Surface Meteorological Observations Research Articles

Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

AbstractThe planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year‐round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLHMOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLHERA5). Comparisons between PBLHMOSAiC and PBLHERA5 from different perspectives reveal that: (a) The overestimation of PBLHERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLHERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLHERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLHERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

Formation mechanism and development potential of wind energy resources on the Tibetan plateau

It is important to understand whether the wind energy resources on the Tibetan Plateau have power potential suitable for development. To this end, we conducted sodar wind measurement experiments in areas of typical terrain, analyzed radiosonde and surface meteorological observations, and performed mesoscale numerical model simulations to study the characteristics and formation mechanisms, and to assess the technical potential, of the wind energy resources on the Tibetan Plateau. Results showed that the wind energy resources on the Tibetan Plateau are very abundant and of high quality with enormous development potential. The total technical potential of the wind energy resources at 100-m height above ground level is 1.02 billion kW, accounting for 26 % of the total technical potential of China's wind energy resources. The technical potential of the wind energy resources at very rich and rich levels accounts for 63 % of the total. The strengthening effect of the superposition of valley wind circulations and the synoptic-scale background wind field is the fundamental formation mechanism of the abundant wind energy resources on the Tibetan Plateau.

Energy Consumption Characteristics Study of Surface Meteorological Observation Station in North and South China

Surface meteorological observation is one of the most important sources of primary data for the meteorological sector and are crucial to the accuracy of weather forecasting. China’s meteorological authorities have built a dense network of surface observation stations, which require a considerable amount of electrical energy to keep the network running normally. There has been a lack of analytical studies dedicated to the energy consumption of surface meteorological observation stations domestically and internationally. This paper selects measured energy consumption data of 4 surface meteorological observation stations in two typical provinces (autonomous regions) in the north and south China over the past two years, and proposes, for the first time, to use the average power of the surface meteorological observation station, adopts the electrical energy consumption calculation method, carries out energy consumption calculation and analysis by month, sub-region and season, derives the characteristics of energy consumption changes of the surface meteorological observation station, and proposes energy saving and consumption reduction measures.

Estimation of the water vapor field by fusing GPS and surface meteorological observations on the Loess Plateau of China

Estimation of the water vapor field by fusing GPS and surface meteorological observations on the Loess Plateau of China

Gap-free 16-year (2005–2020) sub-diurnal surface meteorological observations across Florida

The sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events that impact agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered as the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for years 2005–2020.

Tropical Easterly Waves Over Costa Rica and Their Relationship to the Diurnal Cycle of Rainfall

AbstractUsing an index of tropical easterly wave (TEW) activity derived from spacetime‐filtered outgoing longwave radiation, we construct composites of long‐term hourly surface meteorological observations and morningtime sounding data collected near San José, Costa Rica to investigate how TEWs affect the diurnal cycle of rainfall over land. Our results indicate that TEWs enhance the frequency of occurrence of rain during convectively active conditions over the course of the diurnal cycle. By contrast, rainfall conditional intensity sensitivity to TEW phase appears more nuanced, with indications that active conditions induce a slight delay in the timing of the diurnal peak intensity but a longer duration of heavier rainfall. Analysis of associated hourly surface meteorology along with sounding profiles and derived thermodynamic parameters points to both initial vertical and time‐evolving surface conditions regulating diurnal behavior, such as greater instability and higher precipitable water in morningtime profiles under active phase conditions.

AI-based estimation of precipitation in the Tibetan Plateau using multi-frame FY-4A satellite data

ABSTRACT The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial – temporal matching. The results show that the multi-frame FY-4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored.

Spatio-temporal characteristics of urban heat Island of Jakarta metropolitan

Around 151 million people (56%) of Indonesia's population currently live in cities. When the inner city sees substantially warmer temperatures than neighboring rural areas, a city experiences the urban heat island (UHI) effect. For both day and night time, the inner city within the Jakarta area tends to have a higher land and surface air temperature in comparison to its surroundings. Using available remote sensing satellite data (MODIS and Landsat) and surface meteorological observations, this research detects and defines the spatiotemporal land surface (SUHI) development in the Jakarta agglomeration region. In this paper, how the intensity of land surface (SUHI) and surface air UHI (AUHI) is measured, the trend assessment of 20 years SUHI area, as well as how land use change contributes to UHI development in the Jakarta Metropolitan area, is discussed. The results show that the SUHI intensity is approximately 3°C–6°C and AUHI is approximately 1°C–2.5 °C. This paper offers a comprehensive feature of UHI in Greater Jakarta as a research novelty, which is getting worse in response to global warming.

