Influence of meteorological input data on backtrajectory cluster analysis – a seven-year study for southeastern Spain

Potassium (K) is a critical nutrient for crops, as it is a major constituent in fertilizer formulations. With increasing concentrations of K in agricultural soil, it is necessary to understand its movement and retention in the soil. Sub-surface modeling is an alternative method to overcome the exhausting and uneconomical methods to study and determine the actual concentration of K in soil. HYDRUS-1D is considered an effective finite-element model which is suitable for sub-surface modeling. This model requires the input of ground-station meteorological (GM) data taken at a daily timestep for the simulation period. It can be a limiting factor in the absence of ground stations. The study compares K predictions in surface and sub-surface soil layers under Boro rice cultivation obtained with the usage of different meteorological datasets. Thus, the main hypothesis of the study was to validate that, in the absence of GM data, satellite-based meteorological data could be utilized for simulating the K concentration in soil. The two meteorological datasets that are considered in the study included the GM and satellite-derived NASA-Power (NP) meteorological datasets. The usage of a satellite meteorological product at a field scale may help in applying the method to other regions where GM data is not available. The numerical model results were validated with field experiments from four experimental fields which included varied K doses. The concentration in soil was assessed at the regular depths (0–5, 5–10, 10–15, 15–30, 30–45 and 45–60 cm), and at various stages of crop growth, from bare soil and sowing, to the tillering stages. The concentration of K was measured in the laboratory and also simulated through the optimized model. The modeled values were compared with measured values statistically using relative root mean square error (RMSER) and Nash–Sutcliffe modeling efficiency (E) for simulating K concentration in the soil for the Boro rice cropping pattern with both GM data and NP data. The model was found most suitable for the 0–30 cm depth on all days and for all treatment variations.

Influence of hydro-meteorological data spatial aggregation on streamflow modelling

A dispersion modelling system SILAM and its evaluation against ETEX data

Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of 20 different PET methods on both time series and gridded datasets. The majority of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with 20 PET methods for station and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity and uncertainty analyses, and hindcasting and forecasting studies (especially in data-scarce regions) is provided.

Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large‐domain meteorological data sets enables convenient uncertainty characterization, which however is rarely explored in large‐domain research. This study analyzes how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Data set for Planet Earth (EM‐Earth). EM‐Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables. There are three main findings. (a) The uncertainties in precipitation and temperature lead to substantial uncertainties in hydrological model outputs, some of which exceed 100% of the magnitude of the output variables themselves. (b) The uncertainties of different variables show distinct scale effects caused by spatial averaging or temporal averaging. (c) Precipitation uncertainties have the dominant impact for most basins and variables, while air temperature uncertainties are also nonnegligible, sometimes contributing more to modeling uncertainties than precipitation uncertainties. We find that three snow‐related variables (snow water equivalent, snowfall amount, and snowfall fraction) can be used to estimate the impact of air temperature uncertainties for different model output variables. In summary, this study provides insight into the impact of probabilistic data sets on hydrological modeling and quantifies the uncertainties in cryosphere basin modeling that stem from the meteorological forcing data.

Technological adaptation zone of passive evaporative cooling of China, based on a clustering analysis

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.

Eulerian air quality models that require gridded meteorological inputs have to adapt to recent advances in meteorological models for fully compressible atmosphere. When the input meteorological data are recast with a robust fully compressible governing set of equations, chemistry-transport models can follow the dynamic and thermodynamic descriptions of the meteorological data closely. For evaluating mass consistency in meteorological data, one may take advantage of the characteristics of the governing set of equations applicable for a specific vertical coordinate system. This paper discusses how the data from meteorological models should be used in air quality simulations. It proposes a general methodology to conserve mass of trace species in air quality models by maintaining consistency in the wind and air density fields. Limitations of several simplifying assumptions on atmospheric dynamics are also discussed. In summary, it attempts to bridge the information gap between dynamic meteorologists ...

This paper reports on the performance of Atmospheric Dispersion Modelling System (ADMS) 4.2 in predicting peak and mean ambient sulfur dioxide concentrations at two sites adjacent to the Map Ta Phut Industrial Estate in Eastern Thailand, the centre of the country’s petrochemical industry. The model comprised 100 individual stacks and utilised four separate meteorological datasets from different points around the site. We show that model performance varies according to the location at which the meteorological data were obtained, with considerable differences in model outputs observed for meteorological stations that are relatively close to each other. The best performances were observed when there was co-location of the meteorological data and receptor. In such cases, acceptance criteria for the majority of performance parameters were satisfied across averaging periods ranging from 1 h to 7 days. We have also compared the results from this study with those obtained from a recent literature American Meteorological Society/United States Environmental Protection Agency Regulatory Model (AERMOD) study for the same site and time period; the comparison indicates that AERMOD is likely to be similarly influenced by the choice of meteorological dataset. Using ADMS model simulations for all four meteorological datasets and a breakdown of the local population by electoral ward, we were able to estimate exposure over 1 h, 24 h and yearly averaging periods and compare these to air quality standards and guidelines published by Thailand, the World Health Organisation (WHO) and the European Union (EU). The results of this analysis showed that despite the large variations in overall model performance, the impact of choice of meteorological dataset on prediction of compliance with the standards and guidelines is relatively small: the WHO 24-h guideline of 7.5 ppb (100th percentile) was predicted to be exceeded in all of the wards for all meteorological datasets, whilst compliance with Thai and EU standards was predicted for at least 86 % of the population, with relatively little variation between the different meteorological datasets.

