Meteorological Uncertainty Research Articles

The role of meteorological and hydrological uncertainties in the performance of optimal water allocation approaches

AbstractEfficient reservoir operation and irrigation scheduling are important for the mitigation of water shortages in Iran. For more accuracy, the hydrological and meteorological uncertainties associated with reservoirs and farm levels should be considered. The major contribution of the current paper is to evaluate the uncertainties of evapotranspiration (ET) and inflow, and the issue of constant/variable agricultural demand (CAD/VAD) for optimal irrigation scheduling and reservoir operation. Some optimization approaches were employed and compared during a drought episode in the Zayandeh‐Rud agricultural system. Approaches include: (i) DP‐CAD: dynamic programming (DP), considering CAD and no inflow uncertainty; (ii) SSDP‐CAD: sampling stochastic DP (SSDP) with CAD and inflow uncertainty; (iii) LP‐NLP‐VAD: implementing linear (LP) and non‐linear programming (NLP) modelling for crop types, growing stages, and irrigation systems under deterministic conditions; (iv) SDP‐NLP‐VAD: similar to the third approach, but considers ET uncertainties using a stochastic DP (SDP) rather than an LP model, and uses stochastic crop yield functions in the NLP formulation. DP‐CAD and SDP‐NLP‐VAD were the simplest and most complicated modelling processes, respectively. SDP‐NLP‐VAD was the most time‐consuming to reach a steady state and a global optimal solution. The LP‐NLP‐VAD and SDP‐NLP‐VAD approaches, which account for variability in crop water requirements, conservatively consider water shortages and reservoir release. © 2019 John Wiley & Sons, Ltd.

Probabilistic capacity assessment of single circuit transmission tower-line system subjected to strong winds

The safety and resilience of physical infrastructure of the power systems, including the transmission towers and lines for high-voltage transmission as well as the poles and wires for power distribution at a lower voltage level, is critical to avoid and recover from power outages under the extreme weather related loads such as strong winds from hurricanes, tornados, downbursts or isolated thunderstorms. In the current engineering practices, single circuit lattice steel towers linked by transmission lines are widely used to form power transmission systems. Failure of either a structural component (local failures, such as buckling of a local member or breakage of a single wire) or the entire structure (global failures, such as instability of the entire tower) could propagate to their adjacent structural members or systems (such as neighboring towers or lines). Therefore, there is a strong need to identify the root cause of the failure of the transmission tower-line system and identify the weakest link under various extreme weather scenarios. This study is to develop a probabilistic assessment approach for a typical transmission tower-line system subjected to strong wind loadings under synoptic winds (atmospheric boundary layer wind). Due to the complicated structural details and complex wind-structure interactions, wind tunnel experiments were carried out to obtain the static wind load coefficients for different panels of the transmission towers as well as for the transmission lines. Incremental dynamic analysis (IDA) is carried out to obtain the capacity curve for the transmission tower-line system. Uncertainties from the meteorological parameters are also considered in the analysis. The probabilities of failure for the two predefined limit state functions are obtained considering the meteorological uncertainties. Meanwhile, the probabilities of failure at different return periods are presented for a transmission tower-line system subjected to strong winds in a coastal region. Finally, the effects of the aerodynamic damping for the transmission lines on the dynamic responses of the tower-line system are discussed, as well.

On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development

Dealing with meteorological uncertainty poses a major challenge in air traffic management (ATM). Convective weather (commonly referred to as storms or thunderstorms) in particular represents a significant safety hazard that is responsible for one quarter of weather-related ATM delays in the US. With commercial air traffic on the rise and the risk of potentially critical capacity bottlenecks looming, it is vital that future trajectory planning tools are able to account for meteorological uncertainty. We propose an approach to model the uncertainty inherent to forecasts of convective weather regions using statistical analysis of state-of-the-art forecast data. The developed stochastic storm model is tailored for use in an optimal control algorithm that maximizes the probability of reaching a waypoint while avoiding hazardous storm regions. Both the aircraft and the thunderstorms are modeled stochastically. The performance of the approach is illustrated and validated through simulated case studies based on recent nowcast data and storm observations.

