What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of investigation include Meteorology, Downscaling, Climate change, Statistics and Precipitation.
His studies in Meteorology integrate themes in fields like Weighting and Bias correction.
His Downscaling study combines topics in areas such as Value, Forecast skill, Variable and Scale.
When carried out as part of a general Climate change research project, his work on Climatic gradient is frequently linked to work in Danger signal, North africa and Fire weather index, therefore connecting diverse disciplines of study.
His Precipitation research includes elements of Kriging, Climate model, Extreme value theory and Interpolation.
His Series research integrates issues from Artificial neural network and Nonlinear system.
His most cited work include:
- Expert Systems and Probabilistic Network Models (569 citations)
- Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. (407 citations)
- Development and analysis of a 50‐year high‐resolution daily gridded precipitation dataset over Spain (Spain02) (234 citations)
What are the main themes of his work throughout his whole career to date?
José M. Gutiérrez mostly deals with Downscaling, Meteorology, Climate change, Precipitation and Climate model.
His Downscaling research is multidisciplinary, incorporating perspectives in Value, Statistics, Range and Model output statistics.
His research combines Statistical physics and Statistics.
His study looks at the relationship between Meteorology and fields such as Predictability, as well as how they intersect with chemical problems.
His Climate change study often links to related topics such as Econometrics.
He performs multidisciplinary study in Precipitation and Peninsula in his work.
He most often published in these fields:
- Downscaling (24.90%)
- Meteorology (17.55%)
- Climate change (16.33%)
What were the highlights of his more recent work (between 2017-2021)?
- Downscaling (24.90%)
- Climate change (16.33%)
- Climate model (11.43%)
In recent papers he was focusing on the following fields of study:
José M. Gutiérrez focuses on Downscaling, Climate change, Climate model, Precipitation and Value.
His Downscaling study integrates concerns from other disciplines, such as Hindcast, Econometrics, Model output statistics, Statistics and Extreme events.
The subject of his Hindcast research is within the realm of Meteorology.
José M. Gutiérrez focuses mostly in the field of Climate change, narrowing it down to topics relating to Regional science and, in certain cases, Variance decomposition of forecast errors and Data access.
His studies deal with areas such as General Circulation Model, GCM transcription factors and Scatter plot as well as Climate model.
His Precipitation study combines topics from a wide range of disciplines, such as Reliability and Big data.
Between 2017 and 2021, his most popular works were:
- An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment (75 citations)
- Observational uncertainty and regional climate model evaluation: A pan‐European perspective (51 citations)
- Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications (39 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Downscaling, Climate change, Value, Statistics and Bias correction are his primary areas of study.
With his scientific publications, his incorporates both Downscaling and Added value.
His work deals with themes such as Precipitation and Component, which intersect with Climate change.
His research investigates the connection between Statistics and topics such as Scale that intersect with issues in Econometrics and Reliability.
His studies examine the connections between Bias correction and genetics, as well as such issues in Annual cycle, with regards to Climate model.
His Hindcast study deals with the bigger picture of Meteorology.
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