Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An ...update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period.
International Cooperative for Aerosol Predictions (ICAP) model 550 nm total AOD RMSE of the 72 h forecast versus corresponding mean AODs for AERONET sites listed in Table 2. Large black dots are ICAP multi‐model ensemble consensus means. Individual models are in small coloured dots. Validation of fine‐ and coarse‐modal AODs and dust AOD is also available in Figure 2.
Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy ...Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow-dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two-dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT> 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over-land synoptic-scale aerosol events.
While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. ...Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1 × 1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.
Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further ...degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research. even larger than what was diagnosed here. We conclude with a discussion on how
An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System ...(ENAAPS) at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data assimilation. The optimized ensemble system was compared to the Navy's current operational aerosol forecasting system, which makes use of NAVDAS-AOD (NRL Atmospheric Variational Data Assimilation System for aerosol optical depth), a 2-D variational data assimilation system. Overall, the two systems had statistically insignificant differences in root-mean-squared error (RMSE), bias, and correlation relative to AERONET-observed AOT. However, the ensemble system is able to better capture sharp gradients in aerosol features compared to the 2DVar system, which has a tendency to smooth out aerosol events. Such skill is not easily observable in bulk metrics. Further, the ENAAPS-DART system will allow for new avenues of model development, such as more efficient lidar and surface station assimilation as well as adaptive source functions. At this early stage of development, the parity with the current variational system is encouraging.
The co-transport of aerosol particles and water vapor has
long been noted in the literature, with a myriad of implications such as air
mass characterization, radiative transfer, and data ...assimilation. Here, the
relationship between aerosol optical depth (AOD) and precipitable water
vapor (PW) is evaluated to our knowledge for the first time globally, at
daily to seasonal levels using approximately 20 years of NASA Aerosol Robotic Network (AERONET)
observational data and the 16-year Navy Aerosol Analysis Prediction System (NAAPS) reanalysis v1.0 (NAAPS-RA) model
fields. The combination of AERONET observations with small uncertainties and
the reanalysis fields with global coverage is used to provide a best
estimate of the seasonal AOD and PW relationships, including an evaluation
of correlations, slope, and PW probability distributions for identification
of statistically significant differences in PW for high-AOD events. The
relationships produced from the AERONET and NAAPS-RA datasets were compared
against each other and showed consistency, indicating that the NAAPS-RA
provides a realistic representation of the AOD and PW relationship. The
analysis includes layer AOD and PW relationships for proxies of the
planetary boundary layer and the lower, middle, and upper free troposphere. The
dominant AOD and PW relationship is positive, supported by both AERONET and
model evaluation, which varies in strength by season and location. These
relationships were found to be statistically significant and present across
the globe, observed on an event-by-event level. Evaluations at individual
AERONET sites implicate synoptic-scale transport as a contributing factor in
these relationships at daily levels. Negative AOD and PW relationships were
identified and predominantly associated with regional dry-season timescales
in which biomass burning is the predominant aerosol type. This is not an
indication of dry-air association with smoke for an individual event but is
a reflection of the overall dry conditions leading to more biomass burning
and higher associated AOD values. Stronger correlations between AOD and PW
are found when evaluating the data by vertical layers, including the boundary
layer and the lower, middle, and upper free troposphere (corresponding to typical water
vapor channels), with the largest correlations observed in the free
troposphere – indicative of aerosol and water vapor transport events. By
evaluating the variability between PW and relative humidity in the NAAPS-RA,
hygroscopic growth was found to be a dominant term to (1) amplify positive
AOD–PW relationships, particularly in the midlatitudes; (2) diminish
negative relationships in dominant biomass burning regions; and (3) lead to
statistically insignificant changes in PW for high-AOD events for maritime
regions. The importance of hygroscopic growth in these relationships
indicates that PW is a useful tracer for AOD or light extinction but not
necessarily as strongly for aerosol mass. Synoptic-scale African dust events
are an exception where PW is a strong tracer for aerosol transport shown by
strong relationships even with hygroscopic effects. Given these results, PW
can be exploited in coupled aerosol and meteorology data assimilation for
AOD, and the collocation of aerosol and water vapor should be carefully taken
into account when conducting particulate matter (PM) retrievals from space
and in evaluating radiative impacts of aerosol, with the season and location
in mind.
A chemical mass balance approach is used to determine the relative contributions of evaporative versus tailpipe sources to motor vehicle volatile organic compound (VOC) emissions. Contributions were ...determined by reconciling time‐resolved ambient VOC concentrations measured downwind of Sacramento, California, in summer 2001 with source speciation profiles. A composite liquid fuel speciation profile was determined from gasoline samples collected at Sacramento area service stations. Vapor‐liquid equilibrium relationships were used to determine the corresponding headspace vapor composition. VOC concentrations measured in a highway tunnel were used to define the composition of running vehicle emissions. The chemical mass balance analysis indicated that headspace vapor contributions ranged from 7 to 29% of total vehicle‐related VOC depending on time of day and day of week, with a mean daytime contribution of 17.0 ± 0.9% (mean ± 95% CI). A positive association between the headspace vapor contribution and ambient air temperature was found for afternoon hours. We estimate a 6.5 ± 2.5% increase in vapor pressure‐driven evaporative emissions and at least a 1.3 ± 0.4% increase in daily total (exhaust plus evaporative) VOC emissions from motor vehicles per degree Celsius increase in maximum temperature.
