Meteorological Stations Research Articles

Meteorological drought in the upper Noteć catchment area (Central Poland) in the light of NDVI and SPI indicators

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland.

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.

Forecasting of global solar radiation: A statistical approach using simulated annealing algorithm

In recent years, the share of renewable energy sources in current energy production has been increasing due to the depletion of fossil fuel resources, increasing energy needs and environmental concerns. Solar energy, one of the most important renewable energy sources depending on being clean, sustainable and environmentally friendly energy source. Understanding the solar radiation value is crucial for maximizing the potential of solar energy and ensuring the efficient operation of solar energy systems. In this paper, it is aimed to develop a new global solar radiation (GSR) prediction model by using simulated annealing algorithm (SAA). Modeling solar radiation data with significant variability is a challenging task that necessitates the use of nonlinear approaches. Although the SAA is widely used in engineering and natural sciences, it has not previously been used to develop a GSR prediction model. While developing the SAA forecast model for the Adana region, long-term sunshine duration and solar radiation data obtained from the Turkish General Directorate of State Meteorology were used, as well as geographical features such as latitude and longitude of the selected region. The performance and applicability of the proposed model was examined by comparing it with different GSR estimation methods developed in the literature. The primary contribution of this study lies in the introduction of an inventive model, which has been constructed for the first time in the assessment of GSR, to the current collection of literature. The produced results were statistically compared with the observed data using six separate performance and error measures. The results of all years showed that the relative percentage error (RPE) less than 11.66 %, the mean percentage error (MPE) does not exceed 12.55 %, the correlation coefficient (R2) greater than 0.98, the mean absolute percentage error (MAPE) test results appear to vary close to the value 10, the sum of squared relative error (SSRE) test results seem to approaching 0 and the t-statistic test less than 4.06 for the annual GSR. It has been observed that the developed SAA GSR forecasting model has a successful performance when compared to different forecasting models popularly used in the literature. According to the statistical error results, the estimated GSR data from the novel SAA-GSR forecast model aligns well with the measured meteorological values. Given the efficacy of the established SAA model in GSR estimation, this study is anticipated to offer valuable insights and contributions to the application of the SAA approach in other energy applications.

The genocidal disruption of Johannes Jacob Manissadjian’s (1862–1942) lifework: a biographical approach to mass violence and indigenous knowledge production

ABSTRACT Relying on a biographical approach that reconstructs the life and work of Johannes [Hovhanness/Յովհաննէս] Jakob [Hagop/Յակոբ] Manissadjian [Manisacıyan/Մանիսաճեան] (1862–1942), a highly successful scientist at the Anatolia College (Merzifon/Marsovan/Մարզվան), who established a meteorological station and a natural history museum with an extensive collection of specimens, the paper traces the routes of disappearance, dispersal and ruination of indigenous lives, people, and knowledge within the context of the Armenian genocide. Drawing on documents from Ottoman, German, and American archives, I stress the potential of biographical methods to study the processes and structures of mass violence targeting the Ottoman Armenians, as well as to foreground the agency and subjectivity of genocide survivors. The article also focuses on post-genocide scientific (dis)engagements of Manissadjian in light of Theodor W. Adorno’s ‘after Auschwitz’ discussions and from the perspective of indigenous knowledge production. In particular, his two ‘archival acts’ in the post-genocide context, the ‘Catalogue’ of the collection of the Anatolia College Museum that he prepared as ‘the former Curator’ and his small pamphlet entitled Proverbs of Turkey, which provided an ethnographic portrait of Anatolia, were his humble acts of saving a treasure trove of knowledge that was in danger of becoming debris.

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

Multivariate Imputation Chained Equation on Solar Radiation in Automatic Weather Station

Solar radiation is one of the crucial weather observation variables Its variable has a role in renewable energy solutions, agriculture, meteorology, and hydrology. AWS is one of instrument that use to observing weather especially solar radiation. AWS has a pyranometer sensor used to measure solar radiation. Unfortunately, the instrument has problem like the igh cost of supplying, installing, maintaining, and calibrating the equipment. Due to this, there is a lot of empty data, and the actual data cannot be properly measured. Imputation of solar radiation data using MICE algorithm can be solution. This study using BLR, NRR and RFR estimator to estimating solar radiation data. AWS Staklim Banten as target and other AWS as input. The period from January 1, 2018 - February 12, 2024. The performance evaluation of the solar radiation imputation estimator is still according to WMO operational requirements for solar radiation measurements, which can be seen from the resulting MAPE value < 8%.

