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- A Physical Mechanism-based Scheme for Parameterizing the Fractional
Vegetation Cover-
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Abstract: The leaf area index (LAI) and fractional vegetation cover (FVC) are very important parameters in land–atmosphere interactions. In this study, a very simple but robust and mechanism-based method was developed to derive FVC data based on the relationships between the canopy gap fraction, LAI, and direct solar extinction coefficient. For validation, the LAI data and NDVI-based and mechanism-based FVC data were assimilated into the integrated urban land model (IUM). Using the mechanism-based FVC data as the input, the simulation of the annual average land surface temperatures (LSTs) in the Beijing area were improved compared with those using the NDVI-based FVC data as the input.
PubDate: 2024-05-01
- Forecast Modeling of Invasive and Climate-driven Scenarios of Pest
Outbreaks-
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Abstract: The climate change observed in the zone of boreal forests of the Holarctic since the end of the 20th century initiates the effect of expanding the boundaries of biological species ranges. Climate-driven invasive processes differ in dynamics. In some situations, there are population outbreaks of unwanted species. In addition to the climatic factor, an important aspect is the response of a biotic environment. Special methods are required to predict rapid invasions that can cause extreme changes. The reproductive potential of pests often turns out to be excessive due to warming climate and favorable conditions. Aggressive invasions often develop as oscillating processes that transform when the species adapts to the environment and fades when the autochthonous biota adapts to a new species. Not only new pests, but also the enemies of the main enemies of ordinary pests have become harmful invaders. Computational scenario models of invasions have been developed based on a logically expandable hybrid structure of equations that take into account delayed adaptation, which is manifested depending on climatic factors as an invasion outbreak develops. The scenarios indicate the series of peaks with fading activity after a primary outbreak and make it possible to evaluate the factors that cause repeated activity of a population after a depression when the invasion of a hyperparasite turns out to be essential.
PubDate: 2024-05-01
- Effect of Decision Tree in the ANFIS Models: An Example of Completing
Missing Data-
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Abstract: Missing data in water resources studies prevent planning. For this reason, data estimation studies are carried out. In this study, ANFIS (Adaptive Neural Fuzzy Inference System) was used to complete the missing data. At the study area, the Yesilirmak Basin located in the north of Turkey, input variables from seven stations and output variable from one station were determined. In the research, 80% (378 months of data) of 504 months of the flow data between 1969 and 2011 was used in the training phase and 20% (126 months of data) was employed in the testing one. The decision tree was used instead of the trial and error method in the selection of input variables and determining the number of membership functions in ANFIS models. It was concluded that the ANFIS model established with the information obtained from the decision tree is successful compared to the randomly established ANFIS models. Using the decision tree before ANFIS models are created will not only minimize the time spent on the model development, but also prevent the best of the possible models from being overlooked.
PubDate: 2024-05-01
- Analysis of Average Daily Temperature and Precipitation on the Territory
of Belarus Using Quantile Regression-
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Abstract: Average daily air temperature and daily total precipitation were analyzed using quantile regression. Quantiles corresponding to the selected extremes, i.e., below 0.1 (only for the analysis of air temperature) and above 0.9 were considered. It has been shown for the majority of cases that the spatial distribution of air temperature quantiles is close to the mean for winter and summer seasons. Specific features of temporal changes in the quantiles of air temperature and total precipitation are in line with the modern climate warming. A statistically significant relationship between air temperature and Earth surface characteristics is observed only for quantiles of 0.9 and more. Due to the complexity of the factors responsible for the formation of high-intensity precipitation, there is no clear pattern in the spatial distribution of the quantiles of daily total precipitation. There is statistically significant relationship between daily total precipitation, orography, and forest cover fraction on the territory.
