scholarly journals A Comparison of Temperature Data from Automated and Manual Observing Networks in Georgia and Impacts of Siting Characteristics

2016 ◽  
Vol 33 (7) ◽  
pp. 1473-1494 ◽  
Author(s):  
Jason Allard ◽  
Paul C. Vincent ◽  
Jeromy R. McElwaney ◽  
Gerrit Hoogenboom
Keyword(s):  
Abrupt Change ◽  
Monitoring Network ◽  
Nocturnal Boundary Layer ◽  
Temperature Data ◽  
Land Use Land Cover ◽  
Local Conditions ◽  
Climate Record ◽  
Seasonal Maximum ◽  
Historical Climatology ◽  
Maximum Temperatures

AbstractThe objectives of this study were to compare average monthly and seasonal maximum and minimum temperatures of the Georgia Automated Environmental Monitoring Network (AEMN) to those of geographically close (i.e., paired) manual observations from U.S. Historical Climatology Network (USHCN) stations and Cooperative Observer Program (COOP) stations for the period 2002–13, and to evaluate the extent to which differences in siting characteristics of paired AEMN–USHCN stations contribute to the temperature differences. Correlations for monthly and seasonal maximum and minimum temperatures of paired AEMN–USHCN and AEMN–COOP stations were high and almost always significant, although the correlations for seasonal minimum temperatures were slightly lower than those of maximum temperatures, especially for summer. Monthly maximum and minimum temperatures and seasonal maximum temperatures of paired AEMN–USHCN and AEMN–COOP stations were significantly different in only a few instances, while seasonal minimum temperatures were more often significantly different, particularly for summer. The stronger relationship between maximum temperatures than minimum temperatures for paired stations is logical given that minimum temperatures typically occur when a shallow, decoupled nocturnal boundary layer is more sensitive to local conditions [e.g., land use/land cover (LULC)]. Stepwise regressions confirmed that a portion of the variance of seasonal minimum temperatures of paired AEMN–USHCN stations was explained by differences in LULC, while the variance in seasonal maximum temperatures was explained better by differences in elevation. Despite the generally close relationships between temperatures of paired stations and a portion of the differences being explained, an abrupt change from manual networks to the AEMN without data adjustments would change the Georgia climate record on monthly and seasonal time scales.

scholarly journals Overall Warming with Reduced Seasonality: Temperature Change in New England, USA, 1900–2020

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 176
Author(s):  
Stephen S. Young ◽  
Joshua S. Young
Keyword(s):  
New England ◽  
Winter Season ◽  
The United States ◽  
Temperature Data ◽  
Seasonal Temperature ◽  
Temperature Changes ◽  
Rural Economies ◽  
Historical Climatology ◽  
Average Annual Temperature ◽  
Maximum Temperatures

The ecology, economy, and cultural heritage of New England is grounded in its seasonal climate, and this seasonality is now changing as the world warms due to human activity. This research uses temperature data from the U.S. Historical Climatology Network (USHCN) to analyze annual and seasonal temperature changes in the New England region of the United States from 1900 to 2020 at the regional and state levels. Results show four broad trends: (1) New England and each of the states (annually and seasonally) have warmed considerably between 1900 and 2020; (2) all of the states and the region as a whole show three general periods of change (warming, cooling, and then warming again); (3) the winter season is experiencing the greatest warming; and (4) the minimum temperatures are generally warming more than the average and maximum temperatures, especially since the 1980s. The average annual temperature (analyzed at the 10-year and the five-year average levels) for every state, and New England as a whole, has increased greater than 1.5 °C from 1900 to 2020. This warming is diminishing the distinctive four-season climate of New England, resulting in changes to the region’s ecology and threatening the rural economies throughout the region.

scholarly journals Estimation of seasonal boundaries using temperature data: a case of northwest part of Bangladesh

2020 ◽  
Vol 6 (1) ◽  
pp. 50-62
Author(s):  
Syed Mustafizur Rahman ◽  
Syed Mahbubur Rahman ◽  
Md. Shuzon Ali ◽  
Md. Abdullah Al Mamun ◽  
Md. Nezam Uddin
Keyword(s):  
Spectral Analysis ◽  
Crop Productivity ◽  
Temperature Data ◽  
Daily Temperature ◽  
Temperature Cycle ◽  
Local Climate ◽  
Higher Power ◽  
Cycle Plays ◽  
Maximum Temperatures ◽  
Harmonic Analyses

