The potential benefits of ground-based remote sensing instruments such as ceilometer, cloud radar, wind profiler, and passive radiometers are quite well understood and have attracted attention to the continuous efforts for an improvement (Wilczak et al., 1998; WMO, 2006; Cimini et al., 2015). Among these instruments, the ground-based microwave sounding radiometer (hereafter the radiometer) which takes measurement of the downwelling radiances (in the form of brightness temperature, Tb) in the microwave region has been used to obtain the vertical information of temperature (T) and humidity (q) (Cimini et al., 2006; Löhnert and Maier, 2012; Cadeddu et al., 2013). Its cost efficiency, the capability of autonomous operation, and high temporal resolution with the relatively high accuracy are the most important advantages that have attracted a variety of users. For example, Knupp et al. (2009) show that the high temporal resolution data from the radiometer could resolve the rapidly changing thermodynamic structure of transitioning boundaries, including cold fronts, gust fronts, bores, and gravity waves. It is a significant potential benefit of the radiometer over radiosondes to be able to detect the thermodynamic changes occurring on very short time scales, on the order of 1–10 min, which are far too short to be captured by radiosondes.

On the other hand, the radiometer is also characterized by its limited information contents which are mainly located in the lower atmosphere and by the limited vertical resolution (Löhnert et al., 2009; Candlish et al., 2012; Cadeddu et al., 2013). Due to the limitation of information contents contained within the lower atmosphere, the retrieval accuracy of the radiometer usually decreases with increasing altitude (Cadeddu et al., 2002; Hewison, 2007). Also, as the radiometer provides the volumetric measurement while the radiosonde measures the point value with the higher vertical resolution, the radiometer often fails to capture a sharp temperature change, such as the shallow inversion layer (Ware et al., 2003; Löhnert and Maier, 2012). To overcome these limitations, several efforts such as combining with satellite observation (Westwater et al., 1985; Ho et al., 2002), utilizing other ground-based remote sensing instruments (Han and Westater, 1995; Löhnert et al., 2008, 2009), and combination with the numerical weather prediction (NWP) model (Gaussiat et al., 2007) have been applied with a considerable improvements, although not a consolidated approach has been established.

With the expected applications to the nowcasting and utilizations for the NWP model, Korea Meteorological Administration (KMA) has been operating nine ground-based radiometers since as early as April 2009. The observation sites are selected to complement the radar wind profilers which do not provide the vertical thermodynamic information. Although there have been several attempts to utilize the radiometers in research and operational applications (Ha et al., 2010; Jeon et al., 2008; Won et al., 2009), the application has been limited, partly due to the lack of the instrument characterization or little understanding of the derived products. Indeed, since the beginning of the operation a thorough investigation to characterize the measured data or to conduct a rigorous validation of the derived products have not been undertaken. Thus, for a better utilization, characterization of the instrument calibration through the analysis of the measured radiance data is highly desired.

Here, we apply a process to characterize the observation data, Tb, for a better understanding of the instrument calibration and to improve our understanding of the characteristics of the radiometer products. To take best advantage of the radiometers, i.e., easily operate them without human intervention, most of the radiometers are operated continuously without interruption except for when the regular absolute calibration should be conducted. On the other hand, as there is no additional reference instrument to be used for the direct comparison, we need to find an indirect approach to characterize the observation data. Thus, we utilized a vicarious calibration approach which compares the measured data with the reference data obtained from the simulation of a radiative transfer model (RTM). For the RTM simulation, we use the T and q profiles from the NWP models instead of radiosonde observation to overcome the limitation due to the small number of collocated radiosonde data and to test feasibility of the NWP models for such a purpose. Indeed, application of the NWP model results for the characterization of the radiometer data has been used before, although it is only done for unique occasions (Liljegren and Lesht, 1996; Cadeddu et al., 2013; Güldner, 2013).

The paper is organized as follows. Data used for the characterization along with the methodology including a short description of the radiative transfer model are introduced in Sect. 2. The comparison results between the measured and simulated Tbs are shown in Sect. 3, and the assessments of possible causes of the large bias between the simulated and measured Tb values are described in Sect. 4. The paper is summarized in Sect. 5 with a few lists of the planned future works.

To characterize the measured Tb, we first assess the comparability of the simulated Tb by using the T and q profiles obtained both from radiosonde and the NWP data at seven frequencies of the oxygen absorption band. After the assessment, the TbM values are compared with the simulated Tb values. To better understand the cloud effects on TbM, comparisons are made for both all sky and clear sky conditions.

