Meteorology and Atmospheric Physics最新文献

One of the key attributes of an ensemble prediction system (EPS) is the spread among the members. It plays a crucial role in conveying the uncertainty associated with the forecasted parameters. It is a quantitative measure of forecast uncertainty, provides a range of possible outcomes, and helps in the assessment of risk and decision making. Additionally, the spread can also serve as a diagnostic tool for assessing the reliability and variability among the ensemble members. If the spread is consistently narrow, it may indicate that the ensemble members are not diverse enough and the uncertainties may not be adequately captured resulting in sub-optimal decision making. In this study, the rainfall forecasts from two EPSs over India have been assessed during four monsoon seasons (2019–2022) with an aim to boost the ensemble spread by constructing a ‘Grand Ensemble’. The two high-resolution operational global EPSs of Ministry of Earth Science (MoES) in India are (i) National Centre for Medium Range Weather Forecasting (NCMRWF) EPS (NEPS) which has a 12 km grid, and 23 members and (ii) Global Ensemble Forecast System (GEFS) with a 12 km grid and 21 members. Both EPSs have been used for operational medium range forecasts out to Day-10 since 2018. The MoES Grand Ensemble (MGE) constructed by combining the two EPSs (NEPS & GEFS), features a higher spread and an improved Spread Vs Bias relationship compared to the constituent models. Further, the results indicate lowest CRPS in the MGE compared to the constituent EPSs, over the Indian land region. The improved performance of MGE is also demonstrated for moderate and heavy rainfall events using Brier Skill Score (BSS), Reliability Diagram and ROC curves.

A 41-year dataset from 1982 to 2022 analyzed climatic patterns influencing cyclone formation in the Bay of Bengal (BoB). Results showed a significant increase in sea surface temperature (SST) and a warming trend over the past four decades. Specific humidity increased while wind shear decreased. The moisture budget showed increased precipitation and evaporation rates, possibly due to more warming scenarios. Tropical Cyclones (TC) experienced significant increases in SST anomalies. These anomalies were higher during cyclonic than non-cyclonic years, except for 2015, due to El Niño conditions. Tropical Cyclone Heat Potential (TCHP) values increased in cyclonic years, while specific humidity (SH) anomalies increased 10–15 days before cyclone formation. Moist static energy (MSE) values increased across the BoB region, with TCs Amphan, Yaas, and Asani exhibiting significant positive relative vorticity (RV) anomalies. The Madden-Julian Oscillation (MJO) plays a crucial role in TC initiation and intensification, with recent TC demonstrating this. In general, the Empirical Orthogonal Function (EOF) analysis of SST, upper-level moisture, and low wind shear for May over the BoB reveals more conducive conditions for TC intensification. Furthermore, it is also found that the negative phase of the Indian Ocean Dipole (NIOD) associated pre-monsoon month of May has produced more intense TCs in recent years over BoB. The findings of this study will facilitate augmenting existing knowledge and understanding about the genesis and intensification of pre-monsoon TCs over BoB.

Precipitation observation data from different departments are highly complementary in the application, but there are some differences in observation equipment, data sampling methods, accuracy, and data transmission methods. To better apply the precipitation data of different departments in meteorological services, it is necessary to carry out a quality control method. In this study, rain gauge precipitation data, radar data, and satellite data from the China Meteorological Administration are used to perform collaborative quality control of precipitation data from the Ministry of Water Resources of the People’s Republic of China. The threshold value, spatial consistency, and temporal consistency are verified using meteorological station precipitation data, and the relationship thresholds between satellite and radar products and hourly precipitation are summarized and verified for consistency. Subsequently, collaborative quality control results are derived using a comprehensive scoring method. Testing this quality control method suggests that the method will not classify too many correct data as mistakes and the detection rate of incorrect data can be more than 0.7. Following quality control, the hourly precipitation error for hydrological station data fell dramatically, the False Alarm Rate decreased by 19%, and the anomalous maxima were successfully eliminated. Therefore, this collaborative quality control method can compensate for the deficiencies of a single quality-control source, thus allowing precipitation data not from the meteorological industry to be screened effectively.

