Ground Observation Stations Research Articles

Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China

An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.

Quantitative Short-Term Precipitation Model Using Multimodal Data Fusion Based on a Cross-Attention Mechanism

Short-term precipitation prediction through abundant observation data (ground observation station data, radar data, etc.) is an essential part of the contemporary meteorological prediction system. However, most current studies only use single-modal data, which leads to some problems, such as poor prediction accuracy and little prediction timeliness. This paper proposes a multimodal data fusion precipitation prediction model integrating station data and radar data. Specifically, our model consists of three parts. Firstly, the radar feature encoder comprises a shallow convolution neural network and a stacked convolutional long short term memory network (ConvLSTM), which is used to extract the spatio-temporal features of radar-echo data. The weather station data feature encoder is composed of a fully connected network and an LSTM, which is used to extract the sequential features of the weather station data. Then, the cross-modal feature encoder obtains cross-modal features by aligning and exchanging the feature information of the radar data and the weather station data through the cross-attention mechanism. Finally, the decoder outputs the quantitative short-term precipitation prediction value. Our model can integrate station and radar data characteristics and improve prediction accuracy and timeliness, and can flexibly add other modal features. We have verified our model on four short-term and impending rainfall datasets in South Eastern China, achieving the best performance among the algorithms.

Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Air temperature is the primary indicator of climate change. Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. However, they contain biases in observations, so a bias correction is required to enhance the accuracy of modeling predictions. In this study, we used the temperature lapse-rate method to correct ERA-Interim reanalysis-temperature data in the Qilian Mountains of China from 1979 to 2017. These temperature lapse rates were based on observations (ΓObs) and on model internal vertical lapse rates derived from different ERA-Interim pressure levels (ΓERA). The results showed that the temperature lapse rates in warm periods were larger than those in cold periods. Both the original and corrected ERA-Interim temperature can significantly capture the warming trend exhibited by observations. In general, the temperature lapse rate method was reliable for correcting ERA-interim reanalysis-temperature data. Although ΓObs performed best in bias correction, it depends heavily on the density of ground observation stations and is not appropriate for remote areas with a low data coverage. Correction methods based on ΓERA were shown to be reliable for bias correction, and will be especially applicable to mountainous areas with few observation stations. Our results contribute to the improvement of quality of data products and enhance the accuracy of modeling of climate change effects and risks to the environment and human health.

Quantification of Effects of Errors in the Cloud Properties on the Representation of the Surface Downward Shortwave Flux Based on MERRA-2 in Japan

Abstract The evaluation of the representation of the surface downward shortwave flux (DSF) from atmospheric reanalysis data products is required to obtain reliable information for the resource assessment of surface solar energy. The representation of the DSF from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data product was evaluated using surface solar radiation from ground-based observations in Japan. The cloud fraction (CFR) and cloud optical thickness (COT) from Moderate Resolution Imaging Spectroradiometer (MODIS) were also used as references. The CFR from MERRA-2 tends to be smaller than that from MODIS, and the correlation between the difference in the CFR and that in the DSF is negative. The correlation between the difference in the COT and that in the DSF is weakly negative. To quantify the effects of the difference in the CFR and COT to that of the DSF, a regression model based on an artificial neural network architecture that emulates the process of the DSF in MERRA-2 was constructed. Numerical experiments using the emulator quantify contributions of each of the differences in the CFR and COT and joint contributions of the two variables. In addition, a cluster analysis was performed to clarify the differences in the seasonal changes in the monthly mean bias error (MBE) in the DSF among ground observation stations, and three clusters were identified. Contributions of the differences in the CFR and COT to the seasonal change in the monthly MBE were also clarified using the results of the numerical experiments.

