Article
Oceanography
Xiaoyu Long, Matthew J. Widlansky, Claire M. Spillman, Arun Kumar, Magdalena Balmaseda, Philip R. Thompson, Yoshimitsu Chikamoto, Grant A. Smith, Bohua Huang, Chul-Su Shin, Mark A. Merrifield, William V. Sweet, Eric Leuliette, H. S. Annamalai, John J. Marra, Gary Mitchum
Summary: Coastal high water level events are on the rise due to global sea-level rise, but operational seasonal forecasts of sea-level anomalies are lacking in most coastal regions. Advances in forecasting climate variability using coupled ocean-atmosphere global models provide the opportunity to predict future high water events several months in advance. Multi-model assessments show that skillful seasonal sea-level forecasts are possible in many, but not all, parts of the global ocean.
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
(2021)
Article
Meteorology & Atmospheric Sciences
Shiyuan Liu, Wentao Li, Qingyun Duan
Summary: This study evaluates the daily and cumulative precipitation prediction skills of three subseasonal prediction products over China, revealing that cumulative precipitation predictions are more skillful compared to daily precipitation predictions, with correlation coefficients peaking at 0.7-0.8 after 3-5 days. These results provide valuable information for water resource managers who prioritize general conditions over a specific day's weather events.
JOURNAL OF HYDROMETEOROLOGY
(2023)
Article
Geosciences, Multidisciplinary
M. Kathleen Brennan, Gregory J. Hakim, Edward Blanchard-Wrigglesworth
Summary: We evaluated Linear Inverse Models (LIMs) trained on last millennium model data for Arctic sea-ice prediction. More than 500 years of training data and 100 years of validation data are needed for reliable estimation of LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model (AR1), particularly near the ice edge. However, out-of-sample validation tests show underperformance due to differences in the sea-ice edge location between training and validation data, when using data from various sources.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Environmental Sciences
Pengyou Lai, Jingtao Yang, Lexi Liu, Yu Zhang, Zhaoxuan Sun, Zhefan Huang, Duanzhou Shao, Linbin He, Tomeu Rigo, Stephan Havemann
Summary: This study aimed to correct significant bias in 0-44-day precipitation forecasts under numerical weather conditions. Observational data from 156 surface stations in the Sichuan region and reanalysis grid data were utilized. Statistical analysis of the spatiotemporal characteristics of precipitation in Sichuan was conducted, followed by a correction experiment using the Analog Ensemble algorithm for 0-44-day precipitation forecasts for different seasons. The results showed effective error reduction in model forecast results, with some variations in correction effectiveness due to different precipitation events and forecast parameters.
Article
Environmental Sciences
Shouwen Zhang, Hui Wang, Hua Jiang, Wentao Ma
Summary: The study found a significant decline in forecast skill for El Nino and Southern Oscillation (ENSO) since 2000, especially in the target months from May to August. The weakening relationship between the extratropical Pacific signal and ENSO after 2000 has contributed to the reduction in predictability and skills of ENSO forecasts. Therefore, integrating different methods is necessary to improve the accuracy of ENSO forecasts.
Article
Engineering, Environmental
Han Wang, Ping-an Zhong, Fei-lin Zhu, Qing-wen Lu, Yu-fei Ma, Sun-yu Xu
Summary: Evaluations were conducted on the adaptability of precipitation forecasting in the Huaihe River basin using data from five global ensemble prediction systems. The study found variations in forecast quality among the EPSs, with different performances in terms of QPF and PQPF qualities based on precipitation thresholds and lead times. The study also showed that the multimodel superensemble had little effect on improving PQPF skill but could enhance QPF accuracy in cases where raw EPSs had significantly different QPF accuracies.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Meteorology & Atmospheric Sciences
Lili Lei, Yangjinxi Ge, Zhe-Min Tan, Yi Zhang, Kekuan Chu, Xin Qiu, Qifeng Qian
Summary: This study evaluates the ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) for western North Pacific typhoons in 2016. The results show that the WRF/EnKF system provides better ensemble forecasts and higher predictability for typhoon intensity compared to NCEP and ECMWF ensemble forecasts.
