Data Quality Reports for Session: 127686 User: torreon1n2 Completed: 05/11/2010


TABLE OF CONTENTS

DQR IDSubjectData Streams Affected
D050722.1SGP/MWR/C1 - REPROCESS - Revised Retrieval Coefficientssgp1mwravgC1.c1, sgp5mwravgC1.c1, sgpmwrlosC1.a1, sgpmwrlosC1.b1, sgpmwrtipC1.a1,
sgpqmemwrcolC1.c1
D050915.1SGP/MWR/C1 - Instrument noise problemsgp1mwravgC1.c1, sgp5mwravgC1.c1, sgpmwrlosC1.b1, sgpmwrtipC1.a1


DQRID : D050722.1
Start DateStart TimeEnd DateEnd Time
04/16/2002200006/28/20052300
Subject:
SGP/MWR/C1 - REPROCESS - Revised Retrieval Coefficients
DataStreams:sgp1mwravgC1.c1, sgp5mwravgC1.c1, sgpmwrlosC1.a1, sgpmwrlosC1.b1, sgpmwrtipC1.a1,
sgpqmemwrcolC1.c1
Description:
IN THE BEGINNING (June 1992), the retrieval coefficients used to derive the precipitable 
water vapor (PWV) and liquid water path (LWP) from the MWR brightness temperatures were 
based on the Liebe and Layton (1987) water vapor and oxygen absorption model and the Grant 
(1957) liquid water absorption model.  

Following the SHEBA experience, revised retrievals based on the more recent Rosenkranz 
(1998) water vapor and oxygen absorption models and the Liebe (1991) liquid waer absorption 
model were developed.  The Rosenkranz water vapor absorption model resulted a 2 percent 
increase in PWV relative to the earlier Liebe and Layton model.  The Liebe liquid water 
absorption model decreased the LWP by 10% relative to the Grant model.  However, the 
increased oxygen absorption caused a 0.02-0.03 mm (20-30 g/m2) reduction in LWP, which was 
particularly significant for low LWP conditions (i.e. thin clouds encountered at SHEBA).

Recently, it has been shown (Liljegren, Boukabara, Cady-Pereira, and Clough, TGARS v. 43, 
pp 1102-1108, 2005) that the half-width of the 22 GHz water vapor line from the HITRAN 
compilation, which is 5 percent smaller than the Liebe and Dillon (1969) half-width used in 
Rosenkranz (1998), provided a better fit to the microwave brightness temperature 
measurements at 5 frequencies in the range 22-30 GHz, and yielded more accurate retrievals.  
Accordingly, revised MWR retrieval coefficients have been developed using MONORTM, which 
utilizes the HITRAN compilation for its spectroscopic parameters.  These new retrievals 
provide 3 percent less PWV and 2.6 percent greater LWP than the previous retrievals based on 
Rosenkranz (1998).

Although the MWR data will be reprocessed to apply the new monortm-based retrievals, for 
most purposes it will be sufficient to correct the data using the following factors:

PWV_MONORTM = 0.9695 * PWV_ROSENKRANZ
LWP_MONORTM = 1.026  * LWP_ROSENKRANZ

The Rosenkranz-based retrieval coefficients became active as follows (BCR 456):
SGP/C1 (Lamont)     4/16/2002, 2000
SGP/B1 (Hillsboro)  4/12/2002, 1600
SGP/B4 (Vici)       4/15/2002, 2300
SGP/B5 (Morris)     4/15/2002, 2300
SGP/B6 (Purcell)    4/16/2002, 2200
SGP/E14(Lamont)     4/16/2002, 0000
NSA/C1 (Barrow)     4/25/2002, 1900 
NSA/C2 (Atqasuk)    4/18/2002, 1700
TWP/C1 (Manus)      5/04/2002, 0200
TWP/C2 (Nauru)      4/27/2002, 0600
TWP/C3 (Darwin)     inception

The MONORTM-based retrieval coefficients became active as follows (BCR 984):

