Sam Pimentel | Searcharxiv

arXiv2022

Simulating surface height and terminus position for marine outlet glaciers using a level set method with data assimilation

We implement a data assimilation framework for integrating ice surface and terminus position observations into a numerical ice-flow model. The model uses the well-known shallow shelf approximation (SSA) coupled to a level set method to capture ice motion and changes in the glacier geometry. The level set method explicitly tracks the evolving ice-atmosphere and ice-ocean boundaries for a marine outlet glacier. We use an Ensemble Transform Kalman Filter to assimilate observations of ice surface elevation and lateral ice extent by updating the level set function that describes the ice interface. Numerical experiments on an idealized marine-terminating glacier demonstrate the effectiveness of our data assimilation approach for tracking seasonal and multi-year glacier advance and retreat cycles. The model is also applied to simulate Helheim Glacier, a major tidewater-terminating glacier of the Greenland Ice Sheet that has experienced a recent history of rapid retreat. By assimilating observations from remotely-sensed surface elevation profiles we are able to more accurately track the migrating glacier terminus and glacier surface changes. These results support the use of data assimilation methodologies for obtaining more accurate predictions of short-term ice sheet dynamics.

physics.ao-ph↗

arXiv2014

Variable-Ratio Matching with Fine Balance in a Study of Peer Health Exchange

In observational studies of treatment effects, matched samples are created so treated and control groups are similar in terms of observable covariates. Traditionally such matched samples consist of matched pairs. If a pair match fails to make treated and control units sufficiently comparable, alternative forms of matching may be necessary. One general strategy to improve balance is to match a variable number of control units to each treated unit. A more tailored strategy is to adopt a fine balance constraint. Under a fine balance constraint, a nominal covariate is exactly balanced, but it does not require individually matched treated and control subjects for this variable. In the example, we seek to construct a matched sample for an ongoing evaluation of Peer Health Exchange, an intervention in schools designed to decrease risky health behaviors among youth. We find that an optimal pair match that minimizes distances between pairs creates a matched sample where balance is poor. Here we propose a method to allow for fine balance constraints when each treated unit is matched to a variable number of control units, which is not currently possible using existing matching algorithms. Our approach uses the entire number to first determine the optimal number of controls for each treated unit. For each strata of matched treated units, we can then apply a fine balance constraint. We then demonstrate that a matched sample for the evaluation of the Peer Health Exchange based on a variable number of controls and fine balance constraint is superior to simply using a variable-ratio match.

stat.ME↗