The vertical distribution of dust layers can influence dust transport, radiative forcing, deposition and ultimately, surface particulate matter mass concentration. Although many dust layer height (ALH) products from passive satellite measurements have been developed, most of them are applicable on dark surfaces only. Here, building on the absorbing aerosol optical centroid height (AOCH) retrieval from hyperspectral O2A and B absorption band measurements of TROPOspheric Monitoring Instrument (TROPOMI) for dark target, we further develop dust AOCH retrieval over bright surfaces. Key updates include: (a) the thresholds in cloud mask tests are refined with consideration of the different spectral characteristics of bright surface reflectance; (b) the assumption of Lambertian surface is modified to the Ross-Li Bidirectional Reflectance Distribution Function (BRDF) model to consider the angular dependence of surface reflectance. The validation against the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) for several dust plumes over Saharan Desert illustrates that TROPOMI AOCH has ~1 km uncertainty and ~0.1 km mean bias, better than ~1 km underestimated dust layer mean altitude (ALT) from the Infrared Atmospheric Sounder Interferometer (IASI). With this implement of bright surfaces, our algorithm is ready for global retrieval and will be applicable for similar hyperspectral instrument in the future.

In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts. This approach aims to reduce redundant memory IO costs, a significant factor in latency for high batch sizes and long context lengths. Bifurcated attention achieves this by dividing the attention mechanism during incremental decoding into two distinct GEMM operations, focusing on the KV cache from prefill and the decoding process. This method ensures precise computation and maintains the usual computational load (FLOPs) of standard attention mechanisms, but with reduced memory IO. Bifurcated attention is also compatible with multi-query attention mechanism known for reduced memory IO for KV cache, further enabling higher batch size and context length. The resulting efficiency leads to lower latency, improving suitability for real-time applications, e.g., enabling massively-parallel answer generation without substantially increasing latency, enhancing performance when integrated with post-processing techniques such as reranking.

This study addresses two key uncertainties in the fire radiative power (FRP) retrieval, which is essential for improving global top-down fire emission inventories. First, it proposes a novel FRP retrieval method by combining the ~4 and$\sim 8.6~\mu $m channels based on Monte Carlo simulation, which is verified using the Visible Infrared Imaging Radiometer Suite (VIIRS). The inclusion of the$\sim 8.6~\mu $m channel significantly improves the accuracy of FRP retrieval, especially for highly smoldering fires. Second, atmospheric correction is conducted using outputs from the state-of-the-art unified linearized vector radiative transfer model (UNL-VRTM). The importance of atmospheric correction is demonstrated through the single-channel ($\sim 4~\mu $m) FRP retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire (AF), VIIRS AF, and VIIRS second-generation fire light detection algorithm (FILDA-2) products. Post-correction results show effective mitigation of nighttime FRP angular dependency, achieved by considering the enhanced atmospheric attenuation due to longer path length off-nadir. However, a residual daytime FRP angular dependency remains, likely due to the angular dependency of the thresholds used for daytime fire detection. Additionally, an enhanced agreement is observed between the VIIRS FILDA-2 FRP retrievals from the Suomi National Polar-orbiting Partnership (NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 satellites after correction. Lastly, a global FRP increase is noted across all three products, with VIIRS AF and VIIRS FILDA-2 showing more significant increases (65.8% and 62.5%, respectively) than MODIS AF (20.8%). These advancements in FRP retrievals may enhance the downstream fire emission products, which will benefit the air pollution modeling community.

The Multi-angle Imaging SpectroRadiometer (MISR) measurement with a large range of scattering angles provides valuable information about aerosol microphysical properties. The current MISR algorithm utilizes pre-defined aerosol mixtures in lookup tables (LUT) to infer aerosol types and microphysical parameters, which performs well globally but remains subject to considerable uncertainties in regional scales. To make efficient use of MISR measurement, we developed a physics-informed Deep Learning (PDL) method to retrieve aerosol optical/microphysical parameters over land in eastern China. By combining the physical constraint of radiative transfer simulation and modeling ability of DL methods, each aerosol parameter can be modeled with the whole used MISR measurements separately with high computational efficiency. PDL Aerosol Optical Depth (AOD) and fine AOD(FAOD) have high correlation coefficients (R>0.95) with Aerosol Robotic Network (AERONET) observations, with 89% and 81% values falling into expected error (EE) envelope of ± (0.05+20%AODAERONET) respectively. Despite only a slightly higher accuracy than recent MISR Version 23 products, PDL retrievals have solved the underestimation problem of AOD and FAOD at moderate-high values (>0.4). Besides better constraint of abnormal values in coarse AOD(CAOD), PDL algorithm significantly improves retrieval accuracy of MISR Single Scattering Albedo (SSA). With reliable and robust performance, PDL algorithm provides a flexible and efficient aerosol retrieval framework for emerging multi-angle polarimetric measurements.

