Automatic Meteorological Station Research Articles

Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling

Actual evapotranspiration (ETa) can be commonly estimated using numerical models based on i) weather and plant-based parameters, ii) from remotely sensed data and energy balance algorithms, and lately, iii) through the development and implementation of machine learning (ML) modeling techniques. In this work, supervised ML models were developed from a vineyard located in Talca, Chile, (i) to estimate actual evapotranspiration (ETa) (Model 1; M1) using the micrometeorological approach [Eddy Covariance; EC; sensible (H), latent (LE), soil heat fluxes (G) and net radiation (Rn)] and data from an automatic meteorological station (AMS) in reference conditions as ground-truth (inputs); (ii) to estimate energy balance components (Model 2; M2) from AMS data (inputs) and EC energy balance data as targets; (iii) to estimate ETa from the EC’s measured ETa data as target and thermal time data (degree hours; DH) calculated from air temperature with a base of 5 °C increments from 5 – 45 °C as inputs (Model 3; M3) and iv) to estimate energy balance components (targets from EC) from the same inputs of Model 3 (Model 4; M4). Results showed that the developed ML models had high accuracy and performance with no signs of over or under-fitting with a high correlation (R) and slope (b) close to unity (M1; R=0.94; b=0.89; M2; R=0.97; b=0.93; M3; R=0.97; b=0.89–0.95; M4; R=0.98; b=0.97). Furthermore, models were directly deployed over another vineyard located 22 km West of the modeled vineyard at 60 m lower over the sea level with significant performances and R values (R = 0.64–0.87; b = 0.66–1.00 for M1 to M4, respectively). These models could be used for precision irrigation to increase water use efficiency and better control canopy vigor, balance fruit and vegetative components, and ultimately improve berry and wine quality traits.

Estimation of urban solar radiation for the City of Recife-PE

Urban Global Solar Radiation(RS) is one of the main inputs of urban climatology, and should already be considered from the conception of the construction project of green infrastructures, which mitigate the environmental impacts of urbanization. The objectives of this work were to calibrate and evaluate the performance in estimating the RS, for the capital of Pernambuco, of six existing models in the literature, namely: Hargreaves; Samani (1982), Bristow; Campbell (1984), Jong & Stewart (1993), Chen et al. (2004), Hunt et al. (1998) e Annandale et al. (2002), referring to a rainy period in the year 2023, with data from an automatic meteorological station installed in one of the campuses of the University of Pernambuco, in the city of Recife-PE. The statistical criteria used for the analysis of the calculated data: the root mean square error (RMSE), the Nash-Sulcliffe coefficient (NSE) and the residual mass coefficient (CMR). The models, in ascending order of greater effectiveness are: Hunt et al. (1998), Bristow; Campbell (1984), Jong & Stewart (1993), Chen et al. (2004), Hargreaves; Samani (1982), and Annandale et al. (2002). However, it should be noted that for the use of the model proposed by Hunt et al. (1998), it is necessary to have daily precipitation data, which may not be available, especially in historical series. If only temperature values are available, the best model for estimating RS is the one proposed by Bristow; Campbell (1984), which presented a satisfactory performance and very close to that obtained by the model proposed by Hunt et al. (1998).

Cold units and their relationship with wheat grain yield in the Yaqui Valley, Sonora, Mexico

