Meteorological Applications最新文献

The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintaining land surface observations, thus creating uncertainties in surface energy and hydrological processes. The present study demonstrates the value of the high-resolution land data assimilation system (HRLDAS) integrated at 2 km grid spacing from 2011 to 2021 over Uttarakhand and validated against in situ, satellite, and reanalyzes products. Diurnal variation of sensible heat flux (SHF), and latent heat flux (LHF) are closer to the in situ observations (−35 to 64 Wm⁻²) than the global and regional analysis (−125 to 129 Wm⁻² and −40 to 172 Wm⁻²) during monsoon season. The HRLDAS soil moisture (SM) is overestimated against in situ and exhibited less error against European Space Agency Climate Change Initiative (ESACCI) (0.02 m³ m⁻³ with 30%) and Cyclone Global Navigation Satellite System (CYGNSS) (−0.02 m³ m⁻³ error with 21%). The HRLDAS performs better for soil temperature (ST) with high correlation and less bias (0.94°C and −0.34°C) than the GLDAS (0.83°C and −0.61°C) and IMDAA (0.86°C and 2.2°C), when verified against in situ observations. The spatial distribution of HRLDAS shows maximum ST in the southern parts and minimum ST in the northern parts of the Uttarakhand region and is consistent with the GLDAS and IMDAA during monsoon. HRLDAS shows lesser biases in net radiation (12 Wm⁻²), SHF (−10 Wm⁻²), and LHF (9.7 Wm⁻²) compared to GLDAS (25, −17, 10.3 Wm⁻²), and IMDAA (38, −11, 16 Wm⁻²), respectively. Besides the performance, the HRLDAS products represent better spatial heterogeneity than the coarser global and regional analysis and are useful to initialize numerical models.

Understanding how complex orography influences lower atmospheric winds is essential for accurately characterizing wind conditions, especially in regions considered for wind energy development. Complex terrain alters flow dynamics through mechanisms such as wind channeling, flow separation, and the formation of turbulent eddies and mountain waves, all of which significantly affect near-surface wind speed and direction. High-resolution numerical weather prediction (NWP) models, particularly the weather research and forecasting (WRF) model, have demonstrated substantial improvements in simulating these effects when fine-scale terrain and land surface datasets are employed, outperforming simulations based on coarse-resolution inputs. In this study, the WRF model is benchmarked for the first time using climate reanalysis data for the Askervein Hill campaign—a canonical field study of wind conditions over varying terrain. Multiple model configurations, including vertical and horizontal grid setups and ERA and NCEP/NCAR reanalysis input data, are evaluated to identify optimal settings for flat and complex terrain. Results show that while changes in vertical resolution have limited impact, finer horizontal resolution significantly improves predictions, particularly in complex orographic settings, with ERA data consistently outperforming NCEP/NCAR in all configurations. The model captures velocity profiles on flat terrain with RMSE within 2.5% (10–348 m heights) and turbulence intensity with RMSEs under 3%. Over complex terrain, near-surface flow is not adequately resolved, and the model overpredicts turbulence, which corresponds to an underprediction of the wind profile. However, the model performance improves significantly at wind turbine operational heights, with prediction errors reducing to below 2.4%. This discrepancy can be attributed to model limitations in resolving terrain-induced wind shear and stability gradients, to which the WRF model is particularly sensitive. These findings underscore the critical role of high-resolution terrain and land surface representation in improving WRF model performance for wind energy applications, highlighting the need for careful treatment of model physics, boundary conditions, and domain design to ensure accurate yet computationally efficient simulations.

Weather surveillance radar (WSR) provide distributed quantitative precipitation estimates (QPEs) of great value to the modelling, understanding and management of many hydro-meteorological processes. To obtain these observations over regional or larger scale domains it is necessary to composite data from multiple WSRs. These composites are often produced operationally by national or international meteorological agencies yet valuable data from ad-hoc sources such as research groups and local-level WSR operators are not included in these products. This study presents a methodology for incorporating data from a research radar deployment (the National Centre for Atmospheric Science mobile X-band weather radar, NXPol-1) into a national scale composite (the UK Met Office British Isles gridded composite) using a quality-index. Firstly a quality-index is developed for NXPol-1 using an intuitive, multi-factor approach. The quality-index is then cross-referenced with the existing quality-index for the national composite, to allow production of a dynamically merged two source WSR QPE. The method developed is then evaluated using surface precipitation measurements from an extensive rain gauge network. Merging QPE from the two sources using a quality-index improves the accuracy of WSR QPE when compared to either individual data source, showing it is possible to combine ad-hoc WSR data with national products dynamically such that precipitation estimation is improved. Improving local QPE using additional radar deployments will benefit flood forecasting accuracy and local incident response, particularly when that data is used to enhance existing coverage.

