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observation (instrument reset), as required by adherence to the formal convention mentioned above. Shifting errors are most common in the T max data for observers with a morning ( a.m. ) time of observation. Because on most days the actual time of occurrence of T max is in the afternoon of the prior day, some observers have mistakenly believed that they should record the value on the day that it occurred. The presence of shifting creates problems for spatial quality control (QC) when comparing shifted
observation (instrument reset), as required by adherence to the formal convention mentioned above. Shifting errors are most common in the T max data for observers with a morning ( a.m. ) time of observation. Because on most days the actual time of occurrence of T max is in the afternoon of the prior day, some observers have mistakenly believed that they should record the value on the day that it occurred. The presence of shifting creates problems for spatial quality control (QC) when comparing shifted
with mixed observation times. Note that this problem will be solved if modernization of the coop network provides hourly or subhourly data at most station sites. b. 1993 floods Quality control procedures were applied to the data for the 1993 Midwest floods over the Missouri River basin and part of the upper Mississippi River basin, where heavy rainfall and floods occurred. Data were tested from 1 April 1 to 30 August. Figure 3 shows the interpolated spatial pattern of the fraction of flagged
with mixed observation times. Note that this problem will be solved if modernization of the coop network provides hourly or subhourly data at most station sites. b. 1993 floods Quality control procedures were applied to the data for the 1993 Midwest floods over the Missouri River basin and part of the upper Mississippi River basin, where heavy rainfall and floods occurred. Data were tested from 1 April 1 to 30 August. Figure 3 shows the interpolated spatial pattern of the fraction of flagged
that may contribute to the net exchange of scalars ( Loescher et al. 2006 ). To reduce and quantify these uncertainties, the AmeriFlux quality assurance and quality control (QAQC) laboratory was created to enhance data quality and ensure consistency in EC measurements within and among sites. The primary activities of the AmeriFlux QAQC laboratory involve the use of a portable eddy covariance system (PECS, discussed below). This system includes all necessary hardware and software to make EC
that may contribute to the net exchange of scalars ( Loescher et al. 2006 ). To reduce and quantify these uncertainties, the AmeriFlux quality assurance and quality control (QAQC) laboratory was created to enhance data quality and ensure consistency in EC measurements within and among sites. The primary activities of the AmeriFlux QAQC laboratory involve the use of a portable eddy covariance system (PECS, discussed below). This system includes all necessary hardware and software to make EC
combined gauge accumulations (Σ G ): Rain gauge data were interpolated and quality controlled via a cubic spline–based method described in Wang et al. (2008) . Separate monthly convective and stratiform Z e – R relations were generated using the reflectivity classification criteria defined in Steiner et al. (1995) . Within the KPOL field of view, there are only 15 rain gauge sites. Seven locations are shown in Wolff et al. (2005) , and the additional eight comprise the X-array at Roi
combined gauge accumulations (Σ G ): Rain gauge data were interpolated and quality controlled via a cubic spline–based method described in Wang et al. (2008) . Separate monthly convective and stratiform Z e – R relations were generated using the reflectivity classification criteria defined in Steiner et al. (1995) . Within the KPOL field of view, there are only 15 rain gauge sites. Seven locations are shown in Wolff et al. (2005) , and the additional eight comprise the X-array at Roi
statistical anomalies in the way the NN inputs are computed, especially at the edges of echoes. A method of selective emphasis is followed here to ensure good performance on significant echoes. Last, the technique described in this paper removes or retains entire echo regions, not just individual pixels. A particular challenge in the quality control (QC) of radar reflectivity data is that errors in the QC process can be additive from the point of view of downstream applications. This effect is
statistical anomalies in the way the NN inputs are computed, especially at the edges of echoes. A method of selective emphasis is followed here to ensure good performance on significant echoes. Last, the technique described in this paper removes or retains entire echo regions, not just individual pixels. A particular challenge in the quality control (QC) of radar reflectivity data is that errors in the QC process can be additive from the point of view of downstream applications. This effect is