Two new aerosol data assimilations are presented as part of this work with both assimilations making use of an Ensemble Kalman Filter (EnKF) and the National Center for Atmospheric Research's (NCAR) ...community atmosphere model (CAM) with 60 ensemble members. The first assimilation involves the joint adjustment of the amount of atmospheric aerosol and the relative amount of fine and coarse aerosol using observations of aerosol optical depth (AOD) and angstrom exponent from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Both the amount and relative contribution of fine and coarse aerosol were identified as key parameters for determining aerosol radiative forcing and, therefore, accurately determining these parameters is desirable. The second assimilation presented as part of this work is similar to the first with the addition of a vertical redistribution of coarse aerosol using vertical extinction observations from NASA's Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Studies have shown that the atmospheric lifetime of aerosol is tightly coupled to the vertical profile, therefore, it is expected that vertical adjustments will further reduce aerosol uncertainty, especially in coarse aerosol. Similar to aerosol amount and size, lifetime is important for properly quantifying radiative forcing as it determines the time an aerosol has to impact the climate and influences the horizontal distribution of aerosol that is highly heterogenous in space and time. The two presented assimilations are run for the year 2007 and results are compared against a control run simulation as well observations of AOD, angstrom exponent, and fine aerosol contributions from MODIS and NASA's Aerosol Robotic Network (AERONET). Through the comparison, it is demonstrated that the presented assimilations are able to reduce the model bias with an increase in the predicted aerosol optical depth. The globally averaged control run AOD prediction for 2007 is 0.086(± 0.06). The globally average AOD predictions for the amount and size assimilation and vertical assimilation are 0.115(± 0.05), 0.140(± 0.05), respectively. This is compared to globally averaged MODIS observations of 0.161(± 0.09). Over-ocean averaged angstrom exponent predictions from the control run are 0.65(± 0.35) while the size and amount and vertical assimilation predictions are 0.68 (± 0.15) and 0.66 (± 0.15), respectively. This is compared to globally averaged MODIS observations of 0.65(± 0.30). While it is difficult to determine improvements in angstrom exponent predictions based on the global average, clear reductions in regional biases were observed. Aerosol predictions are also compared to ground-based AERONET observations by site category, including desert dust, biomass burning, rural, industrial pollution, polluted marine, and dirty pollution. While the rural sites have statistically similar averaged AOD values across simulations, improvements are found for the other site categories in the assimilation runs with higher average AOD values and greater temporal variability. In addition to AOD comparison, the predicted amount of AOD due to fine aerosol is compared to AERONET observations by site category. The greatest reduction in bias is observed for polluted marine sites with the assimilation runs predicting a smaller fine aerosol contribution than the control simulation. Size-related observations are concentrated over ocean regions, therefore, the greatest impact of the assimilation with respect to size is expected for marine sites. Additionally, the positive bias in fine aerosol contribution predicted at dusty sites is reduced the most in the vertical assimilation with dust being mostly coarse in size. The results of the assimilation are used to quantify the contribution of anthropogenic aerosol to AOD. Globally averaged, the anthropogenic contribution to AOD is 38.8 percent for the control simulation, 47.6 percent for the amount and size assimilation and 49.5 percent from the vertical assimilation. These results are comparable to previously published anthropogenic AOD percentages which range from 41 to 72 percent (IPCC 2007). Additionally, aerosol loss processes and lifetime are analyzed. The dominant loss processes are condensational growth for nucleation mode aerosol (fine, < 0.1μm), activation and wet deposition for accumulation mode aerosol (fine, 0.1-1μm), and dry deposition and activation for coarse mode aerosol (> 1 μm). The longest aerosol lifetimes are found in the vertical assimilation with most aerosol species showing better comparison to reported AEROCOM lifetimes, except for sulfate. In particular, the lifetimes of coarse mode dust and sea salt increased in the vertical adjustment assimilation, reducing the negative aerosol optical depth bias, especially in dusty regions. The predicted sulfate lifetime is double the reported AEROCOM value and may be the cause of some positive AOD bias regions in the Northern hemisphere predicted in the model runs. (Abstract shortened by UMI.)
Various forms of global compositional forecasting are now commonplace across the world's operational centers. Biomass burning smoke is often forecast just like other aspects of our weather to support ...numerous applications such as air quality, transportation, and climate. Recent developments in the field have been bolstered by a new generation of advanced satellite sensors and algorithms on an international constellation of geostationary and polar orbiting satellites. The academic community frequently solicits operational developers for input on development needs and what should be operationalized. Yet, the volume of new data sources is currently outpacing Moore's Law and the forecasting community's ability to process and utilize new data sources data. Targeted to the academic community and using the 2020 western biomass-burning season as an example, this presentation will provide a brief review of how developers view next generation products for use in coupled observational and data assimilation systems that may be required to meet challenges posed by global extreme smoke event forecasting.
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