Global warming has the potential to cause the reduction of the cold period available during the winter, which may affect the production of wheat and deciduous fruit trees. Wheat requires accumulated cold units (CU) to switch from a vegetative period to a productive one, making the phenological cycle slower and promoting greater productive yield. The objective was to analyze the accumulation of CU and wheat yield during the last five fall-winter agricultural seasons (2018-2019 to 2022-2023) in the Yaqui Valley, Sonora, Mexico. Data of air temperature were obtained from 21 meteorological stations from the network of automatic meteorological stations of the state of Sonora, located in the Yaqui Valley. The number of CU (≤ 10 °C) was calculated for the wheat season (November 15 to April 30) from the years 2018 to 2023, and the grain yield was obtained from the reports by the Agri-Food and Fisheries Information Service. Descriptive statistics of central tendency, dispersion and the Shapiro-Wilk test were performed to determine the normality of the data; also, correlation analysis and the simple linear regression model by least squares were performed. The 2022-23 crop season showed the highest average grain yield (7.79 t ha-1) and the highest average number of CU (882) among the five seasons analyzed. Wheat seasons 2018-2019 and 2019-2020 had an average number of CU lower than 500. The Pearson correlation analysis showed a highly positive correspondence between CU and grain yield. The linear regression model showed that starting from a base of 434 CU, wheat production per hectare would be 5.90 t, and it will increase 214.3 kg for every additional 100 CU.

Assessment of desertification in the brazilian semiarid region using time series of climatic and biophysical variables

The municipality of Irauçuba, located in the Semiarid region of Ceará, Brazil, has faced the effects of desertification resulting from the scarcity of rainfall. This study aimed to assess the progression of desertification in the area from 2003 to 2020, using climatic and biophysical data collected during this period. We used supplementary data from automatic meteorological stations operated by the National Agency of Water and Basic Sanitation to determine the rainfall anomaly index of the historical series. Additionally, we performed annual scale validation of the CHIRPS data using statistical indicators such as Pearson Correlation Coefficient, Mean Error, Percentage BIAS, and Root Mean Square Error. Subsequently, we analyzed land cover and land use changes over the 17 years (2003-2020) in the municipality by processing CHIRPS, MODIS product (MOD16A2 and MOD11A2), and MapBiomas data in the Google Earth Engine digital platform and QGIS software. We analyzed land cover and land use changes using MapBiomas data. Finally, we analyzed the aridity index for the same 17-year period. The research findings revealed a significant correlation between rainfall and biophysical parameters, with the lowest aridity index of 0.255 observed between 2012 and 2014 and the highest of 0.464 between 2018 and 2020. The area's susceptibility to the desertification process was classified as high, which is consistent with the Semiarid climate of the region. The methodology employed during the research proved to be a sustainable technology of low economic cost and telling and easy access, which enabled the creation of a scientific contribution for future works in the region, besides being able to add knowledge for studies of environmental degradation.

The most extreme rainfall erosivity event ever recorded in China up to 2022: the 7.20 storm in Henan Province

Abstract. Severe water erosion occurs during extreme storm events. Such an exceedingly severe storm occurred in Zhengzhou in central China on 20 July 2021 (the 7.20 storm). The magnitude and frequency of occurrence of this storm event were examined in terms of how erosive it was. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to (1) characterize the spatial and temporal distribution of the rainfall amount and rainfall erosivity of the 7.20 storm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the 7.20 storm moved from southeast to northwest in Henan Province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou Meteorological Station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm h−1. The average recurrence intervals of the maximum daily rainfall erosivity (43 354±1863 MJ mm ha−1 h−1) and the maximum event rainfall erosivity (58 874±2351 MJ mm ha−1 h−1) were estimated to be about 19 200 and 53 700 years, respectively, assuming the log-Pearson type-III distribution, and these were the maximum rainfall erosivities ever recorded among 2420 meteorological stations in mainland China up to 2022. The 7.20 storm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and these can occur in mid-latitude around 35∘ N with a moderate mean annual rainfall of 566.7 mm in Zhengzhou.

