What is he best known for?
The fields of study he is best known for:
- Climatology
- Climate change
- Meteorology
His primary scientific interests are in Climatology, Precipitation, Global warming, Atmospheric sciences and Climate model.
Aiguo Dai specializes in Climatology, namely Forcing.
His work in the fields of Precipitation frequency overlaps with other areas such as Intensity.
He connects Global warming with Arid in his research.
Aiguo Dai combines subjects such as Cloud cover, Interdecadal Pacific Oscillation and Global warming hiatus with his study of Atmospheric sciences.
In his study, Humidity, Southern Hemisphere, Northern Hemisphere, Westerlies and Hadley cell is strongly linked to Anomaly, which falls under the umbrella field of Climate model.
His most cited work include:
- Increasing drought under global warming in observations and models (2062 citations)
- Increasing drought under global warming in observations and models (2062 citations)
- The Changing Character of Precipitation (1735 citations)
What are the main themes of his work throughout his whole career to date?
Aiguo Dai mainly focuses on Climatology, Precipitation, Climate model, Atmospheric sciences and Climate change.
His research brings together the fields of Global warming and Climatology.
He works mostly in the field of Precipitation, limiting it down to topics relating to Streamflow and, in certain cases, Surface runoff, as a part of the same area of interest.
His work carried out in the field of Climate model brings together such families of science as Meteorology, Convection, Surface pressure, Weather Research and Forecasting Model and Atmospheric tide.
His work in Atmospheric sciences covers topics such as Cloud cover which are related to areas like Atmospheric temperature.
His Forcing study combines topics from a wide range of disciplines, such as Atmosphere and Internal variability.
He most often published in these fields:
- Climatology (104.89%)
- Precipitation (48.44%)
- Climate model (32.00%)
What were the highlights of his more recent work (between 2017-2021)?
- Climatology (104.89%)
- Precipitation (48.44%)
- Climate model (32.00%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Climatology, Precipitation, Climate model, Global warming and Climate change.
His study in Forcing, Weather Research and Forecasting Model, Teleconnection, North Atlantic oscillation and Pacific decadal oscillation falls within the category of Climatology.
His research in Precipitation intersects with topics in Monsoon, Troposphere and Mesoscale meteorology.
Aiguo Dai interconnects Range and Water cycle in the investigation of issues within Climate model.
The various areas that Aiguo Dai examines in his Global warming study include Humidity, Convection, Atmospheric sciences and Arctic.
His Climate change research is multidisciplinary, relying on both Sea ice and Meteorology.
Between 2017 and 2021, his most popular works were:
- Arctic amplification is caused by sea-ice loss under increasing CO 2 (110 citations)
- Climate Change and Drought: a Precipitation and Evaporation Perspective (81 citations)
- Climate Change and Drought: a Precipitation and Evaporation Perspective (81 citations)
In his most recent research, the most cited papers focused on:
- Climate change
- Meteorology
- Climatology
Aiguo Dai mainly investigates Climatology, Climate model, Precipitation, Global warming and Climate change.
The study of Climatology is intertwined with the study of Arctic in a number of ways.
His Climate model research includes elements of Humidity and Pacific decadal oscillation.
The study incorporates disciplines such as Spatial variability, Seasonality and Weather Research and Forecasting Model in addition to Precipitation.
His research integrates issues of Convection, Convective available potential energy, Atmospheric sciences and Buoyancy in his study of Global warming.
His work deals with themes such as Sea ice, Evapotranspiration, Surface water and Water content, which intersect with Climate change.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