PubDate: 2024-05-01
- Light Climate in Moscow
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Abstract: The light climate of Moscow is presented based on the long-term observations of natural illuminance of the Earth’s surface, illuminance of differently oriented vertical surfaces, and factors influencing their variability, which have been performed at the Moscow State University Meteorological Observatory. The obtained normals are sufficient for any practical applications in most cases. An issue analyzed in the study is conditions that lead to a decrease in illumination below the critical values for which the use of combined or artificial lighting of premises is required. It is shown that illumination can be forecasted based on low-level clouds predicted using a general weather forecast.
PubDate: 2024-05-01
- Variation Patterns of CO2 and CH4 according to the Measurements in the
Surface Atmosphere over Urban and Suburban Areas in 2021–2022-
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Abstract: The patterns and factors of changes in CO2 and CH4 concentrations in the surface atmosphere of an urbanized and suburban environment were analyzed based on the results of the synchronous measurements in Yekaterinburg and the Kourovka Astronomical Observatory (KAO) in September 2021–August 2022. On average, the maximum levels of CO2 in Yekaterinburg (443.2 ppm) were shown to be higher than in KAO (432.4 ppm) and were reached in January. The minima, on the contrary, were lower in the city than in the suburban area (405.4 ppm in July in Yekaterinburg against 412.7 ppm in September in KAO). Enhanced CO2 levels in the warm season in KAO were caused by very high nighttime concentrations (up to 500 ppm), which was not observed in the surface urban atmosphere. For CH4, the seasonal dynamics in the city and in KAO was similar: the maximum levels were reached in January (2.154 and 2.076 ppm), and the minima were registered in June (1.998 and 1.971 ppm). The mutual influence of the territories under consideration was assessed to be moderate. The results of the study can be used to develop a technology for assessing the carbon balances on a regional scale, which is the main task of the Carbon Supersites program in the Urals.
PubDate: 2024-05-01
- Influence of External Parameters on Evapotranspiration in the INM
RAS–MSU Land Surface Model-
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Abstract: The paper investigates the sensitivity of evapotranspiration in the INM RAS–MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002–2012) from the surface of medium-sized (2−50×103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys.
PubDate: 2024-05-01
- Statistical Correction of the SL-AV Model Long-term Forecasts of Surface
Air Temperature for the Territory of Northern Eurasia-
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Abstract: For the territory of Northern Eurasia, a scheme for the statistical correction of surface air temperature forecasts has been developed for periods of 1–4 months on the basis of the SL-AV model using the MOS concept. For statistical correction of operational temperature forecasts, the regression parameters and EOF expansion coefficients obtained by cross-validation on historical forecasts were used. Due to the internal relationships of the model output data, the proposed scheme allows improving the skill of surface characteristic forecasts. A significant improvement in the skill of deterministic air temperature forecasts by using statistical correction is manifested in transition seasons. The scheme of statistical correction is constantly evolving. Further development of the statistical correction technology involves the use of neural networks and forecast indices of atmospheric circulation.
PubDate: 2024-05-01
- Spatiotemporal Changes in Cyclogenesis and Precipitation Regime over the
Euro-Atlantic Sector in 1979–2019-
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Abstract: The study presents the classification of cyclones according to the region of origin and track in the Euro-Atlantic sector. The cyclones have been identified according to the ERA5 reanalysis data. Their seasonal frequency, travel speed, size, and central pressure have been quantified, and their trends have been revealed. Mean and maximum total precipitation associated with the distinguished types of cyclones over the territory of Europe is determined. It is shown that the frequency of the North Atlantic cyclones in the recent 40 years has decreased in winter, summer, and autumn and increased in spring. It has been revealed, that the frequency of the southern cyclones insignificantly decreases in summer and increases in winter. A decrease in minimum central pressure for some types of the North Atlantic cyclones occurs in winter and summer. There is an increase in maximum total precipitation in winter due to the North Atlantic cyclones and in summer due to the southern cyclones. The number of days with cyclonic precipitation decreases for all types of cyclones.