SGP/C1 (Lamont)     6/28/2005, 2300
SGP/B1 (Hillsboro)  6/24/2005, 2100
SGP/B4 (Vici)       6/24/2005, 2100
SGP/B5 (Morris)     6/24/2005, 2100
SGP/B6 (Purcell)    6/24/2005, 1942
SGP/E14(Lamont)     6/28/2005, 2300
NSA/C1 (Barrow)     6/29/2005, 0000 
NSA/C2 (Atqasuk)    6/29/2005, 0000
TWP/C1 (Manus)      6/30/2005, 2100
TWP/C2 (Nauru)      6/30/2005, 2100
TWP/C3 (Darwin)     6/30/2005, 2100
PYE/M1 (Pt. Reyes)  4/08/2005, 1900**

** At Pt. Reyes, the original retrieval coefficients implemented in March 2005 were based 
on a version of the Rosenkranz model that had been modified to use the HITRAN half-width 
at 22 GHz and to be consistent with the water vapor continuum in MONORTM.  These 
retrievals yield nearly identical results to the MONORTM retrievals.  Therefore the Pt. Reyes 
data prior to 4/08/2005 may not require reprocessing.
Measurements:sgp5mwravgC1.c1:
  • Averaged total liquid water along LOS path(liq)
  • MWR column precipitable water vapor(vap)

sgpmwrtipC1.a1:
  • Total liquid water along zenith path using tip-derived brightness temperatures(liqtip)
  • Total water vapor along zenith path using tip-derived brightness temperatures(vaptip)

sgpqmemwrcolC1.c1:
  • Ensemble average for MWR liquid in window centered about balloon release(mean_liq_mwr)
  • Ensemble average for MWR vapor in window centered about balloon release(mean_vap_mwr)

sgp1mwravgC1.c1:
  • MWR column precipitable water vapor(vap)
  • Averaged total liquid water along LOS path(liq)

sgpmwrlosC1.a1:
  • MWR column precipitable water vapor(vap)
  • Averaged total liquid water along LOS path(liq)

sgpmwrlosC1.b1:
  • MWR column precipitable water vapor(vap)
  • Averaged total liquid water along LOS path(liq)


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DQRID : D050915.1
Start DateStart TimeEnd DateEnd Time
07/28/2005140008/05/20051700
Subject:
SGP/MWR/C1 - Instrument noise problem
DataStreams:sgp1mwravgC1.c1, sgp5mwravgC1.c1, sgpmwrlosC1.b1, sgpmwrtipC1.a1
Description:
Various variables including the mixer temperatures were very noisy. After several attempts 
to fix the problem, the instrument was taken off line and returned to the manufacturer 
for repair.
Measurements:sgp5mwravgC1.c1:
  • Standard deviation about the mean for the total water vapor amount(vap_sdev)
  • IR Brightness Temperature(ir_temp)
  • Number of data points averaged out of 15(number_obs_averaged)
  • 23.8 GHz sky brightness temperature(23tbsky)
  • Standard deviation about the mean for the total liquid water amount(liq_sdev)
  • Probability of slope change in ratio of averaged brightness temps(prob_slope_change)
  • Standard deviation about the mean for the 23.8 GHz sky brightness temperature(tbsky23_sdev)
  • Standard deviation about the mean for the IR brightness temperature(ir_temp_sdev)
  • Fraction of data in averaging interval with water on Teflon window(water_flag_fraction)
  • Standard deviation about the mean for the 31.4 GHz sky brightness temperature(tbsky31_sdev)
  • MWR column precipitable water vapor(vap)
  • Probability of outlier in ratio of averaged brightness temps(prob_outlier)
  • 31.4 GHz sky brightness temperature(31tbsky)
  • Flag indicating where the initial surface water measurements are from: 0-> SMOS,
    1-> AERI(water_flag)
  • Probability of level change in ratio of averaged brightness temps(prob_level_change)
  • Sky brightness temperature at 23.8 GHz(tbsky23)
  • Averaged total liquid water along LOS path(liq)
  • Sky brightness temperature at 31.4 GHz(tbsky31)

