Meteorology and Atmospheric Physics最新文献

Abstract

An accurate quantification of fluxes from heterogeneous sites and further bifurcation into contributing homogeneous fluxes is an active field of research. Among such sites, fragmented croplands with varying surface roughness characteristics pose formidable challenges for footprint analysis. We conducted two flux monitoring experiments in fragmented croplands characterized by two dissimilar surfaces with objectives to: (i) evaluate the performance of two analytical footprint models in heterogeneous canopy considering aggregated roughness parameters and (ii) analyze the contribution of fluxes from individual surfaces under changing wind speed. A set of three eddy covariance (EC) towers (one each capturing the homogenous fluxes from individual surfaces and a third, high tower capturing the heterogeneous mixed fluxes) was used for method validation. High-quality EC fluxes that fulfill stationarity and internal turbulence tests were analyzed considering daytime, unstable conditions. In the first experiment, source area contribution from a surface is gradually reduced by progressive cut, and its effect on high-tower flux measurements is analyzed. Two footprint models (Kormann and Meixner ‘KM’; analytical solution to Lagrangian model ‘FFP’) with modified surface roughness parameters were applied under changing source area contributions. FFP model has consistently over predicted the footprints (RMSE_FFP = 0.31 m⁻¹, PBIAS_FFP = 19.00), whereas KM model prediction was gradually changed from over prediction to under prediction towards higher upwind distances (RMSE_KM = 0.02 m⁻¹, PBIAS_KM = 8.50). Sensitivity analysis revealed that the models are more sensitive to turbulent conditions than surface characteristics. This motivated to conduct the second experiment, where the fractional contribution of individual surfaces (α and β) to the heterogeneous fluxes measured by the high tower (T3) was estimated using the principle of superposition (FT3 = α FT1 + β FT2). Results showed that α and β are dynamic during daylight hours and strongly depend on mean wind speed (U) and friction velocity (u*). The contribution of fluxes from adjoining fields [1 − (α + β)] is significant beyond 80% isopleth. Our findings provide guidelines for future analysis of fluxes in heterogeneous, fragmented croplands.

This paper presents a first-time satellite-based air mass analysis over the Indian region using Indian National SATellite System (INSAT)-3DR sounder data. The Indian region is characterized by circulations and air mass fronts which have an influential role in deciding the synoptic weather. Usually, air mass analysis is carried out using radiosonde and atmospheric model-based reanalysis data. Both these datasets have intrinsic limitations due to sparse observations and other error sources. The present study is carried out using meteorologically significant satellite-derived 850 hPa level mixing ratio, optical depth, and equivalent potential temperature. The study elicits relative movements and mixing of different air masses during different seasons over the Indian region. The air mass features are well represented by mixing ratio and optical depth compared to potential temperature. The study brings forth prominent interacting air masses and their relative abundance during different seasons. The statistical analysis of air masses during different seasons at 850 hPa estimates the average values of physical attributes concerning different air masses. From the case analysis of air masses, at 850 hPa, a dry pool of mixing ratio is observed during the pre-monsoon (April–May) months. The analysis suggests that the reason for the formation of dry pool over Bay of Bengal and Arabian Sea is frequent anticyclonic formation during the period. The present study limelights the potential of satellite-derived mixing ratio profiles to understand the weather features associated with air mass interactions over the Indian region.

The nonlinear scaling of meteorological processes is an issue of much interest. The objectives of the present work were (a) to investigate cross-correlations between pairs of meteorological time series using different resolutions and (b) to explore the long-range cross-correlations through different scaling exponents. We used 13 years of daily records of rainfall, relative humidity, cloudiness and vapor pressure ranging from January 1st 1996 to December 31st 2009. Data sets were compiled from Veguita agro-meteorological station at Granma province, Cuba. Detrended cross-correlation analysis, multiscale sample entropy, Lévy-stable laws and Hurst–Kolmogorov dynamics were the main methodological and theoretical tools. The detrended cross-correlation coefficient showed significant cross-correlation between rainfall, relative humidity, cloudiness and actual vapor pressure at all investigated time scales. The individual Hurst exponents were in the range 0.62 ≤ H ≤ 0.72 which is consistent with long-range correlated patterns. Bivariate Hurst exponents (H_xy) were larger than the average exponents of the separate processes (H_x and H_y, respectively). The Hurst–Kolmogorov exponents estimated from the climacograms were in the range 0.6 ≤ H ≤ 0.7 (0.603 ≤ β ≤ 0.798) consistent with the values estimated from detrended fluctuation analysis. Each pair of meteorological variables fitted reasonably well bistable distributions with approximately the same Lévy index (α ≅ 0.736). Hurst–Kolmogorov and infinite variance processes are important drivers of the atmospheric dynamics which can explain the persistence of extreme events (droughts) usually observed in the studied region. The multivariate multiscale sample entropy method and multivariate stable distributions could be valuable candidates for describing daily atmospheric processes.

