Evaluation of multisatellite precipitation products by use of ground‐based data over China

Huang, Anning; Zhao, Yong; Zhang, Yang; Yang, Ben; Zhang, Lujun; Dong, Xinning; Fang, Dexian; Wu, Yang

doi:10.1002/2016jd025456

Cited by 45 publications

(41 citation statements)

References 111 publications

(203 reference statements)

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“…Among all regions, northwestern China and Tibetan Plateau show the most obvious difference, such result is accordant with the findings in previous studies that precipitation estimates possess relative poor performance in the arid and semiarid and high-altitudes areas (Huang et al, 2016;Qin et al, 2014;Tang, Ma, et al, 2016). Beside the abovementioned influence of land surface properties and virga precipitation on the retrieval accuracy in dry areas (Chen et al, 2013), possible reasons for Tibetan Plateau with complex terrain and high altitudes can be orographic precipitation that is more difficult to be detected (Giovannettone & Barros, 2009) or the icy or snowy surface that induces strong scattering (Xu et al, 2017).…”

Section: Analysis Of Different Rain Intensities and Durationssupporting

confidence: 91%

Validating the Integrated Multisatellite Retrievals for Global Precipitation Measurement in Terms of Diurnal Variability With Hourly Gauge Observations Collected at 50,000 Stations in China

Wang

2018

JGR Atmospheres

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The diurnal cycle of precipitation is a key feature in the Earth system and related to multiscale physical processes. The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) data are expected to improve its presentation of diurnal cycle, but how it really performs remains poorly known. This study compares the diurnal characteristics of the half‐hourly IMERG V05 Final Run product with the hourly rain gauge data collected at approximately 50,000 automatic weather stations in China during a 3‐year period (2014–2016). Our results show that IMERG performs well in terms of the diurnal cycle of precipitation amount but less well in terms of frequency and even worse in terms of intensity. Ground observations show that the frequency and intensity are rather stable for the diurnal cycle; however, IMERG shows strong fluctuations. Significant inverse correlation is found between the diurnal cycles of IMERG precipitation frequency and intensity; thus, the biases of these two variables offset and result in a better estimate of amount. A low probability of detection (POD≈50%) and a high false alarm ratio (FAR≈50%) are the major reasons for the inaccurate diurnal cycles of precipitation intensity and frequency. In particular, IMERG presents a false peak of frequency at approximately 23:00–01:00 (Beijing Time) because a large proportion of the data sources at this time contain the indirect estimates from thermal infrared observations. The significant underestimation of the frequency and overestimation of the intensity at approximately 09:00–11:00 and 20:00–22:00 is because the estimates primarily come from less accurate cross‐track microwave sensors.

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Section: Analysis Of Different Rain Intensities and Durationssupporting

confidence: 91%

Validating the Integrated Multisatellite Retrievals for Global Precipitation Measurement in Terms of Diurnal Variability With Hourly Gauge Observations Collected at 50,000 Stations in China

Wang

2018

JGR Atmospheres

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“…However, there are weak correlations in semi-arid and arid regions, particularly in the Tibetan Plateau and Tarim Basin, which is consistent with the results of Chen & Li (2016). This may be attributed to the fact that complex topographic conditions and climates pose a great challenge for estimating accurate precipitation amounts from TRMM or that the number of gauges used in the GPCC product was limited in these regions (Huang et al, 2016). In addition, the RMSE shows a gradually decreasing trend from the southeast to the northwest, which is similar to the spatial pattern of precipitation.…”

Section: Temporal Evaluation Of Precipitation Productsupporting

confidence: 63%

Multidimensional evaluation of the TRMM 3B43V7 satellite-based precipitation product in mainland China from 1998–2016