Anticipation and Response: Emergency Services in Severe Weather Situations in Germany

Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time based on the end-user’s own risk assessment and decision threshold. The presented results of an interview study, conducted with 27 members of German civil protection authorities, show that developments in meteorology and weather forecasting do not necessarily fit the current practices of German emergency services. These practices are mostly carried out based on alarms and ground truth in a superficial reactive manner, rather than on anticipation based on prognoses or forecasts. Emergency managers cope with uncertainty by collecting, comparing, and blending different information about an uncertain event and its uncertain outcomes within the situation assessment to validate the information. Emergency managers struggle most with an increase of emergency calls and missions due to the impacts of severe weather. Because of the additional expenditures, the weather event makes it even harder for them to fulfill their core duties. These findings support the need for impact-based warnings.

Impacts of meteorological uncertainties on the haze formation in Beijing–Tianjin–Hebei (BTH) during wintertime: a case study

Abstract. In the present study, a persistent heavy haze episode from 13 to 20 January 2014 in Beijing–Tianjin–Hebei (BTH) is simulated using the WRF-CHEM model through ensemble simulations to investigate impacts of meteorological uncertainties on the haze formation. Model results show that uncertainties in meteorological conditions substantially influence the aerosol constituent simulations at an observation site in Beijing, and the ratio of the ensemble spread to the ensemble mean (RESM) exceeds 50 %. The ensemble mean generally preforms well in reproducing the fine particles' (PM2.5) temporal variations and spatial distributions against measurements in BTH. The meteorological uncertainties do not alter the PM2.5 distribution pattern in BTH principally or dominate the haze formation and development, but remarkably affect the simulated PM2.5 level, and the RESM for the simulated PM2.5 concentrations can be up to 30 % at the regional scale. In addition, the rather large RESM in PM2.5 simulations at the city scale also causes difficulties in evaluation of the control strategies. Therefore, our results suggest that the ensemble simulation is imperative to take into account the impact of the meteorological uncertainties on the haze prediction.

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 103), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200 m. The start time is determined to within 5 min out of 2 h, and the duration to within 50 min out of 4 h. Over a range of release amounts of 10 to 1000 kg, the estimated amount exceeds the actual amount of 146 kg by only 32 kg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.

A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

Abstract Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) ‘dressing’ of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) ‘dressing’ of an ensemble streamflow forecast by adding an estimate of hydrological uncertainty to each individual streamflow ensemble member. Both approaches aim to produce an estimate of the ‘total uncertainty’ that captures both the meteorological and hydrological uncertainties. They differ in the degree to which they make use of statistical post-processing techniques. In the ‘lumped’ approach, both sources of uncertainty are lumped by post-processing deterministic forecasts using their verifying observations. In the ‘source-specific’ approach, the meteorological uncertainties are estimated by an ensemble of weather forecasts. These ensemble members are routed through a hydrological model and a realization of the probability distribution of hydrological uncertainties (only) is then added to each ensemble member to arrive at an estimate of the total uncertainty.The techniques are applied to one location in the Meuse basin and three locations in the Rhine basin. Resulting forecasts are assessed for their reliability and sharpness, as well as compared in terms of multiple verification scores including the relative mean error, Brier Skill Score, Mean Continuous Ranked Probability Skill Score, Relative Operating Characteristic Score and Relative Economic Value. The dressed deterministic forecasts are generally more reliable than the dressed ensemble forecasts, but the latter are sharper. On balance, however, they show similar quality across a range of verification metrics, with the dressed ensembles coming out slightly better. Some additional analyses are suggested. Notably, these include statistical post-processing of the meteorological forecasts in order to increase their reliability, thus increasing the reliability of the streamflow forecasts produced with ensemble meteorological forcings.

