Percentage occurrence of global tilted deep convective clouds under strong vertical wind shear

Sharma, Neerja; Varma, A. K.; Liu, Guosheng

doi:10.1016/j.asr.2021.12.040

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…To analyze the effect of meteorological vertical structure on air pollution in Hefei, VWS and gradient of temperature must be computed in the beginning. Therefore, VWS is calculated as follows [60]:…”

Section: Calculation Of Vertical Wind Shear and Temperature Gradientmentioning

confidence: 99%

The Effects of Planetary Boundary Layer Features on Air Pollution Based on ERA5 Data in East China

et al. 2023

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The planetary boundary layer (PBL) structure and its evolution can significantly affect air pollution. Here, the PBL’s characteristics and their association with air pollution were analyzed in Hefei, east China, using ERA5 reanalysis data, weather observations and air pollutant measurements from 2016 to 2021. In the near-surface level, air pollution was directly influenced by ground meteorological conditions, and high PM2.5 was normally related to weak wind speed, northwest wind anomalies, low temperature and high relative humidity. Moreover, in the trajectory analysis, air masses from the north and the northwest with short length played an important role in the high PM2.5 with pollutant transport within the PBL. Furthermore, high PM2.5 showed a tight dependence on PBL stratification. There was high temperature and relative humidity and low wind speed and PBL height within all PBL altitudes in the polluted condition. Notably, vertical wind shear (VWS) and temperature gradient tended to be much weaker below 900 hPa, which created a deeply stable stratification that acted as a cap to upward-moving air. Such a PBL structure facilitated more stable stratification and enhanced the generation of air pollution. Finally, the stable stratification in the PBL was related to the special synoptic configuration for the high PM2.5 conditions, which included the block situation at the high level, the southerly wind anomalies at the middle level and the wild range of the uniform pressure field at the near-ground level. Therefore, air pollutant concentrations were regulated by ground factors, PBL structure and the synoptic situation. Our results provide a precise understanding of the role of PBL features in air pollution, which contributes to improving the assimilation method of the atmospheric chemistry model in east China.

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Section: Calculation Of Vertical Wind Shear and Temperature Gradientmentioning

confidence: 99%

The Effects of Planetary Boundary Layer Features on Air Pollution Based on ERA5 Data in East China

et al. 2023

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“…Cloud research is paramount in atmospheric and meteorological systems. The recognition of severe convective clouds has always been important in researching meteorological disaster prevention [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Severe Convective Cloud Based on the Cloud Image Prediction Sequence from FY-4A

Chen,

Yin,

et al. 2023

Remote Sensing

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Severe convective weather is hugely destructive, causing significant loss of life and social and economic infrastructure. Based on the U-Net network with the attention mechanism, the recurrent convolution, and the residual module, a new model is proposed named ARRU-Net (Attention Recurrent Residual U-Net) for the recognition of severe convective clouds using the cloud image prediction sequence from FY-4A data. The characteristic parameters used to recognize severe convective clouds in this study were brightness temperature values TBB9, brightness temperature difference values TBB9−TBB12 and TBB12−TBB13, and texture features based on spectral characteristics. This method first input five satellite cloud images with a time interval of 30 min into the ARRU-Net model and predicted five satellite cloud images for the next 2.5 h. Then, severe convective clouds were segmented based on the predicted image sequence. The root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and correlation coefficient (R2) of the predicted results were 5.48 K, 35.52 dB, and 0.92, respectively. The results of the experiments showed that the average recognition accuracy and recall of the ARRU-Net model in the next five moments on the test set were 97.62% and 83.34%, respectively.

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“…Further, a large part of India's population depends on ISM rainfall data, which plays a vital role in its economy and agriculture. The localized heavy rainfall events in strong wind shear associated with monsoonal systems are often tilted (Sharma et al., 2022; Shige & Kummerow, 2016) and also play a vital role that complicates rainfall estimation further. The study of ISM variability is also of interest to weather and climate modeling researchers who need precise long‐term rainfall estimates.…”

Section: Introductionmentioning

confidence: 99%

Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India

Kumar

Varma

Kubota

et al. 2022

Earth and Space Science

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This study aims to create a 21‐year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° × 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° × 0.5°) and IMD daily gridded rainfall (0.25° × 0.25°) were utilized to generate long‐term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000–2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement final‐run. The results suggest GSMaP_IMD has a smaller root‐mean‐square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3‐hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

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Percentage occurrence of global tilted deep convective clouds under strong vertical wind shear

Cited by 7 publications

References 32 publications

The Effects of Planetary Boundary Layer Features on Air Pollution Based on ERA5 Data in East China

The Effects of Planetary Boundary Layer Features on Air Pollution Based on ERA5 Data in East China

Recognition of Severe Convective Cloud Based on the Cloud Image Prediction Sequence from FY-4A

Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India

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