With high resolution and wide coverage, satellite precipitation products like Global Precipitation Measurement (GPM) could support hydrological/ecological research in the Tianshan Mountains, where the spatial heterogeneity of precipitation is high, but where rain gauges are sparse and unevenly distributed. Based on observations from 46 stations from 2014–2015, we evaluated the accuracies of three satellite precipitation products: GPM, Tropical Rainfall Measurement Mission (TRMM) 3B42, and the Climate Prediction Center morphing technique (CMORPH), in the Tianshan Mountains. The satellite estimates significantly correlated with the observations. They showed a northwest–southeast precipitation gradient that reflected the effects of large-scale circulations and a characteristic seasonal precipitation gradient that matched the observed regional precipitation pattern. With the highest correlation (R = 0.51), the lowest error (RMSE = 0.85 mm/day), and the smallest bias (1.27%), GPM outperformed TRMM and CMORPH in estimating daily precipitation. It performed the best at both regional and sub-regional scales and in low and mid-elevations. GPM had relatively balanced performances across all seasons, while CMORPH had significant biases in summer (46.43%) and winter (−22.93%), and TRMM performed extremely poorly in spring (R = 0.31; RMSE = 1.15 mm/day; bias = −20.29%). GPM also performed the best in detecting precipitation events, especially light and moderate precipitation, possibly due to the newly added Ka-band and high-frequency microwave channels. It successfully detected 62.09% of the precipitation events that exceeded 0.5 mm/day. However, its ability to estimate severe rainfall has not been improved as expected. Like other satellite products, GPM had the highest RMSE and bias in summer, suggesting limitations in its way of representing small-scale precipitation systems and isolated deep convection. It also underestimated the precipitation in high-elevation regions by 16%, suggesting the difficulties of capturing the orographic enhancement of rainfall associated with cap clouds and feeder–seeder cloud interactions over ridges. These findings suggest that GPM may outperform its predecessors in the mid-/high-latitude dryland, but not the tropical mountainous areas. With the advantage of high resolution and improved accuracy, the GPM creates new opportunities for understanding the precipitation pattern across the complex terrains of the Tianshan Mountains, and it could improve hydrological/ecological research in the area.
Progresses in climatological, ecological, and hydrological studies that focused on the Tianshan mountains area (known as “the water tower of central Asia”) have been restricted by the availability of station observations as well as high resolution and quality data set. With the aim to overcome some of these difficulties, the high‐resolution Weather Research and Forecasting (WRF) regional climate model is run over the Tianshan mountains area driven by the ERA‐Interim reanalysis. Double nesting method was used with a horizontal resolution of 40 and 8 km covering the period 1980–2018. A decade of simulation from 1980 to 1989, a period when most abundant station observations are available, is considered for validation. These downscaled results are compared against station observations, ERA‐Interim reanalysis, and three widely used spatially interpolated products in order to investigate the added value of the dynamical downscaling approach. Results of these comparisons show that the WRF‐downscaled data outperform and add significant details to ERA‐Interim reanalysis. A remarkable improvement of the WRF simulation is found at reproducing the observed seasonal cycle of daily extreme temperatures and precipitation. Due to better representation of orography, WRF simulations are able to capture extreme precipitation events that are missing in the high‐quality interpolated products. Refining the resolution from 40 to 8 km further improves the model performance, particularly at depicting orographic enhancement of precipitation. The validated WRF model can be used in future climate projections studies, and this high‐resolution as well as high‐quality climatological data set we present here is useful for impact and further downstream studies.
Precipitation is critical for maintaining ecosystem stability, especially in arid regions.This study was primarily focused on the changes during the present (i.e., from 1985 to 2005) and future (i.e., from 2040 to 2059) periods in Xinjiang, northwest China. To predict the future climate, the Weather Research and Forecasting model was run in Xinjiang using National Climate Research Center Community Climate System Model version 4 for the mid-21st century under representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively). The results indicate that the amount of annual precipitation would increase in the future under RCP4.5 and RCP8.5 in Xinjiang, especially in the mountainous areas. The increase in precipitation was predicted to be much smaller under RCP8.5 than under RCP4.5, except in Southern Xinjiang. Moreover, the increased precipitation predicted in Xinjiang implies that the current humid and warm conditions will continue. In addition, the largest increase in seasonal precipitation was predicted to occur in spring and summer in Tian Shan and Northern Xinjiang, whereas this phenomenon will occur in spring and winter in Southern Xinjiang. In addition, it was predicted that daily heavy precipitation events will occur more frequently in various subregions of Xinjiang, although light rain events will remain dominant. Finally, the increase in the frequency of heavy precipitation events was found to be related to the vertically integrated column precipitation, whereas the relative humidity was observed to be closely related to the changes in annual and seasonal precipitation.
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