scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals Generalization of the Discrete Brier and Ranked Probability Skill Scores for Weighted Multimodel Ensemble Forecasts

2007 ◽  
Vol 135 (7) ◽  
pp. 2778-2785 ◽  
Author(s):  
Andreas P. Weigel ◽  
Mark A. Liniger ◽  
Christof Appenzeller
Keyword(s):  
Practical Importance ◽  
Skill Score ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Model Case ◽  
Skill Scores ◽  
Small Ensemble

Abstract This note describes how the widely used Brier and ranked probability skill scores (BSS and RPSS, respectively) can be correctly applied to quantify the potential skill of probabilistic multimodel ensemble forecasts. It builds upon the study of Weigel et al. where a revised RPSS, the so-called discrete ranked probability skill score (RPSSD), was derived, circumventing the known negative bias of the RPSS for small ensemble sizes. Since the BSS is a special case of the RPSS, a debiased discrete Brier skill score (BSSD) could be formulated in the same way. Here, the approach of Weigel et al., which so far was only applicable to single model ensembles, is generalized to weighted multimodel ensemble forecasts. By introducing an “effective ensemble size” characterizing the multimodel, the new generalized RPSSD can be expressed such that its structure becomes equivalent to the single model case. This is of practical importance for multimodel assessment studies, where the consequences of varying effective ensemble size need to be clearly distinguished from the true benefits of multimodel combination. The performance of the new generalized RPSSD formulation is illustrated in examples of weighted multimodel ensemble forecasts, both in a synthetic random forecasting context, and with real seasonal forecasts of operational models. A central conclusion of this study is that, for small ensemble sizes, multimodel assessment studies should not only be carried out on the basis of the classical RPSS, since true changes in predictability may be hidden by bias effects—a deficiency that can be overcome with the new generalized RPSSD.

scholarly journals A Comprehensive Assessment of CFS Seasonal Forecasts over the Tropics*

2012 ◽  
Vol 27 (1) ◽  
pp. 3-27 ◽  
Author(s):  
K. P. Sooraj ◽  
H. Annamalai ◽  
Arun Kumar ◽  
Hui Wang
Keyword(s):  
Real Time ◽  
El Niño ◽  
El Nino ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Seasonal Forecasts ◽  
Skill Scores ◽  
Different Types ◽  
The Tropics ◽  
Sst Anomalies

Abstract The 15-member ensemble hindcasts performed with the National Centers for Environmental Prediction Climate Forecast System (CFS) for the period 1981–2005, as well as real-time forecasts for the period 2006–09, are assessed for seasonal prediction skills over the tropics, from deterministic (anomaly correlation), categorical (Heidke skill score), and probabilistic (rank probability skill score) perspectives. Further, persistence, signal-to-noise ratio, and root-mean-square error analyses are also performed. The CFS demonstrates high skill in forecasting El Niño–Southern Oscillation (ENSO) related sea surface temperature (SST) anomalies during developing and mature phases, including that of different types of El Niño. During ENSO, the space–time evolution of anomalous SST, 850-hPa wind, and rainfall along the equatorial Pacific, as well as the mechanisms involved in the teleconnection to the tropical Indian Ocean, are also well represented. During ENSO phase transition and in the summer, the skill of forecasting Pacific SST anomalies is modest. An examination of CFS ability in forecasting seasonal rainfall anomalies over the U.S. Affiliated Pacific Islands (USAPI) indicates that forecasting the persistence of dryness from El Niño winter into the following spring/summer is skillful at leads > 3 months. During strong El Niño years the persistence is predicted by all members with a 6-month lead time. Also, the model is skillful in predicting regional rainfall responses during different types of El Niño. Since both deterministic and probabilistic skill scores converge, the suggestion is that the forecast is useful. The model’s skill in the real-time forecasts for the period 2006–09 is also discussed. The results suggest the feasibility that a dynamical-system-based seasonal prediction of precipitation over the USAPI can be considered.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals On the Interpretation and Utility of Skill Information for Seasonal Climate Predictions