The AErosol RObotic NETwork (AERONET) inversion data over East Asia (72°E – 135°E, 30°N – 50°N) in spring from 2018 to 2020 were collected to characterize the microphysical and single scattering properties of the dust aerosols in Asia, and a climatological aerosol model was then built up for the Asian dust. A bimodal log-normal size distribution is assumed for the climatological Asian dust model, and the non-spherical effect was considered for the coarse mode only. The microphysical and single scattering properties of dust aerosols are parameterized by the 675-nm AOD and wavelength in the Asian dust aerosol model. The parameterization is based on the fitting or interpolation of the AERONET data. A lookup table was generated based on the Asian dust model via the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) for retrieving the dust plume height from Earth Polychromatic Imaging Camera (EPIC) measurements.

This article describes the most recent updates on a smart-and-connected low-cost sensor system called I-Canopy Sensor that measures near-surface air(temperature, humidity, and pressure) and soil properties(temperature and moisture) in order to provide necessary data for irrigation systems. Designed and manufactured in Iowa, I-Canopy Sensor is a real-time solar-powered IoT device that can be used in urban or rural areas under a variety of conditions. I-Canopy supports both Wi-Fi and LoRa-WAN for short-distance and long-distance transmission. The updates improve the sensor performance by adding voltage control functions, Internet connection logic optimization, rechargeable NiMH battery research, and temporary data storage function. These improvements enable the sensor with an ability to run under extreme circumstances including but not limited to below-zero freezing weather, interruption of Wi-Fi and LoRa Wan signal, and poor battery energy capacity. I-Canopy sensor has been tested in farms in Nebraska and Iowa and urban areas in Iowa, Illinois, and New York. With the latest update, the frequency of unexpected shutdowns is greatly diminished. In the firmware part, this article will expand on the cause of different kinds of shutdowns and practical solutions implemented to mitigate the problems.

The multi-angle polarimetric (MAP) instruments have been a focus of recent satellite missions dedicated to enhanced detection of global aerosol microphysical properties. Considering that satellite observations can hardly infer all the unknowns of atmosphere and surface, it’s crucial to know how many and which aerosol parameters can be accurately retrieved from these different MAP measurements as well as their uncertainties. In this study, we present a comprehensive insight into the information content of POLDER-3 and 3MI observations for aerosol retrievals and estimate posterior errors of corresponding parameters based on Bayesian theory. The total degree of freedom for signal (DFS) of aerosol retrievals is around 6-8 from POLDER-3, and is raised by ~1.8-3.5 with 3MI. The retrieval accuracy of volume concentration and effective radius are high (<4%) in the fine-dominant case for both POLDER-3 and 3MI, but get much lower (~8% and ~15%) in coarse-dominant conditions. Furthermore, the advanced 3MI measurements can upgrade the retrieval uncertainties of POLDER-3 by ~50%. Though additional shortwave infrared bands of 3MI provide more information regarding coarse particles, the influence of aerosols on surface BRDF leads to a decrease of the total DFS. With a prior assumption that variations of refractive index depending on wavelength, satellite retrieval accuracy of the real (<0.03) and imaginary part (<0.003) reaches close levels with that of ground-based Sun photometers. Our results can provide a fundamental reference for MAP satellite retrieval of aerosol microphysical properties.

We present the second-generation Fire Light Detection Algorithm (FILDA-2), which includes advances in fire detection and retrievals of radiative power (FRP), fire visible energy fraction (VEF), and fire modified combustion efficiency (MCE) at nighttime from the holistic use of multiple-spectral radiances measured by the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP (VNP) and NOAA-20/JPSS-1 (VJ1) satellites. Key enhancements include: (1) a new fast algorithm that maps VIIRS Day/Night Band (DNB) radiances to the pixel footprints of VIIRS Moderate (M) and Imagery (I) bands; (2) identification of potential fire pixels through the use of the DNB anomalies and I-Band thermal anomalies; (3) dynamic thresholds for contextual testing of fire pixels; and (4) pixel-specific estimates of FRP, VEF, and MCE. The global benchmark test demonstrates that FILDA-2 can detect approximately 25-30% more smaller and cooler fires than the operational VIIRS Active Fire 375 m I-band algorithm with the added benefit of providing daily global pixel-level characterizations of MCE for nighttime surface fires. The MCE derived by FILDA-2 is in good agreement with limited ground-based observations near the fires. Additionally, FILDA-2 reduces angular dependence in FRP estimates and significantly reduces the ”bow-tie” (double-counting) effect in fire detection compared to the AF-I product. The cross-validation of FILDA-2 products from VNP and VJ1 retrievals confirms good consistency in FRP and MCE retrievals globally. FILDA-2 is being implemented by NASA to generate a new VIIRS data product for fire monitoring, chemical-speciated fire emission estimates, and fire line characterization.