Numerical weather models often face significant challenges in achieving high prediction accuracy. To enhance the predictive performance of these models, a solution involving the integration of deep learning algorithms has been proposed. This paper introduces a machine learning approach for correcting the numerical weather forecast results from the Weather Research and Forecasting (WRF) model. Initially, the WRF model is used to simulate summer precipitation in the Jinsha River Basin. Subsequently, the adaptive noise-robust empirical mode decomposition (CEEMDAN) method is employed to decompose WRF simulation errors. These decomposed subsequences are then input into four machine learning algorithms and two metaheuristic optimization algorithms to predict the error sequences. Finally, the predicted error subsequences are merged and superimposed on the WRF simulation values to obtain the corrected precipitation. Research findings demonstrate that the integration of machine learning algorithms with WRF significantly improves prediction accuracy. The correlation coefficient of the optimal model increases by 158%, and Nash-Sutcliffe Efficiency (NSE) increases by 149% compared to before correction. This indicates that correcting the WRF model through deep learning methods effectively enhances precipitation forecasting accuracy.

The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions.

The analysis of urban thermal environment based on Local Climate Zone (LCZ) is helpful to understand the fine structure of urban heat island (UHI), so as to provide a scientific basis for urban ecological environment management. This research focused on the three biggest cities, Shanghai, Nanjing and Hangzhou, in Yangtze River Delta (YRD) and the UHI characteristics in a heatwave month (July 2017) were investigated. Based on the observations of automatic weather stations, the spatiotemporal characteristics of air temperature and canopy urban heat island intensity (UHII) of each LCZ in three cities under different weather conditions were compared and analyzed by using the LCZ clustering method, and the effects of water bodies, urban greening and sea breeze on urban heat island were discussed. Results show that the air temperature and urban heat island intensity of different LCZs would vary due to the differences in urban geometry, building materials, the proportion of impervious surface and anthropogenic heat. The LCZ based UHII in the three YRD typical cities showed similar characteristics: compact high-rise (LCZ 1), compact mid-rise (LCZ 2) and open mid-rise (LCZ 5) had higher UHII while sparsely built (LCZ 9) had lower UHII. The diurnal variation of UHII in the three cities are different: the UHII diurnal curves of Nanjing and Hangzhou were “U” type, while that of Shanghai was shallow “W” type, which was because Shanghai was vulnerable to sea breeze during the summer day. In addition to land and sea location, large water bodies and urban greening would also impact the spatiotemporal patterns of urban thermal environment.

In this study, we have tried to find out the influence of air-sea heat fluxes (particularly the Surface Latent Heat Flux (SLHF) and the Surface Sensible Heat Flux (SSHF)) on the intensity of Intense Tropical Cyclones’ (ITCs’). We have analysed 32 ITCs which originated in the Bay of Bengal (BoB) during 1990–2019. We have used IMD best track data for track and vital parameters of ITCs. We have also used high resolution (0.25°×0.25°) ERA5 SLHF, SSHF and SST data for their variations during the lifetime of ITCs. It is observed that the SLHF_max during the whole lifetime and the study period is highly correlated with ITCs’ intensity (i.e. with the central pressure (CP) and the maximum sustained wind speed (MSW)) whereas the SSHF_max shows weak correlations with ITCs’ intensity. This suggests the strong association between the SLHF_max and ITCs intensity and strong latent heat flux exchange between the ocean and atmosphere during the whole lifetime and the study period. Similar results are observed in the pre-monsoon and the post-monsoon season. In the pre-monsoon season the association of SLHF_max with the ITCs intensity is stronger than the post-monsoon season due to strong background conditions, pointing towards the strong air-sea interaction. The SLHF_max in the growing and the decaying stage are also highly correlated with ITCs’ intensity but correlation coefficients of ITCs’ intensity with the SLHF_max in the decaying stage are slightly higher than those of in the growing stage. It is also found that the SSHF_max has an appreciable correlation with ITCs’ intensity during the growing stage whereas it has negligible correlation with ITCs’ intensity during the decaying stage pointing towards the influence of sensible heat flux in the growing stage of ITCs. It is also noticed that during rapid decay (RD) the SLHF_max is highly correlated with ITCs’ intensity possibly due to cold wakes which rapidly diminishes the SLHF but during rapid intensification the SLHF does not increase so rapidly due to the sluggish WISHE feedback, hence the SLHF_max during rapid intensification (RI) is not appreciably correlated with ITCs’ intensity.

