Search

We show that both SALT and LU equations can be recovered using a prescribed definition of a random diffeomorphism used to perturb the physical space. However, compared with SALT and LU settings, the proposed perturbation scheme does not directly rely on a particular physics. Hence, the random mapping is more flexible and can be applied to any PDE.

As the world continues urbanizing, including efforts to forge a new framework of urban development is necessary. Recent studies related to flood prediction and mitigation have shown that Ensemble Prediction Systems (EPSs) constitute a valuable and essential tool for an Early Warning System. However, the use of EPS for flood forecasting in urban zones has yet to be understood. This work has the objective to investigate the potential use of the Operational EPS, issued by the European Centre for Medium-Range Weather Forecasts (ECMWF), for probabilistic urban flood prediction. In this research, a precipitation forecast verification was carried out in two study zones: (1) Mexico Valley Basin and (2) Mexico City, where for the latter, forecasts were compared against real-time observed data. The results showed good forecast reliability for a rain threshold of up to 20 mm in 24-hourly accumulations, with the first 36 h of the forecast horizon being the most reliable. The EPS has sufficient resolution and precision for flood prediction in Mexico City, which represents a further step toward developing a flood warning system at the local level based on ensemble forecasts. HIGHLIGHTS ECMWF's forecast evaluation from real-time measurements and at a temporal resolution of less than 12 h.; Implementation of a predictive model to predict the occurrence of a flood event.; To extend the use of EPS to include them in the chain for emergency and decision-making.; This work seeks to shorten the gap of previous research and direct its efforts to the evaluation of urban and flash floods.

For disaster prevention, it is important to predict the duration and the amount of rainfall brought by line-shaped rainbands. We examined a prediction method of ensemble forecast under a hypothesis that ensemble forecasts for difficult-to-predict events show the characteristic that the ensemble spread does not decrease. We call “𝐺𝐹𝑈” (Growing Forecast Uncertainty) as how became large the spread of updated forecast compare with the past ones. We analyzed ensemble forecast data of the recent line-shaped rainbands events and we found when the precipitation forecast was underpredicted, the 𝐺𝐹𝑈 becomes larger. From these analysis, we think that 𝐺𝐹𝑈 can be used to predict the occurrence and duration of line-shaped rainbands heavy rainfall and how forecast will go wrong.

eLife 12, e81916 (2023). doi:10.7554/eLife.81916, Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.Methods:We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.Results:Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.Conclusions:Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Published by eLife Sciences Publications, Cambridge

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.

India is an agro-based country, receives nearly 80% of the annual rainfall during the summer monsoon season, and has a vital socioeconomic security link. The start of the Indian rainy season is observed over the country's southern tip (Kerala) and is referred to as the monsoon onset. The Indian Summer Monsoon (ISM) onset is one of the key aspects and unique for each year. This paper aims the prediction of ISM onset for different years by using the operational numerical weather forecasts at medium-range time scales by using different atmospheric variables from UKMO, and NCMRWF deterministic (NCUM) and ensemble model (NEPS) weather forecasts. For predicting the 2018 onset, we have adopted objective methods like the rainfall criteria, daily circulation index, potential temperature, moisture transport etc. The results for an early onset year (2018) show that the models could predict the onset date agreeing with the observed India Meteorological Department (IMD) onset date of 29 May 2018 from both UKMO 5-day and NCUM 10-day forecasts. This study also emphasizes on the utilization of the medium-range numerical weather forecasts, providing enough time to plan the farming activities. We also present the models’ performance/ skill for assessing the onset dates for the delayed onset (2019) and timely onset (2020) monsoons with NCUM 10-days lead time, and the results agree with IMD dates. From the UKMO, NCUM, NEPS models’ rainfall forecast show that the onset date on 29 May 2018 as IMD. Similarly, the analysis from circulation index, potential temperature, vertically integrated moisture transport analysis, outgoing long wave radiation, tropospheric temperature index clearly shows the onset date agreeing with IMD observations. Similarly, for the late onset (2019) and timely onset (2020) years, the daily circulation index agrees with IMD observed onset dates. Finally, these results from the deterministic and probabilistic forecasts are consistent with the observed onset dates, and these medium range forecasts are highly helpful to compute the monsoon onset date with higher lead times (10 days) within the error of one day.

The Meteo-France seasonal forecasting system 7 provides a 7-month forecast range with 25 ensembles. The seasonal precipitation re-forecast (from May to November 1993–2015) was evaluated by the Brier score in terms of accuracy and reliability based on tercile probabilities. Multiple analyses were performed to assess the robustness of the score. These results show that the spatial distribution of the Brier score depends significantly on tercile thresholds, reference data, sampling methods, and ensemble types. Large probabilistic errors over the dry regions on land and the Nino regions in the Pacific can be reduced by adjusting the tercile thresholds. The forecast errors were identified when they were insensitive to different analysis methods. All the analyses detected that the errors increase/decrease with the lead time over the tropical Indian/Pacific Ocean. The intra-seasonal analysis reveals that some of these errors are inherited from monthly forecasts, which may be related to large-scale, short-term variability modes. A new confidence interval calculation was formulated for the “uncertain” case in the reference data. The confidence interval at a 95% level for the mean Brier score over the entire tropical region was quantified. The best estimations are ~6% the mean Brier score for both the above and below-normal terciles.

We have investigated the quality of track forecast for Typhoon Hagibis 2019 using ensemble forecast data from four major operational numerical weather forecast centers, ECMWF, NCEP, UKMO, and JMA. From six to four days before the landfall, the JMA forecast was the best among other centers, but the error increased sharply three days before the landfall. Compared to the forecast from 0000 UTC 9 October, the typhoon drifted westward about two days before the landfall in the forecast from 1200 UTC 9 October. Consistent with the westward track error, there was a northwestward bias of the environmental winds. The sensitivity analysis at the verification time close to the landfall indicates a large sensitivity to a low pressure disturbance located to the south of the typhoon.

