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.

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 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 Enhanced regime predictability in atmospheric low-order models due to stochastic forcing

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130286 ◽  
Author(s):  
Frank Kwasniok
Keyword(s):  
Noise Level ◽  
Coarse Grained ◽  
Climate Modelling ◽  
Order Model ◽  
Stochastic Forcing ◽  
Barotropic Flow ◽  
Optimal Value ◽  
Weather And Climate ◽  
Flow Over Topography ◽  
Low Order

Regime predictability in atmospheric low-order models augmented with stochastic forcing is studied. Atmospheric regimes are identified as persistent or metastable states using a hidden Markov model analysis. A somewhat counterintuitive, coherence resonance-like effect is observed: regime predictability increases with increasing noise level up to an intermediate optimal value, before decreasing when further increasing the noise level. The enhanced regime predictability is due to increased persistence of the regimes. The effect is found in the Lorenz '63 model and a low-order model of barotropic flow over topography. The increased predictability is only present in the regime dynamics, that is, in a coarse-grained view of the system; predictability of individual trajectories decreases monotonically with increasing noise level. A possible explanation for the phenomenon is given and implications of the finding for weather and climate modelling and prediction are discussed.

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

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.

scholarly journals Low-Order Stochastic Mode Reduction for a Prototype Atmospheric GCM

2006 ◽  
Vol 63 (2) ◽  
pp. 457-479 ◽  
Author(s):  
Christian Franzke ◽  
Andrew J. Majda
Keyword(s):  
Stochastic Modeling ◽  
Multiplicative Noise ◽  
A Priori ◽  
Climate Modes ◽  
Systematic Strategy ◽  
Eddy Fluxes ◽  
Climate Drift ◽  
Stochastic Mode ◽  
Correction Terms ◽  
Low Order

Abstract This study applies a systematic strategy for stochastic modeling of atmospheric low-frequency variability to a three-layer quasigeostrophic model. This model climate has reasonable approximations of the North Atlantic Oscillation (NAO) and Pacific–North America (PNA) patterns. The systematic strategy consists first of the identification of slowly evolving climate modes and faster evolving nonclimate modes by use of an empirical orthogonal function (EOF) decomposition in the total energy metric. The low-order stochastic climate model predicts the evolution of these climate modes a priori without any regression fitting of the resolved modes. The systematic stochastic mode reduction strategy determines all correction terms and noises with minimal regression fitting of the variances and correlation times of the unresolved modes. These correction terms and noises account for the neglected interactions between the resolved climate modes and the unresolved nonclimate modes. Low-order stochastic models with 10 or less resolved modes capture the statistics of the original model very well, including the variances and temporal correlations with high pattern correlations of the transient eddy fluxes. A budget analysis establishes that the low-order stochastic models are highly nonlinear with significant contributions from both additive and multiplicative noise. This is in contrast to previous stochastic modeling studies. These studies a priori assume a linear model with additive noise and regression fit the resolved modes. The multiplicative noise comes from the advection of the resolved modes by the unresolved modes. The most straightforward low-order stochastic climate models experience climate drift that stems from the bare truncation dynamics. Even though the geographic correlation of the transient eddy fluxes is high, they are underestimated by a factor of about 2 in the a priori procedure and thus cannot completely overcome the large climate drift in the bare truncation. Also, variants of the reduced stochastic modeling procedure that experience no climate drift with good predictions of both the variances and time correlations are discussed. These reduced models without climate drift are developed by slowing down the dynamics of the bare truncation compared with the interactions with the unresolved modes and yield a minimal two-parameter regression fitting strategy for the climate modes. This study points to the need for better optimal basis functions that optimally capture the essential slow dynamics of the system to obtain further improvements for the reduced stochastic modeling procedure.

