Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China

Peng, Fanchen; Zhao, Shuang; Chen, Cheng; Cong, Dianmin; Wang, Yamei; Ouyang, Hongda

doi:10.1016/j.atmosres.2019.104814

Cited by 55 publications

(24 citation statements)

References 51 publications

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“…Although rarely discussed, it must be pointed out that the accuracy of the original satellite precipitation data has a great influence on the downscaling results, regardless of the downscaling methods and the auxiliary variables. A large number of studies have shown that the GPM-era precipitation products have better accuracy as well as higher resolution than those in the TRMM-era (Tang et al, 2016;Chen et al, 2018;Peng et al, 2020). A comparative study has shown that the downscaled results based on IMERG V05B precipitation had better performance than those based on TMPA precipitation over the Tibetan Plateau in 2015 (Ma et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area

Chen

Qin

et al. 2020

Front. Earth Sci.

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Section: Discussionmentioning

confidence: 99%

Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area

Chen

Qin

et al. 2020

Front. Earth Sci.

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“…The CCs and KGE's increase their optimal values from the northwest to the southeast across mainland China, and the RMSEs exhibit the opposite trend. Based on the results of other studies, the low IMERG precipitation accuracy in R7 and R8 may be affected by several factors, e.g., the topography, climate, spatiotemporal distribution of precipitation, and generation procedure of the satellite dataset (error correction and retrieval algorithm) [6,7,28,30,47]. Table 2 displays the distributions of the regional CCs, RMSEs, and KGE's at the 0.05, 0.50, and 0.95 quantiles of the daily, monthly, and annual IMERG data against the observed precipitation data for the eight regions and mainland China.…”

Section: Evaluation Of Imerg Precipitation At Multiple Temporal and Smentioning

confidence: 99%

“…Although the CCs and KGE's at the 0.95 quantile are slightly higher (even close to those of R1-R6), they are much lower at the 0.05 quantile (i.e., KGE' < 0). This result may be strongly related to the overlap of the CSCD and GPCC meteorological stations, including an overlap of certain meteorological stations in China [52], and to the sparse-distributed GPCC meteorological stations in western China for IMERG correction and the sophisticated mountainous terrain in this region [47,56]. According to the results of Figure 2, Figure 3, and Table 2, the IMERG possesses a strong capacity for monitoring intra-annual precipitation variations, and it features maximize performance at the monthly scale in comparison with that at the daily and annual scales.…”

Section: Evaluation Of Imerg Precipitation At Multiple Temporal and Smentioning

confidence: 99%

“…Namely, the IMERG possesses sufficient stability and reliability in capturing intra-annual precipitation variations and detecting drought over mainland China, although there is a lower accuracy in local regions, such as R7 and R8 [51]. This lower accuracy is likely due to the topography, climate, precipitation spatiotemporal distribution, and satellite dataset generation procedure (error correction and retrieval algorithm) [6,7,28,30,47].…”

Section: Usability Of Retrospective Imerg-based Spimentioning

confidence: 99%

“…The satellite precipitation product with the highest spatiotemporal resolution and optimum dependability among the multi-source SPPs should be used to reduce error propagation in the drought detection process [7]. Recently, some research results have proclaimed that the IMERG Final Run precipitation product is a better dataset compared to other SPPs, i.e., the PERSIANN-CDR, CHIRPS, and TMPA, in Huanghuaihai Plain of China [47] and mainland China [48]. The quality of the IMERG product has been demonstrated to estimate precipitation more reliably.…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

et al. 2020

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This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.

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One‐kilometre monthly air temperature and precipitation product over the Mongolian Plateau for 1950–2020

Xin

Yang

Chen

et al. 2023

Intl Journal of Climatology

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The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP.

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Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China

Cited by 55 publications

References 51 publications

Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area

Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area

Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

One‐kilometre monthly air temperature and precipitation product over the Mongolian Plateau for 1950–2020

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