The aim of the paper is to outline a project focussing on the development of a new type of ship weather routing solution with improved uncertainty handling, through better estimation of ship performance and responses to sea conditions. Ensemble forecasting is considered to take into account the uncertainty levels that are typical of operations in a stochastic environment. Increased accuracy of weather prediction is achieved through the assimilation of real-time data, measured by an on-board monitoring system. The proposed system will allow smooth integration of short-term Decision Support Systems for ship handling in dangerous or peculiar situations with long-term Decision Support Systems for weather routing. An appropriate user interface is also a critical part of the project and it will be implemented in a commercial Electronic Nautical Chart environment. A prototype of the full system will be installed and tested on a commercial vessel in regular operations and under real environmental conditions.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

This work is devoted to the development of an ensemble of below-cloud scavenging models of pollutant aerosol transport into the atmosphere. Among other factors contributing to the uncertainty of the forecasts of the dispersion and deposition of technogenic gas-aerosol releases in the atmosphere, precipitation scavenging is one of the least studied and, in case of precipitation, can be the dominant mechanism for aerosol deposition. To form the ensemble of below-cloud scavenging models, appropriate experimental data, raindrop-aerosol capture models, raindrop terminal velocity parameterizations, and raindrop size distributions were chosen. The pool of models was prepared and then evaluated to adequately describe the experimental data using statistical analysis. Rank diagrams were used to analyze the adequacy of meteorological ensembles; together with the ensemble distribution construction, they allowed selecting the groups of models with such properties as to produce unbiased estimates and dispersion corresponding to the dispersion of the experimental data. The model calculations of the concentration fraction deposited due to below-cloud scavenging were performed using a log-normal distribution with characteristics corresponding to those observed during the accidents at the Chernobyl NPP and Fukushima-1 NPP. The results were compared with those obtained using the models of the NAME and FLEXPART codes. The results of this work can be used to improve the current approaches applied for modelling the distribution of pollutants in the atmosphere in the case of emergency, enhancing the reliability of forecasts by taking into account uncertainties in the results. The formed multi-model ensemble will be included in the decision support system used in responding to releases of radioactive substances into the atmosphere.

Coastal marine ecosystems are currently being exposed to climate change at a much faster rate than many other ecosystems, with coastal species being exposed to multiple stressors. Cephalopod mollusks play a pivotal role in marine trophic webs, and most are “keystone” species due to their influence on ecosystem dynamics. In this study, we characterized the global patterns of coastal cephalopod diversity and present, for the first time, a global forecast of potential changes in richness, mean body size, and assemblage composition (i.e., species replacement, nestedness, and combinations of both) for 161 coastal cephalopod species under climate change, using an ensemble of ecological niche models (ENMs) for an end of the century mitigation scenario. We have shown that, for the baseline period, coastal cephalopod diversity is higher in the Central Indo-Pacific area and that body size patterns follow the temperature-size rule, with larger animals occurring at higher latitudes. The end-century projections of habitat suitability show a different picture, with 96% of cephalopod species predicted to experience range contraction and 15% completely losing their environmental space. Nestedness is projected to be the main effect of species compositional change. Maximum body size is projected to increase in 44% of the pixels and decrease in 37%. Regarding fisheries, the projected changes are more favorable to the countries at higher latitudes, although the search for refugia of smaller tropical species might potentially lead to a mitigation of the negative effects of climate change in these areas, as measured by the total capture (ton). While the model has limitations, our findings reflect major climatic drivers of change and highlight the idea that even though cephalopod species seem good candidates to replace overexploited fish stocks in the near future, they may not have the environmental space to do so., This study was supported by the Portuguese Foundation for Science and Technology (FCT) through a Ph.D. grant (SFRH/BD/51514/2011) to JB-P, project grants PTDC/BIA-BEC/103266/2008 and PTDC/MAR/0908066/2008 to RR, and PTDC/AAC-AMB/98163/2008 to MA and FG.

Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform single-run simulations in all categorical metrics but no clear conclusion can be extracted on which is the best option between these two., We acknowledge the use of the ERA5 Fifth generation of ECMWF atmospheric data from the Copernicus Climate Change Service; neither the European Commission nor ECMWF is responsible for the use made of the Copernicus Information and Data. We thank Alexa Van Eaton from USGS for providing us with digitalised Calbuco isopach contours

Although land surface influences atmospheric processes significantly, insufficient studies have been conducted on the ensemble forecasts using the breeding of growing modes (BGM) with perturbed land surface variables. To investigate the practicability of perturbed land variables for ensemble forecasting, we used the ARWv3 mesoscale model to generate ensembles for an event of 24 h heavy rainfall with perturbed atmospheric and land variables by the BGM method. Results show that both atmospheric and land variables can generate initial perturbations with BGM, except that they differ in time and saturation characteristics, e.g., saturation is generally achieved in approximately 30 h with a growth rate of ~1.30 for atmospheric variables versus 102 h and growth rate of 1.02 for land variables. With the increase in precipitation, the importance of the perturbations of land variables also increases as compared to those of atmospheric variables. Moreover, the influence of the perturbations of land variables on simulated precipitation is still relatively large, although smaller than that of atmospheric variables, e.g., the spreads of perturbed atmospheric and land subsets were 7.3 and 3.8 mm, respectively. The benefits of perturbed initialisation can also be observed in terms of probability forecast. All findings indicate that the BGM method with perturbed land variables has the potential to ensemble forecasts for precipitation.

For disaster prevention, it is important to predict the duration and the amount of rainfall brought by line-shaped rainbands. We examined a prediction method of ensemble forecast under a hypothesis that ensemble forecasts for hard-to-predict events, such as line shaped rainband heavy rainfall, show two patterns of characteristic changes when the forecasts are updated. The first pattern is that the ensemble mean does not approach reality even if the forecast is updated. The second pattern is that the dispersion of ensemble does not decrease. We conducted an ensemble forecast taking multiple initial time for the northern Kyushu heavy rainfall occurred in 2017, and examined the prediction information of water vapor, which is the physical quantity preceding precipitation. Pattern 1 was strongly observed around Asakura City. Pattern 2 was observed not only around Asakura City but also in areas upstream of water vapor inflow. These characteristics correspond to the time duration of heavy rainfall to some extent. From these analysis, we showed the possibility of utilization of two patterns for the heavy rainfall exceeding the scenario of ensemble forecast.

