Analysis of the Qinghai-Xizang Plateau Monsoon Evolution and Its Linkages with Soil Moisture

Zhou, Juan; Wen, Jun; Wang, Xin; Jia, Dongyu; Chen, Jinlei

doi:10.3390/rs8060493

Cited by 13 publications

(12 citation statements)

References 30 publications

(34 reference statements)

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“…Furthermore, the timescale of the soil moisture observation data used in the study as of now is approximately 10 years. Consequently, for regional‐scale or long‐term climate research, model‐derived soil moisture data are often utilized as an alternative (Zuo and Zhang, ; Zhou et al ., ). Previous evaluations of several reanalysis data sets showed that the ERA‐40 reanalysis could generally capture the inter‐annual variations of soil moisture from the surface to deeper layers over China (Li et al ., ; Zhang et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…With respect to the reliability of the reanalysis data, some scholars (Qi et al, 2009; Hua et al ., ; Zhou et al ., ) have used ECMWF reanalysis data to calculate plateau monsoon indices. Therefore, it is feasible to utilize ERA‐Interim data for the study of plateau monsoons.…”

Section: Methodsmentioning

confidence: 99%

“…Studies about the influence of spring soil moisture on the latter plateau summer monsoon have rarely been discussed. Our previous study indicated that late spring TP soil moisture is significantly correlated with the plateau summer monsoon; when the spring soil moisture over the central and eastern TP is higher (lower) and soil moisture over the western TP is lower (higher) than normal, the plateau summer monsoon may be stronger (weaker) (Zhou et al ., ); a statistical correlation between the summer plateau monsoon index and preceding spring local soil moisture was simply discussed. However, the possible mechanism that the late spring TP soil moisture affecting the plateau summer monsoon are still unclear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Late spring soil moisture variation over the Tibetan Plateau and its influences on the plateau summer monsoon

Zhou

Wen

Liu

et al. 2018

Intl Journal of Climatology

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Based on the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data from 1979 to 2016, an index for Tibetan Plateau (TP) soil moisture (I TPSM ), which could represent inter-annual variation in spring soil moisture, is defined after removing the linear trend in soil moisture. The late spring soil moisture variation over the TP and its influences on the plateau summer monsoon are analysed. The results show that the TP soil moisture variation is non-uniform, and spring soil moisture over the northeastern TP shows an increasing trend, while the western TP (especially the peripheral area) renders a significantly decreasing trend. When the I TPSM shows positive (negative) anomalies (i.e., spring soil moisture over the northeastern TP is higher (lower), soil moisture over the western TP is lower (higher) than normal), the plateau summer monsoon may be stronger (weaker). Anomalous variations in spring TP soil moisture can eventually affect the plateau summer monsoon by changing atmospheric circulation, diabatic heating, and water vapour transportation. It is found that when the I TPSM exhibits positive anomalies, diabatic heating over the centre and eastern TP is remarkably enhanced, and warm and humid moisture from the Bay of Bengal and Arabian Sea and cold air from high latitudes merge over the central and eastern TP; coupled with lower-level convergence and upper-level divergence, all these contribute to an increase in plateau summer monsoonal precipitation. When the I TPSM shows negative anomalies, diabatic heating over the centre and eastern TP is weakened, the anomalous cyclone to the south of the TP hinders warm and humid air flows transportation to the TP, and the anomalous anticyclone to the southeast of Lake Baikal weakens cold air to the south; coupled with weakened convergence and upwards motion, these factors all lead to a decrease in plateau summer monsoonal precipitation.

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Late spring soil moisture variation over the Tibetan Plateau and its influences on the plateau summer monsoon

Zhou

Wen

Liu

et al. 2018

Intl Journal of Climatology

Self Cite

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show abstract

“…Zhou et al [30] explored the linkage between soil moisture and the Qinghai-Xizang Plateau monsoon evolution, and proposed a new plateau monsoon index (ZPMI). The ZPMI was compared with the existing Plateau Monsoon Indices (PMI): the Traditional Plateau Monsoon Index (TPMI), the Dynamic Plateau Monsoon Index (DPMI), and the QPMI (i.e., PMI developed by Qi et al).…”

Section: Water Cycle and Climate From Spacementioning

confidence: 99%

Preface: Land Surface Processes and Interactions—From HCMM to Sentinel Missions and Beyond

Vekerdy

Zeng

2017

Remote Sensing

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“…It is produced by a data assimilation system based on a 2006 release of the ECMWF Integrated Forecasting System. As discussed in [32][33][34], the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) land-surface model is used for the ERA-Interim model. Specifically, the ERA-Interim model uses the TESSEL [35,36] scheme to evolve the thermal and water storage in the four layers of soil and snow during the forecast.…”

Section: Ecmwf Datamentioning

confidence: 99%

Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

Hang

Liu

et al. 2018

Remote Sensing

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Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6.2% and 2.5% on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.

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Analysis of the Qinghai-Xizang Plateau Monsoon Evolution and Its Linkages with Soil Moisture

Cited by 13 publications

References 30 publications

Late spring soil moisture variation over the Tibetan Plateau and its influences on the plateau summer monsoon

Late spring soil moisture variation over the Tibetan Plateau and its influences on the plateau summer monsoon

Preface: Land Surface Processes and Interactions—From HCMM to Sentinel Missions and Beyond

Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

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