scholarly journals Uncertainty in weather and climate prediction

2011 ◽  
Vol 369 (1956) ◽  
pp. 4751-4767 ◽  
Author(s):  
Julia Slingo ◽  
Tim Palmer
Keyword(s):  
Chaos Theory ◽  
Model Uncertainty ◽  
Lead Times ◽  
Probabilistic Prediction ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
Prediction Systems ◽  
The 1960S ◽  
Support Decision Making ◽  
Adaptation And Mitigation

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation.

scholarly journals Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

2019 ◽  
Vol 12 (7) ◽  
pp. 2797-2809 ◽  
Author(s):  
Sebastian Scher ◽  
Gabriele Messori
Keyword(s):  
Neural Networks ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Climate Model ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Lead Times ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
The Neural Networks

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

scholarly journals Stochastic parametrizations and model uncertainty in the Lorenz ’96 system

2013 ◽  
Vol 371 (1991) ◽  
pp. 20110479 ◽  
Author(s):  
H. M. Arnold ◽  
I. M. Moroz ◽  
T. N. Palmer
Keyword(s):  
Model Uncertainty ◽  
Multiplicative Noise ◽  
Lorenz System ◽  
Initial Conditions ◽  
Short Term ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
The Lorenz System ◽  
Lorenz 96 ◽  
Perturbed Parameter Ensembles

Simple chaotic systems are useful tools for testing methods for use in numerical weather simulations owing to their transparency and computational cheapness. The Lorenz system was used here; the full system was defined as ‘truth’, whereas a truncated version was used as a testbed for parametrization schemes. Several stochastic parametrization schemes were investigated, including additive and multiplicative noise. The forecasts were started from perfect initial conditions, eliminating initial condition uncertainty. The stochastically generated ensembles were compared with perturbed parameter ensembles and deterministic schemes. The stochastic parametrizations showed an improvement in weather and climate forecasting skill over deterministic parametrizations. Including a temporal autocorrelation resulted in a significant improvement over white noise, challenging the standard idea that a parametrization should only represent sub-gridscale variability. The skill of the ensemble at representing model uncertainty was tested; the stochastic ensembles gave better estimates of model uncertainty than the perturbed parameter ensembles. The forecasting skill of the parametrizations was found to be linked to their ability to reproduce the climatology of the full model. This is important in a seamless prediction system, allowing the reliability of short-term forecasts to provide a quantitative constraint on the accuracy of climate predictions from the same system.

scholarly journals Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground

2019 ◽  
Author(s):  
Sebastian Scher ◽  
Gabriele Messori
Keyword(s):  
Neural Networks ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Climate Model ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Lead Times ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
The Neural Networks

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom-up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple General Circulation Models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill, and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

Propagation of Thermohaline Anomalies and their predictive potential in the Northern North Atlantic

2021 ◽  
Author(s):  
Helene R. Langehaug ◽  
Pablo Ortega ◽  
Francois Counillon ◽  
Daniela Matei ◽  
Elizabeth Maroon ◽  
...  
Keyword(s):  
Time Scales ◽  
North Atlantic ◽  
Atlantic Water ◽  
The Arctic ◽  
Lead Times ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Predictive Skill ◽  
Prediction Systems ◽  
Water Pathway

<p>In this study we assess to what extent seven different dynamical prediction systems can retrospectively predict the winter sea surface temperature (SST) in the subpolar North Atlantic and the Nordic Seas in the time period 1970-2005. We focus in particular on the region where warm water flows poleward, i.e., the Atlantic water pathway, and on interannual-to-decadal time scales. To better understand why dynamical prediction systems have predictive skill or lack thereof, we confront them with a mechanism identified from observations – propagation of oceanic anomalies from low to high latitudes – on different forecast lead times. This observed mechanism shows that warm and cold anomalies propagate along the Atlantic water pathway within a certain time frame. A key result from this study is that most models have difficulty representing this mechanism, resulting in an overall poor prediction skill after 1-2 years lead times (after applying a band-pass filter to focus on interannual-to-decadal time scales). There is a link, although not very strong, between predictive skill and the representation of the SST propagation. Observational studies demonstrate predictability several years in advance in this region, thus suggesting a great potential for improvement of dynamical climate predictions by resolving the causes for the misrepresentation of the oceanic link. Inter model differences in simulating surface velocities along the Atlantic water pathway suggest that realistic velocities are important to better circulate anomalies poleward, and hence, increase predictive skill on interannual-to-decadal time scales in the oceanic gateway to the Arctic.</p>

scholarly journals Scale-Selective Precision for Weather and Climate Forecasting

2019 ◽  
Vol 147 (2) ◽  
pp. 645-655 ◽  
Author(s):  
Matthew Chantry ◽  
Tobias Thornes ◽  
Tim Palmer ◽  
Peter Düben
Keyword(s):  
Time Scales ◽  
Weather Forecasting ◽  
Forecast Accuracy ◽  
Spectral Space ◽  
Dynamical Equations ◽  
Length Scales ◽  
Climate Forecasting ◽  
Spectral Models ◽  
Weather And Climate ◽  
Numerical Precision

