A Southern Hemisphere Sea Level Pressure-Based Precursor for ENSO Warm and Cold Events

Past studies have described large-scale sea level pressure (SLP) variations in the Southern Hemisphere that lead to El Nino-Southern Oscillation (ENSO) warm and cold events (WE and CE). By relying on this description and the importance of the related variability in the lead up to WE and CE, Southern Hemisphere SLP variations in May-June-July (MJJ) are shown here to be excellent predictors for the peak warm/cold events in sea-surface temperatures (SST) and sea level pressure that mark the mature phase of a warm/cold event in November-January of the same year. Cyclostationary empirical orthogonal functions (CSEOFs) are used to extract the variability associated with this description of SLP evolution leading to extreme events, underscoring the importance of this signal in the build-up to ENSO events. Using the CSEOF decomposition, an MJJ precursor is established and shown to precede impending warm and cold events in the past sixty years. Furthermore, the precursor developed in this study would have suggested that a significant WE for the latter half of 2014 was unlikely

Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

A Bayesian hierarchical model (BHM) is developed to estimate surface vector wind (SVW) fields and associated uncertainties over the Mediterranean Sea. The BHM–SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and SVW retrievals from the QuikSCAT data record. The process-model stage of the BHM–SVW is based on a Rayleigh friction equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM–SVW parameters are discussed. Ten realizations from the posterior distribution of the BHM–SVW are used to force the data-assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. The sequential data-assimilation method of the Mediterranean forecast system (MFS) is adapted to the ensemble implementation. Analyses of sample ensemble initial conditions for a single data-assimilation period in MFS are presented to demonstrate the multivariate impact of the BHM–SVW ensemble generation methodology. Ensemble initial-condition spread is quantified by computing standard deviations of ocean state variable fields over the ten ensemble members. The methodological findings in this article are of two kinds. From the perspective of statistical modelling, the process-model development is more closely related tophysicalbalances than inpreviousworkwithmodels for the SVW.Fromthe ocean forecast perspective, the generation of ocean ensemble initial conditions via BHM is shown to be practical for operational implementation in an ensemble ocean forecast system. Phenomenologically, ensemble spread generated via BHM–SVW occurs on ocean mesoscale time- and space-scales, in close association with strong synoptic-scale wind-forcing events. A companion article describes the impacts of the BHM–SVW ensemble method on the ocean forecast in comparisons with more traditional ensemble methods

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

This article analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian hierarchical model (BHM) developed in Part I of this series. A new method for ocean ensemble forecasting (OEF), the socalled BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14 day analysis and 10 day forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and in the pycnocline of the eddy field. The new method is compared with an ensemble response forced by European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EEPS) surface winds, and with an ensemble forecast started from perturbed initial conditions derived froman ad hoc thermocline intensified random perturbation (TIRP) method. The EEPS-OEF shows spread on basin scales while the TIRP-OEF response is mesoscale-intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain, while the BHM-SVW-OEF perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean-model response to uncertainty in the surface wind forcing is largest

Ocean Ensemble Forecasting, Part II: Mediterranean Forecast System Response

This paper analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian Hierarchical Model (BHM) developed in Part I (Milliff et al., 2009). A new method for Ocean Ensemble Forecasting (OEF), so-called BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14-day analysis and 10-day forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and pycnocline of the eddy field. The new method is compared to an ensemble response forced by ECMWF Ensemble Prediction System (EEPS) surface winds, and to an ensemble forecast started from perturbed initial conditions derived from an ad hoc Thermocline Intensified Random Perturbation (TIRP) method. The EEPS-OEF shows spread at the basin scales while the TIRP-OEF response is mesoscale intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain while the BHM-SVW-OEF perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean model response to uncertainty in the surface wind forcing is largest. The BHM-SVW-OEF method offers a practical and objective means for producing short-term forecast spread by modeling surface atmospheric forcing uncertainties that have maximum impact at the ocean mesoscales

Ocean Ensemble Forecasting, Part I: Ensemble Mediterranean Winds from a Bayesian Hierarchical Model

