Search Results
You are looking at 1 - 10 of 16 items for
- Author or Editor: Alberto Arribas x
- Refine by Access: All Content x
Abstract
The errors in both the initialization and simulated evolution of weather and climate models create significant uncertainties in forecasts at lead times beyond a few days. Modern prediction systems sample the sources of these uncertainties to produce a probability distribution function of future meteorological conditions to help end users in their risk assessment and decision-making processes. The performance of prediction systems is assessed using data from a set of historical forecasts and the corresponding observations. There are many aspects to the correspondence between forecasts and observations, and various summary scores have been created to measure the different features of forecast quality. The main concern for end users is the usefulness of forecasts. There are two independent and sufficient aspects for the assessment of the usefulness of forecasts to end users: 1) the statistical consistency of forecast statements with observations and 2) the extra information contained in the forecast relative to the situation in which such predictions are unavailable. In this paper two new scores, the full-pdf-reliability R pdf and information quantity IQ, are proposed to measure these two independent aspects of usefulness. In contrast to all existing summary scores, both R pdf and IQ depend upon all moments of the forecast pdf. When taken together, the values of R pdf and IQ offer a general measure of the usefulness of ensemble predictions.
Abstract
The errors in both the initialization and simulated evolution of weather and climate models create significant uncertainties in forecasts at lead times beyond a few days. Modern prediction systems sample the sources of these uncertainties to produce a probability distribution function of future meteorological conditions to help end users in their risk assessment and decision-making processes. The performance of prediction systems is assessed using data from a set of historical forecasts and the corresponding observations. There are many aspects to the correspondence between forecasts and observations, and various summary scores have been created to measure the different features of forecast quality. The main concern for end users is the usefulness of forecasts. There are two independent and sufficient aspects for the assessment of the usefulness of forecasts to end users: 1) the statistical consistency of forecast statements with observations and 2) the extra information contained in the forecast relative to the situation in which such predictions are unavailable. In this paper two new scores, the full-pdf-reliability R pdf and information quantity IQ, are proposed to measure these two independent aspects of usefulness. In contrast to all existing summary scores, both R pdf and IQ depend upon all moments of the forecast pdf. When taken together, the values of R pdf and IQ offer a general measure of the usefulness of ensemble predictions.
Abstract
The limited numbers of start dates and ensemble sizes in seasonal forecasts lead to sampling errors in predictions. Defining the magnitude of these sampling errors would be useful for end users as well as informing decisions on resource allocation to minimize total system error. A numerical experiment has been designed to measure them, and results indicate that sampling errors are substantial in state-of-the-art seasonal forecast systems. The standard solution of increasing sample sizes is of limited benefit in seasonal forecasting because of restrictions imposed by resource costs and nonstationary observations. Alternative options, based on the postprocessing of forecast and hindcast data, are presented in this paper. The spatial and temporal aggregations of data together with the appropriate use of theoretical distributions can reduce the effect of sampling errors on forecast quantities by an amount equivalent to increasing samples sizes by a factor of 4 of more, with insignificant losses of forecast information. These postprocessing techniques can be viewed as cost-effective methods of reducing the effects of sampling errors in seasonal forecast quantities.
Abstract
The limited numbers of start dates and ensemble sizes in seasonal forecasts lead to sampling errors in predictions. Defining the magnitude of these sampling errors would be useful for end users as well as informing decisions on resource allocation to minimize total system error. A numerical experiment has been designed to measure them, and results indicate that sampling errors are substantial in state-of-the-art seasonal forecast systems. The standard solution of increasing sample sizes is of limited benefit in seasonal forecasting because of restrictions imposed by resource costs and nonstationary observations. Alternative options, based on the postprocessing of forecast and hindcast data, are presented in this paper. The spatial and temporal aggregations of data together with the appropriate use of theoretical distributions can reduce the effect of sampling errors on forecast quantities by an amount equivalent to increasing samples sizes by a factor of 4 of more, with insignificant losses of forecast information. These postprocessing techniques can be viewed as cost-effective methods of reducing the effects of sampling errors in seasonal forecast quantities.
