Precipitation Monitoring Research Articles

In Situ Monitoring of Drug Precipitation from Digesting Lipid Formulations Using Low-Frequency Raman Scattering Spectroscopy.

Low-frequency Raman spectroscopy (LFRS) is a valuable tool to detect the solid state of amorphous and crystalline drugs in solid dosage forms and the transformation of drugs between different polymorphic forms. It has also been applied to track the solubilisation of solid drugs as suspensions in milk and infant formula during in vitro digestion. This study reports the use of LFRS as an approach to probe drug precipitation from a lipid-based drug delivery system (medium-chain self-nanoemulsifying drug delivery system, MC-SNEDDS) during in vitro digestion. Upon lipolysis of the digestible components in MC-SNEDDS containing fenofibrate as a model drug, sharp phonon peaks appeared at the low-frequency Raman spectral region (<200 cm-1), indicating the precipitation of fenofibrate in a crystalline form from the formulation. Two multivariate data analysis approaches (principal component analysis and partial least squares discriminant analysis) and one univariate analysis approach (band ratios) were explored to track these spectral changes over time. The low-frequency Raman data produces results in good agreement with in situ small angle X-ray scattering (SAXS) measurements with all data analysis approaches used, whereas the mid-frequency Raman requires the use of PLS-DA to gain similar results. This suggests that LFRS can be used as a complementary, and potentially more accessible, technique to SAXS to determine the kinetics of drug precipitation from lipid-based formulations.

High-resolution precipitation monitoring with a dense seismic nodal array

Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design.

Automatic Detection of Rainfall at Hourly Time Scales from Mooring Near-Surface Salinity in the Eastern Tropical Pacific

Abstract We explore the use of ocean near-surface salinity (NSS), that is, salinity at 1-m depth, as a rainfall occurrence detector for hourly precipitation using data from the Salinity Processes in the Upper-Ocean Regional Studies–2 (SPURS-2) mooring at 10°N, 125°W. Our proposed unsupervised learning algorithm consists of two stages. First, an empirical quantile-based identification of dips in NSS enables us to capture most events with hourly averaged rainfall rate of &gt;5 mm h−1. Overestimation of precipitation duration is then corrected locally by fitting a parametric model based on the salinity balance equation. We propose a local precipitation model composed of a small number of calibration parameters representing individual rainfall events and their location in time. We show that unsupervised rainfall detection can be formulated as a statistical problem of predicting these variables from NSS data. We present our results and provide a validation technique based on data collected at the SPURS-2 mooring. Significance Statement Continuous monitoring of precipitation in the ocean is challenging when a physical rain gauge is not available in the region of interest. Indirect detection of precipitation using available data, such as changes in ocean near-surface salinity (NSS) can be used to construct a virtual rainfall detector. We propose to combine data-based and model-based methods to detect rainfall without the use of a physical rain gauge. We use NSS and precipitation data from a mooring in the eastern tropical Pacific Ocean to develop and test the method.

A dataset of monthly precipitation with resolution of 1 km in China from 1960 to 2020

Precipitation data are greatly valuable in theoretical research and practical application, and have been widely used in hydrological forecasting, agricultural production, ecological protection and other fields. China has a vast territory with large topographic fluctuations, and most meteorological stations are located in densely populated plain areas. Due to the low density of stations, there is a lack of precipitation observation data in some areas such as mountainous areas. Therefore, it is necessary to use the interpolation method to establish high-precision precipitation datasets. Based on the precipitation monitoring data from more than 2,400 meteorological stations on the ground from 1960 to 2020, we used the climate data spatial interpolation software ANUSPLIN to obtain a dataset of monthly precipitation interpolation with a resolution of 1 km in China. According to the comparison between the dataset and the measured precipitation data as well as the hydrological yearbook data in China released by the CRU and Northwest University of agriculture and forestry science and technology, this dataset has the characteristics of high accuracy, high resolution and long time series, with great potential in scientific research and application.

Ecohydrologic Dynamics of Rock Moisture in a Montane Catchment of the Colorado Front Range

