Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

smos > Surface Meteorological Observation System Instruments for SGPInstrument Type(s) > Baseline

The surface meteorological observation system (SMOS) mostly uses conventional in situ sensors to obtain 1-minute, 30-minute, and 1440-minute (daily) averages of surface wind speed, wind direction, air temperature, relative humidity (RH), barometric pressure, and precipitation at the Central Facility and many of the extended facilities of ARM’s Southern Great Plains (SGP) observatory. The SMOSs are not calibrated as systems. The sensors and the data logger (which includes the analog-to-digital converter, or A/D) are calibrated separately. All systems are installed using components that have a current calibration. SMOSs have not been installed at extended facilities located within about 10 km of existing surface meteorological stations, such as those of the Oklahoma Mesonet.

These systems are used to create climatology for each particular location, and to verify the output of numerical weather forecast and other models. They are also used to “ground-truth” other remote-sensing equipment.

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

2021

Krishnamurthy R, R Newsom, L Berg, H Xiao, P Ma, and D Turner. 2021. "On the estimation of boundary layer heights: a machine learning approach." Atmospheric Measurement Techniques, 14(6), 10.5194/amt-14-4403-2021.

Levin M, D Zhang, and K Gaustad. 2021. Tower Water-Vapor Mixing Ratio Value-Added Product Report. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-128. 10.2172/1804448.

2020

Zhang C, S Xie, and C Tao. 2020. ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation. Ed. by Robert Stafford, ARM Climate Research Facility. DOE/SC-ARM-TR-202.

Tai S, J Fast, W Gustafson, D Chand, B Gaudet, Z Feng, and R Newsom. 2020. "Simulation of Continental Shallow Cumulus Populations using an Observation‐Constrained Cloud‐System Resolving Model." Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002091, 10.1029/2020MS002091.
Research Highlight

Han Y, G Zhang, X Huang, and Y Wang. 2020. "A Moist Physics Parameterization Based on Deep Learning." Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002076, 10.1029/2020MS002076.


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