Relationship of mean temperatures with screen temperatures

An attempt has been made to study the variation in daily mean temperatures obtained from maximum and minimum temperatures and that obtained from hourly temperatures recorded by the automatic weather station at the Agrometeorological Observatory, Anand (Gujarat). The mean temperatures obtained from the records of daily maximum and minimum temperatures were higher and fluctuated from -1.5 to 1.5 °C during the months of September to May as compared to the respective values obtained from hourly temperatures recorded by the automatic weather station. However, during May to September, these daily mean temperatures were found to be higher than mean temperatures obtained from the automatic weather station. Different coefficients were deduced from the records of the automatic weather station to estimate the hourly temperatures and a model was developed similar to that of William and Logan (1981). The hourly temperatures and the daily mean temperatures so estimated were in good agreement with the respective actual hourly and daily temperatures record by the automatic weather station. Therefore, by using this model one could estimate the true daily mean temperature from the records of maximum and minimum temperatures.

Deflation activity in the Begovitsa valley (Pirin Mountain)

The article presents the results of deflation activity researches in the upper (2240 m a.s.l.) and lower (1760 m a.s.l.) part of Begovitsa river valley. For the purpose, data from Automatic Weather Station (AWS) and wind direction marks were used and analyzed. Wind roses were performed for both, upper and lower part of the river valley.

A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I²C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

Analysis of Rainfall and Temperature Changes and Variability over Glen Farm, South Africa

In the agricultural domain, decision-making is greatly guided by agricultural meteorology, which is the science that applies knowledge of weather and climate to qualitative and quantitative improvement in agricultural efficiency. The study area is challenged with increasing multifaceted agricultural production risks and complex agricultural ecosystems, which require analysis and understanding of local rainfall and temperature patterns. Digital technologies, such as the automatic weather station, play a pivotal role to monitor the physical environment, successively. This study engaged on a thorough analysis and interpretation of long-term rainfall and temperature data. The results would enable farmers and other users to comprehend valuable knowledge for improved productivity. The objectives of this paper were to analyse long-term climate data for Glen automatic weather station. To determine decadal climate patterns and trends, determine seasonal shifts, climate variability and climate change and quantify the frequency of the occurrence of weather extremes and develop suitable adaptation strategies relating to agronomic, phenological and physiological data necessary for crop modelling, operational evaluation and statistical analysis. The applied methods entailed Microsoft Excel and INSTAT Plus statistical software, which used to detect the interactions of environmental factors and suitable agricultural productivity. Understanding of rainfall and temperature patterns is required for agricultural management decisions, on planting date selection, crop suitability, livestock adaptation, ecosystem conservation. Agro meteorological knowledge derived from meteorological parameters, temperature, rainfall, wind and weather extremes, and may enhance agricultural productivity. Analysis of long-term and decadal trends in the time series indorse a sequence of alternately increasing and decreasing in mean annual rainfall and air temperature in Glen Farm.

Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently, the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate are important for reliable present and future assessment of changes in the Greenland Ice Sheet. Here, we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper, we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic web-based database of known data quality issues is associated with the data products at https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues/ (last access: 7 April 2021). As part of the living data option, the datasets presented and described here are available at https://doi.org/10.22008/promice/data/aws (Fausto et al., 2019).

Greenland bare-ice albedo from PROMICE automatic weather station measurements and Sentinel-3 satellite observations

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) provides surface meteorological and glaciological measurements from widespread on-ice automatic weather stations since mid-2007. In this study, we use 105 PROMICE ice-ablation time series to identify the timing of seasonal bare-ice onset preceded by snow cover conditions. From this collection, we find a bare-ice albedo at ice-ablation onset (here called bare-ice-onset albedo) of 0.565 ± 0.109 that has no apparent spatial dependence among 20 sites across Greenland. We then apply this snow-to-ice albedo transition value to measure the variations in daily Greenland bare-ice area in Sentinel-3 optical satellite imagery covering the extremely low and high respective melt years of 2018 and 2019. Daily Greenland bare-ice area peaked at 153 489 km² in 2019, 1.9 times larger than in 2018 (80 220 km²), equating to 9.0% (in 2019) and 4.7% (in 2018) of the ice sheet area.

MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level