What is he best known for?
The fields of study he is best known for:
- Meteorology
- Climatology
- Statistics
The scientist’s investigation covers issues in Meteorology, Climatology, Sensible heat, Water content and Data assimilation.
In general Meteorology study, his work on Planetary boundary layer and Diurnal cycle often relates to the realm of Available energy, thereby connecting several areas of interest.
His Climatology study combines topics from a wide range of disciplines, such as Meteorological reanalysis, Climate Forecast System, Precipitation and Surface runoff.
His research investigates the connection with Meteorological reanalysis and areas like NCEP/NCAR Reanalysis which intersect with concerns in Water cycle.
His Sensible heat research is multidisciplinary, relying on both Latent heat and Water balance.
In his research on the topic of Water content, Groundwater, Mesonet and Irrigation is strongly related with Anomaly.
His most cited work include:
- The NCEP Climate Forecast System Reanalysis (3319 citations)
- NORTH AMERICAN REGIONAL REANALYSIS (2504 citations)
- Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model (2021 citations)
What are the main themes of his work throughout his whole career to date?
Michael Ek mainly investigates Meteorology, Climatology, Atmospheric sciences, Data assimilation and Water content.
His work on Sensible heat, Snow and Numerical weather prediction as part of general Meteorology study is frequently linked to Surface and Climate model, therefore connecting diverse disciplines of science.
His Climatology research incorporates elements of Drainage basin, Climate Forecast System, Precipitation and Surface runoff.
The study incorporates disciplines such as Moisture and Sea surface temperature in addition to Precipitation.
Michael Ek has included themes like Planetary boundary layer, Boundary layer, Latent heat and Atmosphere in his Atmospheric sciences study.
Michael Ek studied Data assimilation and Evapotranspiration that intersect with Hydrometeorology.
He most often published in these fields:
- Meteorology (41.86%)
- Climatology (35.66%)
- Atmospheric sciences (25.58%)
What were the highlights of his more recent work (between 2017-2020)?
- Precipitation (17.83%)
- Remote sensing (6.20%)
- Climatology (35.66%)
In recent papers he was focusing on the following fields of study:
Michael Ek mostly deals with Precipitation, Remote sensing, Climatology, Data assimilation and Meteorology.
Michael Ek has researched Precipitation in several fields, including Moisture and Atmospheric sciences.
His work on Atmosphere expands to the thematically related Climatology.
The concepts of his Data assimilation study are interwoven with issues in Humidity, Evapotranspiration and Latent heat.
A large part of his Meteorology studies is devoted to Global Forecast System.
His study focuses on the intersection of Climate Forecast System and fields such as Extratropical cyclone with connections in the field of Biosphere model.
Between 2017 and 2020, his most popular works were:
- Satellite and in situ observations for advancing global earth surface modelling: A review (47 citations)
- Infiltration from the Pedon to Global Grid Scales: An Overview and Outlook for Land Surface Modeling (19 citations)
- Role of moisture transport and recycling in characterizing droughts: Perspectives from two recent U.S. droughts and the CFSv2 system (13 citations)
In his most recent research, the most cited papers focused on:
- Meteorology
- Statistics
- Hydrology
His primary scientific interests are in Precipitation, Atmospheric sciences, Moisture, Data assimilation and Infiltration.
Precipitation is a subfield of Meteorology that he studies.
Michael Ek combines subjects such as Hydrometeorology, Water budget, Evapotranspiration, Hydrological modelling and Stream flow with his study of Data assimilation.
His biological study spans a wide range of topics, including Hydraulic conductivity, Climatology, Surface runoff and Soil horizon.
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.
