Meteorological Model Performance Research Articles

Integrating burned area as a complementary performance measure for daily fire danger assessment: A large-scale test

Fire indices are used to describe the weather conditions that influence fire ignition and fire behavior. Although many studies analyzed their performance on fire occurrence at daily resolution, few focused on their ability to capture the burned area, which is usually analyzed at the weekly or monthly scale. Cumulative Logarithmic Area Ranking Efficiency (CLARE) is a newly developed metric that takes burned area into account when assessing daily fire danger. The use of CLARE in addition to the Area Under the receiver operating characteristic Curve (AUC) in the selection process of fire indices or fire occurrence models provides a complementary metric that allows for the evaluation of a model's ability to assess burned area. We evaluated the CLARE performance in 11 regions ranging from the European Alps to the Mediterranean basin. We also assessed the impact of (i) different groups of input variables (meteorological variables vs. fire indices), (ii) model complexity in terms of number of variables, and (iii) the modeling approach (Generalized Linear Models vs. Maxent) on the performance of CLARE. We found that models that achieve a high AUC for predicting fire occurrence may fail to show a high performance when predicting burned area. Using a multi-variable modeling approach is likely to provide higher CLARE performance than using single-variable fire index models, especially among models that have high AUC. Moreover, using this approach led to better multi-variable meteorological model performance than single-variable fire index models for some regions. This may be particularly valuable for regions where the calculation of fire indices is not possible. Finally, the differences between the modeling approaches were mainly related to the region or input variable groups. Overall, our results highlight that including burned area in the fire danger assessment process is feasible across a wide range of environmental conditions and provides valuable insights.

Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands.

A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.

Sub-kilometer dispersion simulation of a CO tracer for an inter-Andean urban valley

Air quality planning and management can benefit from the ability of numerical models to produce skillful forecasts. However, obtaining these forecasts is a continuing challenge, particularly in complex terrain. Here we examine the dispersion of traffic emissions in the Aburrá valley (located in the Colombian Andes) during an episode of severe air pollution, using the WRF-Chem and a Lagrangian model at sub-kilometer resolution. We focus on the identification of areas with higher CO concentrations (the pollutant analyzed), and examine the role of local and regional airflows in the distribution of CO inside the valley. The meteorological model performance, considering different planetary boundary layer parameterizations and grid sizes, is evaluated using available observations. Overall, the meteorological model performance is within recommended benchmarks for complex terrain. Model performance improves with increasing grid resolution for surface temperature and wind direction, but not for wind speed. Both dispersion models reproduce important features of the spatial and diurnal variability of CO in the valley, but underestimate CO concentrations throughout the simulation period. The south and southeast areas of the valley present the largest CO concentrations, associated with a prevalent northerly transport along the valley axis and reduced transport on the eastern slope. The representation of CO improves when the model adequately reproduces observed rainfall, due to effects of rainfall on boundary layer height and stability, which condition dispersion. Better simulation of transport processes is crucial for informing decisions on air quality management in critical areas such as urban valleys.

Simulation of trace metals and PAH atmospheric pollution over Greater Paris: Concentrations and deposition on urban surfaces

Urban areas can be subject not only to poor air quality, but also to contamination of other environmental media by air pollutants. Here, we address the potential transfer of selected air pollutants (two metals and three PAH) to urban surfaces. To that end, we simulate meteorology and air pollution from Europe to a Paris suburban neighborhood, using a four-level one-way nesting approach. The meteorological and air quality simulations use urban canopy sub-models in order to better represent the effect of the urban morphology on the air flow, atmospheric dispersion, and deposition of air pollutants to urban surfaces. This modeling approach allows us to distinguish air pollutant deposition among various urban surfaces (roofs, roads, and walls). Meteorological model performance is satisfactory, showing improved results compared to earlier simulations, although precipitation amounts are underestimated. Concentration simulation results are also satisfactory for both metals, with a fractional bias <0.5. Concentrations of benzo[a]pyrene are overestimated, probably because continental emissions may be overestimated. Concentrations of benzo[b]fluoranthene and indeno[1,2,3,cd]pyrene are underestimated, in part because of null boundary conditions. PAH deposition fluxes are consistent with earlier measurements obtained in the Greater Paris region. The model simulation results suggest that both wet and dry deposition processes need to be considered when estimating the transfer of air pollutants to other environmental media. Dry deposition fluxes to various urban surfaces are mostly uniform for PAH, which are entirely present in fine particles. However, there is significantly less wall deposition compared to deposition to roofs and roads for trace metals, due to their coarse fraction. Meteorology, particle size distribution, and urban morphology are all important factors affecting air pollutant deposition. Future work should focus on the collection of data suitable to evaluate the performance of atmospheric models for both wet and dry deposition with fine spatial resolution.

