Application of DIAL/HSRL and CATCH algorithm-based methodologies for surface PM2.5 concentrations during the KORUS-AQ campaign

Aerosols and droplets are the main factors of visibility reduction by scattering and absorbing light. For visibility predictions in operational NWP models, hydrometeors are often considered to be the dominant factor in the total extinction, whereas aerosol effects are usually simplified or omitted in models developed for relatively clean regions. In China, also many NWP studies related to visibility forecasting during haze-fog processes have been conducted, primarily focusing on severely polluted periods before 2018. These studies often employed visibility parameterizations that considered either aerosol extinction alone or hydrometeor extinction alone. Therefore, the significance of incorporating both aerosol and hydrometeor extinction into visibility forecasting during haze-fog processes remains uncertain, particularly under recent rapid changes in aerosol concentration, composition, and hygroscopicity in China.In this study, we first use the 3-D meteorology fields from the Shanghai Meteorological Service WRF-ADAS Real-Time Modeling System (WARMS) to drive the Community Multiscale Air Quality (CMAQ) model. In this version, CMAQ is used in an off-line mode and visibility is diagnosed by combining extinctions due to hydrometeors and aerosols. Satellited derived NOx emissions using the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm have been incorporated to give more up-to-date emissions. We analyze the results of a one-month forecasting period during the winter of 2021-2022 to assess the model's performance and understand the impact of hydrometeor and aerosol extinction on operational visibility forecasting. We find that for the city of Shanghai, aerosol extinction has a minor impact on the model’s performance when forecasting visibility below 1 km but becomes crucial for predictions spanning 1-10 km. Comparison against observations shows that the model well captures the general contributions from various chemical constituents with nitrate as the most important factor in aerosol extinction (~60%). Furthermore, our assessment of the North China Plain (NCP) highlights that in highly polluted areas aerosols could be significant for visibility below 1 km. Finally, we conduct case studies with the fully coupled WRF-Chem model and compare results with the offline WARMS-CMAQ system. Aerosol effects on fog and visibility forecasting due to feedbacks between aerosols, radiation, and cloud physics will be discussed.

The NASA Langley Research Center High Altitude Lidar Observatory (HALO) is a multi-function and modular lidar developed to address the observational needs of NASA’s weather, climate, carbon cycle, and atmospheric composition focus areas. HALO measures atmospheric H2O mixing ratios, CH4 mole fractions, and aerosol/cloud optical properties using the Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL) techniques, respectively. In 2019 HALO participated in the NASA Atmospheric Carbon and Transport – America campaign on board the NASA C-130 to compliment a suite of greenhouse gas in-situ sensors and provide, for the first time, simultaneous measurements of column CH4 and aerosol/cloud profiles. HALO operated in 18 of 19 science flights where the DIAL and Integrated Path Differential Absorption lidar (IPDA) techniques at 1645 nm were used for column and multi-layer measurements of CH4 mole fractions, the HSRL and backscatter techniques at 532 and 1064 nm, respectively, for retrievals of aerosol backscatter, extinction, depolarization, and mixing layer heights. In this paper we present HALO’s measurement theory for the retrievals of column and multi-layer XCH4, retrieval accuracy and precision including methods for bias correction, and a comprehensive total column XCH4 validation comparison to in-situ observations. Comparisons of HALO XCH4 to in-situ derived XCH4, collected during spiral ascents and descents, indicates mean difference of 2.54 ppb and standard deviation of the differences of 16.66 ppb when employing 15 s along track averaging (< 3 km). A high correlation coefficient of R = 0.9058 was observed for the 11 in-situ spiral comparisons. Column XCH4 measured by HALO over regional scales covered by the ACT-America campaign are compared against in-situ CH4 measurements carried out within the planetary boundary layer (PBL) from both the C-130 and B200 aircraft. Favorable correlation between the in-situ point measurements within the PBL and the remote column measurements from HALO elucidates the sensitivity of a column integrating lidar to CH4 variability within the PBL, where surface fluxes dominate the signal. Novel capabilities for CH4 profiling in regions of clear air using the DIAL technique are presented and validated for the first time. Additionally, profiling of CH4 is used to apportion the PBL absorption from the total column and is compared to previously reported IPDA cloud slicing techniques that estimate PBL columns using strong echoes from fair weather cumulus. The analysis presented here points towards HALO’s ability to retrieve accurate and precise CH4 columns with the prospects for future multi-layer profiling in support of future suborbital campaigns.