Zhou

Guo

et al. 2020

PeerJ

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This study evaluates the applicability of the Tropical Rain Measurement Mission (TRMM) 3B43V7 product for use throughout mainland China. Four statistical metrics were used based on the observations made by rain gauges; these metrics were the correlation coefficient (R), the relative bias (RB), the root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE), and they were chosen to evaluate the performance of the 3B43V7 product at temporal and spatial scales. The results revealed that 3B43V7 performed satisfactorily on all timescales (R > 0.9 and NSE > 0.86); however, it overestimated the results when compared with the rain gauge observations in certain circumstances (RB = 9.7%). Monthly estimates from 3B43V7 were in agreement with rain gauge observations. 3B43V7 can effectively capture the seasonal patterns of precipitation characteristics over mainland China. However, 3B43V7 tends to register a greater overestimation of precipitation in the winter (RB = 14%) than in other seasons while showing greater consistency with the observations made by rain gauges during dry periods. The 3B43V7 product performs well in the eastern part of mainland China, while its performance is poor in the western part of mainland China. In terms of altitude, 3B43V7 performs satisfactorily in areas with moderate to low altitudes (when altitude < 3,500 m, R > 0.9, NSE > 0.8 and RB < 10.2%) but RB values increase with altitude. Overall, 3B43V7 had a favorable performance throughout mainland China.

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“…The root‐mean‐square error (RMSE) is adopted to evaluate the model errors in quantity. The formulas for these statistics (Huang et al, ) are given as follows:

italicTC = \frac{\sum_{i = 1}^{N} ((), S_{i} - \overset{S}{true}) ((), O_{i} - \overset{O}{true})}{\sqrt{\sum_{i = 1}^{N} {(S_{i} - \overset{true¯}{S})}^{2}} \sqrt{\sum_{i = 1}^{N} {(O_{i} - \overset{true¯}{O})}^{2}}}

RMSE = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(S_{i} - O_{i})}^{2}}

where S i ( O i ) is the simulation (observation) at the i th point in time. N is the number of sample.…”

Section: Models Data Experiments and Methodsmentioning

confidence: 99%

“… R 0 is an achievable maximum correlation (here set as 1). It is clear that the Taylor score (TS) ranges from 0 to 1 and better performance is indicated by higher TS (Huang et al, ; Kan et al, ).…”

Section: Models Data Experiments and Methodsmentioning

confidence: 99%

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Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

Huang

Wang

et al. 2019

JGR Atmospheres

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The ability of FLake, WRF‐Lake, and CoLM‐Lake models in simulating the thermal features of Lake Nam Co in Central Tibetan Plateau has been evaluated in this study. All the three models with default settings exhibited distinct errors in the simulated vertical temperature profile. Then model calibration was conducted by adjusting three (four) key parameters within FLake and CoLM‐Lake (WRF‐Lake) in a series of sensitive experiments. Results showed that each model's performance is sensitive to the key parameters and becomes much better when adjusting all the key parameters relative to tuning single parameter. Overall, setting the temperature of maximum water density to 1.1 °C instead of 4 °C in the three models consistently leads to improved vertical thermal structure simulation during cold seasons; reducing the light extinction coefficient in FLake results in much deeper mixed layer and warmer thermocline during warm seasons in better agreement with the observation. The vertical thermal structure can be clearly improved by decreasing the light extinction coefficient and increasing the turbulent mixing in WRF‐Lake and CoLM‐Lake during warm seasons. Meanwhile, the modeled water temperature profile in warm seasons can be significantly improved by further replacing the constant surface roughness lengths by a parameterized scheme in WRF‐Lake. Further intercomparison indicates that among the three calibrated models, FLake (WRF‐Lake) performs the best to simulate the temporal evolution and intensity of temperature in the layers shallower (deeper) than 10 m, while WRF‐Lake is the best at simulating the amplitude and pattern of the temperature variability at all depths.

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Evaluation of multisatellite precipitation products by use of ground‐based data over China

Cited by 45 publications

References 111 publications

Validating the Integrated Multisatellite Retrievals for Global Precipitation Measurement in Terms of Diurnal Variability With Hourly Gauge Observations Collected at 50,000 Stations in China

Validating the Integrated Multisatellite Retrievals for Global Precipitation Measurement in Terms of Diurnal Variability With Hourly Gauge Observations Collected at 50,000 Stations in China

Multidimensional evaluation of the TRMM 3B43V7 satellite-based precipitation product in mainland China from 1998–2016

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

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