Multi-parametric variational data assimilation for hydrological forecasting

Abstract Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Assimilation of water temperature and discharge data for ensemble water temperature forecasting

Recent work demonstrated the value of water temperature forecasts to improve water resources allocation and highlighted the importance of quantifying their uncertainty adequately. In this study, we perform a multisite cascading ensemble assimilation of discharge and water temperature on the Nechako River (Canada) using particle filters. Hydrological and thermal initial conditions were provided to a rainfall-runoff model, coupled to a thermal module, using ensemble meteorological forecasts as inputs to produce 5day ensemble thermal forecasts. Results show good performances of the particle filters with improvements of the accuracy of initial conditions by more than 65% compared to simulations without data assimilation for both the hydrological and the thermal component. All thermal forecasts returned continuous ranked probability scores under 0.8°C when using a set of 40 initial conditions and meteorological forecasts comprising 20 members. A greater contribution of the initial conditions to the total uncertainty of the system for 1-dayforecasts is observed (mean ensemble spread=1.1°C) compared to meteorological forcings (mean ensemble spread=0.6°C). The inclusion of meteorological uncertainty is critical to maintain reliable forecasts and proper ensemble spread for lead times of 2days and more. This work demonstrates the ability of the particle filters to properly update the initial conditions of a coupled hydrological and thermal model and offers insights regarding the contribution of two major sources of uncertainty to the overall uncertainty in thermal forecasts.

Assessing measurement uncertainty in meteorology in urban environments

Measurement uncertainty in meteorology has been addressed in a number of recent projects. In urban environments, uncertainty is also affected by local effects which are more difficult to deal with than for synoptic stations. In Italy, beginning in 2010, an urban meteorological network (Climate Network®) was designed, set up and managed at national level according to high metrological standards and homogeneity criteria to support energy applications. The availability of such a high-quality operative automatic weather station network represents an opportunity to investigate the effects of station siting and sensor exposure and to estimate the related measurement uncertainty. An extended metadata set was established for the stations in Milan, including siting and exposure details. Statistical analysis on an almost 3-year-long operational period assessed network homogeneity, quality and reliability. Deviations from reference mean values were then evaluated in selected low-gradient local weather situations in order to investigate siting and exposure effects. In this paper the methodology is depicted and preliminary results of its application to air temperature discussed; this allowed the setting of an upper limit of 1 °C for the added measurement uncertainty at the top of the urban canopy layer.

Ensemble Streamflow Forecasting across the U.S. Mid-Atlantic Region with a Distributed Hydrological Model Forced by GEFS Reforecasts

Abstract The quality of ensemble streamflow forecasts in the U.S. mid-Atlantic region (MAR) is investigated for short- to medium-range forecast lead times (6–168 h). To this end, a regional hydrological ensemble prediction system (RHEPS) is assembled and implemented. The RHEPS in this case comprises the ensemble meteorological forcing, a distributed hydrological model, and a statistical postprocessor. As the meteorological forcing, precipitation, and near-surface temperature outputs from the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), are used. The Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used as the distributed hydrological model, and a statistical autoregressive model with an exogenous variable is used as the postprocessor. To verify streamflow forecasts from the RHEPS, eight river basins in the MAR are selected, ranging in drainage area from ~262 to 29 965 km2 and covering some of the major rivers in the MAR. The verification results for the RHEPS show that, at the initial lead times (1–3 days), the hydrological uncertainties have more impact on forecast skill than the meteorological ones. The former become less pronounced, and the meteorological uncertainties dominate, across longer lead times (>3 days). Nonetheless, the ensemble streamflow forecasts remain skillful for lead times of up to 7 days. Additionally, postprocessing increases forecast skills across lead times and spatial scales, particularly for the high-flow conditions. Overall, the proposed RHEPS is able to improve streamflow forecasting in the MAR relative to the deterministic (unperturbed GEFSRv2 member) forecasting case.

