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Spatiotemporal Global Climate Model Tracking
Published in Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, Large-Scale Machine Learning in the Earth Sciences, 2017
Scott McQuade, Claire Monteleoni
Climate scientists often work with temperature anomalies as opposed to raw temperatures. A temperature anomaly is the change in temperature at a particular location from the (average) temperature at that same location during a particular benchmark period. Temperature anomalies are used by climate scientists because they tend to have lower variance when averaged across multiple locations than raw temperatures (as discussed further in [13]). Figure 3.7 shows the observed global temperature anomalies from NASA GISTEMP, as well as the anomalies for input data (GCM predictions), over the 1890–2000 period. Both the observed data and the input data had been spatially averaged over all of the valid 5 degree cells (the included regions).
Experimental study on integrated and autonomous conductivity-temperature-depth (CTD) sensor applied for underwater glider
Published in Marine Georesources & Geotechnology, 2021
Bin Lv, Hai-lin Liu, Yi-fan Hu, Cheng-xuan Wu, Jie Liu, Hai-jing He, Jie Chen, Jian Yuan, Zhao-wen Zhang, Lin Cao, Hui Li
The thermal lag effect of CTD sensor is attributed to the heat stored in the conductance cell. The thermal lag effect is the temperature anomaly of the cylinder. Under the assumption of quasi-steady heat transfer, the thermal hysteresis effect is that the temperature of the cylinder or cube changed anomalously, and is caused by the accumulated heat which is stored in the cylinder wall of the conductivity cell when fluid is constantly flowing over the cylinder surface. The thermal lag effect affected the calculation of salinity, and the numerical model of the effect could be derived (Lueck 1990). The effect of thermal lag on salinity data would be up-regulated with the accumulation of temperature errors, and an algorithm could be developed to correct the error amplitude caused by thermal lag (Mensah et al. 2018). Besides, the thermal lag effect of CTD on an underwater glider was well studied. Janzen and Creed (2011) analyzed and corrected the thermal lag effect in the original data of unpumped CTD on a “Seaglider” to optimize salinity calculation. As the glider moves through the thermocline, the heat stored in the conductivity cell diffused around it, thus significantly affecting the measurement accuracy of the conductivity sensor and the temperature sensor. When the glider entered cold water from warm water, it would usually be submerged, and thus the measured value would be larger than the true value, resulting in greater salinity. When the glider moved from cold water to warm water, it would usually float upward, and the measured value was smaller than the true value, resulting in a smaller salinity (Mensah, Menn, and Morel 2009).
i4Ocean: transfer function-based interactive visualization of ocean temperature and salinity volume data
Published in International Journal of Digital Earth, 2021
Fenglin Tian, Qing Mao, Yazhen Zhang, Ge Chen
The system supports the combined display of multiple visual effects. As shown in Figure 12(b) and Figure 12(c), the cold and fresh anomalies are represented by blue and purple colors, respectively. The statistical histogram shows that the minimum values of the temperature (salinity) anomaly are -4.6 (-2 psu). According to the histogram distribution and interactively adjusting the threshold, we take temperature -1.8 as the threshold for distinguishing the cold anomaly, and the threshold for extracting the fresh anomaly is set to -0.2 psu. The opacity of these two threshold key points is set to zero and point at -2.0 is increased to 0.8(point at -0.5 psu is increased to 0.6) to show the core of the anomalous seawater mass. Moreover, we set other areas to zero to highlight the anomaly features without the occlusion of clutter data. The boundaries can be adjusted according to the requirements of oceanographers. In Figure 12(a), we can clearly see the features of cold and fresh seawater masses, which are interlaced with each other in space. In addition, there are a large number of anomalous seawater masses in the Kuroshio extension region of the western Pacific, which is consistent with the fact that there are strong ocean currents and many eddies in this region. There are thousands of eddies in the Kuroshio extension that can significantly influence the distribution of heat and fresh water in the ocean. Ji et al. (2018) consider that the intensity of both the cyclonic and anticyclonic eddies decreases eastward from the result of the spatial distribution of the eddy, which is in agreement with the phenomenon that the abnormal water masses are less in the east, as shown in Figure 12(a). The advantage of using the transfer function for thermohaline anomaly visualization is that it is an interactive method of facilitating the selection of an anomaly threshold for different areas of the ocean. (a) Combined visualization of low temperature (depicted in blue) and low salt (depicted in purple). (b) The transfer function used for the salinity anomaly. Opacity at points -0.2 psu as a threshold is set to zero to extract salt anomalies. (c) The transfer function used for the temperature anomaly. Opacity at points -1.8 is set to zero to extract cold anomalies. Data: HYCOM. Date: 1 January 2012.