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  • Establishing a Series of Dust Event Case Studies for East Asia

    Abstract: Dust aerosols have a wide range of effects on air quality, health, land-management decisions, aircraft operations, and sensor data interpretations. Therefore, the accurate simulation of dust plume initiation and transport is a priority for operational weather centers. Recent advancements have improved the performance of dust prediction models, but substantial capability gaps remain when forecasting the specific location and timing of individual dust events, especially extreme dust outbreaks. Operational weather forecasters and US Army Engineer Research and Development Center (ERDC) researchers established a series of reference case study events to enhance dust transport model evaluation. These reference case studies support research to improve modeled dust simulations, including efforts to increase simulation accuracy on when and where dust is lofted off the ground, dust aerosols transport, and dust-induced adverse air quality issues create hazardous conditions downstream. Here, we provide detailed assessments of four dust events for Central and East Asia. We describe the dust-event lifecycle from onset to end (or when dust transports beyond the area of interest) and the synoptic and mesoscale environ-mental conditions governing the process. Analyses of hourly reanalysis data, spaceborne lidar and aerosol optical depth retrievals, upper-air soundings, true-color satellite imagery, and dust-enhanced false-color imagery supplement the discussions.
  • Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

    Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
  • Simulating Environmental Conditions for Southwest United States Convective Dust Storms Using the Weather Research and Forecasting Model v4.1

    Abstract: Dust aerosols can pose a significant detriment to public health, transportation, and tactical operations through reductions in air quality and visibility. Thus, accurate model forecasts of dust emission and transport are essential to decision makers. While a large number of studies have advanced the understanding and predictability of dust storms, the majority of existing literature considers dust production and forcing conditions of the underlying meteorology independently of each other. Our study works towards filling this research gap by inventorying dust-event case studies forced by convective activity in the Desert Southwest United States, simulating select representative case studies using several configurations of the Weather Research and Forecasting (WRF) model, testing the sensitivity of forecasts to essential model parameters, and assessing overall forecast skill using variables essential to dust production and transport. We found our control configuration captured the initiation, evolution, and storm structure of a variety of convective features admirably well. Peak wind speeds were well represented, but we found that simulated events arrived up to 2 hours earlier or later than observed. Our results show that convective storms are highly sensitive to initialization time and initial conditions that can preemptively dry the atmosphere and suppress the growth of convective storms.
  • The AFWA Dust Emission Scheme for the GOCART Aerosol Model in WRF-Chem v3.8.1

    Abstract: Airborne particles of mineral dust play a key role in Earth’s climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.