相关论文: Spatio-temporal quantile regression analysis revea…

In this study, we introduce a novel and comprehensive extension of a Bayesian spatio-temporal disease mapping model that explicitly accounts for gender-specific effects of meteorological exposures. Leveraging fine-scale weekly mortality and…

We analyze global surface temperature data obtained at 13472 weather stations from the year 1702 to 1990. The mean annual temperature of a station fluctuates from year to year by typically +-0.6oC (one standard deviation). Superimposed on…

This paper shows an analysis of the gridded European precipitation data. We combine simple linear regression with data mining tools like clustering, and evaluate the strength of the results by the modern bootstrap methods. We have used the…

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the…

Urbanization of an area is known to increase the temperature of the surrounding area. This phenomenon -- a so-called urban heat island (UHI) -- occurs at a local level over a period of time and has lasting impacts for historical data…

Using observational data and an elementary rigorous statistical fact it is easily shown that the distribution of Earth's climate is non-stationary. Examination of records of hundreds of local Industrial Era temperature histories in the…

A Chow test for structural breaks in the surface temperature series is used to investigate two common claims about global warming. Quirk (2009) proposed that the increase in Australian temperature from 1910 to the present was largely…

The temporal and spatial trends of 16 climate extreme indices based on daily maximum and minimum temperatures during the period 1987-2016 at 28 stations distributed across Israel and Palestine in the Levant region were annually and…

Both global, intermediate and local scales of Climate Change have been studied extensively, but a unified diagnostic framework for examining all spatial scales concurrently has remained elusive. Here we present a new tool-set using…

It is important to predict how the Global Mean Temperature (GMT) will evolve in the next few decades. The ability to predict historical data is a necessary first step toward the actual goal of making long-range forecasts. This paper…

A nonanticipative analog method is used for the long-term forecast of air temperature extremes. The data to be used for prediction include average daily air temperature, mean visibility, mean wind speed, mean dew point, maximum and minimum…

Understanding how weather and climate influence fire risk is important for many purposes, including climate adaptation planning and decision-making in sectors such as emergency management, finance, health and infrastructure (e.g., for…

Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as…

The importance of the sea ice retreat in the polar regions for the global warming and the role of ice-albedo feedback was recognized by various authors [1,2]. Similar to a recent study of the phenomenon in the Arctic [3] we present a…

We develop a three-timescale framework for modelling climate change and introduce a space-heterogeneous one-dimensional energy balance model. This model, addressing temperature fluctuations from rising carbon dioxide levels and the…

Starting from a classical Budyko-Sellers-Ghil energy balance model for the average surface temperature of the Earth, a nonautonomous version is designed by allowing the solar irradiance and the cloud cover coefficients to vary with time in…

This paper aims to project a climate change scenario using a stochastic paleotemperature time series model and compare it with the prevailing consensus. The ARIMA - Autoregressive Integrated Moving Average Process model was used for this…

Global temperature is a fundamental climate metric highly correlated with sea level, which implies that keeping shorelines near their present location requires keeping global temperature within or close to its preindustrial Holocene range.…

Detrended fluctuation analysis is used to investigate correlations between the monthly average of the maximum daily temperatures for different locations in the continental US and the different climates these locations have. When we plot the…

We write a nonlinear model that predicts the climate (temperature and humidity) on the surface of a small region on Earth, perform numerical investigations using the model, and compare the results to real climate on a variety of regions on…