2007 ◽  
Vol 135 (5) ◽  
pp. 1974-1984 ◽  
Author(s):  
Arun Kumar
Keyword(s):  
Time Series ◽  
General Circulation ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Steady Increase ◽  
Seasonal Forecasts ◽  
Past Performance ◽  
Seasonal Climate ◽  
The Past

Abstract In recent years, there has been a steady increase in the emphasis on routine seasonal climate predictions and their potential for enhancing societal benefits and mitigating losses related to climate extremes. It is also suggested by the users, as well as by the producers of climate predictions, that for informed decision making, real-time seasonal climate predictions should be accompanied by a corresponding level of skill estimated from a sequence of the past history of forecasts. In this paper it is discussed whether conveying skill information to the user community can indeed deliver the promised benefits or whether issues inherent in the estimates of seasonal prediction skill may still lead to potential misinterpretation of the information content associated with seasonal predictions. Based on the analysis of atmospheric general circulation model simulations, certain well-known, but often underappreciated, issues inherent in the estimates of seasonal prediction skill from the past performance of seasonal forecasts are highlighted. These include the following: 1) the stability of estimated skill depends on the length of the time series over which seasonal forecasts are verified, leading to scenarios where error bars on the estimated skill could be of the same magnitude as the skill itself; 2) a single estimate of skill obtained from the verification over a given forecast time series, because of variation in the signal-to-noise ratio from one year to another, is generally not representative of seasonal prediction skill conditional to sea surface temperature anomalies on a case-by-case basis. These issues raise questions on the interpretation, presentation, and utilization of skill information for seasonal prediction efforts and present opportunities for increased dialogue and the exploration of ways for their optimal utilization by decision makers.

scholarly journals The Impact of a Low Bias in Snow Water Equivalent Initialization on CFS Seasonal Forecasts

2017 ◽  
Vol 30 (21) ◽  
pp. 8657-8671 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
Nicholas Dawson
Keyword(s):  
Lead Time ◽  
Snow Water Equivalent ◽  
Seasonal Prediction ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Air Temperatures ◽  
Forecast Lead Time ◽  
Snow Water ◽  
The Impact

Across much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April–June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April–June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April–June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April–June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring–summer transition over snowy regions.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 11 (1) ◽  
pp. 919-944 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
The World ◽  
The Standardized Precipitation Index

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two datasets as initial conditions: the Global Precipitation Climatology Center (GPCC) and the ECMWF ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation: the most current ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast skill is concentrated on verification months where precipitation deficits are likely to have higher drought impacts and grouped over different regions in the world. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. Our results also support recent questions whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

Lagged ensemble vs burst sampling strategy for initializing sub-seasonal forecasts

2020 ◽  
Author(s):  
Frederic Vitart
Keyword(s):  
Real Time ◽  
Current System ◽  
Sampling Strategy ◽  
Seasonal Prediction ◽  
Daily Basis ◽  
Optimal Number ◽  
Lead Times ◽  
Large Set ◽  
Ensemble Size ◽  
Seasonal Forecasts