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

BACKGROUND: Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations. Though generally considered representative of the actual ambient conditions and individual’s exposure, their broader application in environmental epidemiology is often constrained by inhomogeneous records and the sparse density of meteorological stations. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. AIM: The aim of the study is to provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. METHODS: We applied the well-established time-series analyses with quasi-Poisson regression (with distributed lag non-linear models and multivariate meta-regression) to model the location specific temperature-mortality associations, across 612 cities within 39 countries over the period 1985–2019, covering a wide range of climates and including low- and middle-income countries. Briefly, we first systematically compared the correlation between daily temperature series derived from ground station observations and ERA5-Land/ERA5 reanalysis, then we evaluated differences in estimated exposure-response functions of temperature-mortality relationships, and finally we compared their performance using fit statistics. RESULTS: Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. CONCLUSIONS: Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. The consistent spatio-temporal coverage also makes it attractive for quantifying population attributable fractions, an indicator important for health planners and policy makers. KEYWORDS: mortality, temperature, reanalysis, ERA5-Land, distributed lag models.

A D-InSAR method to improve snow depth estimation accuracy

<p indent="0mm">Snow depth is an important parameter that reflects the law of spatiotemporal variation in snow cover and is an indispensable observation variable for studying global and regional climate change and the hydrological cycle. Traditional manual field measurements and ground station observations have limited scopes and cannot fully reflect regional-scale snow conditions. Satellite remote sensing can simultaneously observe surface information over a large area and has been widely used in snow monitoring since the 1970s. Optical remote sensing has difficulty obtaining snow depth information directly through the visible light band and is limited by atmospheric conditions. Microwave remote sensing can penetrate clouds and fog and can effectively capture changes in snow depth. Differential interferometric synthetic aperture radar (D-InSAR) technology uses the differential interferometric phase formed by microwave penetration through the snow layer before and after snowfall to establish a geometric function relationship with the snow depth; it is widely used in regional small-scale snow depth estimation research. However, its estimation accuracy is affected by many factors, such as interferometric image pair coherence, local topography, and snow dielect capability at microwave wavelengths. Based on high-resolution Sentinel-1 SAR data, this study optimizes the snow depth differential interferometric phase unwrapping accuracy by introducing Sentinel-2 optical images and high coherence coefficient regions to select ground control points, which are phase correction benchmarks. Furthermore, introducing field-measured snow parameters such as snow density and satellite local terrain incidence angles based on digital elevation model (DEM) data reduces the error of the differential interferometric phase-slope distance relationship model, thereby enabling estimations of the spatiotemporal distribution of snow depth in the Babao River Basin in the northeastern Qinghai-Tibet Plateau during the 2021 ablation period. Simultaneously, the accuracy of the snow depth estimation ability is evaluated based on the synchronous field measurement data of snow cover satellites. The main factors affecting the accuracy of snow depth estimation are discussed and analysed. Data from 122 ground snow depth measurements (meteorological stations + field measurements) are used to verify the results. The results show that the optimized D-InSAR differential interferometry can improve the snow depth estimation accuracy. The RMSE is <sc>3.9 cm,</sc> the<italic> </italic>MAPE is 20.03%, and the <italic>R</italic>² is 0.92. However, the estimated snow depth is generally underestimated (MBE%=−16.8%), and the maximum underestimation error of the snow depth is <sc>9.1 cm.</sc> In addition to the influencing factors of D-InSAR system interferometric decorrelation, the estimations are affected by the penetration ability of microwaves in the snow and snow parameters such as stratigraphy structure, temperature and humidity. This limits the ability of differential interferometry to estimate snow depth and is more suitable for dry and homogeneous snow cover. This method allows for more precise and faster monitoring of centimetre-level snow depth changes; at the same time, the underlying surface of the study is relatively simple, and the influence of forest and other vegetation coverage is not considered. In follow-up research, snow cover should be optimized with multi-property and multi-environment interferometric models. An InSAR scattering model with a layered factor is introduced to improve the propagation information inside the snow layer then combined with the multiangle radar measurement of the ascending and descending orbits; experiments are carried out using the penetration ability of more bands in microwaves to reduce interferometric error sources and improve the accuracy of snow depth estimation. This study can provide a scientific reference for D-InSAR snow depth estimation research.

