pure and applied geophysics最新文献

Predicting rainfall is crucial across multiple sectors and activities, impacting agriculture, water management and disaster preparedness. In this study, the Particle Swarm Optimization (PSO) algorithm is used to optimize the hyperparameters of hybrid deep learning and machine learning models such as Bidirectional Long Short-Term Memory (BiLSTM), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Artificial Neural Network (ANN) and Support Vector Regression (SVR). The performances of the PSO-optimized models are compared using the monthly rainfall dataset of Aizawl Weather Station and the all-India monthly average rainfall dataset. For the all-India rainfall datasets, the results of the PSO models are also compared with models from previous studies. The results show that, for the all-India rainfall dataset, the hybrid model PSO-BiLSTM IV achieved an RMSE of 225.12 and outperformed an existing RNN model by 14% and an existing single-cell LSTM, Vanilla LSTM and stacked LSTM by 11%, 10% and 8% respectively. In the Aizawl Weather Station dataset, the hybrid model PSO-BiLSTM II achieved the best result with an RMSE of 76.6, a benchmark result for this dataset. Overall, the hybrid PSO-BiLSTM models have the lowest RMSE score and the SVR models achieve the lowest performance for both datasets.

Research and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.

A total of 25 storms were recorded in the autumn–winter period of 2023–2024, with eight of these exhibiting a notable impact on the coast of the South-Eastern Baltic. As a consequence of this phenomenon, the western coast of the Sambian Peninsula of the Kaliningrad Oblast (Russia) was subjected to devastating effects: partial washout of the beach, flooded recreational infrastructure, the direction of the water flow changed and the formation of a local canyon, the dam of a flooded quarry broke through and was completely destroyed. The methodology for the integrated use of field measurement data, meteorological and hydrological information, re-analysis data, as well as satellite images was developed in order to analyses the effects of storms on inland coastal water bodies. Almost all storm events caused sea levels to rise, which had a devastating effect on the coast. As a consequence of the initial storm in October 2023, the inland water body was entirely obliterated, first becoming part of the sea and then a sandy beach. The most significant event was a series of storms in January and February 2024, which resulted in a 90 cm increase in the level rise relative to the pre-storm period. The storms brought with them a vast amount of precipitation, amounting to 51% of the total during the cold period. Rising sea levels and heavy precipitation caused the flooding of coastal lagoon lakes, changes in their thermohaline and oxygen regimes, as well as flooding of adjacent infrastructure.

Information regarding the uncertainty associated with weather forecasts, particularly when they are related to a localized area at convective scales, can certainly play a crucial role in enhancing decision-making. In this study, we discuss and evaluate a short-range forecast (0–75 h) from of a regional ensemble prediction system (NEPS-R) running operationally at the National Centre for Medium Range Weather Forecasting (NCMRWF). NEPS-R operates at a convective scale (~ 4 km) with 11 perturbed ensemble members and a control run. We assess the performance of the NEPS-R in comparison to its coarser-resolution global counterpart (NEPS-G), which is also operational. NEPS-R relies on initial and boundary conditions provided by NEPS-G. The NEPS-G produces valuable forecast products and is capable of predicting weather patterns and events at a spatial resolution of 12 km. The objective of this study is to investigate areas where NEPS-R forecasts could add value to the short-range forecasts of NEPS-G. Verification is conducted for the period from 1st August to 30th September 2019, covering the summer monsoon over a domain encompassing India and its neighboring regions, using the same ensemble size (11 members). In addition to standard verification metrics, fraction skill scores, and potential economic values are used as the evaluation measures for the ensemble prediction systems (EPSs). Near-surface variables such as precipitation and zonal wind at 850 hPa (U850) are considered in this study. The results suggest that, in some cases, such as extreme precipitation, there is a benefit in using regional EPS forecast. State-of-the-art probabilistic measures indicate that the regional EPS has reduced under-dispersion in the case of precipitation compared to the global EPS. The global EPS tends to provide higher skill scores for U850 forecasts, whereas the regional EPS outperforms the global EPS for heavy precipitation events (> 65 mm/day). There are instances when the regional EPS can provide a useful forecast for cases, including moderate rainfall, and can add more value to the global EPS forecast products. The investigation of diurnal variations in precipitation forecasts reveals that although both models struggle to predict the correct timing, the time phase and peaks in precipitation in the convection-permitting regional model are closer to the observations.

