time-series of the underlying datasets. Our research can be instructive for climate scientists and researchers in temporal domain to enable them to choose the right base which would not bias the outcome of the results.

AB - Earth science data consists of a strong seasonality component as indicated by the cycles of repeated patterns in climate variables such as air pressure, temperature and precipitation. The seasonality forms the strongest signals in this data and in order to find other patterns, the seasonality is removed by subtracting the monthly mean values of the raw data for each month. However since the raw data like air temperature, pressure, etc. are constantly being generated with the help of satellite observations, the climate scientists usually use a moving reference base interval of some years of raw data to calculate the mean in order to generate the anomaly time series and study the changes with respect to that. In this paper, we evaluate different measures for base computation and show how an arbitrary choice of base can skew the results and lead to a favorable outcome which might not necessarily be true. We perform a detailed study of different base selection criterion and base periods to highlight that the outcome of data mining can be sensitive to choice of the base. We present a case study of the dipole in the Sahel region to highlight the bias creeping into the results due to the choice of the base. Finally, we propose a generalized model for base selection which uses Monte-Carlo based methods to minimize the expected variance in the anomaly time-series of the underlying datasets. Our research can be instructive for climate scientists and researchers in temporal domain to enable them to choose the right base which would not bias the outcome of the results.

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M3 - Paper

AN - SCOPUS:84879411629

SP - 189

EP - 203

ER -