Analysis of Extreme Rain and Snow Weather Dynamic and Water Vapor Conditions in Northeast China from 17 to 19 November 2020

Based on hourly precipitation data from 2413 national ground observation stations in China and ERA5 (0.25° × 0.25°), this study analyzes the characteristics and causes of extreme rainfall and snow in northeast China from 17–19 November 2020. The results show that extreme precipitation is mainly attributed to the abnormally strong large-scale low vortex and ground cyclone. The significant high-level and low-level coupling in areas with strong rain and snow is conducive to the continuous upward motion, which provides favorable dynamic conditions for the generation and development of extreme precipitation. The frontogenesis effect below the 850 hPa level is obvious, and the extreme precipitation period corresponds to the meeting of the north and south front areas. The symmetrical unstable atmosphere of 925 hPa~700 hPa is forced by the frontogenesis, which strengthens the oblique rising of the low layer and increases the instability, leading to the strengthened development of precipitation. For heavy rainfall and snow in early winter in China, water vapor transport is crucial. The extremely strong low-level jet also provides extremely strong water vapor conditions for the occurrence of heavy rain and snow. The analysis of the extreme rain and snow characteristics and formation mechanism of this weather process can deepen the understanding of extreme weather processes, and provide a useful reference for the research and prediction of extreme precipitation processes.

Estimation of Daily Average Shortwave Solar Radiation under Clear-Sky Conditions by the Spatial Downscaling and Temporal Extrapolation of Satellite Products in Mountainous Areas

The downward surface shortwave radiation (DSSR) received by an inclined surface can be estimated accurately based on the mountain radiation transfer model by using the digital elevation model (DEM) and high-resolution optical remote sensing images. However, it is still challenging to obtain the high-resolution daily average DSSR affected by the atmosphere and local topography in mountain areas. In this study, the spatial downscaling and temporal extrapolation methods were explored separately to estimate the high-resolution daily average DSSR under clear-sky conditions based on Himawari-8, Sentinel-2 satellite radiation products and DEM data. The upper and middle reaches of the Heihe River Basin (UM-HRB) and the Laohugou area of Qilian Mountain (LGH) were used as the study areas because there are many ground observation stations in the UM-HRB that are convenient for DSSR spatial downscaling studies and the high-resolution instantaneous DSSR datasets published for the LHG are helpful for DSSR temporal extrapolation studies. The verification results show that both methods of spatial downscaling and temporal extrapolation can effectively estimate the daily average DSSR. A total of 3002 measurements from six observation sites showed that the 50 m downscaled results of the Himawari-8 10-min 5 km radiation products had quite a high correlation with the ground-based measurements from the UM-HRB. The coefficient of determination (R2) exceeded 0.96. The mean bias error (MBE) and the root-mean-squared error (RMSE) were about 41.57 W/m2 (or 8.22%) and 49.25 W/m2 (or 9.73%), respectively. The fifty-two measurements from two stations in the LHG indicated that the temporal extrapolated results of the Sentinel-2 10 m instantaneous DSSR datasets published previously performed well, giving R2, MBE, and RMSE values of 0.65, 41.06 W/m2 (or 7.89%) and 88.90 W/m2 (or 17.07%), respectively. By comparing the estimation results of the two methods in the LHG, it was found that although the temporal extrapolation method of instantaneous high-resolution radiation products can more finely describe the spatial heterogeneity of solar radiation in complex terrain areas, the overall accuracy is lower than that achieved with the spatial downscaling approach.

Influence of total ozone column (TOC) on the occurrence of tropospheric ozone depletion events (ODEs) in the Antarctic