ADVANCES IN ATMOSPHERIC SCIENCES
(2022)
Article
Environmental Sciences
Yang Yang, Wenbin Sun, Meng Zou, Shaobo Qiao, Qingxiang Li
Summary: The increased climate change has a significant impact on the world, highlighting the importance of effective climate information integration and forecasting for risk reduction and adaptation to climate change. This study evaluates and compares the performance of different seasonal prediction models for global surface temperature, introducing a correction method to enhance ensemble forecast skills.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
Gauri Shanker, Abhijit Sarkar, Ashu Mamgain, S. Kiran Prasad, R. Bhatla, A. K. Mitra
Summary: This study compares the forecast quality of India's National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS-G) using different ensemble configurations. The results show that the inclusion of lagged ensemble members improves the forecast skill, especially during the winter season. Evaluation metrics used include ensemble spread, root-mean-square error (RMSE), brier skill score (BSS), reliability diagram, outliers statistics, relative operating characteristics (ROC) score, and ranked probability score (RPS). The findings suggest that the NEPS-G dataset with fewer computational resources (E23) offers more accurate and nearly as skilled forecasts as the ensemble with all 22 members (E00_22) over a longer forecast lead time.
ATMOSPHERIC RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Amin Shirvani, Willem A. Landman, Mathew Barlow, Andrew Hoell
Summary: The precipitation forecast skill of the North American Multi-Model Ensemble (NMME) over Iran is evaluated using Taylor diagrams and ranked probability skill scores (RPSS). The results show that the forecast skill is highest in November, and the multi-model ensemble means (MMM) have higher temporal and spatial correlations compared to individual models. The study also examines the connection between the El Nino-Southern Oscillation (ENSO) and precipitation over Iran, finding significant correlations in some seasons.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2023)
Article
Engineering, Civil
Xin Liu, Liping Zhang, Dunxian She, Jie Chen, Jun Xia, Xinchi Chen, Tongtiegang Zhao
Summary: This paper develops an integrated postprocessing framework for hydrometeorological ensemble forecasts, which aims to address the data correction issue for hydrological models. By using a reasonable design of canonical events and employing postprocessed ensemble precipitation forecasts, the performance of hydrological forecasts can be improved in terms of lead times and accuracy. The Bayesian model averaging (BMA) scheme further enhances the forecast effect by generating more skillful and reliable probabilistic hydrological forecasts.
JOURNAL OF HYDROLOGY
(2022)
Article
Meteorology & Atmospheric Sciences
Hussen Seid Endris, Linda Hirons, Zewdu Tessema Segele, Masilin Gudoshava, Steve Woolnough, Guleid A. Artan
Summary: The skill of precipitation forecasts from global prediction systems varies by region and month. Among the 11 models evaluated in predicting monthly precipitation over the Greater Horn of Africa, the ECMWF model performs the best, while the BoM, CMA, HMCR, and ISAC models show poorer prediction skill.
WEATHER AND FORECASTING
(2021)
Article
Meteorology & Atmospheric Sciences
Kensuke K. Komatsu, Yuhei Takaya, Takahiro Toyoda, Hiroyasu Hasumi
Summary: This paper evaluates the cause-and-effect relationship between snow cover and surface air temperature in Eurasia, and finds that there is a one-way causality from surface air temperature to snow cover in Europe, while there is causality from snow cover to surface air temperature in the Mongolian Plateau. The study also suggests that the predictability of surface air temperature can be affected by snow cover for up to four weeks in autumn.
JOURNAL OF CLIMATE
(2023)
Article
Meteorology & Atmospheric Sciences
Luciano G. Andrian, Marisol Osman, Carolina S. Vera
Summary: This paper evaluates the predictability and skill of the models from the NMME project in South America, and finds that temperature predictability is higher than precipitation predictability. The multi-model ensemble signal dominates temperature variance in autumn and summer, while inter-model biases dominate in spring and winter. The highest predictability of precipitation is found in tropical latitudes.
Article
Meteorology & Atmospheric Sciences
S. Khan, D. B. Kirschbaum, T. Stanley
Summary: This study compares the performance of NASA's GEOS global precipitation forecast with satellite precipitation estimates, focusing on extreme precipitation events over the contiguous United States. Findings show that seasonality influences the performance of both satellite and model-based precipitation products. The research aims to assess the viability of using a global forecast for landslide predictions and understand the variability between these products.
WEATHER AND CLIMATE EXTREMES
(2021)