Non-rainy Days Research Articles

Volcanic tremor associated with successive gas emission activity at a boiling pool: Analyses of seismic array and visible image data recorded at Iwo-Yama in Kirishima Volcanic complex, Japan

Volcanic tremors are often observed during volcanic activity and volcanic eruptions, and their generation processes provide clues for understanding volcanic fluid activity underground and eruption dynamics. However, tremors are characterized by continuous oscillations that mask P- and S-waves; hence few studies have precisely located the source, which is the most fundamental information for understanding the generation mechanism. In this study, we focus on volcanic tremors excited by continuous gas emissions occurring at a vent called Y2a in Iwo-Yama, the Kirishima Volcanic Complex, Japan, to clarify the source process of the tremor as well as gas emission activity. We simultaneously observed the volcanic tremor by deploying a small aperture array consisting of six seismometers and the gas emission activity by using a newly developed visual IoT system that can be operated without commercial electricity. MUSIC analysis locates the tremor at depths ranging from the ground surface to approximately 200 m beneath the Y2a and Y2b vents, which are approximately 30 m apart, for approximately four months from November 2021 to February 2022. The source locations of the tremors in the 2 Hz (1.2–2.6 Hz), 4 Hz (3–4 Hz), and 5 Hz (4–5.5 Hz) ranges show some differences and changes with time. The source location tends to become deeper when the 2 Hz amplitude is large. The infrasound generated by gas emission activity is dominant in the tremor signals, which are recognized in the wave propagation velocity with an acoustic velocity of 330 m/s when the 2 Hz amplitude is small. The visual IoT system succeeded in detecting long-term changes in the gas emission activity, and we found that the 2 Hz amplitude of tremor was well correlated with the amount of hot water in the boiling pool of Y2a, which was controlled by precipitation and evaporation during non-rainy days. From these observations, we infer that the volcanic tremor is generated by resonance of volcanic gas and hot water in a crack-like structure beneath Y2a. The resonance was triggered by the counterforces of the gas emissions in the boiling pool, and the infrasound was dominant during periods of hot water depletion in the boiling pool. Temporal changes in the source depths may be caused by changes in the fluid properties, configuration of the resonator and/or the strengths of the underground sources and infrasound. Our simultaneous observations of seismic array and visual IoT system clarify that even the continuous gas emission activity that looks stable is controlled by external sources such as precipitation.

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

Divergent Circadian Foraging Strategies in Response to Diurnal Predation Versus Persistent Rain in Asian Weaver Ant, Oecophylla smaragdina, Suggest Possible Energetic Trade-offs.

The study of chronobiology of foraging behavior in social insects offers valuable models for the investigation of circadian rhythms. We scored hourly nest entries and exits of Oecophylla smaragdina (Asian weaver ant) workers in 9 active non-polydomous nests on days with and without rain and with and without a primarily diurnal predator present. After determining that Oecophylla display a high nest fidelity, we focused exclusively on analyzing nest entry counts: we found a significant decrease in overall entry counts of individual ants on rainy days compared with non-rainy days (p < 0.0001). They usually maintain a typical diurnal pattern of foraging activity; however, that regularity was often distorted during rainy periods but appeared to quickly revert to typical patterns following rain. This lack of compensatory foraging activity following a period of rain supports the hypothesis that these ants have enough food reserves to withstand a pure masking-induced suppression of foraging activity. Predation through bird anting, too, decreased foraging activity but appeared to cause a reversal in foraging activity timing from diurnal to nocturnal foraging. Daily periodicity of foraging was significantly disrupted in most nests during rain; however, daily foraging periodicity was disrupted in only one nest due to presence of predators. Thus, rain and predation both exert significant impacts on the overall foraging activity of Asian weaver ants, but while persistent pressure from rain seemed to primarily cause masking (diminution) of circadian foraging activity, predation restricted to the daytime resulted in phase-inversion to nocturnal foraging activity, with little diminution. This is consistent with different energetic strategies being used in response to different pressures by this species.

Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation

Interaction of focused recharge and deep groundwater discharge near a wetland: A study in the Ordos Basin, China

Groundwater mounds and depressions induced by rainfall and evapotranspiration near wetlands/lakes have been widely observed, but their effects on groundwater flow field are poorly known. Here, a sandy hillslope near a wetland in the Ordos Plateau, China, which has a semi-arid climate, is selected for investigation. After identifying temporary groundwater mounds/depressions based on three water level observation wells, a HYDRUS-2D model is setup to obtain the highly transient daily flow field. The variable thickness of unsaturated zone near the wetland, which controls soil water content and unsaturated hydraulic conductivity, is found to be responsible for groundwater mounds resulting from focused recharge and groundwater depressions resulting from nonuniform evapotranspiration. On nonrainy days, evapotranspiration in the lowland is supported by flowpaths from regional groundwater discharge, and in the slope is supported by a flow cell from the upland to the upper part of the slope, which causes water table recession in the upland. On rainy days, focused recharge in the lowland leads to two temporary local flow cells, which break the regional groundwater discharge flowpaths into two temporary flow cells. The two temporary flow cells toward the upland contribute to water table rises in the uplandMoreover, we find focused recharge in rainy days does not change the overall direction of flowpaths controlled by regional groundwater discharge and strong evapotranspiration, but only increase the tortuosity of flowpaths. By coupling daily atmospheric conditions and regional groundwater discharge, this study has implications for groundwater recharge estimation and future groundwater modelling in arid regions.

Extensive analysis of rain in australia by exploratory data analysis, feature engineering and modeling

Accurate rainfall forecasts help in planning outdoor activities, agricultural practices, and water resource management, thereby mitigating the impact of extreme weather events. This article provides an in-depth analysis of rainfall in Australia, focusing on predicting whether it will rain tomorrow using logistic regression. The research aims to develop an accurate model to help predict rainfall events for better preparedness and planning. We obtained datasets from a number of Australian weather stations. The dataset contains 142,193 daily weather observations spanning approximately ten years. The recorded information includes various details such as date, location, humidity, wind direction, clouds, temperature, etc. This shows that the model performs well in distinguishing between rainy and non-rainy days with an accuracy of about 0.875. The findings of this study have important implications for various stakeholders including meteorologists, disaster management agencies, and the public.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: II. Simulation of diurnal cycle

The accurate forecast of the diurnal cycle of the number concentration of trace gases is vital due to their influence on precipitation processes by controlling the number concentration of cloud condensation nuclei (CCN). 1-D hybrid Monte Carlo-Gear solver developed to retrieve vertical profiles of the number concentration of CCNs for microphysics modeling has been tested for representation of the diurnal cycle in the present paper. The retrieved profiles of CH4 and SO2 have been tested with the Copernicus Atmosphere Monitoring Service (CAMS) model at 3-hour time intervals for four megacities: Delhi, Kolkata, Chennai, and Mumbai for rainy and non-rainy days. The retrieved profiles have shown diurnal variation up to 18 UTC at all pressure levels with lead or lag with that of the CAMS model. After 18 UTC there was a furious increase in the number concentrations. During non-rainy days, the 1-D model slightly overestimated (underestimated) the maximum (minimum) number concentrations of CH4 over Delhi whereas concentrations are overestimated over Kolkata, Chennai, and Mumbai. Forecasted CH4 has a good (weak) correlation over Chennai (Mumbai) respectively. The 1-D model overestimated (overestimated) the maximum (minimum) number concentrations of SO2 over Delhi but the maximum (minimum) concentrations are underestimated (overestimated) over Kolkata, Chennai, and Mumbai. The number concentrations of SO2 have shown a good correlation for all megacities except Delhi. CH4 number concentration is overestimated during rainy days. Delhi and Kolkata show a good correlation of CH4 during rainy days. SO2 during rainy days is underestimated except over Chennai and both models show a good correlation except over Mumbai. Overall, it can be stated that the 1-D hybrid solver is successful in simulating the monthly mean diurnal variation of vertical profiles of CH4 and SO2, and its implementation in the global model may estimate the number concentrations with better accuracies.