This article describes the design and development of a smart-and-connected low-cost Iowan-designed canopy (I-Canopy) sensor system that is enabled by the Internet of Things (IoT) devices capabilities, empowered by solar-based rechargeable batteries, and developed for community science applications. The I-Canopy sensor, is designed for real-time monitoring of near-surface air properties (temperature, relative humidity, pressure) and soil properties (temperature and moisture) for a wide range of weather and canopy conditions. The sensor is well suited for rural areas where the real-time data of air and soil is lacking in part due to the lack of broadband internet connection, and in part due to the limited (if any) ground-based weather stations in the current federal and state observation network. The canopy sensor has been tested in rural communities in western Nebraska to provide information for farmer's decision-making of irrigation and agricultural water use in the crop growing season. The sensor is capable to transmit data through both WiFi and LoRaWAN in real-time to a cloud data server and the local data server. Presented here are the first results of the sensor design and sensor data evaluation in various out-door environments, which illustrates the high-level readiness of the sensor for large-scale deployment for either routine or scientific applications for rural areas.

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.

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke and dust over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Our algorithm uses EPIC atmospheric window bands to determine AOD and then takes advantage of oxygen A and B bands to derive ALH. We applied this algorithm on several dust and smoke events. Validation shows our results are of high accuracy.

In the present study, the surface meteorological parameters over Singapore are simulated using WRF-ARW mesoscale model by varying the planetary boundary layer (PBL) parameterization schemes, horizontal resolutions and two land cover data sets (USGS and MODIS). Simulations are conducted with four nested domains having horizontal resolution of 27, 9, 3 and 1 km; 51 vertical levels by using the 1° × 1° NCEP final analysis meteorological fields for initial and boundary conditions. Eight days (20-28 January 2015) are selected for simulating various surface meteorological parameters. The model-simulated parameters of surface temperature, relative humidity, wind speed and wind direction are validated with the available observations over Singapore. It has been found that, improvements in predicting surface meteorological parameters with the increase in model resolution up to 3 km. The experiment with the 3 km grid resolution showed better simulated surface meteorological variables than that of 1 km resolution grid. Further, MODIS land cover data considerably improved the prediction of surface meteorological variables compare to the USGS. The surface meteorological variables simulated using the ACM2 PBL scheme with MODIS data are in better agreement with the observations showing least error statistics than the other PBL schemes used in the study. The better performance by ACM2 could be due to the non-local turbulence closure during unstable conditions and local-closure during stable conditions formulated in this scheme.

Feature extraction and tracking is a fundamental operation used in many geoscience applications. In this paper, we present a scalable method for computing and tracking features on distributed memory machines for large-scale geospatial data. We carefully apply new communication schemes to minimize the data exchanged among the computing nodes in building and updating the global connectivity information of features. We present a theoretical complexity analysis, and show that our method can significantly reduce the communication cost compared to the traditional method.

Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day-night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection” approach from FILDA that lowers the 4-μm brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4-μm BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

Unlike previous spaceborne Earth observing sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) employs onboard sample aggregation to reduce downlink bandwidth requirements and preserve spatial resolution across the scan. To examine the potentially deleterious impacts of onboard sample aggregation when encountering detector saturation, nearly four months of the National Oceanic and Atmospheric Administration's Nightfire product are analyzed, which contains a subset of the hottest observed nighttime pixels. An empirical method for identifying saturation is devised. The M12 band (3.69 μm) is the most frequently saturating band with 0.15% of the Nightfire pixels at or near the ~359-K detector saturation limit; some saturation is also found in M14, M15, and M16 (8.58, 10.74, and 11.86 μm). Artifacts consistent with detector saturation are seen with M12 temperatures as low as 330 K in the scene center. This partial saturation and aggregation influence must be considered when using VIIRS radiances for quantitative characterization of hot emission sources such as fires and gas flaring.