Global Sea Surface Temperature Prediction Using a Multimodel Ensemble

Abstract In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be predicted as a boundary condition to the AGCM. In this study, a global SST prediction system has been developed as a part of the tier-two seasonal prediction system. This system uses predictions from four models—one dynamic, two statistical, and persistence—and a simple composite ensemble method is applied to these models. The simple composite ensemble prediction system has predictive skill over most of the global oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared with other more sophisticated ensemble methods. The simple composite method has forecast skill comparable to the other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.

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A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

Weather and Forecasting ◽

10.1175/2008waf2222140.1 ◽

2009 ◽

Vol 24 (3) ◽

pp. 812-828 ◽

Cited By ~ 33

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.

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Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

Journal of Climate ◽

10.1175/jcli-d-18-0193.1 ◽

2019 ◽

Vol 32 (3) ◽

pp. 957-972 ◽

Cited By ~ 14

Author(s):

Takeshi Doi ◽

Swadhin K. Behera ◽

Toshio Yamagata

Keyword(s):

Seasonal Prediction ◽

Ensemble Prediction ◽

Economic Losses ◽

Prediction System ◽

Ensemble Size ◽

Ensemble Prediction System ◽

Skill Scores ◽

Operational Systems ◽

The Indian Ocean ◽

Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

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Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

10.5194/egusphere-egu21-13673 ◽

2021 ◽

Author(s):

Carlos Velasco-Forero ◽

Jayaram Pudashine ◽

Mark Curtis ◽

Alan Seed

Keyword(s):

Lead Time ◽

Precipitation Forecast ◽

Ensemble Prediction ◽

Lead Times ◽

Prediction System ◽

Short Term ◽

Radar Rainfall ◽

Flash Flooding ◽

Ensemble Prediction System ◽

Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.&#160; Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network.&#160;</p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented.&#160;</p> </div>

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Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Atmosphere ◽

10.3390/atmos10080458 ◽

2019 ◽

Vol 10 (8) ◽

pp. 458 ◽

Cited By ~ 1

Author(s):

Zhenzhen Liu ◽

Qiang Dai ◽

Lu Zhuo

Keyword(s):

Lead Time ◽

Rainfall Variability ◽

Ensemble Prediction ◽

Lead Times ◽

Short Term ◽

Radar Rainfall ◽

Ensemble Prediction System ◽

Predictive Skill ◽

Many Sources

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

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Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

Monthly Weather Review ◽

10.1175/2008mwr2761.1 ◽

2009 ◽

Vol 137 (4) ◽

pp. 1480-1492 ◽

Cited By ~ 18

Author(s):

Frédéric Vitart ◽

Franco Molteni

Keyword(s):

Lead Time ◽

Monsoon Rainfall ◽

Horizontal Resolution ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System ◽

Variable Resolution ◽

Sensitivity Experiments ◽

Extended Range ◽

Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

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Toward Random Sampling of Model Error in the Canadian Ensemble Prediction System

Monthly Weather Review ◽

10.1175/2009mwr3187.1 ◽

2010 ◽

Vol 138 (5) ◽

pp. 1877-1901 ◽

Cited By ~ 95

Author(s):

Martin Charron ◽

Gérard Pellerin ◽

Lubos Spacek ◽

P. L. Houtekamer ◽

Normand Gagnon ◽

...

Keyword(s):

Ensemble Kalman Filter ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System ◽

Predictive Skill ◽

Global Environmental ◽

The Impact ◽

Horizontal Grid ◽

Probabilistic Measures ◽

Global Environmental Multiscale

Abstract An updated global ensemble prediction system became operational at the Meteorological Service of Canada in July 2007. The new elements of the system include the use of 20 members instead of 16, a single dynamical core [the Global Environmental Multiscale (GEM) model], stochastic physical tendency perturbations and a kinetic energy backscatter algorithm, an ensemble Kalman filter with four-dimensional data handling, and a decrease from 1.2° to 0.9° in horizontal grid spacing. This system is compared with the former operational one using a variety of probabilistic measures. For global upper-air dynamical fields, the improvement in predictive skill for equivalent forecast quality is from 9 to 16 h around day 6. Precipitation forecasts, verified over Canada, are also significantly improved. The impact of each of the abovementioned new elements of the ensemble prediction system is also evaluated separately in a series of sensitivity experiments for which one given element is removed from the system.

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GEPS-Based Monthly Prediction at the Canadian Meteorological Centre

Monthly Weather Review ◽

10.1175/mwr-d-16-0138.1 ◽

2016 ◽

Vol 144 (12) ◽

pp. 4867-4883 ◽

Cited By ~ 20

Author(s):

Hai Lin ◽

Normand Gagnon ◽

Stephane Beauregard ◽

Ryan Muncaster ◽

Marko Markovic ◽

...

