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MalaquÍas Peña and Michael W. Douglas

Abstract

This paper describes the mean atmospheric conditions associated with synoptic-scale rainfall fluctuations over Central America during the rainy season. The study is based on composites of wet and dry spells; these composites are generated from six years (1990–94 and 1997) of daily rainfall observations from select Central American stations, one year (1997) of upper-air wind data from an enhanced sounding network over the region, National Center for Environmental Prediction (NCEP) reanalysis data, and outgoing longwave radiation (OLR) data. Wet spells, defined as days when 75% or more of the stations along the Pacific side of Nicaragua, Costa Rica, and Panama reported rainfall, are associated with weaker trade winds over the Caribbean and stronger cross-equatorial flow northward over the eastern Pacific. During wet spells the intensity of eastern Pacific cross-equatorial flow exceeds by several meters per second the seasonal mean in the lower and middle troposphere, and is strongest and deepest one day before the wettest day. Dry spells, defined as the days when 35% or less of these stations reported rainfall, are associated with stronger trade winds over Central America and weaker and shallower cross-equatorial flow. The basic flow patterns seen in the observation-based composites agree well with similar composites produced using reanalysis data, except that the observations show stronger cross-equatorial flow in the lower-mid troposphere over the eastern Pacific. OLR data shows that convective cloudiness anomalies associated with the wet and dry spells extend westward from Central America into the eastern tropical Pacific.

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Malaquias Peña and Huug van den Dool

Abstract

The performance of ridge regression methods for consolidation of multiple seasonal ensemble prediction systems is analyzed. The methods are applied to predict SST in the tropical Pacific based on ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) models, plus two of NCEP’s operational models. Strategies to increase the ratio of the effective sample size of the training data to the number of coefficients to be fitted are proposed and tested. These strategies include objective selection of a smaller subset of models, pooling of information from neighboring grid points, and consolidating all ensemble members rather than each model’s ensemble average. In all variations of the ridge regression consolidation methods tested, increased effective sample size produces more stable weights and more skillful predictions on independent data. While the scores may not increase significantly as the effective sampling size is increased, the benefit is seen in terms of consistent improvements over the simple equal weight ensemble average. In the western tropical Pacific, most consolidation methods tested outperform the simple equal weight ensemble average; in other regions they have similar skill as measured by both the anomaly correlation and the relative operating curve. The main obstacles to progress are a short period of data and a lack of independent information among models.

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Malaquías Peña, Ming Cai, and Eugenia Kalnay

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The impact of the local phase relationship between the low-level atmospheric circulation and the sea surface temperature (SST) on the duration of atmospheric anomalies is statistically evaluated. Using 5-day-average data from the NCEP–NCAR reanalysis, it is found that most of the long-lasting atmospheric anomalies are locally coupled with SST anomalies, with their number decreasing from the equator to the extratropics. The longer-lasting anomalies tend to have relationships of cyclonic-over-cold or anticyclonic-over-warm phase in the extratropics, and cyclonic-over-warm or anticyclonic-over-cold in the Tropics. This preferential phase relationship of the long-lasting anomalies is consistent with a predominant “atmosphere-driving” situation in the extratropics and an “ocean-driving” one in the Tropics.

A similar analysis using data from a one-way interaction model, with the ocean always forcing the atmosphere is carried out to compare the results with those from the reanalysis. The results show that the one-way interaction produces fewer (more) long-lasting anomalies in the extratropics (Tropics). These differences arise mostly in atmosphere-driving situations, namely, the cyclonic-over-cold or anticyclonic-over-warm phase relation. This suggests that ignoring the atmosphere's feedback effect on the ocean can lead to erroneous damping (lengthening) of atmospheric anomalies in the extratropics (Tropics).

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Malaquias Peña, Zoltan Toth, and Mozheng Wei

Abstract

A variety of ad hoc procedures have been developed to prevent filter divergence in ensemble-based data assimilation schemes. These procedures are necessary to reduce the impacts of sampling errors in the background error covariance matrix derived from a limited-size ensemble. The procedures amount to the introduction of additional noise into the assimilation process, possibly reducing the accuracy of the resulting analyses. The effects of this noise on analysis and forecast performance are investigated in a perfect model scenario. Alternative schemes aimed at controlling the unintended injection of noise are proposed and compared. Improved analysis and forecast accuracy is observed in schemes with minimal alteration to the evolving ensemble-based covariance structure.

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Andrew W. Robertson, Arun Kumar, Malaquias Peña, and Frederic Vitart
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Edmund K. M. Chang, Malaquías Peña, and Zoltan Toth
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Samuel P. Lillo, David B. Parsons, and Malaquias Peña

Abstract

A major winter storm took place over Mexico during 7 to 11 March 2016, impacting 28 states and leaving four million families without power. Extensive agricultural damage and livestock deaths were also reported with widespread snow across central and northern Mexico. North of the border, this system resulted in record-breaking flooding and severe weather in Texas and Louisiana. The event was due to a trough that deepened and cut off over central Mexico with 500-hPa heights that were nine standard deviations below normal, well beyond previous records! Motivated by the societal impacts of this event, this study investigates factors that contributed to the extreme trough and influenced its predictability in forecast models. A strong El Niño provided the antecedent conditions, with enhanced tropical convection over the central Pacific, a strengthened subtropical anticyclone, and poleward Rossby wave dispersion. However, unlike past strong El Niños, the North Pacific preceding this event was characterized by significant synoptic-scale Rossby wave activity on the midlatitude jet stream including multiple wave packets tracking around the globe during February and March. The interaction of one of these packets with the subtropical anticyclone aloft resulted in a large anticyclonic wave break over the east Pacific, leading to the amplification of the downstream trough over Mexico. The ability of numerical weather prediction to capture this extreme trough is directly related to the predictability of the Rossby wave packet. These results are also discussed within the context of the relationship between El Niño, Rossby wave activity, and extreme events in western North America.

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Emily J. Becker, Huug van den Dool, and Malaquias Peña

Abstract

Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables.

The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

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Emily J. Becker, Huug van den Dool, and Malaquias Peña
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Juhui Ma, Yuejian Zhu, Dingchen Hou, Xiaqiong Zhou, and Malaquias Peña

Abstract

The ensemble transform with rescaling (ETR) method has been used to produce fast-growing components of analysis error in the NCEP Global Ensemble Forecast System (GEFS). The rescaling mask contained in the ETR method constrains the amplitude of perturbations to reflect regional variations of analysis error. However, because of a lack of suitable three-dimensional (3D) analysis error estimation, in the operational GEFS the mask is based on the estimated analysis error at 500 hPa and is not flow dependent but changes monthly. With the availability of an ensemble-based data assimilation system at NCEP, a 3D mask can be computed. This study generates initial perturbations by the ensemble transform with 3D rescaling (ET_3DR) and compares the performance with the ETR. Meanwhile, the ET_3DR is also applied within the ensemble Kalman filter (EnKF) method (hereafter EnKF_3DR).

Results from a set of experiments indicate that the 3D mask suppresses perturbations less in unstable regions. Relative to the ETR, the large amplitudes of the ET_3DR initial perturbations at 500 hPa better reflect areas of baroclinic instability over the extratropics and deep convection over the tropics. Furthermore, the maxima of the vertical distribution for the ET_3DR initial perturbations correspond to the heights of the subtropical westerly and tropical easterly jet regions. Such perturbations produce faster spread growths. Results with EnKF_3DR also show benefits from an orthonormalization by the ensemble transform algorithm and amplitude constraint by the 3D mask rescaling. Thus, the EnKF_3DR forecasts outperform the EnKF.

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