Abstract
Seasonal forecasts are most commonly issued as anomalies with respect to some multiyear reference period. However, different seasonal forecasting centers use different reference periods. This paper shows that for near-surface temperature, precipitation, and mean sea level pressure, over most regions of the world there is evidence that these differences between reference periods should not be ignored, especially when forecasters combine outputs from several prediction systems. Three methods are presented by which reference periods could be adjusted, and it is shown that the differences between the proposed methods are smaller than the errors that result from not correcting for different reference periods.
Abstract
Seasonal forecasts are most commonly issued as anomalies with respect to some multiyear reference period. However, different seasonal forecasting centers use different reference periods. This paper shows that for near-surface temperature, precipitation, and mean sea level pressure, over most regions of the world there is evidence that these differences between reference periods should not be ignored, especially when forecasters combine outputs from several prediction systems. Three methods are presented by which reference periods could be adjusted, and it is shown that the differences between the proposed methods are smaller than the errors that result from not correcting for different reference periods.
Abstract
Environmental datasets are becoming so large that they are increasingly being hosted in the compute cloud, where they can be efficiently analyzed and disseminated. However, this necessitates new ways of efficiently delivering environmental information across the Internet to users. We visualised a big atmospheric dataset in a web page by repurposing techniques normally used to stream HD video. You can try the prototype at http://demo.3dvis.informaticslab.co.uk/ng-3d-vis/apps/desktop/ or watch a video demonstration at www.youtube.com/watch?v=pzvk1ZNMvFY.
Abstract
Environmental datasets are becoming so large that they are increasingly being hosted in the compute cloud, where they can be efficiently analyzed and disseminated. However, this necessitates new ways of efficiently delivering environmental information across the Internet to users. We visualised a big atmospheric dataset in a web page by repurposing techniques normally used to stream HD video. You can try the prototype at http://demo.3dvis.informaticslab.co.uk/ng-3d-vis/apps/desktop/ or watch a video demonstration at www.youtube.com/watch?v=pzvk1ZNMvFY.
Abstract
A new approach to probabilistic forecasting is proposed, based on the generation of an ensemble of equally likely analyses of the current state of the atmosphere. The rationale behind this approach is to mimic a poor man’s ensemble, which combines the deterministic forecasts from national meteorological services around the world. The multianalysis ensemble aims to generate a series of forecasts that are both as skillful as each other and the control forecast. This produces an ensemble mean forecast that is superior not only to the ensemble members, but to the control forecast in the short range even for slowly varying parameters, such as 500-hPa height. This is something that it is not possible with traditional ensemble methods, which perturb a central analysis.
The results herein show that the multianalysis ensemble is more skillful than the Met Office’s high-resolution forecast by 4.5% over the first 3 days (on average as measured for RMSE). Similar results are found for different verification scores and various regions of the globe. In contrast, the ensemble mean for the ensemble currently run by the Met Office performs 1.5% worse than the high-resolution forecast (similar results are found for the ECMWF ensemble). It is argued that the multianalysis approach is therefore superior to current ensemble methods. The multianalysis results were achieved with a two-member ensemble: the forecast from a high-resolution model plus a low-resolution perturbed model. It may be possible to achieve greater improvements with a larger ensemble.
Abstract
A new approach to probabilistic forecasting is proposed, based on the generation of an ensemble of equally likely analyses of the current state of the atmosphere. The rationale behind this approach is to mimic a poor man’s ensemble, which combines the deterministic forecasts from national meteorological services around the world. The multianalysis ensemble aims to generate a series of forecasts that are both as skillful as each other and the control forecast. This produces an ensemble mean forecast that is superior not only to the ensemble members, but to the control forecast in the short range even for slowly varying parameters, such as 500-hPa height. This is something that it is not possible with traditional ensemble methods, which perturb a central analysis.
