Meteorological Uncertainty Research Articles

Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action

Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types.

Natural ventilation cooling effectiveness classification for building design addressing climate characteristics

The evaluation of natural ventilation potential for effective sustainable options and innovative green building design strategies is of great interest to architects, researchers and governments. From a retrospective review, we found that the potential evaluation of natural ventilation (NV) cooling effectiveness in the same category based on similar meteorological uncertainty, research objectives and objects showed significant differences. Uncertainties added and uncertainty propagation (both model form uncertainties and parameter uncertainties) could result in large discrepancies between simulation outcomes and real scenarios, especially in the design performance modeling (DPM) phase. In this conceptual design stage, a few parameters are available and therefore decisive. It is necessary to review and identify the key performance indicators and explore the extent to which deviations are caused by inconsistencies or biases in model information. As a basis for more concrete research, we propose statistical tests based on quantitative evaluations to explore the rule of natural ventilation potential volatility and identify whether there is a significant potential improvement resulting from the critical parameter enhancement with the optimal relationship. The showcase is applied in China, where there has been a significant amount of criticism regarding the current building climate zoning due to the perceived coarseness of the system and where there has been an active exploration into the possibility of redefining building climate zoning with a view toward improving its accuracy and effectiveness.

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

Predicting Air Traffic Congestion under Uncertain Adverse Weather

This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection.

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Safety in nuclear energy utilization is crucial. In the event of a radioactive release incident, coupled with meteorological uncertainties, a radioactive plume can impact personnel evacuation. This paper presents an integrated solution for radionuclide release accident assessment and emergency evacuation decision making. The solution consists of three processes: atmospheric dispersion calculation, dose calculation, and path planning. The individual processes are connected through data exchange, thus allowing users to choose specific models based on experience. The proposed scheme combination is the Gaussian plume model, the dose conversion factor method, and an improved Dijkstra’s path planning algorithm. This algorithm, combined with dispersion and dose results, weighs nodes using the moving expected dose, facilitating the path with minimum dose risk. A program for Atmospheric Diffusion and Dose Calculation (ADDC) is developed based on the recommended scheme. Advantages include ease of use, minimal data requirements, data accessibility, and efficient evacuation. Dose estimates and optimal evacuation routes can be obtained quickly and at very low cost in response to rapidly changing environmental conditions. In a case study at a Chinese planned nuclear plant, we consider a spent fuel pool water loss scenario, assessing dose risks from 2020 to 2022 meteorological statistics. In dose calculation, results reveal that during an SFP drying accident, the radiation dose in the core area (100 m away) can reach 30–150 mSv within 2 h, and at 500 m away, it can reach 5–15 mSv. The dose in all downwind directions can drop below 250 mSv within 60 m. In path planning, results reveal the program is capable of accurately and efficiently calculating the minimum dose evacuation route. The program’s path reduces the effective dose by up to 67.3% compared to the shortest route, enhancino safety, and guiding post-accident decision making and planning.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12–68 times, 13–73 times, and 18–98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

Abstract. Ensemble predictions of atmospheric dispersion that account for the meteorological uncertainties in a weather forecast are constructed by propagating the individual members of an ensemble numerical weather prediction forecast through an atmospheric dispersion model. Two event scenarios involving hypothetical atmospheric releases are considered: a near-surface radiological release from a nuclear power plant accident and a large eruption of an Icelandic volcano releasing volcanic ash into the upper air. Simulations were run twice-daily in real time over a 4-month period to create a large dataset of cases for this study. The performance of the ensemble predictions is measured against retrospective simulations using a sequence of meteorological fields analysed against observations. The focus of this paper is on comparing the spread of the ensemble members against forecast errors and on the calibration of probabilistic forecasts derived from the ensemble distribution. Results show good overall performance by the dispersion ensembles in both studies but with simulations for the upper-air ash release generally performing better than those for the near-surface release of radiological material. The near-surface results demonstrate a sensitivity to the release location, with good performance in areas dominated by the synoptic-scale meteorology and generally poorer performance at some other sites where, we speculate, the global-scale meteorological ensemble used in this study has difficulty in adequately capturing the uncertainty from local- and regional-scale influences on the boundary layer. The ensemble tends to be under-spread, or over-confident, for the radiological case in general, especially at earlier forecast steps. The limited ensemble size of 18 members may also affect its ability to fully resolve peak values or adequately sample outlier regions. Probability forecasts of threshold exceedances show a reasonable degree of calibration, though the over-confident nature of the ensemble means that it tends to be too keen on using the extreme forecast probabilities. Ensemble forecasts for the volcanic ash study demonstrate an appropriate degree of spread and are generally well-calibrated, particularly for ash concentration forecasts in the troposphere. The ensemble is slightly over-spread, or under-confident, within the troposphere at the first output time step T + 6, thought to be attributable to a known deficiency in the ensemble perturbation scheme in use at the time of this study, but improves with probability forecasts becoming well-calibrated here by the end of the period. Conversely, an increasing tendency towards over-confident forecasts is seen in the stratosphere, which again mirrors an expectation for ensemble spread to fall away at higher altitudes in the meteorological ensemble. Results in the volcanic ash case are also broadly similar between the three different eruption scenarios considered in the study, suggesting that good ensemble performance might apply to a wide range of eruptions with different heights and mass eruption rates.