some cases this may lead to errors on larger scales, as shown by the case studies ( Figs. 11 , 13 ). Such errors may be suppressed by relying more on the scatterometer observations, in particular by relaxing the variational quality control. 6. Conclusions In this paper a new method for ambiguity removal named 2DVAR is presented and applied to SeaWinds data. 2DVAR constructs an incremental analysis from the background and the observations, taking the a priori probabilities of the latter into
some cases this may lead to errors on larger scales, as shown by the case studies ( Figs. 11 , 13 ). Such errors may be suppressed by relying more on the scatterometer observations, in particular by relaxing the variational quality control. 6. Conclusions In this paper a new method for ambiguity removal named 2DVAR is presented and applied to SeaWinds data. 2DVAR constructs an incremental analysis from the background and the observations, taking the a priori probabilities of the latter into
data are to be free, easily accessible in a timely manner (i.e., for “real time” data within 24 h), and generated using uniform procedures. Thus, the majority of the procedures described in the text are not only employed by the U.S. Argo Data Assembly Center (U.S. DAC) but also by the DACs operated by the other countries listed in Table 1 . However, the U.S. DAC is also testing additional real-time quality-control procedures that were not yet accepted by the IADMT. These procedures will be
data are to be free, easily accessible in a timely manner (i.e., for “real time” data within 24 h), and generated using uniform procedures. Thus, the majority of the procedures described in the text are not only employed by the U.S. Argo Data Assembly Center (U.S. DAC) but also by the DACs operated by the other countries listed in Table 1 . However, the U.S. DAC is also testing additional real-time quality-control procedures that were not yet accepted by the IADMT. These procedures will be
FY-3A for the purpose of providing cloud cover, clear-sky sea surface temperatures, and cloud-top brightness temperatures. It is of great value to incorporate the MWTS, MWHS, and IRAS data from the FY-3 series into numerical weather prediction (NWP) models. This study describes a quality control procedure that could be implemented prior to the assimilation of the MWTS data into NWP models. MWTS is a four-channel cross-track microwave scanning radiometer that is similar to the Microwave Sounding
FY-3A for the purpose of providing cloud cover, clear-sky sea surface temperatures, and cloud-top brightness temperatures. It is of great value to incorporate the MWTS, MWHS, and IRAS data from the FY-3 series into numerical weather prediction (NWP) models. This study describes a quality control procedure that could be implemented prior to the assimilation of the MWTS data into NWP models. MWTS is a four-channel cross-track microwave scanning radiometer that is similar to the Microwave Sounding
-mode quality control. Reasons for such inconsistencies could be due to (i) natural variability (even at deeper depths), (ii) the lack of good quality background data, and (iii) difficulty in delineating sensor drift from long-term water mass change. Considering all of the above facts, in the present study, long-term sensor drift in the conductivity cells of floats that completed 5 yr are examined using the “near-linear” subsurface salinity structure in the Sea of Japan (SOJ). In drift analysis, one major
-mode quality control. Reasons for such inconsistencies could be due to (i) natural variability (even at deeper depths), (ii) the lack of good quality background data, and (iii) difficulty in delineating sensor drift from long-term water mass change. Considering all of the above facts, in the present study, long-term sensor drift in the conductivity cells of floats that completed 5 yr are examined using the “near-linear” subsurface salinity structure in the Sea of Japan (SOJ). In drift analysis, one major
system, or simply the solver that takes innovations and produces corrections; context will make it clear which is the correct meaning. Data assimilation procedures typically begin with quality control (QC) procedures to mitigate errors in the observation data. Our QC procedures for AMSU-A radiances include elimination of redundant data, screening of known bad satellite channels and low-peaking channels over land–sea ice, gross error checks for unphysical values, an innovation outlier check
system, or simply the solver that takes innovations and produces corrections; context will make it clear which is the correct meaning. Data assimilation procedures typically begin with quality control (QC) procedures to mitigate errors in the observation data. Our QC procedures for AMSU-A radiances include elimination of redundant data, screening of known bad satellite channels and low-peaking channels over land–sea ice, gross error checks for unphysical values, an innovation outlier check
