What is he best known for?
The fields of study he is best known for:
- Statistics
- Meteorology
- Climatology
His primary areas of study are Climatology, Meteorology, Predictability, Tornado and Atmospheric sciences.
His studies in El Niño Southern Oscillation, Sea surface temperature, Hindcast, Forecast skill and Annual cycle are all subfields of Climatology research.
In his research, Range is intimately related to Spatial distribution, which falls under the overarching field of Meteorology.
His Predictability research includes elements of Kullback–Leibler divergence, Mutual information, Canonical correlation and Linear discriminant analysis.
His work carried out in the field of Tornado brings together such families of science as Thunderstorm, Climatic variability and Extreme value theory.
His Atmospheric sciences study which covers Climate model that intersects with Tropical cyclone.
His most cited work include:
- Ensemble Square Root Filters (658 citations)
- The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction (509 citations)
- Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing? (386 citations)
What are the main themes of his work throughout his whole career to date?
Climatology, Predictability, Meteorology, Forecast skill and Precipitation are his primary areas of study.
Michael K. Tippett focuses mostly in the field of Climatology, narrowing it down to topics relating to Atmospheric sciences and, in certain cases, Climate model.
The concepts of his Predictability study are interwoven with issues in Range, Northern Hemisphere and Econometrics.
His Meteorology research incorporates elements of Spatial distribution and Climate change.
His studies in Forecast skill integrate themes in fields like Ensemble forecasting and Hindcast.
His research in Precipitation tackles topics such as Seasonality which are related to areas like Annual cycle.
He most often published in these fields:
- Climatology (89.88%)
- Predictability (40.47%)
- Meteorology (28.02%)
What were the highlights of his more recent work (between 2019-2021)?
- Climatology (89.88%)
- Predictability (40.47%)
- Tropical cyclone (12.45%)
In recent papers he was focusing on the following fields of study:
Michael K. Tippett focuses on Climatology, Predictability, Tropical cyclone, Statistics and Regression analysis.
His El Niño Southern Oscillation study in the realm of Climatology connects with subjects such as Latitude.
His El Niño Southern Oscillation research integrates issues from Amplitude, Covariance and Mode.
His Tropical cyclone research is multidisciplinary, incorporating elements of Intensity, Climate model and Downscaling.
In general Statistics study, his work on Confidence interval, Akaike information criterion and Sampling distribution often relates to the realm of Uncertainty estimation and Mean squared prediction error, thereby connecting several areas of interest.
His studies deal with areas such as Logistic regression, Common spatial pattern, Elastic net regularization, Statistical model and Extratropical cyclone as well as Regression analysis.
Between 2019 and 2021, his most popular works were:
- Statistical-Dynamical Downscaling Projections of Tropical Cyclone Activity in a Warming Climate: Two Diverging Genesis Scenarios (13 citations)
- Atlantic Ocean influence on Middle East summer surface air temperature (7 citations)
- Low-dimensional representations of Niño 3.4 evolution and the spring persistence barrier (4 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Meteorology
- Mathematical analysis
His primary scientific interests are in Climatology, Tropical cyclone, Monsoon, Precipitation and Predictability.
He studies El Niño Southern Oscillation which is a part of Climatology.
His Tropical cyclone study incorporates themes from Intensity and Climate model.
His Monsoon research incorporates themes from Power law, Atmospheric sciences, Rainfed agriculture and Temporal mean.
His work deals with themes such as Regression analysis and Laplace operator, which intersect with Precipitation.
His Atmosphere research is multidisciplinary, incorporating perspectives in Common spatial pattern, Model output statistics, Probabilistic logic, Forecast skill and Downscaling.
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