This study presents the energy balance dynamics of a mature Pine (Pinus roxburghii) ecosystem of the Indian Himalaya using multiple year (March 2020 to December 2022) eddy covariance-based measurements. Efforts are made to quantify the inter-annual dynamics of surface energy balance at seasonal and annual time scales. The impact of drought conditions, induced by soil moisture and vapor pressure deficit, on energy partitioning of the ecosystem is quantified using Bowen ratio (β) and evaporative fraction (EF). The energy balance closure is assessed for three seasons (i.e., pre-monsoon, monsoon, and post-monsoon) of each observation year. We find that the closure fraction (CF) of the site is more than 80% on an annual scale. Higher CF is observed during pre-monsoon (⁓80%) and monsoon (⁓90%) seasons due to the onset and duration of the growing season. The available energy partitioned into latent heat flux is larger than the sensible heat flux for the ecosystem, signifying that evapotranspiration is one of the dominant components of water and energy budgets. The evaporative cooling at the site takes place during the monsoon season through higher EF; however, the Pine ecosystems sustained the dry pre-monsoon season with higher β values. We find that the soil moisture-induced drought at the site resulted in higher partitioning of the available energy to sensible heat flux, effectively promoting the drought stress condition. However, it is to be noted that a better comprehension could be made for Pine forest behavior under environmental stress if such studies are further replicated.

Understanding the temporal and spatial variability of rainfall extremes is essential for developing effective adaptation strategies and making informed decisions in water resource management, agriculture, and infrastructure development. This study examines the spatial variability and temporal trends of extreme rainfall events in the Kagera sub-basin, using nine climate indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) and the Standardized Precipitation Index (SPI). The Sen's slope estimator was used to quantify the magnitude of the trend, whereas the Mann-Kendall (MK) test was applied to evaluate its statistical significance at a significance level of α = 0.1. The findings revealed significant trends in the rainfall regime across both annual and seasonal time scales. Annually, consecutive dry days (CDD) showed predominantly negative trends, ranging from −0.24 to −0.1 days/year, whereas consecutive wet days (CWD) generally exhibited positive trends, ranging from 0.16 to 1.0 days/year. Both heavy and very heavy rainfall events, as well as the highest 1- and 5-day rainfall totals, displayed increasing trends, especially in the eastern and central regions of the sub-basin. Seasonally, the results show a decreasing trend in consecutive dry days (CDD) ranging from −0.3 to −0.03 days/year, whereas CWD exhibit an increasing trend, ranging between 0.01 and 0.65 days/year. Both heavy and very heavy rainfall events also exhibited a predominant upward trend. The SPI revealed that the sub-basin experienced periods of severe and extreme drought, particularly between 1991 and 2005. However, there is a notable shift towards wetter conditions, as evidenced by predominantly increasing trends in the 3-, 6-, and 12-month SPI. These findings provide critical insights for developing adaptation strategies to address socio-environmental challenges which are often exacerbated by extreme rainfall events.

This study demonstrates a specific application of the hierarchical system development (HSD) approach to investigate, analyze, and attribute model issues within the Unified Forecast System (UFS), with a focus on process isolation. By evaluating a non-precipitating, shallow cumulus case at the Atmospheric Radiation Measurement Southern Great Plains site in the UFS global forecast against the observation, the investigation identifies a warmer and deeper daytime convective planetary boundary layer (PBL) and misrepresented nocturnal PBL transition. Hypothesis testing, which employs the Common Community Physics Package (CCPP) single-column model (SCM) and uses the same physics as the UFS global model, confirms that these issues are attributed to the model physics and initialization. Specifically, misrepresented PBL processes are linked to problematic surface condition and a lack of cloud formation, which may stem from deficiencies in PBL and cloud microphysics parameterizations and their interactions. The UFS initial condition contributes to an earlier, excessively collapsed daytime convective boundary layer and a lack of decoupling between the stable boundary layer and residual layer late in the afternoon. This work introduces an avenue for the community to engage with the application of HSD, along with the CCPP and CCPP SCM, to understand the interplay of model physics, disentangle the roles of model components, as well as facilitate model and forecast improvement.

This study aims to provide forecasters with valuable and practical insights into the effective application of convective-scale ensemble forecasts for precipitation prediction. Statistical verification and subjective analyses were conducted on the forecast performance during a heavy precipitation event in eastern Southwest China. The results indicate that different postprocessed deterministic forecast products each have distinct advantages and limitations that forecasters should consider. The ensemble mean forecast (EMF) has shown strengths in forecasting small magnitude precipitation (i.e., light rain, moderate rain, and heavy rain events), but it tends to smooth out information regarding extreme precipitation. The probability-matched EMF (PMEMF) outperforms the EMF for extreme precipitation predictions. In general, optimal ensemble quantile forecasts outperform the corresponding EMFs and PMEMFs, as well as most individual ensemble members, but notably, the optimal quantiles vary significantly across different cases. The ensemble forecast system is capable of predicting certain probabilities of heavy rainstorms and extraordinary rainstorm events as early as 4 days in advance. Based on the verification results, it is recommended that forecasters should remain cautious even when only a single or few ensemble members predict extremely heavy precipitation (or whether a certain probability of extreme precipitation exists, even if it is relatively low), thus helping to reduce decision-making errors. Furthermore, probabilistic forecasting should be more comprehensively and effectively applied in China.