Addressing the quantification of meteorological uncertainties in the atmospheric transport simulations of the 133Xe industrial background

The French National Data Center (NDC) uses an automated simulation of the 133Xe worldwide atmospheric background as one of the means to categorize the radionuclide measurements of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS). These simulations take into account 133Xe releases from the known or assumed major industrial emitters in the world and global-scale meteorological data. However, a quantification of the simulation uncertainties in this operational set up is yet to be addressed. This work discusses the benefits of meteorological ensemble data as available from National Centers for Environmental Prediction (NCEP) for that purpose. For this study, the daily dispersion of releases from the Institute for Radio Elements (IRE), a medical isotope production facility located in Fleurus (Belgium), was calculated over one year with emissions measured in-site and ensemble meteorological data. The ensemble contains 31 members, which resulted in as many predictions of activity concentration for any given time and place. The resulting distribution statistics (mean, median and spread), and the control run, were confronted to the deterministic run and to measurements at one IMS-like station near Paris (France) and one IMS station in Freiburg (Germany). Overall, the ensemble results have decreased the simulation performance, as expected given the use of meteorological analyses only. However, contrasting patterns were found with a detailed analysis of daily activity concentration over two one-month-and-a-half periods. Noticeably, outlier results were found to carry the best forecast in some significant detections, proving their relevance for the measurement categorization, despite their isolated character. Importantly, the ensemble has allowed the quantification of meteorological uncertainties, which was beneficial in all cases. It either has improved the confidence of IMS data categorization or has pointed to low confidence predictions. A criterion to identify the latter is suggested, based on information provided by the ensemble distributions. In addition, maps of probability of detections and of relative spread are suggested to show additional benefits of ensemble meteorology.

Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area

The effectiveness of air traffic management depends on the amount of obtained information and the accuracy of prediction, leading to a reduction in uncertainty disturbances, and an improvement in the robustness of decision-making results. Among all controlled areas, the Terminal Manoeuvring Area (TMA) is a complex and sophisticated airspace surrounding a coordinated airport in which there exists a high density of traffic, especially with arrivals converging from different directions. With the purpose of improving safety levels and operational efficiency, this paper addresses the aircraft arrival scheduling problem at the microscopic level, integrating the meteorological uncertainty based on wind networking into the optimization model. Wind uncertainty affects the flight speed, which in turn leads to uncertainty in the flight arrival time at one waypoint. Conflict detection and resolution are performed at waypoints and links in a predefined network, based on the predicted time information from the designated route. In order to optimize the time and speed for each aircraft entering TMA, this paper proposes a mathematical model that seeks to minimize the number of potential conflicts, flight delays and the number of flights deviating from the original plan under two assumptions. The first one considers regular weather forecasts for predicting the future positions of aircraft for conflict detection. The second one investigates the potential benefit for aircraft to share their local wind measure thanks to the ADSB-IN and ADSB-OUT channels. A resolution approach is proposed combining a simulated annealing algorithm and a simulation framework. Using data from a case study with 437 flights overflying the Paris Charles de Gaulle (CDG) airport TMA, we demonstrate the validity of the proposed scheduling strategy. A performance analysis is then conducted between standard forecast and wind networking.

Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration

Imperfect land physics introduce significant levels of uncertainty into current land surface models (LSMs) and can cause bias in their representation of land-atmosphere coupling strength (ρ). When LSMs are coupled with atmospheric prediction models, such errors will eventually degrade the accuracy of lower atmosphere forecasts. Here, we investigate the potential of two remote sensing (RS)-based ρ references for addressing LSM ρ bias. To minimize meteorological uncertainty and maximally attribute LSM ρ bias to land sources, we focus specifically on off-line LSM calibration forced using high-quality, observation-based meteorological data. Both ρ references are based on a newly proposed two-system approach for eliminating the impact of random errors in RS retrievals and quantified using the temporal correlations of soil moisture (SM) versus both evapotranspiration (ET) and the diurnal amplitude of surface temperature (dT). Experiments are conducted to calibrate an off-line LSM individually against each resulting ρ reference and using a combination of both dT- and ET-represented ρ references. The resulting calibrated LSM is further evaluated using independent ground-based ET observations and RS dT retrievals. Results show that although dT- and ET-represented ρ references are physically consistent across space, model calibration results based on them are quite different. Specifically, the calibration experiment targeting ET-represented ρ outperforms that targeting dT-represented ρ in ET and dT modeling. Diagnostic results indicate that the failure of dT-based calibration experiments is due to the confounding impacts of transpiration/evapotranspiration partitioning error and large dT uncertainties in LSM. However, results also confirm the potential of both dT- and ET-represented ρ references for jointly diagnosing and understanding LSM ρ bias. As a result, we suggest diagnosing LSM ρ bias using both ET- and dT-represented ρ references – but calibrating LSM using only ET-represented ρ reference data.

Localisation of atmospheric release of radioisotopes using inverse methods and footprints of receptors as sources

Releases of radionuclides to the atmosphere occasionally occur with no warning and with first observation at radioactivity monitoring stations. The Chernobyl accident of 1986 was first detected at Forsmark, Sweden, long before the official announcement by the Soviet Union, and the release of Ruthenium 106 detected across Europe in 2017 still has no official release location. The current study details a method based on footprint analysis of an atmospheric dispersion model to locate the source of an atmospheric release. The method was applied to the European Tracer EXperiment of 1994 to validate the method and to the Ruthenium observations of autumn 2017 to determine likely release locations and time characteristics of this release. The method can readily utilise an ensemble of numerical weather prediction data which improves the localisation results by taking into account meteorological uncertainties compared to only using deterministic weather data. In applying the method to the ETEX scenario, the most likely release location improved from a distance of 113 km from the true release location when using deterministic meteorology, to a distance of 63 km when using ensemble meteorology data, although such improvements may be scenario dependent. The method was constructed to be robust with respect to the choices of model parameters and measurement uncertainties. The localisation method can be useful for decision makers to enact countermeasures to protect the environment against the effects of radioactivity when observations are available from environmental radioactivity monitoring networks.

The European GeoMetre project: developing enhanced large-scale dimensional metrology for geodesy

We provide a survey on the joint European research project “GeoMetre”, which explores novel technologies and their inclusion to existing surveying strategies to improve the traceability of geodetic reference frames to the SI definition of the metre. This work includes the development of novel distance meters with a range of up to 5 km, the realisation of optical multilateration systems for large structure monitoring at an operation distance of 50 m and beyond, and a novel strategy for GNSS-based distance determination. Different methods for refractivity compensation, based on classical sensors, on dispersion, on spectroscopic thermometry, and on the speed of sound to reduce the meteorological uncertainties in precise distance measurements, are developed further and characterised. These systems are validated at and applied to the novel European standard baseline EURO5000 at the Pieniny Kippen Belt, Poland, which was completely refurbished and intensely studied in this project. We use our novel instruments for a reduced uncertainty of the scale in the surveillance networks solutions for local tie measurements at space-geodetic co-location stations. We also investigate novel approaches like close-range photogrammetry to reference point determination of space-geodetic telescopes. Finally, we also investigate the inclusion of the local gravity field to consider the deviations of the vertical in the data analysis and to reduce the uncertainty of coordinate transformations in this complex problem.

Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

AbstractAtmospheric dispersion models are employed in forecasting the atmospheric transport of ash clouds following a volcanic eruption. Errors in input meteorological data can, however, lead to discrepancies between the modeled and observed ash cloud locations. Furthermore, meteorological errors can affect the performance of inversion techniques used to estimate ash emissions. This study explores using ensemble meteorological data sets to overcome issues with meteorological errors in a Bayesian inversion system, Inversion Technique for Emissions Modelling (InTEM) for volcanic ash. Two measures (the evidence value and a cost function) are obtained from the Bayesian framework, and each measure is used to select a “best” meteorological data set from within the ensemble. These two best meteorological data sets are constructed in an iterative manner which is ideally suited for operational use when ash cloud forecasts are updated at regular intervals. The method is applied to the 2011 eruption of the Icelandic volcano Grímsvötn and improvements to ash cloud forecasts using the best meteorological data sets assessed. In this case study, errors in the deterministic meteorological data are significant, resulting in a reduction of ash emissions estimated by InTEM for volcanic ash. We also illustrate how the best meteorological data sets lead to more accurate modeling of the ash cloud.