Published version is accepted in Weather and Forecasting, DOI: 10.1175/WAF-D-18-0149.1; International audience; To satisfy a wide range of end-users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical post-processing methods based on Quantile Regression Forests and Gradient Forests with a semi-parametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called PEARP. They are verified with a cross validation strategy and compete favorably with state-of-the-art methods like Analog Ensemble or Ensemble Model Output Statistics. Our methods do not assume any parametric links between the variable to calibrate and possible covariates. They do not require any variable selection step, and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

Probabilistic forecasting is becoming increasingly important for a wide range of applications, especially for energy systems such as forecasting wind power production. A need for proper evaluation of probabilistic forecasts follows naturally with this, because evaluation is the key to improving the forecasts. Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist, we find that there is a need for an introduction with some practical application. In particular, many forecast scenarios in energy systems are inherently multivariate, and while univariate evaluation methods are well understood and documented, only limited and scattered work has been done on their multivariate counterparts. This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods, primarily scoring rules, as well as practical sections that explain how these methods can be calculated and estimated. In three case studies featuring simple autoregressive models, stochastic differential equations and real wind power data, we implement, apply and discuss the logarithmic score, the continuous ranked probability score and the variogram score for forecasting problems of varying dimension. Finally, the advantages and disadvantages of the three scoring rules are highlighted, and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.

The ensemble technique is considered to be an effective approach in enhancing the model capacity of intra-seasonal climate change. Since El Niño-Southern Oscillation is one of the critical modes of interannual variability in the tropical Pacific, an appropriate ensemble technique may help minimize model bias in ENSO forecast. This research includes a modified stochastically perturbed parameterization tendencies scheme in the Community Earth System Model to investigate its impact on ENSO prediction. This revised scheme uses independent noise patterns to perturb the tendencies from different physical parameterizations. In the original scheme, only the same noise is employed. The result suggests that the altered approach is in a position to further reduce sea surface temperatures and gain more skill in uncertainty estimation compared to the original one. ENSO’s amplitude is improved especially of its warm phase El Niño, but there is a limited improvement in its spatial structure. The modified scheme also ameliorated the variability of ENSO by increasing the magnitude toward observation. The power spectrum exhibits an increased representation. Besides those findings, we notice that simple ensemble mean may not be able to represent the climate status as it smoothes out some useful signals.

In June-July 2020 two remarkable weather events occurred in northern Eurasia. One is a severe heat wave that produced a record-breaking temperature of 38 degrees C at Verkhoyansk, eastern Siberia on 20 June. The other one is heavy rainfall events observed in East Asia (southern China and southwestern Japan) in early July, causing severe floods that brought about considerable damage to infrastructure and the economy, as well as the loss of human lives. Despite the accumulated evidence of stronger and more extreme heat waves and heavy rainfall as a result of global warming, little is known about the linkage between these phenomena. Given that the Arctic is warming twice as fast as the global mean, Arctic warming might be enhancing the increase of heavy rainfall events in East Asia. Here, we investigated the relationship between the Siberian heat wave and the East Asian heavy rainfall that occurred summer in 2020. An empirical orthogonal function (EOF) analysis applied to atmospheric reanalysis data of 1958-2020 period captures dominant summer circulation patterns reflecting atmospheric internal variability and externally forced anomalies. On the basis of these EOF patterns, operational forecasts of summer 2020 using the global model from the Japan Meteorological Agency (JMA) and a global climate model experiment based on 2-K warming future projection are utilized to examine roles of the internal variability and external forcing, respectively. Consistent results between them reveal that development of the blocking high over eastern Siberia has certain impacts on rainfall anomalies over East Asia. By a statistical technique applied to the ensemble forecast data, prediction of the East Asian precipitation is improved by 10-20% of its amplitude. Our research demonstrates possibility that East Asian rainfall is being enhanced by high-latitude atmospheric circulations due to the Arctic warming even in the current climate in which the tropical warming is not yet severe. Suggestions are given that continued Arctic warming and a future increase of tropical warming will lead to increases of the frequency and severity of heavy rainfall events in East Asia.

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy.

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES.

Mesoscale convective systems (MCSs) often lead to events such as floods, and debris flow. Initial condition is difficult to determine for Numerical Weather Prediction (NWP) of rainfall prediction. Data assimilation (DA) using radar data is expected to improve the initial condition in the model. The purpose of this study is to determine the predictabilitiy of MCSs by assimilation of XRAIN data. We investigated the effects of DA on the heavy precipitation event of July 2012 in Kyoto. As a result, this experiments shows the assimilation of ice-water mixing ratios improved predicting accuracy.

Nowadays, ensemble-based numerical weather forecasts provide probabilistic guidance to actors in the renewable energy sector. Ensemble forecasts can however suffer from statistical inconsistencies that affect the forecast reliability. Statistical post-processing techniques address this issue using learning algorithms based on past data. In this study, it is shown that quantile regression is a suitable method for the post-processing of ensemble global radiation forecasts. In a basic approach, conditional quantiles are estimated using the first guess quantile forecasts and a solar geometry variable as predictors. In a more complex approach, adequate meteorological predictors are selected among a pool of ensemble model outputs by means of a regularization scheme. The so-called penalized quantile regression and the basic quantile regression approaches, respectively, are applied to hourly averaged global radiation forecasts of the high-resolution ensemble prediction system COSMO-DE-EPS. Both calibration setups provide reliable probabilistic forecasts at all investigated probability levels, which improves considerably the ensemble forecast skill. Moreover, verification results demonstrate that including rigorously selected predictors in the regression scheme increases the ensemble forecast sharpness and thereby the value of the probabilistic guidance.

Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time. RESUMO A previsão a longo prazo da umidade do solo permite melhor planejamento em áreas como a agricultura. No entanto, há ainda poucos estudos dedicados à especificamente estimar umidade do solo para longos horizontes de tempo, o que reflete as dificuldades associadas a este tipo de previsão. Uma abordagem que pode ajudar a melhorar a performance destas previsões é a utilização de previsões por conjuntos (ensemble). Neste estudo, uma previsão de umidade do solo para horizontes de tempo de um, três e seis meses foi desenvolvida na bacia do rio Ijuí (Brasil) usando uma combinação de previsões por conjuntos de precipitação e simulação hidrológica. Todos os membros dos conjuntos de três diferentes modelos climatológicos foram utilizados para rodar o modelo hidrológico MGB, gerando 207 previsões de umidade do solo, organizadas em quatro grupos: (A) para cada modelo, o intervalo de umidade mais frequentemente previsto entre as previsões feitas com cada membro do ensemble, (B) usando a precipitação média de cada modelo, (C) considerando um superensemble, e (D) o intervalo de umidade do solo médio previsto entre as previsões do grupo B. Os resultados mostram que a umidade do solo a longo prazo estimada com base em previsões de precipitação pode ser útil para identificar períodos mais secos ou mais úmidos do que as condições médias para a região estudada. Contudo, a estimativa dos valores exatos de umidade do solo ainda é bastante limitada. Previsões realizadas com os grupos B e D tiveram performance similar as dos grupos A e C, e requerem menor análise e processamento de dados e esforço computacional.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES The Taim Wetland (BT), located in the extreme south of Brazil, in a region characterized by recurrence of conflicts over water use, which involve region's main economic activity, flood irrigated rice cultivation, and preservation of the aquatic ecosystem present at the BT. Irrigation water withdrawals are made from Mangueira and Mirim lagoons, located upstream and downstream of BT, respectively. These lagoons regulate BT's water exchange and eventually make it more or less able to accommodate the richness of fauna and flora species that depend on it for their survival throughout or part of their life. Water management in this region is necessary for anticipate or mitigate conflicts of this nature, and hydrological forecasting is one of the tools used to assist decision making. Assuming that it is possible to assess future hydrological conditions, considering the interrelationship of water levels with local fauna and flora, it is possible to estimate the environmental condition for the same horizon as the level forecasts. Thus, this work presents a methodology that allows the realization of the hydro-environmental (hydrological plus environmental) forecast for the BT. The hydrological forecast of the BT levels was performed considering the ensemble forecast in the hydrologic-hydrodynamic cell model, which considers the entire Taim Hydrological System (SHT), including Mangueira Lagoon and the downstream influence condition exerted by Mirim Lagoon on the BT levels, although to a lesser extent than Mangueira Lagoon. As a strategy for forecasting, data from the Atmospheric General Circulation Model (AGCM from CPTEC/INPE), the regional statistical model developed for the Rio Grande do Sul State (MRERS from CCPMet/ UFPel) and historical data were used. The best results were obtained for the methodology that used the historical data, which allowed an ensemble forecast with a 12-month horizon, indicating good results for the proposed methodology. For the environmental forecast were considered previously developed Habitat Suitability Indices (HSI), for nine species bioindicators of the BT. For the use of HSI, an alternative methodology based on Suitability Relationships was developed, without the need to model the forecast of environmental suitability considerin specific hydrological conditions. At the end of the process, the hydrological forecast of the the BT was combined with the Suitability Relationships, allowing the identification of the environmental forecast. The feasibility of applying the methodology was presented by conducting a hydro-environmental forecast for the 2019-2020 crop. The application also highlights the strategies used to circumvent the main difficulty observed in the development of the work, related to the lack of data in the region. Due to the influence of Mirim Lagoon on SHT modeling, it was also necessary to develop a statistical model of level forecast for this lagoon, based on monthly levels, precipitation and evaporation data, simplifying the utilization process. More than one regression model had the adequate ability to estimate Mirim Lagoon levels, especially for smaller forecast horizons, as well as for the purposes of fill lack of data and extension of Mirim's historical series of levels. O Banhado do Taim (BT), localizado no extremo sul do Brasil, está inserido numa região caracterizada pela recorrência de conflitos pelo uso da água, que envolvem a principal atividade econômica da região, a rizicultura irrigada por inundação, e a preservação do ecossistema aquático presente no BT. As retiradas de água para irrigação são feitas das Lagoas Mangueira e Mirim, localizadas a montante e jusante do BT, respectivamente. Estas lagoas regulam as trocas de água do BT e acabam por torná-lo mais ou menos apto a comportar a riqueza de espécies de fauna e flora, que dependem dele para sua sobrevivência durante toda a sua vida ou parte dela. A gestão da água nesta região é fundamental para antecipar ou mitigar conflitos desta natureza, sendo a previsão hidrológica uma das ferramentas utilizadas para auxiliar a tomada de decisões. Partindo do pressuposto de que é possível avaliar condições hidrológicas futuras, se considerada a inter-relação dos níveis de água com a fauna e flora local, é possível estimar a condição ambiental para o mesmo horizonte das previsões de níveis. Assim, nesse trabalho é apresentada uma metodologia que permite a realização da previsão hidroambiental (hidrológica mais ambiental) para o BT. A previsão hidrológica de níveis do BT foi realizada considerando a previsão por conjunto, com a utilização do modelo hidrológico-hidrodinâmico de células, que considera todo o Sistema Hidrológico do Taim (SHT) e a condição de influência de jusante que a Lagoa Mirim exerce sobre os níveis do Banhado. Como estratégia para a previsão foram utilizados dados oriundos do Modelo de Circulação Geral Atmosférico (MCGA, do CPTEC/INPE), o modelo regional estatístico desenvolvido para o Rio Grande do Sul (MRERS, do CCPMet/UFPel) e também de dados históricos. Os melhores resultados foram obtidos para a metodologia que utilizou os dados históricos, que permitiram uma previsão por conjunto com horizonte de 12 meses, indicando bons resultados para a metodologia proposta. Para a previsão ambiental foram considerados Índices de Adequabilidade de Habitat (IAH) previamente desenvolvidos, para nove espécies bioindicadoras do BT. Para a utilização dos IAH, foi desenvolvida uma metodologia alternativa, baseada em Relações de Adequabilidade, sem a necessidade de modelar a previsão da adequabilidade ambiental frente a condições hidrológicas específicas. Ao final do processo, a previsão hidrológica do BT foi combinada com as Relações de Adequabilidade, permitindo a identificação da previsão ambiental. A viabilidade da aplicação da metodologia foi apresentada por meio da realização de uma previsão hidroambiental para a safra 2019-2020. Na aplicação também são destacadas as estratégias utilizadas para contornar a principal dificuldade encontrada no desenvolvimento do trabalho, relacionada à ausência de dados na região. Em razão da influência da Lagoa Mirim na modelagem do SHT, também foi necessário desenvolver um modelo estatístico de previsão de níveis para esta lagoa, baseado em dados de cotas, precipitação e evaporação mensais, simplificando o processo de utilização. Chegou-se a mais de um modelo de regressão com capacidade adequada para prever os níveis da Mirim, especialmente para horizontes menores de previsão, além de servir, também, para os propósitos de preenchimento de falhas e extensão da série histórica dos níveis da Mirim.

