Ciaran Broderick

Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods

Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST), we analyze the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining interperiod transferability. DSST is conducted using 2/3 year noncontinuous blocks of (i) the wettest/driest years on record based on precipitation totals and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment, and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan Averaging (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here GRA performed better than the best individual model in 51%–86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments; and (iv) using a multimodel ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment.

Integrating data rescue into the classroom

AbstractOver much of the globe, the temporal extent of meteorological records is limited, yet a wealth of data remains held in paper or image form in numerous archives. To date, little attention has been given to the role that students might play in efforts to rescue these data. Here we summarize an ambitious research-led, accredited teaching experiment in which undergraduate students successfully transcribed more than 1,300 station years of daily precipitation data and associated metadata across Ireland over the period 1860-1939. We explore i) the potential for integrating data rescue activities into the classroom; ii) the ability of students to produce reliable transcriptions and; iii) the learning outcomes for students. Data previously transcribed by Met Eireann (Ireland’s National Meteorological Service) were used as a benchmark against which it was ascertained that students were as accurate as the professionals. Details on the assignment, its planning and execution, and student-aids used are provided...

Clustering of Observed Diurnal Cycles of Precipitation over the United States for Evaluation of a WRF Multiphysics Regional Climate Ensemble

AbstractThe diurnal cycle of precipitation during the summer season over the contiguous United States is examined in eight distinct regions. These were identified using cluster analysis applied to the diurnal cycle characteristics at 2141 rainfall gauges over the 10-yr period 1991–2000. Application of the clustering technique provides a physically meaningful way of identifying regions for comparison of model results with observations. The diurnal cycle for each region is specified in terms of 1) total precipitation, 2) frequency of precipitation occurrence, and 3) intensity of precipitation per occurrence on an hourly basis averaged over the 10-yr period. The amplitude and phase of each element of the diurnal cycle was obtained from harmonic analysis and has been compared with the results of a 24-member multiphysics ensemble of simulations produced by the Weather Research and Forecast (WRF) Model on a region-by-region basis. Three cumulus schemes, two radiation schemes, two microphysics schemes, and two p...