Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from twelve state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, the U.S., August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within one day are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by FRP-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019 mainly over the transported smoke plumes, owing to the underestimated emissions on the 7th. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center’s Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper with the day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported one-day-old smoke. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Pilot provision of EARLINET/ACTRIS lidar profiles to CAMS

<p>In December 2019, a contract between CNR and ECMWF was signed for a pilot ACTRIS/EARLINET data provision to the Copernicus Atmosphere Monitoring Service (CAMS). Such pilot contract (CAMS21b) aims to put in place a first data provision for a set of selected stations and it will demonstrate the feasibility of fully traceable and quality-controlled data provision for the whole network.</p><p>In CAMS21b, the main effort is devoted to design, test and set up the provision of quality-controlled ACTRIS/EARLINET products in Real Real Time (RRT) and/or Near Real Time (NRT) to CAMS. The activities are focused on the automatic centralized data processing and data provision, ensuring the full traceability of the products from the data acquisition level up to the final quality-controlled data level. Most of the activities are done at ARES, the EARLINET/ACTRIS data center node at CNR, for assuring the centralized, harmonized and quality-controlled processing in compliance with FAIR principles.</p><p>New modules and submodules of the ACTRIS/EARLINET Single Calculus Chain (SCC) as well as optimized algorithms for cloud screening have been designed. Additional procedures were implemented for improving the quality of the data provided in NRT, but also for the quality control of the Level 2 products which are delivered with a time delay.</p><p>The release of a new version of SCC and of QC procedure is planned for mid-February.</p><p>The data provision started in October 2020 at the test site of Potenza. A system has been set up for measurement reporting and monitoring of KPIs (Key Performance Indicators). After 3 months of measurements, the overall data provision system showed no critical points.</p><p>In January 2021, the provision started for a group of 9 stations which are seen as representative for the whole network in terms of instrumental capability, but also ensuring a good geographical coverage of the European continent.</p><p>In order to accommodate also measurements from non-continuous operation systems, a measurement schedule has been set up, compromising between the need of a large number of measurements and costs/efforts at each station. The measurement schedule has been designed through a representativeness study and foresees 6 slots of measurements per week, 3 in daytime and 3 in nighttime conditions.</p><p>The successful implementation of the pilot allows the provision of aerosol optical property profiles to the CAMS services. from a set of observational sites distributed over the different European regions. These profiles is expected to be of interest for the assimilation, near real time evaluation and re-analysis evaluation of several CAMS products, including the aerosol load over Europe for air quality issues, atmospheric composition, climate forcing and solar and UV products. This allows for having a systematic solution for looking into specific events as they develop (e.g. the dust plume that you investigated earlier this month or the Californian fires in September), supporting or contradicting model forecasts. This pilot is the first provision of aerosol profiles from a high-quality ground-based network in NRT for this kind of applications. It is expected that these efforts will be continued in the next phase of CAMS/Copernicus (2021-2027).</p>

Multi-sensor comparison of aerosol optical properties profiles observed during a pollution episode in a mega city of the North China Plain

A fine aerosol particle pollution episode happened in mega city Beijing during February 25th – March 5th. Aerosol extinction coefficient profiles were observed at 7 L idar s ta ti ons. Different aerosol optical property instruments were used to verify Lidar retrievals. Ground visibility observations were used to verify near ground Lidar observations. High correlation coefficient was found (0.8) between visiometer and Lidar observations. Lidar’s Column integrated aerosol extinction coefficient was also verified by Himawari-8 aerosol optical depth (AOD) data. The correlation coefficient between two AOD retrievals was 0.4. And the mean bias was -0.17. CALIPSO aerosol property profiles was also used to verify Lidar retrievals. Lidar retrievals was inconsistent with CALIPSO in the boundary layer. Overall, the Lidar retrievals are consistent with other optical property sensors. And the retrials could be used for observation research and model validation.

PERFORMANCE OF AHI HOURLY AEROSOL OPTICAL PROPERTY DURING FREQUENT HAZE-FOG EVENTS: A CASE STUDY OF BEIJING

The Advanced Himawari Imager (AHI) onboard Himawari-8, a next-generation geostationary meteorological satellite, provided firstly the full-disk aerosol observations every 10 min at sub-kilometer spatial resolution(5 km). This is responsible for retrieving the ground-level particulate matter of fewer than 2.5 micrometers and improving assimilation model. However, the representativeness of AHI L3 hourly Aerosol Optical Thickness (AOT) products remains unclear under different air quality conditions, Especially, over frequently polluted urban areas that feature complex surface characteristics and aerosol models. In this study, One-to-one comprehensive comparisons were conducted to evaluate the performance of three types of AHI L3 AOT products (version 3.0) based on the Aerosol Robotic NETwork (AERONET) aerosol measurements over Beijing. The overall comparisons of AHI and ground AOTs show the AHI merged AOT perform best, which the R is 0.87, RMSE is 0.25 and 52.5% of retrievals fall within the envelope of Expect Error (EE, ±(0.05 + 0.2 * AOTground)). For the different primary pollutants, the results suggested the three types AHI hourly AOT products are more suitable for the fine particulate matters (PM2.5) retrievals, especially the merged AOT with 0.87 of R, 0.29 of RMSE and 58.8% of within EE. Furthermore, when the slight and moderate pollution happened over Beijing, the AHI hourly AOT products perform well. And when the heavy pollution happened, the performance of the AHI merge AOT and L2 mean AOT is better. a case during low to high pollution suggested that AHI merged AOT can capture the similar spatial pattern to the MODIS (Deep Blue) DB or (Dark Target) DTDB merged AOT and has good consistency with ground-based air quality monitoring. These results demonstrate the AHI hourly merged AOT is a promising aerosol retrieval for air quality.