IM Systems (United States)

<strong class="journal-contentHeaderColor">Abstract.</strong> We perform an inverse modelling analysis to quantify biomass burning emissions of carbon monoxide (CO) from the extreme wildfires in Canada between May and September 2023. Using the GEOS-Chem model, we assimilated observations from the Tropospheric Monitoring Instrument (TROPOMI) separately and then jointly with Total Carbon Column Observing Network (TCCON) measurements. We also evaluated prior emissions from the Quick Fire Emissions Dataset (QFED), Blended Global Biomass Burning Emissions Product eXtended (GBBEPx), Global Fire Assimilation System (GFAS), and Canadian Forest Fire Emissions Prediction System (CFFEPS). The assimilation of TROPOMI-only measurements estimated posterior North America emissions for QFED, GBBEPx, GFAS, and CFFEPS of 110.4&plusmn;20, 112.8&plusmn;20, 127.2&plusmn;17, and 125.6&plusmn;18 Tg CO compared to prior estimates of 37.1, 42.7, 91.0, and 90.2 Tg CO, respectively. The joint assimilation of TROPOMI+TCCON reduced the uncertainty on the North American emission estimates by up to about 30 %, while showing only a modest impact (&lt; 5 %) on the magnitude of the inferred emissions. An evaluation against independent measurements reveals that adding TCCON data increases the correlations and slightly lowers the biases and standard deviations. Additionally, including an experimental TCCON product at East Trout Lake with higher surface sensitivity, we find better agreement of assimilation results with nearby in situ tall tower and aircraft measurements. This highlights the potential importance of vertical sensitivity in these experimental data for constraining local surface emissions. Our results demonstrate the complementarity of the greater temporal coverage provided by TCCON with the spatial coverage of TROPOMI when these data are jointly assimilated.

<strong class="journal-contentHeaderColor">Abstract.</strong> The performance and consistency of satellite observations in characterizing the saline dust emission from the newly formed Aralkum Desert have remained poorly understood. We address this knowledge gap by providing a review of satellite techniques capable of detecting the presence, column burden, and vertical height of airborne dust over desert surfaces. Then we evaluate the consistency between different aerosol products in observing an intense Aralkum dust storm in 2018, via synergistic analyses of the ultraviolet aerosol index (UVAI) from OMPS, TROPOMI and EPIC, aerosol optical depth (AOD) from MODIS and VIIRS, and aerosol optical centroid height (AOCH) from CALIOP and EPIC. The UVAI products consistently delineate the areal extent of the freshly emitted dust plume if the dynamic range of each product is considered. The heavy dust plume is however erroneously masked as clouds in the AOD products. All UVAI products show large positive values over the Garabogazk&ouml;l gulf and northern Caspian Sea due to enhanced UV absorption by turbid and saline waters, suggesting that caution must be taken to avoid misinterpreting the surface effect as dust signal over ephemeral or dried lakes. The AOD products show generally good agreement in observing the total and coarse-mode AOD associated with the dust outflow to Caspian Sea. Over-land AOD retrievals show strong non-linear relationships between aerosol algorithms. The NOAA Enterprise Processing System (EPS) product yields significantly lower AOD than other algorithms, likely due to the misuse of an urban aerosol optical model for dust retrieval. The EPIC AOCH retrieval shows the best agreement with CALIOP over heavy dust burden areas, with both mean bias and RMSE below 0.5 km. This study reveals significant inconsistency between satellite aerosol products and the potential of multi-sensor approaches for identifying the product biases and limitations in Central Asia.

<strong class="journal-contentHeaderColor">Abstract.</strong> The performance and consistency of satellite observations in characterizing the saline dust emission from the newly formed Aralkum Desert have remained poorly understood. We address this knowledge gap by providing a review of satellite techniques capable of detecting the presence, column burden, and vertical height of airborne dust over desert surfaces. Then we evaluate the consistency between different aerosol products in observing an intense Aralkum dust storm in 2018, via synergistic analyses of the ultraviolet aerosol index (UVAI) from OMPS, TROPOMI and EPIC, aerosol optical depth (AOD) from MODIS and VIIRS, and aerosol optical centroid height (AOCH) from CALIOP and EPIC. The UVAI products consistently delineate the areal extent of the freshly emitted dust plume if the dynamic range of each product is considered. The heavy dust plume is however erroneously masked as clouds in the AOD products. All UVAI products show large positive values over the Garabogazk&ouml;l gulf and northern Caspian Sea due to enhanced UV absorption by turbid and saline waters, suggesting that caution must be taken to avoid misinterpreting the surface effect as dust signal over ephemeral or dried lakes. The AOD products show generally good agreement in observing the total and coarse-mode AOD associated with the dust outflow to Caspian Sea. Over-land AOD retrievals show strong non-linear relationships between aerosol algorithms. The NOAA Enterprise Processing System (EPS) product yields significantly lower AOD than other algorithms, likely due to the misuse of an urban aerosol optical model for dust retrieval. The EPIC AOCH retrieval shows the best agreement with CALIOP over heavy dust burden areas, with both mean bias and RMSE below 0.5 km. This study reveals significant inconsistency between satellite aerosol products and the potential of multi-sensor approaches for identifying the product biases and limitations in Central Asia.

