Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

Narapusetty, B.; Collins, Dan C.; Murtugudde, Raghu; Gottschalck, Jon; Peters‐Lidard, Christa D.

doi:10.1002/asl.818

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Novel bias correction and calibration methods with indications of improved prediction skill, reliability, or other assessment statistics, have been developed using NMME. These include a bias correction for precipitation based on dynamically linking it with temperature (Narapusetty et al 2018) and experimental calibration methods applied to probabilistic ENSO prediction (Graziani et al 2021). Van den Dool et al (2017) discusses the probability anomaly correlation calibration technique, which is employed by the NMME probabilistic forecasts shown on NOAA's NMME website.…”

Section: Bias Correction and Multi-model Techniquesmentioning

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: Bias Correction and Multi-model Techniquesmentioning

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

View full text Add to dashboard Cite

show abstract

“…In practice, there is an opportunity to extend the trend-aware method for multivariate post-processing of hydro-meteorological variables (Schepen et al, 2020c). For example, temperature information could be utilised to post-process precipitation forecasts (Narapusetty et al, 2018) in a multivariate model configuration. Apart from forecast post-processing, it is also feasible to use the trend-aware method for statistical forecasting of hydrometeorological variables.…”

Section: Limitations and Extension Opportunitiesmentioning

confidence: 99%

Post-processing sub-seasonal to seasonal climate forecasts under climate change

Shao¹

2022

Water e-Journal

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For managing the impacts of climate variability and change, climate outlooks on sub-seasonal and seasonal timescales are attracting more interest from climate-sensitive communities, such as water resource management, agriculture, and energy. With a profound knowledge of the sources of climate predictability, modelling techniques are rapidly developed for forecasting future climate conditions. Recent advancements are dynamical global climate models (GCMs), which typically integrate atmosphere, land surface, ocean, and sea ice components to comprehensively simulate earth climate system and output a wide array of climate forecasts. However, GCMs usually suffer from long-standing modelling issues, such as systematic errors and the failure of reproducing the observed trends in seasonal climate forecasts. Statistical post-processing techniques are frequently employed to improve forecast performance. Many commonly used methods are found to be effective at removing biases, maximising forecast skill, and improving forecast reliability in terms of ensemble spread, but they are seldom designed to resolve the trend disparity issue in the post-processed climate forecasts. This issue should not be neglected as global and regional land surface temperature and precipitation have shown discernible temporal trends over recent decades. To address this gap, the overarching objective of this thesis is to develop and demonstrate the merit of a new, trend-aware forecast post-processing method that eliminates the trend disparity between climate forecasts and observations while making the forecasts bias-free, skillful, and reliable.The first part of this research aims to develop a new statistical post-processing method to embed the observed trend into seasonal temperature forecasts. I extend the capability of a calibration method, the Bayesian joint probability (BJP) modelling approach, by introducing a new trend component into the algorithm. The new model (named BJP-t) is applied to calibrate January mean forecasts of daily maximum temperatures from the SEAS5 seasonal forecasting system, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) in three test stations in Australia. In these cases, the BJP-t calibrated forecasts are shown to accurately reproduce the observed trends, and are more skillful, more reliable, and sharper than raw and BJP calibrated forecasts.ii In the BJP-t model, the trend is entirely inferred from the training data. In practice, given limited available periods of retrospective forecasts for model training, these inferred trends are subject to large sampling errors, and may not reflect true underlying trends in the observations. Accordingly, the second part of my thesis further develops the BJP-t model to account for trend uncertainty.The extended trend-aware forecast post-processing method is applied to SEAS5 seasonal mean minimum and maximum temperature forecasts, and the evaluations are upscaled to the Australian continent. After trend-aware post-processing that deals with trend uncertainty...

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Advancing irrigation management: integrating technology and sustainability to address global food security

Su,

Singh

2024

Environ Monit Assess

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Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

Cited by 7 publications

References 28 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

Post-processing sub-seasonal to seasonal climate forecasts under climate change

Advancing irrigation management: integrating technology and sustainability to address global food security

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