Rainfall variability has a significant impact on hydrological cycle. Understanding rainfall variability over Fiji Islands is important for decision-making in the backdrop of global warming. Reanalysis rainfall products are commonly used to overcome observed data quality challenges especially over ungauged highland areas. However, an evaluation of reanalysed datasets is important to ensure accurate and reliable climate information generated using such datasets, especially for small Island with high variable topography like Fiji. This work aims to validate the spatiotemporal performance of European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis rainfall (ERA5) data against ground-based station data from 19 stations for the period 1971–2020 over Fiji Islands. Correlation coefficient and difference statistics: bias, and root mean square error, are used to assess the performance of the data. Further, common Empirical Orthogonal Function (common EOFs) analysis was used to evaluate spatiotemporal performance of ERA5 datasets. The results of the station-by-station comparison shows that interpolated ERA5 annual rainfall matches the corresponding results from rain gauges remarkably well for many stations. The correlation coefficient values range from 0.5 to 0.85, while the bias spans from a negative 282 to a positive 575, and the root mean square error (RMSE) varies between 285 and 662 mm for the annual rainfall across the study area. However, there is overestimation and underestimation of the observed rainfall by ERA5 datasets. The leading common EOF principal component for annual rainfall suggests that the inter-annual variability in ERA5 dataset is generally consistent with observed station datasets, cross validation results indicated high scores (correlations of 0.82), with limited spatial variation. This work presents a reliable data assessment of the ERA5 data over Fiji Islands, indicating there is good match of the annual observed rain gauged station data and ERA5. The findings give accuracy references for further use of the ERA5 data in understanding rainfall variability and change over the region.

On October 13th, 2020, at approximately 1940 UTC, a hailstorm struck the city of Itajubá, located in the south of the Minas Gerais state, Brazil. This hailstorm produced hail with a diameter of 5 cm causing damages in roofs of houses and shelters. In this sense, the objective of this study is to describe the synoptic-scale environment that lead to the “ingredients” necessary for the mesoscale development of the storm, and to provide a description of cloud microphysical and lightning properties. Several data sources were used in this study as: surface observations, reanalysis data, and atmospheric remote sensing information. The synoptic-scale environment conducive to storm formation was associated with an inverted trough at surface and a shortwave trough at upper-level levels, which were important to organize upward movements in the atmosphere. High reflectivity (> 60 dBZ) was registered in the convective cell from 1940 to 2010 UTC, according to the São Roque radar data, indicating the presence of large raindrops and/or hail on the ground. The total lightning rates increased from the beginning of the storm, reaching ~ 80 lightning/5 min around 20 min before the hail precipitation, which occurred at 1920 UTC. This study highlights the importance of associating synoptic and physical information for understanding the environment and the main features of hailstorms. It also emphasizes the significance of producing information that can aid in nowcasting tools.

The climate indices demonstrate temporal and spatial variability of large-scale ocean–atmosphere patterns. This study has been carried out to analyze the streamflow data in Turkey to understand the effects of climate indices such as the Southern Oscillation (SO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO). The periodical relationship of the streamflow data of Turkey over different atmospheric oscillations was investigated. For this purpose, the average annual and seasonal flows at the current 72 stations in other regions of Turkey have been studied. In this context, the correlation analysis determined the relationship between NAO, AO, SO indices, and stream-flows. Besides, the original observed data were separated into parts by discrete wavelet transform to obtain the periodic components. The correlations between the found periodical components and atmospheric indices were also examined. The correlations between the streamflow and the AO/NAO showed a strong negative relationship during the annual/winter and spring periods, especially in western Turkey. Besides, the periodic components showed us the multi-annual connections between the global indices and the streamflow data of Turkey.