Quantifying the contribution of local drivers to observed weakening of spring dust storm frequency over northern China (1982–2017)

Recent studies have suggested that spring dust storm (SDS) events in northern China (NC) have exhibited substantial decline over the past 30 years. However, it is unclear which local factors are most responsible for the decline in SDS events, and the contribution of each dominant factor remains to be determined. This study utilized high-density DS records and collocated homogenized surface meteorological observations from 1982 to 2017, in conjunction with land surface products, to examine the local drivers that influence the long-term variation in SDS frequency (SDSF) over the entire NC area and its three dust-source areas: northwestern China (NWC), north-central China (NCC), and northeastern China (NEC). Results indicated that the observed SDSF averaged over NC, NWC, NCC, and NEC has decreased by 144.4 %, 109.3 %, 166.4 %, and 92.2 %, respectively, during 1982–2017. The variation in SDSF is largely explained by variation in wind speed (WS), precipitation, volumetric soil moisture, and surface bareness. A multivariable linear regression model incorporating these local drivers accounted for 81.0 %, 74.0 %, and 46.9 % of the variance in SDSF in NWC, NCC, and NEC, respectively. Statistical analyses on the local drivers suggested that weakening of WS was the dominant factor in the reduction in SDSF over recent decades, contributing 76.9 %, 54.7 %, and 33.6 % of the variation in NWC, NCC, and NEC, respectively. More importantly, we revealed that the interannual variation in regional SDSF was not only controlled by local drivers, but also influenced by cross-regional transport of dust aerosols emitted from upstream source areas.

Regional Tropospheric Correction Model from GNSS–Saastamoinen–GPT2w Data for Zhejiang Province

Tropospheric delay models based on GNSS observations are essential for studying tropospheric changes. However, the uneven distribution of GNSS stations reduces the accuracy of GNSS tropospheric delay models in remote areas. Moreover, the accuracy of the tropospheric delay calculated by traditional models, which rely on meteorological parameters, is lower compared to the accuracy achieved by GNSS tropospheric models. At present, there are sufficient surface meteorological observation facilities around the world that can obtain surface meteorological parameters in real time. It is of great importance to make full use of the measured meteorological parameters to establish tropospheric correction models. Moreover, the empirical tropospheric models use free and open data, and one can obtain tropospheric parameters through the model without requiring any auxiliary information. We established a provincial real-time regional tropospheric fusion model using ground-based GNSS observations, a meteorological model, and empirical model data. Results showed that the tropospheric delay observations of the three models can be fused to establish a real-time tropospheric delay model with better accuracy and higher spatiotemporal resolution. The accuracy of Zenith Total Delay (ZTD) estimated by the fusion model reached 0.96/1.04/3.11 cm during the tropospheric quiet/active/typhoon period.

Impact of thermal structure of planetary boundary layer on aerosol pollution over urban regions in Northeast China

This study investigated the characteristics of aerosol pollution in three provincial capitals (Shenyang, Changchun, and Harbin) in Northeast China and the impact of the thermal structure of the planetary boundary layer (PBL) using surface air quality monitoring data and meteorological observations and sounding data from January 1 to January 20, 2020. The number of pollution days in Shenyang, Changchun, and Harbin were 13, 17, and 19, respectively, and the number of heavy pollution days was 4, 3, and 13, respectively. Harbin suffered the heaviest and longest pollution with the smallest diurnal variation between daytime low and nighttime high PM2.5. The atmosphere in Harbin was more stable than that in Shenyang and Changchun, and PM2.5 concentration in the three cities tended to increase with decreasing planetary boundary layer height (PBLH). The lower winter air temperature in Northeast China led to a shallower PBL and weaker ventilation coefficient, which in turn led to an increase in PM2.5 concentration, indicating that air temperature is the key meteorological factor affecting local pollution in Northeast China. The analysis of two typical pollution events showed that a warm advection aloft suppressed the development of the PBL in Shenyang. In contrast, the suppressed effect of warm advection on the PBL was weakened in Harbin, which originally had a low PBLH due to cold weather. Moreover, the daytime convective boundary layer in Harbin developed 1–2 h later than that in Shenyang; the delayed development of PBL during the daytime in Harbin was also conducive to poor air quality.

