Combining environmental information from multiple and disparate data sources for minimum temperature prediction

2014 ◽  
Author(s):  
S. Hernandez ◽  
P. Sallis ◽  
S. Baltierra ◽  
G. Becerra
Keyword(s):  
Minimum Temperature ◽  
Environmental Information ◽  
Data Sources ◽  
Temperature Prediction ◽  
Disparate Data

scholarly journals Topoclimatic modeling for minimum temperature prediction at a regional scale in the Central Valley of Chile*

Agronomie ◽  
1997 ◽  
Vol 17 (6-7) ◽  
pp. 307-314 ◽  
Author(s):  
F. Santibáñez ◽  
L. Morales ◽  
J. de la Fuente ◽  
P. Cellier ◽  
A. Huete
Keyword(s):  
Minimum Temperature ◽  
Regional Scale ◽  
Central Valley ◽  
Temperature Prediction

Using multiple disparate data sources to map heat vulnerability: Vancouver case study

2016 ◽  
Vol 60 (3) ◽  
pp. 356-368 ◽  
Author(s):  
Mehdi Aminipouri ◽  
Anders Knudby ◽  
Hung Chak Ho
Keyword(s):  
Data Sources ◽  
Disparate Data ◽  
Heat Vulnerability

Multisensor data fusion with disparate data sources

2010 ◽  
Author(s):  
Christian P. Minor ◽  
Mark H. Hammond ◽  
Kevin J. Johnson ◽  
Susan L. Rose-Pehrsson
Keyword(s):  
Data Fusion ◽  
Data Sources ◽  
Multisensor Data Fusion ◽  
Disparate Data ◽  
Multisensor Data

scholarly journals Temperature Trend Analysis and Extreme High Temperature Prediction Based on Weighted Markov Model in Lanzhou

2021 ◽  
Author(s):  
Zhiqiang Pang ◽  
Zhaoxu Wang
Keyword(s):  
High Temperature ◽  
Markov Model ◽  
Prediction Model ◽  
Trend Analysis ◽  
High Temperatures ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Temperature Prediction ◽  
Real Situation ◽  
Change Trend

Abstract In this study, temporIn this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high-temperature prediction was constructed. Several non-parametric methods were used to analyze the time trend. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature, and average temperature in Lanzhou have a significant rising trend and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation. and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation.

scholarly journals An Evolutionary Schema for Using “it-is-what-it-is” Data in Official Statistics

2019 ◽  
Vol 35 (1) ◽  
pp. 137-165
Author(s):  
Jack Lothian ◽  
Anders Holmberg ◽  
Allyson Seyb
Keyword(s):  
Data Sources ◽  
Data Sets ◽  
Data Set ◽  
Policy Makers ◽  
Survey Error ◽  
Time Space ◽  
Central Statistical ◽  
Disparate Data ◽  
Survey Methodologies ◽  
Biased Estimates

Abstract The linking of disparate data sets across time, space and sources is probably the foremost current issue facing Central Statistical Agencies (CSA). If one reviews the current literature looking for the prevalent challenges facing CSAs, three issues stand out: 1) using administrative data effectively; 2) big data and what it means for CSAs; and 3) integrating disparate data set (such as health, education and wealth) to provide measurable facts that can guide policy makers. CSAs are being challenged to explore the same kind of challenges faced by Google, Facebook, and Yahoo, which are using graphical/semantic web models for organizing, searching and analysing data. Additionally, time and space (geography) are becoming more important dimensions (domains) for CSAs as they start to explore new data sources and ways to integrate those to study relationships. Central agency methodologists are being pushed to include these new perspectives into their standard theories, practises and policies. Like most methodologists, the authors see surveys and the publications of their results as a process where estimation is the key tool to achieve the final goal of an accurate statistical output. Randomness and sampling exists to support this goal, and early on it was clear to us that the incoming “it-is-what-it-is” data sources were not randomly selected. These sources were obviously biased and thus would produce biased estimates. So, we set out to design a strategy to deal with this issue. This article presents a schema for integrating and linking traditional and non-traditional datasets. Like all survey methodologies, this schema addresses the fundamental issues of representativeness, estimation and total survey error measurement.

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