Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

Development and evaluation of an advanced National Air Quality Forecast Capability using the NOAA Global Forecast System version 16

A new dynamical core, known as the Finite Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA’s Global Forecast System (GFS) and in limited area models (LAMs) for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), and includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecast Capability (NAQFC) that will continue to protect human and ecosystem health in the U.S. Here we describe the development of the FV3GFSv16 coupling with a “state-of-the-science” CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), which became the next operational NAQFC system (i.e., NACC-CMAQ) on July 20, 2021. NACC-CMAQ has a number of scientific advancements that include satellite- based data acquisition technology to improve land cover and soil characteristics, and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ has significantly different meteorological and chemical predictions than the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72-hour (3-day) forecast with this system.

Air Quality Prediction Using Supervised Machine Learning Technique

Generally, air contamination alludes to the arrival of different contamination into the air which are compromising the human wellbeing and planet also. The air contamination is the major hazardous horrendous to humankind at any point confronted. It causes major harm to creatures, plants and so forth, if this continues proceeding, the individuals will confront major circumstances in the forthcoming years. The significant toxins are from the vehicle and enterprises. In this way, to forestall this issue significant areas need to foresee the air quality from transport and ventures .In existing undertaking there are numerous hindrances. The venture is tied in with assessing the PM2.5 fixation by planning a photo based strategy. In any case photographic technique isn’t the only one adequate to compute PM2.5 since it contains just one of the grouping of toxins furthermore, it ascertains just PM2.5 so there are some passing up a great opportunity of the significant toxins and the data required for controlling the contamination .So along these lines we proposed the AI procedures by UI of GUI application. In this numerous dataset can be joined from the diverse source to shape a summed up dataset and different AI calculations are used to get the outcomes with the most extreme precision. From looking at different AI calculations we can get the best precision result. Our assessment gives the thorough manual to affectability assessment of model boundaries concerning generally speaking execution in forecast of air great contaminations through exactness computation. Furthermore to examine and think about the presentation of AI calculations from the dataset with assessment of GUI based UI air quality forecast by credits.

Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of −0.2 ∘C for temperature at 2 m, 0.4 % for relative humidity at 2 m, and 0.4 m s−1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 µm or less (PM2.5) for warm months (May–September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

Fog dissipation through ground warming monitored by satellite image : an approach to support regional forecasting

<p>By reducing the atmospheric visibility, fog events have strong impacts on several humans activities. Transport security, military operations, air quality forecast and solar energy production are critical activities considering fog dissipation time as a high valuable information.</p><p>Fog dissipation occurs through these two following processes. (1) An adiabatic cloud elevation converts the fog into a low stratus, increasing the visibility at ground level while keeping an overcast sky. (2) A radiative warming can break through a large continuous fog deck. Then, the cleared area increases progressively by heating the ground of the neighboured fog covered area.</p><p>These two events are particularly difficult to forecast using NWP models as many non-linear local processes at short-time scale are involved. Moreover, current network of fog presence sensors is too scarce to analyse and/or anticipate the phenomena. Subsequent images of geostationary meteorological satellite offer a high temporal resolution that enables to monitor large fog decks and detect punctual clear areas that induce dissipation (case 2). However, fog detection using satellite images suffers from a lack of distinction between fog and very low stratus.</p><p>In this work, we explored the potential of MSG SEVIRI radiometer through radiance observations and more advanced cloud products to analyse fog events effectively observed at the SIRTA atmospheric observatory (Palaiseau, France). We assumed that, during these events, pixels classified as “very low cloud” according to SAF-NWC algorithm were covered by fog. We monitored the evolution of these pixels using a cloud index derived from HRV channels, providing a more detailed spatial distribution of cloud cover during day time. We analysed the evolution of brightness temperature spatial gradient from the SEVIRI infrared window channel (IR 10.8µm). We isolated cases where ground warming situation could anticipate an irreversible fog dissipation. Then we deduced some fog dissipation forecasting principles.</p><p>This approach has the potential to provide to users information on morning fog sustainability with a higher accuracy and finer temporal resolution than NWP. Ongoing work focuses on characterizing favourable situations for accurate forecasts, while further predictors are investigated using recent products providing a smart distinction between fog and low stratus using SEVIRI images.</p>

Simulation of O₃ and NO_<i>x</i> in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)

We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3) and nitrogen oxides (NOx) concentrations within the urban street canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations are performed inside the Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the São Paulo Environmental Agency (CETESB), providing data (both pollutant concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) local climate zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compound (VOC) speciation is approximated using information from the São Paulo air quality forecast emission file and non-methane hydrocarbon concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gas pollutants emitted by the vehicles.