Impacts of snow cover on micrometeorological conditions and rural air quality in Northeast China

Snow cover is one of the most typical underlying surface types in Northeast China in winter. Snow cover can affect air quality potentially through changing atmospheric diffusion capacity, with the underlying physics awaiting a further exploration for Northeast China. Therefore, the impacts of snow cover on micrometeorological conditions and air quality were investigated using surface and tower meteorological observations from October 2019 to March 2020 in Fujin, a typical rural area in the Sanjiang Plain in Northeast China. Low atmospheric visibility and poor air quality were observed to occur more frequently on snow-cover days than on snow-free days, with visibility exhibiting a decreasing trend with increasing snow depth (< 8 cm). Compared with snow-free days, the presence of snow cover was found to produce weakened unstable stratification in the near-surface layer and lower daily surface sensible heat flux (reduced by 54.8%), mainly related to higher snow surface albedo (0.5–0.8), which can suppress the development of daytime atmospheric boundary layer height and vertical dispersion of air pollutants. Moreover, moister ambient conditions on snow-cover days presumably favored secondary aerosol formation and particle hygroscopic growth. Stronger wind speeds and shears, friction velocity, and turbulence kinetic energy were observed on snow-cover days than on snow-free days when the air was clean, probably due to smaller aerodynamic roughness length on snow cover. However, when air pollution occurred on snow cover, winds and mechanic turbulence decreased significantly, favoring aerosols' accumulation near the surface. These findings can facilitate a comprehensive understanding of the physical mechanism of air pollution occurring over a large portion of the snow-cover region in wintertime Northeast China and helpful for the pollution controls.

Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign

Abstract. The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

Possible influence of sudden stratospheric warmings on the atmospheric environment in the Beijing–Tianjin–Hebei region

Abstract. Using European Centre for Medium-Range Weather Forecasts fifth-generation (ERA5) and second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis and surface meteorological observation data, this study explores the possible impact of sudden stratospheric warming (SSW) events on air quality in the Beijing–Tianjin–Hebei (BTH) region. Major SSW events are divided into polar vortex displacement SSW and polar vortex split SSW. As the duration of split SSW events is longer and the stratospheric signal pulses propagate further downward than displacement SSWs, subseasonal variability of the atmospheric particulates in the BTH is larger during split SSWs. The air particulate concentration is light before the SSW onset due to the enhanced perturbation in the troposphere associated with strengthened planetary waves. The air particulate concentration around the SSW onset dates begins to rise due to weakening of the tropospheric disturbance as the enhanced planetary waves enter the stratosphere. In the decaying period of the SSW, the air particulate concentration decreases as the stratospheric negative northern annular mode (NAM) signal propagates downward. Specifically, in the pre-SSW period of displacement (split) SSW events, a wavenumber-1-like (wavenumber-2-like) anomaly pattern is strengthened. The East Asian winter monsoon intensifies as the East Asian trough is deepened, especially before the split SSW event onset, leading to a cleaning period. Around the SSW onset period as the tropospheric perturbation diminishes and the East Asian winter monsoon weakens, a surge of air particulate concentration is observed. After the SSW onset, due to the downward propagation of the stratospheric negative NAM signal, cold anomalies form in northeastern East Asia, especially for split SSWs, corresponding to a cleaning period in the BHT region. The local meteorological conditions during the SSWs are also discussed.

ASSESSMENT OF CHANGE OF AGROCLIMATIC POTENTIAL OF ULYANOVSK REGION FOR PLANT PRODUCTION

The article discusses the main approaches to an increase ofgrain production volume and maximum usage of bioclimatic potential in changing climatic conditions.Comprehensive assessment of factors of potential and actually possible crop production was studied. The initial information for the research was data of surface meteorological observations at six meteorological stations located on the territory of Ulyanovsk region from 1990 to 2019. For assessment of long-term changes of agrometeorological parameters, Fourier series expansion was used, parameters of the best sinusoidal approximation of the maximum and minimum points were determined. It was revealed that over the past three decades, there has been a steady increase oof the average annual temperature (5.2 ° C), especially in the cold season. Moisture supply is more chaotic, the change is heterogeneous, varying from 280 mm (2012) to 664 mm (2017) and the average value is 498 mm for 1990-2019. Barley has the greatest potential among grain crops in terms of photosynthetically active radiation (PAR) (8.3 t/ha), rye has the smallest potential (6.4 t/ha). In terms of the amount of heat resources and moisture, rye has the maximum crop production, wheat is sensitive to moisture, barley - to temperature regime.