A dataset of soil water and heat dynamics from Pinus massoniana plantations under close-to-nature management in South Subtropics (2016–2021)

This study was conducted at the Tropical Forestry Experimental Center of the Chinese Academy of Forestry in Youyiguan Pingxiang Chongzuo Forest Ecosystem Observation and Research Station of Guangxi. Based on the automatic meteorological observation station, we monitored and analyzed the soil hydrothermal dynamics in Pinus massoniana plantations under close-to-nature management. The sample plots layout, index monitoring, and data quality control were carried out in accordance with relevant specifications. This dataset contains the data of soil temperature, soil volume water content, soil heat flux, as well as air temperature and air humidity in Pinus massoniana plantations of close-to-nature management in South Subtropics from 2016 to 2021. After standard data processing, including outlier elimination and data interpolation, we statistically derived the daily data of soil water and heat dynamics of Pinus massoniana, and explained the construction of the dataset. The establishment and sharing of this dataset are valuable for facilitating a comprehensive understanding of the characteristics of soil water and heat in Pinus massoniana plantations under close-to-nature management in South Subtropics. Furthermore, the dataset can serve as an important resource for in-depth research on the response of mixed coniferous broad-leaved forest to global climate change. It can provide essential to support the sustainable management and conservation of forest resources in South Subtropics.

Modeling Urban Heat Islands and Thermal Comfort During a Heat Wave Event in East China With CLM5 Incorporating Local Climate Zones

AbstractThe urban expansion‐induced heat can exacerbate heat stress for urban dwellers, especially during heat waves. With a focus on the intra‐urban variability of urban heat islands (UHIs) and thermal comfort, the urban parameterization within the Community Land Model version 5 (CLM5) was modified incorporating the local climate zones (LCZs) framework, named CLM5‐LCZs, to simulate the urban climate during a heat wave (HW) event in the summer of 2013. The evaluation of model performance demonstrated that it did a reasonable job of simulating surface energy balance and thermal regimes in cities against observational fluxes from a flux tower measurement site and temperatures from automatic meteorological stations in Nanjing, China. Then we investigated the characteristics and causes of UHIs associated with local background climate, intra‐urban inhomogeneity and HW intensity in East China. The results exhibited that daytime and nighttime canopy urban heat island intensity (CUHII) were highest in the Compact Low Rise (LCZ3) and the Compact High Rise (LCZ1) areas, respectively, while surface urban heat island intensity (SUHII) peaked in the Large Low Rise (LCZ8) and the Compact High Rise (LCZ1) areas during daytime and nighttime, respectively. Urban dwellers were easier exposed to serious heat environment in LCZ3 and LCZ1 areas over the north subtropical climate zone. Contrasts of CUHII and SUHII among different urban classes could exceed 1.7°C and 5.4°C. The intra‐urban heterogeneity may alter the dominant factors controlling SUHII, which were also modulated by local climate and HW intensity. Unlike other controlling factors, the impact of local climate on the contribution from the urban‐rural contrast of convection efficiency was larger than urban features. Overall, CLM5‐LCZs displayed potential of implementing detailed simulations for inter‐ and intra‐city UHIs at a larger scale, and enhancing the capabilities in modeling urban climate and exploring the causes and controls of UHIs.

Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

Accurate wind speed forecasting is of great societal importance. In this study, the short-term wind speed forecasting bias at automatic meteorological stations in Hangzhou, Zhejiang Province, China, was corrected using an XGBoost machine learning model called WSFBC-XGB. The products of the local NWP (numerical weather prediction) system were used as the inputs of WSFBC-XGB. The WSFBC-XGB-corrected results were compared with those corrected using the traditional MOS (model output statistics) method. Results showed that WSFBC-XGB performed better than MOS, with the root-mean-square errors (RMSEs)/accuracy rates of the wind speed forecasting (ACCs) of WSFBC-XGB being reduced/ promoted by 26.1% and 7.64%/35.6% and 7.02% relative to NWP and MOS, respectively. The RMSEs/ACCs of WSFBC-XGB were smaller/higher than those of MOS at 90% stations. In addition, the mean decrease in impurity method was used to analyze the interpretability of WSFBC-XGB to help users gain trust in the model. Results showed that the four most important features were the wind speed at 10 m (47.35%), meridional component of wind at 10 m (12.73%), diurnal cycle (9.97%), and meridional component of wind at 1000 hPa (7.45%). The WSFBC-XGB model will help improve the accuracy of short-term wind speed forecasting and provide support for large-scale outdoor activities.摘要准确的风速预报具有重要的社会意义. 在本研究中, 使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正. WSFBC-XGB使用本地数值天气预报系统的产品作为输入. 将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较. 结果表明: WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%; 对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS. 此外, 采用平均杂质减少法对WSFBC-XGB的可解释性进行分析, 以帮助用户增加对模型的信任. 结果表明: 10米风速(47.35%), 10米风的经向分量(12.73%), 日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征. WSFBC-XGB模型将有助于提高短期风速预报的准确性, 为大型户外活动提供支持.