Evaluations of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (Southern Ethiopia) to enhance the quality of input data for hydro-climate studies

The sparse distribution and lack of meteorological stations due to deficit infrastructure in developing countries is one of the limiting factors for hydro-climate studies, and dependency on globally available data is often prone to various level of errors. Thus, this study aimed to evaluate the performance of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (BER) in Southern Ethiopia. This study evaluated performances of three rainfall products such as the Climate Hazards Group Infrared Precipitation with Stations, version 2.0 (CHIRPS v2.0), Tropical Applications of Meteorology using SATellite and ground-based observations, version 3.1 (TAMSAT v3.1) and Multi-Source Weighted-Ensemble Precipitation, version 2.8 (MSWEP v2.8). The two temperature products evaluated in this study are ERA5 and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA v2). Evaluations of these satellite/reanalysis rainfall and temperature products were undertaken against observed data (1995–2014) in temperate, sub-tropical and tropical agro-ecological zones (AEZs) across multiple temporal scales ranging from the daily to annual. For assessing the performances of satellite/reanalysis rainfall and temperature products, a point-pixel evaluation approach was undertaken using five continuous evaluation scores such as correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS) and Kling–Gupta efficiency (KGE). Categorical sores such as Probability of Detection (POD), False Alarm Ratio (FAR) and Frequency Bias Index (FBI) were also used for assessing the rainfall products. The findings revealed that MSWEP v2.8 has better performance than CHIRPS v2.0 and TAMSAT v3.1 in temperate and tropical AEZs at the daily, dry season and annual time steps as well as in sub-tropical AEZ in dry season and annual temporal periods, but MSWEP v2.8 displayed comparable performance with TAMSAT v3.1 in the daily time step. CHIRPS v2.0 exhibit superior performance in the monthly time scale in the three AEZs as well as in the wet season in temperate and tropical AEZs, but TAMSAT v3.1 has outperformed than CHIRPS v2.0 in the wet season in sub-tropical AEZ. The finding also indicated that the capability of MSWEP v2.8 to detect the rainy days (79–86%) and frequency of rainy days (0.99–1.79) is better than CHIRPS v2.0 and TAMSAT v3.1, but TAMSAT v3.1 has shown the best performance for identifying the non-rainy days (14–38%) than MSWEP v2.8 and CHIRPS v2.0. With regard to temperature, MERRA v2 outperformed over ERA5 in temperate and tropical AEZs for estimating both maximum and minimum temperatures from the daily to annual time scales, but ERA5 has shown superior performance than MERRA v2 in the sub-tropical AEZ. In view of the finding, we concluded that the best performing rainfall and temperature products for each AEZ can be used for data scarce regions such as the BER. The findings of this study provide important insights about the need to identify best performing rainfall and temperature products for different AEZs to enhance the quality of hydro-climate study outputs in the decision-making process.

Natural attenuation and remobilization of arsenic in a small river contaminated by the volcanic eruption of Mount Iou in southern Kyushu Island, Japan

The active volcano Mount Iou, in the southern part of Japan, erupted in 2018 for the first time in approximately 250 years. Geothermal water discharged from Mount Iou had high concentrations of toxic elements, such as arsenic (As), which could seriously contaminate the adjacent river. In this study, we aimed to clarify the natural attenuation of As in the river through daily water sampling for approximately eight months. The risk of As in the sediment was also evaluated using the sequential extraction procedures. The highest As concentration (2000 μg/L) was observed upstream but typically remained below 10 μg/L downstream. Dissolved As was the main form in the river water on non-rainy days. Arsenic concentration in the river naturally decreased through dilution and sorption/coprecipitation with Fe, Mn, and Al (hydr)oxides during flow. However, peaks in As concentration were frequently observed during rainfall events, possibly due to sediment resuspension. Furthermore, the range of pseudo-total As in the sediment was 4.62–14.3 mg/kg. Total As content was highest upstream before decreasing further along the flow. When using the modified Keon method, 44–70% of the total As existed as more reactive fractions associated with (hydr)oxides.