<p>The WWRP/WCRP Sub-seasonal to Seasonal Prediction (S2S) database contains real-time and re-forecasts from 11 operational centres. Several S2S models are initialized frequently with a small ensemble size (e.g. 4 ensemble members every day). In order to inflate the ensemble size, real-time forecasts are produced by combining all the forecasts produced over a window of several days to produce a “lagged ensemble” in which ensemble members have different lead times. The other S2S models are initialized less frequently (e.g. once or twice a week) but with a large ensemble size (e.g. 51 members). This initialization strategy is referred to as “burst sampling”. Both strategies have advantages and inconvenience and it is not clear which strategy is optimal for sub-seasonal prediction. <br>The ECMWF sub-seasonal forecasts are produced using the burst-sampling strategy: a 51-member ensemble is run twice a week (every Monday and Thursday). A large set of re-forecasts, run on a daily basis, have been produced to assess the potential benefit of replacing this current ensemble configuration by a lagged-ensemble approach. We are interested in answering the following two questions, if the current 51-member ensemble run twice a week is replaced by a sub-seasonal ensemble run every day with an ensemble size Ne:</p><p>• What is the minimum value of Ne so that there is a lagged ensemble forecast (Nd forecast days combined) which is at least as skilful as the current system on Mondays and Thursdays?</p><p>• For a given value of Ne, what is the optimal number Nd of forecast days to combine? Greater values of Nd produce larger lagged ensemble size, but also reduce the accuracy of the forecasts by adding ensemble members with older start dates. </p><p>Results indicate that:</p><p>1. A lagged ensemble is more beneficial in the Tropics than in the Northern Extratropics particularly for shorter lead times (weeks 1 and 2).  </p><p>2. The minimum daily ensemble size to produce sub-seasonal forecasts (beyond week 1) at least as skilful as the current ECMWF forecasts on Mondays and Thursdays is Ne=20 with an optimal number of lag days Nd=3. The values of Ne (Nd) decrease (increase) with increased lead time. </p><p>These results suggest that a lagged-ensemble could be a viable alternative to the current ensemble extended-range forecasting system at ECMWF. </p>

Seasonal prediction of the austral summer Southern Annular Mode, and investigation of its connection to the Southern Ocean

2020 ◽  
Author(s):  
Tim Hempel ◽  
André Düsterhus ◽  
Johanna Baehr
Keyword(s):  
Southern Ocean ◽  
Austral Summer ◽  
Seasonal Prediction ◽  
Southern Annular Mode ◽  
Skill Score ◽  
Prediction Skill ◽  
High Latitudes ◽  
Max Planck ◽  
Annular Mode ◽  
Atmospheric Variables

<div>The Southern Annular Mode (SAM) modulates the eddy-driven-westerly jet in the southern mid- to high-latitudes. This modulation has major impacts on the seasonal climate in the southern hemisphere. Thus, a seasonal prediction of the SAM is desirable. Still, only few studies show a significant prediction skill on this timescale. In this contribution the prediction skill of the SAM is improved by using its physical links to the Southern Ocean.</div><div>We use the seasonal prediction system based on the Max-Planck-Institute Earth-System-Model (MPI-ESM) in mixed resolution (MR). In ensemble reforecasts for 1982 to 2016 we find large regions of the surface ocean in the southern mid- to high-latitudes to be significantly predictable on seasonal timescales. In contrast, the atmospheric variables in the same regions show only very little skill. In the austral summer season (December-January-February (DJF)) different ensemble members evolve considerably different in the ocean and the atmosphere. With physical links between the Southern Ocean and the SAM, identified in ERA-Interim, we only select ensemble members that also show these links. This process is repeated every year and leads to a new time series with a reduced number of ensemble members. To evaluate the prediction skill of the new ensemble mean SAM we use the correlation coefficient and the Heidke Skill Score (HSS). The reduced ensemble has a correlation with ERA of 0.50, while the full ensemble shows a correlation of 0.31. Similarly the reduced ensemble has a HSS of 0.35 compared to the HSS of the full ensemble of 0.17.</div><div>We additionally show that choosing the same ensemble members we selected for the SAM also increases the prediction skill for other atmospheric variables. The reduced ensemble has an increased prediction skill for pressure, wind, and temperature in the southern mid- to high-latitudes, to which the selection is targeted.</div>

scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2017 ◽  
Vol 21 (8) ◽  
pp. 4103-4114 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Warning System ◽  
Skill Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Global Water

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

scholarly journals Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United States

2012 ◽  
Vol 13 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jin-Ho Yoon ◽  
Kingtse Mo ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Initial Conditions ◽  
Meteorological Drought ◽  
Seasonal Prediction ◽  
Distribution Functions ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Simple Method ◽  
Princeton University

Abstract A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.

Sign in / Sign up

Export Citation Format

Share Document