Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.

In order to conduct real-time quantitative monitoring of dust storms, Ka-band millimeter wave radar (MMWR) was utilized for the consecutive detection of dust storms over the Taklimakan Desert from April to June 2018. The retrievals of the reflectivity factor, dust spectrum distribution and dust mass concentration were carried out with the power spectrum data detected by MMWR for three dust storm processes. The analysis shows that: The probability density distribution of dust conforms to the lognormal distribution. During the dust storm processes, the effective detection height of the reflectivity factor was within 2000 m and the range of the reflectivity factors was between $-25\mathrm{dBZ}$ and $25\mathrm{dBZ}$ . During the floating dust period, the effective height of the dust spectrum distribution was lower than 300 m and the values of dust mass concentration were less than 31.62 $\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ , at a height of 200 m. Furthermore, during the blowing sand stage, the effective height of the dust spectrum distribution was normally lower than 600 m and the values of dust mass concentration were mainly less than $316.23\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ , at a height of 200 m. During the dust storm period, the effective height of the dust spectrum distribution exceeded 1000 m; when the height was 100 m, the values of dust mass concentration were between $1220\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}\mathrm{and}\mathrm{42,146}\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ and the average mass concentration was $9287\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ ; whereas, the values of dust mass concentration were between $2\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}\mathrm{and}820\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ when the height was 1200 m and the average mass concentration was $24\mathsf{\mu }\mathrm{g}·{\mathrm{m}}^{-3}$ . The relationship between the reflectivity factor Z and the dust mass concentration M is defined as $Z=651.6M^{0.796}$ . Compared with the observational data from Grimm180 particle detector, the data of the retrieved dust mass concentration are basically accurate and this retrieved method proves to be feasible. Thus, the MMWR cans be used as a new device for quantitative monitoring of dust storms.

To assess the quality of the retrieved products from ground-based microwave radiometers, the “clear-sky” Level-2 data (LV2) products (profiles of atmospheric temperature and humidity) filtered through a radiometer in Beijing during the 24 months from January 2010 to December 2011 were compared with radiosonde data. Evident differences were revealed. Therefore, this paper investigated an approach to calibrate the observed brightness temperatures by using the model-simulated brightness temperatures as a reference under clear-sky conditions. The simulation was completed with a radiative transfer model and National Centers for Environmental Prediction final analysis (NCEP FNL) data that are independent of the radiometer system. Then, the least-squares method was used to invert the calibrated brightness temperatures to the atmospheric temperature and humidity profiles. A comparison between the retrievals and radiosonde data showed that the calibration of the brightness temperature observations is necessary, and can improve the inversion of temperature and humidity profiles compared with the original LV2 products. Specifically, the consistency with radiosonde was clearly improved: the correlation coefficients are increased, especially, the correlation coefficient for water vapor density increased from 0.2 to 0.9 around the 3 km height; the bias decreased to nearly zero at each height; the RMSE (root of mean squared error) for temperature profile was decreased by more than 1 degree at most heights; the RMSE for water vapor density was decreased from greater than 4 g/m³ to less than 1.5 g/m³ at 1 km height; and the decrease at all other heights were also noticeable. In this paper, the evolution of a temperature inversion process is given as an example, using the high-temporal-resolution brightness temperature after quality control to obtain a temperature and humidity profile every two minutes. Therefore, the characteristics of temperature inversion that cannot be seen by conventional radiosonde data (twice daily) were obtained by radiometer. This greatly compensates for the limited temporal coverage of radiosonde data. The approach presented by this paper is a valuable reference for the reprocessing of the historical observations, which have been accumulated for years by less-calibrated radiometers.

Radar data are essential to convection nowcasting and nudging-based radar data assimilation through diabatic initialization is one of the most effective approaches for forecasting convective systems with numerical weather prediction (NWP) models, used at several advanced global weather centers. It is desired to assess the uncertainty and physical consistency of this assimilation process. This paper investigated impacts of relaxation coefficient, radar data update intervals and continuous assimilation time duration and addressed the key issues and possible solutions of the radar data assimilation based on the WRF hydrometeor and latent heat nudging (HLHN) developed at the National Center for Atmospheric Research (NCAR). It is revealed that excessively large relaxation coefficient forced the model to observations with a tendency greater than the physical terms of the convection, causing the dynamic imbalances and serious convection “ramp-down” right after the free forecast starts. Assimilating high update frequency radar data can make the tendency terms moderate and sustained thereby maintaining the assimilation effect and reducing fortuitous convection. HLHN requires a minimum continuous assimilation duration to contain the initial forced disturbance of the model. For a summer Meiyu precipitation case studied, the minimum duration is ~1 h. Appropriate selection of the HLHN parameters is able to effectively improve the temperature, humidity, and dynamic fields of the model. In addition, several issues still remain to be solved to further enhance HLHN.

To better understand how severe storms form and evolve in the outer rainbands of typhoons, in this study, we investigate the evolutionary characteristics and possible formation mechanisms for severe storms in the rainbands of Typhoon Mujigae, which occurred during 2–5 October 2015, based on the NCEP–NCAR reanalysis data, conventional observations, and Doppler radar data. For the rainbands far from the inner core (eye and eyewall) of Mujigae (distance of approximately 70–800 km), wind speed first increased with the radius expanding from the inner core, and then decreased as the radius continued to expand. The Rankine Vortex Model was used to explore such variations in wind speed. The areas of strong stormy rainbands were mainly located in the northeast quadrant of Mujigae, and overlapped with the areas of high winds within approximately 300–550 km away from the inner core, where the strong winds were conducive to the development of strong storms. A severe convective cell in the rainbands developed into waterspout at approximately 500 km to the northeast of the inner core, when Mujigae was strengthening before it made landfall. Two severe convective cells in the rainbands developed into two tornadoes at approximately 350 km to the northeast of the inner core after Mujigae made landfall. The radar echo bands enhanced to 60 dBZ when mesocyclones occurred in the rainbands and induced tornadoes. The radar echoes gradually weakened after the mesocyclones weakened. The tops of parent clouds of the mesocyclones elevated at first, and then suddenly dropped about 20 min before the tornadoes appeared. Thereby, the cloud top variation has the potential to be used as an early warning of tornado occurrence.