In aviation, accurate wind prediction is crucial, especially during takeoff and landing at complex sites like Gran Canaria Airport. This study evaluated five Deep Learning models: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), One-Dimensional Convolutional Neural Network (1dCNN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Gated Recurrent Unit (GRU) for forecasting wind speed and direction. The LSTM model demonstrated the highest precision, particularly for extended forecasting periods, achieving a mean absolute error (MAE) of 1.23 m/s and a circular MAE (cMAE) of 15.80° for wind speed and direction, respectively, aligning with World Meteorological Organization standards for Terminal Aerodrome Forecasts (TAF). While the GRU and CNN-LSTM also showed promising results, and the 1dCNN excelled in wind direction forecasting over shorter intervals, the vRNN lagged in performance. Additionally, the autoregressive integrated moving average model underperformed relative to the DL models, underscoring the potential of DL, particularly LSTM, in enhancing TAF accuracy at airports with intricate wind patterns. This study not only confirms the superiority of DL over traditional methods but also highlights the promise of integrating artificial intelligence into TAF automation.

This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

The work presents the development of Rhines' theory for mixed topographic-planetary waves in a stratified ocean on a background current. The vertical anisotropy of baroclinic Rossby waves has been established depending on the slopes of the topography. If for positive slopes (water shallowing to the north) the baroclinic mode node shifts downward, then for negative slopes the frequency and phase velocity decrease and the vertical node shifts upward. Estimates of frequency variability for vertical modes at a negative bottom slope were obtained. For weak changes in bottom topography in the long-wave limit, an analytical asymptotic expression of the dispersion relation for the surface mode is constructed for positive and negative slopes. The dispersion curves in one-dimensional and two-dimensional cases were analyzed numerically. It is shown that the range of influence of topography on baroclinic waves reaches maximum deviations of the order of 50% in the long-wave part of the spectrum.

Extremely severe cyclonic storm (ESCS) ‘Fani’ formed in the North Indian Ocean and crossed at Puri in Orissa State on the east coast of India on 03 May 2019. In this study, we examine the sensitivity of convection permitting WRF simulations (3 km) of ‘Fani’ to cloud microphysics (CMP) schemes using radar and multi-satellite data products. Five CMP schemes, namely Thompson, Goddard, WSM6, Morrison and Lin are tested in WRF. Results show that the changes in the CMP schemes primarily affect the simulated intensity and have lesser impact on the track predictions. Simulations with Thompson followed by Goddard produced the best predictions for both track and intensity estimates. Our analysis reveals significant variations in vertical motions associated with Fani across different CMP schemes; the WSM6, Goddard and Lin schemes produced relatively stronger vertical motions. The explicit WRF simulations could reproduce the wind profiler radar observed intense convective motions during the transit of Fani between 1 and 2 May 2019 at Gadanki station. Experiments with Thompson and Goddard schemes simulated the mean vertical velocities in lower, middle and upper layers in better agreement with radar data. The Lin, WSM6 and Goddard CMP predicted stronger updraft velocities (~ 0.35 m/s); Thompson produced moderate updraft velocities (~ 0.25 m/s) in the upper troposphere over a relatively wider area of high theta-e (385–390 K) indicating the simulation of a convectively stronger and warmer core compared to Morrison. Our analysis suggests that the differences in vertical motions in various CMP simulations are mainly due to the variations in the warming in simulations. It has been found that WSM6, Lin and Goddard produced a deeper core (up to 200 hPa) with a stronger diabatic heating of ~ 6° C followed by Thompson, which simulated a moderately deep core extending to ~ 250 hPa with moderate heating of ~ 5 °C whereas Morrison produced a relatively weak core with a heating of ~ 4 °C limited to 300 hPa. The stronger simulated diabatic heating in Lin, WSM6 and Goddard produces stronger inflow, moisture convergence in the lower levels and stronger outflow and divergence in the upper levels leading to stronger convection in the core region in these cases. The Lin, WSM6 and Goddard mixed phase schemes with more solid hydrometeors simulated stronger radar reflectivities, and stronger eyewalls, due to more latent heat release leading to the development of a strong warm core in the upper troposphere and thus a stronger TC.