Abstract Seasons are the divisions of the year into months or days according to the changes in weather, ecology and the intensity of sunlight in a given region. The temperature cycle plays a major role in defining the meteorological seasons of the year. This study aims at investigating seasonal boundaries applying harmonic analysis in daily temperature for the duration of 30 years, recorded at six stations from 1988 to 2017, in northwest part of Bangladesh. Year by year harmonic analyses of daily temperature data in each station have been carried out to observe temporal and spatial variations in seasonal lengths. Periodic nature of daily temperature has been investigated employing spectral analysis, and it has been found that the estimated periodicities have higher power densities of the frequencies at 0.0027 and 0.0053 cycles/day. Some other minor periodic natures have also been observed in the analyses. Using the frequencies between 0.0027 to 0.0278 cycles/day, the observed periodicities in spectral analysis, harmonic analyses of minimum and maximum temperatures have found four seasonal boundaries every year in each of the stations. The estimated seasonal boundaries for the region fall between 19-25 February, 19-23 May, 18-20 August and 17-22 November. Since seasonal variability results in imbalance in water, moisture and heat, it has the potential to significantly affect agricultural production. Hence, the seasons and seasonal lengths presented in this research may help the concerned authorities take measures to reduce the risks for crop productivity to face the challenges arise from changing climate. Moreover, the results obtained are likely to contribute in introducing local climate calendar.

scholarly journals Impact of Rainfall Variability on Rural Tea Roads in Kericho, Kenya

2021 ◽  
Vol 3 (1) ◽  
pp. 38-48
Author(s):  
Nancy Chemutai Koech ◽  
Sammy C. Letema ◽  
James Kibii Koske
Keyword(s):  
Climate Variability ◽  
Agricultural Sector ◽  
Rainfall Variability ◽  
Linear Regression Analysis ◽  
Heavy Rain ◽  
Temperature Data ◽  
Rural Road ◽  
Maximum Temperatures ◽  
Period Pattern ◽  
Emergency Funds

Climate variability is a global phenomenon that is posing a threat to the infrastructure and agricultural sector. Intense precipitation often results in the deterioration of rural road infrastructure resulting in inaccessibility. Rainfall and temperature data from 1989 to 2019 was obtained from Kenya Meteorological Department. Data on Greenleaf and cost of repairs and maintenances are from selected tea factories managed by Kenya Tea Development Agency. Data on perception is based on a questionnaire survey of 398 randomly selected tea farmers. Results show that climate variability is experienced in Kericho (p < 0.005). There is a varsity variation in mean maximum temperatures F(0.05, 29) ꞊ 5.564 (p ꞊ 0.009) and mean minimum temperature F(29) =8.503 (p ꞊  0.000). However, the linear regression analysis shows that rainfall has decreased (y ꞊ 2.5476x - 40.778) while the temperature has increased (y ꞊ 0.028x - 0.4473). There is a significant positive correlation between the amount of rainfall and cost of repairs and maintenances for five factories (r ꞊ 0.122, r = 0.046, r = 0.029, r = 0.255) except one (r = -.261, p ꞊ .466). Therefore, the climate has significantly varied from 1989-2019 and heavy rains occur periodically that damage rural tea roads, thus impacting negatively on tea transportation. There is a need, therefore, for heavy investment of emergency funds for repair and maintenance of rural tea roads based on rainfall variability and heavy rain return period pattern.

scholarly journals Spatio-temporal Analysis of Maximum and Minimum Temperature in Bangladesh

2015 ◽  
Vol 7 (2) ◽  
pp. 73-77 ◽  
Author(s):  
MN Uddin ◽  
MSA Mondal ◽  
NMR Nasher
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Maximum Temperatures ◽  
Kendall Method

The analysis of annual mean maximum and annual mean minimum temperature data are studied in GIS environment, obtained from 34 meteorological stations scattered throughout the Bangladesh from 1948 to 2013. IDW method was used for the spatial distribution of temperature over the study area, using ArcGIS 10.2 software. Possible trends in the spatially distributed temperature data were examined, using the non-parametric Mann-Kendall method with statistical significance, and the magnitudes of available trends were determined using Sen’s method in ArcMap depiction. The findings of the study show positive trends in annual mean maximum temperatures with 90%, 95%, 99% and 99.9% significance levels.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22210 J. Environ. Sci. & Natural Resources, 7(2): 73-77 2014

scholarly journals Temperature Trends of the U.S. Historical Climatology Network Based on Satellite-Designated Land Use/Land Cover

1999 ◽  
Vol 12 (5) ◽  
pp. 1344-1348 ◽  
Author(s):  
Kevin P. Gallo ◽  
Timothy W. Owen ◽  
David R. Easterling ◽  
Paul F. Jamason
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Temperature Trends ◽  
Historical Climatology ◽  
The U.S

scholarly journals An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3

2011 ◽  
Vol 116 (D19) ◽  
Author(s):  
Jay H. Lawrimore ◽  
Matthew J. Menne ◽  
Byron E. Gleason ◽  
Claude N. Williams ◽  
David B. Wuertz ◽  
...  
Keyword(s):  
Temperature Data ◽  
Data Set ◽  
Mean Temperature ◽  
Historical Climatology ◽  
Global Historical Climatology Network

scholarly journals Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Zain Nawaz ◽  
Xin Li ◽  
Yingying Chen ◽  
Xufeng Wang ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Abrupt Change ◽  
Reference Data ◽  
Temperature Data ◽  
Trend Detection ◽  
Preliminary Evaluation ◽  
Change Analysis ◽  
Study Region ◽  
Data Products ◽  
Abrupt Change Analysis ◽  
Temperature Indices

Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.

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