Improved Trend-Aware Postprocessing of GCM Seasonal Precipitation Forecasts

Shao, Yawen; Wang, QJ; Schepen, Andrew; Ryu, Dongryeol; Pappenberger, Florian

doi:10.1175/jhm-d-21-0099.1

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…While no trend-based calibration is currently applied to operational NMME predictions, Shao et al (2021aShao et al ( , 2021b, using ECMWF's SEAS5 model, propose a calibration method for model representation of temperature trends that leads to improved forecast skill and reliability. Shao et al (2021c) expands this methodology to trends in precipitation, finding substantial improvement to forecasts in some regions.…”

Section: Emerging and Future Directionsmentioning

confidence: 99%

A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions

Becker

Kirtman

L’Heureux

et al. 2022

Bulletin of the American Meteorological Society

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Ten years, sixteen fully coupled global models, and hundreds of research papers later, the North American Multi-Model Ensemble (NMME) monthly-to-seasonal prediction system is looking ahead to its second decade. The NMME comprises both real-time, initialized predictions and a substantial research database; both retrospective and real-time forecasts are archived and freely available for research and development. Many US-based and international entities, both private and public, use NMME data for regional or otherwise tailored forecasts. The system’s built-in evolution, with new models gradually replacing older ones, has been demonstrated to gradually improve the skill of 2 m temperature and sea surface temperature, although precipitation prediction remains a difficult problem. This paper reviews some of the NMME-based contributions to seasonal climate prediction research and applications, progress on scientific understanding of seasonal prediction and multi-model ensembles, and new techniques. Several prediction-oriented aspects are explored, including model representation of observed trends and the under-prediction of below-average temperature. We discuss potential new directions, such as higher-resolution models, hybrid statistical-dynamical techniques, or prediction of environmental hazards such as coastal flooding and the risk of mosquito-borne diseases.

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Section: Emerging and Future Directionsmentioning

confidence: 99%

A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions

Becker

Kirtman

L’Heureux

et al. 2022

Bulletin of the American Meteorological Society

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“…To do this, future work needs to conceive new strategies on building the daily postprocessor that considers computational efficiency and data availability. Shao et al (2021c) extended and applied the trendaware model to postprocess seasonal precipitation forecasts. Technically, the method developed for seasonal precipitations is also applicable for handling subseasonal precipitation forecasts as the original BJP model was employed to effectively postprocess subseasonal to seasonal forecasts of rainfall (Schepen et al, 2018;Li et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…We set m i as m i =δ i × m 00 i , where δ i is the MAP estimate of the standard deviation of y 0 i obtained from the variable transformation step. We follow the prior specification scheme elaborated in Shao et al (2021c) to estimate m 00 i using spatial and temporal neighbourhood information on a cell-by-cell basis. First, for each cell in Australia, for each of four lead times, for each of 12 months, for observation anomalies and for raw forecast anomalies separately, we run the BJP-t model (Shao et al, 2021a;2021b), known as a member of the trendaware method with noninformative uniform priors for trend parameters, and record the median value of sampled trend parameters α i in the parameter inference without cross-validation.…”

Section: Trend-aware Forecast Calibrationmentioning

confidence: 99%

Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing

Shao

Wang

Schepen

et al. 2022

Intl Journal of Climatology

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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 trendaware 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 decisionmaking in a changing climate.

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Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Yang

Wang

Western

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

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Improved Trend-Aware Postprocessing of GCM Seasonal Precipitation Forecasts

Cited by 3 publications

References 37 publications

A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions

A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions

Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

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