An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.

Disparate roost sites drive intraspecific physiological variation in a Malagasy bat

Many species are widely distributed and individual populations can experience vastly different environmental conditions over seasonal and geographic scales. With such a broad ecological reality, datasets with limited spatial and temporal resolution may not accurately represent a species and could lead to poorly informed management decisions. Because physiological flexibility can help species tolerate environmental variation, we studied the physiological responses of two separate populations of Macronycteris commersoni, a bat widespread across Madagascar, in contrasting seasons. The populations roost under the following dissimilar conditions: either a hot, well-buffered cave or within open foliage, unprotected from the local weather. We found that flexible torpor patterns, used in response to prevailing ambient temperature and relative humidity, were central to keeping energy budgets balanced in both populations. While bats’ metabolic rate during torpor and rest did not differ between roosts, adjusting torpor frequency, duration and timing helped bats maintain body condition. Interestingly, the exposed forest roost induced extensive use of torpor, which exceeded the torpor frequency of overwintering bats that stayed in the cave for months and consequently minimised daytime resting energy expenditure in the forest. Our current understanding of intraspecific physiological variation is limited and physiological traits are often considered to be fixed. The results of our study therefore highlight the need for examining species at broad environmental scales to avoid underestimating a species’ full capacity for withstanding environmental variation, especially in the face of ongoing, disruptive human interference in natural habitats.

PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

Statistical interpretation of general circulation model: A prospect for automation of medium range local weather forecast in India

The General Circulation Models (GCM), though able to provide reasonably good medium range weather forecast. have comparatively less skill in forecasting location-specific weather. This is mainly due to the poor representation of 16cal topography and other features in these models. Statistical interpretation (SI) of GCM is very essential in order to improve the location-specific medium range local weather forecast. An attempt has been made at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi to do this type of objective forecasting. Hence location-specific SI models are developed and a bias free forecast is obtained. One of the techniques for accomplishing this, is the Perfect Prog. Method (PPM). PPM models for precipitation (quantitative, probability, yes/no) and maximum minimum temperature are developed for monsoon season (June to August) for 10 stations in lndia. These PPM models and the output from the GCM (R-40) operational at NCMRWF, are then used to obtain the SI forecast. An indirect method based upon SI forecast and observed values of previous one or two seasons, for getting bias free forecast is explained. A comparative study of skill of bias free SI and final forecast, with the observed, issued from NCMRWF to 10 Agromet Field Units (AMFU) during monsoon season 1993, has indicated that automation of medium range local weather forecast can be achieved with the help of SI forecast.

The Importance of Hail Events for Landslides and Bedload Transport in Austrian Alpine Torrents

Landslides and bedload transport can be a threat to people, infrastructure, and vegetation. Many detailed hydrometeorological trigger mechanisms of such natural hazards are still poorly understood. This is in particular valid concerning hail as a trigger of these processes. Therefore, this study aims to determine the influence of hail on landslides and bedload transport in alpine torrents. Based on a generated table from an event register of mountain processes maintained by the Avalanche and Torrent Control Unit (WLV) and weather data provided by the Centre for Meteorology and Geodynamics (ZAMG), 1,573 observed events between 1980 and 2019 in 79 Austrian alpine sites are analysed. Thiessen polygons are used to regionalise local weather data to adjacent regions. The spatial extend of these regions are merged with the registered torrential events. As a result of a stepwise filtering of the used data, the final inventory was created.The results show that 95.1% of the investigated torrential processes triggered by hailstorms are debris flows or debris flow-like transports. Within the study period, a peak of hail-triggered landslides and bedload transport can be recognised in the first 10 days of August in all 39 years. Furthermore, the results suggest that hail is rather a direct than an indirect trigger for landslides and bedload transport.Overall, we conclude that the influence of hail on landslides and bedload transport is significant. Respective hydrometeorological triggering conditions should be included in any regions. Further research for this topic is required to explore the process dynamics in greater detail.

The Relationship between Clouds Containing Multiple Layers 7.5–30 m Thick and Surface Weather Conditions

Previous studies have identified finely laminated, or layered, features within Arctic clouds. This study focuses on quasi-horizontal layers that are 7.5 to 30 m thick, within clouds from 0 to 5 km altitude. No pre-selection for any particular cloud types was made prior to the identification of laminations. We capitalize on the 4-year measurement record available from Eureka, Nunavut (79.6∘ N, 85.6∘ W), using the Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) Rayleigh–Mie–Raman Lidar (CRL; 1 min, 7.5 m resolution). Laminated features are identified on 18% of all days, from 2016–2019. Their presence is conclusively excluded on 12% of days. March, April, and May have a higher measurement cadence and show laminations on 41% of days. Individual months show laminations on up to 50% of days. Our results suggest that laminations are not rare phenomena at Eureka. To determine laminations’ likely contribution to Arctic weather and climate, local weather reports were obtained from the nearby Environment and Climate Change Canada (ECCC) weather station. Days with laminated clouds are strongly correlated with precipitating snow (r = 0.63), while days with non-laminated clouds (r = −0.40) and clear sky days (r = −0.43) are moderately anti-correlated with snow precipitation.

EVALUATING HYDROLOGIC MODEL PERFORMANCE OF GLOBAL AND LOCAL WEATHER DATA INPUTS

Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.