Improving estimates of PM2.5 concentration and chemical composition by application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from chemistry (CATCH) algorithm

Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol-cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically-resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES) project, which took place over the Southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018. Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN – HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN – backscatter and CCN – extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from WRF-CAM5. These results demonstrate the utility of deriving vertically-resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.

Tropical cyclones (TCs) are destructive meteorological phenomena, necessitating accurate predictions of TC track and intensity to reduce risks to human life. This study evaluates three Transformer-based models – vanilla Transformer (Transformer), inverted Transformer (iTransformer), and temporal-variate Transformer (TVFormer) – which are trained, validated, and tested on best track data from 1980 to 2021 from the China Meteorological Administration for TC prediction, integrating temporal, variate, and hybrid token embeddings to analyze temporal and variate correlations. Comparative analysis with four recurrent neural network (RNN) models demonstrates the superiority of the refined Transformer models over RNNs: iTransformer reduces mean absolute error (MAE) and root mean square error (RMSE) by 29.55% and 25.80% (latitude), 50.31% and 46.18% (longitude), 8.71% and 9.98% (pressure), and 8.68% and 9.45% (wind speed), while TVFormer achieves MAE and RMSE reductions of 13.98% and 13.84% (latitude), 39.11% and 38.02% (longitude), 13.69% and 14.02% (pressure), and 12.84% and 12.94% (wind speed) on average. Among Transformer variants, iTransformer excels in track prediction, outperforming Transformer with 21.74% lower MAE and 18.26% lower RMSE for latitude, and 32.73% lower MAE and 24.01% lower RMSE for longitude. TVFormer dominates intensity prediction, reducing pressure errors by 4.42% (MAE) and 3.92% (RMSE) and wind speed errors by 19.21% (MAE) and 14.79% (RMSE) compared to Transformer, while outperforming iTransformer with 4.59% lower MAE and 3.68% lower RMSE for pressure and 3.83% lower MAE and 3.18% lower RMSE for wind speed. Notably, TVFormer also enhances track prediction, with 7.10% reduction in MAE and 7.02% reduction in RMSE for latitude, and 22.84% reduction in MAE and 17.09% reduction in RMSE for longitude compared to Transformer. These results highlight the superiority of iTransformer in track prediction and the efficacy of TVFormer in intensity prediction, thanks to their ability to exploit temporal and variate dependencies, offering potential for TC disaster preparedness systems.

Taking advantage of the broad spectrum of the Cabannes-Brillouin scatter from atmospheric molecules, the high spectral resolution lidar (HSRL) technique employs a narrow spectral filter to separate the aerosol and molecular scattering components in the lidar return signals and therefore can obtain the aerosol optical properties as well as the lidar ratio (i.e., the extinction-to-backscatter ratio) which is normally selected or modeled in traditional backscatter lidars. A polarized HSRL instrument, which employs an interferometric spectral filter, is under development at the Zhejiang University (ZJU), China. In this paper, the theoretical basis to retrieve the aerosol lidar ratio, depolarization ratio and extinction and backscatter coefficients, is presented. Error analyses and sensitivity studies have been carried out on the spectral transmittance characteristics of the spectral filter. The result shows that a filter that has as small aerosol transmittance (i.e., large aerosol rejection rate) and large molecular transmittance as possible is desirable. To achieve accurate retrieval, the transmittance of the spectral filter for molecular and aerosol scattering signals should be well characterized.