Temperature and moisture transport during atmospheric blocking patterns around the Antarctic Peninsula

We assess temperature and moisture transport in and around the Antarctic Peninsula (AP) associated with atmospheric blocking over two domains, one located to the west (150–90°W, 50–70°S, Western AP (WAP)) and the other over and to the east (90–30°W, 50–70°S, Eastern AP (EAP)) of the AP. We make use of surface meteorological observations, ERA5 reanalysis data, and a state-of-the-art atmospheric river (AR) database. Observed temperature anomalies indicate that the WAP and EAP blocking patterns are characterized by significant cold and warm anomalies over the AP, respectively, particularly in austral autumn, winter and spring. Consistent with these changes, cold anomalies depicted by ERA5 are associated with the transport of cold and dry air from the Antarctic continent by southerly and southeasterly flow over the eastern flank of the WAP blocking. ERA5 results highlight the importance of blocking days over the EAP domain (largely centered over the Drake Passage) to the occurrence of warm events associated with northerly and northwesterly warm air transport. Significant increases in integrated vapor transport (IVT) and AR frequency are also evident during the EAP blocking, particularly on the windward side of the AP. During the most extreme blocking days in this domain, there exist significant increases in latent and sensible heat fluxes on the windward side of the AP and the Larsen C Ice Shelf, respectively, indicating the contribution of foehn events to warm anomalies, especially in austral autumn and winter. The co-occurrences between landfalling ARs and blocking are found to amplify foehn effect due to higher IVT and associated latent heat condensation compared to blocking days without ARs. We conclude that blocking patterns are important to understand the occurrence of extremely warm events and landfalling ARs in the AP and their potential impacts on the surface cryospheric processes.

The Snowline and 0°C Isotherm Altitudes During Precipitation Events in the Dry Subtropical Chilean Andes as Seen by Citizen Science, Surface Stations, and ERA5 Reanalysis Data

Understanding the variability of the snowline and the closely related 0°C isotherm during infrequent precipitation events in the dry Andes in Chile is fundamental for precipitation, snow cover, and discharge predictions. For instance, it is known that on the windward side of mountains, the 0°C isotherm can be several hundreds of meters lower than on the free air upwind counterpart, but little is understood about such effects in the Andes due to missing in situ evidence on the precipitation phase. To bridge this gap, 111 photographs of the snowline after precipitation events between 2011 and 2021 were gathered in the frame of a citizen science programme to estimate the snowline altitude. Since photographs of the mountain snowline are in good agreement with Sentinel-2 imagery, they have great potential to validate empirical snowline estimations. Using the snowline altitude from the photos, we evaluated different methods to estimate the snowline and 0°C isotherm altitude during precipitation events based on surface meteorological observations and ERA5 reanalysis data. We found a high correlation between the observed snowline altitude and the extrapolated 0°C isotherm based on constant lapse rates (−5.5 to −6.5°C km−1) applied to air temperature from single, near stations. However, uncertainty increases for distances &amp;gt;10 km. The results also indicate that the linear regression method is a good option to estimate ZSL, but the results strongly depend on the availability of high-elevation station datasets. During half of the precipitation events, the 0°C isotherm lies between ∼1,800 and ∼2,400–2,500 m asl. in winter, and the snowline is on average ∼280 m below this altitude. Our results indicate the presence of a mesoscale lowering of the 0°C isotherm over the windward slopes compared to the free-air upwind value during precipitation events and a possible isothermal layer of near-freezing air temperatures comparable to other mountain ranges. Due to this mesoscale and local behavior, ERA5 data generally overestimate the snow–rain transition in high-elevation areas, especially for relatively intense events. On the other hand, the 0°C isotherm altitude is underestimated if only low-elevation valley stations are considered, highlighting the importance of high-altitude meteorological stations in the network.

Comparison of Visual Features for Image-Based Visibility Detection

Abstract The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection. Significance Statement The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions.

Heat stress in Beijing and its relationship with boundary layer structure and air pollution

Both extreme heat and heavy air pollution can cause adverse health impacts on urban inhabitants. To understand heat stress and its relationship with boundary layer structure and air pollution in Beijing, this study analyzed surface meteorological observations, radiosonde measurements, and ground-level PM2.5 and O3 concentrations in summer from 2015 to 2019, in conjunction with simulated air quality and MERRA-2 data. We measured the heat stress using a heat index that combines temperature and humidity to quantify the sensible temperature as perceived by humans, and found that high heat risk in Beijing was often associated with a low boundary layer height and poor air quality. To reveal the underlying physical mechanism involved, we objectively classified the synoptic conditions in North China. The typical synoptic pattern associated with the coupling of heat and pollution in Beijing was found to feature a southeast-to-north pressure gradient at the 700-hPa level, leading to westward warm advection above planetary boundary layer (PBL) and southward movement of warm, humid, and polluted air masses within the PBL towards Beijing. The elevated warm advection can enhance the capping of thermal inversion over the PBL and suppress the PBL's development and the vertical dispersion of pollutants. With mountains to the north and west, pollutants and heat can be trapped in a limited volume in Beijing, increasing the health risk from heat and pollution. These findings on the meteorological mechanisms of the coupling between heat and pollution in Beijing may have important implications for limiting the current health risk and preparing for any projected changes in it in the future.