Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations

Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.

Investigation of precipitation characteristics under the action of acoustic waves in the source region of the Yellow River

Acoustic agglomerations have increasingly attracted widespread attention as a cost-effective and environmentally friendly approach for fog removal and weather modification. In this study, research on precipitation interference and the agglomeration performance of droplet aerosols under large-scale acoustic waves was presented. In total, 49 field experiments in the source region of the Yellow River (SRYR) in the summer of 2019 were performed to reveal the influences of acoustic waves on precipitation, such as the radar reflectivity factor (Z), rain rate (R), and raindrop size distribution (DSD). A monitoring system that consisted of rain gauges and raindrop spectrometers was employed to monitor near-ground rainfall within a 5 km radius of the field site. The ground-based observations showed that acoustic waves could significantly affect the rainfall distribution and microstructure of precipitation particles. The average values of rainfall increased by 18.98%, 10.61%, and 8.74% within 2 km, 3 km and 5 km of the operation center with acoustic application. The changing trend of microphysical parameters of precipitation was roughly in line with variation of acoustic waves for stratiform cloud. Moreover, there was a good quadratic relationship between the spectral parameters λ and μ. Raindrop kinetic energy (eK) and the radar reflectivity factor (Z) both exhibited a power function relationship with the rain rate (R).

Analysis of the Vertical Air Motions and Raindrop Size Distribution Retrievals of a Squall Line Based on Cloud Radar Doppler Spectral Density Data

A squall line is a type of strongly organized mesoscale convective system that can cause severe weather disasters. Thus, it is crucial to explore the dynamic structure and hydrometeor distributions in squall lines. This study analyzed a squall line over Guangdong Province on 6 May 2016 that was observed using a Ka-band millimeter-wave cloud radar (CR) and an S-band dual-polarization radar (PR). Doppler spectral density data obtained by the CR were used to retrieve the vertical air motions and raindrop size distribution (DSD). The results showed the following: First, the CR detected detailed vertical profiles and their evolution before and during the squall line passage. In the convection time segment (segment B), heavy rain existed with a reflectivity factor exceeding 35 dBZ and a velocity spectrum width exceeding 1.3 m s−1. In the PR detection, the differential reflectivity factor (Zdr) was 1–2 dB, and the large specific differential phase (Kdp) also represented large liquid water content. In the transition and stratiform cloud time segments (segments B and C), the rain stabilized gradually, with decreasing cloud tops, stable precipitation, and a 0 °C layer bright band. Smaller Kdp values (less than 0.9) were distributed around the 0 °C layer, which may have been caused by the melting of ice crystal particles. Second, from the CR-retrieved vertical air velocity, before squall line passage, downdrafts dominated in local convection and weak updrafts existed in higher-altitude altostratus clouds. In segment B, the updraft air velocity reached more than 8 m s−1 below the 0 °C layer. From segments C to D, the updrafts changed gradually into weak and wide-ranging downdrafts. Third, in the comparison of DSD values retrieved at 1.5 km and DSD values on the ground, the retrieved DSD line was lower than the disdrometer, the overall magnitude of the DSD retrieved was smaller, and the difference decreased from segments C to D. The standardized intercept parameter (Nw) and shape parameter (μ) of the DSD retrieved at 1.8 km showed good agreement with the disdrometer results, and the mass-weighted mean diameter (Dm) was smaller than that on the ground, but very close to the PR-retrieved Dm result at 2 km. Therefore, comparing with the DSD retrieved at around 2 km, the overall number concentration remained unchanged and Dm got larger on the ground, possibly reflecting the process of raindrop coalescence. Lastly, the average vertical profiles of several quantities in all segments showed that, first of all, the decrease of Nw and Dm with height in segments C and D was similar, reflecting the collision effect of falling raindrops. The trends were opposite in segment B, indicating that raindrops underwent intense mixing and rapid collision and growth in this segment. Then, PR-retrieved Dm profiles can verify the rationality of the CR-retrieved Dm. Finally, a vertical velocity profile peak generated a larger Dm especially in segments C and D.

