Available via license: CC BY 4.0
Content may be subject to copyright.
A preview of the PDF is not available
Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation
Abstract and Figures
In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill scores (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance.
Figures - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
... The same patterns have also been used to infer information about the likelihood of coastal flooding and of droughts (Richardson et al., 2020a) at mediumand extended-range lead times. In a similar concept, weather analogues of atmospheric variability over Europe have been used to infer information about precipitation (and temperature) at subseasonal scales (Krouma et al., 2021;Yiou and Déandréis, 2019). ...
Extreme precipitation events (EPEs) can have devastating consequences, such as floods and landslides, posing a great threat to society and economy. Predicting such events long in advance can support the mitigation of negative impacts. Here, we focus on EPEs over the Mediterranean, a region that is frequently affected by such hazards. Previous work identified strong connections between localized EPEs and large‐scale atmospheric flow patterns, affecting weather over the entire Mediterranean. We analyse the predictive skill of these patterns in the European Centre for Medium‐Range Weather Forecasts (ECMWF) extended range forecasts and assess if and where these patterns can be used for indirect predictions of EPEs, using the Brier skill score. The results show that the ECMWF model provides skilful predictions of the Mediterranean patterns up to 2 weeks in advance. Moreover, using the forecasted patterns for indirect predictability of EPEs outperforms the reference score up to ∼10 days lead time for many locations. For high orography locations or coastal areas in particular, like parts of western Turkey, western Balkans, Iberian Peninsula, and Morocco, this limit extends to 11–14 days lead time. This study demonstrates that the connections between localized EPEs and large‐scale patterns over the Mediterranean extend the forecasting horizon of the model by over 3 days in many locations, in comparison with forecasting based on the predicted precipitation. Thus, it is beneficial to use the predicted patterns rather than the predicted precipitation at longer lead times for EPE forecasting. The model's performance is also assessed from a user perspective, showing that the EPE forecasting based on the patterns increases the economic benefits at medium/extended range lead times. Such information could support higher confidence in the decision‐making of various users; for example, the agricultural sector and (re)insurance companies.
... Similar analysis is also implemented for other locations and domains, e.g., for Europe. For the latter, weather analogues of atmospheric variability over Europe have been used to infer information about precipitation (and temperature) at sub-seasonal scales (Krouma et al., 2021;Yiou and Déandréis, 2019). ...
Extreme Precipitation Events (EPEs) can have devastating consequences such as floods and landslides, posing a great threat to society and economy. Predicting such events long in advance can support the mitigation of negative impacts. Here, we focus on EPEs over the Mediterranean, a region that is frequently affected by such hazards. Previous work identified strong connections between localised EPEs and large-scale atmospheric flow patterns, affecting weather over the entire Mediterranean. We analyse the predictive skill of these patterns in the ECMWF extended range forecasts and assess if and where these patterns can be used for indirect predictions of EPEs, using the Brier Skill Score. The results show that the ECMWF model provides skilful predictions of the Mediterranean patterns up to 2 weeks in advance. Moreover, using the forecasted patterns for indirect predictability of EPEs outperforms the reference score up to ~10 days lead time for many locations. Especially for high orography locations or coastal areas, like parts of western Turkey, western Balkans, Iberian Peninsula and Morocco this limit extends to 11-14 days lead time. This study demonstrates that connections between localised EPEs and large-scale patterns over the Mediterranean extends the forecasting horizon of the model by over 3 days in many locations, in comparison to forecasting based on the predicted precipitation. Thus, it is beneficial to use the predicted patterns rather than the predicted precipitation at longer lead times for EPEs forecasting. The model's performance is also assessed from a user perspective, showing that the EPEs forecasting based on the patterns increases the economic benefits at medium/extended range lead times. Such information could support higher confidence in the decision-making of various users, e.g., the agricultural sector and (re)insurance companies.