A Bayesian Hierarchical Model (BHM) is developed to estimate surface vector wind fields (SVW), and associated uncertainties, over the Mediterranean Sea. The BHM-SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and from the QuikSCAT data record. The process model stage of the BHM-SVW is based on a Rayleigh Friction Equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM-SVW parameters are discussed. Ten realizations from the posterior distribution the BHM-SVW are used to force the data assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. Ensemble initial condition spread is quantified by computing standard deviations of ocean state variable fields over the 10 ensemble members, driven by 10 realizations from the BHM-SVW posterior distribution over a 14-day sequential data assimilation period. Ensemble spread occurs on mesoscale time and space scales, in close association with strong synoptic scale wind forcing events. A companion paper compares the performance of the MFS ensemble forecasts given initial condition generation and forecast forcing from the BHM-SVW, with forecasts based on more traditional methods of ensemble generationSubmitted1.8. Osservazioni di geofisica ambientaleJCR Journalope

Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

A Bayesian hierarchical model (BHM) is developed to estimate surface vector wind (SVW) fields and associated uncertainties over the Mediterranean Sea. The BHM–SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and SVW retrievals from the QuikSCAT data record. The process-model stage of the BHM–SVW is based on a Rayleigh friction equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM–SVW parameters are discussed. Ten realizations from the posterior distribution of the BHM–SVW are used to force the data-assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. The sequential data-assimilation method of the Mediterranean forecast system (MFS) is adapted to the ensemble implementation. Analyses of sample ensemble initial conditions for a single data-assimilation period in MFS are presented to demonstrate the multivariate impact of the BHM–SVW ensemble generation methodology. Ensemble initial-condition spread is quantified by computing standard deviations of ocean state variable fields over the ten ensemble members. The methodological findings in this article are of two kinds. From the perspective of statistical modelling, the process-model development is more closely related tophysicalbalances than inpreviousworkwithmodels for the SVW.Fromthe ocean forecast perspective, the generation of ocean ensemble initial conditions via BHM is shown to be practical for operational implementation in an ensemble ocean forecast system. Phenomenologically, ensemble spread generated via BHM–SVW occurs on ocean mesoscale time- and space-scales, in close association with strong synoptic-scale wind-forcing events. A companion article describes the impacts of the BHM–SVW ensemble method on the ocean forecast in comparisons with more traditional ensemble methods.Published858–878JCR Journalembargoed_2014050

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

This article analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian hierarchical model (BHM) developed in Part I of this series. A new method for ocean ensemble forecasting (OEF), the socalled BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14 day analysis and 10 day forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and in the pycnocline of the eddy field. The new method is compared with an ensemble response forced by European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EEPS) surface winds, and with an ensemble forecast started from perturbed initial conditions derived froman ad hoc thermocline intensified random perturbation (TIRP) method. The EEPS-OEF shows spread on basin scales while the TIRP-OEF response is mesoscale-intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain, while the BHM-SVW-OEF perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean-model response to uncertainty in the surface wind forcing is largest.Published879–893JCR Journalembargoed_2014050

Ocean Ensemble Forecasting, Part II: Mediterranean Forecast System Response

This paper analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian Hierarchical Model (BHM) developed in Part I (Milliff et al., 2009). A new method for Ocean Ensemble Forecasting (OEF), so-called BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14-day analysis and 10-day forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and pycnocline of the eddy field. The new method is compared to an ensemble response forced by ECMWF Ensemble Prediction System (EEPS) surface winds, and to an ensemble forecast started from perturbed initial conditions derived from an ad hoc Thermocline Intensified Random Perturbation (TIRP) method. The EEPS-OEF shows spread at the basin scales while the TIRP-OEF response is mesoscale intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain while the BHM-SVW-OEF perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean model response to uncertainty in the surface wind forcing is largest. The BHM-SVW-OEF method offers a practical and objective means for producing short-term forecast spread by modeling surface atmospheric forcing uncertainties that have maximum impact at the ocean mesoscales.Submitted3.8. Geofisica per l'ambienteJCR Journalope