Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

Low-altitude Sensing of Urban Atmospheric Turbulence with UAV

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to main regularities in the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov-Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

Algorithms for computerised monitoring the performance of DCP network

India Meteorological Department (IMD) has been operating a network of one hundred Automatic Weather Stations called Data Collection Paltform (DCP) since 1986. All these stations are unmanned and there is no way to know the working status of DCP equipment except to extract such Information from the data transmitted by them. Hence suitable algorithms were developed to evaluate the working status of various sub-systems of DCP stations by analysing the data received from them, which is essential for their effective and efficient maintenance. The concept used in developing these algorithms is described here.

Characterization of Spatio-temporal Trends in Long-term Rainfall of Three Weather Stations in Benin: Implications for Agricultural Decision Making.

A better understanding of rainfall variability and trends is vital for agricultural production systems. This study evaluates the spatio-temporal variability and trends in annual, seasonal and daily rainfall in Benin. Daily rainfall data for the 1970-2016 period measured at three weather stations (Savè, Malanville, and Tanguiéta) were obtained from the Benin National Weather Agency. Descriptive statistics, standardized anomaly of rainfall (SAR) and rainfall intensity were used to analyze rainfall variability. For rainfall trends analysis, we tested for auto-correlation and used the Mann-Kendall and Modified Mann-Kendall tests for non-auto-correlated and auto-correlated data, respectively. Trend magnitude was estimated using Sen’s slope. Globally a moderate-to-high seasonal rainfall and low variability of yearly rainfall were observed. The SAR indicated more than 50% of the years in the studies period experienced dry years. Between 1970 and 2016, a significant 20 % increase was observed in the yearly rainfall in Tanguiéta whereas no significant trends were observed in Malanville (10% increase) and Savè (0.6% decrease). The general rainfall increase observed during the post-monsoon season (October to November) in the three weather stations potentially increases flood frequencies during the harvest period of some crops, which can reduce crop yields. The changes in the pre-monsoon season (March to May) and monsoon season (June to September) were not globally uniform and can have positive/negative impact on agriculture, certainly when no adaptation strategies are applied. These findings are essential to the resilience building and climate risk management in agriculture which is largely dependent on weather conditions.

Santa Ana Winds: Fractal-Based Analysis in a Semi-Arid Zone of Northern Mexico

A fractal analysis based on the time series of precipitation, temperature, pressure, relative humidity, and wind speed was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Days on which the phenomenon known as Santa Ana winds occurs were identified based on the corresponding criteria of wind speed (≥4.5 m/s) and wind direction (between 0° and 90°). Subsequently, the time series was formed with data representing the days on which this phenomenon occurs in each of the analyzed weather stations. A time series was additionally formed from the days in which the Santa Ana winds condition does not occur. Hurst exponents and fractal dimension were estimated applying the rescaled range method to characterize the established time series in terms of characteristics of persistence, anti-persistence, or randomness along with the calculation of the climate predictability Index. This enabled the behavior and correlation analysis of the meteorological variables associated with Santa Ana winds occurrence. Finally, this type of research study is instrumental in understanding the regional dynamics of the climate in the basin, and allows us to establish a basis for developing models that can forecast the days of occurrence of the Santa Ana winds, in such a way that actions or measures can be taken to mitigate the negative consequences generated when said phenomenon occurs, such as fires and droughts.

An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

A wide variety of engineering applications requires the use of maximum values of rainfall intensity and wind speed related to short recording intervals, which can often only be estimated from available less exhaustive records. Given that many locations lack exhaustive climatic records that would allow accurate empirical correlations between different recording intervals to be identified, generic equations are often used to estimate these extreme values. The accuracy of these generic estimates is especially important in fields such as the study of wind-driven rain, in which both climatic variables are combined to characterise the phenomenon. This work assesses the reliability and functionality of some of these most widespread generic equations, analysing climatic datasets gathered since 2008 in 109 weather stations in Spain and the Netherlands. Considering multiple recording intervals at each location, it is verified that most of these generic estimations, used especially in the study of wind-driven rain, have functional limitations and can cause significant errors when characterising both variables for subdaily intervals and extreme conditions. Finally, an alternative approach is proposed to accurately extrapolate extreme values of both variables related to any subdaily recording interval in a functional manner and from any available records.

Multiple comparisons of precipitation variations in different areas using simultaneous confidence intervals for all possible ratios of variances of several zero-inflated lognormal models

Flash flooding and landslides regularly cause injury, death, and homelessness in Thailand. An advancedwarning system is necessary for predicting natural disasters, and analyzing the variability of daily precipitation might be usable in this regard. Moreover, analyzing the differences in precipitation data among multiple weather stations could be used to predict variations in meteorological conditions throughout the country. Since precipitation data in Thailand follow a zero-inflated lognormal (ZILN) distribution, multiple comparisons of precipitation variation in different areas can be addressed by using simultaneous confidence intervals (SCIs) for all possible pairwise ratios of variances of several ZILN models. Herein, we formulate SCIs using Bayesian, generalized pivotal quantity (GPQ), and parametric bootstrap (PB) approaches. The results of a simulation study provide insight into the performances of the SCIs. Those based on PB and the Bayesian approach via probability matching with the beta prior performed well in situations with a large amount of zero-inflated data with a large variance. Besides, the Bayesian based on the reference-beta prior and GPQ SCIs can be considered as alternative approaches for small-to-large and medium-to-large sample sizes from large population, respectively. These approaches were applied to estimate the precipitation variability among weather stations in lower southern Thailand to illustrate their efficacies.

Impacts of climate warming on reindeer herding require new land-use strategies

Climate in the Arctic has warmed at a more rapid pace than the global average over the past few decades leading to weather, snow, and ice situations previously unencountered. Reindeer herding is one of the primary livelihoods for Indigenous peoples throughout the Arctic. To understand how the new climate state forces societal adaptation, including new management strategies and needs for preserved, interconnected, undisturbed grazing areas, we coupled changes in temperature, precipitation, and snow depth recorded by automatic weather stations to herder observations of reindeer behaviour in grazing areas of the Laevas Sámi reindeer herding community, northern Sweden. Results show that weather and snow conditions strongly determine grazing opportunities and therefore reindeer response. We conclude that together with the cumulative effects of increased pressures from alternative land use activities, the non-predictable environmental conditions that are uniquely part of the warming climate seriously challenge future reindeer herding in northern Sweden.

Network of Automatic Weather Stations : Pseudo random burst sequence type

A network of 125 Automatic Weather Stations (AWS) has been set up by India Meteorological Department (IMD) during the year 2006-07 across India. Each station is configured to measure air temperature, hourly maximum temperature, hourly minimum temperature, relative humidity, station level pressure, hourly rainfall and cumulative rainfall for the day, Wind speed and Wind direction. In addition to these parameters, 25 stations provide data for global solar radiation and soil temperature. Five stations also provide soil moisture in addition to soil temperature. Each station transmits a data stream at an interval of an hour in a Pseudo Random Burst Sequence (PRBS) manner via UHF transmitter and a dedicated meteorological satellite KALPANA-1/ INSAT-3A to the central AWS data receiving Earth Station facility established at IMD, Pune. Mean sea level pressure, dew point temperature, duration of bright sunshine and daily maximum & minimum temperature are derived at the receiving Earth Station. Data archival in near real time is done at the receiving Earth Station. Data dissemination in WMO code form is also done in near real time through Global Telecommunication System. This paper provides technical description of various sub-systems of PRBS type Indian Automatic Weather Station network including instrument, satellite transmission technique, sensor characteristics, siting and exposure conditions and performance of a representative station.