Aerosol Optical Depth Forecasts Research Articles

Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”

AbstractWildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi‐model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU‐CMAQ, NACC‐CMAQ, and HYSPLIT), global models (GEFS‐Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP‐MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS‐SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.

Evaluation of Aerosol Optical Depth Forecasts from NOAA’s Global Aerosol Forecast Model (GEFS-Aerosols)

Abstract The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model. Significance Statement The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China

Based on the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme of the Weather Research and Forecasting model coupled with online Chemistry (WRF-Chem) and the three-dimensional variational (3DVAR) assimilation method, a 3DVAR data assimilation (DA) system for aerosol optical depth (AOD) and aerosol concentration observations was developed. A case study on assimilating the Himawari-8 satellite AOD and/or fine particulate matter (PM2.5) was conducted to investigate the improvement of DA on the analysis accuracy and forecast skills of the spatial distribution characteristics of aerosols, especially in the vertical dimension. The aerosol extinction coefficient (AEC) profile data from The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), surface PM2.5 and Himawari-8 AOD measurements were used for verification. One control experiment (without DA) and two DA experiments including a PM2.5 DA experiment denoted by Da_PM and a combined PM2.5 and AOD DA experiment denoted by Da_AOD_PM were conducted. Both DA experiments had positive effects on the surface PM2.5 mass concentration forecast skills for more than 60 h. However, the Da_PM showed a slight improvement in the analysis accuracy of the AOD distribution compared with the control experiment, while the Da_AOD_PM showed a considerable improvement. The Da_AOD_PM had the best positive effect on the AOD forecast skills. The correlation coefficient (CORR), root mean square error (RMSE), and mean fraction error (MFE) of the 24 h AOD forecasts for the Da_AOD_PM were 0.73, 0.38, and 0.54, which are 0.09 (14.06%), 0.08 (17.39%), and 0.22 (28.95%) better than that of the control experiment, and 0.05 (7.35%), 0.06 (13.64%), and 0.19 (26.03%) better than that of the Da_PM, respectively. Moreover, improved performance for the Da_AOD_PM occurred when the AEC profile was used for verification, as when the AOD was used for verification. The Da_AOD_PM successfully simulated the first increasing and then decreasing trend of the aerosol extinction coefficients below 1 km, while neither the control nor the Da_PM did. This indicates that assimilating AOD can effectively improve the analyses and forecast accuracy of the aerosol structure in both the horizontal and vertical dimensions, thereby compensating for the limitations associated with assimilating traditional surface aerosol observations alone.

Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem

We implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing of AOD is performed using historical analog forecasts and a Kalman Filter (KF). Analog forecasts are selected from WRF-Chem simulations based on a set of environmental predictors (AOD, wind speed, precipitable water, and particulate matter) that exhibit past values similar to the current forecasts. Space-borne AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites corresponding to the analogs are used to build the analog ensemble. This study focuses on a spatial domain covering the AERONET sites in contiguous United States. We use the analog ensemble weighted mean (AN) and Kalman filter analog (KFAN) algorithms, which are both trained using WRF-Chem AOD forecasts for the months of June to August during 2008–2011 and tested during the same months for 2012. Overall, the AOD forecast are more skillful when the forecast errors are corrected using a combination of analogs and Kalman filter in KFAN. This is especially true for the western US where the correlation of AOD with PM2.5, PM10, and surface horizontal wind speed are higher than those for other predictors. In fact, the overall biases in AOD are significantly reduced close to zero, with KFAN AOD being statistically indistinguishable to MODIS. However, both methods show mixed results (albeit still showing overall improvements) in eastern and central U.S., where AOD and its variability are highest. We find that, during the summer, PM is not the only predominant factor driving AOD in these regions, unlike western United States (U.S.) (except New Mexico and Arizona). We note, however, that the quality of the analogs depends on the model's capability to accurately simulate total precipitable water, which in turn influences aerosol sources and sinks.