The development, rate, and duration of extreme rainfall events over a region depend on the coexistence and strength of multiple atmospheric physical conditions. Then, understanding the synoptic and cloud-scale aspects is a continuous, crucial integrated task between universities and operational centers aiming for early warning and risk management. This study first evaluates the large-scale atmospheric circulation, instability behavior, and moisture parameters before and after the start of rainfall. It also investigates the dynamic triggering for an extreme rainfall event in Rio de Janeiro between April 08th and 09th, 2019. Secondly, this study intended to examine the microphysics cloud aspects using data from the Geostationary Operational Environmental Satellite (GOES-16). From monthly records and the 99th percentile of accumulated daily rainfall, it was possible to highlight the spatial rainfall dependence on seasonal and topography with higher rainfall values recorded in the south portion of the city of Rio de Janeiro. From the large-scale synoptic aspects, concomitant circulations related to upper, middle, and lower atmospheric levels creating a dynamic vertical structure favorable to convective development were verified over southeastern Brazil. The thermodynamic parameters showed different characteristics before and after rainfall started, suggesting multi-parameters' importance as so-called "instability ingredients" for evaluating the atmospheric potential for clouds and rainfall development. The velocity divergence in upper atmospheric levels was a determinant dynamic forcing for deep convection evolution. Lastly, regarding the wind circulations, northwest winds before precipitation and a change in wind direction were related to the region's frontal systems passage. The cloud microphysics aspects showed that the channel differences approach showed that monitoring top cloud glaciation, vertical development, and particle size are indicators of heavy rainfall when the cloud top offering considerable vertical growth was a helpful tool to identify regions with huge potential to develop heavy rain.

The Advanced Research Weather Research and Forecasting (WRF-ARW) model was used to simulate the downscale evolving atmospheric dynamical processes conducive to the intensification and propagation of the Tubbs Fire (2017). This wildfire impacted Napa and Sonoma Counties, California, spreading quickly and erratically through complex mountainous terrain due in large part to downslope Diablo Winds. The Tubbs Fire spread over 36,000 acres and destroyed 5,636 structures, killing 22. The simulations and supporting observations during the pre-Diablo Wind period indicate a well-defined inverted surface trough in Northern California’s Central Valley, along with a strong amplifying trough in the mid-troposphere and attendant cold frontogenesis over the Sierra Nevada. Mid-upper tropospheric jet streak flow, along with simulated and observed soundings from Reno, Nevada, indicate a mid-upper tropospheric jet indirect, exit-region descending, secondary circulation in conjunction with lower mid-tropospheric cold air advection caused by the southwestward low-level jet under the upper level jet’s entrance region. These adjustments enabled the organization of a deepening and ascending inversion over the Sierra Nevada, as well as a self-induced wave critical layer between 850 and 700 hPa prior to Diablo Wind formation. As the organizing jet streak departed, the discontinuously stratified atmosphere over the Sierra Nevada and coastal mountains in Northern California provided a favorable environment for mountain wave amplification. Intensifying leeside sinking motion coupled with wave steepening resulted in strong downslope winds in Northern California. Upward propagating mountain waves are present coinciding with the steepening of the isentropic surfaces consistent with the resonant interaction of nonlinear gravity waves. The model also simulated the development of a hydraulic jump in the lower troposphere on the lee side of the mountain range during Diablo Wind development. The simulation and observations indicate that the favorable environment for Diablo Winds resulted from the baroclinic jet-front system propagating over the Sierra Nevada when it produced a highly discontinuously stratified atmosphere favorable for nonlinear mountain wave amplification. However, the main surge of momentum down the leeside is only indirectly coupled with the jet streak’s exit region, being the result of cold frontogenesis, which allows for vertically differential cold air advection and its attendant discontinuously stratified vertical atmospheric structure.