The operational eEMEP model version 10.4 for volcanic SO<sub>2</sub> and ash forecasting

Abstract. This paper presents a new version of the EMEP MSC-W model called eEMEP developed for transportation and dispersion of volcanic emissions, both gases and ash. EMEP MSC-W is usually applied to study problems with air pollution and aerosol transport and requires some adaptation to treat volcanic eruption sources and effluent dispersion. The operational set-up of model simulations in case of a volcanic eruption is described. Important choices have to be made to achieve CPU efficiency so that emergency situations can be tackled in time, answering relevant questions of ash advisory authorities. An efficient model needs to balance the complexity of the model and resolution. We have investigated here a meteorological uncertainty component of the volcanic cloud forecast by using a consistent ensemble meteorological dataset (GLAMEPS forecast) at three resolutions for the case of SO2 emissions from the 2014 Barðarbunga eruption. The low resolution (40 × 40 km) ensemble members show larger agreement in plume position and intensity, suggesting that the ensemble here does not give much added value. To compare the dispersion at different resolutions, we compute the area where the column load of the volcanic tracer, here SO2, is above a certain threshold, varied for testing purposes between 0.25 and 50 Dobson units. The increased numerical diffusion causes a larger area (+34 %) to be covered by the volcanic tracer in the low resolution simulations than in the high resolution ones. The higher resolution (10 × 10 km) ensemble members show higher column loads farther away from the volcanic eruption site in narrower clouds. Cloud positions are more varied between the high resolution members, and the cloud forms resemble the observed clouds more than the low resolution ones. For a volcanic emergency case this means that to obtain quickly results of the transport of volcanic emissions, an individual simulation with our low resolution is sufficient; however, to forecast peak concentrations with more certainty for forecast or scientific analysis purposes, a finer resolution is needed. The model is further developed to simulate ash from highly explosive eruptions. A possibility of increasing the number of vertical layers, achieving finer vertical resolution, as well as a higher model top, is included in the eEMEP version. Ash size distributions may be altered for different volcanic eruptions and assumptions. Since ash particles are larger than typical particles in the standard model, gravitational settling across all vertical layers is included. We attempt finally a specific validation of the simulation of ash and its vertical distribution. Model simulations with and without gravitational settling for the 2010 Eyjafjallajökull eruption are compared to lidar observations over central Europe. The results show that with gravitation the centre of the ash mass can be 1 km lower over central Europe than without gravitation. However, the height variations in the ash layer caused by real weather situations are not captured perfectly well by either of the two simulations, playing down the role of gravitation parameterization imperfections. Both model simulations have on average an ash centre of mass below the observed values. Correlations between the observed and corresponding model centres of mass are higher for the model simulation with gravitational settling for four of the six stations studied here. The inclusion of gravitational settling is suggested to be required for a volcanic ash model.

A Comparison of Ensemble Strategies for Flash Flood Forecasting: The 12 October 2007 Case Study in Valencia, Spain

Abstract On 12 October 2007, several flash floods affected the Valencia region, eastern Spain, with devastating impacts in terms of human, social, and economic losses. An enhanced modeling and forecasting of these extremes, which can provide a tangible basis for flood early warning procedures and mitigation measures over the Mediterranean, is one of the fundamental motivations of the international Hydrological Cycle in the Mediterranean Experiment (HyMeX) program. The predictability bounds set by multiple sources of hydrological and meteorological uncertainty require their explicit representation in hydrometeorological forecasting systems. By including local convective precipitation systems, short-range ensemble prediction systems (SREPSs) provide a state-of-the-art framework to generate quantitative discharge forecasts and to cope with different sources of external-scale (i.e., external to the hydrological system) uncertainties. The performance of three distinct hydrological ensemble prediction systems (HEPSs) for the small-sized Serpis River basin is examined as a support tool for early warning and mitigation strategies. To this end, the Flash-Flood Event–Based Spatially Distributed Rainfall–Runoff Transformation–Water Balance (FEST-WB) model is driven by ground stations to examine the hydrological response of this semiarid and karstic catchment to heavy rains. The use of a multisite and novel calibration approach for the FEST-WB parameters is necessary to cope with the high nonlinearities emerging from the rainfall–runoff transformation and heterogeneities in the basin response. After calibration, FEST-WB reproduces with remarkable accuracy the hydrological response to intense precipitation and, in particular, the 12 October 2007 flash flood. Next, the flood predictability challenge is focused on quantitative precipitation forecasts (QPFs). In this regard, three SREPS generation strategies using the WRF Model are analyzed. On the one side, two SREPSs accounting for 1) uncertainties in the initial conditions (ICs) and lateral boundary conditions (LBCs) and 2) physical parameterizations are evaluated. An ensemble Kalman filter (EnKF) is also designed to test the ability of ensemble data assimilation methods to represent key mesoscale uncertainties from both IC and subscale processes. Results indicate that accounting for diversity in the physical parameterization schemes provides the best probabilistic high-resolution QPFs for this particular flash flood event. For low to moderate precipitation rates, EnKF and pure multiple physics approaches render undistinguishable accuracy for the test situation at larger scales. However, only the multiple physics QPFs properly drive the HEPS to render the most accurate flood warning signals. That is, extreme precipitation values produced by these convective-scale precipitation systems anchored by complex orography are better forecast when accounting just for uncertainties in the physical parameterizations. These findings contribute to the identification of ensemble strategies better targeted to the most relevant sources of uncertainty before flash flood situations over small catchments.

