Observed Changes in Agroclimate Metrics Relevant for Specialty Crop Production in California

Every decade, a suite of standardized climatological metrics known as climate normals are updated, providing averages of temperature and precipitation data over the previous 30-year period. Although some of these climate normals are directly applicable to agricultural producers, there are additional agroclimate metrics calculated from meteorological data that provide physiologically relevant information for on-farm management decisions. In this study, we identified a suite of energy-based agroclimate metrics and calculated changes over the two most recent normal periods (1981–2010 and 1991–2020), focusing on specialty crop production regions in California. Observed changes in agroclimate metrics were largely consistent with broader global warming trends. While most metrics showed small changes between the two periods, during the 1991–2020 period, the last spring freeze occurred ~5 days earlier as compared to the 1981-2010 period, contributing to a >6 day longer frost-free period in the Sacramento and Salinas Valleys; likewise an additional 6.4 tropical nights (Tn > 20 °C) occurred in the Coachella Valley during the 1991-2020 period. A complementary trend analysis of the agroclimate metrics over the 1981–2020 period showed significant increases in growing degree days across all agricultural regions, while significant increases in heat exposure were found for the Salinas and Imperial Valleys and over the Central Coast region. Moreover, summer reference evapotranspiration increased approximately 40 mm in California’s Central Valley during 1981–2020, with implications for agricultural water resources. Quantifying the shifts in these agroclimate metrics between the two most recent 30-year normal periods and the accompanying 40-year trends provides context for understanding and communicating around changing climatic baselines and underscores the need for adaptation to meet the challenge that climate change poses to agriculture both in the future and in the present.

Inter-spatial heat vulnerability assessment of Summer - 2018 over Madhya Pradesh using discomfort (wind and thermal) indices

Due to global warming, increase in air temperature is a growing concern at present. This rise in temperature may cause mild to severe thermal discomfort and heat related hazards mostly for the people who are engaged in outside activities throughout the day. The present study shows the inter-spatial monthly distribution of thermal patches over major stations of Madhya Pradesh, viz., Bhopal, Gwalior, Indore, Jabalpur, Hoshangabad, Rewa, Ratlam, Ujjain, Dhar etc. In this study, various Heat Indices applicable for tropical climate including Wet Bulb Globe Temperature (WBGT) are used to estimate the thermal stress by analyzing the meteorological data of Summer-2018 in Madhya Pradesh. Study was carried out for computing indoor, shady and outdoor heat stress separately and heat transfer rates to identify the places vulnerable to severe heat stroke in the month of March, April and May in 2018.It is observed that declaration of heat wave alone at any station is not sufficient for the administration and health organizations to take precautionary actions; also, discomfort indices should be referred for impact based monitoring and making work schedules. It is found that March and April fall in the partial discomfort category for at least half of the districts in Madhya Pradesh. It is interesting to note that several districts fall in discomfort category in outdoor conditions but not in indoor or shady conditions in May month. Severe stresses are observed mainly in the West and Central Madhya Pradesh during April and May months. Comparison of various Heat Indices is too performed along with computing Tropical Summer Index (TSI) and Apparent Temperature (AT) to indicate real feel-like temperatures in Madhya Pradesh during extreme temperature events.

Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study has been tried to analyze the most important climatic variables, viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.

A countrywide analysis of 27 solar power plants installed at different climates

The world is still heavily using nonconventional energy sources, which are worryingly based on carbon. The step is now alternative energy sources hoping that they will be more environmentally friendly. One of the important energy conversion forms by using these sources is photovoltaic solar systems. These type of power plants is on the increase in everyday on the world. Before investment a solar power plant in a specified region, a techno-economic analyse is performed for that power plant by using several meteorological data like solar irradiance and ambient temperature. However, this analyses generally lacks evaluation on effects of climatic and geographical conditions. In this work, 5 years of data of 27 grid-connected photovoltaic power plants are investigated, which are installed on seven different climate types in Turkey. Firstly, the power plants are categorized considering the tilt angles and Köppen–Gieger climate classification. The performance evaluations of the plants are mainly conducted using monthly average efficiencies and specific yields. The monthly average efficiencies, which were classified using the tilts and climate types were from 12 to 17%, from 12 to 16% and from 13 to 15% for tilts 30°/10°, 25° and 20°, respectively. The variation in the specific yields decrease with elevation as y(x) = − 0.068x + 1707.29 (kWh/kWp). As the performances of photovoltaic systems for some locations within the Csb climatic regions may relatively lower than some other regions with same climate type. Thus, techno-economic performance for PVPP located in this climate classification should be carefully treated.

Spatial Analysis of Urban Thermal Island in Northern Iran (Case Study: Rasht City)

Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance.The one-window algorithm of Kim et al. Uses Landsat satellite imagery to model the earth's surface temperature.These trends are validated using meteorological data. Two main and basic factors play a major role in the temporal and spatial trend of the thermal islands of Rasht. These two factors of climate change that have occurred in the last two decades in the region of Gilan province and the city of Rasht. The second factor that has greatly enhanced the effect of the first factor is the human factor that has greatly included other urban factors in Rasht, including urban management and proper urban planning in the province and the city of Rasht. These two factors in the temporal and spatial trend of urban thermal islands have caused thermal islands to rapidly increase the growth of the city and urban population from the urban center to the western and southwestern regions and have very negative effects on land use changes and human areas. It has caused the construction of Rasht city.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Assimilative capacity of the atmosphere at Gorakhpur with respect to air pollution

The present study aims at seasonal and diurnal pollution potential at Gorakhpur in east Uttar Pradesh. To assess the pollution potential, meteorological data for five year period (1982-86) of Gorakhpur have been analyzed for four seasons viz; winter (December-February), summer (March-May), monsoon (June-September) and post monsoon (October-November). Season wise wind roses, stability, stability wind roses have been prepared and season wise diurnal variation of mixing height and ventilation coefficient have also been worked out. It is found that Gorakhpur has a better diffusion capacity in summer and poor in post monsoon followed by winter. Afternoon hours are better for vertical mixing. The winds are predominantly from southwest to west in all seasons except in monsoon when it blows from northeast to east. Based on this study, an appropriate location for industrialization has been suggested.

Perception of Climate Variability and Its Impact by Smallholders in Pastoral Systems of Abudwak District, Galmudug State, Somalia

it is true in all the regions of the country; there is no stable and reliable documented meteorological data for reasonable tracking of the climate change and variability. Thus, this study explores the perception of significant variability in climate and related impact on local livestock holders among smallholders in pastoral systems of Abudwak district, Somalia. We drew on empirical data obtained from pastoral communities surveys conducted in 4 villages, 169 pastoral associations. Using this data, this study analyses smallholders' perception of climate variability and its associated impact on local livelihood, and the effect of several household on perception. Respondents interviewed during the study period, however, believed that there has been significant variability in the rainfall and temperature patterns for the last twenty years and considered climate variability as a salient risk to their future livelihoods and economic development. Likewise, the general perception of the people is that both rainfall and temperature have highly been fluctuating for decades now became unpredictable with less rainfall in shorter duration and warmer temperature over year now than usual. Different levels of perception were expressed in terms of climate variability and the impact on traditional rain-fed. Age, education level, livestock holding, access to climate information and extension services significantly affected perception levels.