PubDate: 2024-05-01
- Preliminary Data Processing of the MSU-GS/VE Device aboard the Arktika-M
No. 1 Highly Elliptical Satellite Using Machine Learning Methods-
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Abstract: The paper presents the research work aimed at improving the quality characteristics of information products based on the MSU-GS/VE radiometer aboard the Arktika-M No. 1 satellite, as well as at obtaining data preprocessing products. All described methods are based on using machine learning algorithms, namely, neural networks of various architectures. The results of developing a technology for minimizing the interference that occurs in the channels of the satellite device are provided. The work on detecting cloud formations based on processing the channel data in the visible and infrared ranges is presented. It is shown that the use of neural networks makes it possible to implement automatic algorithms for obtaining thematic products that take into account various factors and have an accuracy that is commensurate with statistical and physical approaches and reduces the time of satellite data processing.
PubDate: 2024-04-01
- Assessment of Atmospheric Ozone from Reanalysis and Ground-based
Measurements in the Baikal Region-
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Abstract: The machine learning model used to predict ozone concentrations at the Listvyanka monitoring station in the Baikal region is described. The model was trained and verified using automatic ground-based gas analyzer ozone measurements. Random forest and boosting machine learning models were used. According to the ERA5 reanalysis, the mean absolute error of ozone values exceeds 16 ppb, and the mean percentage error is 80%. The respective errors in the ozone values calculated using machine learning models are 6.7 ppb and 29%. The results of forecasting are the most sensitive to the season, air temperature, and vegetation. The ozone values for 2017–2022 were simulated and analyzed using the trained model and reanalysis data.
PubDate: 2024-04-01
- Artificial Intelligence and Its Application in Numerical Weather
Prediction-
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Abstract: Artificial intelligence is one of the most popular, frequently discussed, and, meanwhile, ambiguous and controversial metaphorical concepts, which defines a scientific direction in computer science that studies the techniques for gaining knowledge, their computer representation, transformation, and application. Presently, it is intensively penetrating into many areas of human activities, including hydrometeorological ones. The concept of artificial intelligence, the history of its origin, and its methods and technologies are considered. The author analyzes the studies related to the use of artificial intelligence in short- and medium-range weather forecasting, including the collection and quality control of meteorological information, assimilation of data in order to generate initial conditions for numerical weather prediction models, development of forecast models and parameterization schemes for physical processes, postprocessing and physical-statistical interpretation of the output data of numerical weather prediction models.
PubDate: 2024-04-01
- Using Machine Learning Methods to Develop an Algorithm for Recognizing a
Risk of Waterspout Occurrence off the Black Sea Coast of Russia-
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Abstract: Every year about 50 waterspouts occur over the sea off the Black Sea coast of Russia. Over the past few years, the cases of waterspouts have occurred in the immediate vicinity of the coast with their subsequent destruction. The vortex destruction is often accompanied by short-term wind strengthening up to storm levels. The present study solves the problem of nowcasting the Black Sea waterspouts (building a detailed forecast of their formation for the next 2–6 hours) using machine learning methods. Learning by precedents is considered based on the labeled dataset of the radar characteristics of convective systems with and without waterspouts, models for classifying systems in terms of the risk of waterspout occurrence are constructed. The testing of the models showed that it is fundamentally possible to use them to diagnose systems with already formed waterspouts, as well as to identify the risk of waterspouts in advance (within two hours).
PubDate: 2024-04-01
- A Method for Object-oriented Detection of Deep Convection from
Geostationary Satellite Imagery Using Machine Learning-
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Abstract: Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013–2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode.
PubDate: 2024-04-01
- Satellite Data Processing for Hydrometeorologal Research with the Use of
Neural Network Technologies: The Approaches Used at Planeta State Research
Center on Space Hydrometeorology-
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Abstract: The paper presents an experience of using artificial intelligence techniques, in particular, neural networks to solve relevant problems of hydrometeorology. The results of the investigations at the Planeta State Research Center on Space Hydrometeorology in detecting clouds and snow cover from the Himawari, Electro-L, and Meteor-M satellite data, as well as on classifying cloud types according to the AHI instrument data (Himawari-8) are reported. The findings of the work on retrieving values of total ozone and water vapor according to the infrared sensing devices are demonstrated. The work on detecting the boundaries of the ice cover and river floods from medium- and high-resolution satellite instruments, as well as the technologies for temperature and humidity sensing in the microwave spectrum are considered. The studies have shown that the use of neural network technologies provides the required accuracy of the received hydrometeorological information and high speed of processing incoming data.