As shown in Fig. 5, even after removal of data contaminated by the faulty calibration and cloud contamination, TbM and TbE show a clear offset from the one-to-one line with the relatively small variability, particularly at 52.28 and 53.86 GHz channels. The apparent systematic bias is also shown in the comparison made with other simulated data including radiosonde and KLAPS analysis in addition to the other similar microwave radiometers (Hewison et al., 2006; Löhnert and Maier, 2012); their plausible causes are traced to center-frequency offset among other sources of uncertainties such as the band pass effect, band width effect, and so on. However, as mentioned also in Löhnert and Maier (2012) and Maschwitz et al. (2013), the RPG radiometer (Generation 2) used for current study is known to have uncertainty of 1 MHz. Thus, for an assessment of the proposition of the frequency shift as the source of the systematic bias, a simple approach which searches a new frequency value that gives the least difference between TbM and TbE is applied followed by the error analysis.

To find the new frequency, we first select the clear sky TbM with free of the faulty absolute calibration to minimize uncertainties other than the frequency uncertainty. Then the NWP data corresponding to the observation time of the clear sky TbM are used to simulate the high-resolution (10 MHz interval) simulated Tb values as a function of frequency values. Here, it should be mentioned that the simulation resolution is coarser than the known uncertainty of 1 MHz. However, as the interested frequencies, 52.28 and 53.86 GHz, are in the valleys of absorption line centers (e.g., 52.021, 52.542, 53.066, 53.595, 54.130 GHz) where absorption is smooth with frequency due to the pressure broadening at the ground, the 10 MHz-interval simulation would give meaningful results. Once the simulated Tb spectrum is prepared, the TbM is used to find the best matching frequency value which gives the least difference between the TbM and simulated Tb. As the simulated Tb spectrum covers all four frequency channels, the best matching frequency for each channel is simultaneously found. Then, the difference between the known frequency value and newly found frequency value could be considered as the uncertainty in the center frequency. To increase the characterization accuracy, we use all of the selected data and also utilize both the ECMWF and KLAPS data to check the NWP model dependence of the frequency deviation. The results from all selected individual cases are used to derive the bias and variability and are summarized in Table 4.

From the Table 4, several interesting conclusions could be derived. First of all, the results from the two NWP models provide almost the same frequency shift and its variability, verifying the model inputs would not produce the systematic bias, at least for the current study. Secondly, to remove the Tb difference between the TbM and simulated Tb, a frequency shift of as much as -0.04 (0.02) GHz for the 52.28 (53.86) GHz channel is necessary. However, the uncertainties in the estimated frequency shift (given as the variability in the Table 4) show rather large values (as much as 0.03 GHz in the 52.28 GHz), compared to the known frequency uncertainties of about 1 MHz (Löhnert and Maier, 2012). Thus, to further check whether the Tb difference is indeed due to the frequency uncertainty, the characteristics of the newly derived TbE (or Tbnew), corresponding to the simulated Tb at the shifted frequency (for example, 52.24 GHz instead of the original 52.28 GHz), are compared to the TbM. Figure 6 shows the Tb differences, both the original Tb difference (black crosses) and the new Tb difference (i.e., TbM-Tbnew; red crosses), as a function of the TbM. With the shifted frequency, average of the Tb difference reduces to almost 0.0 at both channels. However, the variability and the slope (between the Tb differences vs. TbM) do not show any meaningful change that carries an important meaning. The non-zero slope shown in the original data seemed closely related with the frequency uncertainty (Wu and Yu, 2013; Ahn et al., 2015b), and thus it could be readily argued that the Tb difference is due to the frequency shift. In particular, when the spectral Tb is highly dependent on the frequency such as the case for the two lower channels, a small uncertainty in the center frequency produces a significant uncertainty in the estimated Tb (called frequency sensitivity). Furthermore, the frequency sensitivity increases with increasing target temperature, i.e. TbM, thus the frequency shift introduces the non-zero slope as shown in the original data (the black crosses). With the same argument, the frequency sensitivity of the new Tb difference should be reduced to 0 values (see the Fig. 5 of Ahn et al., 2015b) because the Tbnew is supposed to be free from the frequency uncertainty. However, as shown in Fig. 6, the red crosses have almost the same slope as the original data (the black crosses) implying the frequency shift does not remedy the large frequency sensitivity shown in the original Tb difference. It should also be noted that the variability of TbM-Tbnew does not show any improvement either from the original data (from 1.80 to 1.81 K for the 52.28 GHz channel). Thus, based on these two characteristics of the Tb difference, it should be concluded that the systematic bias shown in the two channels are not caused by the frequency uncertainty.

