Search Results (5)

Sunshine duration (SD) is one of the essential meteorological variables. It represents the sum of time for which direct solar radiation with an intensity above 120 W∙m⁻² reaches the Earth’s surface. In the contemporary observational routine, automatic electronic devices are in use. The pyranometric method based on the measurements of global solar radiation measurements (K_glob) is also proposed by WMO to assess SD. The aim of the paper is to study the accuracy of the Slob–Monna method (SD-WMO), recommended by WMO to calculate sunshine duration. Alternatively, the author’s method, which is based on the Ångström clearness index (SD-ACI), was used to approximate SD. In this purpose, two years series of SD and K_glob observations at four locations in Poland (well representing Central European transitional climate zone) were analyzed. The result shows that, for SD-WMO, sunshine duration values are on average 16% higher than observed ones. For the SD-ACI method, they are only 5% higher. When verifying the accuracy of SD-WMO and SD-ACI approximations, we have found that both for daily and monthly periods the calculated SD sums are closer to the observed ones in the case of SD-ACI than for the SD-WMO method. The correlation coefficients are, respectively, 0.98 and 0.82 (for daily sums) as well as 0.99 and 0.88 for monthly sums. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed methodology and implemented product in this study serves as a proof of concept that could be replicated by WMO members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for model input selection based on large-scale indicators leveraging artificial intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction in the wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead. Full article

The tropopause is described as the distinction between the troposphere and the stratosphere, and the tropopause height (TPH) is an indicator of climate change. GNSS Radio Occultation (RO) can monitor the atmosphere globally under all weather conditions with a high vertical resolution. In this study, four different techniques for identifying the TPH were investigated. The lapse rate tropopause (LRT) and cold point tropopause (CPT) methods are the traditional methods for determining the TPH based on temperature profiles according to the World Meteorological Organization (WMO) definition. Two advanced methods based on the covariance transform (CT) method are used to estimate the TPH from the refractivity (TPH_N) and the TPH from the bending angle (TPH_α). Data from the Sentinel-6 satellite were used to evaluate the different algorithms for the identification of the TPH. The analysis shows that the CPT height is greater than the LRT height and that the CPT is only valid in tropical regions. The CPT height, TPH_N, and TPH_α were compared with the LRT height. In general, the TPH_α had the largest value, followed by the TPH_N, and the LRT had the lowest value of TPH at and near the equator. In the equatorial region, the maximum TPH results from the TPH_α (approximately 17.5 km), and at the poles, the minimum TPH results from the LRT (approximately 9 km). The results were also compared with the European Center for Medium-Range Weather Forecasts (ECMWF), and there was a strong correlation of 0.999 between the different approaches for identifying the TPH from RO and the ECMWF model. The identification of the TPH is critical for the transfer of mass, water, and trace gases between the troposphere and stratosphere. Full article

Evapotranspiration plays an inevitable role in various fields of hydrology and agriculture. Reference evapotranspiration (ET₀) is mostly applied in irrigation planning and monitoring. An accurate estimation of ET₀ contributes to decision and policymaking processes governing water resource management, efficiency, and productivity. Direct measurements of ET₀, however, are difficult to achieve, often requiring empirical methods. The Penman–Monteith FAO56 (PM-FAO56) method, for example, is still considered to be the best way of estimating ET₀ in most regions of the globe. However, it requires a large number of meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Furthermore, the objectivity of some elements of the empirical equations often used can be highly variable from region to region. The result is a need to find an alternative, objective method that can more accurately estimate ET₀ in regions of interest. This study was conducted in the Hexi corridor, Northwest China. In it we aimed to evaluate the applicability of 32 simple empirical ET₀ models designed under different climatic conditions with different data inputs requirements. The models evaluated in this study are classified into three types of methods based on temperature, solar radiation, and mass transfer. The performance of 32 simple equations compared to the PM-FAO56 model is evaluated based on model evaluation techniques including root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE). The results show that the World Meteorological Organization (WMO) and the Mahringer (MAHR) models perform well and are ranked as the best alternative methods to estimate daily and monthly ET₀ in the Hexi corridor. The WMO and MAHR performed well with monthly mean RMSE = 0.46 mm and 0.56 mm, PBIAS = 12.1% and −11.0%, and NSE = 0.93 and 0.93, before calibration, respectively. After calibration, both models showed significant improvements with approximately equal PBIAS of −2.5%, NSE = 0.99, and RMSE of 0.24 m. Calibration also significantly reduced the PBIAS of the Romanenko (ROM) method by 82.12% and increased the NSE by 16.7%. Full article