Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableImproved Trend-Aware Postprocessing of GCM Seasonal Precipitation Forecasts(AMER METEOROLOGICAL SOC, 2022-01)Abstract Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.
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ItemIntroducing long-term trends into subseasonal temperature forecasts through trend-aware postprocessing(WILEY, 2022-07)Abstract Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend‐aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30‐year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30‐year trends is to robustly represent long‐term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20‐year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day‐of‐year climatology, raw week‐1 forecasts reproduce temperature trends of the 20‐year observations in many regions while raw week‐4 forecasts do not exhibit the 20‐year observed trends. After trend‐aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week‐1 forecasts show apparent trends consistent with the 20‐year observations, as the calibration transfers forecast skill and embeds the 20‐year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week‐4 forecasts exhibit the 30‐year observed trends, as the calibration reverts the forecasts to the 30‐year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend‐aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend‐aware method can be applied to deliver high‐quality subseasonal forecasts and support decision‐making in a changing climate.
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ItemParsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems(MDPI, 2022-03)Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
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ItemEmbedding trend into seasonal temperature forecasts through statistical calibration ofGCMoutputs(Wiley, 2021-01)Accurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration.