We compare the precipitable water vapor (PWV) determined using a domestic ground-based microwave radiometer (MWR PWV) with PWV measurements from radiosondes (RS PWV), the Global Navigation Satellite System (GNSS PWV), and reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) (EC PWV). The MWR PWV is affected by precipitation, and thus it differs greatly from the other three observations. The correlation coefficient between the MWR PWV and RS PWV (EC PWV) is 0.934 (0.933), and the root mean square error (RMSE) is 17.19 mm (16.05 mm), whereas the correlation coefficient between the GNSS PWV and RS PWV (EC PWV) is 0.989 (0.986), and the RMSE is 17.04 mm (15.83 mm). The scatter distributions of the MWR PWV and the other observations show a systematic deviation that is negatively correlated with the surface air temperature. After polynomial fitting and corrections are applied, the correlation coefficients between the MWR PWV and the RS PWV and EC PWV increase to 0.993 and 0.99, and the RMSEs decrease to 14.13 and 15.86 mm, respectively. The temperature and water vapor density profiles are retrieved from the bright temperature and can reflect the quality of the bright temperature. Because the PWV retrieved from the ground-based MWR has a linear relationship with the brightness temperature, the accuracy of the PWV can be analyzed in terms of the quality of the brightness temperature. We found that the differences in the temperature profile below 2000 m are smaller, whereas those in the water vapor density profile below 2000 m show the largest difference. This finding reflects the differences in the brightness temperature, which may be the cause of the inaccurate PWV observations. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.
In this study, the temperature and relative humidity profiles retrieved from five ground-based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall performed best by assimilating the ground-based microwave radiometer profiles.
An improved algorithm to calculate cloud-base height (CBH) from infrared temperature sensor (IRT) observations that accompany a microwave radiometer was described, the results of which were compared with the CBHs derived from ground-based millimeter-wavelength cloud radar reflectivity data. The results were superior to the original CBH product of IRT and closer to the cloud radar data, which could be used as a reference for comparative analysis and synergistic cloud measurements. Based on the data obtained by these two kinds of instruments for the same period (January–December 2016) from the Beijing Nanjiao Weather Observatory, the results showed that the consistency of cloud detection was good and that the consistency rate between the two datasets was 81.6%. The correlation coefficient between the two CBH datasets reached 0.62, based on 73 545 samples, and the average difference was 0.1 km. Higher correlations were obtained for thicker clouds with a larger echo intensity. A low-level thin cloud cannot be regarded as a blackbody because of its high transmittance, which results in higher CBHs derived from IRT data. Because of a smaller cloud radiation effect for high-level thin cloud above 8 km, the contribution of the atmospheric downward radiation below the cloud base to the IRT cannot be ignored, as it results in lower CBHs derived from IRT data. Owing to the seasonal variation of atmospheric downward radiation reaching the IRT, the difference between the two CBHs also has a seasonal variation. The IRT CBHs are generally higher (lower) than the cloud radar CBHs in winter (summer).
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