The results herein show that the multianalysis ensemble is more skillful than the Met Office’s high-resolution forecast by 4.5% over the first 3 days (on average as measured for RMSE). Similar results are found for different verification scores and various regions of the globe. In contrast, the ensemble mean for the ensemble currently run by the Met Office performs 1.5% worse than the high-resolution forecast (similar results are found for the ECMWF ensemble). It is argued that the multianalysis approach is therefore superior to current ensemble methods. The multianalysis results were achieved with a two-member ensemble: the forecast from a high-resolution model plus a low-resolution perturbed model. It may be possible to achieve greater improvements with a larger ensemble.
Abstract
The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization.
The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space–time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER.
Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts.
Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown.
Concerning the forecasts started in August, ENSEMBLES exhibits mixed results over both tropics and northern midlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced.
Abstract
The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization.
The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space–time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER.
Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts.
Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown.
Concerning the forecasts started in August, ENSEMBLES exhibits mixed results over both tropics and northern midlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced.
Abstract
An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.
Abstract
An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.
Abstract
December 2010 was unusual both in the strength of the negative North Atlantic Oscillation (NAO) intense atmospheric blocking and the associated record-breaking low temperatures over much of northern Europe. The negative North Atlantic Oscillation for November–January was predicted in October by 8 out of 11 World Meteorological Organization Global Producing Centres (WMO GPCs) of long-range forecasts. This paper examines whether the unusual strength of the NAO and temperature anomaly signals in early winter 2010 are attributable to slowly varying boundary conditions [El Niño–Southern Oscillation state, North Atlantic sea surface temperature (SST) tripole, Arctic sea ice extent, autumn Eurasian snow cover], and whether these were modeled in the Met Office Global Seasonal Forecasting System version 4 (GloSea4). Results from the real-time forecasts showed that a very robust signal was evident in both the surface pressure fields and temperature fields by the beginning of November. The historical reforecast set (hindcasts), used to calibrate and bias correct the real-time forecast, showed that the seasonal forecast model reproduces at least some of the observed physical mechanisms that drive the NAO. A series of ensembles of atmosphere-only experiments was constructed, using forecast SSTs and ice concentrations from November 2010. Each potential mechanism in turn was systematically isolated and removed, leading to the conclusion that the main mechanism responsible for the successful forecast of December 2010 was anomalous ocean heat content and associated SST anomalies in the North Atlantic.
Abstract
December 2010 was unusual both in the strength of the negative North Atlantic Oscillation (NAO) intense atmospheric blocking and the associated record-breaking low temperatures over much of northern Europe. The negative North Atlantic Oscillation for November–January was predicted in October by 8 out of 11 World Meteorological Organization Global Producing Centres (WMO GPCs) of long-range forecasts. This paper examines whether the unusual strength of the NAO and temperature anomaly signals in early winter 2010 are attributable to slowly varying boundary conditions [El Niño–Southern Oscillation state, North Atlantic sea surface temperature (SST) tripole, Arctic sea ice extent, autumn Eurasian snow cover], and whether these were modeled in the Met Office Global Seasonal Forecasting System version 4 (GloSea4). Results from the real-time forecasts showed that a very robust signal was evident in both the surface pressure fields and temperature fields by the beginning of November. The historical reforecast set (hindcasts), used to calibrate and bias correct the real-time forecast, showed that the seasonal forecast model reproduces at least some of the observed physical mechanisms that drive the NAO. A series of ensembles of atmosphere-only experiments was constructed, using forecast SSTs and ice concentrations from November 2010. Each potential mechanism in turn was systematically isolated and removed, leading to the conclusion that the main mechanism responsible for the successful forecast of December 2010 was anomalous ocean heat content and associated SST anomalies in the North Atlantic.
Abstract
Predictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts.
The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992–2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: with over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.
Abstract
Predictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts.
The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992–2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: with over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.
Abstract
Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.
Abstract
Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.
