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N. Vigaud, A. W. Robertson, and M. K. Tippett

Abstract

Probabilistic forecasts of weekly and week 3–4 averages of precipitation are constructed using extended logistic regression (ELR) applied to three models (ECMWF, NCEP, and CMA) from the Subseasonal-to-Seasonal (S2S) project. Individual and multimodel ensemble (MME) forecasts are verified over the common period 1999–2010. The regression parameters are fitted separately at each grid point and lead time for the three ensemble prediction system (EPS) reforecasts with starts during January–March and July–September. The ELR produces tercile category probabilities for each model that are then averaged with equal weighting. The resulting MME forecasts are characterized by good reliability but low sharpness. A clear benefit of multimodel ensembling is to largely remove negative skill scores present in individual forecasts. The forecast skill of weekly averages is higher in winter than summer and decreases with lead time, with steep decreases after one and two weeks. Week 3–4 forecasts have more skill along the U.S. East Coast and the southwestern United States in winter, as well as over west/central U.S. regions and the intra-American sea/east Pacific during summer. Skill is also enhanced when the regression parameters are fit using spatially smoothed observations and forecasts. The skill of week 3–4 precipitation outlooks has a modest, but statistically significant, relation with ENSO and the MJO, particularly in winter over the southwestern United States.

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N. Vigaud, A.W. Robertson, and M. K. Tippett

Abstract

Four recurrent weather regimes are identified over North America from October to March through a k-means clustering applied to MERRA daily 500-hPa geopotential heights over the 1982–2014 period. Three regimes resemble Rossby wave train patterns with some baroclinicity, while one is related to an NAO-like meridional pressure gradient between eastern North America and western regions of the North Atlantic. All regimes are associated with distinct rainfall and surface temperature anomalies over North America. The four-cluster partition is well reproduced by ECMWF week-1 reforecasts over the 1995–2014 period in terms of spatial structures, daily regime occurrences, and seasonal regime counts. The skill in forecasting daily regime sequences and weekly regime counts is largely limited to 2 weeks. However, skill relationships with the MJO, ENSO, and SST variability in the Atlantic and Indian Oceans suggest further potential for subseasonal predictability based on wintertime large-scale weather regimes.

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N. Vigaud, M. K. Tippett, J. Yuan, A. W. Robertson, and N. Acharya

Abstract

The extent to which submonthly forecast skill can be increased by spatial pattern correction is examined in probabilistic rainfall forecasts of weekly and week-3–4 averages, constructed with extended logistic regression (ELR) applied to three ensemble prediction systems from the Subseasonal-to-Seasonal (S2S) project database. The new spatial correction method projects the ensemble-mean rainfall neighboring each grid point onto Laplacian eigenfunctions and then uses those amplitudes as predictors in the ELR. Over North America, individual and multimodel ensemble (MME) forecasts that are based on spatially averaged rainfall (e.g., first Laplacian eigenfunction) are characterized by good reliability, better sharpness, and higher skill than those using the gridpoint ensemble mean. The skill gain is greater for week-3–4 averages than week-3 leads and is largest for MME week-3–4 outlooks that are almost 2 times as skillful as MME week-3 forecasts over land. Skill decreases when using more Laplacian eigenfunctions as predictors, likely because of the difficulty in fitting additional parameters from the relatively short common reforecast period. Higher skill when increasing reforecast length indicates potential for further improvements. However, the current design of most subseasonal forecast experiments may prove to be a limit on the complexity of correction methods. Relatively high skill for week-3–4 outlooks with winter starts during El Niño and MJO phases 2–3 and 6–7 reflects particular opportunities for skillful predictions.

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Michael K. Tippett, Jeffrey L. Anderson, Craig H. Bishop, Thomas M. Hamill, and Jeffrey S. Whitaker

Abstract

Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.

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