Search

The dynamic and non-linear nature of complex chemical processes often leads to low prediction accuracy of key quality variables by traditional soft sensors, thus affecting the overall system control accuracy and operational efficiency. Therefore, this paper proposes a semi-supervised soft sensor modelling method based on improved the northern goshawk optimization (INGO)-variable mode decomposition (VMD)-echo state network (ESN). Firstly, a new semi-supervised fusion method is proposed to address the problem of model training difficulty due to the scarcity of labelled samples and process dynamics, which reconstructs the sample dataset by fusing labelled and unlabelled samples into more representative new samples, improving the model's generalization ability. Secondly, for the noise interference present in the reconstructed data, the input data is denoised using the VMD method to improve the quality of data. Then, a soft sensor model is built based on ESN. Additionally, the denoising and prediction performance of VMD and ESN is significantly affected by parameters, therefore the paper utilizes the INGO algorithm to achieve parameter rectification for VMD and ESN. Finally, the method is validated based on actual sulphur recovery data from a refinery. The results demonstrate that the method effectively mitigates the impact of dynamics and nonlinearity in the complex chemical process which enhances prediction accuracy. [ABSTRACT FROM AUTHOR]

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California's wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990-2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

Using a combination of dynamical and statistical downscaling techniques, we projected mid-21stcentury warming in the Los Angeles region at 2-km (1.2-mile) resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions and other factors affecting the planet's energy balance, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios of the Fifth Coupled Model Intercomparison Project. To account for the considerable uncertainty associated with choice of global climate model, we downscaled results for all available global models. For the business-as-usual scenario, we find that by the mid-21stcentury, the most likely warming is roughly 4.6°F averaged over the region's land areas, with a 95% confidence that the warming lies between 1.7 and 7.5°F. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. Therefore adaptation to a changing climate over the next few decades is likely to be inevitable in the Los Angeles region.

This study examines the slow modulation of El Niño-Southern Oscillation (ENSO) intensity and its underlying mechanism. A 10-15-yr ENSO intensity modulation cycle is identified from historical and paleoclimate data by calculating the envelope function of boreal winter Niño-3.4 and Niño-3 sea surface temperature (SST) indices. Composite analyses reveal interesting spatial asymmetries between El Niño and La Niña events within the modulation cycle. In the enhanced intensity periods of the cycle, El Niño is located in the eastern tropical Pacific and La Niña in the central tropical Pacific. The asymmetry is reversed in the weakened intensity periods: El Niño centers in the central Pacific and La Niña in the eastern Pacific. El Niño and La Niña centered in the eastern Pacific are accompanied with basin-scale surface wind and thermocline anomalies, whereas those centered in the central Pacific are accompanied with local wind and thermocline anomalies. The El Niño-La Niña asymmetries provide a possible mechanism for ENSO to exert a nonzero residual effect that could lead to slow changes in the Pacific mean state. The mean state changes are characterized by an SST dipole pattern between the eastern and central tropical Pacific, which appears as one leading EOF mode of tropical Pacific decadal variability. The Pacific Walker circulation migrates zonally in association with this decadal mode and also changes the mean surface wind and thermocline patterns along the equator. Although the causality has not been established, it is speculated that the mean state changes in turn favor the alternative spatial patterns of El Niño and La Niña that manifest as the reversed ENSO asymmetries. Using these findings, an ENSO-Pacific climate interaction mechanism is hypothesized to explain the decadal ENSO intensity modulation cycle. © 2009 American Meteorological Society.

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity.

The annual cycle of sea surface temperature (SST) in the eastern Pacific warm pool and its relation to Central America gap winds are examined in this study. Locally enhanced annual harmonics of SST are found underneath the regions where the Tehuantepec and Papagayo gap winds blow. The SSTs underneath the Tehuantepec gap wind undergo larger annual variations than those underneath the Papagayo gap wind. This suggests that the Tehuantepec gap wind has a stronger influence on the annual cycle of SST than the Papagayo gap wind. A series of ocean model experiments are performed to demonstrate the enhancement effect of the gap winds. Further heat budget analyses of the experiments show that the gap winds increase the amplitude of the SST annual cycle primarily by enhancing the vertical entrainment process in the ocean. The thermal forcing effect of the gap winds is less important in modulating the SST annual cycle.