Variation of virtual temperature and wind in the atmospheric boundary layer over the pearl river estuary during 2011–2020

Most studies of the effects of urbanisation on local climate have been based on ground observation data. In contrast, we used observation data from a boundary layer radar wind profiler, radio-acoustic sounding system, and automatic meteorological station located at Shenzhen Bao’an International Airport to analyse changes in wind and virtual temperature in the upper level atmosphere, with a top height of 1,200 m, over the Pearl River Estuary between 2011 and 2020. Our results show that during the decade evaluated, the wind speed and virtual temperature of the upper level atmosphere over the Pearl River Estuary changed very significantly and faster than the changes observed at ground level. During the study period, the linear warming rate of the virtual temperature of the upper level atmosphere reached 0.24°C/a, whereas that on the land surface was 0.17°C/a. The mean decreases in the upper level atmosphere and land surface wind speeds were −0.12 and −0.05 m/s·a, respectively. Additionally, the rate of change in the upper level climate was faster in winter than in summer for both wind speed and virtual temperature. These changes in the climate of the upper level atmosphere over the Pearl River Estuary may be related to the rapid increase in the number of high-rise buildings in the region during that decade, which generally negatively affected the atmospheric environment.

Validation of 3-day rainfall forecast at the regional scale

Nearly half of the natural disasters in the world are due to hydro-geomorphological hazards. Therefore, rainfall forecast is a key parameter for the implementation of landslides and flash-floods early warning systems. In this work we developed a routine in R software that enables the validation of a 3-day rainfall forecast by comparison with the daily rainfall data recorded in 101 automatic meteorological stations available in mainland Portugal. The routine integrates the pre-processing of base data, the matching between the 3-day rainfall forecast and the daily rainfall registered in the automatic meteorological stations based on sequence of days, the estimation of the difference between the forecasted and the real rainfall values and the computation of error measures, such as the bias, the mean absolute error, the mean absolute percentage error and the root mean square error. The results from the error measures, estimated for the 101 automatic meteorological stations, are then exported to an excel file. The routine is implemented for mainland Portugal and tested using data from February 2015, however, the spatial and temporal data can be easily updated for other regions.•A routine to validate the rainfall forecast at the regional scale using R programming language is implemented.•The automated routine can be easily updated and adapted with different spatial and temporal scales.

Data quality of automatic meteorological stations located in three states of Mexico

El objetivo del presente trabajo fue aplicar algunos procedimientos para evaluar la calidad de datos provenientes de diferentes estaciones meteorológicas automáticas (EMA) ubicadas en tres estados de la república mexicana. Se obtuvieron datos meteorológicos diarios de estaciones automáticas ubicadas en los siguientes puntos: una en el estado de México, cinco en Guanajuato, y una en Sinaloa. Los procedimientos utilizados para la temperatura máxima, media y mínima, y radiación global fueron la prueba de rango flexible o efeméride meteorológica; para las variables temperatura y humedad relativa se empleó la prueba de consistencia interna; a la variable temperatura se le aplicó la prueba de salto térmico y de ceros; a la humedad relativa, la prueba de consistencia interna y la de persistencia. Para la estación ubicada en Sinaloa, la variable radiación global diaria, en la prueba de rango flexible, presentó 23.25 % de datos erróneos. Los resultados de los procedimientos aplicados a datos históricos de las EMA apoyan en determinar la confiabilidad de la información meteorológica de éstas para su uso.

