Toward a framework for the multimodel ensemble prediction of soil nitrogen losses

2021 ◽  
Vol 456 ◽  
pp. 109675
Author(s):  
Kaihua Liao ◽  
Ligang Lv ◽  
Xiaoming Lai ◽  
Qing Zhu
Keyword(s):  
Soil Nitrogen ◽  
Nitrogen Losses ◽  
Ensemble Prediction ◽  
Multimodel Ensemble

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

scholarly journals Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

2019 ◽  
Vol 147 (6) ◽  
pp. 1967-1987 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle
Keyword(s):  
Lead Time ◽  
East Coast ◽  
Time Frame ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Winter Storms ◽  
East Coast Winter Storms ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

scholarly journals Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data

2020 ◽  
Vol 35 (2) ◽  
pp. 367-377
Author(s):  
Hyun-Ju Lee ◽  
Woo-Seop Lee ◽  
Jong Ahn Chun ◽  
Hwa Woon Lee
Keyword(s):  
South Korea ◽  
Heat Wave ◽  
Large Scale ◽  
Heat Waves ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Large Scale Circulation ◽  
Circulation Patterns ◽  
Wave Forecast ◽  
Prediction Systems

Abstract Forecasting extreme events is important for having more time to prepare and mitigate high-impact events because those are expected to become more frequent, intense, and persistent around the globe in the future under the warming atmosphere. This study evaluates the probabilistic predictability of the heat wave index (HWI) associated with large-scale circulation patterns for predicting heat waves over South Korea. The HWI, reflecting heat waves over South Korea, was defined as the vorticity difference at 200 hPa between the South China Sea and northeast Asia. The forecast of up to 15 days from five ensemble prediction systems and the multimodel ensemble has been used to predict the probabilistic HWI during the summers of 2011–15. The ensemble prediction systems consist of different five operational centers, and the forecast skill of the probability of heat waves occurrence was assessed using the Brier skill score (BSS), relative operating characteristics (ROC), and reliability diagram. It was found that the multimodel ensemble is capable of better predicting the large-scale circulation patterns leading to heat waves over South Korea than any other single ensemble system through all forecast lead times. We concluded that the probabilistic forecast of the HWI has promise as a tool to take appropriate and timely actions to minimize the loss of lives and properties from imminent heat waves.

Improved modelling of soil nitrogen losses

2015 ◽  
Vol 5 (8) ◽  
pp. 705-706 ◽  
Author(s):  
Qing Zhu ◽  
William J. Riley
Keyword(s):  
Soil Nitrogen ◽  
Nitrogen Losses

Review: Reducing residual soil nitrogen losses from agroecosystems for surface water protection in Quebec and Ontario, Canada: Best management practices, policies and perspectives

2014 ◽  
Vol 94 (2) ◽  
pp. 109-127 ◽  
Author(s):  
Sogol Rasouli ◽  
Joann K. Whalen ◽  
Chandra A. Madramootoo
Keyword(s):  
Surface Water ◽  
Soil Nitrogen ◽  
Best Management Practices ◽  
Management Practices ◽  
Nitrogen Losses ◽  
Agricultural Fields ◽  
Residual Soil ◽  
Water Protection ◽  
N Losses ◽  
Best Management

Rasouli, S., Whalen, J. K. and Madramootoo, C. A. 2014. Review: Reducing residual soil nitrogen losses from agroecosystems for surface water protection in Quebec and Ontario, Canada: Best management practices, policies and perspectives. Can. J. Soil Sci. 94: 109–127. Eutrophication and cyanobacteria blooms, a growing problem in many of Quebec and Ontario's lakes and rivers, are largely attributed to the phosphorus (P) and nitrogen (N) emanating from intensively cropped agricultural fields. In fact, 49% of N loading in surface waters comes from runoff and leaching from fertilized soils and livestock operations. The residual soil nitrogen (RSN), which remains in soil at the end of the growing season, contains soluble and particulate forms of N that are prone to being transported from agricultural fields to waterways. Policies and best management practices (BMPs) to regulate manure storage and restrict fertilizer and manure spreading can help in reducing N losses from agroecosystems. However, reduction of RSN also requires an understanding of the complex interactions between climate, soil type, topography, hydrology and cropping systems. Reducing N losses from agroecosystems can be achieved through careful accounting for all N inputs (e.g., N credits for legumes and manure inputs) in nutrient management plans, including those applied in previous years, as well as the strategic implementation of multiple BMPs and calibrated soil N testing for crops with high N requirements. We conclude that increasing farmer awareness and motivation to implement BMPs will be important in reducing RSN. Programs to promote communication between farmers and researchers, crop advisors and provincial ministries of agriculture and the environment are recommended.

scholarly journals Dynamical downscaling of a multimodel ensemble prediction system: Application to tropical cyclones

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Manpreet Kaur ◽  
R. Phani Murali Krishna ◽  
Susmitha Joseph ◽  
Avijit Dey ◽  
Raju Mandal ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
System Application ◽  
Dynamical Downscaling ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

scholarly journals Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

2020 ◽  
Vol 148 (6) ◽  
pp. 2591-2606 ◽  
Author(s):  
Luying Ji ◽  
Xiefei Zhi ◽  
Clemens Simmer ◽  
Shoupeng Zhu ◽  
Yan Ji
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Diagnostic Evaluation ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Object Based ◽  
Forecast Technique ◽  
Point To Point ◽  
The Individual

Abstract We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

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