Smaller land-sea contrast on probability of precipitation (POP) of warm clouds over globe

<p>Proper observation of global warm rain and understanding of its formation processes can significantly advance our understanding on aerosol-cloud-precipitation interactions. Previous study shows that due to smaller cloud effective radii (Re), rain from liquid clouds over land is sharply reduced compared to oceans (Mülmenstädt, 2015). However, in our study, we use A-Train satellite observations to show that there should be smaller land-sea difference on probability of precipitation (POP) of warm clouds between land and oceans. The discrepancy is probably because the algorithm bias in CloudSat precipitation flag products over land, which may mistakenly treat drizzle as no rain. We also find that if Re is smaller than 14 mm, no matter how thick the warm cloud is it can hardly produce significant precipitation (here defined as radar reflectivity factor lager than 0dBZ), which can generate dynamic feedback on the development of clouds.</p>

Application of radar data assimilation on convective precipitation forecasts based on water vapor retrieval

Based on a short-time heavy rainfall in Anhui and the weather research and forecasting (WRF) model, the water vapor in the initial field of the model is retrieved using the statistical relationships of the reflectivity factor from the Doppler weather radar with the relative humidity and hydrometeor. Three-dimensional variational (3DVAR) assimilation method is used to assimilate the radar reflectivity factor and radial velocity, and then the impact of assimilating retrieved water vapor on the analysis and forecast of the torrential rain is assessed. The results show that, after assimilating the retrieved water vapor, the water vapor field in the model is significantly improved. The water vapor content in the middle layer of the model in the analyzed field is increased, corresponding well with the convective region. Meanwhile, the precipitation distribution during this weather process is successfully simulated. The mesoscale characteristics are better presented by the imageries of radar reflectivity factor, and false echoes are partially reduced. Besides, the prediction of short-time heavy rainfall regions is closer to the actual observations. After assimilating the retrieved water vapor, the simulated one-hour accumulated rainfall is closer to the actual observation, and the fraction skill score (FSS) is higher.

Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars

The quality of radar data is crucial for its application. In particular, before radar mosaic and quantitative precipitation estimation (QPE) can be conducted, it is necessary to know the quality of polarimetric parameters. The parameters include the horizontal reflectivity factor, ZH; the differential reflectivity factor, ZDR; the specific differential phase, KDP; and the correlation coefficient, ρHV. A novel radar data quality index (RQI) is specifically developed for the Chinese polarimetric radars. Not only the influences of partial beam blockages and bright band upon radar data quality, but also those of bright band correction performance, signal-to-noise ratio, and non-precipitation echoes are considered in the index. RQI can quantitatively describe the quality of various polarimetric parameters. A new radar mosaic QPE algorithm based on RQI is presented in this study, which can be used in different regions with the default values adjusted according to the characteristics of local radar. RQI in this algorithm is widely used for high-quality polarimetric radar data screening and mosaic data merging. Bright band correction is also performed to errors of polarimetric parameters caused by melting ice particles for warm seasons in this algorithm. This algorithm is validated by using nine rainfall events in Guangdong province, China. Major conclusions are as follows. ZH, ZDR, and KDP in bright band become closer to those under bright band after correction than before. However, the influence of KDP correction upon QPE is not as good as that of ZH and ZDR correction in bright band. Only ZH and ZDR are used to estimate precipitation in the bright band affected area. The new mosaic QPE algorithm can improve QPE performances not only in the beam blocked areas and the bright band affected area, which are far from radars, but also in areas close to the two radars. The sensitivity tests show the new algorithm can perform well and stably for any type of precipitation occurred in warm seasons. This algorithm lays a foundation for regional polarimetric radar mosaic precipitation estimation in China.

Observed Microphysical Characteristics of Stratiform and Convective Precipitation over an Inland Arid Region of the Qinghai–Tibet Plateau

This study analyzed the microphysical characteristics of stratiform and convective precipitation over an inland arid region of Qinghai–Tibet Plateau in summer for the first time. The observed precipitation data were from the OTT Parsivel2 laser raindrop spectrometer and the raindrop size distribution can be described by a gamma distribution and a general exponential distribution. The results indicate that: (1) compared to the exponential distribution, the gamma distribution is the better function with which to describe the raindrop size distribution in this region; (2) the raindrop sizes are mainly below 1 mm, and the raindrop sizes which contribute most to the rainfall intensity are below 2 mm for stratiform precipitation and convective precipitation; (3) the mean values of microphysical parameters, e.g., rainfall intensity, radar reflectivity factor, and liquid water content, are higher for convective precipitation than stratiform precipitation; and (4) the standard Z–R relationship underestimates the radar reflectivity factor in this region. Overall, the obtained results will enhance our understanding and facilitate future studies regarding the microphysical characteristics of precipitation in such regions. For example, the obtained Z–R relationship can be a reference for estimating the radar reflectivity factor in this region with higher accuracy.