Land use and land cover (LULC) changes significantly influence the dynamics of weather systems, particularly in regions prone to rapid land cover changes and extreme weather events like monsoon depression (MD). This study employs the Weather Research and Forecasting (WRF) model with a high-resolution (2 km) configuration to investigate the impact of updated LULC data on the predictability of MDs over India. Two experiments were conducted with LULC data from different sources: (i) the United States Geological Survey (USGS) with 1 km resolution and (ii) the National Remote Sensing Centre (NRSC) with 56 m resolution. The simulations are validated against observational and reanalysis datasets, including Automated Weather Stations (AWS), ERA5 reanalysis, IMD best track data, and GPM IMERG rainfall estimates. The results indicate that the NRSC dataset, which reflects current updated land cover conditions, provides a more accurate representation of land surface-atmosphere interactions, leading to improved simulations of MDs' track, intensity, and associated rainfall. Key meteorological parameters such as wind profiles, potential vorticity, moisture transport, and diabatic heating exhibit better agreement with observed/reanalysis data in the NRSC experiment than in the USGS. The NRSC experiment consistently shows lower mean Direct Position Errors (DPEs) in MD tracks throughout the forecast period, with an average improvement of 45–60 km over USGS. Additionally, RMSE in surface variables like temperature, humidity, and wind is reduced by 5%–10%. This study highlights the critical role of accurate and up-to-date LULC data in numerical models for enhancing their forecast capability of MDs, particularly in regions undergoing rapid LULC changes.

Unmanned aerial vehicles (UAVs) play a significant role in the aviation industry nowadays. Their portability and lower cost compared to traditional meteorological towers mean that their use is gaining momentum in many meteorological applications. In particular, UAV-based wind measurements are exploited in atmospheric energy balance research, precision agriculture, climate change studies, among others. This work aims to review the state-of-the-art of UAV-based wind measurement techniques by comparing the different working principles and highlighting their main challenges. The analyzed methodologies are divided into two categories: direct wind measurements (using anemometers mounted on UAVs) and indirect wind measurements (using velocity and force balances). Key aspects, such as the use of computational fluid dynamics (CFD) simulations, the most common sensor onboarding strategies, and the set-up of experimental tests in wind tunnels or in the field to validate the wind measurement accuracy, are addressed. Furthermore, novel developments based on machine learning and data filtration techniques for data quality enhancement are detailed. Based on a quantitative analysis of the recent relevant literature on this topic, we can conclude that multirotor UAVs are preferred to fixed-wing UAVs for scientific purposes, with the main challenge being the effect of propeller perturbation in the case of direct method wind measurements. Finally, it is shown that in most of the studies analyzed, sonic anemometers are chosen among all other types of sensors. Alternatively, the simplest version of the indirect method, namely the tilt model, is a common choice.

Tropical cyclones (TCs) pose significant threats to life and property across global ocean basins, and forecasting their structural evolution, track, and intensity remains a major scientific challenge. This review synthesizes the current understanding of TCs across major basins, that is, the Pacific, Atlantic, and North Indian Oceans, with a focus on the key environmental factors influencing TC behavior, such as sea surface temperature (SST), vertical wind shear (VWS), mid-tropospheric moisture, and land surface conditions. A special emphasis is further placed on the comparative skill of operational numerical weather prediction (NWP) models employed globally for TC forecasting. The review also discusses TC tracking algorithms, structural diagnostics, and the evolution of forecasting frameworks, along with emerging research trends revealed through scientometric mapping. The 51 peer-reviewed studies were selected and analyzed, and scientometric analysis was conducted on these 51 studies. Out of these selected studies, 37.25% focused on the Pacific, 23.52% on the Atlantic, and 17.64% on the North Indian Ocean (NIO, that is, the Bay of Bengal (BoB) and Arabian Sea). Out of these 51 studies, it has been found that while most studies utilized satellite-based methods, data assimilation (DA) techniques were emerging during 2006–2013, gaining momentum with machine learning (ML) applications post-2019. Notably, research since 2019 highlights a shift toward machine-based algorithms aimed at improving intensity predictions. While these AI/ML-based TC prediction models show promise, challenges remain in scalability, interpretability, and integration into forecasting workflows. The review emphasizes the need for assimilating next-generation satellite datasets (e.g., CYGNSS, TROPICS, rapid-scan AMVs, LIDAR), improved storm surge modeling, and real-time ensemble forecasting with high spatiotemporal resolution. Ultimately, advancing TC forecasting requires a collaborative, interdisciplinary approach involving model developers, operational centers, and observational programs. Bridging short-term forecasting with climate-informed strategies will be pivotal in enhancing global resilience to cyclonic hazards in a warming world.