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A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit’’’

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A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit’’’ 1014 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 30 Reply to ‘‘Comments on ‘A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit’’’ STEPHEN PO-CHEDLEY AND QIANG FU Department of Atmospheric Sciences, University of Washington, Seattle, Washington (Manuscript received 2 July 2012, in final form 26 November 2012) ABSTRACT The main finding by Po-Chedley and Fu was that the University of Alabama in Huntsville (UAH) mi- crowave sounding unit (MSU) product has a bias in its NOAA-9 midtropospheric channel (TMT) warm target factor, which leads to a cold bias in the TMT trend. This reply demonstrates that the central arguments by Christy and Spencer to challenge Po-Chedley and Fu do not stand. This reply establishes that 1) Christy and Spencer found a similar, but insignificant, bias in the UAH target factor because their radiosonde data lack adequate sampling and measurement errors were considered twice; 2) the UAH individual satellite TMT difference between NOAA-9 and NOAA-6 reveals a bias of 0.082 6 0.011 in the UAH NOAA-9 target factor; 3) comparing the periods before and after NOAA-9 is not an adequate method to draw conclusions about NOAA-9 because of the influence of other satellites; 4) using the Christy and Spencer trend sensitivity 2 value, UAH TMT has a cold bias of 0.035 K decade 1 given a target factor bias of 0.082; 5) similar trends from UAH and Remote Sensing Systems (RSS) for the lower tropospheric temperature product (TLT) do not indicate that the UAH TMT and TLT NOAA-9 target factor is unbiased; and 6) the NOAA-9 warm target temperature signal in UAH TMT indicates a problem with the UAH empirical algorithm to derive the target factor. 1. Background and after correction, respectively; a is the target factor; C is a constant offset; and T is the warm target temperature Po-Chedley and Fu (2012) worked toward reconciling WT of the satellite (Christy et al. 2000; Mears et al. 2003). Each a previously noted, long-standing difference between the team solves for this target factor differently, but in order to University of Alabama in Huntsville (UAH) and the achieve the same goal: to remove warm target temperature Remote Sensing Systems (RSS) Microwave Sounding contamination in the final MSU products (Christy et al. Unit (MSU) midtropospheric temperature (TMT) data- 2000; Mears et al. 2003; Zou et al. 2009). sets. This difference is also evident between the UAH Figure 1 shows the differences between the RSS and and the National Oceanic and Atmospheric Adminis- UAH TMT and lower tropospheric temperature (TLT) tration (NOAA) MSU TMT dataset. The difference is datasets over the NOAA-9 satellite time period versus related to the warm target calibration of the MSU mea- the warm target temperature on board NOAA-9. We see surements (Mears et al. 2003; Karl et al. 2006). UAH, that the differences in both TMT and TLT are signifi- RSS, and NOAA utilize a correction factor, referred to as cantly related to the warm target temperature of the a warm target factor, that is multiplied by the warm target NOAA-9 satellite, indicating that there are still large temperature of the satellite to remove the residual signal warm target temperature signals in one or both of the of the warm target temperatures in the MSU measure- UAH and RSS MSU products. The differences between ments. For each satellite, the correction has the form the UAH and the NOAA TMT products are also sig- T 5 T 2 a 3 T 1 C,whereT and T MSU,C MSU WT MSU MSU,C nificantly related to the NOAA-9 warm target temper- are the measured Earth brightness temperatures before ature (the slope is 20.053 6 0.010 and the r2 value is 0.81). The difference between the RSS and NOAA TMT products versus the NOAA-9 warm target temper- Corresponding author address: Stephen Po-Chedley, Department 2 6 2 of Atmospheric Sciences, Box 351640, University of Washington, ature has a slope of 0.010 0.007 with an r value of Seattle, WA 98195. 