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Monitoring and modeling aerosol particle life cycle in Southeast Asia (SEA) is challenged by high cloud cover, complex meteorology, and the wide range of aerosol species, sources, and transformations found throughout the region. Satellite observations are limited, and there are few in situ observations of aerosol extinction profiles, aerosol properties, and environmental conditions. Therefore, accurate aerosol model outputs are crucial for the region. This work evaluates the Navy Aerosol Analysis and Prediction System Reanalysis (NAAPS-RA) aerosol optical thickness (AOT) and light extinction products using airborne aerosol and meteorological measurements from the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP.sup.2 Ex) conducted in 2019 during the SEA southwest monsoon biomass burning season. Modeled AOTs and extinction coefficients are compared to those retrieved with a high spectral resolution lidar (HSRL-2). Agreement between simulated and retrieved AOT (R.sup.2 = 0.78, relative bias =-5 %, normalized root mean square error (NRMSE) = 48 %) and aerosol extinction coefficients (R.sup.2 = 0.80, 0.81, and 0.42; relative bias = 3 %, -6 %, and -7 %; NRMSE = 47 %, 53 %, and 118 % for altitudes between 40-500, 500-1500, and 1500 m, respectively) is quite good considering the challenging environment and few opportunities for assimilations of AOT from satellites during the campaign. Modeled relative humidities (RHs) are negatively biased at all altitudes (absolute bias =-5 %, -8 %, and -3 % for altitudes 500 500-1500 and 1500 m, respectively), motivating interest in the role of RH errors in AOT and extinction simulations. Interestingly, NAAPS-RA AOT and extinction agreement with the HSRL-2 does not change significantly (i.e., NRMSE values do not all decrease) when RHs from dropsondes are substituted into the model, yet biases all move in a positive direction. Further exploration suggests changes in modeled extinction are more sensitive to the actual magnitude of both the extinction coefficients and the dropsonde RHs being substituted into the model as opposed to the absolute differences between simulated and measured RHs. Finally, four case studies examine how model errors in RH and the hygroscopic growth parameter, γ, affect simulations of extinction in the mixed layer (ML). We find NAAPS-RA overestimates the hygroscopicity of (i) smoke particles from biomass burning in the Maritime Continent (MC) and (ii) anthropogenic emissions transported from East Asia. This work mainly provides insight into the relationship between errors in modeled RH and simulations of AOT and extinction in a humid and tropical environment influenced by a myriad of meteorological conditions and particle types. These results can be interpreted and addressed by the modeling community as part of the effort to better understand, quantify, and forecast atmospheric conditions in SEA.

Light Detection and Ranging (LiDAR) systems become more prevalent in remote sensing for modeling buildings, engineering structures, or their deformations and displacements. Processing data from such systems, usually point clouds, can be performed using different methods, including M[sub.split] estimation. The method in question is relatively novel but it has several variants. From a practical point of view, the variants that are globally or locally robust against outliers seem very promising. The paper addresses robustness and the problem of different types of outliers that might disturb LiDAR point cloud processing by M[sub.split] estimation. The basic variants, the squared and the absolute M[sub.split] estimations, are often sensitive to global outliers and cannot always deal with local outliers. The comparative analyses show that the modifications of the basic M[sub.split] estimation variants complement each other. Hence, one can always find an M[sub.split] estimation variant that is appropriate for processing LiDAR data disturbed by different types or share of outliers. The paper points out such variants and their application range. It also gives clues on using the methods in question in practice.

Unpiloted aerial system (UAS) light detection and ranging (lidar) and structure-from-motion (SfM) photogrammetry have emerged as viable methods to map high-resolution snow depths (â¼1 m). These technologies enable a better understanding of snowpack spatial distribution and its evolution over time, advancing hydrological and ecological applications. This is particularly critical in mixed vegetation environments, where both forest canopy and open areas influence snow accumulation and melt patterns. In this study, a series of UAS lidar/SfM snow depth maps were collected during the 2020/2021 winter season in Durham, New Hampshire, USA, with three objectives: (1) quantifying UAS lidar/SfM snow depth retrieval performance using in situ magnaprobe measurements, (2) conducting a quantitative comparison of lidar and SfM retrievals of shallow snow depths (35 cm) throughout the winter, and (3) understanding the spatial distribution of snow depth and its relationship with terrain features. Eight UAS surveys were conducted over approximately 0.35 km.sup.2 including both open fields and a mixed forest. In the field, lidar had a slightly lower error than SfM, compared with in situ observations, with a mean absolute difference (MAD) of 3.5 cm for lidar and 4.0 cm for SfM. Snow depth maps from SfM and lidar were fairly consistent in the field, with only marginal differences on most dates. In the forest, SfM greatly overestimated in situ snow depths compared with lidar (lidar MAD = 6.3 cm, SfM MAD = 31.4 cm). There was no clear agreement between SfM and lidar snow depth values for individual 1 m.sup.2 pixels in the forest (MAD = 55.7 cm). Using the concept of temporal stability, we found that the spatial distribution of snow depth captured by lidar was generally consistent throughout the period, indicating a strong influence from static land characteristics. Considering both areas (forest and field), the spatial distribution of snow depth was primarily influenced by vegetation type while also reflecting the effects of soil variables (e.g., soil organic matter). When the field and forest areas were analyzed separately, the spatial distribution was distinctly affected by slope and the shadowing effects of the forest canopy.