The objective of this project was to provide vertically and horizontally resolved data on aerosol optical properties to assess and ultimately improve how models represent these aerosol properties and their impacts on atmospheric radiation. The approach was to deploy the NASA Langley Airborne High Spectral Resolution Lidar (HSRL) and other synergistic remote sensors on DOE Atmospheric Science Research (ASR) sponsored airborne field campaigns and synergistic field campaigns sponsored by other agencies to remotely measure aerosol backscattering, extinction, and optical thickness profiles. Synergistic sensors included a nadir-viewing digital camera for context imagery, and, later in the project, the NASA Goddard Institute for Space Studies (GISS) Research Scanning Polarimeter (RSP). The information from the remote sensing instruments was used to map the horizontal and vertical distribution of aerosol properties and type. The retrieved lidar parameters include profiles of aerosol extinction, backscatter, depolarization, and optical depth. Products produced in subsequent analyses included aerosol mixed layer height, aerosol type, and the partition of aerosol optical depth by type. The lidar products provided vertical context for in situ and remote sensing measurements from other airborne and ground-based platforms employed in the field campaigns and was used to assess the predictions of transport models. Also, the measurements provide a data base for future evaluation of techniques to combine active (lidar) and passive (polarimeter) measurements in advanced retrieval schemes to remotely characterize aerosol microphysical properties. The project was initiated as a 3-year project starting 1 January 2005. It was later awarded continuation funding for another 3 years (i.e., through 31 December 2010) followed by a 1-year no-cost extension (through 31 December 2011). This project supported logistical and flight costs of the NASA sensors on a dedicated aircraft, the subsequent analysis and archival of the data, and the presentation of results in conferences, workshops, and publications. DOE ASR field campaigns supported under this project included - MAX-Mex /MILAGRO (2006) - TexAQS 2006/GoMACCS (2006) - CHAPS (2007) - RACORO (2009) - CARE/CalNex (2010) In addition, data acquired on HSRL airborne field campaigns sponsored by other agencies were used extensively to fulfill the science objectives of this project and the data acquired have been made available to other DOE ASR investigators upon request.

A simple high spectral resolution lidar technique using a multi-longitudinal mode laser is proposed for measuring aerosol extinction and backscattering coefficients. A scanning interferometer having the same free spectral range as the mode spacing of the laser is used to separate Rayleigh from Mie scattering. Scanning the interferometer in the span of one fringe, the lidar signals at the minimum and maximum Mie-scattering transmission are measured. The Rayleigh scattering signal is analyzed from these signals, and the aerosol extinction coefficient is derived. The interferometer transmittance for Mie scattering is calibrated with the reference signals taken with a portion of the transmitted laser beam.

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.

Assumptions made in the analysis of both Raman lidar measurements of aerosol extinction and differential absorption lidar (DIAL) measurements of an absorbing species are tested. Statistical analysis techniques are used to enhance the estimation of aerosol extinction and aerosol extinction error that is usually handled using a linear model. It is determined that the most probable extinction value can differ from that of the linear assumption by up to 10% and that differences larger than 50% can occur in the calculation of extinction error. Ignoring error in the number density alters the calculated extinction by up to 3% and that of extinction error by up to 10%. The preceding results were obtained using the least-squares technique. The least-squares technique assumes that the data being regressed are normally distributed. However, the quantity that is usually regressed in aerosol extinction and DIAL calculations is not normally distributed. A technique is presented that allows the required numerical derivative to be determined by regressing only normally distributed data. The results from this technique are compared with the usual procedure. The same concerns raised here regarding appropriate choice of a model in the context of aerosol extinction calculations should apply to DIAL calculations of absorbing species such as water vapor or ozone as well because the numerical derivative that is required is identical.

Evaluation of large language models for carbohydrate counting in type 1 diabetes mellitus: a comparative study with patient estimations.

Ice clouds in the Arctic are expected to have different radiative properties compared to mid latitude cirrus, because of the different humidity and temperature profile and also the prevalent aerosol loading in northern latitudes which govern their formation. During the late winter and early spring 2022 the HALO-(AC)3 campaign was conducted out of Kiruna (Sweden) to probe artic clouds with an airborne remote sensing payload. For this purpose, the German research aircraft HALO was equipped with a water vapor Differential Absorption Lidar (DIAL), a cloud radar, micro-wave radiometers, radiation measurements in the visible, near infrared and thermal region and a drop-sonde dispenser. A total of 25 flights where performed mainly over the sea between Svalbard and Greenland and up to nearly 90&amp;#176;N.The primary observable to study ice cloud formation is the relative humidity, which is not directly measurable by lidar, but can only be computed with the aid of additional temperature information. By comparison with a large number of dropsondes launched during flight, we will show that the temperature field from ECMWF IFS analyses and short-term forecasts provides sufficient accuracy to retrieve the relative humidity for ice cloud studies. Using this method we will analyse different scenarios of arctic cirrus formation: under stable artic conditions, during a warm air intrusion and while a cold air outbreak. An interesting special case is the modification of cirrus properties by the presence of an aerosol layer which is most probably composed of long range transported Sharan dust.&amp;#160;

Background:Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.Methods:Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).Results:The optimum ES model was Brown’s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.Conclusion:The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.