Abstract Attempts to include the vast range of length scales and physical processes at play in Earth’s atmosphere push weather and climate forecasters to build and more efficiently utilize some of the most powerful computers in the world. One possible avenue for increased efficiency is in using less precise numerical representations of numbers. If computing resources saved can be reinvested in other ways (e.g., increased resolution or ensemble size) a reduction in precision can lead to an increase in forecast accuracy. Here we examine reduced numerical precision in the context of ECMWF’s Open Integrated Forecast System (OpenIFS) model. We posit that less numerical precision is required when solving the dynamical equations for shorter length scales while retaining accuracy of the simulation. Transformations into spectral space, as found in spectral models such as OpenIFS, enact a length scale decomposition of the prognostic fields. Utilizing this, we introduce a reduced-precision emulator into the spectral space calculations and optimize the precision necessary to achieve forecasts comparable with double and single precision. On weather forecasting time scales, larger length scales require higher numerical precision than smaller length scales. On decadal time scales, half precision is still sufficient precision for everything except the global mean quantities.

scholarly journals Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models

2013 ◽  
Vol 20 (2) ◽  
pp. 199-206
Author(s):  
I. Trpevski ◽  
L. Basnarkov ◽  
D. Smilkov ◽  
L. Kocarev
Keyword(s):  
Linear Regression Method ◽  
Atmospheric Models ◽  
Time Intervals ◽  
Average Value ◽  
Climate Forecasting ◽  
Empirical Correction ◽  
Weather And Climate ◽  
Real World Applications ◽  
Correction Terms ◽  
Low Order

Abstract. Contemporary tools for reducing model error in weather and climate forecasting models include empirical correction techniques. In this paper we explore the use of such techniques on low-order atmospheric models. We first present an iterative linear regression method for model correction that works efficiently when the reference truth is sampled at large time intervals, which is typical for real world applications. Furthermore we investigate two recently proposed empirical correction techniques on Lorenz models with constant forcing while the reference truth is given by a Lorenz system driven with chaotic forcing. Both methods indicate that the largest increase in predictability comes from correction terms that are close to the average value of the chaotic forcing.

Model error in weather and climate forecasting

2010 ◽  
pp. 391-427 ◽  
Author(s):  
Myles Allen ◽  
David Frame ◽  
Jamie Kettleborough ◽  
David Stainforth
Keyword(s):  
Model Error ◽  
Climate Forecasting ◽  
Weather And Climate

Visualising the invisible: performing chaos theory

Leonardo ◽  
2020 ◽  
pp. 1-8
Author(s):  
Emma Weitkamp
Keyword(s):  
Complex Systems ◽  
Chaos Theory ◽  
Initial Conditions ◽  
Starting Point ◽  
Weather And Climate ◽  
Butterfly Effect ◽  
Long Term Behavior ◽  
The Invisible

Edward Lorenz, the pioneering figure in the field of chaos theory coined the phrase “butterfly effect” and posed the famous question “Does the flap of a butterfly's wings in Brazil set off a tornado in Texas?” In posing the question, Lorenz sought to highlight the intrinsic difficulty of predicting the long term behavior of complex systems that are sensitive to initial conditions, like, for example, the weather and climate; these systems are often referred to as chaotic. Taking Lorenz' butterfly as a starting point, Chaos Cabaret sought to explore the nuances of chaos theory through performance and music.

scholarly journals Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms

2019 ◽  
Vol 147 (12) ◽  
pp. 4389-4409 ◽  
Author(s):  
Yunji Zhang ◽  
David J. Stensrud ◽  
Fuqing Zhang
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Lead Times ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Radar Observations ◽  
Weather Radars ◽  
Infrared Radiance ◽  
Severe Thunderstorms ◽  
Ensemble Analysis

Abstract This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when in-storm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones.

Facilitating the Self-Directed Learning Efforts of Professional Meteorologists

2018 ◽  
Vol 99 (12) ◽  
pp. 2519-2527 ◽  
Author(s):  
Daphne S. LaDue ◽  
Ariel E. Cohen
Keyword(s):  
Professional Practice ◽  
Formal Education ◽  
The Self ◽  
Learning Needs ◽  
Self Directed Learning ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
Two Factors ◽  
Directed Learning ◽  
Over Time

AbstractProfessional meteorologists gain a great deal of knowledge through formal education, but two factors require ongoing learning throughout a career: professionals must apply their learning to the specific subdiscipline they practice, and the knowledge and technology they rely on becomes outdated over time. It is thus inherent in professional practice that much of the learning is more or less self-directed. While these principles apply to any aspect of meteorology, this paper applies concepts to weather and climate forecasting, for which a range of resources, from many to few, for learning exist. No matter what the subdiscipline, the responsibility for identifying and pursuing opportunities for professional, lifelong learning falls to the members of the subdiscipline. Thus, it is critical that meteorologists periodically assess their ongoing learning needs and develop the ability to reflectively practice. The construct of self-directed learning and how it has been implemented in similar professions provide visions for how individual meteorologists can pursue—and how the profession can facilitate—the ongoing, self-directed learning efforts of meteorologists.

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