On the other hand, such a characteristic of the Tb difference is also known to be introduced by the uncertainties in the spectroscopic data and/or the uncertainty in the absolute calibration of the radiometer (Cimini et al., 2004; Hewison et al., 2006; Cadeddu et al., 2007; Maschwitz et al., 2013). Although it is not possible to provide definitive evidence toward one cause, several aspects of the Tb difference imply the root cause as the uncertainty in the spectroscopic data. First of all, the positive slopes shown in Fig. 6 are all consistent regardless of the input data used for the estimation of the Tbnew, i.e. positive slopes are shown when the simulated Tb is obtained with the KLAPS data. Exactly the same characteristics of the spectral sensitivity are also shown in the similar frequency channels used for the Radiometric TP/WVP-3000 instrument (see Fig. 1 of Hewison et al., 2006). Furthermore, the time series of the new Tb difference shown in Fig. 7 shows a clear seasonality and consequently the TbM dependence (less variable with negative bias during the wintertime and increased variability with a positive bias during the summer time). The data affected by the faulty absolute calibration further also shows that a calibration error introduces a much different shape of the spectral sensitivity (higher value during the winter time in the 52.28 GHz channel, with no seasonal feature in the 53.86 GHz channel; not shown). However, as described by Maschwitz et al. (2013) the calibration accuracy also decreases with decreasing TbM value, and thus for a detailed quantitative analysis, it is required to understand the definitive cause of the discrepancy.

Nine ground-based microwave sounding radiometers of KMA have been operating since 2010 without a rigorous sensor characterization. For a better utilization of the measurement data, a quality assessment process on the accuracy of the radiometers has been applied. The reference measurements for this, the simulated downwelling radiances (or Tbs), are prepared by the radiative transfer modeling with the T and q profiles from the NWP data instead of the conventional radiosonde observations. Before its application, the simulated Tb with the NWP data is validated with the simulated Tb with the limited number of radiosonde data. With the application of NWP data (from KLAPS and ECMWF), the study period could be extended, and 3 years of measurement data starting from the beginning of the radiometer observation at the Changwon Weather Station since 2010 are utilized.

Direct comparison between the simulated Tb and measured Tb revealed the three important characteristics associated with the instrument calibration. First of all, when the absolute calibration is not properly performed, the inter-comparison between the measured and simulated Tbs reveals a clear offset in the measured Tb, giving large values of bias and variability with the quite low number of correlation coefficient. Secondly, the clouds with an appreciable optical thickness introduce a significant uncertainty in the comparison results which require a solid cloud detection algorithm for a better characterization of the instrument calibration. Finally, with the removal of the data affected by the two important degrading components, the comparison results between the measured and simulated Tbs agree within the previously known accuracy, better than 1 K in the bias and variability, except at the two frequency channels, the 52.28 and 53.86 GHz channels which show the bias value of -2.6 and 1.0 K, respectively.

The proposition that uncertainty in the center frequency as the root cause of the bias is rejected by two characteristics shown in the Tb difference. First of all, the dependence of Tb difference on the measured Tb does not disappear even after a new center frequency is applied to derive the Tb difference. Secondly, the uncertainty of the necessary frequency shift to best match the measured Tb and simulated Tb is way too large compared to the known frequency uncertainty. Thus, instead of the frequency uncertainty, the plausible cause for the discrepancy is traced to the spectroscopy data used in the preparation of the simulated Tb, in view of the consistent spectral sensitivity shown with the simulated Tbs with different input profiles and results given by a different type of the radiometer along with the time series of the Tb difference. However, to determine the definitive cause of the discrepancy a detailed quantitative analysis is required.

Even with the limitation, it is clearly shown that the simulated Tb values using the readily available NWP data could well be used for the characterization of the radiometer. With the same application process, we plan to expand the assessment activities to the other radiometers being operated at other weather stations. Through the activities, overall quality of the measurement data along with the identification of necessary improvements for a better utilization of the instruments could be derived. Also, with the microwave sounding radiometer that is manufactured by a different company and has been operating at the same weather station for a limited period of time, we would be in better position to understand the issues related with the Tb difference, such as effects on the temperature retrieval. A similar approach, but with an additional care for the cloudy data, could be used for the water vapor channels. Finally, it should be mentioned here that even the characteristics of instrument calibration could be assessed with the NWP data, it would be always better to have a sufficient number of in situ observation data such as the radiosonde observation. This would be more important for the evaluation of the retrieval performance.