Water flow probabilistic predictions based on a rainfall–runoff simulator: a two-regime model with variable selection

Probabilistic forecasting aims at producing a predictive distribution of the quantity of interest instead of a single best guess point-wise estimate. With regard to water flow forecasts, the two main sources of uncertainty stem from unknown future rainfall and temperature (input error, i.e., meteorological uncertainty) and from the inadequacy of the deterministic simulator mimicking the rainfall–runoff (RR) transformation (hydrological uncertainty or RR error). These two sources of uncertainty can be dealt with separately and only the latter will be considered here. Only hydrological uncertainty is at stake when recorded meteorological data (instead of meteorological forecasts) are used as inputs to feed the RR simulator (RRS) for probabilistic predictions. The predictive performance of the RRS may strongly depend on the hydrological regimes: rapid flood variations induce large errors of anticipation but a series of dry events will translate into a much more smoother sequence of river levels due to the easily predictable behavior of the soil reservoir emptying. Consequently, a model with several regimes adapted to different error structures appears as a solution to cope with the issue of unstationary predictive variance. The river regime is modeled as a latent variable, the distribution of which is based on additional outputs of the RRS to be selected. Inference is performed by the EM algorithm with both steps leading to explicit analytic expressions. Asymptotic confidence regions for the estimates are provided within the same EM framework. Model selection is also performed, including the length of the model memory as well as the choice of explanatory variables for the latent regimes. The model is applied to a series of water flow forecasts routinely issued by two hydroelectricity producers in France and in Quebec and compared with their present operational forecasting methods.

Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO<sub>2</sub>

Abstract. A new model for greenhouse gas transport has been developed based on Environment and Climate Change Canada's operational weather and environmental prediction models. When provided with realistic posterior fluxes for CO2, the CO2 simulations compare well to NOAA's CarbonTracker fields and to near-surface continuous measurements, columns from the Total Carbon Column Observing Network (TCCON) and NOAA aircraft profiles. This coupled meteorological and tracer transport model is used to study the predictability of CO2. Predictability concerns the quantification of model forecast errors and thus of transport model errors. CO2 predictions are used to compute model–data mismatches when solving flux inversion problems and the quality of such predictions is a major concern. Here, the loss of meteorological predictability due to uncertain meteorological initial conditions is shown to impact CO2 predictability. The predictability of CO2 is shorter than that of the temperature field and increases near the surface and in the lower stratosphere. When broken down into spatial scales, CO2 predictability at the very largest scales is mainly due to surface fluxes but there is also some sensitivity to the land and ocean surface forcing of meteorological fields. The predictability due to the land and ocean surface is most evident in boreal summer when biospheric uptake produces large spatial gradients in the CO2 field. This is a newly identified source of uncertainty in CO2 predictions but it is expected to be much less significant than uncertainties in fluxes. However, it serves as an upper limit for the more important source of transport error and loss of predictability, which is due to uncertain meteorological analyses. By isolating this component of transport error, it is demonstrated that CO2 can only be defined on large spatial scales due to the presence of meteorological uncertainty. Thus, for a given model, there is a spatial scale below which fluxes cannot be inferred simply due to the fact that meteorological analyses are imperfect. These unresolved spatial scales correspond to small scales near the surface but increase with altitude. By isolating other components of transport error, the largest or limiting error can be identified. For example, a model error due to the lack of convective tracer transport was found to impact transport error on the very largest (wavenumbers less than 5) spatial scales. Thus for wavenumbers greater than 5, transport model error due to meteorological analysis uncertainty is more important for our model than the lack of convective tracer transport.

