| 1. |
- Wei, Linyong, et al.
(författare)
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An Extended Triple Collocation Method With Maximized Correlation for Near Global-Land Precipitation Fusion
- 2023
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Ingår i: Geophysical Research Letters. - 0094-8276. ; 50:24
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Tidskriftsartikel (refereegranskat)abstract
- An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi-global land by combining three independent precipitation products. The performance of the ETCC-merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC-merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth.
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| 2. |
- Wei, Linyong, et al.
(författare)
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Bias correction of GPM IMERG Early Run daily precipitation product using near real-time CPC global measurements
- 2022
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Ingår i: Atmospheric Research. - : Elsevier BV. - 0169-8095. ; 279
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Tidskriftsartikel (refereegranskat)abstract
- This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards.
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| 3. |
- Wei, Linyong, et al.
(författare)
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Fusion of gauge-based, reanalysis, and satellite precipitation products using Bayesian model averaging approach : Determination of the influence of different input sources
- 2023
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694. ; 618
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Tidskriftsartikel (refereegranskat)abstract
- Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art ensemble-based product, MSWEP. The results indicate that the BMA predictions performed substantially better than the reanalysis and satellite precipitation datasets in both daily statistics and seasonal analyses. MCE, MCI, and MCEG demonstrated better performances relative to CPC in terms of individual metrics. Moreover, MCI, MCG, and MCEI generally outperformed MSWEP over the entire study area, particularly in local regions, such as southwestern China and the eastern Tibetan Plateau. During Typhoon Rammasun in 2014, MCG and MCEG provided greater detail for heavy rainfall events than the four ensemble members and the MSWEP product. Thus, the performance of the BMA predictions exhibited evident differences because of various input sources. CPC was the major internal influencing factor with the highest weight score. Meanwhile, the increased-input CPC dataset into the BMA-based schemes exerted a significant influence on the precipitation estimates, which markedly facilitated the performance improvement of the fusion model, and its improved degree (greater than 14 %) was obtained using a ‘changed-initial’ comparison method. Our results demonstrate that the developed modifiable BMA framework is useful for analyzing the impacts of ensemble members on BMA predictions and suggests that it is considerate in the use of different input sources for generating ensemble-based precipitation products.
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| 4. |
- Wei, Linyong, et al.
(författare)
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Preliminary utility of the retrospective IMERG precipitation product for large-scale drought monitoring over Mainland China
- 2020
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Ingår i: Remote Sensing. - 2072-4292. ; 12:18
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Tidskriftsartikel (refereegranskat)abstract
- 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 Precipitation Estimation from 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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| 5. |
- Wei, Linyong, et al.
(författare)
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Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data
- 2021
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694. ; 598
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Tidskriftsartikel (refereegranskat)abstract
- Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.
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