Prospects for Improving Subseasonal Predictions

Abstract Extending atmospheric prediction skill beyond the predictability limit of about 10 days for daily weather rests on the hope that some time-averaged aspects of anomalous circulations remain predictable at longer forecast lead times, both because of the existence of natural low-frequency modes of atmospheric variability and coupling to the ocean with larger thermal inertia. In this paper the week-2 and week-3 forecast skill of two global coupled atmosphere–ocean models recently developed at NASA and NOAA is compared with that of much simpler linear inverse models (LIMs) based on the observed time-lag correlations of atmospheric circulation anomalies in the Northern Hemisphere and outgoing longwave radiation (OLR) anomalies in the tropics. The coupled models are found to beat the LIMs only slightly, and only if an ensemble prediction methodology is employed. To assess the potential for further skill improvement, a predictability analysis based on the relative magnitudes of forecast signal and forecast noise in the LIM framework is conducted. Estimating potential skill by such a method is argued to be superior to using the ensemble-mean and ensemble-spread information in the coupled model ensemble prediction system. The LIM-based predictability analysis yields relatively conservative estimates of the potential skill, and suggests that outside the tropics the average coupled model skill may already be close to the potential skill, although there may still be room for improvement in the tropical forecast skill.

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The Global Influence of the Madden–Julian Oscillation on Extreme Temperature Events*

Journal of Climate ◽

10.1175/jcli-d-14-00625.1 ◽

2015 ◽

Vol 28 (10) ◽

pp. 4141-4151 ◽

Cited By ~ 28

Author(s):

Satoko Matsueda ◽

Yuhei Takaya

Keyword(s):

Extreme Events ◽

Time Lag ◽

Extreme Temperature ◽

Forecast Skill ◽

Japan Meteorological Agency ◽

Ensemble Prediction ◽

Boreal Winter ◽

Madden Julian Oscillation ◽

Ensemble Prediction System ◽

Extreme Temperature Events

Abstract The authors investigated the influence of the Madden–Julian oscillation (MJO) on extreme warm and cold events, which may have large social and economic impacts. The frequencies of extreme temperature events were analyzed and compared between active and inactive MJO periods by using the 7-day running average of the 850-hPa temperature during the extended boreal winter (November–April). The results show that the frequency of extreme events is significantly modulated (i.e., increased by a factor of more than 2) by the MJO with a time lag over some areas in the extratropics as well as in the tropics. In the extratropics, the modulation of the frequency of the extreme events is roughly associated with midlatitude wave responses to tropical forcing and anomalous lower-level circulation due to the MJO. The relationship between the MJO and forecast skill of extreme temperature events was also investigated by using a suite of hindcasts made with the operational one-month ensemble prediction system of the Japan Meteorological Agency. Forecast skill of extreme events occurring after active MJO periods tend to be better over some areas, compared with after inactive MJO periods. These results suggest that a realistic representation of the MJO and of the atmospheric response to the MJO in forecast models is important for providing reliable early warning information about extreme events.

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Sensitivity of Global Ensemble Forecasts to the Initial Ensemble Mean and Perturbations: Comparison of EnKF, Singular Vector, and 4D-Var Approaches

Monthly Weather Review ◽

10.1175/2010mwr3296.1 ◽

2010 ◽

Vol 138 (10) ◽

pp. 3886-3904 ◽

Cited By ~ 4

Author(s):

Mark Buehner ◽

Ahmed Mahidjiba

Keyword(s):

Ensemble Prediction ◽

Boreal Summer ◽

Prediction System ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Var Analysis ◽

