| 1. |
- de Vries, Hylke, et al.
(author)
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How Gulf-Stream SST-fronts influence Atlantic winter storms : Results from a downscaling experiment with HARMONIE to the role of modified latent heat fluxes and low-level baroclinicity
- 2019
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In: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 52:9-10, s. 5899-5909
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Journal article (peer-reviewed)abstract
- The strong horizontal gradients in sea surface temperature (SST) of the Atlantic Gulf Stream exert a detectable influence on extratropical cyclones propagating across the region. This is shown in a sensitivity experiment where 24 winter storms taken from ERA-Interim are simulated with HARMONIE at 10-km resolution. Each storm is simulated twice. First, using observed SST (REF). In the second simulation a smoothed SST is offered (SMTH), while lateral and upper-level boundary conditions are unmodified. Each storm pair propagates approximately along the same track, however their intensities (as measured by maximal near-surface wind speed or 850-hPa relative vorticity) differ up to +/- 25%. A 30-member ensemble created for one of the storms shows that on a single-storm level the response is systematic rather than random. To explain the broad response in storm strength, we show that the SST-adjustment modifies two environmental parameters: surface latent heat flux (LHF) and low-level baroclinicity (B). LHF influences storms by modifying diabatic heating and boundary-layer processes such as vertical mixing. The position of each storm's track relative to the SST-front is important. South of the SST-front the smoothing leads to lower SST, reduced LHF and storms with generally weaker maximum near-surface winds. North of the SST-front the increased LHF tend to enhance the winds, but the accompanying changes in baroclinicity are not necessarily favourable. Together these mechanisms explain up to 80% of the variability in the near-surface maximal wind speed change. Because the mechanisms are less effective in explaining more dynamics-oriented indicators like 850 hPa relative vorticity, we hypothesise that part of the wind-speed change is related to adjustment of the boundary-layer processes in response to the LHF and B changes.
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| 2. |
- Hochman, Assaf, et al.
(author)
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A new view of heat wave dynamics and predictability over the eastern Mediterranean
- 2021
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In: Earth System Dynamics. - : Copernicus Publications. - 2190-4979 .- 2190-4987. ; 12:1, s. 133-149
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Journal article (peer-reviewed)abstract
- Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two complementary approaches to diagnose the predictability of extreme weather: recent developments in dynamical systems theory and numerical ensemble weather forecasts. The former allows us to define atmospheric configurations in terms of their persistence and local dimension, which provides information on how the atmosphere evolves to and from a given state of interest. These metrics may be used as proxies for the intrinsic predictability of the atmosphere, which only depends on the atmosphere's properties. Ensemble weather forecasts provide information on the practical predictability of the atmosphere, which partly depends on the performance of the numerical model used. We focus on heat waves affecting the eastern Mediterranean. These are identified using the climatic stress index (CSI), which was explicitly developed for the summer weather conditions in this region and differentiates between heat waves (upper decile) and cool days (lower decile). Significant differences are found between the two groups from both the dynamical systems and the numerical weather prediction perspectives. Specifically, heat waves show relatively stable flow characteristics (high intrinsic predictability) but comparatively low practical predictability (large model spread and error). For 500 hPa geopotential height fields, the intrinsic predictability of heat waves is lowest at the event's onset and decay. We relate these results to the physical processes governing eastern Mediterranean summer heat waves: adiabatic descent of the air parcels over the region and the geographical origin of the air parcels over land prior to the onset of a heat wave. A detailed analysis of the mid-August 2010 record-breaking heat wave provides further insights into the range of different regional atmospheric configurations conducive to heat waves. We conclude that the dynamical systems approach can be a useful complement to conventional numerical forecasts for understanding the dynamics and predictability of eastern Mediterranean heat waves.
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| 3. |
- Hochman, A., et al.
(author)
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Dynamics and predictability of cold spells over the Eastern Mediterranean
- 2022
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In: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 58:7-8, s. 2047-2064
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Journal article (peer-reviewed)abstract
- The accurate prediction of extreme weather events is an important and challenging task, and has typically relied on numerical simulations of the atmosphere. Here, we combine insights from numerical forecasts with recent developments in dynamical systems theory, which describe atmospheric states in terms of their persistence (θ−1) and local dimension (d), and inform on how the atmosphere evolves to and from a given state of interest. These metrics are intuitively linked to the intrinsic predictability of the atmosphere: a highly persistent, low-dimensional state will be more predictable than a low-persistence, high-dimensional one. We argue that θ−1 and d, derived from reanalysis sea level pressure (SLP) and geopotential height (Z500) fields, can provide complementary predictive information for mid-latitude extreme weather events. Specifically, signatures of regional extreme weather events might be reflected in the dynamical systems metrics, even when the actual extreme is not well-simulated in numerical forecasting systems. We focus on cold spells in the Eastern Mediterranean, and particularly those associated with snow cover in Jerusalem. These rare events are systematically associated with Cyprus Lows, which are the dominant rain-bearing weather system in the region. In our analysis, we compare the ‘cold spell Cyprus Lows’ to other ‘regular’ Cyprus Low days. Significant differences are found between cold spells and ‘regular’ Cyprus Lows from a dynamical systems perspective. When considering SLP, the intrinsic predictability of cold spells is lowest hours before the onset of snow. We find that the cyclone’s location, depth and magnitude of air-sea fluxes play an important role in determining its intrinsic predictability. The dynamical systems metrics computed on Z500 display a different temporal evolution to their SLP counterparts, highlighting the different characteristics of the atmospheric flow at the different levels. We conclude that the dynamical systems approach, although sometimes challenging to interpret, can complement conventional numerical forecasts and forecast skill measures, such as model spread and absolute error. This methodology outlines an important avenue for future research, which can potentially be fruitfully applied to other regions and other types of weather extremes.