BackgroundArtificial intelligence (AI)–driven pose estimation (PE) offers a scalable and cost-effective solution to track exercises in mobile health apps. However, occlusion, influenced by camera angle and distance, can reduce detection accuracy and repetition counting precision. The influence of smartphone positioning on these performance metrics remains underexplored in controlled studies.ObjectiveThe study aimed to examine how smartphone camera angle (front, side, and diagonal) and distance (90 cm, 180 cm, 200 cm, and 360 cm) affect detection performance and repetition counting accuracy during push-ups and squats using AI-based PE.MethodsIn this cross-sectional, within-subject study, 44 healthy university students (9 [20.5%] female participants; mean age 20.3 y, SD 0.4 y; mean BMI 23.2, SD 0.6 kg/m²) were assigned to perform either squats or push-ups. Each participant completed their assigned exercise across 12 predefined smartphone camera configurations, yielding approximately 264 squat trials (n=22) and 264 push-up trials (n=22). Each trial consisted of an average of 5 repetitions, totaling approximately 1320 repetitions per exercise. PE performance was assessed using binary classification accuracy, detection rate, and mean absolute error (MAE) for repetition counting. Generalized linear mixed-effects models evaluated classification odds, linear mixed-effects models analyzed MAE, and Tukey-adjusted post hoc tests followed significant effects.ResultsThe mean detection rate was 61.1% (SD 48.8%) for push-ups and 61.5% (SD 48.7%) for squats, with MAEs of 1.08 (SD 1.78) and 1.11 (SD 1.82) repetitions, respectively. Push-ups were most accurately detected from diagonal views at 90 to 180 cm (up to 85.7% detection; MAE=0.28) and least accurately from the front at 360 cm (20%; MAE=2.70). Squats performed best from a diagonal view at 200 cm (95.5%; MAE=0.05) and worst from the side at 90 cm (0%; MAE=5). Generalized linear mixed models showed that for push-ups, the front 90 cm and diagonal 360 cm views significantly reduced classification odds compared to the side 90 cm view (P=.03 and P=.04, respectively), whereas for squats, diagonal and front views significantly outperformed side views across all distances (P<.001). Post hoc tests confirmed that for push-ups, diagonal close or mid-range views had significantly lower MAEs than far front views, and for squats, diagonal and front views at 180 to 200 cm achieved the highest accuracy and lowest MAEs (P<.05).ConclusionsAI-based PE effectiveness for exercise tracking is significantly affected by smartphone positioning. Diagonal and frontal views at mid-range distances (180‐200 cm) provided the highest detection accuracy and counting precision. These findings offer actionable guidance for developers, clinicians, coaches, and users optimizing mobile health exercise monitoring.

Rating is one of the most frequently used metrics in the television industry to evaluate television programs or channels. This research is an attempt to develop a prediction model of television program ratings using rating data gathered from UseeTV (interned-based television service from Telkom Indonesia). The machine learning methods (Random Forest and Extreme Gradient Boosting) were tried out utilizing a set of rating data from 20 television programs collected from January 2018 to August 2019 (train dataset) and evaluated using September 2019 rating data (test dataset). Research results show that Random Forest gives a better result than Extreme Gradient Boosting based on evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). On the training dataset, prediction using Random Forest produced lower RMSE and MAE scores than Extreme Gradient Boosting in all programs, while on the testing dataset, Random Forest produced lower RMSE and MAE scores in 16 programs compared with Extreme Gradient Boosting. According to MAPE score, Random Forest produced more good quality prediction (4 programs in the training dataset, 16 programs in the testing dataset) than Extreme Gradient Boosting method (1 program in the training dataset, 12 programs in the testing dataset) both in training and testing dataset.