Initial Ensemble ◽

Ensemble Mean ◽

The Tropics ◽

The Impact

Abstract This study examines the sensitivity of global ensemble forecasts to the use of different approaches for specifying both the initial ensemble mean and perturbations. The current operational ensemble prediction system of the Meteorological Service of Canada uses the ensemble Kalman filter (EnKF) to define both the ensemble mean and perturbations. To evaluate the impact of different approaches for obtaining the initial ensemble perturbations, the operational EnKF approach is compared with using either no initial perturbations or perturbations obtained using singular vectors (SVs). The SVs are computed using the (dry) total-energy norm with a 48-h optimization time interval. Random linear combinations of 60 SVs are computed for each of three regions. Next, the impact of replacing the initial ensemble mean, currently the EnKF ensemble mean analysis, with the higher-resolution operational four-dimensional variational data assimilation (4D-Var) analysis is evaluated. For this comparison, perturbations are provided by the EnKF. All experiments are performed over two-month periods during both the boreal summer and winter using a system very similar to the global ensemble prediction system that became operational on 10 July 2007. Relative to the operational configuration that relies on the EnKF, the use of SVs to compute initial perturbations produces small, but statistically significant differences in probabilistic forecast scores in favor of the EnKF both in the tropics and, for a limited set of forecast lead times, in the summer hemisphere extratropics, whereas the results are very similar in the winter hemisphere extratropics. Both approaches lead to significantly better ensemble forecasts than with no initial perturbations, though results are quite similar in the tropics when using SVs and no perturbations. The use of an initial-time norm that does not include information on analysis uncertainty and the lack of linearized moist processes in the calculation of the SVs are two factors that limit the quality of the resulting SV-based ensemble forecasts. Relative to the operational configuration, use of the 4D-Var analysis to specify the initial ensemble mean results in improved probabilistic forecast scores during the boreal summer period in the southern extratropics and tropics, but a near-neutral impact otherwise.

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A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

Monthly Weather Review ◽

10.1175/2007mwr2109.1 ◽

2008 ◽

Vol 136 (2) ◽

pp. 443-462 ◽

Cited By ~ 41

Author(s):

Xiaoli Li ◽

Martin Charron ◽

Lubos Spacek ◽

Guillem Candille

Keyword(s):

Deep Convection ◽

Horizontal Resolution ◽

Forecast Skill ◽

Convective Precipitation ◽

Ensemble Prediction ◽

Limited Area ◽

Prediction System ◽

Ensemble Prediction System ◽

Stochastic Perturbations ◽

Singular Vectors

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

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Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System

Monthly Weather Review ◽

10.1175/2008mwr2498.1 ◽

2008 ◽

Vol 136 (11) ◽

pp. 4092-4104 ◽

Cited By ~ 32

Author(s):

Linus Magnusson ◽

Martin Leutbecher ◽

Erland Källén

Keyword(s):

Singular Vector ◽

Initial Perturbation ◽

Error Variance ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Prediction System ◽

Perturbation Energy ◽

Analysis Error ◽

The Tropics ◽

Model Environment

Abstract In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) in the same model environment is presented. A simple breeding system (simple BV-EPS) as well as one with regional rescaling dependent on an estimate of the analysis error variance (masked BV-EPS) were used. The ECMWF Integrated Forecast System has been used and the three experiments are compared for 46 forecast cases between 1 December 2005 and 15 January 2006. By studying the distribution of the perturbation energy it was possible to see large differences between the experiments initially, but after 48 h the distributions have converged. Using probabilistic scores, these results show that SV-EPS has a somewhat better performance for the Northern Hemisphere compared to BV-EPS. For the Southern Hemisphere masked BV-EPS and SV-EPS yield almost equal results. For the tropics the masked breeding ensemble shows the best performance during the first 6 days. One reason for this is the current setup of the singular vector ensemble at ECMWF yielding in general very low initial perturbation amplitudes in the tropics.