Reducing Data Uncertainty in Surface Meteorology Using Data Assimilation: A Comparison Study

Data assimilation in weather forecasting is a well-known technique used to obtain an improved estimation of the current state of the atmosphere (analysis). The Meteorological Service of Catalunya (SMC) is seeking for a real time high resolution analysis of surface parameters over Catalonia (north-east of Spain), in order to know the current weather conditions at any point of that region. For this purpose, a comparative study among several data assimilation experiments based on LAPS (Local Analysis and Prediction System) and STMAS (Space-Time Multiscale Analysis System) and multi-regression technique designed at SMC, has been performed to determine which one delivers best results. The comparison has been done using as true state independent observational data provided by the Spanish Meteorological State Agency (Agencia Estatal de METeorologia, AEMET). The results show that the multi-regression technique provides more accurate analyses of temperature and relative humidity than the LAPS/STMAS experiments, mainly due to the fact that multi-regression methodology only uses observations and consequently the model biases are avoided.

Impact of inherent meteorology uncertainty on air quality model predictions

AbstractIt is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short‐Range Ensemble Forecast system to drive the four‐dimensional data assimilation in the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone‐mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone‐mixing ratios of the ensemble can vary as much as 10–20 ppb or 20–30% in areas that typically have higher pollution levels.

Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE) ~ 1.8 g C m−2 d−1). However, when predictions were based on only remotely sensed data the accuracy was close to the optimum (ρ ~ 0.8; RMSE ~ 1.9 g C m−2 d−1) and to a commonly used light use efficiency model (MOD17) with parameters optimised for the applied study sites (the MOD17 +, ρ ~ 0.79; RMSE ~ 2.04 g C m−2 d−1). Remotely sensed data were key drivers for the accurate prediction of GPP in ecosystems with high variability of green biomass over the phenological cycle (e.g., deciduous broad-leaved forests) or highly affected by the human management (e.g. croplands). In contrast, in the ecosystems with low variability of greenness (e.g., evergreen broad-leaved forests), the predictions were poor when meteorological information were not used. At a European spatial scale, when modelled grids of meteorological, land cover and fPAR data were used as inputs, the propagation of their uncertainty, not accounted in the models training, had significant effects on the uncertainty of the mean annual GPP. At this scale, the effects of meteorological uncertainty were higher than the misclassification error. These findings suggested that a strategy based on satellite-measured data could be a favourable improvement for the spatial upscaling of GPP, because avoiding the propagation of the uncertainties of the modelled grids.

Short-term management of hydropower reservoirs under meteorological uncertainty by means of multi-stage optimization

Abstract Uncertainty in meteorology, market volatility and balancing requirements for introducing renewable energy resources into the power grid, environmental obligations require robust management of non-intermittent energy sources such as hydropower. In this paper, a probalistic management system is shown and its performance is discussed in relation to the deterministic one. In the system, scenario trees enable to setup a multi-stage stochastic optimization approach as the mathematical formulation of the short-term system management. The Federal Columbia River Power System (FCRPS), managed by the Bonneville Power Administration, the US Army Corps of Engineers and the Bureau of Reclamation, serves as a large-scale test case for the application of the management system and proves that the stochastic approach is feasible and verify the operational applicability within a real-time environment.