Using GEOS-5 forecast products to represent aerosol optical depth in operational day-ahead solar irradiance forecasts for the southwest United States

This study aims to improve operational day-ahead direct normal irradiance (DNI) forecasts in clear-sky conditions using the Weather and Research Forecasting model. To create three different forecasting methods targeting the direct effect of aerosols on radiation, we use three different types of aerosol optical depth (AOD) data: (1) the Tegen aerosol climatology, (2) the persistence of measured AERONET AOD, and (3) the Goddard Earth Observing System model version 5 (GEOS-5) gridded forecasts of AOD. We evaluate each method at the Solana Generating Station, a concentrating solar power plant near Gila Bend, Arizona, and the University of Arizona, Tucson. We perform a retrospective DNI forecast analysis and find that including GEOS-5 forecast AOD improved the DNI forecast compared to using an aerosol climatology at both locations. At Tucson, where AOD is measured, we find that the persistence of measured AOD gives the best DNI forecast. However, the accuracy of that measured AOD reduces when translating it 225 km to Solana to forecast DNI 48 hours later. We then include the GEOS-5 AOD forecasts in one member of an operational forecast system and evaluate it against the other ensemble members that use the aerosol climatology. In clear-sky conditions, including GEOS-5 forecast AOD instead of the Tegen aerosol climatology, reduces the DNI forecast root mean square error by 27% at Solana. We found no significant differences during all-sky conditions because the relatively poor performance during cloudy conditions outweighs the improvements made in clear-sky conditions.

Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods.

This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three‐dimensional variational (3D‐Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D‐Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.

Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia

A new aerosol optical depth (AOD) data assimilation (DA) module was developed in Gridpoint Statistical Interpolation (GSI) 3-dimensional variational (3DVAR) system, named FastJ/CRTM-AOD DA module. And applied to the Modal Aerosol Dynamics Model for Europe with the Secondary Organic Aerosol Model (MADE/SORGAM) in the Weather Research and Forecasting/Chemistry model (WRF/Chem). The Fast-J optical module in WRF/Chem was used as the observation operator of AOD. The corresponding Jacobian code was modified from the one of CRTM-AOD in GSI. This way obviated the need for the Jacobian code's generation, which was complex and difficult for the highly nonlinear observation operator. During the studying period (January and April of 2014), compared to the ground AOD observations, AOD DA reduced about 20% fractional error (FE) with MADE/SORGAM. The original DA framework, which applied to the Goddard Chemistry Aerosol Radiation and Transport (GOCART) mechanism, performed slightly better than the new assimilation scheme for the low-value AOD situations (value < 0.4). However, compared to the original DA framework, the new DA scheme show a notable improvement for the high-value (0.4 < value ≤ 1.2) and extreme-high-value (value > 1.2) AOD situations. FE can be reduced by 48% and 64%, respectively. It indicates that the AOD DA impacts on AOD forecasts vary significant between different aerosol mechanisms. Moreover, FastJ/CRTM-AOD DA module can be easily and efficiently applied to the other aerosol schemes and the other optical modules, which is important to the development on AOD assimilation.

Interannual variation of the East Asia Jet Stream and its impact on the horizontal distribution of aerosol in boreal spring

Interannual variation of the aerosol optical depth (AOD) in East Asia has been investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Modern Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data for 2000–2018. The data analysis focuses on boreal spring when Siberian biomass burning is at its seasonal maximum. The results indicate that the significant increase in organic and black carbon is primarily caused by emissions from biomass burning in East Asia, which leads to significant interannual variations in aerosol loading and pan-Pacific transport. The anomalous large-scale climate variability associated with the East Asia Jet Stream (EAJS) provides favorable conditions for increasing the AOD of organic and black carbon in Northeast Asia and may represent an underlying physical mechanism. When the EAJS shows greater weakening than normal, abnormal high-pressure anomalies are maintained in East Asia, which tend to drive warm advection over Northeast Asia. This warm advection expedites the melting of the Eurasian snow cover, which helps increase surface dryness in late spring and provides favorable conditions for biomass burning. The EAJS index can be predictable with statistical significance up to lead 1 month by the dynamical ensemble seasonal forecasts, suggesting a possible implementation of the empirical AOD forecasts using climate forecast models.

Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 2: Evaluation)

High frequency knowledge of the spatio-temporal distribution of the downwelling surface shortwave flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface–atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (MSG Downwelling Surface Short-wave radiation Fluxes—Total and Diffuse fraction) product with an operational status since the year 2019. The method for retrieval was presented in a companion paper. Part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents a comparison of the corresponding outputs, i.e., total DSSF and diffuse fraction (fd) components, against in situ measurements acquired at four Baseline Surface Radiation Network (BSRN) stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20 Wm−2 for DSSF &lt; 200 Wm−2, and 10% for DSSF ≥ 200 Wm−2. The mean bias error (MBE) and relative mean bias error (rMBE) compared to the ground measurements are 3.618 Wm−2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd &lt; 0.5, and 20% for fd ≥ 0.5. The MBE and rMBE compared to the ground measurements are −0.044% and −17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud–aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the aerosol optical depth (AOD) forecasts currently available is accurate enough to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP).