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ENSO predictability over the past 137 years based on a CESM ensemble prediction system

Journal of Climate ◽

10.1175/jcli-d-21-0450.1 ◽

2021 ◽

pp. 1-38

Author(s):

Ting Liu ◽

Xunshu Song ◽

Youmin Tang ◽

Zheqi Shen ◽

Xiaoxiao Tan

Keyword(s):

General Circulation ◽

Southern Oscillation ◽

Ensemble Prediction ◽

Coupled Models ◽

Abrupt Decrease ◽

Ensemble Prediction System ◽

Enso Events ◽

Intermediate Complexity ◽

Enso Predictability ◽

Dispersion Component

AbstractIn this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models.

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Skill Evaluation of Extended-Range Forecasts of Rainfall and Temperature over the Meteorological Subdivisions of India

Weather and Forecasting ◽

10.1175/waf-d-18-0055.1 ◽

2019 ◽

Vol 34 (1) ◽

pp. 81-101 ◽

Cited By ~ 3

Author(s):

Susmitha Joseph ◽

A. K. Sahai ◽

R. Phani ◽

R. Mandal ◽

A. Dey ◽

...

Keyword(s):

Lead Time ◽

Coupled Model ◽

Southwest Monsoon ◽

Tropical Meteorology ◽

Ensemble Prediction ◽

Northeast Monsoon ◽

Ensemble Prediction System ◽

Extended Range ◽

The Government ◽

Skill Evaluation

Abstract Under the National Monsoon Mission Project initiated by the government of India’s Ministry of Earth Sciences, an indigenous dynamical ensemble prediction system (EPS) has been developed at the Indian Institute of Tropical Meteorology based on the state-of-the-art Climate Forecast System Model version 2 (CFSv2) coupled model, for extended-range (~15–20 days in advance) prediction. The forecasts are generated for the entire year covering the southwest monsoon, the northeast monsoon, and the summer and winter seasons. As the forecast of rainfall is important during the southwest and northeast monsoon seasons, along with that of the temperature during the summer and winter seasons, the present study documents the deterministic as well as probabilistic skill of the EPS in predicting the results in the respective seasons, over various meteorological subdivisions throughout India, on a pentad-lead time scale. The EPS is found to be skillful in predicting rainfall during the southwest and northeast monsoon seasons, as well as temperature during the summer and winter seasons, across different subdivisions of India. In addition, the EPS is noted to be skillful in predicting selected extremes in rainfall and temperature. This affirms the reliability and usefulness of the present EPS from an operational perspective.

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Ensemble-Based Forecast Uncertainty Analysis of Diverse Heavy Rainfall Events

Weather and Forecasting ◽

10.1175/2010waf2222378.1 ◽

2010 ◽

Vol 25 (4) ◽

pp. 1103-1122 ◽

Cited By ~ 24

Author(s):

Russ S. Schumacher ◽

Christopher A. Davis

Keyword(s):

United States ◽

Tropical Cyclones ◽

Heavy Rainfall ◽

Warm Season ◽

The United States ◽

Forecast Skill ◽

Ensemble Prediction ◽

Lead Times ◽

Cool Season ◽

Ensemble Prediction System

Abstract This study examines widespread heavy rainfall over 5-day periods in the central and eastern United States. First, a climatology is presented that identifies events in which more than 100 mm of precipitation fell over more than 800 000 km2 in 5 days. This climatology shows that such events are most common in the cool season near the Gulf of Mexico coast and are rare in the warm season. Then, the focus turns to the years 2007 and 2008, when nine such events occurred in the United States, all of them leading to flooding. Three of these were associated with warm-season convection, three took place in the cool season, and three were caused by landfalling tropical cyclones. Global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System are used to assess forecast skill and uncertainty for these nine events, and to identify the types of weather systems associated with their relative levels of skill and uncertainty. Objective verification metrics and subjective examination are used to determine how far in advance the ensemble identified the threat of widespread heavy rains. Specific conclusions depend on the rainfall threshold and the metric chosen, but, in general, predictive skill was highest for rainfall associated with tropical cyclones and lowest for the warm-season cases. In almost all cases, the ensemble provides very skillful 5-day forecasts when initialized at the beginning of the event. In some of the events—particularly the tropical cyclones and strong baroclinic cyclones—the ensemble still shows considerable skill in 96–216-h precipitation forecasts. In other cases, however, the skill drops off much more rapidly as lead time increases. In particular, forecast skill at long lead times was the lowest and spread was the largest in the two cases associated with meso-α-scale to synoptic-scale vortices that were cut off from the primary upper-level jet. In these cases, it appears that when the vortex is present in the initial conditions, the resulting precipitation forecasts are quite accurate and certain, but at longer lead times when the model is required to both develop and correctly evolve the vortex, forecast quality is low and uncertainty is large. These results motivate further investigation of the events that were poorly predicted.