Abstract. The occurrence of tropospheric ozone depletion events (ODEs) in the Antarctic can be influenced by many factors, such as the total ozone column (TOC). In this study, we analyzed the observational data obtained from ground observation stations and used two numerical models (TUV and KINAL), to discover the relationship between the TOC and the occurrence of ODEs in the Antarctic. A sensitivity analysis was also performed on ozone and major bromine species (BrO, HOBr and HBr) to find out key photolysis reactions determining the impact on the occurrence of tropospheric ODEs brought by TOC. From the analysis of the observational data and the numerical results, we suggest that the occurrence frequency of ODEs in the Antarctic is negatively associated with TOC, after screening out the impact on ODEs caused by the solar zenith angle (SZA). This negative impact of TOC on the occurrence of ODEs was suggested to be exerted through altering the solar radiation reaching the ground surface and changing the rates of photolysis reactions. Moreover, major ODE accelerating reactions (i.e., photolysis of tropospheric ozone, H2O2 and HCHO) and decelerating reactions (i.e., photolysis of BrO and HOBr), which heavily control the start of ODEs, were also identified. We found that when TOC decreases, the major ODE accelerating reactions significantly speed up. In contrast, the major ODE decelerating reactions are only slightly affected. As a result of the different impacts of TOC on photolysis reactions, the occurrence of ODEs depends negatively on TOC.

MODELLING THE AIR QUALITY INDEX FOR BOLU, TURKEY

The monthly air quality index (AQI) derived from ground observation stations that obtained daily air pollutants information for 1990- through 2010 was analyzed in this study. AQI was evaluated using the common comparative index method presented by the U.S. Environmental Protection Agency (USEPA), and a statistically based approach was used for predicting the AQI value. With the first method, AQI was predicted using the USEPA subindex formula for different pollutants, such as particulate matter and sulfur dioxide, which contribute the most to air pollution. A combination of the principal component analysis (PCA) and multiple linear regression (MLR) methods were used with the measured values of climate variables obtained from the ground stations for the most effective contributors and a prediction was modelled. The results of these two methods were compared and evaluated for consistency. Two methods were presented for determining the AQI value. According to the findings, the common comparative index method was consistent with the statistical prediction models, and the best results were obtained using PCA models with varimax rotation.

An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone

Surface ozone (O₃) pollution is a severe environmental problem that endangers human health. It is necessary to obtain high spatio-temporal resolution O₃ data to provide support for pollution monitoring and prevention. For this purpose, this study makes comprehensive use of remote sensing data, reanalysis data, and ground station observations, and develops an enhanced geographically and temporally weighted neural network (EGTWNN) model to acquire high spatial and temporal resolutions of O₃ data. The EGTWNN model is nested by two neural networks. The first neural network automatically learns the spatio-temporal proximity relationship to obtain spatio-temporal weights for the samples, and the spatio-temporal weights are then inputted into the second neural network to conduct weighted modeling of the relationship between O₃ and influencing variables. The contribution of the proposed model is that the first neural network replaces the traditional empirical weighting method, and represents the spatio-temporal proximity relationship more accurately to improve estimation accuracy. Results indicate that the cross-validation R² and RMSE of EGTWNN are 0.81 and 21.24 &#x03BC;g/m³, respectively, which are increased by 0.02 and decreased by &#x007E;1 &#x03BC;g/m³ relative to those of the traditional empirical weighting method based geographically and temporally weighted neural network model. The results also show that compared with the geographically and temporally weighted regression model, the proposed model achieves superior performance. In addition, the spatio-temporal weights obtained by the first neural network of EGTWNN are highly consistent with those obtained by the traditional empirical weighting method, indicating that the results of neural networks are highly interpretable.

Simulated seasonal impact on middle atmospheric ozone from high-energy electron precipitation related to pulsating aurorae

