Malaquias Peña

The NCEP Climate Forecast System

Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric compon...

RISE: Undergraduates Find That Regime Changes in Lorenz's Model are Predictable

Abstract No Abstract Available. Note from the BAMS editors: This article was originally reviewed and submitted to BAMS before the student section editorial board was formed, but is included here as an outstanding example of educational opportunities now available to undergraduates.

Characteristics of Wet and Dry Spells over the Pacific Side of Central America during the Rainy Season

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 crossequatorial 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.

Consolidation of Multimodel Forecasts by Ridge Regression: Application to Pacific Sea Surface Temperature

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 bene...

Life Span of Subseasonal Coupled Anomalies

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 longerlasting 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).

Estimation of analysis and forecast error variances

Accurate estimates of error variances in numerical analyses and forecasts (i.e. difference between analysis or forecast fields and nature on the resolved scales) are critical for the evaluation of forecasting systems, the tuning of data assimilation (DA) systems and the proper initialisation of ensemble forecasts. Errors in observations and the difficulty in their estimation, the fact that estimates of analysis errors derived via DA schemes, are influenced by the same assumptions as those used to create the analysis fields themselves, and the presumed but unknown correlation between analysis and forecast errors make the problem difficult. In this paper, an approach is introduced for the unbiased estimation of analysis and forecast errors. The method is independent of any assumption or tuning parameter used in DA schemes. The method combines information from differences between forecast and analysis fields (‘perceived forecast errors’) with prior knowledge regarding the time evolution of (1) forecast error variance and (2) correlation between errors in analyses and forecasts. The quality of the error estimates, given the validity of the prior relationships, depends on the sample size of independent measurements of perceived errors. In a simulated forecast environment, the method is demonstrated to reproduce the true analysis and forecast error within predicted error bounds. The method is then applied to forecasts from four leading numerical weather prediction centres to assess the performance of their corresponding DA and modelling systems. Error variance estimates are qualitatively consistent with earlier studies regarding the performance of the forecast systems compared. The estimated correlation between forecast and analysis errors is found to be a useful diagnostic of the performance of observing and DA systems. In case of significant model-related errors, a methodology to decompose initial value and model-related forecast errors is also proposed and successfully demonstrated.

Controlling Noise in Ensemble Data Assimilation Schemes

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.

Ensemble Transform with 3D Rescaling Initialization Method

AbstractThe 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, ...

Impact of Sea Surface Temperature Forcing on Weeks 3 and 4 Forecast Skill in the NCEP Global Ensemble Forecasting System

AbstractThe Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing ex...

Finding the driver of local ocean–atmosphere coupling in reanalyses and CMIP5 climate models

Identification of the driver of coupled anomalies in the climate system is of great importance for a better understanding of the system and for its use in predictive efforts with climate models. The present analysis examines the robustness of a physical method proposed three decades ago to identify coupled anomalies as of atmospheric or oceanic origin by analyzing 850 mb vorticity and sea surface temperature anomalies. The method is then used as a metric to assess the coupling in climate simulations and a 30-year hindcast from models of the CMIP5 project. Analysis of the frequency of coupled anomalies exceeding one standard deviation from uncoupled NCEP/NCAR and ERA-Interim and partially coupled CFSR reanalyses shows robustness in the main results: anomalies of oceanic origin arise inside the deep tropics and those of atmospheric origin outside of the tropics. Coupled anomalies occupy similar regions in the global oceans independently of the spatiotemporal resolution. Exclusion of phenomena like ENSO, NAO, or AMO has regional effects on the distribution and origin of coupled anomalies; the absence of ENSO decreases anomalies of oceanic origin and favors those of atmospheric origin. Coupled model simulations in general agree with the distribution of anomalies of atmospheric and oceanic origin from reanalyses. However, the lack of the feedback from the atmosphere to the ocean in the AMIP simulations reduces substantially the number of coupled anomalies of atmospheric origin and artificially increases it in the tropics while the number of those of oceanic origin outside the tropics is also augmented. Analysis of a single available 30-year hindcast surprisingly indicates that coupled anomalies are more similar to AMIP than to coupled simulations. Differences in the frequency of coupled anomalies between the AMIP simulations and the uncoupled reanalyses, and similarities between the uncoupled and partially coupled reanalyses, support the notion that the nature of the coupling between the ocean and the atmosphere is transmitted into the reanalyses via the assimilation of observations.