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A Study of Subseasonal Predictability

Monthly Weather Review ◽

10.1175//2558.1 ◽

2003 ◽

Vol 131 (8) ◽

pp. 1715-1732 ◽

Cited By ~ 68

Author(s):

Matthew Newman ◽

Prashant D. Sardeshmukh ◽

Christopher R. Winkler ◽

Jeffrey S. Whitaker

Keyword(s):

Signal To Noise Ratio ◽

Forecast Error ◽

Grid Point ◽

Time Lag ◽

Forecast Skill ◽

Stochastic Noise ◽

Signal To Noise ◽

Noise Ratio ◽

The Tropics ◽

Tropical Heating

Abstract The predictability of weekly averaged circulation anomalies in the Northern Hemisphere, and diabatic heating anomalies in the Tropics, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics. In both winter and summer, the model's forecast skill at week 2 (days 8–14) and week 3 (days 15–21) is comparable to that of a comprehensive global medium-range forecast (MRF) model developed at the National Centers for Environmental Prediction (NCEP). Its skill at week 3 is actually higher on average, partly due to its better ability to forecast tropical heating variations and their influence on the extratropical circulation. The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges. The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent. An average signal-to-noise ratio is estimated at each grid point on the hemisphere and is then used to estimate the potential predictability of weekly variations at the point. In general, this predictability is about 50% higher in winter than summer over the Pacific and North America sectors; the situation is reversed over Eurasia and North Africa. Skill in predicting tropical heating variations is important for realizing this potential skill. The actual LIM forecast skill has a similar geographical structure but weaker magnitude than the potential skill. In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. This contrasts with the traditional emphasis in studies of shorter-term predictability on flow-dependent instabilities, that is, on the predictable variations of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM's propagator, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and the Northern Hemispheric circulation is not only potentially but also actually more predictable than at other times.

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On Ensemble Prediction Using Singular Vectors Started from Forecasts

Monthly Weather Review ◽

10.1175/mwr3018.1 ◽

2005 ◽

Vol 133 (10) ◽

pp. 3038-3046 ◽

Cited By ~ 13

Author(s):

Martin Leutbecher

Keyword(s):

Singular Vector ◽

Ensemble Prediction ◽

Prediction System ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Singular Vectors ◽

The Tropics ◽

The Impact ◽

Very High ◽

Tropical Cyclone Tracks

Abstract The impact on the ECMWF Ensemble Prediction System of using singular vectors computed from 12-h forecasts instead of analyses has been studied. Results are based on 34 cases in November–December 1999 and 28 cases in September 2003. The similarity between singular vectors started from a 12-h forecast and singular vectors started from an analysis is very high for the extratropical singular vectors in each of the 62 cases and for both hemispheres. Consistently, ensemble scores and spread measures show close to neutral impact on geopotential height in the extratropics. The sensitivity of the singular vectors to the choice of trajectory is larger in the Tropics than in the extratropics. However, the spread in tropical cyclone tracks is not significantly decreased if singular vectors computed from 12-h forecasts are used. The computation of singular vectors from forecasts could be used to disseminate the ensemble forecasts earlier or to allocate more resources to the nonlinear forecasts. Furthermore, it greatly facilitates the implementation of computationally more demanding configurations for the singular-vector-based initial perturbations.

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