Summary Climate and land-use change are expected to substantially alter future plant species distributions leading to higher extinction rates. However, little is known about how plant species ranges, richness and phylogenetic diversity of continents will be affected by these dynamics. We address this gap here by examining the patterns of species' distributions and phylogenetic relationships for 7465 seed plant taxa in North America. An ensemble of species distribution models was used to estimate the potential suitable habitat of species under different sets of climate, land-use and dispersal constraint scenarios. We then evaluated the vulnerability and extinction risk of individual species to changes in climate and land use, and examined whether rare, endangered and evolutionarily distinct species were disproportionally threatened by climate and land-use change. We show that ~2000 species may lose >80% of their suitable habitats under the A1b emission scenario for the 2080s, while ~100 species may experience >80% range expansions (a 20 : 1 ratio of loss to gain). When considering >50% range retraction and expansion, the ratio of loss to gain was 13 : 1. A greater loss of species diversity is expected at low latitudes, while larger gains are expected at high latitudes. Evolutionarily distinct species are predicted to have significantly higher extinction risks than extant species. This suggests a disproportionate future loss of phylogenetic diversity for the North American flora. Synthesis and applications. Our study provides continental-scale evidence of plant species extinction risk caused by future climate and land-use change, and highlights the importance of integrating phylogenetic measures into conservation risk assessments. This work provides insight into the status, trends and threats for a large share of North America's plant species by identifying risks and prioritizing conservation in a rapidly changing world.

This paper has been submitted to Solar Energy. Abstract: We introduce a computational framework to forecast cloud index (CI) fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.

Severity of drought in California (U.S.) varies from year-to-year and is highly influenced by precipitation in winter months, causing billion-dollar events in single drought years. Improved understanding of the variability of drought on decadal and longer timescales is essential to support regional water resources planning and management. This paper presents a soft-computing approach to forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly data covering more than two centuries (1801&ndash, 2014) was used for the design of ensemble projections to understand and quantify the uncertainty associated with interannual-to-interdecadal predictability. With a predictable structure elaborated by exponential smoothing, the projections indicate for the horizon 2015&ndash, 2054 a weak increase of drought, followed by almost the same pace as in previous decades, presenting remarkable wavelike variations with durations of more than one year. Results were compared with a linear transfer function model approach where Pacific Decadal Oscillation and El Ni&ntilde, o Southern Oscillation indices were both used as input time series. The forecasted pattern shows that variations attributed to such internal climate modes may not provide more reliable predictions than the one provided by purely internal variability of drought persistence cycles, as present in the PDSI time series.

Sophisticated strategies are required for flood warning in urban areas regarding convective heavy rainfall events. An approach is presented to improve short-term precipitation forecasts by combining ensembles of radar nowcasts with the numerical weather predictions COSMO-DE-EPS of the German Weather Service. The combined ensemble forecasts are produced operationally every 5 min. Applications involve the Flood Warning Service Hamburg (WaBiHa) and real-time hydrological simulations with the model KalypsoHydrology. Ensemble forecast flood hydrographs for the urban catchment Kollau are presented in this work. The range and quantiles are illustrated for a convective storm event in summer 2016. A flood management measure is improved by using precipitation forecasts in a flood control reservoir.

Lecacheux, S.; Bonnardot, F.; Rousseau, M.; Paris F.; Pedreros, R.; Nicolae Lerma, A.; Quetelard, H., and Barbary, D., 2018. Probabilistic forecast of coastal waves for flood warning applications at Reunion Island (Indian Ocean). In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 776–780. Coconut Creek (Florida), ISSN 0749-0208.La Reunion being a small isolated island, the coastal impacts of tropical cyclones transiting nearby are tightly related to the relative position of the track, wind intensity and size. Thus, the forecast of cyclone-induced waves should be conducted in a probabilistic manner.Using ensemble track and intensity forecasts of Bonnardot and Quetelard (2016), the approach presented here relies on two steps: (1) Considering that the use of parametric models leads to an underestimation of wave heights distant from the cyclone eye, the forecasted vortex is bogused into the large-scale wind field provided by ECMWF's IFS model for each track and time step. A short simulation is conducted with Meso-NH model at 8 km resolution so that it can create realistic wind and pressure fields well balanced with the large-scale conditions (2) A combination of a two-way nested Wavewatch 3 modelling framework enables to compute the waves associated to each track from 10 km to 300 m resolution at the coast.Results extracted at 50 m depth are presented as exceedance values and probabilities of exceedance of wave heights for different coastal segments and thresholds. The comparison with measurements and simulations performed with Best-Track data show that probabilistic wave forecasts performed with the ensemble sets of Bonnardot and Quetelard (2016) upgrade the precision and the level of information provided compared to the deterministic method. Nevertheless, the study also points out that the size and shape parameters of the cyclones should be considered in the ensemble generation method to produce more accurate wave forecast.

International audience; Medium-range forecasts (one day to two weeks) of solar radiation are commonly assessed with a single forecast at a given location. In this paper, we forecast maps of surface solar irradiance, using ensembles of forecasts from the THORPEX Interactive Grand Global Ensemble (TIGGE) with a 6-h timestep. We compare our forecasts with observations derived from MeteoSat Second Generation (MSG) and provided by the HelioClim-3 database as gridded observations over metropolitan France. First, we study the ensembles from six meteorological centers. Second, we use sequential aggregation to linearly combine all the forecasts with weights that vary in space and time. Sequential aggregation updates the weights before any forecast, using available observations. We use the global numerical weather prediction from the European Center for Medium-range Weather Forecasts (ECMWF) as a reference forecast. The issue of spatial resolution is discussed because the low resolution forecasts from TIGGE are compared to high resolution irradiance estimated from MSG data. We found that the TIGGE ensembles are under-dispersed but rather different from one to another. Aggregation decreases the forecast error by 20%, and produces a more realistic spatial pattern of predicted irradiance.