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.

Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

Aerosol optical depth (AOD) is an important parameter characterizing the optical properties of atmospheric aerosols and can be used to indicate aerosol loading and evaluate air quality. In this study, a FY-3/medium-resolution spectral imager (MERSI) AOD data assimilation (DA) system was developed using a three-dimensional variational DA method to assess the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts. Two typical sand-dust weather events occurred during the spring season of years 2010 and 2011 were selected as case study. The DA system and Weather Research and Forecasting model coupled with a chemical model (WRF-Chem) were used to evaluate the impacts of FY-3/MERSI AOD DA on air quality forecasts. This was based on comparisons between modeled AOD data and AOD data acquired by the Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. Results from both case studies revealed that FY-3/MERSI AOD DA apparently improved the air quality forecasts. Key findings of the FY-3/MERSI AOD DA experiments included: (1) FY-3/MERSI AOD DA adjusted the simulated aerosol particle content of the WRF-Chem model and efficiently improved the extinction coefficient fields below 500 hPa. Moreover, AOD DA had the strongest effect on adjusting the extinction coefficients at 750 hPa (approximately 2 km). Compared with the AOD background field, the AOD analysis field was similar to the satellite observation field. (2) Compared with the control experiments without DA, the AOD DA experiment produced more accurate 24-h AOD forecasts, more consistent with the AERONET and satellite observations. (3) Due to the spatial distribution and intensity difference of satellite AOD data, satellite AOD data assimilation has obvious individual characteristics for the improvement of particle concentration prediction. Our study findings suggest that the developed DA system can facilitate the effective use of AOD data acquired by Chinese satellites in air quality forecasting models and can improve dust forecasting results.

Simultaneous three‐dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth

Total 550 nm aerosol optical depth (AOD) retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface fine particulate matter (PM2.5) observations were assimilated with the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three‐dimensional variational (3DVAR) data assimilation (DA) system. Parallel experiments assimilated AOD and surface PM2.5observations both individually and simultaneously. New 3DVAR aerosol analyses were produced every 6 h between 0000 UTC 01 June and 1800 UTC 14 July 2010 over a domain encompassing the continental United States. The analyses initialized Weather Research and Forecasting‐Chemistry (WRF‐Chem) model forecasts. Assimilating AOD, either alone or in conjunction with PM2.5 observations, produced better AOD forecasts than a control experiment that did not perform DA. Additionally, individual assimilation of both AOD and PM2.5 improved surface PM2.5 forecasts compared to when no DA occurred. However, the best PM2.5 forecasts were produced when both AOD and PM2.5 were assimilated. Considering the goodness of both AOD and PM2.5 forecasts, the results unequivocally show that concurrent DA of PM2.5 and AOD observations produced the best overall forecasts, illustrating how simultaneous DA of different aerosol observations can work synergistically to improve aerosol forecasts.

A case study to prepare for the utilization of aerosol forecasts in solar energy industries

Precise aerosol information is indispensable in providing accurate clear sky irradiance forecasts, which is a very important aspect in solar facility management as well as in solar and conventional power load prediction. In order to demonstrate the need of detailed aerosol information, direct irradiance derived from Aerosol Robotic Network (AERONET) ground based measurements of aerosol optical depth (AOD) was compared in a case study over Europe to irradiance calculated using a standard aerosol scenario. The analysis shows an underestimation of measurement-derived direct irradiance by the scenario-derived direct irradiance for locations in Northern Europe and an overestimation for the Mediterranean region. Forecasted AOD of the European Dispersion and Deposition Model (EURAD) system was validated against ground based AERONET clear sky AOD measurements for the same test period of February 15th to 22nd, 2004. For the time period analyzed, the modelled AOD forecasts of the EURAD system slightly underestimate ground based AERONET measurements. To quantify the effects of varying AOD forecast quality in their impact on the application in solar energy industry, measured and forecasted AOD were used to calculate and compare direct, diffuse, and global irradiance. All other influencing variables (mainly clouds and water vapour) are assumed to be modelled and measured correctly for this analysis which is dedicated to the specific error introduced by aerosol forecasting. The underestimated AOD results in a mean overestimation of direct irradiance of +28 W/m 2 (+12%), whereas diffuse irradiance is generally underestimated (−19 W/m 2 or −14%). Mean global irradiance values where direct and diffuse irradiance errors compensate each other are very well represented (on average +9 W/m 2 or +2%).