Analysis Of Precipitation Patterns Research Articles

Improving Tropical Cyclone Precipitation Forecasting With Deep Learning and Satellite Image Sequencing

AbstractPrecipitation forecasting in tropical cyclones (TC) is vital for warning systems and disaster management. Artificial intelligence (AI)‐based methods show promise in this domain. Here, we investigate two aspects of AI forecasting for TC precipitation: modeling satellite image sequencing and analyzing predictability. To the former, using the Global Precipitation Measurement, we establish a high‐accuracy regional and intensity forecasting method. Through an analysis of precipitation patterns and intensities, we have demonstrated the effectiveness, reliability, and robustness of forecasting TC precipitation. To the latter, we conduct predictability research, which covers different intensity categories and landfall versus non‐landfall TC precipitation. The conclusions are: (a) TC precipitation varies regionally with predictability differences among intensity categories; (b) Forecasting landfalling TC precipitation is less challenging than non‐landfalling, considering TC intensity and paths. The proposed method also demonstrates strong forecasting capabilities in handling extreme and accumulated precipitation within 0–120 min, achieving an accuracy rate of 87%.

Historical Climate Trends and Extreme Weather Events in the Tri-State Area: A Detailed Analysis of Urban and Suburban Differences

This study analyzes daily temperature and precipitation data collected from 44 weather stations throughout New York, New Jersey, and Connecticut to assess and quantify the historical climatic changes within these states. The study conducts a detailed examination of spatial and temporal trends, focusing on specific stations that best represent the climatic diversity of each area. A critical analysis aspect involves comparing temperature trends in urban and suburban areas, mainly focusing on New York City. The findings reveal a significant upward increasing trend in average temperatures across all seasons, with urban areas, especially NYC, exhibiting the most marked increases. This trend is notably sharp in the spring, reflecting climate change’s escalating influence. The study also observes an increase in the annual average temperatures and a concurrent decrease in the variability of temperature ranges, suggesting a stabilization of temperature fluctuations over time. Also, we identified a notable increase in heat wave frequency, more so in urban locales than in their suburban counterparts. Analysis of precipitation patterns, particularly in NYC, reveals a decline in snowfall days, consistent with the general warming trend. The results demonstrate significant trends in seasonal average temperatures, a decrease in the variability of temperatures, and a rise in heat wave occurrences, with urban areas typically experiencing warmer conditions. This comprehensive study highlights the need for a more in-depth analysis of spatial precipitation trends. It underscores the importance of continued research in understanding the multifaceted impacts of climate change, particularly in differentiating urban and rural experiences.

A New Approach to Reconstruct Paleogene Atmospheric Hydrology at High Latitudes

Reconstruction of ancient atmospheric conditions through the analysis of precipitation patterns is a novel endeavor in the study of paleoclimate. A new approach is now available for a quantitative reconstruction of Paleogene atmospheric hydrological conditions in High Arctic. It is based on 1) the discovery of exceptionally-preserved Paleogene plant fossils from the Canadian Arctic which yielded in situ labile biomolecules; 2) the development of compound-specific hydrogen isotope analysis which can be applied to small amount of plant material; and 3) taxon-specific apparent hydrogen isotope fractionation factors obtained from empirical measurement. A new moisture recycling model is established to explain the reconstructed paleohydrologic pattern in the High Arctic during Paleogene.

A principal component analysis of precipitation patterns over northern Italy

A principal component analysis of rainfalls recorded during five winters (1981–1985) at about 70 stations in Northern Italy has been performed. The spatial distribution of the first four eigenvectors (whose principal components cumulatively account for about 81% of the total variance) is presented and discussed. A subsequent rotation of the first four principal components led to new spatial distributions which are are also described. Moreover, the rotation allowed associating the first three rotated principal components with atmospheric circulation patterns typically occurring in the area and belonging to a classification scheme of weather types on the Po Valley previously introduced by other authors on the base of a visual scanning of three years of daily maps.

Horizontal Variability of Seasonal Precipitation over the Eastern Tennessee River Valley

Abstract Rainfall data from 38 stations over a 32-year period are used to study the horizontal variability of seasonal precipitation over the complex terrain of the eastern Tennessee River Valley region. Subjective analysis of precipitation patterns suggests that horizontal variability is large during summer and small during winter. Objective measures of horizontal variability show that summertime variability is about twice as great as winter and annual variability and that variability is about four times greater across the valley than along the valley during winter and about two times greater during summer. A comparison of seasonal precipitation variability over uniform and complex terrain is made. Horizontal variability is always greater over the eastern Tennessee River Valley region with the greatest difference in summer and the least in winter; also, spatial correlations are always greater over uniform terrain.