Aim Forecasting potential patterns in species’ distributions and diversity under climate change is crucial for biodiversity conservation. Although high-latitude regions are expected to experience some of the greatest increases in temperature due to global warming, little is known on how individual responses in species will affect patterns in phylogenetic diversity (PD). Location Alberta, Canada. Methods We used 160,589 occurrence records for 1541 species of seed plants in Alberta (nearly 90% of the province's seed flora) and ensemble niche models to project current and future suitable habitats. We then examined climate change vulnerability of individual species and the potential impacts of climate change on species richness, PD and both taxonomic and phylogenetic endemism (PE). We also assessed whether predicted losses of PD were distributed randomly across the plant tree of life. Results We found that 368 species (24%) may lose on average > 80% of their current suitable climates (habitats), while 539 species (35%) were projected to more than double their current suitable range. Both species richness and PD were predicted to increase in most areas, except for the species-rich Rocky Mountains, which are predicted to experience future declines. Maps of taxonomic and PE identified several regions with high conservation value and climate change threat suggesting priorities for conservation and climate change adaptation. Overall, a non-random extinction risk was found for Alberta's flora, demonstrating potential future impacts of climate change on the loss of evolutionary history. Main conclusions Our analyses suggest that climate change will have asymmetrical effects on the distribution of Alberta's plant diversity and endemism and a non-random extinction risk of the current state of species evolutionary history. Our results provide practical guidance for biodiversity conservation and management in this region by prioritizing species' vulnerabilities and places with higher taxonomic or evolutionary risk due to future climate change.

A series of reforecast experiments using the ensemble prediction system of the Meteorological Research Institute is conducted to reveal the predictability and downward influence of a reflection event of stratospheric planetary waves in February 2014, which recorded the highest level of the downward propagation of the wave activity. The reflection is predictable from at least 7 days in advance. It is found that the skill of forecasts with a prediction period around the predictability limit crucially depends on whether planetary waves are reflected or absorbed in the stratosphere. Composite analyses also reveal that the persistence of the tropospheric wave source in the eastern hemisphere is a key in determining the propagating property of stratospheric planetary waves. When the reflection occurs in association with a weak persistence of the wave source, the subsequent downward propagating wave from the stratosphere enforces anomalous tropospheric circulation in the western hemisphere.

The method of ensemble Typhoon was applied to a strong tropical cyclone in thw Bay of Bengal, Aila, in 2009. An ensemble of 63 members was successfully generated which are used to assess the impact to the society along the Bay of Bengal.

Climate change has modified the structure and functions of ecosystems, affecting human well-being. Evergreen plants in the warm-temperate ecosystems will lose climatically suitable habitats under climate change but have not drawn much scholarly interest. Therefore, the present research aimed to predict the future climatic niches of eight coastal warm-adapted evergreen trees under climate change to provide information for an effective management practice. For this purpose, we used the ensemble species distribution models (SDMs) weighted by the TSS value in modelling the climatic niches of those evergreen trees and then ensembled their future distributions predicted under 20 future climate scenarios. Except for Neolitsea sericea (True Skill Statistic (TSS) = 0.79), all projections for the current climatic niches of evergreens showed excellent predictive powers (TSS > 0.85). The results showed that the climatic niches of the four evergreens—Castanopsis cuspidata, Pittosporum tobira, Raphiolepis indica var. umbellate, and Eurya emarginata—would expand to the northern part of the Korean Peninsula (KP) under climate change, but the ones of the remaining four—Kadsura japonica, Neolitsea sericea, Ilex integra, and Dendropanax morbiferus—would shrink. While the climatic niches of Pittosporum tobira showed the rapidest and greatest expansion under climate change, Dendropanax morbiferus was predicted to experience the greatest loss of habitat. On the other hand, regardless of whether the future distributions of climatically suitable habitats would expand or contract, the highly suitable habitats of all species were predicted to decline under climate change. This may indicate that further climate change will degrade habitat suitability for all species within the distribution boundary and restrict continuous habitat expansions of expanding species or accelerate habitat loss of shrinking species. In addition, the future distributions of most coastal evergreens were found to be confined to coastal areas; therefore, sea-level rise would accelerate their habitat loss under climate change. The present study provides primary and practical knowledge for understanding climate-related coastal vegetation changes for future conservation planning, particularly on the Korean Peninsula.

This paper reviews different statistical methods dedicated to the post-processing of Numerical Weather Predictions and Ensemble Forecast. We focus on the application of the post-processing to problems linked to the production of electricity by eolian devices. The basic idea is to give a concise panorama of the methods commonly used nowadays. We pay a particular attention to the mathematics involved in the methods. We do not compare the methods and do not provide some preferences. Classification. 62-02; 62P12.

Summary This paper evaluates how post-processing can enhance raw precipitation forecasts made by different numerical weather prediction (NWP) models archived in TIGGE (THORPEX Interactive Grand Global Ensemble) database. Ensemble Pre-Processor (EPP), developed at U.S. National Weather Service, is used to post-process raw precipitation forecasts. EPP involves several major steps: (1) deriving the joint distributions of raw forecasts and observations corresponding to different canonical events; (2) obtaining the probability distributions of observations given the raw forecasts; and (3) constructing ensemble forecasts from the conditional probability distributions given the raw forecasts. Raw precipitation forecasts from five NWP models (CMA, ECMWF, JMA, NCEP and UKMO) during the summer-fall period (rainy season) from 2007 to 2011 were evaluated over the Huai river basin in China. The lead time for the precipitation forecasts is set to 9 days, which are divided into 11 canonical events (defined as daily precipitation events or aggregate precipitation events over a period of several consecutive days). Our experiments show that post-processed precipitation forecasts shows substantial improvement over the raw forecasts. Post-processing reduces both the biases and the root mean squared error of the raw forecasts significantly. In terms of ensemble spread, both the Brier skill scores and continuous ranked probability skill score are improved appreciably after post-processing. Reliability diagrams and rank histograms also confirm that post-processed ensemble forecasts possess better ensemble spread property compared to the raw forecasts. Among the five NWP models, ECMWF and JMA have the best overall performance in both raw and post-processed forecasts. The raw and post-processed UKMO and NCEP forecasts outperform other models in certain events. Post-processing can improve the CMA raw forecasts substantially, but still its performance is consistently worse than that of the other models.

International audience; Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts' availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.

International audience; This paper presents the application of an ensemble forecasting approach to the Prev'Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide and particulate matter. It relies on several air quality models which differ by their physical parameterizations, their input data and numerical strategies, so that one model may perform better with respect to observations for a given pollutant, at a given time and location. We apply sequential aggregation methods to this ensemble of models, which has already proved good potential in previous research papers. Compared to these studies, the novelties of this paper are the variety of models, the real operational context, which requires robustness assessment, and the application to several pollutants. In this paper, we first introduce the ensemble forecasting methods and the operational platform Prev'Air along with its models. Then, the sequential aggregation performance and robustness are assessed using two different data sets. The results with the discounted ridge regression method show that the errors of the forecasts are respectively reduced by at least 29%, 35% and 19% for hourly, daily and peak O3 concentrations, by 19%, 26% and 20% for hourly, daily and peak NO2 concentrations, and finally by 17%, 19% and 11% for hourly, daily and peak PM10 concentrations. At last, we give a first insight of the ensemble ability to forecast threshold exceedances.