Abstract. Recent simulation studies have provided evidence that a pulsating aurora (PsA) associated with high-energy electron precipitation is having a clear local impact on ozone chemistry in the polar middle mesosphere. However, it is not clear if the PsA is frequent enough to cause longer-term effects of measurable magnitude. There is also an open question of the relative contribution of PsA-related energetic electron precipitation (PsA EEP) to the total atmospheric forcing by solar energetic particle precipitation (EPP). Here we investigate the PsA-EEP impact on stratospheric and mesospheric odd hydrogen, odd nitrogen, and ozone concentrations. We make use of the Whole Atmosphere Community Climate Model and recent understanding on PsA frequency, latitudinal and magnetic local time extent, and energy-flux spectra. Analysing an 18-month time period covering all seasons, we particularly look at PsA-EEP impacts at two polar observation stations located at opposite hemispheres: Tromsø in the Northern Hemisphere (NH) and Halley Research Station in the Southern Hemisphere (SH). We find that PsA EEP can have a measurable impact on ozone concentration above 30 km altitude, with ozone depletion by up to 8 % seen in winter periods due to PsA-EEP-driven NOx enhancement. We also find that direct mesospheric NOx production by high-energy electrons (E&gt; 100 keV) accounts for about half of the PsA-EEP-driven upper stratospheric ozone depletion. A larger PsA-EEP impact is seen in the SH where the background dynamical variability is weaker than in the NH. Clearly indicated from our results, consideration of polar vortex dynamics is required to understand PsA-EEP impacts seen at ground observation stations, especially in the NH. We conclude that PsA-EEP has the potential to make an important contribution to the total EPP forcing; thus, it should be considered in atmospheric and climate simulations.

Global Land High-Resolution Cloud Climatology Based on an Improved MOD09 Cloud Mask

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.

Quantifying the impact of cropland wind erosion on air quality: A high-resolution modeling case study of an Arizona dust storm

Dust storms fueled by wind erosion of desert and cropland soils in south-central Arizona have caused numerous highway accidents and health risks by creating hazardous air quality conditions. Despite much speculation that a major fraction of windblown dust during these storms originates at an active or abandoned cropland surface over the region, its quantification is largely missing in the literature. This study estimated the contribution of cropland wind erosion to the regional air quality degradation, in terms of concentration of particulate matter with diameter 10 μm and smaller (PM10), during a typical springtime dust storm, by conducting high-resolution (1 km horizontally) simulations utilizing the Weather Research and Forecasting-FENGSHA (a dust emission model)-Community Multiscale Air Quality (WRF-FENGSHA-CMAQ) modeling framework. The dust model for this study employed up-to-date and very high-resolution data on land use, soil texture, and vegetation index. The modeling system used in this study simulated reasonably well a regional dust storm (that raised concerns for air quality and highway safety) of 08–09 April 2013 compared against ground (station) observations of PM10 and satellite observations of dust optical depth and vertical structure of aerosol subtypes. The model evaluations also included the use of radar observations.Furthermore, the model results from this study revealed that the cropland wind erosion was responsible for 55% and 51% of total PM10 over Phoenix (33.35°N–33.85°N, 112.36°W–111.485°W) and western Pinal County (32.50°N–33.35°N, 112.2°W–111.2°W), respectively, during the simulated dust storm. The large contributions suggest that the inclusion and accurate representation of cropland dust sources are crucial in preparing emission inventories and in air quality modeling for the region. Such representation is particularly important in view of the projected future aridity over this region (that is already under a long-term drought), which may lead to fallowing of currently active farmlands, making them potential hotspots of dust emission. The modeling approach developed for this work can be used to carry out more comprehensive studies, over extended spatial and temporal extents, to identify the sources of windblown dust and quantify their climatological dust emission potentials for this region or elsewhere. Such studies are highly desirable to help decision-making processes to reduce windblown dust and the associated health and safety risks.

Post-processing correction method for surface solar irradiance forecast data from the numerical weather model using geostationary satellite observation data

The numerical weather prediction (NWP) model still produces forecast errors. In this study, a correction method for the data from the NWP model is developed to obtain more skillful forecast data. The proposed method fuses information about surface global horizontal solar irradiance (GHI) from the NWP model and the satellite observations and gives corrected forecast data over the domain of the NWP model. There are three steps in the correction method algorithm. First, grid cells of the NWP model are grouped into clusters based on atmospheric conditions and regions. Second, cumulative distribution functions of the GHI from both datasets are inferred for each cluster separately. Last, correction functions are built using the statistical transformation. The correction effect is evaluated using satellite observation data as the reference. These analyses are performed using data for Japan. The correction function generally improves the forecast quality measured with the mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). When the forecast quality is measured with MBE, the improvement lasts for two forecast days, but when measured with MAE and RMSE, the improvement lasts for only one forecast day. The correction effects have seasonal and regional characteristics. The correction is effective from spring to autumn and the correction effect weakens in winter. Forecast quality at all ground observation stations in Japan, except for those in northern Japan, is improved owing to the correction. In contrast, the forecast quality at stations in northern Japan tends to be degraded.