Combining short-range dispersion simulations with fine-scale meteorological ensembles: probabilistic indicators and evaluation during a 85Kr field campaign

Abstract. Numerical atmospheric dispersion models (ADMs) are used for predicting the health and environmental consequences of nuclear accidents in order to anticipate countermeasures necessary to protect the populations. However, these simulations suffer from significant uncertainties, arising in particular from input data: weather conditions and source term. Meteorological ensembles are already used operationally to characterize uncertainties in weather predictions. Combined with dispersion models, these ensembles produce different scenarios of radionuclide dispersion, called “members”, representative of the variety of possible forecasts. In this study, the fine-scale operational weather ensemble AROME-EPS (Applications of Research to Operations at Mesoscale-Ensemble Prediction System) from Météo-France is coupled with the Gaussian puff model pX developed by the IRSN (French Institute for Radiation Protection and Nuclear Safety). The source term data are provided at 10 min resolution by the Orano La Hague reprocessing plant (RP) that regularly discharges 85Kr during the spent nuclear fuel reprocessing process. In addition, a continuous measurement campaign of 85Kr air concentration was recently conducted by the Laboratory of Radioecology in Cherbourg (LRC) of the IRSN, within 20 km of the RP in the North-Cotentin peninsula, and is used for model evaluation. This paper presents a probabilistic approach to study the meteorological uncertainties in dispersion simulations at local and medium distances (2–20 km). First, the quality of AROME-EPS forecasts is confirmed by comparison with observations from both Météo-France and the IRSN. Then, the probabilistic performance of the atmospheric dispersion simulations was evaluated by comparison to the 85Kr measurements carried out during a period of 2 months, using two probabilistic scores: relative operating characteristic (ROC) curves and Peirce skill score (PSS). The sensitivity of dispersion results to the method used for the calculation of atmospheric stability and associated Gaussian dispersion standard deviations is also discussed. A desirable feature for a model used in emergency response is the ability to correctly predict exceedance of a given value (for instance, a dose guide level). When using an ensemble of simulations, the “decision threshold” is the number of members predicting an event above which this event should be considered probable. In the case of the 16-member dispersion ensemble used here, the optimal decision threshold was found to be 3 members, above which the ensemble better predicts the observed peaks than the deterministic simulation. These results highlight the added value of ensemble forecasts compared to a single deterministic one and their potential interest in the decision process during crisis situations.

Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption

Abstract. Due to the remote location of many volcanoes, there is substantial uncertainty about the timing, amount and vertical distribution of volcanic ash released when they erupt. One approach to determine these properties is to combine prior estimates with satellite retrievals and simulations from atmospheric dispersion models to create posterior emission estimates, constrained by both the observations and the prior estimates, using a technique known as source inversion. However, the results are dependent not only on the accuracy of the prior assumptions, the atmospheric dispersion model and the observations used, but also on the accuracy of the meteorological data used in the dispersion simulations. In this study, we advance the source inversion approach by using an ensemble of meteorological data from the Met Office Global and Regional Ensemble Prediction System to represent the uncertainty in the meteorological data and apply it to the 2019 eruption of Raikoke. Retrievals from the Himawari-8 satellite are combined with NAME dispersion model simulations to create posterior emission estimates. The use of ensemble meteorology provides confidence in the posterior emission estimates and associated dispersion simulations that are used to produce ash forecasts. Prior mean estimates of fine volcanic ash emissions for the Raikoke eruption based on plume height observations are more than 15 times higher than any of the mean posterior ensemble estimates. In addition, the posterior estimates have a different vertical distribution, with 27 %–44 % of ash being emitted into the stratosphere compared to 8 % in the mean prior estimate. This has consequences for the long-range transport of ash, as deposition to the surface from this region of the atmosphere happens over long timescales. The posterior ensemble spread represents uncertainty in the inversion estimate of the ash emissions. For the first 48 h following the eruption, the prior ash column loadings lie outside an estimate of the error associated with a set of independent satellite retrievals, whereas the posterior ensemble column loadings do not. Applying a risk-based methodology to an ensemble of dispersion simulations using the posterior emissions shows that the area deemed to be of the highest risk to aviation, based on the fraction of ensemble members exceeding predefined ash concentration thresholds, is reduced by 49 %. This is compared to estimates using an ensemble of dispersion simulations using the prior emissions with ensemble meteorology. If source inversion had been used following the eruption of Raikoke, it would have had the potential to significantly reduce disruptions to aviation operations. The posterior inversion emission estimates are also sensitive to uncertainty in other eruption source parameters and internal dispersion model parameters. Extending the ensemble inversion methodology to account for uncertainty in these parameters would give a more complete picture of the emission uncertainty, further increasing confidence in these estimates.