A series of reforecast experiments by using the ensemble prediction system of the Meteorological Research Institute is conducted to investigate the predictability and occurrence mechanism of a stratospheric sudden warming event in January 2009, which is a typical polar vortex-splitting event. The polar vortex splitting is predicted for forecasts initialized after 8 days before the warming peak. It is found that in the failed forecast, the upward propagating planetary waves from the troposphere are reflected by the polar night jet and propagate downward into the troposphere. Thus, this study indicates that the propagating property of planetary waves in the stratosphere would be another important control factor for the occurrence of the polar vortex-splitting event.

Simplified methods are developed to identify initial perturbations that have largest influence on a given norm in a user-defined verification region. Our methods do not require a data assimilation system such as an adjoint model or an ensemble Kalman filter but an ensemble forecast with simple matrix operations. Two ensemble-based methods are formulated based on the linear perturbation theory: one that approximates the singular vector method and the other the adjoint method. The sensitive initial perturbations are obtained by finding the coefficients of the linear combination of ensemble members. Our formulations have been applied to the weekly ensemble forecasts of the Japan Meteorological Agency with the verification region over Japan. Our methods successfully identify sensitive perturbations associated with mid-latitude and tropical disturbances.

This thesis develops, analyzes and demonstrates several valuable applications of randomized fluid dynamics models referred to as under location uncertainty. The velocity is decomposed between large-scale components and random time-uncorrelated small-scale components. This assumption leads to a modification of the material derivative and hence of every fluid dynamics models. Through the thesis, the mixing induced by deterministic low-resolution flows is also investigated. We first applied that decomposition to reduced order models (ROM). The fluid velocity is expressed on a finite-dimensional basis and its evolution law is projected onto each of these modes. We derive two types of ROMs of Navier-Stokes equations. A deterministic LES-like model is able to stabilize ROMs and to better analyze the influence of the residual velocity on the resolved component. The random one additionally maintains the variability of stable modes and quantifies the model errors. We derive random versions of several geophysical models. We numerically study the transport under location uncertainty through a simplified one. A single realization of our model better retrieves the small-scale tracer structures than a deterministic simulation. Furthermore, a small ensemble of simulations accurately predicts and describes the extreme events, the bifurcations as well as the amplitude and the position of the ensemble errors. Another of our derived simplified model quantifies the frontolysis and the frontogenesis in the upper ocean. This thesis also studied the mixing of tracers generated by smooth fluid flows, after a finite time. We propose a simple model to describe the stretching as well as the spatial and spectral structures of advected tracers. With a toy flow but also with satellite images, we apply our model to locally and globally describe the mixing, specify the advection time and the filter width of the Lagrangian advection method, as well as the turbulent diffusivity in numerical simulations.; Cette thèse concerne le développement, l'extension et l'application d'une formulation stochastique des équations de la mécanique des fluides introduite par Mémin (2014). La vitesse petite échelle, non-résolue, est modélisée au moyen d'un champ aléatoire décorrélé en temps. Cela modifie l'expression de la dérivée particulaire et donc les équations de la mécanique des fluides. Les modèles qui en découlent sont dénommés modèles sous incertitude de position. La thèse s'articulent autour de l'étude successive de modèles réduits, de versions stochastiques du transport et de l'advection à temps long d'un champ de traceur par une vitesse mal résolue. La POD est une méthode de réduction de dimension, pour EDP, rendue possible par l'utilisation d'observations. L'EDP régissant l'évolution de la vitesse du fluide est remplacée par un nombre fini d'EDOs couplées. Grâce à la modélisation sous incertitude de position et à de nouveaux estimateurs statistiques, nous avons dérivé et simulé des versions réduites, déterministe et aléatoire, de l'équation de Navier-Stokes. Après avoir obtenu des versions aléatoires de plusieurs modèles océaniques, nous avons montré numériquement que ces modèles permettaient de mieux prendre en compte les petites échelles des écoulements, tout en donnant accès à des estimés de bonne qualité des erreurs du modèle. Ils permettent par ailleurs de mieux rendre compte des évènements extrêmes, des bifurcations ainsi que des phénomènes physiques réalistes absents de certains modèles déterministes équivalents. Nous avons expliqué, démontré et quantifié mathématiquement l'apparition de petites échelles de traceur, lors de l'advection par une vitesse mal résolu. Cette quantification permet de fixer proprement des paramètres de la méthode d'advection Lagrangienne, de mieux le comprendre le phénomène de mélange et d'aider au paramétrage des simulations grande échelle en mécanique des fluides.

For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP). With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD), against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