Analysis of Weight, Composition, Density, and Recycling Potential of Floating Marine Debris in Pasie Nan Tigo Area, Padang City

Floating marine debris is a solid materials that is produced and disposed of in the marine environment. This study aimed to analyze the weight, composition, density, and recycling of potential marine debris. The samples were taken at three locations on rainy and non-rainy days. Location was selected based on trawl activities, estuary, and public activities. The method of sampling was using trawl. The weight on rainy days was 1.2 to 2.2 g/m2, while on non-rainy days was 1.6 to 2.7 g/m2. Total weight 689.2 kg non rainy conditions and on rainy conditions 895.4 kg. There were four types of waste obtained, which were plastic, wood, fiber, and rubber. The highest percentage in the composition was plastic, which was 48 to 61%. The density of waste on non-rainy days was 0.1 -1.3 g/m2 and on rainy days conditions range 0.2 - 1.7 g/m2. The highest density of waste was plastic of 1.6 g/m2. The recycling potential of plastic, wood, and fabric was 100% respectively. The recycling potential of plastic, wood, and fabric was 100% respectively. The recommendation for floating marine debris is 3R + 1P (recycling, reuse, recovery, and participant).

The Photophysiology of Benthic Diatoms in the Intertidal Flats of Pulau Pinang (Malaysia)

The in-situ photosynthetic activity in tropical intertidal benthic diatom in response to environmental variation was assessed in this study by measuring chlorophyll fluorescence. The investigation was carried out during the lowest tide in January (non-rainy day) and February 2013 (post-rainy day) at two sampling sites (A and B) from each selected location (Pantai Jerejak, Teluk Bahang and Tanjung Bungah, Pulau Pinang, Malaysia). Samples of surface sediment (top 0.5 cm) were collected, and chlorophyll a extracted as biomass estimation. Assessments of the photosynthetic activity of benthic diatoms were made using a pulse-amplitude modulated (PAM) fluorometer. Fifty-three species were identified, representing 27 genera from the three studied locations. Both locations showed similarities in species diversity and abundance. Two-way ANOVA showed no significant differences (p = 0.430) in species richness (Margalef Index) among sampling locations, with an average value of 6.33±0.247. Both intertidal flats were dominated by Cocconeis, Navicula, Actinoptychus, and Diploneis. The community has low maximum quantum yields, Fv/Fm (ranging from 0.170 to 0.340) and is often light-limited (Photoacclimation Index, Ek, ranging from 67.96 to 236.71 mol photons m-2 s-1). The relative electron transport rate (rETRmax) was low, with values ranging from 3.45 to 35.51 across three sampling locations. Fluctuation in salinity has caused a decrease in photosynthetic activity. This study suggests that the low values indicated a poorly adapted benthic microalgal community that is constantly light-limited. However, time-series data is needed to determine the ability of these communities to adapt to the changing environment.

The Lagging Effect of Precipitation on NAIs Concentrations on Rainy Days in Wuyi Mountain National Park, China

Precipitation (PRE) is an essential factor that affects the negative air ions (NAIs) concentrations. However, the mechanism of NAIs concentrations and their influencing factors on rainy and non-rainy days remains unclear. Here, we used hourly data of NAIs concentrations and meteorological data in 2019 to analyze the distribution of NAIs concentrations and its influencing factors on rainy and non-rainy days in the Wuyi Mountain National Park (WMNP) of China, which was listed as a World Cultural and Natural Heritage Site in 1999. The results indicated that the NAIs concentrations on rainy days were significantly higher than on non-rainy days. However, the NAIs concentrations on rainy days were slightly higher than on the first and second days after rainy days. Then, the NAIs concentrations were significantly reduced on the third day and after that. Thus, rainy days lead to a 2-day lag in the smooth reduction of NAIs on non-rainy days after rainy days. NAIs concentrations were significantly correlated with the relative humidity (RHU) on both rainy and non-rainy days. By analyzing the meteorological factors on NAIs for ranking the feature importance scores on rainy and non-rainy days, PRE was ranked first on rainy days, and sea level pressure (PRS_Sea) and temperature (TEM) were ranked first and second on non-rainy days, respectively. Based on the univariate linear regression model (ULRM), NAIs concentrations responded strongly (higher absolute slope values) to RHU on rainy days and to pressure (PRS), visibility (VIS), water vapor pressure (VAP), TEM, and ground surface temperature (GST) on non-rainy days. The results highlight the importance of PRE in the lag time of NAIs concentrations on rainy and non-rainy days.

Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa

Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).

Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling.

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method.

Long-Term Wastewater Surveillance for SARS-CoV-2: One-Year Study in Brazil.

Wastewater-based epidemiology (WBE) is a tool involving the analysis of wastewater for chemicals and pathogens at the community level. WBE has been shown to be an effective surveillance system for SARS-CoV-2, providing an early-warning-detection system for disease prevalence in the community via the detection of genetic materials in the wastewater. In numerous nation-states, studies have indicated the presence of SARS-CoV-2 in wastewater. Herein, we report the primary time-course monitoring of SARS-CoV-2 RNA in wastewater samples in São José do Rio Preto-SP/Brazil in order to explain the dynamics of the presence of SARS-CoV-2 RNA during one year of the SARS-CoV-2 pandemic and analyze possible relationships with other environmental parameters. We performed RNA quantification of SARS-CoV-2 by RT-qPCR using N1 and N2 targets. The proportion of positive samples for every target resulted in 100% and 96.6% for N1 and N2, respectively. A mean lag of -5 days is observed between the wastewater signal and the new SARS-CoV-2-positive cases reported. A correlation was found between the air and wastewater temperatures and therefore between the SARS-CoV-2 viral titers for N1 and N2 targets. We also observed a correlation between SARS-CoV-2 viral titers and media wastewater flow for the N1 target. In addition, we observed higher viral genome copies within the wastewater samples collected on non-rainy days for the N1 target. Thus, we propose that, based on our results, monitoring raw wastewater may be a broadly applicable strategy that might contribute to resolving the pressing problem of insufficient diagnostic testing; it may represent an inexpensive and early-warning method for future COVID-19 outbreaks, mainly in lower- and middle-income countries.

Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

The reconstruction of daily precipitation data is a much-debated topic of great practical use, especially when weather stations have missing data. Missing data are particularly numerous if rain gauges are poorly maintained by their owner institutions and if they are located in inaccessible areas.In this context, an attempt was made to assess the possibility of reconstructing daily rainfall data from other climatic variables other than the rainfall itself, namely atmospheric pressure, relative humidity and prevailing wind direction.The pilot area for the study was identified in Central Italy, especially on the Adriatic side, and 119 weather stations were considered.The parameters of atmospheric pressure, humidity and prevailing wind direction were reconstructed at all weather stations on a daily basis by means of various models, in order to obtain almost continuous values rain gauge by rain gauge. The results obtained using neural networks to reconstruct daily precipitation revealed a lack of correlation for the prevailing wind direction, while correlation is significant for humidity and atmospheric pressure, although they explain only 10–20% of the total precipitation variance. At the same time, it was verified by binary logistic regression that it is certainly easier to understand when it will or will not rain without determining the amount. In this case, in fact, the model achieves an accuracy of about 80 percent in identifying rainy and non-rainy days from the aforementioned climatic parameters. In addition, the modelling was also verified on all rain gauges at the same time and this showed reliability comparable to an arithmetic average of the individual models, thus showing that the neural network model fails to prepare a model that performs better from learning even in the case of many thousands of data (over 400,000). This shows that the relationships between precipitation, relative humidity and atmospheric pressure are predominantly local in nature without being able to give rise to broader generalisations.

Evaluation of Performance of Three Satellite-Derived Precipitation Products in Capturing Extreme Precipitation Events over Beijing, China

Extreme precipitation events have a more serious impact on densely populated cities and therefore reliable estimation of extreme precipitation is very important. Satellite-derived precipitation products provide precipitation datasets with high spatiotemporal resolution. For improved applicability to estimating urban extreme precipitation, the performance of such products must be evaluated regionally. This study evaluated three satellite-derived precipitation products, the Integrated Multi-satellite Retrievals for GPM (IMERG_V06), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2), and China Meteorological Forcing Dataset (CMFD), in capturing extreme precipitation using observations acquired at 36 rainfall stations during 2001–2016 in Beijing, China. Results showed that MSWEP had the highest accuracy regarding daily precipitation data, with the highest correlation coefficient and the lowest absolute deviation between MSWEP and the rainfall station observations. CMFD demonstrated the best ability for correct detection of daily precipitation events, while MSWEP maintained the lowest rate of detecting non-rainy days as rainy days. MSWEP performed better in estimating precipitation amount and the number of precipitation days when daily precipitation was &lt;50 mm; CMFD performed better when daily precipitation was &gt;50 mm. All three products underestimated extreme precipitation. The Structural Similarity Index, which is a map comparison technique, was used to compare the similarities between the three products and rainfall station observations of two extreme rainstorms: “7.21” in 2012 and “7.20” in 2016. MSWEP and CMFD showed higher levels of similarity in terms of spatial–temporal structure. Overall, despite systematic underestimation, MSWEP performed better than IMERG and CMFD in estimating extreme precipitation in Beijing.

Binary classification of rainfall time-series using machine learning algorithms

Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

Inter-Annual Variations of Precipitation Modulate the Dry Spell Length.

Dry spell length (DSL), consecutive non‐rainy days between two precipitation events, play an important role in regulating soil moisture dynamics, terrestrial energy exchange as well as vegetation growth. According to the Clausius‐Clapeyron (C‐C) relationship, global warming can result in prolonged DSL. However, usually the amount of precipitation and its characteristics coincidentally varied with the changes of DSL under global warming, it remains unclear how the inter‐annual variation of precipitation interacts with the evolution of dry spells. In this study, the global long‐term in‐situ observation data set of daily precipitation during 1976–2019 was used to examine the spatiotemporal trends of growing season DSL and precipitation. Our results showed that the global mean growing season DSL significantly increased by 0.3 days decade−1 during 1976–1998 while no significant trend of that was observed during 1999–2019. In contrast, the growing season precipitation (Prec_GS) showed no significant trend in 1976–1998 whereas significant increase trend of that was observed in 1999–2019. To explore the impacts of precipitation on the evolution of dry spells, we examined the relationship between the growing season DSL and Prec_GS. We found that prevalent negative relationship was observed between growing season DSL and Prec_GS in 88% and 86% stations during the period of 1976–1998 and 1999–2019, respectively. Spatially, the mean annual Prec_GS and DSL showed significantly negative relationship, that is, the stations with more precipitation showed shorter DSL in growing season, and vice versa. The changes of mean annual Prec_GS explained 81% spatial variation of growing season DSL. Moreover, during the period of 1999–2019 significant increase of precipitation frequency and decrease of dry day frequency were also observed in addition to the increase of Prec_GS in this period. The decreased dry day frequency further resulted in the decrease of growing season DSL. By excluded the impacts of precipitation, the DSL/Prec_GS ratio showed significant decreasing trend during 1999–2019. Our study suggested that the spatiotemporal variations of DSL were modulated by the variation of precipitation. The impacts of precipitation changes on ecosystem by altering the dry spell evolution should be considered in modeling the terrestrial carbon and hydrological cycling in response to climate changes.

Low-Cost Ka-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies.

Satellite tropospheric propagation studies strongly rely on beacon receiver measurements. We were interested in performing a measurement campaign to characterize rain attenuation statistics. In this article, we outline some of the characteristics and drawbacks one faces when trying to perform a radio wave satellite beacon propagation experiment at the Ka-band with low-cost measurement equipment. We used an affordable beacon receiver consisting of a commercial low-noise block down-converter, an outdoor dual-reflector antenna, and a software-defined radio unit. To measure rain attenuation events, we needed to work out where the reference signal level was at all times. However, as we did not have a radiometer to remove the impact of gases and clouds, since it is a very expensive device, we used a procedure that involved the subtraction of a stable and reliable reference level (template) from the raw received beacon level. This template was extracted from observations during non-rainy periods. The procedure implemented for extracting the template was based on the same data processing methodology used by other authors in this field. Here, we describe through specific examples the main characteristics of the templates extracted on non-rainy days, as well as the impact of some meteorological parameters and unavoidable, but small antenna pointing errors.