IASI-Derived Sea Surface Temperature Data Set for Climate Studies

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long-term homogeneous satellite-derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level-1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT-released IASI level-2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution

Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.

Estimating exposure to hydrogen sulfide from animal husbandry operations using satellite ammonia as a proxy: Methodology demonstration

Husbandry trace gases that have climate change implications such as carbon dioxide (CO₂), methane (CH₄) and ammonia (NH₃) can be quantified through remote sensing; however, many husbandry gases with health implications such as hydrogen sulfide (H₂S), cannot. This pilot study demonstrates an approach to derive H₂S concentrations by coupling in situ and remote sensing data. Using AMOG (AutoMObile trace Gas) Surveyor, a mobile air quality and meteorology laboratory, we measured in situ concentrations of CH₄, CO₂, NH₃, H₂S, and wind at a southern California university research dairy. Emissions were 0.13, 1.93, 0.022 and 0.0064 Gg yr^-1; emission factors (E_F) were 422, 6333, 74, and 21 kg cow^-1 yr^-1, respectively, for the 306 head herd. Contributing to these strong E_F were spillway emissions from a grate between the main cowshed and the waste lagoon identified in airborne remote sensing data acquired by the hyperspectral thermal infrared imager, Mako. NH₃ emissions from the Chino Dairy Complex, also in southern California, were calculated from Infrared Atmospheric Sounding Interferometer (IASI) satellite data for 2008-2017 using average morning winds, yielding a flushing time of 2.7 h, and 8.9 Gg yr^-1. The ratio of E_F(H₂S) to E_F(NH₃) for the research dairy from AMOG data were applied to IASI NH₃ emissions to derive H₂S exposure concentration maps for the Chino area, which ranged to 10-30 ppb H₂S for many populated areas. Combining remote sensing with in situ concentrations of multiple emitted gases can allow derivation of emissions at the sub-facility, facility, and larger scales, providing spatial and temporal coverage that can translate into exposure estimates for use in epidemiology studies and regulation development. Furthermore, with high fidelity information at the sub-facility level we can identify best practices and opportunities to sustainably and holistically reduce husbandry emissions.

Validation of mobile in situ measurements of dairy husbandry emissions by fusion of airborne/surface remote sensing with seasonal context from the Chino Dairy Complex

Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context. For the Chino dairies, MISTIR-AMOG ammonia (NH₃) agreement was within 5% (15.7 versus 14.9 Gg yr^-1, respectively) using all information. Methane (CH₄) emissions were 30 Gg yr^-1 for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported. Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH₄, NH₃, and CO₂ emissions were 91, 209, and 8200 Mg yr^-1, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion. NH₃ downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

Ammonia Emissions May Be Substantially Underestimated in China

China is a global hotspot of atmospheric ammonia (NH₃) emissions and, as a consequence, very high nitrogen (N) deposition levels are documented. However, previous estimates of total NH₃ emissions in China were much lower than inference from observed deposition values would suggest, highlighting the need for further investigation. Here, we reevaluated NH₃ emissions based on a mass balance approach, validated by N deposition monitoring and satellite observations, for China for the period of 2000 to 2015. Total NH₃ emissions in China increased from 12.1 ± 0.8 Tg N yr^-1 in 2000 to 15.6 ± 0.9 Tg N yr^-1 in 2015 at an annual rate of 1.9%, which is approximately 40% higher than existing studies suggested. This difference is mainly due to more emission sources now having been included and NH₃ emission rates from mineral fertilizer application and livestock having been underestimated previously. Our estimated NH₃ emission levels are consistent with the measured deposition of NH_x (including NH₄⁺ and NH₃) on land (11-14 Tg N yr^-1) and the substantial increases in NH₃ concentrations observed by satellite measurements over China. These findings substantially improve our understanding on NH₃ emissions, implying that future air pollution control strategies have to consider the potentials of reducing NH₃ emission in China.

Remote sensing and in situ measurements of methane and ammonia emissions from a megacity dairy complex: Chino, CA

Methane (CH₄) and ammonia (NH₃) directly and indirectly affect the atmospheric radiative balance with the latter leading to aerosol generation. Both have important spectral features in the Thermal InfraRed (TIR) that can be studied by remote sensing, with NH₃ allowing discrimination of husbandry from other CH₄ sources. Airborne hyperspectral imagery was collected for the Chino Dairy Complex in the Los Angeles Basin as well as in situ CH₄, carbon dioxide (CO₂) and NH₃ data. TIR data showed good spatial agreement with in situ measurements and showed significant emissions heterogeneity between dairies. Airborne remote sensing mapped plume transport for ∼20 km downwind, documenting topographic effects on plume advection. Repeated multiple gas in situ measurements showed that emissions were persistent on half-year timescales. Inversion of one dairy plume found annual emissions of 4.1 × 10⁵ kg CH₄, 2.2 × 10⁵ kg NH₃, and 2.3 × 10⁷ kg CO₂, suggesting 2300, 4000, and 2100 head of cattle, respectively, and Chino Dairy Complex emissions of 42 Gg CH₄ and 8.4 Gg NH₃ implying ∼200k cows, ∼30% more than Peischl et al. (2013) estimated for June 2010. Far-field data showed chemical conversion and/or deposition of Chino NH₃ occurs within the confines of the Los Angeles Basin on a four to six h timescale, faster than most published rates, and likely from higher Los Angeles oxidant loads. Satellite observations from 2011 to 2014 confirmed that observed in situ transport patterns were representative and suggests much of the Chino Dairy Complex emissions are driven towards eastern Orange County, with a lesser amount transported to Palm Springs, CA. Given interest in mitigating husbandry health impacts from air pollution emissions, this study highlights how satellite observations can be leveraged to understand exposure and how multiple gas in situ emissions studies can inform on best practices given that emissions reduction of one gas could increase those of others.

Using satellite-based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-based measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ammonia (NH₃), formaldehyde (HCHO), and ozone (O₃)), aerosol optical properties (aerosol optical depth (AOD) and Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI) and temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD × AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatial variability in skill of simple generalized nucleation schemes in reproducing observed NPF. In contrast to more simple proxies developed in prior studies (e.g., based on AOD, AE, SO₂, and UV), use of additional predictors (NO₂, NH₃, HCHO, LAI, T, and O₃) increases the explained temporal variance of UFP concentrations at all sites.