Evaluating the detectability of methane point sources from satellite observing systems using microscale modeling.

This study evaluates the efficacy of current satellite observing systems to detect methane point sources from typical oil and gas production (O&G) facilities using a novel very high-resolution methane concentration dataset generated using a microscale model. Transport and dispersion of typical methane emissions from seven well pads were simulated and the column enhancements for pseudo satellite pixel sizes of 3, 1, and 0.05 km were examined every second of the 2-h simulations (7200 realizations). The detectability of plumes increased with a pixel resolution, but two orders of magnitude change in emission rates at the surface results only in about 0.4%, 1.6%, and 47.8% enhancement in the pseudo-satellite retrieved methane column at 3, 1, and 0.05 km, respectively. Average methane emission rates estimated by employing the integrated mass enhancement (IME) method to column enhancements at 0.05 km showed an underestimation of the mean emissions by 0.2-6.4%. We show that IME derived satellite-based inversions of methane emissions work well for large persistent emission sources (e.g., super emitters), however, the method is ill-suited to resolve short-term emission fluctuations (< 20 min) in typical well site emissions due to the limitations in satellite detection limits, precision, overpass timing, and pixel resolution.

Reimagine fire science for the anthropocene.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Evaluating Methods To Estimate Methane Emissions from Oil and Gas Production Facilities Using LES Simulations.

Large-eddy simulations (LES) coupled to a model that simulates methane emissions from oil and gas production facilities are used to generate realistic distributions of meteorological variables and methane concentrations. These are sampled to obtain simulated observations used to develop and evaluate source term estimation (STE) methods. A widely used EPA STE method (OTM33A) is found to provide emission estimates with little bias when averaged over six time periods and seven well pads. Sixty-four percent of the emissions estimated with OTM33A are within ±30% of the simulated emissions, showing a slightly larger spread than the 72% found previously using controlled release experiments. A newly developed method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to account for multiple sources within the well pad. This method can considerably reduce the spread of the emissions estimates compared to OTM33A (92-95% within ±30% percent error), but it is associated with a substantial increase in computational cost due to the LES. It thus provides an alternative when the additional costs can be afforded to obtain more precise emission estimates.