Sensor Actuator Network for In Situ Studies of Antarctic Plants Physiology.

This article documents a custom sensor-actuator network designed and implemented as a part of experimental setup, where a long-term phenological response of antarctic plants is studied. The first part of our work presents the context of the study, reports experimental methods used in antarctic plant field studies, and characterizes the environmental conditions and logistics facilities available on the measurement spot. After contextualization of the research, we present, in detail, both the network itself and some results obtained during the Antarctic summer seasons between 2019 and 2022 on the King George Island, South Shetlands. The results collected with our network and correlated with selected data registered with a reference automatic meteorological station reveal the thermal plants response. The groups of plants individuals, which were actively warmed using thermal actuators, show the nighttime temperature difference, in reference to the air temperature, of 5 ∘C, which complements the daytime difference caused by the passive method of open top chamber (OTC) used in previous studies carried out in the same localization.

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).

Application of artificial intelligence technology in typhoon monitoring and forecasting

In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting.

Aspectos científicos y sociales del diseño, construcción, instalación y operación de una estación meteorológica para la comunidad cabécar de Pú Ksogë, Talamanca, Limón

In mid-2018, a group of researchers from the Geophysical Research Center and the School of Physics of the University of Costa Rica started a project to share knowledge about weather and climate sciences with the cabécar community of Pú Ksogë (9,591 ° N, 82.978 ° W, 114.4 masl), Talamanca, Limón, Costa Rica. The initiative was supported by leaders and educational authorities of that indigenous community at the end of that year. With local labor input and the respective authorization, an automatic meteorological station (Siwá Etka) was installed and left in operation on the grounds of the community school at the beginning of 2019. Details of this project and the first atmospheric data observed in the history of that community are described in this paper. The lack of adequate community control and the lack of interest of the school authorities in the care of the instruments caused problems that did not allow the project to continue, and the station had to be uninstalled in October 2019. The experience had positive phases, but other aspects such as cell phone and internet connectivity, sometimes of very low quality or non-existent at all, also influenced the decision to discontinue the initiative. Contacts made with an association for local development in the region could be an alternative to give continuity to the project.

Analysis of trends of meteoclimatic characteristics at Enderby Land, East Antarctica based on land observations data and reanalysis

The article studies the presence and possible direction of climate change in Enderby Land, East Antarctica, where from 1963 to 1999, the only meteorological station of the WMO network operated at the Molodezhnaya research station (now the summer field base of the RAE), and now an automatic meteorological station has been installed. The trends in surface air temperature, wind speed and direction for the period from the mid-1960s to the present have been analyzed. As initial data, we used the results of ground-based observations at the Molodezhnaya station, as well as reanalysis datasets, which made it possible to complete the instrumental series and evaluate the dynamics of temperature and wind characteristics for the last decades. Monthly values of climatic parameters were used as initial values, but for wind characteristics presented as the V and U components of the speed vector, single values were taken, according to which the average monthly wind speeds and wind distributions in directions were estimated. From different reanalysis systems, by comparison with ground-based observations, series were selected that showed the greatest similarity in the dynamics of the climatic characteristics studied. Good agreement was found between the temperature series of observations at the Molodezhnaya meteorological station and the datasets UDEL_AirT, ERA5, and GISS. The correlation between the wind observation series is less strong; ERA5 reanalysis data shows the wind speed dynamics slightly better. According to the analysis of the long-term dynamics of average annual and monthly temperatures in the region of Enderby Land, the warming trend has not been statistically confirmed, however, a progressive increase in the number of months of the year with positive temperature dynamics has been noted. Based on the analysis of wind characteristics, it is shown that only an increase in average annual wind speeds of 0,1–0,3 m/s/10 years is statistically significant.