Keyword(s):

North America ◽

Seasonal Prediction ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System ◽

The Past ◽

The Pacific ◽

Summer Half ◽

Forecasting System ◽

Set Up

Abstract Dynamical monthly prediction at the Canadian Meteorological Centre (CMC) was produced as part of the seasonal forecasting system over the past two decades. A new monthly forecasting system, which has been in operation since July 2015, is set up based on the operational Global Ensemble Prediction System (GEPS). This monthly forecasting system is composed of two components: 1) the real-time forecast, where the GEPS is extended to 32 days every Thursday; and 2) a 4-member hindcast over the past 20 years, which is used to obtain the model climatology to calibrate the monthly forecast. Compared to the seasonal prediction system, the GEPS-based monthly forecasting system takes advantage of the increased model resolution and improved initialization. Forecasts of the past 2-yr period (2014 and 2015) are verified. Analysis is performed separately for the winter half-year (November–April), and the summer half-year (May–October). Weekly averages of 2-m air temperature (T2m) and 500-hPa geopotential height (Z500) are assessed. For Z500 in the Northern Hemisphere, limited skill can be found beyond week 2 (days 12–18) in summer, while in winter some skill exists over the Pacific and North American region beyond week 2. For T2m in North America, significant skill is found over a large part of the continent all the way to week 4 (days 26–32). The distribution of the wintertime T2m skill in North America is consistent with the influence of the Madden–Julian oscillation, indicating that a significant part of predictability likely comes from the tropics.

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Predictability of Frontal Waves and Cyclones

Weather and Forecasting ◽

10.1175/waf-d-15-0039.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1291-1302 ◽

Cited By ~ 5

Author(s):

Thomas H. A. Frame ◽

John Methven ◽

Nigel M. Roberts ◽

Helen A. Titley

Keyword(s):

Lead Time ◽

Spatial Scales ◽

Statistical Properties ◽

Forecast Skill ◽

Ensemble Prediction ◽

Prediction System ◽

Number Density ◽

Ensemble Prediction System ◽

Probability Forecasts ◽

Stochastic Physics

Abstract The statistical properties and skill in predictions of objectively identified and tracked cyclonic features (frontal waves and cyclones) are examined in the 15-day version of the Met Office Global and Regional Ensemble Prediction System (MOGREPS-15). The number density of cyclonic features is found to decline with increasing lead time, with analysis fields containing weak features that are not sustained past the first day of the forecast. This loss of cyclonic features is associated with a decline in area-averaged enstrophy with increasing lead time. Both feature number density and area-averaged enstrophy saturate by around 7 days into the forecast. It is found that the feature number density and area-averaged enstrophy of forecasts produced using model versions that include stochastic energy backscatter saturate at higher values than forecasts produced without stochastic physics. The ability of MOGREPS-15 to predict the locations of cyclonic features of different strengths is evaluated at different spatial scales by examining the Brier skill (relative to the analysis climatology) of strike probability forecasts: the probability that a cyclonic feature center is located within a specified radius. The radius at which skill is maximized increases with lead time from 650 km at 12 h to 950 km at 7 days. The skill is greatest for the most intense features. Forecast skill remains above zero at these scales out to 14 days for the most intense cyclonic features, but only out to 8 days when all features are included irrespective of intensity.

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Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts

Monthly Weather Review ◽

10.1175/2009mwr2977.1 ◽

2010 ◽

Vol 138 (1) ◽

pp. 176-189 ◽

Cited By ~ 36

Author(s):

Felix Fundel ◽

Andre Walser ◽

Mark A. Liniger ◽

Christoph Frei ◽

Christof Appenzeller

Keyword(s):

Return Period ◽

Lead Time ◽

Precipitation Forecast ◽

Ensemble Prediction ◽

Small Scale ◽

Limited Area ◽

Return Periods ◽

Model Errors ◽

Ensemble Prediction System ◽

Forecast Lead Time

Abstract The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts increases the precipitation forecast skill substantially (about 1 day in forecast lead time). The largest improvements are achieved during winter months. The reasonable choice of the length of the reforecast climatology is estimated for an efficient use of this computational expensive calibration method.

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Dynamical Seasonal Prediction of Tropical Cyclone Activity Using the FGOALS-f2 Ensemble Prediction System

Weather and Forecasting ◽

10.1175/waf-d-20-0189.1 ◽

2021 ◽

Vol 36 (5) ◽

pp. 1759-1778

Author(s):

Jinxiao Li ◽

Qing Bao ◽

Yimin Liu ◽

Guoxiong Wu ◽

Lei Wang ◽

...

Keyword(s):

Tropical Cyclone ◽

Real Time ◽

Large Scale ◽

Southern Oscillation ◽

Correlation Coefficients ◽

Seasonal Prediction ◽

Prediction Skill ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System

AbstractThere is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.

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