Fraser Experimental Forest Headquarters Station Hourly Temperature Data: 1976-2003

2011 ◽  
Author(s):  
Kelly Elder
Keyword(s):  
Temperature Data ◽  
Hourly Temperature ◽  
Hourly Temperature Data

scholarly journals Modeling Leaf Development of the African Violet (Saintpaulia ionantha Wendl.)

1993 ◽  
Vol 118 (6) ◽  
pp. 747-751 ◽  
Author(s):  
James E. Faust ◽  
Royal D. Heins
Keyword(s):  
Leaf Development ◽  
Maximum Rate ◽  
Temperature Data ◽  
Optimum Temperature ◽  
Leaf Unfolding ◽  
African Violet ◽  
Air Temperatures ◽  
Hourly Temperature ◽  
Growth Chambers ◽  
Hourly Temperature Data

Leaf unfolding rate (LUR) was determined for `Utah' African violet plants grown in growth chambers under 20 combinations of temperature and photosynthetic photon flus (PPF). A nonlinear model was used to predict LUR as a function of shoot temperature and daily integrated PPF. The maximum predicted LUR was 0.27 leaves/day, which occurred at 25C and a daily integrated PPF of 10 mol/m2 per day. The optimum temperature for leaf unfolding decreased to 23C, and the maximum rate decreased to 0.18 leaves/day as the daily integrated PPF decreased from 10 to 1 mol/m2 per day. A greenhouse experiment using 12 combinations of air temperature and daily integrated PPF was conducted to validate the LUR model. Plant temperatures used in the model predicted leaf development more accurately than did air temperatures, but using average hourly temperature data was no more accurate than using average daily temperature data.

scholarly journals Assessment of current methods of positive degree-day calculation using in situ observations from glaciated regions

2015 ◽  
Vol 61 (226) ◽  
pp. 329-344 ◽  
Author(s):  
L.M. Wake ◽  
S.J. Marshall
Keyword(s):  
Variable Temperature ◽  
Quadratic Function ◽  
Absolute Error ◽  
Common Assumption ◽  
Degree Day ◽  
Monthly Temperature ◽  
Positive Degree ◽  
Hourly Temperature ◽  
Hourly Temperature Data

AbstractThe continued use of the positive degree-day (PDD) method to predict ice-sheet melt is generally favoured over surface energy-balance methods partly due to the computational efficiency of the algorithm and the requirement of only one input variable (temperature). In this paper, we revisit some of the assumptions governing the application of the PDD method. Using hourly temperature data from the GC-Net network we test the assumption that monthly PDD total (PDDM) can be represented by a Gaussian distribution with fixed standard deviation of monthly temperature (σM). The results presented here show that the common assumption of fixed σM does not hold, and that σM may be represented more accurately as a quadratic function of average monthly temperature. For Greenland, the mean absolute error in predicting PDDM using our methodology is 3.9°C d, representing a significant improvement on current methods (7.8°C d, when σM = 4.5°C). Over a range of glaciated settings, our method reproduces PDDM, on average, to within 1.5–8.5°C d, compared to 4.4–15.7°C d when σM = 4.5°C. The improvement arises because we capture the systematic reduction in temperature variance that is observed over melting snow and ice, when surface temperatures cannot warm above 0°C.

scholarly journals Evaluation of Long-Term Degree-Days Estimated with Several Methods for Corn in Nebraska, USA

2021 ◽  
Author(s):  
Shuwei Dai ◽  
Martha D. Shulski ◽  
Haishun Yang ◽  
Roger W. Elmore
Keyword(s):  
Estimation Method ◽  
Absolute Error ◽  
Temperature Data ◽  
Thermal Time ◽  
Estimation Methods ◽  
Degree Days ◽  
Hourly Temperature ◽  
Agricultural Community ◽  
Corn Plants

Abstract The concept of thermal time, measured in degree-days, is widely used among the agricultural community in Nebraska to make decisions in corn (Zea Mays L.) production. Instead of the real-time temperatures that are experienced by corn plants, most of the widely available temperature data are limited to daily timescale observations from standard meteorological stations. And a variety of equations are used by different agricultural groups (e.g., researchers, advisors, farmers, and seed companies) to estimate thermal time for corn. Two problems could arise: a) the estimation method is lacking in accuracy; and b) different estimation methods are used for the same purpose by different groups. Consequently, citing these inaccurate and maybe inherently different thermal time results could lead to biased decisions in corn production. The goal of this study is to evaluate six commonly used estimation methods by comparing the estimated thermal time with the hourly-temperature approximated thermal time. We analyzed the root mean square error and mean absolute error for six metrics of total growing season (from May through September) degree-days based on the temperature data from a total of 14 long-term observing locations in Nebraska. In particular, we selected four location-extreme year cases to demonstrate the six methods’ estimation performance on a daily timescale. We found that the most commonly used adjusted Tmax and Tmin rectangle method provided poor estimation in the study area. Instead, single-sine, double-sine, or Tavg-based method was more superior depending on the metric of degree-days.

The New Interference Test: Reservoir Connectivity Information from Downhole Temperature Data

2007 ◽  
Author(s):  
David Anthony Hutchinson ◽  
Najiya Kuramshina ◽  
Ali Chingiz Oglu Sheydayev ◽  
Simon N.J. Day
Keyword(s):  
Temperature Data ◽  
Connectivity Information ◽  
Interference Test ◽  
Downhole Temperature ◽  
Reservoir Connectivity

An Evaluation of Selected Virtual Temperature Data Acquired at the APRF in 1994.

1995 ◽  
Author(s):  
Glenn B. Hoidale ◽  
Wayne L. Flowers ◽  
Linda Parker-Sedillo
Keyword(s):  
Temperature Data ◽  
Virtual Temperature
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