For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere, with its data used for a variety of tasks ranging from weather forecasting to wildfire detection. Located on the GOES-16, GOES-17, and GOES-18 platforms, the Advanced Baseline Imager (ABI) is the first GOES-series imager that meets the precision requirements (e.g., ≥ 10 bits per datum) for high-quality, aerosol-related research. Here, we present a pixel-level (up to 1 km) Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that leverages the ABI instruments on the GOES-16 and GOES-17 platforms, as well as the differences in autocorrelation time-scales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 minutes), fine-and-coarse mode aerosol particle properties at a daily cadence, and surface properties under a framework that combines the unique information content in multi-angle radiances (e.g., sensitivity to aerosol type from multiple scattering-angle observations) with the robust surface characterization inherent to temporally tiled algorithms such as the MAIAC method. We present three case studies, tiling radiances for several days over the Desert Southwest (2 cases) and the Pacific Northwest (1 case). We observed/retrieved smoke from the following major fires: the Camp Fire (November 5 th -12 th , 2018), the Williams Flats Fire (July 29 th -August 8 th , 2019), and the Kincade Fire (October 23 rd -November 1 st , 2019). Because GOES-17 was not making observations during the Camp Fire, we present this as a unique case demonstrating the efficacy of the multi-angle algorithm using only a single ABI sensor. We compare MAGARA retrievals of fine-mode (FM) AOD, coarse-mode (CM) AOD, and single-scattering albedo (SSA) with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period. We also compare MAGARA results against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD.

This study investigated the impact of COVID-19 lockdowns on satellite aerosol optical depth (AOD), to explore the hypothesis that if changes in economic activity are seen in emissions of NO2, an aerosol precursor, then AOD should change commensurably. We developed a technique to filter AOD data to isolate changes associated with anthropogenic emissions. Overall, in 37 of the 43 cities that were identified as top oxides of nitrogen (NO x ) emitters from their transportation sectors, AODs decreased by 21.2% ± 7.8%, 18.9% ± 11.7%, 27% ± 12.4%, 22.9% ± 7.6% in the United States, India, western Europe, and China, respectively—an average of 22.4% ± 7.4%. In contrast, AODs increased on average by 11.7% ± 8.4% in Taiwan, where economic stimulus was used as a strategy during the pandemic. This analysis implies NO x and volatile organic compounds emissions reductions from the transportation sector can be targeted, and by transitioning 6 million light duty vehicles from gasoline to electricity, the US can achieve 21% improvement in AOD.

Methane (CH4) is the second most significant contributor to climate change after carbon dioxide (CO2), accounting for approximately 20% of the contributions from all the well-mixed greenhouse gases. Understanding the spatiotemporal distributions, and the relevant long-term trends are crucial to identifying the sources, sinks, and impacts on climate. Hyperspectral thermal infrared (TIR) sounders, including the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI), have been used to measure global CH4 concentrations since 2002. This study analyzed nearly twenty years of data from AIRS and CrIS and confirmed a significant increase in CH4 concentrations in the mid-upper troposphere (around 400 hPa) from 2003 to 2020, with a total increase of approximately 85 ppb, representing a +4.8% increase in 18 years. The rate of increase was derived using global satellite TIR measurements is consistent with in-situ measurements, indicating a steady increase starting in 2007 and became stronger in 2014. The study also compared CH4 concentrations derived from the AIRS and CrIS against ground-based measurements from NOAA Global Monitoring Laboratory (GML) and found phase shifts in the seasonal cycles in the middle to high latitudes in the northern hemisphere, which is attributed to the influence of stratospheric CH4 that varies at different latitudes. These findings provide insights into the global budget of atmospheric composition and the understanding of satellite measurement sensitivity of CH4.

BACKGROUND: Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. AIM: We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. METHODS: Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)’s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. RESULTS: Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 &amp;#x3bc;g/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 &amp;#x3bc;g/m3) and 0.73 (16.58 &amp;#x3bc;g/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 &amp;#x3bc;g/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 &amp;#x3bc;g/m3) and 0.79 (11.89 &amp;#x3bc;g/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 &amp;#x3bc;g/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 &amp;#x3bc;g/m3) and 0.85 (14.88 &amp;#x3bc;g/m3), respectively. CONCLUSIONS: Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode. KEYWORDS: PM2.5, GOES16, Wildland fire, Remote sensing, AOD, SMOTE, Weighted Random Forest

Abstract The 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field experiment obtained a diverse set of in situ and remotely sensed measurements before and during a pyrocumulonimbus (pyroCb) event over the Williams Flats fire in Washington State. This unique dataset confirms that pyroCb activity is an efficient vertical smoke transport pathway into the upper troposphere and lower stratosphere (UTLS). The magnitude of smoke plumes observed in the UTLS has increased significantly in recent years, following unprecedented wildfire and pyroCb activity observed worldwide. The FIREX-AQ pyroCb dataset is therefore extremely relevant to a broad community, providing the first measurements of fresh smoke exhaust in the upper troposphere, including from within active pyroCb cloud tops. High-resolution remote sensing reveals that three plume cores linked to localized fire fronts, burning primarily in dense forest fuels, contributed to four total pyroCb “pulses.” Rapid changes in fire geometry and spatial extent dramatically influenced the magnitude, behavior, and duration of pyroCb activity. Cloud probe measurements and weather radar identify the presence of large ice particles within the pyroCb and hydrometers below cloud base, indicating precipitation development. The resulting feedbacks suggest that vertical smoke transport efficiency was reduced slightly when compared with intense pyroCb events reaching the lower stratosphere. Physical and optical aerosol property measurements in pyroCb exhaust are compared with previous assumptions. A large suite of aerosol and gas-phase chemistry measurements sets a foundation for future studies aimed at understanding the composition of smoke plumes lifted by pyroconvection into the UTLS and their role in the climate system.

Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size.