Based on hourly rain gauge observation, cloud amount, and radiative fluxes data from the Clouds and the Earth’s Radiant Energy System (CERES) and ECMWF Reanalysis v5 (ERA5) dataset, the precipitation process during the Meiyu period in the middle and lower reaches of the Yangtze River in 2020 was simulated in WRF to reveal the influence of cloud radiative heating process on the diurnal variation of precipitation using multiple cloud microphysical schemes. The statistical evaluation of three microphysical parameterization schemes shows that the two-moment scheme WDM6 is more reasonable than the other two schemes in simulating the precipitation distribution, central intensity, and cloud characteristic distribution. There are significant bimodal characteristics in the diurnal variation of precipitation during the Meiyu period by analyzing the observation data. The numerical experiment accurately simulated the time and magnitude of the early morning peak in the heavy rain area but failed to reproduce the peak in the late afternoon, resulting in a false weak rainfall accumulation. The comparison of simulation results with the observed cloud macroscale and microscale characteristics revealed that the reason for the deviation of precipitation simulation was closely related to the inaccurate description of cloud microphysical quantities. The lack of ice phase cloud droplets led to excessively strong radiative heating rate at 200–500 hPa, resulting in anomalous warming in the mid-upper troposphere. Meanwhile, the cold advection at 850 hPa led to anomalous cooling in the lower troposphere, increasing atmospheric stability and further inhibiting the development of the afternoon thermal convection process.

The prolonged positive phase of the Arctic Oscillation (AO) and associated quasi-stationary equivalent barotropic Euro-Atlantic blocking high (EABH) and the Siberian high (SH) existed during January 2020. The presence of the persistent quasi-stationary barotropic high resulted in higher (lower) than normal surface temperatures in most parts of northern (southern) Eurasia. The large-scale analysis suggests the detouring of the mid-latitudinal westerlies from EABH and the formation of the west–east trough from East Atlantic to Northwest Pacific across North Africa, the Middle East, North India and China. The convergence of the moisture and positive convection anomalies along the region of the trough is perceived from the analysis. In the backdrop of this large-scale circulation anomalies, higher than normal precipitation was received in most of the central and north Indian regions with thunderstorms/hailstorm events. The variations in the AO index (AOI) and EABH were found to be in concurrence with the precipitation anomalies over the Indian region. The detailed analysis of a selected thunderstorm/hailstorm case suggests that the lowering of the 0 °C isotherm due to the intrusion of mid-latitudinal westerlies and the development of atmospheric instability with moisture supply from the adjacent seas facilitated the occurrence of the thunderstorms/Hailstorm events during January 2020.

This study focuses on the importance of reliable surface wind forecasts for various sectors, particularly energy production. Traditional numerical weather prediction models are facing limitations and increasing complexity, leading to the development of machine learning models as alternatives or supplements. The research consists of two stages. In the first stage, the ERA5 database is used to evaluate the long-term performance of different combinations of features and two tree-based algorithms for predicting surface wind characteristics (speed and direction) in Cairo. The XGBoost algorithm slightly outperforms the Random Forest algorithm, especially when combined with appropriate feature selection. Even three years after the training period, the results remain very good, with an RMSE of 0.59 m/s, rRMSE of 17%, and R² of 0.84. The second stage assesses the multivariate approach's ability to forecast wind speed evolution at different time horizons (1–12 h) during a week characterized by significant wind dynamics. The forecasts demonstrate excellent agreement with observations at a 1-h time horizon, with an RMSE of 0.35 m/s, rRMSE of 7.6%, and R² of 0.98, surpassing or comparable to other literature results. However, as the time lag increases, the RMSE (0.86, 1.14, and 1.51 m/s for 3, 6, and 12 h, respectively) and rRMSE (18.7%, 24.8%, and 32.9% for 3, 6, and 12 h, respectively) also increase, while R² decreases (0.86, 0.79, and 0.60). Furthermore, the wind variations' amplitude is underestimated. To address this bias, a simple correction method is proposed.