Foehn warming distributions in nonlinear and linear flow regimes: a focus on the Antarctic Peninsula

The structure of lee-side warming during foehn events is investigated as a function of cross-barrier flow regime linearity. Two contrasting cases of westerly flow over the Antarctic Peninsula (AP) are considered – one highly nonlinear, the other relatively linear. Westerly flow impinging on the AP provides one of the best natural laboratories in the world for the study of foehn, owing to its maritime setting and the Larsen C Ice Shelf (LCIS) providing an expansive, homogeneous and smooth surface on its east side. Numerical simulations with the Met Office Unified Model (at 1.5 km grid size) and aircraft observations are utilized. In case A, relatively weak southwesterly cross-Peninsula flow and an elevated upwind inversion dictate a highly nonlinear foehn event, with mountain wave breaking observed. The consequent strongly accelerated downslope flow leads to high-amplitude warming and ice-shelf melt in the immediate lee of the AP. However this foehn warming diminishes rapidly downwind due to upward ascent of the foehn flow via a hydraulic jump. In case C, strong northwesterly winds dictate a relatively linear flow regime. There is no hydraulic jump and strong foehn winds are able to flow at low levels across the entire ice shelf, mechanically mixing the near-surface flow, preventing the development of a strong surface inversion and delivering large fluxes of sensible heat to the ice shelf. Consequently, in case C ice-melt rates are considerably greater over the LCIS as a whole than in case A. Our results imply that although nonlinear foehn events cause intense warming in the immediate lee of mountains, linear foehn events will commonly cause more extensive lee-side warming and, over an ice surface, higher melt rates. This has major implications for the AP, where recent east-coast warming has led to the collapse of two ice shelves immediately north of the LCIS

Fine-Tuning Generative Models as an Inference Method for Robotic Tasks

Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly affected many areas of robotics. Harnessing modern GPU acceleration, we investigate how to quickly adapt the sample generation of neural network models to observations in robotic tasks. We propose a simple and general method that is applicable to various deep generative models and robotic environments. The key idea is to quickly fine-tune the model by fitting it to generated samples matching the observed evidence, using the cross-entropy method. We show that our method can be applied to both autoregressive models and variational autoencoders, and demonstrate its usability in object shape inference from grasping, inverse kinematics calculation, and point cloud completion.Comment: 7th Conference on Robot Learning, 2023. Project website at https://www.orrkrup.com/mac

Island disaster para-diplomacy in the commonwealth

This chapter covers one particular aspect of the foreign relations of non-sovereign island jurisdictions (SNIJs), namely relations arising from disaster-related activities. Islands are among the territories most seriously affected by calamities, including the spectre of rising seas that may come with climate change. Yet non-sovereign islands are not so well equipped to speak and act effectively for themselves in the face of such threats. This may be true even within the governing structures in which these islands find themselves, but it is even more serious given the weaknesses that may exist in their capacity to speak to and act in the international community on disaster-related activities.peer-reviewe

Classroom re-design to facilitate student learning: a case-study of changes to a university classroom.

Open accessThis case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning.Ye

Summertime cloud phase strongly influences surface melting on the Larsen C ice shelf, Antarctica

Surface melting on Antarctic Peninsula ice shelves can influence ice shelf mass balance, and consequently sea level rise. We show that summertime cloud phase on the Larsen C ice shelf on the Antarctic Peninsula strongly influences the amount of radiation received at the surface and can determine whether or not melting occurs. While previous work has separately evaluated cloud phase and the surface energy balance (SEB) during summertime over Larsen C, no previous studies have examined this relationship quantitatively. Furthermore, regional climate models frequently produce surface radiation biases related to cloud ice and liquid water content. This study uses a high-resolution regional configuration of the UK Met Office Unified Model (MetUM) to assess the influence of cloud ice and liquid properties on the SEB, and consequently melting, over the Larsen C ice shelf. Results from a case-study show that simulations producing a vertical cloud phase structure more comparable to aircraft observations exhibit smaller surface radiative biases. A configuration of the MetUM adapted to improve the simulation of cloud phase reproduces the observed surface melt most closely. During a five-week simulation of summertime conditions, model melt biases are reduced to <2 W·m −2: a four-fold improvement on a previous study that used default MetUM settings. This demonstrates the importance of cloud phase in determining summertime melt rates on Larsen C

PopSparse: Accelerated block sparse matrix multiplication on IPU

Reducing the computational cost of running large scale neural networks using sparsity has attracted great attention in the deep learning community. While much success has been achieved in reducing FLOP and parameter counts while maintaining acceptable task performance, achieving actual speed improvements has typically been much more difficult, particularly on general purpose accelerators (GPAs) such as NVIDIA GPUs using low precision number formats. In this work we introduce PopSparse, a library that enables fast sparse operations on Graphcore IPUs by leveraging both the unique hardware characteristics of IPUs as well as any block structure defined in the data. We target two different types of sparsity: static, where the sparsity pattern is fixed at compile-time; and dynamic, where it can change each time the model is run. We present benchmark results for matrix multiplication for both of these modes on IPU with a range of block sizes, matrix sizes and densities. Results indicate that the PopSparse implementations are faster than dense matrix multiplications on IPU at a range of sparsity levels with large matrix size and block size. Furthermore, static sparsity in general outperforms dynamic sparsity. While previous work on GPAs has shown speedups only for very high sparsity (typically 99\% and above), the present work demonstrates that our static sparse implementation outperforms equivalent dense calculations in FP16 at lower sparsity (around 90%). IPU code is available to view and run at ipu.dev/sparsity-benchmarks, GPU code will be made available shortly

4: Recent Works Show Card

Show card featuring Alumni Nancy Ferro.https://digitalcommons.udallas.edu/alumni_89-90/1001/thumbnail.jp

Foehn jets over the Larsen C Ice Shelf, Antarctica

Previously unknown foehn jets have been identified to the east of the Antarctic Peninsula (AP) above the Larsen C Ice Shelf. These jets have major implications for the east coast of the AP, a region of rapid climatic warming and where two large sections of ice shelf have collapsed in recent years. During three foehn events across the AP, leeside warming and drying is seen in new aircraft observations and simulated well by the Met Office Unified Model (MetUM) at ∼1.5 km grid spacing. In case A, weak southwesterly flow and an elevated upwind inversion characterise a highly nonlinear flow regime with upwind flow blocking. In case C strong northwesterly winds characterise a relatively linear case with little upwind flow blocking. Case B resides somewhere between the two in flow regime linearity. The foehn jets – apparent in aircraft observations where available and MetUM simulations of all three cases – are mesoscale features (up to 60 km in width) originating from the mouths of leeside inlets. Through back trajectory analysis they are identified as a type of gap flow. In cases A and B the jets are distinct, being strongly accelerated relative to the background flow, and confined to low levels above the Larsen C Ice Shelf. They resemble the ‘shallow foehn’ of the Alps. Case C resembles a case of ‘deep foehn’, with the jets less distinct. The foehn jets are considerably cooler and moister relative to adjacent regions of calmer foehn air. This is due to a dampened foehn effect in the jet regions: in case A the jets have lower upwind source regions, and in the more linear case C there is less diabatic warming and precipitation along jet trajectories due to the reduced orographic uplift across the mountain passes