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T N Palmer - One of the best experts on this subject based on the ideXlab platform.
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the primacy of doubt evolution of numerical weather Prediction from determinism to probability
Journal of Advances in Modeling Earth Systems, 2017Co-Authors: T N PalmerAbstract:Over the last 25 years, the focus of operational numerical weather Prediction has evolved from that of estimating the most likely evolution of weather to that of estimating probability distributions of future weather associated with inevitable uncertainties in both initial conditions and model equations. This evolution from determinism to uncertainty has not only increased the scientific rigor of weather Prediction, it has also increased the value of weather forecasts for users. In addition, it has opened up a new approach to solving the equations of motion, likely to be of importance for both weather and Climate Prediction in an age where high-performance computing is limited by power consumption. However, despite all this, the numerical weather Prediction community has yet to embrace fully the concept of the primacy of doubt. It is now time to take the final step in this direction.
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the use of imprecise processing to improve accuracy in weather Climate Prediction
Journal of Computational Physics, 2014Co-Authors: Peter Duben, Hugh Mcnamara, T N PalmerAbstract:The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing bit-reproducibility and precision in exchange for improvements in performance and potentially accuracy of forecasts, due to a reduction in power consumption that could allow higher resolution. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud-resolving atmospheric modelling.The impact of both hardware induced faults and low precision arithmetic is tested using the Lorenz '96 model and the dynamical core of a global atmosphere model. In the Lorenz '96 model there is a natural scale separation; the spectral discretisation used in the dynamical core also allows large and small scale dynamics to be treated separately within the code. Such scale separation allows the impact of lower-accuracy arithmetic to be restricted to components close to the truncation scales and hence close to the necessarily inexact parametrised representations of unresolved processes. By contrast, the larger scales are calculated using high precision deterministic arithmetic. Hardware faults from stochastic processors are emulated using a bit-flip model with different fault rates.Our simulations show that both approaches to inexact calculations do not substantially affect the large scale behaviour, provided they are restricted to act only on smaller scales. By contrast, results from the Lorenz '96 simulations are superior when small scales are calculated on an emulated stochastic processor than when those small scales are parametrised. This suggests that inexact calculations at the small scale could reduce computation and power costs without adversely affecting the quality of the simulations. This would allow higher resolution models to be run at the same computational cost.
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toward a new generation of world Climate research and computing facilities
Bulletin of the American Meteorological Society, 2010Co-Authors: J Shukla, Jochem Marotzke, T N Palmer, Brian J Hoskins, Renate Hagedorn, James L Kinter, M J Miller, J M SlingoAbstract:The impending threat of global Climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather Prediction and in our understanding and ability to model the Climate system suggest that it is both necessary and possible to revolutionize Climate Prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the Climate system, and because the regional manifestations of Climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requireme...
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a nonlinear dynamical perspective on model error a proposal for non local stochastic dynamic parametrization in weather and Climate Prediction models
Quarterly Journal of the Royal Meteorological Society, 2001Co-Authors: T N PalmerAbstract:Conventional parametrization schemes in weather and Climate Prediction models describe the effects of subgrid-scale processes by deterministic bulk formulae which depend on local resolved-scale variables and a number of adjustable parameters. Despite the unquestionable success of such models for weather and Climate Prediction, it is impossible to justify the use of such formulae from first principles. Using low-order dynamical-systems models, and elementary results from dynamical-systems and turbulence theory, it is shown that even if unresolved scales only describe a small fraction of the total variance of the system, neglecting their variability can, in some circumstances, lead to gross errors in the climatology of the dominant scales. It is suggested that some of the remaining errors in weather and Climate Prediction models may have their origin in the neglect of subgrid-scale variability, and that such variability should be parametrized by non-local dynamically based stochastic parametrization schemes. Results from existing schemes are described, and mechanisms which might account for the impact of random parametrization error on planetary-scale motions are discussed. Proposals for the development of non-local stochastic-dynamic parametrization schemes are outlined, based on potential-vorticity diagnosis, singular-vector analysis and a simple stochastic cellular automaton model.