Comparing Standard to Feature-Based Meteorological Model Evaluation Techniques in Bogotá, Colombia

AbstractStandard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

Improving ozone modeling in complex terrain at a fine grid resolution – Part II: Influence of schemes in MM5 on daily maximum 8-h ozone concentrations and RRFs (Relative Reduction Factors) for SIPs in the non-attainment areas

Part II presents a comprehensive evaluation of CMAQ for August of 2002 on twenty-one sensitivity simulations (detailed in Part I) in MM5 to investigate the model performance for O 3 SIPs (State Implementation Plans) in the complex terrain. CMAQ performance was quite consistent with the results of MM5, meaning that accurate meteorological fields predicted in MM5 as an input resulted in good model performance of CMAQ. In this study, PBL scheme plays a more important role than its land surface models (LSMs) for the model performance of CMAQ. Our results have shown that the outputs of CMAQ on eighteen sensitivity simulations using two different nudging coefficients for winds (2.5 and 4.5 × 10 −4 s −1, respectively) tend to under predict daily maximum 8-h ozone concentrations at valley areas except the TKE PBL sensitivity simulations (ETA M-Y PBL scheme with Noah LSMs and 5-layer soil model and Gayno-Seaman PBL) using 6.0 × 10 −4 s −1 with positive MB (Mean Bias). At mountain areas, none of the sensitivity simulations has presented over predictions for 8-h O 3, due to relatively poor meteorological model performance. When comparing 12-km and 4-km grid resolutions for the PX simulation in CMAQ statistics analysis, the CMAQ results at 12-km grid resolution consistently show under predictions of 8-h O 3 at both of valley and mountain areas and particularly, it shows relatively poor model performance with a 15.1% of NMB (Normalized Mean Bias). Based on our sensitivity simulations, the TKE PBL sensitivity simulations using a maximum value (6 × 10 −4) among other sensitivity simulations yielded better model performance of CMAQ at all areas in the complex terrain. As a result, the sensitivity of RRFs to the PBL scheme may be considerably significant with about 1–3 ppb in difference in determining whether the attainment test is passed or failed. Furthermore, we found that the result of CMAQ model performance depending on meteorological variations is affected on estimating RRFs for attainment demonstration, indicating that it is necessary to improve model performance. Overall, G_c (Gayo-Seaman PBL scheme) using the coefficient for winds, 6 × 10 −4 s −1, sensitivity simulation predicts daily maximum 8-h ozone concentration closer to observations during a typical summer period from May to September and provides generally low future design values (DVFs) at valley and mountain areas compared to other simulations.

Improving ozone modeling in complex terrain at a fine grid resolution: Part I – examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model

Meteorological variables such as temperature, wind speed, wind directions, and Planetary Boundary Layer (PBL) heights have critical implications for air quality simulations. Sensitivity simulations with five different PBL schemes associated with three different Land Surface Models (LSMs) were conducted to examine the impact of meteorological variables on the predicted ozone concentrations using the Community Multiscale Air Quality (CMAQ) version 4.5 with local perspective. Additionally, the nudging analysis for winds was adopted with three different coefficients to improve the wind fields in the complex terrain at 4-km grid resolution. The simulations focus on complex terrain having valley and mountain areas at 4-km grid resolution. The ETA M–Y (Mellor–Yamada) and G–S (Gayno–Seaman) PBL schemes are identified as favorite options and promote O 3 formation causing the higher temperature, slower winds, and lower mixing height among sensitivity simulations in the area of study. It is found that PX (Pleim–Xiu) simulation does not always give optimal meteorological model performance. We also note that the PBL scheme plays a more important role in predicting daily maximum 8-h O 3 than land surface models. The results of nudging analysis for winds with three different increased coefficients' values (2.5, 4.5, and 6.0 × 10 −4 s −1) over seven sensitivity simulations show that the meteorological model performance was enhanced due to improved wind fields, indicating the FDDA nudging analysis can improve model performance considerably at 4-km grid resolution. Specifically, the sensitivity simulations with the coefficient value (6.0 × 10 −4) yielded more substantial improvements than with the other values (2.5 and 4.5 × 10 −4). Hence, choosing the nudging coefficient of 6.0 × 10 −4 s −1 for winds in MM5 may be the best choice to improve wind fields as an input, as well as, better model performance of CMAQ in the complex terrain area. As a result, a finer grid resolution is necessary to evaluate and access of CMAQ results for giving a detailed representation of meteorological and chemical processes in the regulatory modeling. A recommendation of optimal scheme options for simulating meteorological variables in the complex terrain area is made.

