The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions

Wang, Jun; Bhattacharjee, Partha; Tallapragada, Vijay; Lu, Cheng-Hsuan; Kondragunta, Shobha; Silva, Arlindo da; Zhang, Xiaoyang; Chen, Sheng-Po; Wei, Shih-Wei; Darmenov, Anton; McQueen, Jeff; Lee, Pius; Koner, Prabhat K.; Harris, Anthony M.

doi:10.5194/gmd-11-2315-2018

Cited by 26 publications

(37 citation statements)

References 67 publications

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“…For channels 13-15 and channels 5-7, both the biases and also the standard deviations increased with precipitation intensities. However, for channels 11 and 4, standard deviations of O−B in the moderate (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and intense precipitation (>35 dBZ) categories were even smaller than those under light precipitation (5-20 dBZ) and precipitation-free (0-5 dBZ) conditions. It is abnormal that standard deviations decreased with precipitation intensities for channels 11 and 4, which is contradictory with the results from Kulie et al [40].…”

Section: Discussionmentioning

confidence: 87%

“…The community radiative transfer model (CRTM) [11,27] developed by the US Joint Center for Satellite Data Assimilation (JCSDA) was used in this work to simulate brightness temperatures of MWHS-2. This model has been widely used for microwave and infrared satellite data assimilation and remote sensing applications, as well as aerosol assimilation [28,29]. It includes modules that compute the satellite-measured radiation from gaseous absorption, and scattering of radiation by aerosols and clouds, as well as emissions and reflection of radiation by the Earth's surface.…”

Section: The Community Radiative Transfer Model (Crtm)mentioning

confidence: 99%

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Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

et al. 2019

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Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

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Section: Discussionmentioning

confidence: 87%

Section: The Community Radiative Transfer Model (Crtm)mentioning

confidence: 99%

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

et al. 2019

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“…NGACv2 underestimates total AOT which is caused by low smoke emission (both OC and BC) data used by the model for this fire event. Wei et al (2017) studied both forecast and analysis of MERRA-2 aerosol fields and compared this with NGACv2. That study also compared aerosol analysis increments (defined as difference between analysis and model first guess) of all four cycles of MERRA-2 and found large AOT analysis increment (0.6-0.8) in the 06:00 Z DA cycle which contributed to higher AOT in MERRA-2.…”

Section: Discussionmentioning

confidence: 99%

The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness

Bhattacharjee

Wang

et al. 2018

Geosci. Model Dev.

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Abstract. An accurate representation of aerosols in global numerical weather prediction (NWP) models is important to predict major air pollution events and to also understand aerosol effects on short-term weather forecasts. Recently the global aerosol forecast model at NOAA, the NOAA Environmental Modeling System (NEMS) GFS Aerosol Component (NGAC), was upgraded from its dust-only version 1 to include five species of aerosols (black carbon, organic carbon, sulfate, sea salt and dust). This latest upgrade, now called NGACv2, is an in-line aerosol forecast system providing three-dimensional aerosol mixing ratios along with aerosol optical properties, including aerosol optical thickness (AOT), every 3 h up to 5 days at global 1∘×1∘ resolution. In this paper, we evaluated nearly 1.5 years of model AOT at 550 nm with available satellite retrievals, multi-model ensembles and surface observations over different aerosol regimes. Evaluation results show that NGACv2 has high correlations and low root mean square errors associated with African dust and also accurately represented the seasonal shift in aerosol plumes from Africa. Also, the model represented southern African and Canadian forest fires, dust from Asia, and AOT within the US with some degree of success. We have identified model underestimation for some of the aerosol regimes (particularly over Asia) and will investigate this further to improve the model forecast. The addition of a data assimilation capability to NGAC in the near future is expected to provide a positive impact in aerosol forecast by the model.

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“…Here we focused on the wildfire intrusion, since it was more difficult to capture the sudden outbreak of wildfire signal than the long-range transport dust intrusion. In addition, the operational NGAC dust forecast has been available to NAQFC (Wang et al, 2018).…”

Section: Aot Derived Lateral Boundary Conditionsmentioning

confidence: 99%

“…As a regional chemical forecast system, the existing National Air Quality Forecast Capability (NAQFC) operated at NOAA needs proper CLBCs for its daily prediction. The current NAQFC uses the dust aerosol LBC from the NOAA Environmental Modeling System (NEMS) Global Forecast System (GFS) Aerosol Component (NGAC) (Lu et al, 2016;Wang et al, 2018), which is the GFS model coupled with Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol mechanism (Chin et al, 2000(Chin et al, , 2002Colarco et al, 2010). Before the implementation of NGAC LBC, NAQFC used the background static profile LBC for aerosols described in Lee et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Chemical Lateral Boundary Conditions for Air Quality Predictions over the Contiguous United States during Intrusion Events

Tang¹,

Bian²,

Tao³

et al. 2020

Preprint

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Abstract. The existing National Air Quality Forecast Capability (NAQFC) operated at NOAA provides operational forecast guidance for ozone and particle matter with aerodynamic diameter less than 2.5 μm (PM2.5) over the contiguous 48 U.S. states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. Currently NAQFC is using chemical lateral boundary conditions (CLBCs) from a monthly climatology, which cannot capture pollutant intrusion events originated outside of the model domain. In this study, we developed a model framework to introduce the time-varying chemical simulation from the Goddard Earth Observing System Model, version 5 (GEOS) as the CLBCs to drive NAQFC. The method of mapping GEOS chemical species to CMAQ CB05-Aero6 species was also developed. We then evaluated NAQFC's performance using the new CLBCs from GEOS. The utilization of the GEOS dynamic CLBCs showed an overall best score when comparing the NAQFC simulation with the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015: the PM2.5 correlation coefficient R was improved from 0.18 to 0.37 and the mean bias was narrowed from −6.74 μg/m3 to −2.96 μg/m3 over CONUS. The CLBCs' influences depended on not only the distance from the inflow boundary, but also species and their regional characteristics. For the PM2.5 prediction, the CLBC's effect on the correlations was mainly near the inflow boundary, and its impact on the background could reach farther inside the domain. The CLBCs also altered background ozone through the inflows of ozone itself and its precursors. It was further found that aerosol optical thickness (AOT) from VIIRS retrieval correlated well to the column CO and elemental carbon from GEOS, based on which the new CLBCs for wildfire intrusion event was derived. The AOT derived CLBCs successfully captured the wildfire intrusion events in our case study for summer 2018. It can be a useful alternative in case the CLBCs of GEOS are not available.

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The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions

Cited by 26 publications

References 67 publications

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness

Comparison of Chemical Lateral Boundary Conditions for Air Quality Predictions over the Contiguous United States during Intrusion Events

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