0.25, which is also statistically significant. NOAA does E-mail: [email protected] not yet produce a TLT dataset. DOI: 10.1175/JTECH-D-12-00131.1 Ó 2013 American Meteorological Society MAY 2013 C O R R E S P O N D E N C E 1015 FIG. 1. Scatterplots of the differences between UAH and RSS for (a) TMT and (b) TLT vs the warm target temperature of NOAA-9 for the 26-month NOAA-9 time period (January 1985–February 1987). There are large differences in the magnitudes of the this difference is actually the difference between MSU target factors from each group for the NOAA-9 satellite errors and radiosonde errors, and that the latter is (Po-Chedley and Fu 2012). The UAH NOAA-9 target unrelated to the satellite warm target temperature factor is more than 2 and 3 times larger than the target (Po-Chedley and Fu 2012). Thus, a significant relation- factors used by RSS and NOAA, respectively. We rec- ship would indicate a detectable warm target tempera- ognize that target factors may not be exactly the same ture signal in the MSU datasets. We found that we could because of different algorithms used in the satellite only detect a significant relationship using the UAH merging process, but there should be no detectable warm dataset with a mean slope of 20.051 6 0.031. This value target signal in the final MSU product. Because the represents the magnitude of the bias in the target factor NOAA-9 warm target temperature explains most of the for UAH TMT (the target factor bias and the slope have discrepancies between the datasets during the NOAA-9 the same magnitude but opposite signs) (Po-Chedley era (Fig. 1), it is not possible that all target factors are and Fu 2012). Christy and Spencer (2013) repeated our equally valid (Po-Chedley and Fu 2012). Since the warm calculation using U.S. VIZ radiosondes (see their Fig. 1, target temperature for NOAA-9 drifts over time, a target top). They obtained a statistically insignificant slope of factor that is too large or too small would leave a spurious 20.042 6 0.047. They further asserted that this calcula- signal related to the warm target temperature drift in the tion assumes no errors for the individual points. By add- final MSU TMT and TLT time series. The difference in ing an error bar as the ‘‘measurement error’’ in the VIZ the magnitude of the target factor has been well docu- radiosondes at each point, they produced a relationship mented as an important discrepancy between the UAH of 20.042 6 0.073. Below we demonstrate that their and RSS datasets, contributing to about half of the trend claim cannot be substantiated because they used a limited difference between the groups (Mears et al. 2003; Karl number of radiosonde stations and their consideration of et al. 2006). Po-Chedley and Fu (2012) sought to reconcile measurement errors is incorrect. this specific, yet important, difference and not other dif- Figure 2 shows the effect of the number of radiosonde ferences between the three groups. stations on the bias detection threshold by randomly subsampling the Radiosonde Innovation Composite Homogenization (RICH) global radiosonde dataset 2. Radiosonde comparisons (Haimberger et al. 2008). We obtain similar results from In Po-Chedley and Fu (2012), we attempted to detect other radiosonde datasets utilized in Po-Chedley and artificial residuals related to the warm target calibration Fu (2012). It shows that the uncertainty declines dra- in MSU TMT products from each group using five matically as we incorporate more radiosonde locations. global, homogenized, peer-reviewed radiosonde data- There is a large uncertainty when few radiosonde sta- sets as references. In particular, we examined whether tions are used, owing to the large error noise from there is a significant relationship between the difference individual radiosonde stations. For this reason, it is of collocated MSU and radiosonde TMT measurements important to use a large number of radiosonde sites. The and the NOAA-9 warm target temperature. Note that Christy and Spencer (2013) value is insignificant, owing to 1016 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 30 FIG. 2. Effects of sample size on statistics in Po-Chedley and Fu (2012). We computed the (a) p value and (b) target factor bias, Da, for the regression of UAH-RICH radiosondes vs the NOAA-9 warm target temperature for different numbers of radiosonde stations (collocated with UAH data over the NOAA-9 time period). For this calculation we randomly subsample a certain number of stations and create an average, collocated UAH minus radiosonde difference time series, which we then regress against the NOAA-9 warm target temperature. We redo this calculation 1000 times for each number of stations sampled and then present the mean p value and Da value; p values less than 0.05 indicate significant biases in the UAH target factor with 95% confidence. The shaded region around the Da value is the 95% confi- dence interval from the subsampling statistics only. The results become significant when about 35 stations are included in the global average; below this number, the signal-to-noise ratio is too low. poor radiosonde sampling; Christy and Spencer (2013) is incorrect. Note that the radiosonde measurement er- utilized 31 radiosonde stations, while Po-Chedley and Fu ror that Christy and Spencer (2013) reference is simply (2012) used between 45 and 451 stations.
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