Analysis of northern events on the Gulf of Tehuantepec and their effects on navigation

The maritime areas of greatest risk in the Gulf of Tehuantepec, are derived from the strong winds and waves presented during the northern events that occurred during the cold front seasons, these meteorological phenomena affect activities related to navigation and represent a danger to the civil population due to the strong intensities of the wind and the height of the waves. The objective of the present investigation was to identify the maritime zones of greatest risk to navigation on the Gulf of Tehuantepec, derived from the analysis of the events of the north during the period 2015-2019, with the purpose of warning and making the maritime community aware. regional and national, the areas where the effects of the northern events are more intense. Likewise, for the analysis, interpretation and identification of the northern events, wind data recorded at the Automatic Meteorological Station (EMA) of the Secretary of the Navy (SEMAR), located in the Port of Salina Cruz, Oaxaca, and reanalysis data of the set were used. of wind and wave data from ERA5, for its subsequent processing through the Python programming language for the generation of maps of the variables. This gave as a result that a radius of the first 80 Nautical Miles (NM) of the Port of Salina Cruz, Oaxaca has intense wind and wave conditions during the month of January and very intense conditions appearing in a radius of 120 NM around said zone. Of the 56 northern events associated with cold fronts, 40 were classified as strong and 16 were classified as very strong, being of great relevance to avoid navigation accidents.

Temporal behavior of air and sea surface temperature in a marine protected area of Cuba

The Cuban Archipelago -particularly its coastal zones- is exposed to Global Warming. The rise of air and sea surface temperature value is a good indicator of its consequences to biodiversity. Air temperature was measured using an Automatic Meteorological Station at the Jardines de la Reina National Park between 2016 and 2017. Sea surface temperature was measured using temperature data loggers twice in the years 2000 (January and September) and 2016 (from January to June) in coral, mangrove and seagrass habitats. Air and sea surface temperature satellite data of the same studied sites were analyzed in the period 2003-2017 using in situ measurements. Results showed that all temperature values were similar to those reported in similar studies in Cuba and the Caribbean, and that extreme values were within the tolerance ranges previously reported for the studied habitats. Air temperature did not have a significant trend; however, sea surface temperature had a significant increase (0.01 °C) during the studied period. These results show the great influence of the Caribbean Sea over the marine waters and climate of this area. A combined monitoring system was proposed using in situ data logger measurements and satellite data temperature analysis to watch this important marine reserve of the Caribbean Region

RAINFALL EROSIVITY IN THE DESERTIFICATION NUCLEUS OF CARIRI AND SERIDÓ DA PARAIBA

Desertification is a process of land degradation and occurs predominantly in arid, semi-arid and dry sub-humid areas. In view of this, we sought to determine the main characteristics of rainfall erosivity in the desertification nuclei of Cariri (Cabaceiras) and Seridó (Santa Luzia) in Paraiba, located in the intermediate geographic regions of Campina Grande and Patos, with these determinations being the main objectives. Using monthly and hourly rainfall data, collected from automatic meteorological stations, from these locations, the rainfall erosivity (EI), the kinetic energy of the maximum intensity of the rain, in 30 minutes (EI30), the number of events with Imax and the relationships between the averages of total rainfall >100 mm and the EIs. The main results show that, in deforested areas, the erosivity of the rain potentiates the erosion process and the interstate desertification nuclei have different characteristics of rainfall regime and EI. The maximum EI30 occurred in the late afternoon/early evening, with lower values in Cabaceiras, where most classes were low. In Santa Luzia, 1/3 of the classes were high to very high, although predatory anthropic interventions contribute to aggravating them.

Study on protection measures of lightning electromagnetic pulse in automatic weather station

Due to automatic meteorological station, the distribution of the unique geographical location and environment, the equipment has a large number of sensors and electronic components, and a highly integrated, combined with the electronic components of low dielectric strength, compression ability and the ability to resist electromagnetic interference and weak ability to resist shock current and lightning electromagnetic pulse is easy to damage to electronic equipment. Therefore, this paper analyzes the existing problems and harms in lightning protection design of automatic weather station system in Shanxi Province, and puts forward improvement and protection measures.