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a nonlinear dynamical perspective on Climate Prediction
Journal of Climate, 1999Co-Authors: T N PalmerAbstract:Abstract A nonlinear dynamical perspective on Climate Prediction is outlined, based on a treatment of Climate as the attractor of a nonlinear dynamical system D with distinct quasi-stationary regimes. The main application is toward anthropogenic Climate change, considered as the response of D to a small-amplitude imposed forcing f. The primary features of this perspective can be summarized as follows. First, the response to f will be manifest primarily in terms of changes to the residence frequency associated with the quasi-stationary regimes. Second, the geographical structures of these regimes will be relatively insensitive to f. Third, the large-scale signal will be most strongly influenced by f in rather localized regions of space and time. In this perspective, the signal arising from f will be strongly dependent of D’s natural variability. A theoretical framework for the perspective is developed based on a singular vector decomposition of D’s tangent propagator. Evidence for the dyamical perspective ...
Francisco J Doblasreyes - One of the best experts on this subject based on the ideXlab platform.
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land surface initialisation improves seasonal Climate Prediction skill for maize yield forecast
Scientific Reports, 2018Co-Authors: Andrej Ceglar, Francisco J Doblasreyes, Andrea Toreti, Chloe Prodhomme, Matteo Zampieri, Marco TurcoAbstract:Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of Climate information favouring adaptation strategies. As Climate variability and extremes have significant influence on agricultural production, the early Prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal Climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal Climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed Climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal Climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.
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seasonal Climate Prediction a new source of information for the management of wind energy resources
Journal of Applied Meteorology and Climatology, 2017Co-Authors: Veronica Torralba, Francisco J Doblasreyes, Dave Macleod, Isadora Christel, Melanie DavisAbstract:AbstractClimate Predictions tailored to the wind energy sector represent an innovation in the use of Climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, Climate Predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the Climate Predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic Climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter...
Brian J Hoskins - One of the best experts on this subject based on the ideXlab platform.
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the potential for skill across the range of the seamless weather Climate Prediction problem a stimulus for our science
Quarterly Journal of the Royal Meteorological Society, 2013Co-Authors: Brian J HoskinsAbstract:Predictability is considered in the context of the seamless weather-Climate Prediction problem, and the notion is developed that there can be predictive power on all time-scales. On all scales there are phenomena that occur as well as longer time-scales and external conditions that should combine to give some predictability. To what extent this theoretical predictability may actually be realised and, further, to what extent it may be useful is not clear. However the potential should provide a stimulus to, and high profile for, our science and its application for many years. Copyright © 2012 Royal Meteorological Society
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toward a new generation of world Climate research and computing facilities
Bulletin of the American Meteorological Society, 2010Co-Authors: J Shukla, Jochem Marotzke, T N Palmer, Brian J Hoskins, Renate Hagedorn, James L Kinter, M J Miller, J M SlingoAbstract:The impending threat of global Climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather Prediction and in our understanding and ability to model the Climate system suggest that it is both necessary and possible to revolutionize Climate Prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the Climate system, and because the regional manifestations of Climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requireme...
Arlan Dirkson - One of the best experts on this subject based on the ideXlab platform.
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canadian snow and sea ice assessment of snow sea ice and related Climate processes in canada s earth system model and Climate Prediction system
The Cryosphere, 2018Co-Authors: Paul J Kushner, Lawrence Mudryk, William J Merryfield, Jaison Thomas Ambadan, Aaron A Berg, Adeline Bichet, Ross Brown, Chris Derksen, Stephen J Dery, Arlan DirksonAbstract:Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a Climate research network focused on developing and applying state-of-the-art observational data to advance dynamical Prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal Climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related Climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal Prediction and for generating large ensembles of multidecadal simulations. Improvements in Climate-Prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.
Thomas Hopson - One of the best experts on this subject based on the ideXlab platform.
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quantifying streamflow forecast skill elasticity to initial condition and Climate Prediction skill
Journal of Hydrometeorology, 2016Co-Authors: Andrew W Wood, Thomas Hopson, Andrew J Newman, Levi D Brekke, Jeffrey R Arnold, Martyn P ClarkAbstract:AbstractWater resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such Predictions derives from the ability to estimate a watershed’s initial moisture and energy conditions and to forecast future weather and Climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow Prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in eithe...
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evaluation of high resolution satellite precipitation products over very complex terrain in ethiopia
Journal of Applied Meteorology and Climatology, 2010Co-Authors: Feyera A Hirpa, Mekonnen Gebremichael, Thomas HopsonAbstract:Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.