## 1 Introduction
The inversion procedures include (i) precise orbit determination and clock synchronization to eliminate the effects of the geometric Doppler and relative transmitter-receiver oscillator drift, (ii) the procedure to convert Doppler to bending angle, (iii) the extrapolation of the ionospheric correction into the lower troposphere, necessary because of the influence of the ionosphere on measured phase delay of GPS signals, (iv) the initialization of the Abelian integral transform which converts atmospheric bending angles to profiles of refractivity, (v) variational algorithm to invert the refractivity profiles into the temperature and moisture profiles in the troposphere and lower stratosphere, and (vi) the quality control (QC) algorithm used to distinguish acceptable data from unacceptable data. The general descriptions of the CDAAC inversion procedures, including implementation methodologies, are detailed in [21; 22; 23]. This inversion package's maturity has been extensively tested by numerous validation studies (also see [22; 23]) using COSMIC data. Ref. [1] has summarized the potential applications using COSMIC-2 for climate and NWP, and space weather. Before applying COSMIC-2 data for science studies and operational applications, it is critically important to carefully quantify COSMIC-2 data quality. This paper aims to assess (i) COSMIC-2 data characteristics in terms of their penetration, precision, and stability, and (ii) COSMIC-2 data quality in terms of accuracy and uncertainty of the accuracy for COSMIC-2 neutral atmospheric profiles. This study will use the first few weeks of data to validate COSMIC-2 precision when six COSMIC-2 flight modules were close in time and locations. We will also use consistently processed GPS RO refractivity profiles from different RO missions, including COSMIC, KOMPSAT-5, MetOp-A, -B GRAS, and TerraSAR-X (TSX), to examine the stability of COSMIC-2. We will compare the precision and stability of COSMIC-2 to those of COSMIC. To demonstrate the COSMIC-2 data quality, we will assess the accuracy of the COSMIC-2 bending angle, refractivity, temperature, and moisture profiles in this study. We will use Vaisala RS41 (fourth generation) radiosonde observation (RAOB) to quantify the quality of COSMIC-2 neutral atmospheric profiles. RS41 was introduced in the fall of 2013 to replace the RS92-SGP radiosondes. With an advanced measurement technology compared to those from RS92, RS41 radiosondes provide the most accurate atmospheric temperature, humidity, and pressure currently available with an improved precision ([24], also see Section 2.4). We will assess the accuracy and uncertainty of the COSMIC-2 temperature in the lower stratosphere, moisture in the troposphere, and refractivity profiles in the stratosphere and troposphere. The causes of errors in the specific inversion step listed above are not the focus of this study. In this study, we also focus on investigating whether the high Signal-to-Noise ratio (SNR) from COSMIC-2 measurements will reduce retrieval errors. As shown in [1], one of the remaining challenges for using COSMIC (and other RO missions) is to detect and mitigate their negative refractivity biases owing to the super-refraction (SR) in the lower troposphere. Ref. [16] (see Figure 2 in [16]) depicts that the COSMIC-1 refractivity is biased negatively comparing with collocated European Centre for Medium-Range Weather Forecasts (ECMWF) global analysis. To improve the gain and signal quality, COSMIC-2 is with a significantly increased SNR compared to other RO missions. Figure 1 depicts the histograms of the normalized accumulation (in %) of L1 SNR, which is computed from the average SNR values from 60 km to 80 km geometric height range of the L1 signal. Figure 1 shows COSMIC-2 L1 SNR for both GPS and GLONASS. COSMIC-2 SNR ranges from 200 \(\
icefrac{{\eta}}{{\var b}}\) to about 2800 \(\
icefrac{{\eta}}{{\var b}}\) with a mean SNR of 1100 \(\
icefrac{{\eta}}{{\var b}}\) for GLONASS and a mean SNR of 1250 \(\
icefrac{{\eta}}{{\var b}}\) for GPS.