Related papers: A Nonanticipative Analog Method for Long-Term Fore…
Current techniques for predicting climate change are mainly based on "massive" deterministic numerical modeling. However, the ocean-atmosphere system is a so-called "complex system", made up of a large number of interacting elements. We…
Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from…
Forecasting multiscale properties of the solar wind is one of the important aspects of space weather prediction as mesoscales, larger than one minute, can affect the magnetosphere. Amongst forecasting techniques, the Analog Ensemble (AnEn)…
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting…
Air temperature is an essential factor that directly impacts the weather. Temperature can be counted as an important sign of climatic change, that profoundly impacts our health, development, and urban planning. Therefore, it is vital to…
We use the Global Historical Climatology Network--daily database to calculate a nonparametric statistic that describes the rate at which all-time daily high and low temperature records have been set in nine geographic regions (continents or…
Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction…
Motivated by the analysis of extreme rainfall data, we introduce a general Bayesian hierarchical model for estimating the probability distribution of extreme values of intermittent random sequences, a common problem in geophysical and…
Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not…
Weather extremes produce major impacts on society and ecosystems and are likely to change in likelihood and magnitude with climate change. However, very low probability events are hard to characterize statistically using observations or…
AI weather foundation models now achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their predictability for high-impact extremes across dynamical regimes remains uncertain. We evaluate…
We assess evidence for changes in tail characteristics of wind, solar irradiance and temperature variables output from CMIP6 global climate models (GCMs) due to climate forcing. We estimate global and climate zone annual maximum and annual…
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record…
Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies…
With the press of global climate change, extreme weather and sudden weather changes are becoming increasingly common. To maintain a comfortable indoor environment and minimize the contribution of the building to climate change as much as…
This paper compares different forecasting methods and models to predict average values of solar irradiance with a sampling time of 15 min over a prediction horizon of up to 3 h. The methods considered only require historic solar irradiance…
Accurately predicting long-term rainfall is challenging. Global climate indices, such as the El Ni\~no-Southern Oscillation, are standard input features for machine learning. However, a significant gap persists regarding local-scale indices…
The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment…
As a form of "small A", quantile machine learning is used to forecast diurnal and nocturnal $Q(.90)$ air temperatures for Paris, France from late spring through the summer months of 2021. The data are provided by the Paris-Montsouris…
High temporal resolution CO2 emission data are crucial for understanding the drivers of emission changes, however, current emission dataset is only available on a yearly basis. Here, we extended a global daily CO2 emissions dataset…