AbstractWarming across the western United States continues to reduce snowpack, lengthen growing seasons, and increase atmospheric demand, leading to uncertainty about moisture availability in montane forests. As many upland forests have thin soils and extensive rooting into weathered bedrock, deep vadose‐zone water may be a critical late‐season water source for vegetation and mitigate forest water stress. A key impediment to understanding the role of the deep vadose zone as a reservoir is quantifying the plant‐available water held there. We quantify the spatiotemporal dynamics of rock moisture held in the deep vadose zone in a montane catchment of the Rocky Mountains. Direct measurements of rock moisture were accompanied by monitoring of precipitation, transpiration, soil moisture, leaf‐water potentials, and groundwater. Using repeat nuclear magnetic resonance and neutron‐probe measurements, we found depletion of rock moisture among all our monitored plots. The magnitude of growing season depletion in rock moisture mirrored above‐ground vegetation density and transpiration, and depleted rock moisture was from ∼0.3 to 5 m below ground surface. Estimates of storage indicated weathered rock stored at least 4%–12% of mean annual precipitation. Persistent transpiration and discrepancies between estimated soil matric potentials and leaf‐water potentials suggest rock moisture may mitigate drought stress. These findings provide some of the first measurements of rock moisture use in the Rocky Mountains and indicated rock moisture use is not just confined to periods of drought or Mediterranean climates.

Uncertainty Quantification of Satellite Soil Moisture Retrieved Precipitation in the Central Tibetan Plateau

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.

Underlying Microphysical Processes in the Melting Layer during Moderate Precipitation: Evidence from Ground-Based Data

Abstract We performed a detailed analysis of ground-based data to investigate changes in the morphological properties and particle size distribution of precipitation particles as they fall through the melting layer (ML). In July 2013, we started continuous precipitation monitoring in Sapporo (Japan) with a two-dimensional video disdrometer, an electrical balance–type snow gauge, and an X-band marine radar. We used data collected from 0943 to 1040 Japan standard time (JST) 10 March 2015 for analysis, when the bright band progressively descended to the ground surface and precipitation intensity was moderate and approximately steady (∼10 mm h−1). We found that the aggregation of aggregates in the upper half of the ML did not necessarily result in large raindrops. Almost all of the snow particles with a melted diameter (Dm) ≥ 4 mm broke up before they melted into raindrops of equivalent size. The apparent one-to-one relationship between melting snow particles and raindrops held for particles with 2 &lt; Dm &lt; 3 mm. Most small raindrops were generated by the successive breakup of melting particles in the lower half of the ML.

Precipitation Forecasting and Monitoring in Degraded Land: A Study Case in Zaghouan

The study aimed to forecast and monitor drought over degraded land based on monthly precipitation using the Seasonal Autoregressive Integrated Moving Average (SARIMA) approach. Several statistical parameters to select the most appropriate model were applied. The results indicate that the SARIMA (1,1,1) (0,1,1)12 is the most suitable for 1981 to 2019 CHIRPS time-series data. The combination of precipitation data and this approved model will subsequently be applied to compute, assess, and predict the severity of drought in the study area. The forecasting performance of the generated SARIMA model was evaluated according to the mean absolute percentage error (15%), which indicated that the proposed model showed high performance in forecasting drought. The forecasting trends showed adequate results, fitting well with the historical tendencies of drought events.

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

ABSTRACT Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement. However, precipitation monitoring data are collected through a limited number of precipitation stations in practice, and they are often sparse and discontinuous, particularly in spatial domain. Furthermore, regional precipitation data exhibits characteristics of seasonality, periodicity and highly non-stationarity on a long-time scale. Therefore, it is challenging to obtain a spatio-temporal variation of precipitation with high spatial resolution from monitoring data measured at a limited number of precipitation stations. To address these challenges, this study develops a non-parametric spatio-temporal Bayesian compressive sensing (ST-BCS) method for interpolation of spatio-temporally varying, but sparsely measured precipitation data in the spatial domain. The proposed method is able to not only provide precipitation interpolation results with high spatial resolution from a limited number of monitoring stations, but also quantify the associated interpolation uncertainty simultaneously. In addition, ST-BCS is directly applicable to the non-stationary spatio-temporal meteorological data. Furthermore, real precipitation datasets are established to benchmark different spatio-temporal interpolation methods. The benchmarking results show that the proposed ST-BCS method performs well and outperforms the spatial BCS method.

Assessing the importance of drainage layers over geomembrane liners within engineered cover systems: Seven years of field monitoring at three mine waste rock piles

Geomembrane liners are commonly subject to defects that can allow water leakage through these openings and significantly undermine their performance. While defects are difficult to prevent and characterize, minimization of potential leakage can still be attained with appropriate lateral drainage over the liner. The objective of this study is to assess the performance of different drainage layer materials within HDPE-lined cover systems at three mine waste rock piles (WRPs) in the Sydney Coalfield, Nova Scotia, Canada. Natural soil, clean gravel and a geocomposite net were employed at the Summit, Victoria Junction and Franklin WRPs, respectively. Extensive field monitoring of precipitation (PPT), moisture content within the cover materials, and head of water above the liner, was performed. The head of water and moisture was consistently high at Summit throughout the year, while consistently low at Victoria Junction and Franklin, with the geocomposite net being just as effective as the gravel layer. Using typical assumptions of defect size/number, leakage rates at Victoria Junction and Franklin remained very low due to limited water head and flow gradient. Therefore, while the prevention of defects is highly challenging at HDPE-lined cover systems, potential defect leakage can be proactively controlled during cover system design with the implementation of adequate drainage layers above the liner.

Extreme precipitations and their influence on the River flood Hazards: A case study of the Sana River Basin in Bosnia and Herzegovina

The subject of the research paper is the use of remote sensing in monitoring and analyzing the impact of climate change on the occurrence of extreme precipitation, and the cause-and-effect occurrence of floods in the area of the Sana River Basin in Bosnia and Herzegovina. The goal is to process the "product" of remote sensing to identify the time intervals of occurrence of extreme precipitation, to assess their impact on water levels, and to map potential floods in space. Spatial identification of zones that are at risk of flooding is an integral part of the aforementioned goal. Precipitation monitoring was performed by processing Climate Hazards Group InfraRed Precipitation with Station Data through the Google Earth Engine platform. The observed 30-year period (1992-2022) was compared with the average precipitation for 2017, 2018 and 2019. The impact of extreme precipitation on the water level of the Sana River was analyzed. Flooding periods have been identified: February and December 2017, March 2018 and May 2019. Mapping of flooded areas was carried out by pre-processing and post-processing of Sentinel-1 radar satellite images. The total flooded area is: 710.38 ha (February 2017), 496.79 ha (December 2017), 417.86 ha (March 2018) and 422.42 ha (May 2019). Based on the identified flooded areas, a flood risk map was created on the main course of the Sana River. The research contributes to a better understanding of the changes that occur in the area under the influence of climate change, and the data presented are important for numerous practical issues in the field of water resource management and flood protection.

Precipitation as a Possible Risk Factor for Development of Lyme Nephritis in Dogs.

To determine whether increased precipitation in the months before diagnosis is predictive of Lyme nephritis in dogs, a symmetrical bidirectional case-crossover study was performed on 87 dogs with presumptive Lyme nephritis. Cases were geographically linked to the nearest precipitation monitoring station, and monthly total precipitation for the 4 mo preceding diagnosis was compared to monthly total precipitation 1 yr before and 1 yr after. Breeds affected included Labrador retrievers (n = 31), mixed-breed dogs (n = 19), and golden retrievers (n = 10), with a possible seasonal bimodal peak in spring and fall. In age-adjusted analysis, each additional inch of precipitation 3 mo before diagnosis was associated with an increase in the odds of developing Lyme nephritis (odds ratio 1.13, 95% confidence interval 1.01-1.25). The results suggest that increased precipitation is a possible risk factor for Lyme nephritis in dogs. These findings may provide useful information for the accurate diagnosis of dogs with protein-losing nephropathy and may guide future studies of risk factors for Lyme nephritis. The methodology employed here reinforces the concept that case-crossover designs have utility beyond acute exposures and outcomes and offer an attractive alternative for studies in companion animals.

Towards Innovative Solutions for Monitoring Precipitation in Poorly Instrumented Regions: Real-Time System for Collecting Power Levels of Microwave Links of Mobile Phone Operators for Rainfall Quantification in Burkina Faso

Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall, the exploitation of records of this attenuation is an innovative and an inexpensive solution for precipitation monitoring purposes. Performance data from mobile operators’ networks are crucial for the implementation of this technology. Therefore, a real-time system for collecting and storing CML power levels from the mobile phone operator “Telecel Faso” in Burkina Faso has been implemented. This new acquisition system, which uses the Simple Network Management Protocol (SNMP), can simultaneously record the transmitted and received power levels from all the CMLs to which it has access, with a time resolution of one minute. Installed at “Laboratoire des Matériaux et Environnement de l’Université Joseph KI-ZERBO (Burkina Faso)”, this acquisition system is dynamic and has gradually grown from eight, in 2019, to more than 1000 radio links of Telecel Faso’s network in 2021. The system covers the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadougou to these cities.

EXPERIENCE IN STRENGTHENING UNDERGROUND PEDESTRIAN CROSSINGS IN WATER-SATURATED CITY SOILS

Purpose: to analyze the problem of subsidence of vertical and horizontal elements of underground pedestrian crossings in the conditions of the city of Kyiv (Intersection of Mineralna – Ukrainian streets) taking into account the water-logged structure of the soil. Find a modern solution to this problem using a scientific approach. Using the example of an underground pedestrian crossing, identify the most deformed areas in the elements of the joints of vertical and horizontal structures for further processing and drawing a conclusion for the elimination of similar ones in the future.Methodology: in order to establish the reasons that caused the deformation of structures, to solve the task of engineering survey of the technical condition of their reinforced concrete structures with an instrumental survey of the actual strength, geometry and monitoring of uneven precipitation. Based on the results of field and chamber work, a complex ofstrengthening and restoration engineering and technical measures was developed and recommended to the customer for implementation, taking into account the requirements. Originality: the article provides an example of the implementation of the construction of the foundations of underground pedestrian crossings erected on water-saturated soils in complex engineering and geological conditions using construction water subsidence, usingtechnological methods, with the aim of excluding uneven deformations of foundation sediments above the zones of depression funnels from water subsidence or artificial foundations from vertical cement-soil reinforcing elements, the frontal part of which must be wrapped in reliable base soils.Findings and practical implications. The sealing of horizontal and vertical technological and sedimentary-temperature seams in the construction of foundations, walls and the covering of underground structures should be carried out using modern types of hydraulic plugs that perceive the hydraulic pressure from underground water and exhibit the property of swelling when in contact with flooding water. Such technological solutions can be used in the construction of underground pedestrian crossings in the conditions of dense urban development, in the presence of significant possible vertical deformations in water-saturated soils.Keywords: subsidence, sediment, crack, depression curve, mountain pressure, waterproofing, pile, cement-soil reinforcing element, monitoring

Long-term declines in atmospheric nitrogen and sulfur deposition reduce critical loads exceedances at multiple Canadian rural sites, 2000–2018

Abstract. Daily air concentrations of inorganic nitrogen (N) species, including gaseous HNO3 and particulate-bound (p)NH4+ and pNO3-, and sulfur (S) species, including SO2 and pSO42-, and precipitation concentrations of NO3-, NH4+, and SO42-, have been routinely monitored by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1983. Data at 15 rural sites from 2000–2018 were used to estimate dry and wet N and S deposition fluxes, which were then used to explore their spatiotemporal trends and assess ecosystem damage through a retrospective analysis of critical loads (CLs) exceedances. Total (dry + wet) N deposition ranged from 1.7–9.5 kgNha-1yr-1 among the 15 sites, though dry deposition of NH3 and some oxidized N species were not included due to lack of monitoring data. Based on additional N measurements in 2010 at one of the sites, annual total N deposition may be underestimated by up to 32 %. Total N deposition was dominated by wet NO3- and wet NH4+ deposition, which together comprised 71 %–95 %. Contributions to dry N deposition were 40 %–74 % by HNO3, 11 %–40 % by pNH4+, and 5 %–25 % by pNO3-. Total S deposition ranged from 1.3–8.5 kgSha-1yr-1 and was dominated by wet deposition of SO42- and dry deposition of SO2. Relative percentages of wet and dry S deposition were 45 %–89 % and 11 %–55 %, respectively. Acidic ion fluxes were greatest in southeastern Canada and were comparable among the west coast, prairie, remote, and eastern Canadian sites. Oxidized N (dry HNO3, dry pNO3-, wet NO3-) deposition was greater than that of reduced N (dry pNH4+, wet NH4+) in the early 2000s. In 2014–2018, reduced N deposition surpassed that of oxidized N in southeastern Canada. Total N and S deposition decreased significantly at a rate of −0.03 to −0.25 kgNha-1yr-1 (−1.1 % yr−1 to −3.3 % yr−1) and −0.08 to −0.66 kgSha-1yr-1 (−3.5 % yr−1 to −6.6 % yr−1), respectively, among the sites. The weak declining trend in total N deposition at the west coast site was consistent with the slower decline in NOx emissions in western Canada. Reductions in total N deposition were driven by its oxidized form as trends in reduced N were non-significant. As a result, reduced N contributions to total N deposition increased on average from 42 % in 2000–2004 to 53 % in 2014–2018. Anthropogenic NOx and SO2 emissions reductions in both eastern Canada and eastern US were highly effective in reducing total oxidized N and total S deposition, respectively, in eastern Canada. Acidic deposition exceeded terrestrial CL at 5 of the 14 sites and aquatic CL at 2 of the 5 sites in the early 2000s. However, exceedances have been trending downwards and acidic deposition fluxes were mostly near or below CL after 2012 for the subset of sites assessed, which support recovery from historical acidification. Further assessments of CL exceedances are required in other Canadian regions susceptible to acidification and affected by elevated or increasing N and S emissions.

Real-time and in situ monitoring of evaporation rate and salt precipitation during interfacial solar evaporation

Interfacial solar evaporation has received extensive attention for sustainable seawater desalination. Despite great progress in interfacial solar evaporators (ISEs) with high evaporation performance, the poor collection of the dynamic evaporation rate of exact ISE in large-scale application as well as the monitoring of the salt precipitation still face great challenges. Herein, the electrical conductivity of the supporting substrate is firstly employed as an indicator to monitor the real-time and in situ evaporation rate of an ISE. This method is based on a linear dependence between the electrical conductivity and the evaporation rate, both of which are dependent on the fluid flow as well as the ion mobility inside the supporting substrate. Moreover, the electrical conductivity-based evaporation rate monitoring is generally applicable for various kinds of ISEs. As a proof-of-concept, six evaporators in a prototype system were simultaneously managed by in situ tracking each electrical conductivity over time. Furthermore, the salt clogging inside the ISE can be sensitively detected almost 30 min earlier than the observation of salt crystals on the ISE surface. Based on its easy accessibility, wide applicability, good stability and high accuracy, this in situ evaporation rate monitoring has important implications to promote the intelligent management of large-scale ISEs.

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).

Evaluation of Extreme Precipitation Indices in Brazil’s Semiarid Region from Satellite Data

Brazil’s semiarid region (SAB) has a heterogeneous precipitation distribution, with the occurrence of periodic droughts and occasional extreme rainfall events. The precipitation monitoring system in this region is insufficient, but remote sensing products can provide information on rainfall in areas with low data coverage. Thus, the main objective of this study was to evaluate 12 extreme precipitation indices calculated using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in comparison with indices calculated from data measured by rain gauges for different SAB locations. To evaluate the IMERG product, we used rainfall data measured by 56 rain gauges during the period from 1 January 2000 to 31 December 2020. The satellite product was evaluated through juxtaposition between the IMERG and actual rainfall data, by calculating the statistical indices bias, root-mean-squared error, Spearman correlation, and probability density function. The results showed that most of the extreme precipitation indices were well represented by the satellite data, except for the simple precipitation intensity index (SDII), in which case the correlation coefficient was 0.2. This result can be explained as this index is calculated from the exact value of daily precipitation, while the other indices are estimated by rainfall values above some thresholds. On the other hand, total annual precipitation and precipitation above 1 mm presented Spearman correlation reaching 0.97 in some locations. We conclude that the IMERG database is adequate to represent the maximum precipitation in the Brazilian semiarid region, and the extreme precipitation indices had good performance according to the region where the maximum rain occurred.

One-step fabrication of sandwiched film based triboelectric nanogenerator for large-area energy harvester and precise self-powered sensor

Triboelectric nanogenerator (TENG) which can work as novel power provider and self-powered sensor has been in rapidly developed recently. However, fabricating large-area TENG through a one-step, continuous, and environmentally friendly method still remains a challenge but highly desired. Herein, large-area sandwiched film based triboelectric nanogenerator (LSF-TENG) was successfully fabricated via coextrusion blow film molding in a one-step way, whose skin (triboelectric layer) and core layer (electrode) are respectively pure polypropylene (PP) and carbon nanotubes compounded with PP (CNTs/PP) phase. Moreover, the as-fabricated LSF-TENG can realize effectively harvest mechanical energy and serves as a self-powered biomimetic whisker (SPBW) for automated guided vehicle (AGV). Furtheromre, thanks to Internet of Things (IoT) technology, the raindrop energy harvesting & precipitation monitoring (REH&PM) system has been successfully developed. This work proposes a facile, continuous and environmentally friendly method for developing low-cost, durable and large-area TENG, opening a new pathway to develop TENG based on the concept of polymer “structuring” processing.

A Multi-Component Synthesis Scoring Method of Gridded Fusion Products for Precipitation Quality Control

A crucial and fundamental type of precipitation monitoring is the automatic observation of precipitation by ground stations. However, the typical quality control methods that are applied to rainfall present restrictions because of their discontinuities in time and space. The multi-component comprehensive consistency approach is a subjective quality control method that uses changes in other weather components associated with the precipitation process. The present study determined the reference weather components for precipitation by calculating the correlation coefficients between weather components and precipitation, and the proportions that passed the significance tests. These components included air pressure, visibility, relative humidity, wind speed, temperature difference, and the sum of cloud covers of 125–875 hPa. This study introduced various gridded fusion products to fill in the empty spaces of weather components, and put forward a scoring method to convert a subjective method into an objective method. The outcomes of the scoring method are assessed using the artificial quality control results. The evaluation’s findings demonstrate that the scoring method’s accuracy in every period of precipitation is greater than 0.75, meeting the need for objective quality control of real-time surface precipitation.