The Siang Valley of Arunachal Pradesh, North-East India, is one of the seismotectonically active region that lie in the eastern Himalayan Syntaxis (EHS); we have investigated seismicity, fault plane solutions (FPS) and P (Pressure) axis orientation in this region. We have analyzed 756 local earthquakes of magnitude range (1.0 ≤ M_L ≤ 5.9) in the region during the period from January 2019 to December 2021. From the spatial distribution of local seismicity, it is estimated that the concentration of seismicity is in Namcha-Barwa, western and eastern flanks of Siang Antiform, respectively. The depth distribution of seismicity extends upto a focal depth of 60 km with a higher concentration in the upper crustal part. Further, we determined 15 fault plane solutions (FPS) using waveform inversions (ISOLA) for events with a magnitude range of 3.5 to 5.9. The waveform inversion has been performed for the events with maximum azimuthal coverage. The frequency band used for the inversion is in the 0.01–0.1 Hz range corresponding to the maximum signal to noise ratio to precise crustal velocity structures, hypocenter positions, and appropriate frequency ranges were used to obtain reliable FPS. The FPS obtained for the shallow focused earthquakes shows Normal faulting with Strike-slip components. The compressional axes orientations of the thrust FPS show a north-east direction. The intense seismic activity and compressional axis orientation in this study area is due to the collision between Indian and Eurasian plate in the north and and east-ward subduction of Indian plate below the Burmese plate.

Knowledge of the tracer characteristics, such as water vapor and ozone in the tropical tropopause layer (TTL), is vital in quantifying the radiation budget and critical to understanding the exchange processes between the troposphere and stratosphere. In this study, we have characterized the tropical tropopause parameters such as the cold point tropopause (CPT) height (CPT-H) and temperature (CPT-T), convective tropopause (COT) height (COT-H) and temperature (COT-T), and the tropical tropopause layer (TTL) using radiosonde observations during 2014–2019 over Chennai (13.0° N, 80.06° E) located in the northeast (NE) monsoon region. The water vapor and ozone data from the microwave limb sounder (MLS) simultaneous to the radiosonde observations are also utilized to understand their roles on the CPT variations for different convective conditions obtained from Infrared brightness temperature (IRBT) data. CPT over Chennai becomes higher (17.6 ± 0.3 km) and colder (189.7 ± 0.9 K) during the winter season and lower (16.6 ± 0.2 km) and warmer (192.1 ± 1.0 K) during the summer monsoon season, however, not in the same month. The water vapor (CPT-W) and ozone (CPT-O) mixing ratios at CPT are found to be lower (~ 70 ± 1.4 ppmv and 3.1 ± 0.4 ppmv) during the winter season and higher (153 ± 4.2 ppbv and 4.8 ± 0.6 ppmv) during summer monsoon season. COT, however, becomes lower (12.4 ± 0.3 km) and higher (13.3 ± 0.3 km) during pre-monsoon and summer monsoon seasons, respectively. The TTL thickness is lesser (3.5 ± 0.6 km) during the winter and greater (4.8 ± 0.8 km) during the summer monsoon seasons. Over Chennai, the seasonal variation of the upper troposphere and lower stratospheric temperature, water vapor, and ozone anomalies are in phase. We have categorized tropical convections as non-penetrative and penetrative using IRBT data. It is observed that the TTL temperature warms with the increasing strength of the non-penetrative convections and cools for the penetrative convection.