sgpmwrtipC1.a1:
  • 23.8 GHz sky signal(tipsky23)
  • 31.4 GHz sky signal(tipsky31)
  • Noise injection temp at 31.4 GHz derived from this tip(tnd31I)
  • 23.8 GHz goodness-of-fit coefficient(r23)
  • 31.4 GHz blac2body+noise injection signal(bbn31)
  • Noise injection temp at nominal temperature at 23.8 GHz(tnd_nom23)
  • Ambient temperature(tkair)
  • 31.4 GHz sky brightness temperature derived from tip curve(tbsky31tip)
  • Noise injection temp at 23.8 GHz adjusted to tkbb(tnd23)
  • Temperature correction coefficient at 23.8 GHz(tc23)
  • Total liquid water along zenith path using tip-derived brightness temperatures(liqtip)
  • Noise injection temp at 23.8 GHz derived from this tip(tnd23I)
  • 23.8 GHz sky brightness temperature derived from tip curve(tbsky23tip)
  • Mixer kinetic (physical) temperature(tkxc)
  • (tknd)
  • Blackbody kinetic temperature(tkbb)
  • Water on Teflon window (1=WET, 0=DRY)(wet_window)
  • 23.8 GHz Blackbody signal(bb23)
  • Noise injection temp at 31.4 GHz adjusted to tkbb(tnd31)
  • Temperature correction coefficient at 31.4 GHz(tc31)
  • Total water vapor along zenith path using tip-derived brightness temperatures(vaptip)
  • 23.8 GHz blackbody+noise injection signal(bbn23)
  • 31.4 GHz goodness-of-fit coefficient(r31)
  • Noise injection temp at nominal temperature at 31.4 GHz(tnd_nom31)
  • 31.4 GHz blackbody(bb31)

sgp1mwravgC1.c1:
  • Sky brightness temperature at 31.4 GHz(tbsky31)
  • 31.4 GHz sky brightness temperature(31tbsky)
  • Fraction of data in averaging interval flagged by Dynamic Linear Model as poten(dlm_flag_fraction)
  • Sky brightness temperature at 23.8 GHz(tbsky23)
  • Number of contiguous periods in averaging interval with water on Teflon window(water_flag_periods)
  • Standard deviation about the mean for the total water vapor amount(vap_sdev)
  • MWR column precipitable water vapor(vap)
  • Standard deviation about the mean for the 31.4 GHz sky brightness temperature(tbsky31_sdev)
  • 23.8 GHz sky brightness temperature(23tbsky)
  • Averaged total liquid water along LOS path(liq)
  • Standard deviation about the mean for the IR brightness temperature(ir_temp_sdev)
  • Standard deviation about the mean for the total liquid water amount(liq_sdev)
  • Standard deviation about the mean for the 23.8 GHz sky brightness temperature(tbsky23_sdev)
  • Fraction of data in averaging interval with water on Teflon window(water_flag_fraction)
  • IR Brightness Temperature(ir_temp)
  • Number of contiguous periods in averaging interval flagged by Dynamic Linear Mo(dlm_flag_periods)

sgpmwrlosC1.b1:
  • Water on Teflon window (1=WET, 0=DRY)(wet_window)
  • 31.4 GHz blac2body+noise injection signal(bbn31)
  • Ambient temperature(tkair)
  • 23.8 GHz sky signal(sky23)
  • Noise injection temp at 23.8 GHz adjusted to tkbb(tnd23)
  • Noise injection temp at 31.4 GHz adjusted to tkbb(tnd31)
  • (tknd)
  • 23.8 GHz blackbody+noise injection signal(bbn23)
  • Mixer kinetic (physical) temperature(tkxc)
  • Noise injection temp at nominal temperature at 31.4 GHz(tnd_nom31)
  • Temperature correction coefficient at 31.4 GHz(tc31)
  • Sky brightness temperature at 23.8 GHz(tbsky23)
  • Averaged total liquid water along LOS path(liq)
  • Blackbody kinetic temperature(tkbb)
  • 31.4 GHz sky signal(sky31)
  • Temperature correction coefficient at 23.8 GHz(tc23)
  • 31.4 GHz blackbody(bb31)
  • MWR column precipitable water vapor(vap)
  • 23.8 GHz Blackbody signal(bb23)
  • Noise injection temp at nominal temperature at 23.8 GHz(tnd_nom23)
  • Sky brightness temperature at 31.4 GHz(tbsky31)
  • Sky Infra-Red Temperature(sky_ir_temp)


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