Accounting for climate changes when constructing the Rossander graph

The temperature of the atmospheric air affects the design and indicators of energy efficiency of all elements of heat supply systems. An extremely important tool to develop heat sources that allows us to assess changes in consumer heat loads and model algorithms for heat source operation, taking into account the variability of outdoor air temperature in the geographical conditions of the design objects, is a graph of the duration of heat loads during the heating period (Rossander’s graph). It is impossible to construct the Rossander’s graph without data on the duration of the outdoor temperature standing during the heating period. In this paper, the problem of the lack of temperature data necessary for the construction of the Rossander’s graph in the current regulatory documents is revealed. The analysis of previous studies that produced temperature data with the duration of temperature standing is carried out, and the need for their refinement is substantiated. The arrays of hourly outdoor air temperatures were processed according to two weather models, as well as the array of average daily temperatures obtained as a result of measurements from the weather station for the geographical location of the main building of KNUCA. As a result of the comparative analysis, a model closer to reality was identified and, on its basis, actual data on the duration of outdoor temperatures standing for the city of Kyiv with a temperature step of 1℃ were developed. The obtained temperature data were compared with those that were developed in previous studies, as well as with the temperature data that are contained in the cancelled standard. The curve of the long-term run of the moving average outdoor air temperatures for the winter periods of 1973-2023 and its polynomial trend are constructed and compared with the curve that characterizes the run of the moving average five-year temperatures for the winter periods of 1861-1970 and its polynomial trend.

МЕТОДЫ ОПРЕДЕЛЕНИЯ РАДИАЦИОННОГО БАЛАНСА ПОЧВЕННОГО И РАСТИТЕЛЬНОГО ПОКРОВА СЕЛЬСКОХОЗЯЙСТВЕННЫХ УГОДИЙ

In this article, an analysis of calculation methods for determining the radiation balance and photosynthetically active radiation of soil and vegetation cover of agricultural land is conducted. The lack of continuous actinometric observations of solar radiation and radiation balance at meteorological stations in the Eurasian region leads to the widespread use of empirical and semi-empirical models based on climatic and geographical indicators. Mathematical models based on climatic indicators, which vary in spatial and temporal aspects and are formed on the basis of the principle of natural analogies, provide reliable and accurate data. However, calculation methods that use geographical indicators do not have physical and mathematical meaning, resulting in significant errors in the data. The aim of the research is to analyze indirect methods to identify their physical and mathematical justification. The need for the development of quantitative methods for assessing the effective use of photosynthetically active radiation to improve forecasting and management of agricultural land is emphasized.

Evolution of the Lower Barun lake and its exposure to potential mass movement slopes in the Nepal Himalaya

The Lower Barun Lake, the largest glacier-fed lake in the Nepal Himalaya, has been designated as critically or highly vulnerable to Glacier Lake Outburst Floods (GLOFs) due to the lake's massive volume and steep side walls that are susceptible to mass movements. The current study estimates the future evolution of the lake's extent and its exposure to potential avalanche under different climate scenarios by simulating the evolution of the Lower Barun glacier, which feeds the lake. We then assess this exposure (i) at the lake's current extent, (ii) when the lake length grows to 75 % of its maximum length, and (iii) when the lake length reaches its maximum possible length. We use a mass conservation based numerical flowline model for our analyses. The model was forced by the glacier surface mass balance (SMB) and meteorological data collected from weather stations in Kathmandu, Nepal and Darjeeling, India. Modelled lake lengths matched measured lengths within an RMSE of ∼200 m. Analyses show that under SSP2–4.5 and SSP5–8.5 scenarios, the lake will reach its maximum length by 2075 ± 2 and 2061 ± 1, respectively. The largest uncertainty in future lake length fluctuations is approximately 200 m. Our study reveals that in current conditions, the zone where the angle of reach of potential avalanches is highest lies on the slopes along the right shore (south side) of the lake. The angle of reach shifts upstream and steepens—and the mass movement hazard increases—as the lake grows in the future.

ОЦЕНКА АГРОБИОЛОГИЧЕСКИХ РЕСУРСОВ ЛАНДШАФТОВ СЕЛЬСКОХОЗЯЙСТВЕННОГО ИСПОЛЬЗОВАНИЯ ТУРКЕСТАНСКОЙ ОБЛАСТИ РЕСПУБЛИКИ КАЗАХСТАН В УСЛОВИЯХ ИЗМЕНЯЮЩЕГОСЯ КЛИМАТА

The article presents the results of assessment of agrobiological resources of landscapes of agricultural use of Turkestan region under conditions of changing climate, which were based on the developed concept of reference yields of H. Tooming. To develop an algorithm for assessing the agrobiological resources of the region, a climatic research base for 1941...2020 was created for 18 meteorological stations located in different natural zones, which made it possible to determine the peculiarities of the formation of different categories of agricultural yields due to climate change. The obtained results of the research on the state of agrobiological resources of landscapes of the region on potential productivity of agricultural lands, on photosynthetically active radiation (PAR) input using the concept of reference yields in spatial and temporal scales have shown that there is a stable trend to increase the potential productivity of agricultural lands on photosynthetically active radiation input, climatic potential-possible productivity and decrease the potential productivity of agricultural lands on - photosynthetically active radiation input, climatic potential-possible productivity and decrease the potential productivity of agricultural lands on - photosynthetically active radiation input.