EM-Earth: The Ensemble Meteorological Dataset for Planet Earth

Abstract Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dewpoint temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.

Impact of Meteorological Uncertainties in the Methane Retrieval Ground Segment of the MERLIN Lidar Mission

MERLIN (MEthane Remote sensing LIdar missioN) is a Franco-German space mission designed to provide weighted columns of atmospheric methane through an inversion of the lidar signal using a priori information on the atmospheric state. Uncertainties about the meteorological parameters of the observed scene used in the ground segment contribute to the error budget on the retrieved methane column. With the LIDSIM (LIDar SIMulator) data simulator and the PROLID (PROcessor LIDar) inversion processor developed for MERLIN, we perform an impact experiment using ECMWF (European Centre for Medium Weather Range Forecast) ensemble forecast data. In addition, we estimate the standard deviation of the error in the methane column due to the meteorological uncertainties to be about 0.6 ppb. In addition, we innovate by discussing the impact of interpolations both in time and space, focusing on vertical extrapolations under the topography by using state-of-the-art methods to determine from the scatter between these methods the range in which the actual profile should be. We conclude that, in areas where the topography variations exceed 10 m over 10 km, an additional random error of 0.1 ppb is due to our lack of knowledge of the adjustment of atmospheric profiles to terrain. Finally, we point out that further work needs to be performed on temporal interpolation. Indeed, the 3 h time interpolation of atmospheric tides can create regional biases of up to 2 ppm (which is a major problem for models trying to identify methane sinks and sources).

Solar irradiance short-term prediction under meteorological uncertainties: survey hybrid artificial intelligent basis music-inspired optimization models

Solar irradiance short-term prediction under meteorological uncertainties: survey hybrid artificial intelligent basis music-inspired optimization models

Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0

Abstract. A particle-filter-based inversion system is presented, which enables us to derive time- and altitude-resolved volcanic ash emission fluxes along with its uncertainty. The system assimilates observations of volcanic ash column mass loading as retrieved from geostationary satellites. It aims to estimate the temporally varying emission profile endowed with error margins. In addition, we analyze the dependency of our estimate on wind field characteristics, notably vertical shear, within variable observation intervals. Thus, the proposed system addresses the special challenge of analyzing the vertical profile of volcanic ash clouds given only 2D high temporal-resolution column mass loading data as retrieved by geostationary satellites. The underlying method rests on a linear combination of height–time emission finite elements of arbitrary resolution, each of which is assigned to a model run subject to ensemble-based space–time source inversion. Employing a modular concept, this setup builds the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem). It comprises a particle smoother in combination with a discrete-grid ensemble extension of the Nelder–Mead minimization method. The ensemble version of the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) is integrated into ESIAS-chem but can be replaced by other models. As initial validation of ESIAS-chem, the system is applied to simulated artificial observations of both ash-contaminated and ash-free atmospheric columns using identical-twin experiments. Thus, in this idealized initial performance test the underlying meteorological uncertainty is neglected. The inversion system is applied to two notional sub-Plinian eruptions of the Eyjafjallajökull volcano, Iceland, with strong ash emission changes with time and injection heights. It demonstrates the ability of ESIAS-chem to retrieve the volcanic ash emission fluxes from the assimilation of column mass loading data only. However, the analyzed emission profiles strongly differ in their levels of accuracy depending of the strength of wind shear conditions. While the error is only 10 %–20 % for the estimated emission fluxes under strong wind conditions, it increases up to 60 % under weak wind shear conditions. In case of increasing wind shear, the performance of the analysis may benefit from extending the assimilation window, in which new observations potentially contribute valuable information to the analysis system. For our test cases using an artificial volcanic eruption, we found an assimilation window length of 18 h, i.e., 10 h after the eruption terminated, to be sufficient for analyzing the extent and location of the artificial ash cloud. In the performed test cases, the analysis ensemble predicts the location of high volcanic ash column mass loading in the atmosphere with a very high probability of > 95 %. Additionally, the analysis ensemble is able to provide a vertically resolved probability map of high volcanic ash concentrations to a high accuracy for both high and weak wind shear conditions.

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.