The Iroise Sea has been observed since 2006 by High Frequency (HF) radars, which estimate surface currents. These measurements offer high resolution and high frequency to capture the dynamics of the coastal domain. This thesis aims at designing and applying a method of assimilation of these data in a realistic numerical model to optimize the bottom friction and to correct the model state in order to improve the representation of the residual tidal circulation and the positions of the Ushant fronts in the Iroise Sea. The method of data assimilation used is the Ensemble Kalman Filter. The originality of this method is the use of a stochastic modeling to estimate the model error. First, ensemble simulations were carried out from the perturbation of various model parameters which are the model error sources: meteorological forcing, bottom friction, horizontal turbulent closure and surface roughness. These ensembles have been explored in terms of dispersion and correlation. An Ensemble Kalman smoother was used to optimize the bottom friction (z0) from the surface current data and from an ensemble produced from a perturbed and spatialized z0. The method is tested with a twin experiment and then with real observations. The optimized maps of parameter z0, produced with the real currents, were used in the model over another period and the results were compared with independent observations. Finally, twin experiments were conducted to test the model state correction. Two approaches were compared; first, only the low frequency, by filtering the tide in the data and in the model, is used to perform the analysis. The other approach takes the whole signal into account. With these experiments, we assess the filter's ability to control both the observed part of the state vector (currents) and the unobserved part of the system (Sea surface Temperature)., La Mer d’Iroise est observée depuis 2006, par des radars à haute fréquence (HF) qui estiment les courants de surface. Ces mesures ont une finesse temporelle et spatiale pour permettre de capturer la dynamique fine du domaine côtier. Ce travail de thèse vise à la conception et l’application d’une méthode d’assimilation de ces données dans un modèle numérique réaliste pour optimiser le frottement sur le fond et corriger l’état du modèle afin de mieux représenter la circulation résiduelle de marée et les positions des fronts d’Ouessant en mer d’Iroise. La méthode d’assimilation de données utilisée est le Filtre de Kalman d’Ensemble dont l’originalité est l’utilisation d’une modélisation stochastique pour estimer l’erreur du modèle. Premièrement, des simulations d’ensemble ont été réalisées à partir de la perturbation de différents paramètres du modèle considérés comme sources d’erreur : le forçage météo, la rugosité de fond, la fermeture turbulente horizontale et la rugosité de surface. Ces ensembles ont été explorés en termes de dispersion et de corrélation d’ensemble. Un Lisseur de Kalman d’Ensemble a ensuite été utilisé pour optimiser la rugosité de fond (z0) à partir des données de courant de surface et d’un ensemble modèle réalisé à partir d’un z0 perturbé et spatialisé. La méthode a d’abord été testée en expérience jumelle puis avec des observations réelles. Les cartes du paramètre z0, optimisés, réalisées avec des observations réelles, ont ensuite été utilisées dans le modèle sur une autre période et les résultats ont été comparés avec des observations sur la zone. Enfin, des expériences jumelles ont été mises en place pour corriger l’état modèle. Deux méthodes ont été comparées, une prenant en compte la basse fréquence en filtrant la marée des données et du modèle pour réaliser l’analyse ; l’autre prenant en compte tout le signal. Avec ces expériences, on a tenté d’évaluer la capacité du filtre à contrôler à la fois la partie observée du vecteur d’état (courant de surface) et la partie non-observée du système (température de surface).

International audience; Ensemble forecasting and filtering are widely used in geophysical sciences for numerical weather forecasting and climate projection application. In practice to be efficient these methods require an accurate physical modeling of the dynamical model errors. These errors evolve along time and strongly interact with the large-scale state variables of interest. The generic design of large-scale geophysical models incorporating errors or uncertainty is consequently far from being an easy task. To address this issue, we propose as initiated by Mémin (2014) to model the fluid flow by a continuous semi-martingale. Within this simple assumption and Ito-Wentzell formula (Kunita, 1997), the material derivative, -- the derivative along the flow trajectory -- of a tracer has to be modified.Compared to a usual transport equation, three new terms appear in this expression: (i) a drift correction, (ii) an inhomogeneous and anisotropic diffusion and (iii) a multiplicative noise. These three terms are strongly linked together, which ensures desired properties such as energy conservation.With this stochastic version of the transport equation, it is possible to express the fundamental conservation laws of classical mechanics and to derive stochastic versions of a priori any fluid dynamics models. Following this procedure, we have derived and simulated a stochastic version of the Surface Quasi-Geostrophic (SQG) model. We have shown that the realizations of this stochastic version allows us to better resolve the small-scales in comparizon to the usual SQG model. Besides, we have evidenced that an ensemble of realization was able to accurately estimate at each time step the amplitudes and positions of the model errors in both spatial and spectral domains. In comparison a classical randomization of the initial state leads to an underestimation of one order of magnitude. Our ensemble also succeeded to predict density skewness and extreme events of the tracer at small scales. The presentation will explicit our stochastic version of the material derivative and comment the numerical results obtained.

気象庁1ヶ月アンサンブル予報実験データを用いて, 夏季の北西太平洋域における対流活動が中高緯度域大気循環の予測可能性に及ぼす影響について解析を行った。特に, 日本の気候に影響を及ぼす循環パターンとして知られるPJ(Pacific-Japan)パターンと中高緯度域の大気循環の予測精度との関連に注目した。その結果, PJパターンに伴う高気圧性(低気圧性)循環偏差が大気下層の北西太平洋域で卓越するとき, 予報6日目から8日目における北太平洋域での対流圏下層の大気循環の予測誤差が有意に小さく(大きく)なることが示された。この北西太平洋域における循環偏差と中高緯度域の予測精度との関係は, PJパターンに伴うロスビー波列の伝播特性がPJパターンの極性に依存することと密接に関連している。, Influence of the summertime convective activity over the Northwest Pacific to the forecast skill of the extratropicalcirculation is investigated by using JMA (Japan Meteorological Agency) 1-month ensemble forecast experiment dataset. In particular, we focus on the relationship between the polarity of the PJ (Pacific-Japan) pattern and the forecast skill of extratropical circulation. It is found that when an anticyclonic (cyclonic) circulation prevails over the Northwest Pacific associated with the PJ pattern, the forecast skill of the lower tropospheric circulation over the North Pacific region is significantly improved (degraded) for 6-8 day forecast. This relationship is intimately related to the dependence of the propagating property of Rossby wave train on the polarity of the PJ pattern.

2009年, 2010年冬季における北半球環状モード(Northern hemisphere Annular Mode: NAM)の予測可能性変動を, 気象庁1ヶ月アンサンブル予報データ, および6時間毎のハインドキャストデータを用いて調べた。特に, NAMの極性と振幅を表すNAM指数の予測可能性変動と, 成層圏突然昇温(Stratospheric Sudden Warming: SSW)との関連に着目して解析を行った。その結果, 成層圏におけるNAM指数の予測可能性はSSWの発生と密接に関連していることがわかった。さらに, アンサンブル予報における対流圏NAM指数の予測スプレッドは, SSW発生前に比べ発生後で小さくなる傾向にあることが示された。これらの事実は, SSWがNAM指数の予測可能性に影響を与えていることを示唆している。, Predictability of the Northern hemisphere Annular Mode (NAM) during 2009 and 2010 winters is examined using JMA (Japan Meteorological Agency) 1-month ensemble forecast dataset and a series of hindcast experiment conducted every 6 h. In particular, we focus on the dependence of the variation of the NAM index predictability on the Stratosphere Sudden Warming (SSW). It is found that the predictability of the stratospheric NAM index is intimately related to the occurrence of the SSW. Moreover, the ensemble spread of the tropospheric NAM index after the SSW becomes smaller than that before the SSW. These facts indicate the influence of the SSW to the predictability of the NAM index.