A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network

Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind speed. The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, and air pressure, to predict the wind speed in the next hour. Hourly data collected from two ground observation stations in Yanqing and Zhaitang in Beijing were divided into training and test sets. The training sets were used to train the model, and the test sets were used to evaluate the model with the root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE) metrics. The proposed method is compared with two other forecasting methods (the autoregressive moving average model (ARMA) method and the single-variable long short-term memory network (LSTM) method, which inputs only historical wind speed data) based on the same dataset. The experimental results prove the feasibility of the MV-LSTM method for short-term wind speed forecasting and its superiority to the ARMA method and the single-variable LSTM method.

Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; concentrations over the North China Plain

Abstract. China has made great efforts to monitor and control air pollution in the past decade. Comprehensive characterization and understanding of pollutants in three-dimensions are, however, still lacking. Here, we used data from an observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations combined with a data assimilation technique to conduct a comprehensive analysis of extreme heavy aerosol pollution (HAP) over the North China Plain (NCP) from November–December 2017. During the studied period, the maximum hourly mass concentration of surface PM2.5 reached ∼390 µg m−3. After assimilation, the correlation between model results and the independent observation sub-dataset was ∼50 % higher than that without the assimilation, and the root mean square error was reduced by ∼40 %. From pollution development to dissipation, we divided the HAP in the NCP (especially in Beijing) into four phases: an early phase (EP), a transport phase (TP), an accumulation phase (AP), and a removal phase (RP). We then analyzed the evolutionary characteristics of PM2.5 concentration during different phases on the surface and in 3-D space. We found that the particles were mainly transported from south to north at a height of 1–2 km (during EP and RP) and near the surface (during TP and AP). The amounts of PM2.5 advected into Beijing with the maximum transport flux intensity (TFI) were through the pathways in the relative order of the southwest &gt; southeast &gt; east pathways. The dissipation of PM2.5 in the RP stage (with negative TFI) was mainly from north to south with an average transport height of ∼1 km above the surface. Our results quantified the multi-dimensional distribution and evolution of PM2.5 concentration over the NCP, which may help policymakers develop efficient air pollution control strategies.

A Closed-Form Localization Algorithm in Scan-Based Sonar

Multi-station TDOA positioning is a more accurate positioning method, which can locate the acoustic emitter by processing the arrival time of signals collected by three or more measuring stations. This paper presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based sonar (SBS).The basic principle of multi station TDOA passive location is proposed. Location scheme based on the combination of different platforms is used. The basic principle of TDOA passive location is discussed, as well as the realization scheme of the main problems such as the synchronization of observation station and acoustic target, the selection of ground observation station, error correction and so on.

Assessment of characteristics of surface solar irradiance on consecutive days using a self‐organizing map and clustering methods

AbstractThe assessment of surface solar irradiance as a resource is a major research topic in renewable energy. A method for assessing the characteristics of global horizontal solar irradiance (GHI) at the surface on consecutive days is proposed in this work. A self‐organizing map and cluster analysis are used as core analysis techniques in the proposed method. Using this method, the regional and seasonal characteristics of GHI for five consecutive days are analysed. One hour accumulated GHI data from ground observation stations in Japan are used. Time series assigned to each node on the topological map of the self‐organizing map are characterized by strength and variability. The probabilities of occurrence of each node on the map differ between stations. Then, using the probabilities as a feature vector for quantifying the characteristics of each observation station, a cluster analysis is conducted to divide stations into clusters according to the dissimilarity of the features. The results show that there are three major regions in which the station characteristics are similar in Japan. Additionally, another cluster analysis is conducted to investigate the seasonal characteristics of the occurrence of time series patterns. It is found that consecutive cloudy days occur frequently in winter and during the rainy season, whereas consecutive clear days occur frequently in spring and summer.