10 ms·

Honest question. Why does weather behave different over oceans when compared to land?

Keyframe 13y ago
No obstacles for wind, water vapor, temperature gradients of water..?
thematt 13y ago
Probably because wind speeds are faster over the ocean due to less friction. Mountains, trees, structures, etc. all affect that over land.
- acadien 13y ago
  Also a larger heat exchange.
- aaren 13y ago
  This. Even a little terrain is a lot more dynamically interesting than the ocean. There are also strong heat / surface moisture gradients on land (think edges of forests, cities, coasts), which play a big role in convection. By comparison, the ocean is an infinite plane so the dynamics are much more dependent on large scale forcings like the Earth's rotation and there is less local variability.
Oculus 13y ago
I'd wager a guess it's the water vapor.
mturmon 13y ago
Great question. I think this is a data issue, not a weather issue. The data they are using over oceans comes from microwave scatterometers, which use radar to measure disturbances of the ocean surface caused by near-surface winds. Basically, the power of the radar return is affected by the interaction of the radar waves with the irregularities in the water surface (https://earth.esa.int/applications/data_util/SARDOCS/spaceborne/Radar_Courses/Radar_Course_III/bragg_scattering.htm https://earth.esa.int/applications/data_util/SARDOCS/spacebo...). To get a wind speed and direction from the radar power, you have to hit the surface from several different angles and perform an inversion. There are several ocean winds space missions that do this continuously from ~90-minute Earth orbits. For reference, one is QuikSCAT -- http://science.nasa.gov/missions/quikscat/ http://science.nasa.gov/missions/quikscat/ There is no analogous sensor that works over land. Hence, the data over land is not nearly as uniform, dense, and regularly observed. All that said, this particular visualization seems to have a calibration issue, because the land winds seem to be off in magnitude. I'm not enough of an expert to know if this is a problem with their data merging, sensor selection, stratification (e.g., winds at land surface may be lower than at sea surface, but up 50m they may be comparable), or something else. You can get other surprising things from remote sensing over oceans, including salinity, temperature, and incredibly precise elevation.
- aaren 13y ago
  Can you estimate the density of that data in space/time? Doesn't this vary with the overflights of the satellites? The land data is scarce in e.g. the Sahara, but the density over western Europe is pretty high. The land based data is continuous as well, and not affected by cloud cover.
  - mturmon 13y ago
    I think it's a 50km spacing grid over the ocean. There are multiple satellites, so they get multiple scans per day. Also, they might ingest the data into a model, and report the model outputs, so this could enhance temporal resolution. Another issue with in situ sensors is calibration and uniformity. Unless they were installed by a central authority, they are likely to all be different. This makes analysis harder.

No obstacles for wind, water vapor, temperature gradients of water..?

Probably because wind speeds are faster over the ocean due to less friction. Mountains, trees, structures, etc. all affect that over land.

acadien 13y ago
Also a larger heat exchange.
aaren 13y ago
This. Even a little terrain is a lot more dynamically interesting than the ocean. There are also strong heat / surface moisture gradients on land (think edges of forests, cities, coasts), which play a big role in convection. By comparison, the ocean is an infinite plane so the dynamics are much more dependent on large scale forcings like the Earth's rotation and there is less local variability.

Oculus 13y ago

I'd wager a guess it's the water vapor.

mturmon 13y ago

Great question. I think this is a data issue, not a weather issue. The data they are using over oceans comes from microwave scatterometers, which use radar to measure disturbances of the ocean surface caused by near-surface winds. Basically, the power of the radar return is affected by the interaction of the radar waves with the irregularities in the water surface (https://earth.esa.int/applications/data_util/SARDOCS/spaceborne/Radar_Courses/Radar_Course_III/bragg_scattering.htm https://earth.esa.int/applications/data_util/SARDOCS/spacebo...). To get a wind speed and direction from the radar power, you have to hit the surface from several different angles and perform an inversion. There are several ocean winds space missions that do this continuously from ~90-minute Earth orbits. For reference, one is QuikSCAT -- http://science.nasa.gov/missions/quikscat/ http://science.nasa.gov/missions/quikscat/ There is no analogous sensor that works over land. Hence, the data over land is not nearly as uniform, dense, and regularly observed. All that said, this particular visualization seems to have a calibration issue, because the land winds seem to be off in magnitude. I'm not enough of an expert to know if this is a problem with their data merging, sensor selection, stratification (e.g., winds at land surface may be lower than at sea surface, but up 50m they may be comparable), or something else. You can get other surprising things from remote sensing over oceans, including salinity, temperature, and incredibly precise elevation.

aaren 13y ago
Can you estimate the density of that data in space/time? Doesn't this vary with the overflights of the satellites? The land data is scarce in e.g. the Sahara, but the density over western Europe is pretty high. The land based data is continuous as well, and not affected by cloud cover.
- mturmon 13y ago
  I think it's a 50km spacing grid over the ocean. There are multiple satellites, so they get multiple scans per day. Also, they might ingest the data into a model, and report the model outputs, so this could enhance temporal resolution. Another issue with in situ sensors is calibration and uniformity. Unless they were installed by a central authority, they are likely to all be different. This makes analysis harder.