Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.

Zhang, Lujun; Kim, Taereem; Yang, Tiantian; Hong, Yang; Zhu, Qiankun

doi:10.1016/j.jhydrol.2021.127058

Cited by 25 publications

(14 citation statements)

References 83 publications

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“…The correlation maps in Figures 1 and 2 indicate that for the raw model forecasts, generally there is no correlation over most parts of South Africa, except for week 4. The improved correlation in week 4 for the raw GCM could be because of noise, not the accuracy of the forecasts, as documented in literature that the skill deteriorates over time (e.g., Zhang et al, 2021). This result gives confidence that downscaling of forecasts is beneficial for climate variables at S2S timescales.…”

Section: Resultsmentioning

confidence: 76%

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

Phakula,

Landman,

Engelbrecht

2024

Meteorological Applications

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The probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed.

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Section: Resultsmentioning

confidence: 76%

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

Phakula,

Landman,

Engelbrecht

2024

Meteorological Applications

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“…Dynamical models have shown increasing skill in accurately forecasting the weather [32], but they still contain systematic biases that compound on subseasonal time scales and suppress forecast skill [33,34,35,36]. Our proposed solution, ABC, learns to correct these biases by adaptively integrating dynamical forecasts, historical observations, and recent weather trends.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Bias Correction for Improved Subseasonal Forecasting

Mouatadid

Orenstein

Flaspohler

et al. 2022

Preprint

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Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and government agencies from the local to the national level. In fact, weather forecasts 0-10 days ahead and climate forecasts seasons to decades ahead are currently used operationally in decision-making, and the accuracy and reliability of these forecasts has improved consistently in recent decades. However, many critical applications require subseasonal forecasts with lead times in between these two timescales. Subseasonal forecasting&#8212;predicting temperature and precipitation 2-6 weeks ahead&#8212;is indeed critical for effective water allocation, wildfire management, and drought and flood mitigation. Yet, accurate forecasts for the subseasonal regime are still lacking due to the chaotic nature of weather.While short-term forecasting accuracy is largely sustained by physics-based dynamical models, these deterministic methods have limited subseasonal accuracy due to chaos. Indeed, subseasonal forecasting has long been considered a &#8220;predictability desert&#8221; due to its complex dependence on both local weather and global climate variables. Nevertheless, recent large-scale research efforts have advanced the subseasonal capabilities of operational physics-based models, while parallel efforts have demonstrated the value of machine learning and deep learning methods in improving subseasonal forecasting.To counter the systematic errors of dynamical models at longer lead times, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We evaluate our adaptive bias correction method in the contiguous U.S. over the years 2011-2020 and demonstrate consistent improvement over standard meteorological baselines, state-of-the-art learning models, and the leading subseasonal dynamical models, as measured by root mean squared error and uncentered anomaly correlation skill. When applied to the United States&#8217; operational climate forecast system (CFSv2), ABC improves temperature forecasting skill by 20-47% and precipitation forecasting skill by 200-350%. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 8-38% and precipitation forecasting skill by 40-80%.Overall, we find that de-biasing dynamical forecasts with our learned adaptive bias correction method yields an effective and computationally inexpensive strategy for generating improved subseasonal forecasts and building the next generation of subseasonal forecasting benchmarks. To facilitate future subseasonal benchmarking and development, we release our model code through the subseasonal_toolkit Python package and our routinely updated SubseasonalClimateUSA dataset through the subseasonal_data Python package.

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“…The model's rank showed ECMWF, UKMO, and KMA as the top scoring models. Zhang et al [18] comprehensively evaluated the S2S precipitation forecasts from the NMME-2 dataset over the contiguous United States (CONUS) and during the study period from 1982 to 2011. Three aspects of precipitation forecast capabilities are compared and analyzed: bias, skill scores, and the ability to predict extreme precipitation events.…”

Section: Introductionmentioning

confidence: 99%

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

et al. 2023

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To clarify the precipitation forecast skills of climate forecast operations in the flood season in Liaoning Province of China, this study examines the forecast accuracies of China’s national and provincial operational climate prediction products and the self-developed objective prediction methods and climate model products by Shenyang Regional Climate Center (SRCC) in the flood season in Liaoning. Furthermore, the forecast accuracies of the main influencing factors on the precipitation in the flood season of Liaoning are assessed. The results show that the SRCC objective methods have a relatively high accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecast initialized at the sub-nearest time has the best performance in June. The National Climate Center (NCC) Climate System Model sub-seasonal forecast initialized at the sub-nearest time, and the ECMWF seasonal and sub-seasonal forecasts initialized at the nearest time, perform the best in July. The NCC sub-seasonal forecast initialized at the sub-nearest time has the best performance in August. For the accuracy of the SRCC objective method, the more significant the equatorial Middle East Pacific sea surface temperature (SST) anomaly is, the higher the evaluation score of the dynamic–analogue correction method is. The more significant the North Atlantic SST tripole is, the higher the score of the hybrid downscaling method is. For the forecast accuracy of the main influencing factors of precipitation, the tropical Atlantic SST and the north–south anti-phase SST in the northwest Pacific can well predict the locations of the southern vortex and the northern vortex in early summer, respectively. The warm (clod) SST in China offshore has a good forecast performance on the weak (strong) southerly wind in midsummer in Northeast China. The accuracy of using the SST in the Nino 1+2 areas to predict the north–south location of the western Pacific subtropical high is better than that of using Kuroshio SST. The accuracy of predicting northward-moving typhoons from July to September by using the SST in the west-wind-drift area is better than using the SST in the Nino 3 area. The above conclusions are of great significance for improving the short-term climate prediction in Liaoning.

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Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.

Cited by 25 publications

References 83 publications

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

Adaptive Bias Correction for Improved Subseasonal Forecasting

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

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