Using the generalized F distribution to model limnetic temperature profile and estimate thermocline depth

2005 ◽  
Vol 188 (2-4) ◽  
pp. 374-385 ◽  
Author(s):  
Victor Chan ◽  
Robin A. Matthews
2021 ◽  
Author(s):  
Kristina Šarović ◽  
Zvjezdana Klaić

Abstract. A simple 1-D energy budget model (SIMO) for the prediction of the vertical temperature profiles in small, monomictic lakes forced by a reduced number of input meteorological variables is proposed. The model estimates the net heat flux and thermal diffusion using only routinely measured hourly mean meteorological variables (namely, the air temperature, relative humidity, atmospheric pressure, wind speed, and precipitation), hourly mean ultraviolet B radiation (UVB), and climatological monthly mean cloudiness data. Except for the initial vertical temperature profile, the model does not use any lake-specific variables. The model performance was evaluated against lake temperatures measured continuously during an observational campaign in two lakes belonging to the Plitvice Lakes, Croatia (Lake 1 and Lake 12). Temperatures were measured at 15 and 16 depths ranging from 0.2 to 27 in Lake 1 (maximum depth of 37.4 m) and 0.2 to 43 m in Lake 12 (maximum depth of 46 m). A sensitivity analysis of the simulation length was performed for simulation lengths from 1 to 30 days. The model performed reasonably well and it was able to satisfactorily reproduce the vertical temperature profile at the hourly scale, the deepening of the thermocline with time, and the annual variation in the vertical temperature profile. A yearlong simulation initiated with an approximately constant vertical profile of the lake temperature (≈ 4 °C) was able to reproduce the onset of stratification and convective overturn. However, the thermocline depth was underestimated while the epilimnion temperatures were overestimated. Nevertheless, the values of the model performance measures obtained for a yearlong simulation were comparable with those reported for other more complex models. Thus, the presented model can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.


2013 ◽  
Vol 70 (2) ◽  
pp. 169-181 ◽  
Author(s):  
Charles K. Minns ◽  
Brian J. Shuter

A semi-mechanistic seasonal temperature-profile model (STM) for stratified lakes was successfully tested with six Ontario data sets. The STM has eight parameters: the days of year for the start and end of stratification and the midsummer surface temperature peak ([Formula: see text]); the temperature at the onset and end of stratification and [Formula: see text]; the seasonal development of the thermocline depth (two parameters); and the steepness of the depth transition from epilimnion to hypolimnion. The STM was fitted using nonlinear least squares with R2 ranging from 0.91 to 0.99 and root mean square errors of 0.80 to 1.42 °C. Residuals were evenly distributed with respect to date and depth with 69.4% to 95.9% ± 1.0 °C. On average, observed-estimated temperature differences range from –0.24 to –0.03 °C and +0.16 to +0.37 °C for estimated temperatures of 5 and 25 °C, respectively. Simulations with daily data sets showed the STM fit was insensitive to reductions in the length of sampling duration and sampling frequency. Several STM parameters were well-estimated by published equations based on morphometric, climatic, and location variables.


2012 ◽  
Vol 17 (4) ◽  
pp. 51-57
Author(s):  
Andrzej Frązyk ◽  
Piotr Urbanek ◽  
Jacek Kucharski

Abstract Fixed, placed at regular distances inductors for induction heating of a rotating steel cylinder do not provide sufficiently uniform temperature profile along cylinder axis required by modern technologies,. The article examines the influence of inductors movement along the cylinder axis on the reduction of pick-to- pick temperature amplitude.


2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Syamsul Hidayat ◽  
Mulia Purba ◽  
Jorina Waworuntu

The purposes of this study were to determine the variability of temperature and its relation to regional processes in the Senunu Bay. The result showed clear vertical stratifications i.e., mixed layer thickness about 39-119 m with isotherm of 27°C, thermocline layer thickness about 83-204 m with isotherm of 14–26°C, and  the deeper layer from the thermocline lower limit to the sea bottom with isotherm <13°C. Temperature and the thickness of each layers varied with season in which during the Northwest Monsoon the temperature was warmer and the mixed layer was thicker than those during Southeast Monsoon. During Southeast Monsoon, the thermocline layer rose  about 24 m. The 2001, 2006, and 2009 (weak La Nina years),  the Indonesia Throughflow (ITF) carried warmer water, deepening thermocline depth and reducing upwelling strength.  In 2003 and 2008 thickening of mixed layer occurred in transition season  was believed  associated with the  arrival of Kelvin Wave from the west. In 2002 and 2004 (weak El Nino period,) ITF carries colder water shallowing thermocline depth and enhancing upwelling strength. In 2007 was believed to be related with positive IODM where the sea surface temperature were decreasing due to intensification of southeast wind which induced strong upwelling. The temperature spectral density of mixed layer and thermocline was influenced by annual, semi-annual, intra-annual and inter-annual period fluctuations. The cross-correlation between wind and temperature showed significant value in the annual period.  Keywords: temperature, thermocline, variability, ENSO, IODM.


2020 ◽  
Author(s):  
Ahmad Sudi Pratikno

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.


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