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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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Muhammad Rezaul Haider, Malaquias Peña, and Emmanouil Anagnostou

Abstract

The performance of first-moment and full-distribution bias-correction methods of monthly temperature distributions for seasonal prediction is analyzed by comparing two approaches: the standard all-in-data procedure and the 6-hourly stratification of data. Five models are applied to remove the systematic errors of the CFSv2 forecasts of temperature for the rainy season in the Ethiopian Blue Nile River basin domain. Using deterministic evaluation measures, it is found that the stratification marginally increases the forecast skill especially in regions where the data distribution of temperature is prominently multimodal. The improvement may be attributed to a split of the mixed distribution into a set of unimodal distributions. A necessary condition for this splitting into unimodal distributions is that the amplitude of the diurnal cycle be larger than the interannual variability in the sample. The maximum improvement of stratification is achieved by the first-moment correction model.

Significance Statement

This paper evaluates bias-correction methods of monthly forecast distributions of temperature to improve seasonal forecast skill. It is found that marginal skill is gained when bias correction of the diurnal cycle is performed. This paper contributes to the discussion on the value of subdaily model output data.

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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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Malaquías Peña, Ming Cai, and Eugenia Kalnay

Abstract

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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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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Edmund K. M. Chang, Malaquías Peña, and Zoltan Toth

The Observing System Research and Predictability Experiment (THORPEX) is an international research and development program conducted under the auspice of the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO), with the goal to accelerate the improvements in the accuracy of one-day to two-week high-impact weather forecasts for the benefit of society, the economy, and the environment. THORPEX was launched in 2004 and is scheduled to conclude in 2014.

Under THORPEX, interactions between the weather research and operational communities have significantly increased worldwide. Numerous science symposia, workshops, working group meetings, and training programs have been conducted to promote

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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

Because of an oversight, there are two sign errors in Eq. (2) of Becker et al. (2013). The correct equation is shown below as it should have appeared:

We regret any inconvenience these errors may have caused.

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Andrew W. Robertson, Arun Kumar, Malaquias Peña, and Frederic Vitart

There is growing interest in the scientific, operational, and applications communities in developing forecasts that fill the gap between medium-range weather forecasts (up to 2 weeks) and long-range or seasonal ones (3–6 months). A new World Weather Research Programme/World Climate Research Programme (WWRP/WCRP initiative on subseasonal to seasonal (S2S) prediction has recently been launched to foster collaboration and research in the weather and climate communities, with the goals of improving forecast skill and physical understanding, promoting forecast uptake by operational centers, and exploitation by the applications community. A key component of the project is to create an archive of S2S

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