ИССЛЕДОВАНИЕ НАВОДНЕНИЙ И ЗАТОПЛЕНИЙ НА РЕКЕ ЕСИЛЬ У ГОРОДА ПЕТРОПАВЛОВСК

This article examines the problem, causes, and factors of floods and inundations on the Esil River near the city of Petropavlovsk from 1941 to 2020. The characteristics of floods and their consequences in the North Kazakhstan region are highlighted and described.The long- term course of discharge and water level on the Esil River – Petropavlovsk city is presented, as well as a climatogram of the Petropavlovsk weather station for 1941...2020. The values of the maximum water flow tend to decrease, which may primarily be associated with the beginning of a period of intense anthropogenic impact on the Yesil River. Thus, the average of the maximum water consumption and their average square deviations have decreased by 1.6...2 times since the late 60s-early 70s in connection with the creation of

Seasonal sonic patterns reveal phenological phases (sonophases) associated with climate change in subarctic Alaska

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January–30 June) over three years (2019–2021) near a weather station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures >0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multi-dimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments.

Analysis of Land Water Balance in Various Rainfall Conditions and Its Utilization to Determine Planting Patterns of Food Crops in the Eastern Part of Seram District of Seram Island

Soil water balance calculation is one of the methods used to estimate the dynamics of soil water content during plant growth. This study aims to describe rainfall conditions and analyze soil water balance as well as determine the growing season and composition of food crop combination patterns based on the available growing season in East Seram Island. This research uses rainfall data for 30 years of observation period 1992-2021 from Geser Meteorological Station (Data analysis with the following stages: (i) rainfall analysis (ii) calculation of average rainfall (iii) determination of rainfall with a 75% chance (iv) calculation of soil water balance using the Thornthwaite and Mather method (v) determination of the growing season and cropping pattern. The results showed that the average rainfall in East Seram was 2,194 mm/year with a water deficit (D) occurring in October and November, then the rainfall had a 75% chance of being exceeded at 1,439 mm/year with a water deficit of 228 mm/year which lasted for eight months, namely August - March. The growing season under normal rainfall conditions was available throughout the year, while under high rainfall conditions, a 75% chance of the growing season was available for five months, namely May and October. The cropping patterns and crop combinations used were monoculture, polyculture, and intercropping with combinations of cassava, maize, sweet potato, groundnut, mung bean, cassava-corn, cassava-peanuts/green beans, maize-peanuts, cassava-corn-peanuts/green beans, cassava-corn-horticultural crops.

A novel error decomposition and fusion framework for daily precipitation estimation based on near-real-time satellite precipitation product and gauge observations

Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research.

Methodology for the assessment of poor-data water resources.

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Drivers of seasonal rainfall variability over the Angolan and Namibian plateaus

AbstractSouthern Africa has been strongly affected by ongoing climate change in recent decades. Rainfall variability is modulated by regional patterns of moisture advection and convergence in the lower troposphere. Using reanalysis, ground and satellite‐based rainfall products and observations from 10 weather stations, we perform a synoptic and climatological analysis focussing on atmospheric circulation, moisture transport and their relationship with rainfall anomalies over the Angolan and Namibian Plateau region. Results clearly show that a stronger (weaker) Zambezi low level jet (LLJ) magnitudes are associated with above (below) normal rainfalls over the main Angolan and Namibian plateaus. With lower confidence, a stronger Limpopo LLJ may also lead to enhanced rainfall over Namibia and southeast Angola. The Zambezi LLJ moisture fluxes are moderately controlled by Mozambique Chanel Trough and Angola Low intensities, while the Limpopo LLJ intensities have very low influence from the Mozambique Chanel Trough and Angola Low, respectively. The Angola Low in its tropical phase is associated with deeper moisture convergence and stronger vertical velocities, leading to higher amounts of precipitable water within the air column, thus enhancing precipitation over the region. It is shown that the major moisture source of rainfall, which is advected to via the Zambezi LLJ, is the Indian Ocean. Meanwhile, the Atlantic Ocean plays a minor role. Given the current lack of observations and projected climate change, further research and investments are urgently needed in the region, for example, regarding the expansion of the surface data network.

