mwrret: MWR Retrievals

Quicklook image using standard y-axis ranges.

There are 2-channel (23.8 and 31.4GHz) microwave radiometers (MWRs) deployed at each ARM Climate Research Facility site. The observed brightness temperatures from these MWRs can be inverted to retrieve precipitable water vapor (PWV) and cloud liquid water path (LWP), both of which are critical variables to understanding radiative transfer in the atmosphere and clouds. The ARM Facility routinely has provided retrieved values of PWV and LWP with the MWR raw (mwrlos) data. These retrievals are based upon a statistical methodology that uses site-dependent monthly retrieval coefficients (Liljegren and Lesht 1996). Therefore, if the atmospheric conditions are significantly different than the historical mean conditions that are captured in the retrieval coefficients, the PWV and/or LWP retrievals will be biased.

A physical retrieval methodology that includes more information about the atmospheric state in the retrieval process is implemented in this VAP. The physical retrieval uses interpolated radiosonde profiles to provide the thermodynamic structure of the atmosphere and information on the cloud temperature (Turner et al. 2004), an optimal estimation in an iterative scheme to retrieve PWV and LWP, and the monoRTM radiative transfer model (Clough et al. 2005). Before performing the retrievals, the VAP also performs an extensive quality check of the input data to identify, and potentially fix, spurious or suspect input data.

Turner et al. 2004, also demonstrated that a clear-sky bias in the retrieved LWP exists, and that the bias has a monthly and site dependence. Non-zero LWP data retrieved from MWRs have been noted by others, both inside and outside of ARM (e.g., Marchand et al. 2003, van Meijgaard and Crewell 2005). This VAP attempts to reduce the size of the LWP bias by removing small offsets from the observed brightness temperatures. These offsets are determined from clear-sky data and are time-dependent; thus the apparent seasonal dependence is, to first order, greatly reduced.

Additionally, offsets in the 23.8 GHz channel, which reduce bias in the retrieved PWV, are determined once per year for each site and facility and as such are referred to as ‘static’ offsets. When running in real time on the ARM production system, static Tb offsets are not applied. Additionally, if ARSCL (Active Remote Sensing of Cloud Layers) data are not available in real-time, cloud base height is determined from ceilometer data. This real-time processing produces a .c1-level file.

At the end of each year, the data from the year are analyzed and the appropriate static offset value at 23.8 GHz is determined. The Tb offset configuration file is updated with the value of newly determined static offsets, and the data are re-run through the algorithm to apply the new static offsets. At the same time, the VAP checks for ARSCL data and uses it, rather than ceilometer, if available. This yearly processing produces .c2-level files, which are considered more accurate than the .c1-level files due to the updated static offsets and the use of ARSCL data.

The VAP outputs best-estimate values of LWP and PWV, ‘be_LWP’ and ‘be_PWV’, which are the recommended variables for the general user.

For more details of the operational implementation of the VAP, see the technical report.



  • Fixed
  • AMF1
  • AMF2
  • AMF3


mwr: Microwave Radiometer

Related Publications


Zhang C, S Xie, S Klein, H Ma, S Tang, K Van Weverberg, C Morcrette, and J Petch. 2018. "CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site." Journal of Geophysical Research: Atmospheres, 123(6), doi:10.1002/2017JD027200.


Ahlgrimm M and RM Forbes. 2016. "Regime dependence of cloud condensate variability observed at the Atmospheric Radiation Measurement Sites." Quarterly Journal of the Royal Meteorological Society, 142(697), 10.1002/qj.2783. ONLINE.


Shupe MD, DD Turner, A Zwink, MM Thieman, EJ Mlawer, and T Shippert. 2015. "Deriving Arctic Cloud Microphysics at Barrow, Alaska: Algorithms, Results, and Radiative Closure." Journal of Applied Meteorology and Climatology, 54(7), 10.1175/jamc-d-15-0054.1.


Chiu JC, JA Holmes, RJ Hogan, and EJ O'Connor. 2014. "The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements." Atmospheric Chemistry and Physics, 14, 10.5194/acp-14-8389-2014.

Mann JA, JC Chiu, RJ Hogan, EJ O'Connor, TS L'Ecuyer, TH Stein, and A Jefferson. 2014. "Aerosol impacts on drizzle properties in warm clouds from ARM Mobile Facility maritime and continental deployments." Journal of Geophysical Research: Atmospheres, 119(7), 10.1002/2013jd021339.


Wagner TJ, DD Turner, LK Berg, and SK Krueger. 2013. "Ground-Based Remote Retrievals of Cumulus Entrainment Rates." Journal of Atmospheric and Oceanic Technology, 30(7), 10.1175/jtech-d-12-00187.1.

Sivaraman S. 2013. ARM Climate Research Facility Quarterly Value-Added Product Report January 1–March 31, 2013. Ed. by U.S. Department of Energy, DOE/SC-ARM-13-010.

Zhang Y and SA Klein. 2013. "Factors Controlling the Vertical Extent of Fair-Weather Shallow Cumulus Clouds over Land: Investigation of Diurnal-Cycle Observations Collected at the ARM Southern Great Plains Site." Journal of the Atmospheric Sciences, 70(4), 10.1175/jas-d-12-0131.1.

Sivaraman C. 2013. ARM Climate Research Facility Quarterly Value-Added Product Report October 01–December 31, 2012. U.S. Department of Energy. DOE/SC-ARM-13-002.


Ahlgrimm M and R Forbes. 2012. "The Impact of Low Clouds on Surface Shortwave Radiation in the ECMWF Model." Monthly Weather Review, 140(11), 10.1175/mwr-d-11-00316.1.

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