Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983-2003) and real-time forecasts (2008-2013)

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.

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REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

Experimental Agriculture ◽

10.1017/s0014479710000876 ◽

2011 ◽

Vol 47 (2) ◽

pp. 205-240 ◽

Cited By ~ 148

Author(s):

JAMES W. HANSEN ◽

SIMON J. MASON ◽

LIQIANG SUN ◽

ARAME TALL

Keyword(s):

Smallholder Farmers ◽

Seasonal Forecast ◽

Sub Saharan Africa ◽

Seasonal Forecasting ◽

Seasonal Forecasts ◽

Climate Services ◽

Seasonal Climate ◽

Climate Forecasting ◽

Probabilistic Forecasts ◽

Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

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Applicability of Multimodel Ensemble Prediction of Heavy Precipitation for the Perm Region: A Case Study for the Summer of 2019

Russian Meteorology and Hydrology ◽

10.3103/s1068373921070037 ◽

2021 ◽

Vol 46 (7) ◽

pp. 444-453

Author(s):

A. L. Vetrov ◽

S. V. Kostarev

Keyword(s):

Heavy Precipitation ◽

Ensemble Prediction ◽

Multimodel Ensemble ◽

Perm Region

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“Flowering Walnuts in the Wood” and Other Bases for Seasonal Climate Forecasting

Seasonal Forecasts, Climatic Change and Human Health ◽

10.1007/978-1-4020-6877-5_2 ◽

2008 ◽

pp. 13-29 ◽

Cited By ~ 2

Author(s):

Simon J. Mason

Keyword(s):

Seasonal Climate ◽

Climate Forecasting

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Seasonal Climate Forecasting and the Management of Rangelands: Do Production Benefits Translate into Enterprise Profits?

Applications of Seasonal Climate Forecasting in Agricultural and Natural Ecosystems - Atmospheric and Oceanographic Sciences Library ◽

10.1007/978-94-015-9351-9_17 ◽

2000 ◽

pp. 271-289 ◽

Cited By ~ 2

Author(s):

Mark Stafford Smith ◽

Rosemary Buxton ◽

Greg McKeon ◽

Andrew Ash

Keyword(s):

Seasonal Climate ◽

Climate Forecasting

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PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-18-0041.1 ◽

2019 ◽

Vol 100 (7) ◽

pp. 1245-1258 ◽

Cited By ~ 11

Author(s):

Brett Roberts ◽

Israel L. Jirak ◽

Adam J. Clark ◽

Steven J. Weiss ◽

John S. Kain

Keyword(s):

Real Time ◽

Data Extraction ◽

Deep Convection ◽

Weather Prediction ◽

Ensemble Forecast ◽

Ensemble Prediction ◽

Data Volume ◽

Explicit Simulation ◽

Prediction Systems ◽

Visualization Techniques

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

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

Monthly Weather Review ◽

10.1175/mwr-d-18-0052.1 ◽

2019 ◽

Vol 147 (6) ◽

pp. 1967-1987 ◽

Cited By ~ 2

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.

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

Weather and Forecasting ◽

10.1175/waf-d-19-0188.1 ◽

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.

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