An adapted vector autoregressive expectation maximization imputation algorithm for climate data networks

Missingness in historical climate data networks is a pervasive phenomenon due to the conditions under which these measurements are made. Accurate estimation of these data is a critical issue as projections of future climate depend on a reliable historical climate record. After all, how can the impac...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Wiley interdisciplinary reviews. Computational statistics Ročník 12; číslo 6; s. e1494 - n/a
Hlavní autoři: Washington, Benjamin J., Seymour, Lynne
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.11.2020
Wiley Subscription Services, Inc
Témata:
Algorithms
Climate change
climate data
Climatic data
Computer applications
Environmental impact
expectation–maximization
Iterative methods
Missing data
Multivariate analysis
multivariate infilling
Networks
Numerical weather forecasting
Root-mean-square errors
Stations
Statistical methods
Stochastic processes
Temperature data
vector autoregression
Weather forecasting
ISSN:1939-5108, 1939-0068
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Missingness in historical climate data networks is a pervasive phenomenon due to the conditions under which these measurements are made. Accurate estimation of these data is a critical issue as projections of future climate depend on a reliable historical climate record. After all, how can the impact of climate change be reliably forecasted when a large proportion of historical climate records are permeated with missing data? We propose an iterative multivariate infilling algorithm and explore its effectiveness on three United States temperature networks of varying densities (number of stations per unit area). Although other multivariate data are not explored here, the application of this infilling procedure is not restricted to climate networks exclusively. We also explore its utility as a function of the type of missing data (i.e., whether or not there is a mechanism or pattern behind the missing observations themselves) and the proportion of missing data within the network. As one may expect, we observe a slightly smaller root mean square error (RMSE) for temperature networks with more stations and less missingness. Somewhat surprisingly, the RMSE tends to be lower for data which is missing at random (there is some mechanism behind the missing data) rather than for data that is missing completely at random (no mechanism behind the missing data). Not surprisingly, the RMSE is largest for data which is missing not at random (missing data are directly related to the values of the observations themselves). Perhaps the most surprising result observed in these networks is that the inclusion of lagged temperature data does not necessarily improve the accuracy of data imputation. The typical RMSE for monthly minimum temperature networks varies from around 1.5 to 2.5°C. This article is categorized under: Data: Types and Structure > Massive Data Data: Types and Structure > Image and Spatial Data Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting The RMSE of an iterative adapted vector autoregressive expectation–maximization algorithm as a function of Iteration (left) and autoregressive order (right) for three types of missing data MCAR (top), MAR (middle) and MNAR (bottom).
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-5108
1939-0068
DOI:10.1002/wics.1494