Short-wavelength infrared photodetectors based on metamorphic InGaAs grown on GaSb substrates and InP substrates are demonstrated. The devices have a pBn structure that employs an AlGaAsSb thin layer as the electron barrier to suppress dark current density. The strain effect on the electrical performance of the devices was specifically studied through the growth of the pBn structure on different substrates, e.g., InP and GaSb, via a specific buffering technique to optimize material properties and minimize dark current. A lower device dark current density, down to 1 × 10[sup.−2] A/cm[sup.2] at room temperature (295 K), was achieved for the devices grown on the GaSb substrate compared to that of the devices on the InP substrate (8.6 × 10[sup.−2] A/cm[sup.2]). The improved properties of the high-In component InGaAs layer and the AlGaAsSb electron barrier give rise to the low dark current of the photodetector device.

Near-infrared (NIR) photodetectors are essential for LiDAR, optical communication, and sensing technologies requiring fast response and low power consumption. This work reports a PN photodiode incorporating a co-sputtered MoS[sub.2]–Al[sub.2]O[sub.3] composite layer to enhance NIR photoresponse for LiDAR and environmental sensing applications. The composite layer improves device performance through defect passivation, dielectric screening, and modified carrier transport behavior. Under 100 mW·cm[sup.−2] illumination at 4 V, the device delivers a photocurrent of 10 mA with a response time of 155 µs, corresponding to an approximately threefold ( 300%) improvement compared to a reference structure. Spectral measurements show peak responsivity at 970 nm with extended sensitivity up to 1100 nm. These results indicate that embedding Al[sub.2]O[sub.3] within the MoS[sub.2] improves the MoS[sub.2]/Si interface and facilitates infrared photon absorption in the Si substrate, leading to enhanced vertical carrier collection and reduced recombination compared with conventional surface-passivated MoS[sub.2]/dielectric layers-based devices. The proposed device demonstrates a low-cost, broadband photodiode architecture suitable for eye-safe LiDAR and environmental monitoring applications.

In this study we focus on the retrieval of aerosol and trace gas vertical profiles from multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations for the first time over Thessaloniki, Greece. We use two independent inversion algorithms for the profile retrievals: the Mexican MAX-DOAS Fit (MMF) and the Mainz Profile Algorithm (MAPA). The former is based on the optimal estimation method (OEM), while the latter follows a parameterization approach. We evaluate the performance of MMF and MAPA, and we validate their retrieved products with ancillary data measured by other co-located reference instruments. The trace gas differential slant column densities (dSCDs), simulated by the forward models, are in good agreement, except for HCHO, where larger scatter is observed due to the increased spectral noise of the measurements in the UV. We find an excellent agreement between the tropospheric column densities of NO.sub.2 retrieved by MMF and MAPA (slope=1.009, Pearson's correlation coefficient R=0.982) and a good correlation for the case of HCHO (R=0.927). For aerosols, we find better agreement for the aerosol optical depths (AODs) in the visible (i.e., at 477 nm) compared to the UV (at 360 nm), and we show that the agreement strongly depends on the O.sub.4 scaling factor that is used in the analysis. The agreement for NO.sub.2 and HCHO near-surface concentrations is similar to the comparison of the integrated columns with slightly decreased correlation coefficients. The seasonal mean vertical profiles that are retrieved by MMF and MAPA are intercompared, and the seasonal variation in all species along with possible sources is discussed. The AODs retrieved by the MAX-DOAS are validated by comparing them with AOD values measured by a CIMEL sun photometer and a Brewer spectrophotometer. Four different flagging schemes were applied to the data in order to evaluate their performance. Qualitatively, a generally good agreement is observed for both wavelengths, but we find a systematic bias from the CIMEL sun photometer and Brewer spectrophotometer measurements, due to the limited sensitivity of the MAX-DOAS in retrieving information at higher altitudes, especially in the UV. An in-depth validation of the aerosol vertical profiles retrieved by the MAX-DOAS is not possible since only in very few cases is the true aerosol profile known during the period of study. However, we examine four cases, where the MAX-DOAS provided a generally good estimation of the shape of the profiles retrieved by a co-located multi-wavelength lidar system. The NO.sub.2 near-surface concentrations are validated against in situ observations, and the comparison of both MMF and MAPA revealed good agreement with correlation coefficients of R=0.78 and R=0.73, respectively. Finally, the effect of the O.sub.4 scaling factor is investigated by intercomparing the integrated columns retrieved by the two algorithms and also by comparing the AODs derived by MAPA for different values of the scaling factor with AODs measured by the CIMEL sun photometer and the Brewer spectrophotometer.