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| 4. |
- Jewson, Stephen, et al.
(author)
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Communicating Properties of Changes in Lagged Weather Forecasts
- 2022
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In: Weather and forecasting. - : American Meteorological Society. - 0882-8156 .- 1520-0434. ; 37:1, s. 125-142
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Journal article (peer-reviewed)abstract
- Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.
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| 5. |
- Jewson, Stephen, et al.
(author)
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Communicating Properties of Changes in Lagged Weather Forecasts
- 2022
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In: Weather and forecasting. - 0882-8156 .- 1520-0434. ; 37:1, s. 125-142
-
Journal article (peer-reviewed)abstract
- Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.
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| 6. |
- Jewson, Stephen, et al.
(author)
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Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations
- 2021
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In: Monthly Weather Review. - : American Meteorological Society. - 0027-0644 .- 1520-0493. ; 149:6, s. 1637-1650
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Journal article (peer-reviewed)abstract
- Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost–loss model. Within this extended cost–loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.
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| 7. |
- Jewson, Stephen, et al.
(author)
-
Decide Now or Wait for the Next Forecast? Testing a Decision Framework Using Real Forecasts and Observations
- 2021
-
In: Monthly Weather Review. - 0027-0644 .- 1520-0493. ; 149:6, s. 1637-1650
-
Journal article (peer-reviewed)abstract
- Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost-loss model. Within this extended cost-loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.
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| 8. |
- Molinder, Jennie, et al.
(author)
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Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests
- 2021
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In: Energies. - BASEL, SWITZERLAND : MDPI. - 1996-1073. ; 14:1
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Journal article (peer-reviewed)abstract
- A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.
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| 9. |
- Rasp, Stephan, et al.
(author)
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WeatherBench : A Benchmark Data Set for Data-Driven Weather Forecasting
- 2020
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In: Journal of Advances in Modeling Earth Systems. - 1942-2466. ; 12:11
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Journal article (peer-reviewed)abstract
- Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common data set and evaluation metrics make intercomparison between studies difficult. Here we present a benchmark data set for data-driven medium-range weather forecasting (specifically 3-5 days), a topic of high scientific interest for atmospheric and computer scientists alike. We provide data derived from the ERA5 archive that has been processed to facilitate the use in machine learning models. We propose simple and clear evaluation metrics which will enable a direct comparison between different methods. Further, we provide baseline scores from simple linear regression techniques, deep learning models, as well as purely physical forecasting models. The data set is publicly available at and the companion code is reproducible with tutorials for getting started. We hope that this data set will accelerate research in data-driven weather forecasting.
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| 10. |
- Scher, Sebastian, 1990-
(author)
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Artificial intelligence in weather and climate prediction : Learning atmospheric dynamics
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Weather and climate prediction is dominated by high dimensionality, interactions on many different spatial and temporal scales and chaotic dynamics. This makes many problems in the field quite complex ones, and also state-of-the-art numerical models are - despite their immense computational costs - not sufficient for many applications. Therefore, it is appealing to use emerging new technologies such as artificial intelligence to tackle these problems.We show that it is possible to use deep neural networks to emulate the full dynamics of a strongly simplified general circulation model, providing both good forecasts of the model state several days ahead as well as stable long-term climate timeseries. This method partly also works on more complex and realistic models, but only for forecasting the model's weather several days ahead, not for creating climate runs. It is sufficient to use 50-100 years of data for training the networks. The same neural network method can be combined with singular value decomposition from numerical ensemble weather forecasting in order to generate probabilistic ensemble forecasts with the neural networks.On a more fundamental level, we show that in a simple dynamical systems setting there seem to be limitations in the ability of feed-forward neural networks to generalize to new regions of the system. This is caused by different parts of the network learning to model different parts of the system. Contradictory, for another simple dynamical system this is shown not to be an issue, raising doubts on the usefulness of results from simple models in the context of more complex ones. Additionally, we show that neural networks are to some extent able to “learn” the influence of slowly changing external forcings on the dynamics of the system, but only given broad enough forcing regimes.Finally, we present a method to complement operational weather forecasts. Given the initial fields and the error of past weather forecasts, a neural network is used to predict the uncertainty in new forecasts, given only the initial field of the new forecast.
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