A high spatiotemporal gauge-satellite merged precipitation analysis over China

Shen, Yan; Zhao, Ping; Yang, Pan; Yu, Jinxing

doi:10.1002/2013jd020686

Cited by 399 publications

(284 citation statements)

References 29 publications

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“…However, the spatial resolution of the LGC-corrected hourly radar QPE used in this study (0.01 • ) is much higher than that used by [15] (0.1 • ). In order to obtain the analysis at the target grid box, an influence range is first set at a given grid box and the deviation between the observed and first-guess precipitation field then corrected according to weight.…”

Section: Parameters In Oi-based Merging Methodsmentioning

confidence: 77%

“…The essence of OI method is to properly define the weight, which is determined from the error variance, σ f i (σ o i ), and error correlation, µ f ij (µ o ij ), for the first-guess (observed) precipitation fields. However, the OI skill has been shown to be sensitive to error and error covariance, which are dependent on the spatiotemporal scale of the product and the precipitation value considered [15,31]. Therefore, at the hourly and 0.01 • resolutions, errors for the gauge precipitation analysis and the local-gauge-corrected radar QPE need to be tuned regionally because they tend to be more localized.…”

Section: Parameters In Oi-based Merging Methodsmentioning

confidence: 99%

“…In the development of the hourly gauge-satellite merged precipitation analysis at the 0.1 • × 0.1 • grid box, the error is defined by limited samples at a specific region [15]. However, at the 0.01 • × 0.01 • grid box, the characteristics of precipitation and precipitation error are more regionally dependent, so the spatial dependence of the error for radar QPE needs to be accounted for first.…”

Section: Error Variance Estimate For the Radar Qpe: σ F Imentioning

confidence: 99%

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China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Shen¹,

Zhou

Yang³

et al. 2018

Remote Sensing

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Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01 • resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01 • resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03 • × 0.03 • spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01 • resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.

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Section: Parameters In Oi-based Merging Methodsmentioning

confidence: 77%

Section: Parameters In Oi-based Merging Methodsmentioning

confidence: 99%

Section: Error Variance Estimate For the Radar Qpe: σ F Imentioning

confidence: 99%

See 1 more Smart Citation

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Shen¹,

Zhou

Yang³

et al. 2018

Remote Sensing

Self Cite

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“…China Merged Precipitation Analysis (CMPA) with hourly, 0.1 • × 0.1 • resolution data [29] were collected to filter invalid soil moisture observations measured at the same date and time (06:00 a.m.-02:00 p.m.) as the in situ soil ground measurements.…”

Section: (3) Precipitation Datamentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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“…2 for the time period from 1200 UTC 2 July to 0000 UTC 4 July 2016. The rainfall observations were obtained from merging the hourly rain gauge data at more than 30,000 automatic weather stations in China with the Climate Precipitation Center Morphing (CMORPH) precipitation product using a probability density function based optimal interpolation method (Shen et al, 2014). To associate the rainfall distributions with precipitation case over eastern China is selected for such a study.…”

Section: Experiments Designmentioning

confidence: 99%

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

Qin

Zou

Weng

2017

Tellus A: Dynamic Meteorology and Oceanography

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The launch of the Japanese Advanced Himawari Imager (AHI) on 7 October 2014 represents a new era of geostationary operational environmental satellite (GOES) imagers, providing many more channels than any previously launched GOES imagers for the first time. In this study, we compare the impacts of assimilating all AHI versus GOES-like infrared channels radiances on regional forecasts over Eastern China. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and Advanced Research Weather Research and Forecast model are employed. Positive impacts are obtained on quantitative precipitation forecasts (QPFs) associated with a typical summer precipitation case over eastern China in both set-ups, i.e. one assimilating all 10 AHI infrared channels (AHIA) and the other assimilating only four GOES-like AHI channels (AHIG). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward-propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHIA experiment generated the largest improvement on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHIG experiment and a control experiment without assimilating any AHI channel.

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A high spatiotemporal gauge-satellite merged precipitation analysis over China

Cited by 399 publications

References 29 publications

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

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