The threshold method is commonly used to determine cloud in a sky image. This paper evaluates the method by numerical simulation and shows that the aerosol optical depth (AOD) is a key factor that influences the accuracy. Particularly when the visibility is low, a single threshold method is inappropriate. To improve the accuracy of cloud determination from low-visibility sky images, an integrated cloud-determination algorithm is presented that is based on the fast Fourier transform, symmetrical image features, and threshold methods. The preliminary comparison tests show that the new integrated method improves the ability to determine cloud under lower-visibility conditions.

Estimating cloud-base height (CBH) from satellite-based passive imaging radiometers is very challenging because the conventional visible and infrared radiance measured by satellite cannot be employed to profile clouds from top to base. The Advanced Himawari Imager (AHI) aboard Himawari-8 is the first of Japanese new-generation visible-infrared imaging spectroradiometer with an improved temporal and spatial resolution. A CBH estimation algorithm for Himawari-8 is imminently needed for aviation safety and climate research. This study presents a machine learning methodology for CBH estimation from Himawari-8 data using a random forest (RF) algorithm, which was conducted for the daytime focusing on single-layer clouds. About 2,876,230 matchups in the period from August 2015 to July 2016 are used in both RF training and validation processes for the purpose of learning the relationship between inputs from Himawari-8 Level-2 cloud products together with geolocation information and target CBH from CloudSat/CALIPSO combined measurements. With the trained RF model, the CBH prediction can be independently generated using Himawari-8 upstream products. Data of ground-based K_a radar in Beijing during 2017 and 2019 are used for independent validation. The results indicate that the Himawari-8 CBH estimation agrees well with those of the K_a radar. The CBH estimations for single-layer nonprecipitation clouds show the best performance with a mean difference of −0.2 ± 1.7 km. The presence of multi-layer clouds mostly leads to overestimated CBHs relative to the radar values, particularly for situations that cirrus clouds overlapping clouds at low-level. Since the ability of the K_a radar is limited to retrieve CBH in precipitation cases, significant overestimation also occurs for precipitation clouds with mean difference of 1.7 ± 2.4 km.

High-resolution and accurate rainfall monitoring is of great importance to many applications, including meteorology, hydrology, and flood monitoring. In recent years, microwave backhaul links from wireless communication networks have been suggested for rainfall monitoring purposes, complementing the existing monitoring systems. With the advances in microwave technology, new microwave backhaul solutions have been proposed and applied for 5G networks. Examples of the latest microwave technology include E-band (71–76 and 81–86 GHz) links, multi-band boosters, and line-of-sight multiple-input multiple-output (LOS-MIMO) backhaul links. They all rely on millimeter-wave (mmWave) technology, which is the fastest small-cell backhaul solution. In this paper, we will study the rain attenuation characteristics of these new microwave backhaul techniques at different mmWave frequencies and link lengths. We will also study the potential of using these new microwave solutions for rainfall monitoring. Preliminary results indicate that all the test mmWave links can be very effective for estimating the path-averaged rain rates. The correlation between the mmWave link measurement-derived rain rate and the local rain gauge is in the range of 0.8 to 0.9, showing a great potential to use these links for precipitation and flood monitoring in urban areas.

To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing are compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs are found to be underestimated by−1.10 ± 2.53 km on average and 49 % of CTH differences are within 1.0 km. The AHI CTHs are underestimated by −1.10 ± 2.27 km and 42 % are within 1.0 km. Both the MODIS and AHI CTH retrieval accuracy depends strongly on the cloud depth (CD). Large differences are mainly due to the retrieval of thin clouds of CD <1 km, especially when the cloud base height is higher than 4 km. For clouds with CD >1 km, the mean CTH difference decreases to $-\mathrm{0.48}\pm \mathrm{1.70}$ km for MODIS and to $-\mathrm{0.76}\pm \mathrm{1.63}$ km for AHI. It is found that MODIS CTHs with higher values (i.e. >6 km) show smaller discrepancy with radar CTH than those MODIS CTHs with lower values (i.e. <4 km). Statistical analysis illustrate that the CTH difference between the two satellite instruments is lower than the difference between the satellite instrument and the ground-based Ka-band radar. The monthly accuracy of both CTH retrieval algorithms is investigated and it is found that summer has the smallest retrieval difference.

Naked-eye observation of cloud cover has widely resisted automation. Replacement of human observation by instruments is an inexorable trend for the development of ground-based macroscopic cloud observation. In this paper, cloud covers from an all-sky imager (ASI) are compared with those from a meteorological observer (MO) through field experiments performed at three sites in China. The correlation coefficient between ASI and MO is 0.77 for all cases. The ASI cloud fractions have great agreement with MO for clear sky, overcast sky, and sky loaded with low- and middle-level clouds. About 78% of the ASI cases had deviations between ±1 tenth compared to MO cloud cover. High-level cloud (or aerosol) is the main reason causing this difference. It is partially due to MO, who takes aerosol as high, thin cloud. Another reason might be that ASI made a wrong estimation for high-level cloud (or aerosol) because of its detector and the cloud-determination algorithm. Distinguishing high, thin cloud from aerosol is a challenge, and is the main problem that needs to be resolved for future developments of ASI. A new, improved method is discussed at the end of this paper.

Nowcasting is a critical technology for disaster prevention and mitigation, and the accuracy of radar echo extrapolation directly impacts forecasting performance. In most deep learning-based models, accurately predicting heavy precipitation remains a challenging task. Focusing on the region of China, this study proposes an improved model based on residual and attention mechanisms—RA-UNet—for precipitation nowcasting with a lead time of 3 h. The model introduces the residual neural network (ResNet) and the convolutional block attention module (CBAM) to integrate multi-scale features into the U-Net encoder–decoder architecture, enhancing its ability to capture the spatiotemporal evolution of precipitation systems. Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. To evaluate the model’s performance, experiments were conducted using 6 min resolution radar echo data from China in 2024, with comparisons made against the optical flow (OF) method and the U-Net model. The experimental results show that RA-UNet demonstrates significant advantages in 3 h forecasting: its mean absolute error (MAE) is reduced by approximately 7%, the false alarm rate (FAR) decreases by about 20%, and it outperforms the comparison models in metrics such as the critical success index (CSI) and structural similarity index (SSIM). Notably, RA-UNet effectively mitigates intensity degradation in long-term forecasts, successfully predicting the trend of >40 dBZ strong echo cores in two typical cases and significantly improving the premature dissipation problem of precipitation fields. This study provides a new approach to refined forecasting of complex precipitation systems, and future work will combine multi-source data fusion with physical constraint mechanisms to further enhance precipitation event prediction capabilities.