Meteorological Controls on Local and Regional Volcanic Ash Dispersal

Changes in holopelagic Sargassum spp. biomass composition across an unusual year

The year 2021 marked a decade of holopelagic sargassum (morphotypes Sargassum natans I and VIII, and Sargassum fluitans III) stranding on the Caribbean and West African coasts. Beaching of millions of tons of sargassum negatively impacts coastal ecosystems, economies, and human health. Additionally, the La Soufrière volcano erupted in St. Vincent in April 2021, at the start of the sargassum season. We investigated potential monthly variations in morphotype abundance and biomass composition of sargassum harvested in Jamaica and assessed the influence of processing methods (shade-drying vs. frozen samples) and of volcanic ash exposure on biochemical and elemental components. S. fluitans III was the most abundant morphotype across the year. Limited monthly variations were observed for key brown algal components (phlorotannins, fucoxanthin, and alginate). Shade-drying did not significantly alter the contents of proteins but affected levels of phlorotannins, fucoxanthin, mannitol, and alginate. Simulation of sargassum and volcanic ash drift combined with age statistics suggested that sargassum potentially shared the surface layer with ash for ~50 d, approximately 100 d before stranding in Jamaica. Integrated elemental analysis of volcanic ash, ambient seawater, and sargassum biomass showed that algae harvested from August had accumulated P, Al, Fe, Mn, Zn, and Ni, probably from the ash, and contained less As. This ash fingerprint confirmed the geographical origin and drift timescale of sargassum. Since environmental conditions and processing methods influence biomass composition, efforts should continue to improve understanding, forecasting, monitoring, and valorizing sargassum, particularly as strandings of sargassum show no sign of abating.

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

High-altitude vertical wind profile estimation using multirotor vehicles

Capturing vertical profiles of the atmosphere and measuring wind conditions can be of significant value for weather forecasting and pollution monitoring however, collecting such data can be limited by current approaches using balloon-based radiosondes and expensive ground-based sensors. Multirotor vehicles can be significantly affected by the local wind conditions, and due to their under-actuated nature, the response to the flow is visible in the changes in the orientation. From these changes in orientation, wind speed and direction estimates can be determined, allowing accurate estimation with no additional sensors. In this work, we expand on and improve this method of wind speed and direction estimation and incorporate corrections for climbing flight to improve estimation during vertical profiling. These corrections were validated against sonic anemometer data before being used to gather vertical profiles of the wind conditions around Volcan De Fuego in Guatemala up to altitudes of 3000 m Above Ground Level (AGL). From the results of this work, we show we can improve the accuracy of multirotor wind estimation in vertical profiling through our improved model and some of the practical limitations of radiosondes that can be overcome through the use of UAS in this application.

The Use of a Numerical Weather Prediction Model to Simulate Near-Field Volcanic Plumes

In this paper, a state-of the art numerical weather prediction (NWP) model is used to simulate the near-field plume of a Plinian-type volcanic eruption. The NWP model is run at very high resolution (of the order of 100 m) and includes a representation of physical processes, including turbulence and buoyancy, that are essential components of eruption column dynamics. Results are shown that illustrate buoyant gas plume dynamics in an atmosphere at rest and in an atmosphere with background wind, and we show that these results agree well with those from theoretical models in the quiescent atmosphere. For wind-blown plumes, we show that features observed in experimental and natural settings are reproduced in our model. However, when comparing with predictions from an integral model using existing entrainment closures there are marked differences. We speculate that these are signatures of a difference in turbulent mixing for uniform and shear flow profiles in a stratified atmosphere. A more complex implementation is given to show that the model may also be used to examine the dispersion of heavy volcanic gases such as sulphur dioxide. Starting from the standard version of the weather research and forecasting (WRF) model, we show that minimal modifications are needed in order to model volcanic plumes. This suggests that the modified NWP model can be used in the forecasting of plume evolution during future volcanic events, in addition to providing a virtual laboratory for the testing of hypotheses regarding plume behaviour.