Modelling the photochemical pollution over the metropolitan area of Porto Alegre, Brazil

The main purpose of this study is to evaluate the photochemical pollution over the Metropolitan Area of Porto Alegre (MAPA), Brazil, where high concentrations of ozone have been registered during the past years. Due to the restricted spatial coverage of the monitoring air quality network, a numerical modelling technique was selected and applied to this assessment exercise. Two different chemistry-transport models – CAMx and CALGRID – were applied for a summer period, driven by the MM5 meteorological model. The meteorological model performance was evaluated comparing its results to available monitoring data measured at the Porto Alegre airport. Validation results point out a good model performance. It was not possible to evaluate the chemistry models performance due to the lack of adequate monitoring data. Nevertheless, the model intercomparison between CAMx and CALGRID shows a similar behaviour in what concerns the simulation of nitrogen dioxide, but some discrepancies concerning ozone. Regarding the fulfilment of the Brazilian air quality targets, the simulated ozone concentrations surpass the legislated value in specific periods, mainly outside the urban area of Porto Alegre. The ozone formation is influenced by the emission of pollutants that act as precursors (like the nitrogen oxides emitted at Porto Alegre urban area and coming from a large refinery complex) and by the meteorological conditions.

Coupling high-resolution precipitation forecasts and discharge predictions to evaluate the impact of spatial uncertainty in numerical weather prediction model outputs

River hydrograph forecasts are highly sensitive to the space–time variability of the meteorological inputs, particularly in the case of watersheds characterised by a complex topography and whose hydrological processes are simulated by means of distributed rainfall–runoff models. An accurate representation of the space–time structure of the event that might occur is, therefore, essential when atmospheric and hydrological models are coupled in order to achieve successful streamflow predictions for medium-sized catchments. Even though the scale compatibility between atmospheric and hydrological models no longer seems to represent a serious problem for a direct one-way coupling, the quality and the reliability of deterministic quantitative precipitation forecasts (QPFs) are often unsatisfactory in driving hydrological models. This is because uncertainties in QPFs are, nowadays, still considerable at the scales of interest for hydrological purposes. In this work, different configurations of the non-hydrostatic meteorological model Lokal Modell (LM) have been tested for four rain events, with the aim of improving the description of the phenomena related to the precipitation. Then, LM QPFs have been coupled with the distributed rainfall–runoff model TOPKAPI, in order to assess the results in terms of discharge forecast over the Reno river basin, a medium-sized catchment in northern Italy. The coupling of atmospheric and hydrological models offers a complementary tool to evaluate the meteorological model performance. In addition, an empirical approach is proposed in order to take into account the spatial uncertainty affecting the precipitation forecast. The methodology is based on an ensemble of future rainfall scenarios, which is built by shifting in eight different directions the precipitation patterns forecasted by LM. An ensemble of discharge forecasts is then generated by feeding the hydrological model with these rain time series, thus, enabling a probabilistic approach for flood management. This empirical methodology has been tested for the same four events. Moreover, a statistical analysis has been performed on the discharge predictions driven by the operational version of LM for the autumn seasons in the years 2003–2005, with the aim of assessing systematic model deficiencies. In particular, this investigation aims to define, in terms of the number of LM grid points, the spatial shift more suitable for taking into account the spatial uncertainty of the precipitation forecast, and to establish which shift directions would improve the forecast over the area of interest. The results show that the very-high-resolution (2.8-km) configuration of LM, where an explicit description of the deep precipitating convection is adopted, improves the rainfall forecast considerably, both in terms of the total amount and the timing. Furthermore, the south, west and south-west shift-adjusted scenarios turn out to be useful in improving the discharge forecast for the Reno river basin when the main error source which affects the LM forecast is the spatial localisation of the precipitation field.