The analysis of temperature fluctuations by pulse-counting techniques

1959 ◽  
Vol 6 (2) ◽  
pp. 261-271 ◽  
Author(s):  
A. A. Townsend
Keyword(s):  
Time Derivative ◽  
Temperature Fluctuations ◽  
Mean Value ◽  
Time Intervals ◽  
Mean Values ◽  
Average Value ◽  
Electrical Pulses ◽  
The Mean ◽  
The Stability ◽  
Better Than

To determine experimentally the mean value of a randomly fluctuating quantity, it may be necessary to measure the average value over a considerable interval of time. This problem arose in a recent study of the temperature fluctuations over a heated horizontal plate, and a system was used that depended on the counting of electrical pulses generated at a rate proportional to the quantity being measured. The advantage of this technique is that mean values may be measured over time intervals of almost unlimited length with little added difficulty for the experimenter. Circuits are described which measure: (a) the mean square of a fluctuating quantity and of its time-derivative, (b) the statistical distribution of the fluctuations, (c) the mean frequency of the fluctuation assuming a particular value, and (d) the mean product of two fluctuating quantities. Over the range of use, the stability and linearity of the calibrations is better than 1%, more than sufficient for work on natural convection. In its present form, the equipment responds uniformly to all frequencies below 100 c/s, but it would not be difficult to extend this range of response to higher frequencies.

scholarly journals ВИЗНАЧЕННЯ ПАРАМЕТРІВ ФОТОЕЛЕКТРИЧНОЇ СИСТЕМИ З АКУМУЛЯТОРОМ ДЛЯ ПОТРЕБ ЛОКАЛЬНОГО ОБ’ЄКТУ

2022 ◽  
pp. 41-50
Author(s):  
OLEXANDER SHAVOLKIN ◽  
RUSLAN MARCHENKO ◽  
YEVHEN STANOVSKYI ◽  
MYKOLA PIDHAINYI ◽  
HENNADII KRUHLIAK
Keyword(s):  
Archival Data ◽  
Time Intervals ◽  
Average Value ◽  
Cost Of Energy ◽  
Photoelectric System ◽  
Local Facility ◽  
Load Schedule ◽  
The Cost ◽  
Energy Processes ◽  
Generation Power

Purpose. Improving the methodology for determining the parameters of a photoelectric system with a battery for the needs of a local object using archival data of the generation of a photoelectric battery with planning the cost of energy consumption from the network for all seasons of the year.Methodology. Using an archive of data on the power generation of a photoelectric battery and analysis of energy processes in a photoelectric system with a battery using computer simulation.Findings. Calculated according to the archive data for five years, the average monthly values of photoelectric battery generation power for time intervals during the day determined according to tariff zones. Dependencies to determine the recommended average value load power of a local object at time intervals.Originality. It is proposed to determine the base schedule of the local facility and the parameters of the photoelectric system based on the average monthly values of photoelectric battery generation in the transition seasons – October, March and the expected cost of energy consumed from the grid during the year. The recalculation of the base value of power during the year is substantiated taking into account the duration of daylight. A method for determining the recommended load schedule of a local object with the formation of the battery charge according to the average monthly value of the photoelectric battery generation power at time intervals during the day, which are determined by archival data for the object location.Practical value. The obtained solutions are the basis for designing photoelectric systems with a battery to meet the needs of local objects.

scholarly journals Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4

2021 ◽  
Author(s):  
Nina Črnivec ◽  
Bernhard Mayer
Keyword(s):  
Climate Models ◽  
Three Dimensional ◽  
Surface Flux ◽  
Atmospheric Models ◽  
Data Set ◽  
Conventional Calculation ◽  
Cloud Regime ◽  
Weather And Climate ◽  
Horizontal Inhomogeneity ◽  
Research Challenge

Abstract. Although the representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud-radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the state-of-the-art Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction, where the thicker is associated with the presence of convective updraft elements. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the conventional calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud regime dependent methodologies in next-generation atmospheric models. This study highlighting the Tripleclouds potential for three essential cloud types signifies the need for more research examining a broader spectrum of cloud morphologies.

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.

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