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R2 (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies.

Predicting noise levels due to military blasts is important near training installations, since surrounding communities may be adversely affected. Prediction models, however, rely on atmospheric or weather data that is not readily available or easily attainable. In this study, blast noise level predictions 1-8 km from the source are made using two sources of meteorological data: publicly available data derived from weather stations that are tens of kilometers from a detonation site, and near-ground weather stations that are 2-6 km from the source. To predict the blast noise levels, statistical learning regression models are trained on each set of meteorological data and over 1000 measured blast events. Predictions from both meteorological data sources are compared to actual blast noise levels recorded during a field experiment by Valente et al. [2012], which were captured under a wide variety of atmospheric conditions. The root-mean-square errors for each model are compared, and the most important input pa...

A sensitivity and uncertainty analysis of the Penman‐Brutsaert evapotranspiration (ET) model was conducted using meteorological data collected from the humid South Florida region. Since net radiation and relative humidity were found to be correlated (ρ = −0.74) in this region, a method was developed to analyze the sensitivity with the correlation effect of these two independent variables. After conducting sensitivity analyses with and without the correlation effect, the results were evaluated using conditional probability density functions. Both theoretical and computed results show that the conditional probability with the correlation effect increases in proportion to the increasing absolute correlation coefficient of two independent variables. This finding suggests that the sensitivity analysis with the correlation effect represents more adequately a given set of meteorological data in South Florida than that without the correlation effect. After defining the random errors of the independent variables based on multiple measurements, the ET model error as well as the propagated error caused by erroneous independent variables are presented.

Global climate change is becoming increasingly crucial. With the continuous strengthening of environmental supervision and governance in China, the haze （O3） weather mainly caused by PM2.5 pollution has gradually improved, while the emissions of invisible ozone pollutants have been increasing annually. In particular, the problem of near-ground ozone pollution is more crucial, which poses a severe threat to urban production, life, and health of people. To gain a deeper understanding of the pollution characteristics of ozone, this study was conducted based on ozone monitoring, ground meteorological, and global data assimilation system （GDAS） meteorological data from January 2020 to February 2023 in Tianshan District, Urumqi City, to explore the characteristics of ozone mass concentration changes and potential source areas in Urumqi City at different time scales using the cluster analysis of the backward trajectory model （HYSPLIT） and trajectory （Tianshan District） and the concentration weight trajectory analyses （CWT）. The results showed that： ① The maximum 8-hour O3 （O3-8h） mass concentration in Tianshan District of Urumqi City had been increasing yearly from 2020 to 2022 and had obvious seasonal variation characteristics, with summer （110.33 μg·m-3） > spring （87.67 μg·m-3） > autumn （64.33 μg·m-3） > winter （39.67 μg·m-3）. ② The temperature, relative humidity, and air pressure of each season had a significant impact on O3-8h mass concentration, with correlation coefficients r of 0.871,-0.752, and 0.644 （P < 0.01）, respectively. The annual wind speed variation was relatively low, which was a secondary meteorological factor and had no significant impact on ozone mass concentration. ③ The HYSPLIT clustering analysis results showed that overseas long-distance transportation had less impact on the O3-8h mass concentration in Tianshan District, Urumqi City. The CWT results indicated that ozone pollution sources were mainly concentrated in the northern area of Urumqi in Xinjiang and the high-value areas in summer [O3-8h mass concentration > 70 μg·m-3] mainly appeared in areas with higher temperatures in Urumqi, Changji, Altay, and Tacheng. ④ Especially in autumn, the pollutants emitted by local anthropogenic activities had a great influence. To summarize, spatiotemporal concentration changes were observed in ozone pollution in Urumqi and the prevention and control of urban ozone pollution in Urumqi requires strengthening the emission of ozone precursors while considering implementing coordinated emission reduction measures for surrounding areas.

Abstract. The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open-source community model designed to address research questions that explore the role of the land surface in the global climate system. Here, we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing, we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Future model development should address (i) a positive albedo bias and resulting shortwave radiation bias in parts of the Northern Hemisphere (NH) extratropics and Tibetan Plateau, (ii) an out-of-phase seasonal gross primary productivity cycle in the humid tropics of South America and Africa, (iii) a lacking spatial correlation of annual mean net ecosystem exchange with site-level measurements, (iv) an underestimation of fractional area burned and corresponding emissions in the boreal forests, (v) a negative soil organic carbon bias in high latitudes, and (vi) a time lag in seasonal leaf area index maxima in parts of the NH extratropics. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.

A method for nonlinear approximation of stochastic processes is derived for calculation of climatic trends of long-term meteorological data sets. The method mainly makes use of spline approximation, Green's function and spectral transfer function of the Chauchy problem. Generally applicable for a wider range of problems the method is specified for special conditions typical for meteorological and climatological data. The applicability of the method described is examined on the basis of long-term (40... 228 years) meteorological data sets for temperature and precipitation data from one Australian and five European locations. The results obtained are discussed in comparison with climate trend results from temperature and precipitation data taken from literature.