PubDate: 2024-04-01
- Using the Neural Network Technique for Lead Detection in Radar Images of
Arctic Sea Ice-
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Abstract: The paper describes an algorithm to differentiate leads from sea ice using the dual polarization synthetic aperture radar (SAR) data from the Sentinel-1 satellite in an extrawide swath mode. The algorithm uses the polarimetric features of the sea surface signal obtained in the SAR images: the ratio between co- and cross-polarization. A technique is proposed for classifying the SAR images to identify discontinuities (cracks, leads) in drifting sea ice using the ratio and difference of polarizations together with texture features and the neural network implementation. The method was tested using the satellite data obtained over the Arctic seas in the Russian Federation.
PubDate: 2024-04-01
- Application of Convolutional Neural Networks for Detecting Sea Ice Leads
in the Laptev Sea with Landsat-8 Satellite Imagery-
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Abstract: A method for detecting leads in the ice of the Arctic seas from satellite images of the visible range is presented. It is shown that sea ice leads are formed under the influence of dynamic processes in the ice cover, such as convergence, drift, and deformation of sea ice, as well as during the interaction of drifting ice with icebergs that have gone aground. The method for identifying sea ice leads is based on the use of artificial intelligence. To analyze the Landsat-8 satellite imagery, a convolutional neural network (U-Net architecture) was used. The method was tested using the satellite images of the visible spectral range that were obtained for the Laptev Sea. The results showed that the lead detection accuracy was above 80%. The method of the minimum rotated rectangle surrounding the polygon was used to determine the geometric parameters of the leads (length, width, inflection points).
PubDate: 2024-04-01
- Application of Physical and Neural Network Methods in Operational Water
Surface Detection-
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Abstract: The paper presents some methods of satellite data preprocessing for the elimination of atmospheric effects on the electromagnetic radiation detected by the target equipment of a satellite and subsequent detection of floods in the Amur River basin. The atmospheric correction algorithm that has been used for the preprocessing is based on the use of a lookup table obtained by applying the Second Simulation of a Satellite Signal in the Solar Spectrum, which is a model of atmosphere radiative transfer. The subsequent flood detection in the Amur River basin water bodies builds on a neural network algorithm, the core of which is the upgraded U-Net. The developed algorithms for atmospheric correction and subsequent flood detection make it possible to receive information in an automatic near-real-time mode for monitoring flood conditions. Some groundwork has been made for applying the algorithm to the data of the Russian satellite instruments for spacecraft planned for launch.
PubDate: 2024-04-01
- A Method for Predicting Fog and Identifying Its Type Based on Neural
Networks for the Saint Petersburg (Pulkovo) Airfield-
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Abstract: Fogs have a serious impact on human activity, in particular, on aviation, since they significantly impair visibility and therefore make aircraft landing difficult. In most cases, fogs cause irregularity of flights and sometimes lead to disasters, so timely and accurate forecasting of the onset of fog and its type is very important. At present, numerical methods greatly facilitate the forecasters’ work, but the problem of predicting visibility and fog remains relevant. Artificial intelligence technologies, in particular, deep learning algorithms using various kinds of neural networks are currently becoming more widespread in hydrometeorological activities. In the present study, the main objective is to develop a method for predicting the appearance of fog and to identify its type based on neural networks. The results of testing the method have showed its practical usefulness.
PubDate: 2024-04-01
- Using the Methods of Neural Network Learning for Peak Water Level
Prediction: A Case Study for the Rivers in the Dvina-Pechora Basin-
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Abstract: The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10–20% as compared with regression dependencies.
PubDate: 2024-04-01
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