Modeling monthly temperature data in Lisbon and Prague

2009 ◽  
Vol 20 (7) ◽  
pp. 835-852 ◽  
Author(s):  
Teresa Alpuim ◽  
Abdel El-Shaarawi
Keyword(s):  
Temperature Data ◽  
Monthly Temperature

Climatic comparisons with tree-ring data from montane forests: are the climatic data appropriate?

1988 ◽  
Vol 18 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Kenneth D. Kimball ◽  
MaryBeth Keifer
Keyword(s):  
Tree Ring ◽  
Montane Forest ◽  
Temperature Data ◽  
Growth Patterns ◽  
Forest Growth ◽  
Precipitation Data ◽  
Montane Forests ◽  
Monthly Temperature ◽  
Tree Ring Data ◽  
Mount Washington

The appropriateness of relating spatially proximate (40-km radius) temperature and precipitation data from different elevations to montane forest growth patterns was investigated for Mount Washington, New Hampshire. Monthly mean temperature and total precipitation data (1933–1983) were correlated (p < 0.05) among all pairs of meteorological stations (280, 420, 610, 1915 m and regional averages) on or near Mount Washington. The unexplained variance (1 − r2) for precipitation comparisons between meteorological stations was greater relative to temperature. When correlated with the average tree-ring index chronology of 90 red spruce trees on Mount Washington (800–1200 m), the monthly temperature data yielded similar correlative patterns among the four meteorological stations. However, the monthly temperature data from the meteorological stations (610 and 1915 m) most proximate to the montane forest study site were correlated (p < 0.10) with the tree-ring indices for two to three times as many months as the temperature data from the lower elevations. There was no consistency in correlative results of tree-ring indices with monthly precipitation data among the four meteorological stations. However, precipitation measurements and Palmer drought indices are poor indicators of moisture availability in montane forests. We conclude that spatially proximate, low elevation temperature data can underestimate correlative relationships between temperature and montane tree-ring data in the northeastern United States.

scholarly journals Sarima Modeling of Monthly Temperature in the Northern part of Ghana

2021 ◽  
pp. 37-45
Author(s):  
Emmanuel Ayitey ◽  
Justice Kangah ◽  
Frank B. K. Twenefour
Keyword(s):  
Northern Region ◽  
Temperature Data ◽  
Information Criteria ◽  
Sarima Model ◽  
Monthly Temperature ◽  
Augmented Dickey Fuller ◽  
Best Fitting ◽  
Adf Test ◽  
Fitting Model ◽  
Sarima Models

The Sarima model is used in this study to forecast the monthly temperature in Ghana's northern region. The researchers used temperature data from January 1990 to December 2020. The temperature data was found to be stationary using the Augmented Dickey Fuller (ADF) test. The ACF and PACF plots proposed six SARIMA models: SARIMA (1,0,0) (1,0,0) (12), SARIMA (2,0,0) (1,0,0) (12), SARIMA (1,0,1) (1,0,0) (12), SARIMA (0,0,1) (1,0,0) (12), SARIMA (0,0,1) (0,0,1) (12), SARIMA (0,0,1) (0,0,1) (12). The best model was chosen based on the lowest Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) values. The Ljung-Box data, among others, were used to determine the model's quality. All diagnostic tests are passed by the SARIMA (1,0,0) (1,0,0) (12) model. As a result, the SARIMA (1,0,0) (1,0,0) (12) is the best-fitting model for predicting monthly temperatures in Ghana's northern region.

scholarly journals The international surface temperature initiative global land surface databank: monthly temperature data release description and methods

2014 ◽  
Vol 1 (2) ◽  
pp. 75-102 ◽  
Author(s):  
J. J. Rennie ◽  
J. H. Lawrimore ◽  
B. E. Gleason ◽  
P. W. Thorne ◽  
C. P. Morice ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Monthly Temperature ◽  
Data Release ◽  
Global Land

Homogenization of Temperature Series via Pairwise Comparisons

2009 ◽  
Vol 22 (7) ◽  
pp. 1700-1717 ◽  
Author(s):  
Matthew J. Menne ◽  
Claude N. Williams
Keyword(s):  
Pairwise Comparison ◽  
Temperature Data ◽  
Temperature Series ◽  
Reference Series ◽  
Monthly Temperature ◽  
History Information ◽  
Difference Series ◽  
Apparent Shift ◽  
Abrupt Shifts ◽  
Lower False Alarm Rate

Abstract An automated homogenization algorithm based on the pairwise comparison of monthly temperature series is described. The algorithm works by forming pairwise difference series between serial monthly temperature values from a network of observing stations. Each difference series is then evaluated for undocumented shifts, and the station series responsible for such breaks is identified automatically. The algorithm also makes use of station history information, when available, to improve the identification of artificial shifts in temperature data. In addition, an evaluation is carried out to distinguish trend inhomogeneities from abrupt shifts. When the magnitude of an apparent shift attributed to a particular station can be reliably estimated, an adjustment is made for the target series. The pairwise algorithm is shown to be robust and efficient at detecting undocumented step changes under a variety of simulated scenarios with step- and trend-type inhomogeneities. Moreover, the approach is shown to yield a lower false-alarm rate for undocumented changepoint detection relative to the more common use of a reference series. Results from the algorithm are used to assess evidence for trend inhomogeneities in U.S. monthly temperature data.

The U.S. Historical Climatology Network Monthly Temperature Data, Version 2

2009 ◽  
Vol 90 (7) ◽  
pp. 993-1008 ◽  
Author(s):  
Matthew J. Menne ◽  
Claude N. Williams ◽  
Russell S. Vose
Keyword(s):  
Temperature Data ◽  
Monthly Temperature ◽  
Historical Climatology ◽  
The U.S

scholarly journals Thornthwaite Models for Estimating Potential evapotranspiration in Medan City

2021 ◽  
Vol 912 (1) ◽  
pp. 012095
Author(s):  
N Anggraini ◽  
B Slamet
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Water Balance ◽  
Air Temperature ◽  
Potential Evapotranspiration ◽  
Meteorological Station ◽  
Temperature Data ◽  
Monthly Temperature ◽  
Region I ◽  
Meteorological Observations

Abstract Evapotranspiration plays a big role in the hydrology process. Potential Evapotranspiration (PET) always keeps soil moisture available, although an amount of water evaporates through evaporation and transpiration. The Thornthwaite equation uses air temperature and latitude from meteorological observations for estimating PET. Medan City is one of the biggest cities in Indonesia that have a problem with land-use change that affected water balance. This study is to estimate the PET and to learn the water balance in Medan City. The monthly temperature data for the period 2011-2020 is collected from three meteorological stations for estimating PET using the Thornthwaite equation. The highest monthly temperature is in Belawan Maritime Meteorological Station yet the lowest rainfall. The trends of PET depend on the month. The highest PET in Jan.-Apr. and Sep.-Dec. are in Belawan Maritime Meteorological Station, while the highest PET in May-Aug. is in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan. The P-PET has shown negative and positive values. The lowest P-PET is found in Belawan Maritime Meteorological Station in March and the highest P-PET is found in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan in October.

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