Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment
VALUE is an open European collaboration to intercompare downscaling approaches for
climate change research, focusing on different validation aspects (marginal, temporal …
climate change research, focusing on different validation aspects (marginal, temporal …
Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation
Abstract Statistical downscaling of Global Climate Models (GCMs) allows researchers to
study local climate change effects decades into the future. A wide range of statistical models …
study local climate change effects decades into the future. A wide range of statistical models …
A simple equation to study changes in rainfall statistics
We test an equation for the probability of heavy 24 h precipitation amountsPr (X> x) as a
function of the wet-day frequency and the wet-day mean precipitation. The expression was …
function of the wet-day frequency and the wet-day mean precipitation. The expression was …
The VALUE perfect predictor experiment: evaluation of temporal variability
Temporal variability is an important feature of climate, comprising systematic variations such
as the annual cycle, as well as residual temporal variations such as short-term variations …
as the annual cycle, as well as residual temporal variations such as short-term variations …
Subsampling impact on the climate change signal over Poland based on simulations from statistical and dynamical downscaling
Most impact studies using downscaled climate data as input assume that the selection of few
global climate models (GCMs) representing the largest spread covers the likely range of …
global climate models (GCMs) representing the largest spread covers the likely range of …
Development of statistical downscaling methods for the assessment of rainfall characteristics under climate change scenarios
The objective of this research was to develop a statistical downscaling approach in the
Phetchaburi River Basin, Thailand, consisting of two main processes: predictor selection …
Phetchaburi River Basin, Thailand, consisting of two main processes: predictor selection …
Convolutional conditional neural processes for local climate downscaling
A new model is presented for multisite statistical downscaling of temperature and
precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are …
precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are …
[HTML][HTML] Application of machine learning techniques to delineate homogeneous climate zones in river basins of Pakistan for hydro-climatic change impact studies
Climatic data archives, including grid-based remote-sensing and general circulation model
(GCM) data, are used to identify future climate change trends. The performances of climate …
(GCM) data, are used to identify future climate change trends. The performances of climate …
Climate change and projections for the Barents region: what is expected to change and what will stay the same?
We present an outlook for a number of climate parameters for temperature, precipitation, and
storm statistics in the Barents region. Projected temperatures exhibited strongest increase …
storm statistics in the Barents region. Projected temperatures exhibited strongest increase …
Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures
Empirical statistical downscaling (ESD) under the perfect prognosis approach was carried
out to simulate daily maximum (Tx) and minimum temperatures (Tn) in 101 meteorological …
out to simulate daily maximum (Tx) and minimum temperatures (Tn) in 101 meteorological …