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Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon and heavy rain cases are slightly better than for squall-line cases. [ABSTRACT FROM AUTHOR]

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 for estimating LCBH by combining relative humidity (RH) threshold methods with convective condensation level (CCL) (RHs-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 being able to reach 243 and 321 m. 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 when 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. Significance Statement: The new algorithm developed in this study can accurately estimate low cloud-base heights from vertical profiles of atmospheric variables. It provides us a much more computationally efficient approach for predicting low cloud-base height relative to running cloud models, which is critical for weather forecasting at locations lacking computational resources and/or cloud modeling capability. In areas such as Southwest China, low clouds are very common, and they pose major threats to aviation safety. The new algorithm has been successfully integrated into the daily operation at Guiyang Airport in Southwest China and demonstrated excellent skills in estimating cloud-base heights. The implementation of the algorithm in aviation forecasting over a broader region is on the horizon. [ABSTRACT FROM AUTHOR]

The coarseModerate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta--Fujian--Pearl River delta (YRD--FJ--PRD) region of China. A first-stage random forest--integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest--derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD--FJ--PRD region, and we achieved a site-based cross validation (CV) R² of 0.87 and a time-based CV R² of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 µg m-3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas. [ABSTRACT FROM AUTHOR]

Investigations of variations and causes of near-surface wind speed (NWS) further understanding of atmospheric changes and improve the ability of climate analysis and projections. NWS varies on multiple temporal scales; however, the centennial-scale variability in NWS and associated causes over China remains unknown. In this study, we employ the European Centre for Medium-Range Weather Forecasts (ECMWF) Twentieth Century Reanalysis (ERA-20C) to study the centennial-scale changes in NWS from 1900 to 2010. Meanwhile, a forward stepwise regression algorithm is used to reveal the relationships between NWS and large-scale ocean–atmosphere circulations. The results show three unique periods in annual mean NWS over China from 1900 to 2010. The annual mean NWS displayed decreasing trends of −0.87% and −11.75% decade−1 from 1900 to 1925 and from 1957 to 2010, respectively, which were caused by the decreases in the days with strong winds, with trends of −6.64 and −4.66 days decade−1, respectively. The annual mean NWS showed an upward trend of 55.47% decade−1 from 1926 to 1956, which was caused by increases in the days with moderate (0.43 days decade−1) and strong winds (23.55 days decade−1). The reconstructed wind speeds based on forward stepwise regression algorithm matched well with the original wind speeds; therefore, the decadal changes in NWS over China at the centennial scale were mainly induced by large-scale ocean–atmosphere circulations, with the total explanation power of 66%. The strongest explanation power was found in winter (74%), and the weakest explanation power was found in summer (46%). [ABSTRACT FROM AUTHOR]

This study elucidates drought characteristics in China during 1980-2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized precipitation-evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998-2015 compared with those during 1980-97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the threedimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity-area-duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEIdetected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km² and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km² and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the most important factors affecting climate and the environment. Routine measurements of irradiance are valuable for climate change research because of long time series and areal coverage. In this study, a set of quality assessment (QA) algorithms is used to test the quality of daily solar global, direct, and diffuse radiation measurements taken at 122 observatories in China during 1957–2000. The QA algorithms include a physical threshold test (QA1), a global radiation sunshine duration test (QA2), and a standard deviation test applied to time series of annually averaged solar global radiation (QA3). The results show that the percentages of global, direct, and diffuse solar radiation data that fail to pass QA1 are 3.07%, 0.01%, and 2.52%, respectively; the percentages of global solar radiation data that fail to pass the QA2 and QA3 are 0.77% and 0.49%, respectively. The method implemented by the Global Energy Balance Archive is also applied to check the data quality of solar radiation in China. Of the 84 stations with a time series longer that 20 yr, suspect data at 35 of the sites were found. Based on data that passed the QA tests, trends in ground solar radiation and the effect of the data quality assessment on the trends are analyzed. There is a decrease in ground solar global and direct radiation in China over the years under study. Although the quality assessment process has significant effects on the data from individual stations and/or time periods, it does not affect the long-term trends in the data. [ABSTRACT FROM AUTHOR]