Abstract. The potential impacts of dust aerosols and atmospheric convective available potential energy (CAPE) on the vertical development of precipitating clouds in southeastern China (20–30∘ N, 110–125∘ E) in June, July, and August from 2000 to 2013 were studied using multisource observations. In southeastern China, heavy-dust conditions are coupled with strong northerly winds that transport air masses containing high concentrations of mineral dust particles, with cold temperatures, and with strong wind shear. This leads to weaker CAPE on dusty days compared with that on pristine days. Based on satellite observations, precipitating drops under dusty conditions grow faster in the middle atmospheric layers (with a temperature of between −5 and +2 ∘C) but slower in the upper and lower layers compared with their pristine counterparts. For a given precipitation top height (PTH), the precipitation rate under dusty conditions is lower in the upper layer but higher in the middle and lower layers. Moreover, the associated latent heating rate released by precipitation in the middle layer is higher. The precipitation top temperature (PTT) shows a fairly good linear relationship with the near-surface rain rate (NSRR): the linear regression slope between the PTT and NSRR is stable under dusty and pristine conditions. However, the PTT0 (the PTT related to rain onset) at the onset of precipitation is highly affected by both the CAPE and aerosol conditions. On pristine days, a stronger CAPE facilitates the vertical development of precipitation and leads to a decrease in PTT0, at a rate of −0.65 ∘C per 100 J kg−1 of CAPE for deep convective precipitation (with a variation of 15 %) and at a rate of −0.41 ∘C per 100 J kg−1 of CAPE for stratiform precipitation (with variation of 12 %). After removing the impacts of CAPE on PTT, dust aerosols led to an increase in PTT0, at a rate of +4.19 ∘C per unit aerosol optical depth (AOD) for deep convective precipitation and at a rate of +0.35 ∘C per unit AOD for stratiform precipitation. This study showed clear evidence that meteorological conditions and aerosol conditions combine to impact the vertical development of precipitation clouds. A quantitative estimation of the sensitivity of PTT to CAPE and dust was also provided.

A heavy dust storm originating in Mongolia and Inner Mongolia traveled to Northeast China and met a midlatitude frontal system on May 3, 2017. The potential ice nuclei (IN) effects of mineral dust aerosols on the vertical structure of clouds, precipitation, and latent heat (LH) were studied using Global Precipitation Mission (GPM) satellite observations and Weather Research and Forecasting (WRF) model simulations. The WRF simulations correctly captured the main features of the system, and the surface rain rate distribution was positively correlated with data retrieved from the GPM Microwave Imager. Moreover, the correlation coefficient increased from 0.31 to 0.54 with increasing moving average window size. The WRF-simulated rainfall vertical profiles are generally comparable to the GPM Dual-Frequency Precipitation Radar (DPR) observations, particularly in low layers. The joint probability distribution functions of the rain rate at different altitudes from the WRF simulation and GPM observations show high positive correlation coefficients of ~0.80, indicating that the assumptions regarding the raindrop size distribution in the WRF model and DPR retrieval were consistent. Atmospheric circulation analysis and aerosol optical depth observations from the Himawari-8 satellite indicated that the dust storm entered only a narrow strip of the northwest edge of the frontal precipitation system. The WRF simulations showed that in carefully selected areas of heavy dust, dust can enhance the heterogeneous ice nucleation process and increase the cloud ice, snowfall, high-altitude precipitation rate, and LH rate in the upper layers. This effect is significant at temperatures of −15 °C to −38 °C and requires dust number concentrations exceeding 10⁶ m⁻³. It is important to accurately classify the dusty region in this type of case study. In the selected vertical cross section, the WRF-simulated and DPR-retrieved LH have comparable vertical shapes and amplitudes. Both results reflect the structure of the tilted frontal surface, with positive LH above it and negative LH below it. The simulated area-averaged LH profiles show positive heating in the entire column, which is a convective-dominated region, and this feature is not significantly affected by dust. DPR-based LH profiles show stratiform-dominated or convective-dominated shapes, depending on the DPR retrieval product.

The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially Low Cloud Base Height(LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm (RHs-CCL) for estimating LCBH by combining relative humidity threshold methods with Convective Condensation Level(CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: (1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average and the error of RHs-CCL is smaller than those of existing algorithms with the minimum mean absolute error and root mean square error at the four airports studies can reach 243m and 321m,(2) The bias score of RHs-CCL is 0.97 on average and low clouds classification utilizing RHs-CCL attains the highest accuracy up to 86%, (3) The errors of ERA5 CBH are the largest compared with the others, (4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models.

In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud’s top by about 300 m and underestimates the error of the cloud’s area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in Z=134.59·R1.184 (sector) and Z=127.35·R1.221 (dendrite) below −40 °C.