The Microwave Radiometer (MWR) provides time-series measurements of column-integrated amounts of water vapor and liquid water. The instrument itself is a sensitive microwave receiver that detects the microwave emissions of the vapor and liquid water molecules in the atmosphere at two frequencies: 23.8 and 31.4 GHz.
|Vapor-sensing channel||Liquid-sensing channel|
|Frequency||23.8 GHz||31.4 GHz|
|Bandwidth||0.4 GHz||0.4 GHz|
|Beamwidth||5.9 degrees||4.5 degrees|
|Estimated Temperature Error||0.3 K||0.3 K|
Integrated water vapor and liquid water path are derived from radiance measurements with a statistical retrieval algorithm that uses monthly derived and location-dependent linear regression coefficients.
More details on the calibration algorithm can be found in:
Liljegren, JC. 1999. "Automatic self-calibration of ARM microwave radiometers." In Microwave Radiometry and Remote Sensing of the Earth’s Surface and Atmosphere. Ed. P. Pampaloni and S. Paloscia, pp. 433-443.
For more information see MWR.
The MWR metrics should mostly be green. It is not uncommon for a flag to get tripped every now and then. If a flag is tripped for an extended period of time, then that may be cause for further investigation.
This shows most of the MWR variables available.
These plots are also created for 1 week.
Noise Injection Temperatures
The noise injection temperatures are also plotted out a second time for 1 day and 1 month.
The comparison plot are the exact same as many of the radiation instruments (GVR, GVRP, etc), please see the GVR page or below for more details.
Liquid Water Path and Precipitable Water Vapor Comparison
This is a comparison plot of all the different instruments that measure/calculate Liquid Water Path (LWP) and Precipitable Water Vapor (PWV). The sonde data is plotted as a dot on the precipitable water vapor plot. There is normally some disagreement between the different instruments as they all have different ways of measuring/calculating these values.
Precipitable Water Vapor vs. SONDE Comparison
The sonde is an in-situ measurement and the data from it can be used to calculate the precipitable water vapor. This value is then compared to the other instruments that calculate the value, including GVR, MWRLOS, MWRP, and more. The values should follow the 1-to-1 line, but may deviate more at higher values.
Sky Brightness Temperature Comparison
The brightness temperatures from multiple instruments are compared for 1 day and 1 week. Because the instruments are actually measuring the sky temperature at different bands, the absolute values will not match, but a general comparison can be made to see if the instrument is picking up subtle changes expected when Liquid and Water Vapor changes occur. The relative changes in the plots will not match up directly either. Mainly we are looking for cases where the the instruments detect a significant change in temperature due to the presence of Liquid or Water Vapor, but one of the instruments do not detect this change, or vice versa.
As you can see in the plots below, the GVR has some noise showing up in the data, most notably the +/-14 GHz channel.
The rain flag plotted is either from the MWRHF (1st) or MWR3C (2nd) when available.
At NSA and OLI, where the sun angle can be very low, buildings or other objects may sometimes cast shadows on the MWR. See Past Problems below.
Known issues for this instrument that MAY NOT need to be mentioned in your DQA's:
Heaters Constantly Operating
Occasionally, the wet window flag can be flagged consistently -- this means that the heater was on at the times the samples were taken. Normally, a fan blows air over the Teflon window of the MWR to keep it free of dust. During condensing or precipitating conditions, the heater turns on to prevent the formation of dew or the settling of fog on the window as well as to promote the evaporation of rain and snow. If there are elevated levels of liquid water path (likely due to frost or snow on the window), then the heater may continuously (but properly) operate. This was the case for DQPR 401: While it may have seemed like something was wrong with the data because the heater was on for such a long time (in this case, it was on for 10 days), it was merely a period of frost and ice on the MWR window. An example of the data during the time the wet window was flagged is shown below.
However, this is not to say that the wet window flag being flagged consistently is never an indication of something being wrong with the instrument, so it is still a good idea to mention this in your DQAs. DQPR 1812, for instance, is an example of a time when the wet window was incorrectly flagging consistently. See the "wet_window flag incorrect" example listed below in Past Problems for more explanation.
Oscillating Noise Injection Temperatures
Oscillating noise injection temperatures do not always mean that there is a problem with the data. In the plots below, you can see that the noise injection temps began to oscillate, with the oscillations being greater at night and lower during the day. It turned out that this was not a problem, as the retrievals were still in good agreement with the MWRP and did not display any diurnal dependence. If you notice oscillating noise injection temperatures, however, it is still worth mentioning in your DQAs (and possibly in a DQPR) so that the mentor will take a look and determine each instance of oscillating temps on a case-by-case basis.
Past problems for this instrument that DO need to be mentioned in your DQA's and possibly requiring a DQPR submittal:
Overestimation of PWV
This is a case where all variables seem normal, but the MWR PWV is consistently too high (see point 10). In this case a bad calibration was responsible for the wrong retrievals. Display of rms errors in correspondence of Radiosonde soundings could help in these situations.
Noisy Data - Bad Analog Board**
This is a case where the time series were particularly noisy (see point 1). The Mixer kinetic temperature Txc was inconstant (see point 13) and the PWV was too high (see point 10). The problem was later identified as a bad analog board.
In this case, the mixer kinetic temperature was unstable (see point 13). The instrument was thermally unstable.
Sun in field of view of instrument**
|Site||Fall Dates||Spring Dates|
|TWP C1||~ September 10 to October 15||~ March 1st to April 5|
|TWP C3||~ October 15 to November 15||---|
Other objects in field of view of instrument
Noisy Mixer Kinetic Temperature
Mixer temperature and blackbody temperature incorrect
Noisy Instrument Temperatures
Ice accumulation on MWR cover
Non-hydrophobic MWR teflon window
Instrument is thermally unstable**
wet_window flag incorrect
Jumps in TAIR/TKAIR values**
Issues with the temperature sensor occur quite often with the MWR, either as a result of corrosion that needs to be cleaned up, or due to the sensor needing to be replaced entirely. Any time you notice the data for tkair acting up, there's probably a good chance a DQPR is necessary, in addition to mentioning it in your DQAs.
This can be a tough issue to diagnose, as the problems with the data can sometimes be intermittent and get progressively worse over time. As shown in the figures below, you can see that the data from the MWR are very noisy and measurements are unreasonable. This ended up being due to the screw that keeps the mirror in place becoming loose, which allowed the mirror to freely rotate and not scan as it should. See DQPR 2187 for further explanation.
Another example of incorrect data due to mirror position is described in DQPR 4358. Below, you can see that the data began to no longer trend well with co-located sondes after a period of missing data.
Incorrect instrument computer setup
Improper computer settings can sometimes lead to intermittent data outages and incorrect data. This was the case for DQPR 2468.
Interference with MWR
Another tough one to diagnose, interference with the MWR can sometimes affect the data and lead to it being extremely noisy. Issues like this can sometimes be fixed by adjusting the orientation of the radiometer, but there is not always a way to avoid all obstructions and interference. In cases like this, there is nothing to be done except DQR all data that may be affected by the interference, as in the case of DQPR 6047.