Published: Wed, 26 Apr 2017 19:56:15 GMTCopyright: Public Domain
Wed, 26 Apr 2017 15:50:30 GMTThe presentation builds on the work presented last year at the 14th CMAS meeting and it is applied to the work performed in the context of the AQMEII-HTAP collaboration. The analysis is conducted within the framework of the third phase of AQMEII (Air Quality Model Evaluation International Initiative) and encompasses the gauging of model performance through measurement-to-model comparison, error decomposition and time series analysis of the models biases. Through the comparison of several regional-scale chemistry transport modelling systems applied to simulate meteorology and air quality over two continental areas, this study aims at i) apportioning the error to the responsible processes through time-scale analysis, and ii) help detecting causes of models error, and iii) identify the processes and scales most urgently requiring dedicated investigations. The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while the apportioning of the error into its constituent parts (bias, variance and covariance) can help assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the previous phases of AQMEII.
Wed, 26 Apr 2017 15:43:54 GMTThrough the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.
Wed, 26 Apr 2017 15:03:51 GMTThe National Stormwater Calculator is a simple to use tool for computing small site hydrology for any location within the US. It estimates the amount of stormwater runoff generated from a site under different development and control scenarios over a long term period of historical rainfall. The analysis takes into account local soil conditions, slope, land cover and meteorology. Different types of low impact development (LID) practices (also known as green infrastructure) can be employed to help capture and retain rainfall on-site. Future climate change scenarios taken from internationally recognized climate change projections can also be considered. The calculator provides planning level estimates of capital and maintenance costs which will allow planners and managers to evaluate and compare effectiveness and costs of LID controls.The calculator’s primary focus is informing site developers and property owners on how well they can meet a desired stormwater retention target. It can be used to answer such questions as:• What is the largest daily rainfall amount that can be captured by a site in either its pre-development, current, or post-development condition?• To what degree will storms of different magnitudes be captured on site?• What mix of LID controls can be deployed to meet a given stormwater retention target?• How well will LID controls perform under future meteorological projections made by global climate change models?• What are the relative cost (capital and maintenance) differences for various mixes of LID controls?The calculator seamlessly accesses several national databases to provide local soil and meteorological data for a site. The user supplies land cover information that reflects the state of development they wish to analyze and selects a mix of LID controls to be applied. After this information is provided, the site’s hydrologic response to a long-term record of historical hourly precipitation, possibly modified by a particular climate change scenario, is computed. This allows a full range of meteorological conditions to be analyzed, rather than just a single design storm event. The resulting time series of rainfall and runoff are aggregated into daily amounts that are then used to report various runoff and retention statistics. In addition, the site’s response to extreme rainfall events of different return periods is also analyzed. The calculator uses the EPA Storm Water Management Model (SWMM) as its computational engine (http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/). SWMM is a well-established, EPA developed model that has seen continuous use and periodic updates for 40 years. Its hydrology component uses physically meaningful parameters making it especially well-suited for application on a nation-wide scale. SWMM is set up and run in the background without requiring any involvement of the user.The calculator is most appropriate for performing screening level analysis of small footprint sites up to several dozen acres in