Evaluation and refinement of ERA5-land 2 m atmospheric temperature in GNSS precipitable water vapor

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas.

New secondary decomposition ensemble support vector regression for monthly drought index forecasting

Drought prediction on a monthly timescale is conducive to the timely identification of drought characteristics and provides a reference for drought mitigation. Current research is generally based on drought indices on timescales of three months or more for drought prediction, whereas the presence of highly nonlinear and drastic changes in the characteristics of drought indices leads to suboptimal drought prediction for drought index sequences on monthly timescales. In this study, a secondary decomposition model based on extreme-point symmetric mode decomposition (ESMD), variational mode decomposition (VMD), and support vector regression (SVR) was investigated to predict a monthly-scale drought index. To evaluate the predictive ability of the model, it was applied to 100 meteorological stations in the Pearl River Basin of China and compared and analyzed using four comparative models. The results showed that the model proposed in this study has better performance index values, and the prediction accuracies of all stations are improved compared with the four comparative models, with the most significant improvement in the Nash–Sutcliffe efficiency (NSE) index value, followed by the root mean square error (RMSE) and mean absolute error (MAE) index values, and the smallest improvement in the Wilmot consistency index (WIA) index value. The monthly-scale drought quadratic decomposition (ESMD-VMD) combined prediction model established in this study provides a novel method for drought prediction.

Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine

Renewable energies play a significant role in mitigating the climate change impacts. Wind and solar energy are projected to supply larger amount to the total renewable energy capacity of many nations. Long-term wind and solar data availability and analysis is vital for sustainable renewable energy farming. In this research, we analyze wind speed u- and v-components at 10 m height of the ERA5 daily reanalysis aggregates at a mesh of 31 Km during a long period (2010–2020) over the Red Sea and the Gulf of Suez coasts of Egypt. Google Earth Engine (GEE) code is developed and used for the analysis. The prime objective is to derive estimates on the spatio-temporal variability of speed, direction, and the wind energy estimates in the area. Further, the solar energy potential has been evaluated using reference data of the PVOUT – Photovoltaic power potential (kWh/m2/day) data, at nearly 1 km resolution, which is normalized at the evaluated sites, and then used to rank sites with their solar energy potential. Evaluation of estimates is carried out at selected thirteen sites distributed along the coasts to better understand the hybrid wind-solar energy potential. Frequency analysis is carried out to the wind speed spatial estimates to demarcate highest frequency wind events potential for wind farming. Reference in-situ wind speed observations available at daily and monthly time scales for Hurghada weather station is used for investigating the reanalysis data performance. The Hurghada site had the highest average wind speed (6.7 m s−1), frequency of events (75 %), and mean daily (60.4 kWh) and annual (22.02 MWh) wind energy. The windiest time intervals were the September month, 2017 year, and the summer season. The north had wind energy directions of 130°, while the south had 110°. Trend analysis showed declining trends for northern locations and rising yearly wind speed estimates for Berenice (0.005) and Halayeb (0.002). El-Tor location had the highest solar energy output, with a daily mean of 5.52 kWh/m2 and an annual mean of almost 2015 kWh/m2. The Hurghada, Jemsa, G. Zeit, and El-Tor sites achieved the highest rankings in terms of hybrid wind-solar energy potential. The results can be very valuable for optimal location of hybrid solar-wind energy facilities and provide viable and promising solutions for sustainably meeting Egypt's growing clean energy demands.

Precipitation and cloud Parameters's characteristics of stratus in summer in Ningxia, eastern of northwest China

Stratus is the main artificial seeding target for weather modification in Ningxia. Meteorological satellites can detect macro and microcosmic physical parameters of large-scale stratus. Researching precipitation and cloud parameter characteristics of stratus can provide scientific and technological support for understanding the development and evolution of stratus to identify seeding conditions and effects. Eight typical stratus precipitation events of the summers of 2016–2021 of Ningxia were selected, using the nine point average method, that matched characteristic cloud parameters retrieved by FY-2G satellite with precipitation classification data of 25 national meteorological observation stations in Ningxia. These stratus precipitation characteristics were correlated with cloud parameters. The results indicate that precipitation decreased rapidly from the southeast to the northwest of Ningxia. In the synoptic background of precipitation, the cloud top height, super-cooled layer thickness, cloud optical thickness, cloud particle effective radius, and cloud liquid water content were all larger than those in the synoptic background of no precipitation. The spatial distribution feature manifests as gradually increased from southeast to northwest. The cloud top temperature and cloud blackbody temperature were smaller than those in the synoptic background of no precipitation. The cloud top temperature was near the cloud “seeding temperature window”, and the depth of the super-cooled layer was thick. This was in line with the “Seeder-Feeder” structure characteristic of cold stratus.