Potential for long-range regional precipitation prediction over India

The potential for long-range precipitation prediction over the Indian monsoon region is generally good where climate noise (i.e., variability due to daily weather fluctuations) is small as compared to the climate signal (i.e., variability due to year to year fluctuations in monthly/seasonal means) being in the tropical belt. In order to understand the potential on smaller spatial scales, the ratios of inter-annual variability to that associated with climate noise have been computed for precipitation of four seasons as well as SW monsoon sub-seasons/months over 1656 stations in the Indian subcontinent. Precipitation in SW monsoon has been found potentially predictable on seasonal as well as intra-seasonal scale. The west coast and contiguous northwest India, part of the 'northeast India are more predictable. Potential for long-range prediction over northwest India is highest during the active monsoon period from July to September. Over eastern peninsula potential for prediction is generally found low whereas over north-central India it is always moderate. Over northern latitudes precipitation due to western disturbances during January to May is potentially predictable. Precipitation over southeast India and Sri Lanka during October to February due to northeast (NE) monsoon shows good potential for long-range prediction. It is manifested that long-range precipitation forecasting schemes for SW monsoon season, sub-seasons and months and for the other seasons over India on point to regional scale have good scope by taking into account the potential predictability at the individual stations as well as at contiguous resemblance areas over the country.

Characteristics of Vapor Based on Complex Networks in China

The uneven spatial distribution of stations providing precipitable water vapor (PWV) observations in China hinders the effective use of these data in data assimilation, nowcasting, and prediction. . In this study, we propose a complex network framework for exploring the topological structure and the collective behavior of PWV in mainland China. We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up undirected and directed complex networks, respectively. Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations. Specifically, the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other (the common interaction mode for vapor stations and their locations). The betweenness results displayed different features. The largest betweenness ratio for directed networks tended to be larger than that of undirected networks, implying the transfer of directed PWV networks was more efficient than that of undirected networks. The findings of this study are heuristic and will be useful for constructing the best strategy for the application of PWV data in applications such as vapor observational networks design and precipitation prediction.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations

Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation nowcasting performance. Moreover, it has been known that multi-modal information from various sources could improve deep learning performance. This study introduces the RAIN-F+ dataset, which is the fusion dataset for rainfall prediction, and proposes the benchmark models for precipitation prediction using the RAIN-F+ dataset. The RAIN-F+ dataset is an integrated weather observation dataset including radar, surface station, and satellite observations covering the land area over the Korean Peninsula. The benchmark model is developed based on the U-Net architecture with residual upsampling and downsampling blocks. We examine the results depending on the number of the integrated dataset for training. Overall, the results show that the fusion dataset outperforms the radar-only dataset over time. Moreover, the results with the radar-only dataset show the limitations in predicting heavy rainfall over 10 mm/h. This suggests that the various information from multi-modality is crucial for precipitation nowcasting when applying the deep learning method.

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.