A recurrent issue encountered in environmental, ecological or agricultural impact studies in which climate is an important driving force is to provide fast and realistic simulations of atmospheric variables such as temperature, precipitation and wind at a few specific locations, at daily or hourly temporal scales. Spatio-temporal dynamics and correlation structures among the variables of interest, as well as weather persistence and natural variability have to be reproduced accurately in a distributional sense. This quest leads to a large variety of so-called stochastic weather generators (WGs) in the literature. Here, we provide an up-to-date overview of weather type WG models.Weather types classically represent daily characteristics of the relevant atmospheric information at hand. There are many ways to build such weather states, either hidden or observed, and to infer their properties. This overview should help statisticians as well as meteorologists and climate product users to understand the probabilistic concepts and models behind weather type WGs, and to identify their advantages and limits.
Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA‐Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general set‐up of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general, low‐frequency variability is found to be well represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses.
This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.
We propose in this article an analogy approach for characterizing the similarity between daily atmospheric state sequences—in our case large‐scale geopotential height fields. The similarity is measured using two indices—the persistence and the singularity. The persistence of a sequence is defined through the average distance between its consecutive states. Its singularity is the average distance between its states and their closest analogues. We apply these indices to geopotential heights over Western Europe in view of characterizing the sequences yielding record rainfall accumulations over several days in the Northern French Alps, more specifically in the Isère River catchment at Grenoble. We show that these indices remarkably stratify the heaviness of rainfall sequences in the region. We find that the less singular and the more persistent the atmospheric state sequence, the wetter the rainfall sequence. Although the persistence of atmospheric states leading to extremes was expected, their low singularity might be a feature of the Northern French Alps, which usually experiences roughly westerly winds triggering orographically enhanced precipitation. Relying on some choices that may be improvable, our study opens the door to future research on the characterization of the atmospheric state sequences favoring regional climate extremes based on analogy. We propose an analogy approach for characterizing the similarity between daily atmospheric state sequences ‐ in our case large scale geopotential height fields. The similarity is measured using two indices ‐ the persistence (celerity) and the singularity. We show that these indices remarkably stratify the heaviness of rainfall sequences in the Isère River catchment in northern French Alps. We find that the less singular and the more persistent the atmospheric state sequence, the wetter the rainfall sequence.
The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators.
The intensity of European heatwaves is connected to specific synoptic atmospheric circulation. Given the relatively small number of observations, estimates of the connection between the circulation and temperature require ad hoc statistical methods. This can be achieved through the use of analogue methods, which allow to determine a distribution of temperature conditioned to the circulation. The computation of analogues depends on a few parameters. In this article, we evaluate the influence of the variable representing the circulation, the size of the domain of computation, the length of the dataset, and the number of analogues on the reconstituted temperature anomalies. We tested the sensitivity of the reconstitution of temperature to these parameters for four emblematic recent heatwaves: June 2003, August 2003, July 2006 and July 2015. The paper provides general guidelines for the use of flow analogues to investigate European summer heatwaves. We found that Z500 is better suited than SLP to simulate temperature anomalies, and that rather small domains lead to better reconstitutions. The dataset length has an important influence on the uncertainty. We conclude by a set of recommendations for an optimal use of analogues to probe European heatwaves.
Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns. These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: “the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid”. The average dimension D of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of D has proved challenging. Moreover, D does not provide information on transient atmospheric motions, such as those leading to weather extremes. Using recent developments in dynamical systems theory, we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at lead times of over two weeks. Instantaneous properties of the attractor therefore provide an efficient way of evaluating and informing operational weather forecasts.
Nowcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others.
The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog–stochastic nowcasting technique.
Until recently, long range forecast systems showed only modest levels of skill in predicting surface winter climate around the Atlantic basin and associated fluctuations in the North Atlantic Oscillation at seasonal lead times. Here we use a new forecast system to assess seasonal predictability of winter north Atlantic climate. We demonstrate that key aspects of European and North American winter climate and the surface North Atlantic Oscillation are highly predictable months ahead. We demonstrate high levels of prediction skill in retrospective forecasts of the surface North Atlantic Oscillation, winter storminess, near surface temperature and wind speed; all of which have high value for planning and adaptation to extreme winter conditions. Analysis of forecast ensembles suggests that while useful levels of seasonal forecast skill have now been achieved, key sources of predictability are still only partially represented and there is further untapped predictability.
Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files “at home” with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named ‘flyingpigeon’ that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC), to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely. Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues, illustrating the flexibility of WPS in designing custom outputs. Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets.