Rain Hours Research Articles

Synoptic-Scale Meteorological Patterns Associated with Heavy Rainfall in the Minnesota Region

Abstract Situated in the Upper Midwest, Minnesota’s midcontinental location places it in a climate transition zone between eastern U.S. humid conditions and western semiarid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May–September 1959–2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural network, self-organizing map (SOM) technique to identify sea level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed that favorable conditions related to moisture and lift were associated with heavy rainfall.

Crowdsourced Meteorological Data to Supplement Limited Official Sources: A Survey and Case Study of Precipitation Monitoring in Guangzhou, China

Abstract Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely, cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.

A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction

Beijing, Shanghai, Singapore, and London are regions with high population density and industrial activities. In this sense, accurate prediction of the rate of particulate matter 2.5 (PM_2.5), one of the most important air pollutants, is very important for creating a healthy ecological system. This study proposes a hybrid method to predict PM_2.5 with high success. This method aims to optimize the Long Short-Term Memory (LSTM) deep learning model, which successfully solves time series problems with a Genetic Algorithm (GA). Here, the hyperparameters of the LSTM model, such as the number of layers, number of neurons, number of epochs, and kernel size, are optimized to achieve the highest prediction rate. Against this backdrop, the present study uses for the first time a hybrid GA-LSTM model to predict PM_2.5 concentrations in Beijing, Shanghai, Singapore, and London. The Beijing dataset consists of 13 attributes, the Shanghai dataset consists of 10 attributes, the London dataset consists of 5 attributes, and the Singapore dataset consists of 7 attributes. In this study, input variables such as PM_2.5, DEWP, TEMP, PRESS, WS, WD, PRE, Snowfall hours, Rain hours, and Season were used. To assess the effectiveness of this hybrid method, the developed GA-LSTM model was compared with k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), LSTM, and Gated Recurrent Units (GRU). Experimental results showed that GA-LSTM outperformed the compared models. In addition, one of the best results was obtained compared to other models in the literature.

The extreme precipitation events of August 2018 and 2019 over southern Western Ghats, India: A microphysical analysis using in-situ measurements

The extreme rainfall events observed at the High-Altitude Cloud Physics Observatory (HACPO) in Rajamallay, Munnar (10° 9′19.94″N, 77° 1′6.65″E; 1820 m above MSL) over the southern Western Ghats (India) during the floods in 2018 and 2019 monsoon periods are investigated. The observations from Micro Rain Radar (MRR) and Ceilometer are used to analyze precipitation microphysics and the vertical distribution of clouds during the intense rainfall episodes on 14 - 16th August 2018 and on 8th August 2019. The drop size distribution (DSD) spectra during the 2018 event is characterized by a large number of small to medium-sized drops resulting in maximum reflectivity of 48dBZ with mass-weighted mean diameter (Dm) value of 1.2 mm. At the same time, the 2019 event is characterized by larger drops and resulted in high reflectivity of 53dBZ and Dm value shifted to 1.4 mm. The consistent increment of Dm and σm with slight variation in Nt during the intensive rain hours on 8th August 2019 shows a mixed-phase microphysical process that can invigorate the production of convective rainfall from deep cloud bands (217 K of cloud top temperature) with enhanced rain water content (22 gm−3). The parameters of scaled raindrop size distributions corresponding to higher rain rates (>50 mmhr−1) suggest that the microphysical process that control the variations in DSD is strongly number controlled during these extreme rainfall events. The DSDs are evolved from a consistent, widespread rainfall supported by anomalous moisture advection from the Arabian Sea in 2018 monsoon period. The moisture convergence occurred on the elevated terrains leads to an intense spell of rainfall in two consecutive hours and satisfies the occurrence of a mini-cloud burst (MCB) event on 8th August 2019 causing flash flood in the region.

SISTEM PENGELOLAAN SAMPAH DI RUMAH SAKIT UMUM DAERAH TGK. CHIK DITIRO SIGLI KABUPATEN PIDIE

Hospital sanitation efforts are one of the supporting efforts in providing health services. Meanwhile, one of the hospital sanitation efforts is hospital waste management efforts (Permenkes No. 1204/Menkes/SK/X/2004). This research is descriptive with a cross-sectional design, where the author wants to know the description of the waste management system at the Tgk Regional General Hospital. Chik Ditiro Sigli, Pidie Regency in 2020. Sorting medical and non-medical waste at the Tgk Regional General Hospital. Chik Ditiro Sigli, Pidie Regency, Each room is separated and has a lid that is intact and easy to open, but the trash cans also have a plastic bag color according to the specified symbol and the garbage collection is made of materials that are not easy to rust, strong and water-resistant so that they are easy to clean and emptied, the shelter is resistant to sharp and pointed objects so that transportation is also available for transporting carts/garbage trolleys with a flat bottom surface and watertight so that the waste does not stick to the means of transportation and does not become a nest of insects, but the transportation of waste is transported to the TPS 2 times a day and to the TPA every day and garbage storage has a complete and open lid, the garbage storage place is also far from the Tgk Chik Ditiro Sigli General Hospital space agency, Pidie Regency and medical waste collection is carried out 24 hours a day and storage is according to the tropical climate, maximum during the rainy season. 48 hours of rain and a maximum of 24 dry season hours but the Tgk Chik Ditiro Sigli General Hospital, Pidie Regency does not have a waste disposal site, waste destruction is carried out or cooperates with a 3rd party PT. Cahaya Tanjung Tiram and final waste disposal, non-medical waste in collaboration with the Sanitation Department and do not destroy medical waste every day so that from the overall results of direct observations in the field. Based on the results of research on the Waste Management System at the Tgk Regional General Hospital. Chik Di Tiro Sigli, Pidie Regency in 2020 consisting of 59 samples or the entire room, the results that meet the requirements are 57 sample units or 96.61% while those who do not meet the requirements are 2 sample units or equal to 3.39%. Keywords: Waste Management, Hospital

Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.

An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi.

Weather conditions during hunting season affect the number of harvested roe deer (Capreolus capreolus).

Due to human‐induced climate and landscape changes, distribution and abundance of many ungulate species have increased worldwide. Especially in areas where natural predators are absent, hunting is the essential management tool for regulating ungulate populations. Therefore, understanding the factors associated with harvest rates is the first step toward an adaptive management approach. Weather influences hunter and ungulate behavior and thus presumably harvest, but how and which meteorological parameters are linked to harvest numbers have rarely been evaluated. We used nearly 65,000 “sit and wait” and driven hunt harvests of roe deer (Capreolus capreolus) in Bavaria, Germany, and weather data from 2008 to 2017 to test for factors affecting roe deer harvests (i.e., temperature, rain hours, wind speed, sunshine duration, snow depth, workdays vs. weekends, month) using zero‐inflated negative binomial mixed‐effect models. Our results reveal that, besides workdays, high temperatures and prolonged rain resulted in fewer harvested animals, whereas sunshine duration in summer and snow height in snow‐rich areas partially favored harvests during sitting hunts in summer and winter, respectively. The influence of wind speed varied over the course of the year. In summer and autumn, wind speed commonly had a negative effect, positively affecting harvests in winter in some regions. Daily harvest numbers decreased during the summer and autumn hunting periods (May till mid‐October), while they increased during the winter period (mid‐October till mid‐January). Interestingly, harvest success during driven hunts, which are planned well in advance and therefore take place largely independent of weather conditions, was similarly affected by the weather. This result suggests that the inferred weather influence is not only due to the hunters' decisions but also due to deer behavior. Since many ungulate populations may further benefit from climate change, building an understanding of the relationship between hunting success and weather will aid adaptive ungulate management.

Impact of Weather, Activities, and Service Disruptions on Transportation Demand

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

Challenges in Water Resources Management in Taiwan - Chain Reactions from Increased Rainfall Intensity under Global Warming

In the summer of 2015, a super typhoon strike in the Taipei City, the capital of Taiwan [1][2]. This super typhoon which strikes northern Taiwan was triggering a massive landslide in the upstream of the water treatment plant. Due to this condition, most of the Taipei citizen suffered from the turbid tap water [3,4]. The river water was too turbid to be treated due to the sediment from the landslide; therefore the turbid water was delivered directly through the pipe to people houses[2]. In the last view decade, this event has become the most significant threat the water resources management in Taipei. On the other hand, this event can be evidence of climate change. Last years, in 2018, there are lots of flooding event all over the world for example in France[5], Myanmar[6], India, Spain, Indonesia[7], Mexico and Taiwan.

Under temperate weather conditions, dairy goats use an outdoor run more with increasing warmth and avoid light wind or rain

Access to an outdoor run might provide some benefits for the social and activity behavior of dairy goats. The aim of this study was to investigate the influence of weather parameters on outdoor run use by dairy goats under temperate weather conditions. Data were collected from February to April and in October, 2014, on 14 commercial dairy goat farms in Switzerland and Germany for 14 d per farm. Temperature, humidity, solar radiation, wind speed, and rain amount were measured continuously using a weather station. The outcome variables, average proportion of the herd in the outdoor run (proportion of herd in run) and average frequency of goats entering the outdoor run per hour in relation to group size (frequency of entries to run), were assessed by video data. Temperature, humidity, and solar radiation were combined to create the parameter "warmth" by means of a principal component analysis, whereas wind speed and rain (rain events and rain amount) were used in their original form as explanatory variables. Additionally, the effect of the comprehensive climate index on outdoor run use was investigated. Data were analyzed using linear mixed effects models. Increasing "warmth" increased, whereas increasing wind speed reduced, the proportion of herd in run. With increasing comprehensive climate index, the proportion of herd in run and the frequency of entries to run increased. On days without rain, the proportion of herd in run and the frequency of entries to run were markedly higher than during comparable daytime hours with rain. In the hour before the rain started and after it had stopped, more goats were in the outdoor run than during rain hours, but still fewer than during comparable daytime hours on days without rain. Furthermore, the proportion of herd in run and the frequency of entries to run decreased to a very low level when the rain amount exceeded 1 mm/h. To conclude, in the measured ranges of temperature (-0.8 to +16.5°C), humidity (34.8 to 99.2%), solar radiation (25.9 to 519.8 W/m2), and wind speed (0 to 4 m/s), warmer conditions increased the outdoor run use, whereas the goats clearly avoided light wind and rain. Therefore, in the outdoor runs, protection against rain and wind could promote their use under temperate weather conditions. Furthermore, ensuring optimized access to solar radiation by considering the exposure of the outdoor run might be of advantage, as direct sun exposure induces warmer conditions.

A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities.

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

El aluvión del 9 agosto 2015 en Alto Patache, región de Tarapacá, Desierto de Atacama 1

During the 09 August 2015 (strong El Nino year) heavy rains fell in the coastal Atacama Desert, including our Research Station Atacama UC_Alto Patache (cordillera de la Costa, Tarapaca Region). In this study we provide a detailed analysis of the meteorology and hydrogeomorphic response to the 51,6 mm in 6 hours of rain recorded in Alto Patache. In our control basin (7.2 Ha) we estimated a maximum erosion of ~1070 m3, which were partially stored in the distal alluvial fan, where we quantified 637 m3 of material. We es timated a mean water discharge of 11 m3 s-1 at the lower basin. The comparison between the meteorological and morphostratigraphical data allows us to define three phases that best characterize the anatomy of the 9A: Initial, Peak and End. We conclude that Alto Pa tache embraces an overall geomorphic stability that is interrupted recurrently by debris flows linkable to ENSO variability.

Ứng dụng mạng nơron nhân tạo để dự báo tốc độ ăn mòn thép CT3 trong khí quyển

Predicting atmospheric corrosion rate of metals has important practical significance. The linear model was often used to predict corrosion rate of metals but this provides low precision. This is because the influence of environmental factors to the corrosion rate is not linear. This paper presents the result of building up one artificial neural network using Matlab software, which can predict atmospheric corrosion rate of CT3 carbon steel in Vietnam tropical climate with input and output data of 26 test areas in 15 provinces of the North, Central and South of Vietnam. This neural network has 05 input parameters including the temperature, humidity, time of wetness - TOW, Cl- concentration, number of hours of rain (average of one year). The obtained results of corrosion rate of CT3 steel at 03 locations (Yen Bai, Da Nang, Thai Binh) by using the neural network show much higher accuracy (relative error < 30%) compared with the linear regression models.

Like Hours of Rain on Piles of Brown Leaves

Like Hours of Rain on Piles of Brown Leaves

Studies on the Rain Scavenging Process of Tritium in a Tropical Site at Narora in India

This study presents the results of systematic experiments on tritium (3H) concentrations in ground level air against those in rainwater near a pressurized heavy water reactor in a tropical region. The samples were collected over the rainy season of year 2011 from eight locations in the environment around Narora Atomic Power Station. The specific activity ratio of 3H between rainwater and air moisture at ground level was calculated for each data set. The average specific activity ratio was found to be ranged from 0.12 to 1.1. A correlation ( to 0.76, ) was observed between the total rain hours in a day and the rainwater 3H activity. Higher rain duration with slower rain rate yielded higher 3H concentrations as more time was available for the scavenging/wash-out process to take effect together with lower dilution. Annual tritium (HTO) wet deposition has been measured and calculated for the year 2011 within 0.8 km distance from 145 m high stack of Narora Atomic Power Station (NAPS) at nine locations in different directions. The range of deposition velocity, (m·s−1), at nine locations for the years 2011 is found to be from 4.43E − 04 to 6.42E − 03. The average value for wet deposition velocity for NAPS site is estimated as 3.17E − 03 m·s−1.

Using atmospheric trajectories to model the isotopic composition of rainfall in central Kenya

The isotopic composition of rainfall (δ2H and δ18O) is an important tracer in studies of the ecohydrology, plant physiology, climate and biogeochemistry of past and present ecosystems. The overall continental and global patterns in precipitation isotopic composition are fairly well described by condensation temperature and Rayleigh fractionation during rainout. However, these processes do not fully explain the isotopic variability in the tropics, where intra‐storm and meso‐scale dynamics may dominate. Here we explore the use of atmospheric back‐trajectory modeling and associated meteorological variables to explain the large variability observed in the isotopic composition of individual rain events at the study site in central Kenya. Individual rain event samples collected at the study site (n = 41) range from −51‰ to 31‰ for δ2H and the corresponding monthly values (rain volume‐weighted) range from −15‰ to 15‰. Using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we map back‐trajectories for all individual rain hours occurring at a research station in central Kenya from March 2010 through February 2012 (n = 544). A multiple linear regression analysis demonstrates that a large amount of variation in the isotopic composition of rainfall can be explained by two variables readily obtained from the HYSPLIT model: (1) solar radiation along the trajectory for 48 hours prior to the event, and (2) distance covered over land. We compare the measurements and regression model results to the isotopic composition expected from simple Rayleigh distillation along each trajectory. The empirical relationship described here has applications across temporal scales. For example, it could be used to help predict short‐term changes in the isotopic composition of plant‐available water in the absence of event‐scale sampling. One can also reconstruct monthly, seasonal and annual weighted mean precipitation isotope signatures for a single location based only on hourly rainfall data and HYSPLIT model results. At the study site in East Africa, the annual weighted mean δ2H from measured and modeled values are −7.6‰ and −7.4‰, respectively, compared to −18‰ predicted for the study site by the Online Isotopes in Precipitation Calculator.

Studies on the rain scavenging process of tritium in a tropical site in India

This study presents the results of one of the first systematic experiments on tritium ( 3H) concentrations in ground level air against that in rainwater near a pressurized heavy water reactor in a tropical region. The samples were collected over the rainy season of three years (2007, 2008 and 2010). For this study, 31 data sets were collected and interpreted based on the theoretical information available in the literature. The specific activity ratio of 3H between rainwater and air moisture at ground level was calculated for each data set. The average specific activity ratio was found to be 1.96 ± 2.72. A correlation ( r = 0.82, p < 0.001) was observed between the total rain hours in a day and the rainwater 3H activity. Higher rain duration with slower rain rate yielded higher 3H concentrations as more time was available for the scavenging/wash out process to take effect together with lower dilution. The present data also suggested the need to further investigate the influence of raindrop distribution and other local meteorological parameters on the 3H wash out process. An attempt was also made to predict the 3H concentration in air moisture samples using a Gaussian plume dispersion model and the values were compared with the measured 3H activity. The measured values were generally lower than the predictions.

Characteristic features of hourly rainfall in India

AbstractHourly rainfall data recorded at 72 Self Recording Raingauge Stations (SRRG) for the period 1969–2006 are utilized to study the characteristic features of hourly rainfall and diurnal variations of rainfall in India. Temporal changes in the short duration (less than 12 h) rainfall extremes are examined. Time distribution of a heavy rain spell of 24 h duration, which is an important component in water resources management and flood control studies, is analysed. Different aspects of hourly rainfall such as, average number of rain hours in a year, empirical probability distribution functions have also been examined. Analysis indicates, many stations in India have recorded more than 10 cm of rainfall in an hour's duration. Such stations are located along the west coast, foothills of Himalayas and along the tracks of monsoon disturbances. Extreme rainfall events of various duration (3–12 h) show increasing trends, significant at 5% level, at many stations located in central India and peninsular India except for southern part of west coast. Time distribution of hourly rainfall shows that, on average, 25% of the daily rainfall can be received in just 3 h, while 80% can occur in 12 h during the heavy rain spells of 24‐h duration. Copyright © 2011 Royal Meteorological Society

Use of meteorological radar to estimate leaf wetness as data input for application of territorial epidemiological model (downy mildew— Plasmopara viticola)

The grapevine downy mildew ( Plasmopara viticola) represents the most important disease of the grapevine in Friuli-Venezia Giulia Region (Italy). The development of this disease depends from the meteorological conditions and particularly by air humidity, rain and leaf wetness (LW here after). Forecast models can help the technicians of the extension services to predict the timing and the best technique to use in operative programs. Unfortunately these models require data, coming from meteorological stations which are often variable in space (e.g. rain, leaf wetness) and hardly spatializable. In the first part of this work, a case study is presented to show the great difference between maps of daily rain duration, obtained by radar, and those created by spatialization of data and obtained by weather stations. Then the possibility to use the radar rain maps appears very interesting to estimate LW over a large area. LW and daily rain measurements, obtained by 14 weather stations of Friuli-Venezia Giulia plain (Italy), are compared with rain maps obtained by polarimetric radar GPM-500 placed in Fossalon di Grado (Friuli-Venezia Giulia, Italy). The reference measurements are made during two periods: from 1/4/2000 to 30/9/2000 and from 1/4/2001 to 30/9/2001. From radar maps rain measurements estimated are extracted above each weather station and these data are integrated for every hour. These radar data of hourly rain are compared to the corresponding measurementes of LW and rain obtained by weather stations. From this analysis it appears that there is a good correlation between the number of rain hours estimated by radar and the number of LW hours measured by stations: in the observed cases, the error found is lower than 2%; then radar has a good precision to estimate LW due to rain. Therefore the use of Radar is foretold to give meteorological inputs in simulation models that can work to evaluate the development of fungal diseases. In the second part a model to daily display the infections of downy mildew all over the plain of Friuli-Venezia Giulia is described. Elements of this model are: • the creation of a daily grid of rain estimated by a meteorological polarimetric radar (GPM-500) located in Fossalon di Grado; • the creation of a daily grid of temperature, air humidity, solar radiation, wind speed using data coming from 14 synoptic meteorological stations located in the plain of the region; • the creation of a daily grid of leaf-wetness computed using the SWEB model for the data measured or estimated in the previous point; • the estimation of the P. viticola infection level using a forecast model (in this case Goidanich). • The daily graphical output in every point of the grid is: number of the actual cycles of P. viticola infections; number of days required for the next infection; annual amount of infective cycles. The model has been checked in a limited area (about 200 km 2) with an high presence of grapevine (DOC Aquileia) in the period from may 2000 to september 2000. In this area the data from eight meteorological automatic stations installed for diseases–defence purposes are used as test, and has been calculated the downy mildew infections using the Goidanich model. These grids have been compared with the maps calculated using data coming from meteorological radar and synoptic stations. The outputs are similar and the proposed method can be considered a good approach in the operative use of radar in the crop protection.