Assessment of the quality of NCEP-2 and CFSR reanalysis daily temperature in China based on long-range correlation

He, Wenping; Zhao, Shanshan

doi:10.1007/s00382-017-3622-0

Cited by 38 publications

(19 citation statements)

References 61 publications

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“…The precipitation data (daily average) was collected from the CHIRPS (Climate Hazard Infrared Precipitation with Stations) database, which provides a high resolution (0.05°) dataset with integrated global climatology, satellite estimates, and rain gauge observations ( Funk et al., 2015 ). The daily temperature data for the study area was collected from National Centres for Environmental Prediction’s Climate Forecast System Realanalysis (CFSR), which provides 3 rd generation real analysis data at high resolution ( He and Zhao, 2018 ). The predominant wind direction in the study area was south and south-western and the average wind speed varied between 3 and 4 ms −1 .…”

Section: Methodsmentioning

confidence: 99%

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh

Kumar

Singh²,

Kumar

et al. 2021

Environmental Pollution

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Section: Methodsmentioning

confidence: 99%

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh

Kumar

Singh²,

Kumar

et al. 2021

Environmental Pollution

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“…The reliability of various reanalysis data is relatively low (e.g., [19,20]). Observational data recorded at meteorological stations are fundamental to the data processing and analysis that underpins climate research.…”

Section: Introductionmentioning

confidence: 99%

Diurnal Variations in Surface Wind over the Tibetan Plateau

Zhao

Zhang

et al. 2019

Atmosphere

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This study uses hourly surface wind direction and wind speed observations from 53 meteorological stations on the Tibetan Plateau (TP) (70–105° E, 25–45° N) between 1995 and 2017 to investigate diurnal variations in the surface wind. The results show large diurnal variations in surface wind on the TP. The minimum wind speed occurs in the morning and the maximum in the afternoon. In all four seasons, the prevailing meridional wind is a southerly, and this is typically evident for more than two-thirds of each day. However, in the mornings during December–February and September–November, this southerly wind is replaced by a northerly, but remains southerly in the afternoon. The TP shows remarkable regional characteristics with respect to diurnal variations in wind speed. In the eastern region, the minimum and maximum daily wind speeds occur about 1 h later than in the west. Among the 53 meteorological stations, 79% observed that it took less time for the minimum speed to rise to the maximum speed than for the maximum to drop to the minimum. The blocking effect of the high surrounding terrain causes the diurnal variations seen in the surface winds at the three stations in the Qaidam Basin to differ significantly from those observed at the other stations elsewhere on the plateau. These Qaidam Basin stations recorded their maximum wind speeds around noon, with the minimum at dusk, which is around 1900 LST. The EOF1 (EOF = empirical orthogonal function) of the hourly wind speed on the TP indicates the key daily circulation feature of the region; i.e., the wind speed is high in the afternoon and low in the morning. The EOF2 reflects the regional differences in the diurnal variations of wind speed on the TP; i.e., the eastern region reaches the daily maximum and minimum wind speeds slightly later than the western region.

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“…In recent years, state‐of‐the‐art dynamic models have been used for operational rainfall prediction (Kang et al, ; Liu et al, ; Gong et al, ). The performances of such models have shown significant progresses; however, limitations of dynamic rainfall prediction still exist (Li and Chou, ; He and Zhao, ). The reason may be that the summer rainfall in China involves several complex atmospheric processes, for example, the low‐latitude Indian summer monsoon (Huang et al, ), the East Asian summer monsoon (EASM; Ding and Chan, ), and the western North Pacific (WNP) subtropical high (Zhou et al, ), and the dynamic models have difficulties in reproducing the complex interactions among these processes.…”

Section: Introductionmentioning

confidence: 99%

Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model

Wang

Liu

et al. 2019

Intl Journal of Climatology

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In this study, the performance of Beijing Climate Center's Climate System Model (BCC_CSM) is analysed in terms of China summer rainfall prediction, especially its capability to reproduce the spatial structure and temporal variation of the leading modes of empirical orthogonal function decomposition. Results show that the BCC_CSM has predictability for the summer mean rainfall pattern, namely, reproducing more rainfall in South China and maritime area, and less rainfall in North China and inland area. The model can reproduce the spatial pattern of the first two leading modes of observed summer rainfall; however, it fails to show correct interannual variability of the first two principle components, which may lead to a low skill in summer rainfall anomaly prediction over most parts of China. The leading mode-based correction (LMC) method is then applied for the post processing procedure to correct prediction errors. Cross validation confirms that the LMC method can integrate the historical spatial pattern information of observed summer rainfall with the original model prediction, making the corrected model output better reflect spatial structure of the leading modes and their interannual variation. Compared with the original model output, the positive temporal correlation coefficient area for the corrected China summer rainfall anomalies is thus increased, and the average spatial correlation coefficient during 1991-2015 is improved from −0.01 before the correction to 0.17. The LMC method shows its potential for improving the prediction skill of BCC_CSM, which can be used in operational prediction.

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Assessment of the quality of NCEP-2 and CFSR reanalysis daily temperature in China based on long-range correlation

Cited by 38 publications

References 61 publications

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh

Diurnal Variations in Surface Wind over the Tibetan Plateau

Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model

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