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Abstract:The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products.

Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes' status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatialresolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China's newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region.

An international programme dedicated to the study of the Third Pole Environment (TPE) is now developing. The TPE region is centred on the Tibetan Plateau and concerns the interests of the surrounding countries and regions. To improve input for hydrological research, we collected precipitation data on 241 meteorological stations across the TPE region; these data were obtained from various countries, thus including various types of gauges. Employing the procedure recommended by the World Meteorological Organization (WMO), a full version of bias adjustment was applied to the data, including adjustments for wind-induced error, wetting loss, evaporation loss and trace amount for each station. The results reveal that the average annual precipitation has increased considerably from a minimum of 4 mm to a maximum of 409 mm with an overall mean of 27% from the adjustment, the largest bias being found in the Chinese standard precipitation gauge (CSPG) which was used in the central TPE region. In addition, the bias shows variable spatial and temporal patterns in different climate zones throughout this area. It is expected that this study and its results will be beneficial for hydrological and climatic studies over the TPE region.

Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic Bayesian model averaging (BMA) algorithm. The blended experiment was performed at a daily 0.25° grid scale for 2007–2012 among Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT and 3B42V7, Climate Prediction Center MORPHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR). First, the BMA weights were optimized using the expectation‐maximization (EM) method for each member on each day at 200 calibrated sites and then interpolated to the entire plateau using the ordinary kriging (OK) approach. Thus, the merging data were produced by weighted sums of the individuals over the plateau. The dynamic BMA approach showed better performance with a smaller root‐mean‐square error (RMSE) of 6.77 mm/day, higher correlation coefficient of 0.592, and closer Euclid value of 0.833, compared to the individuals at 15 validated sites. Moreover, BMA has proven to be more robust in terms of seasonality, topography, and other parameters than traditional ensemble methods including simple model averaging (SMA) and one‐outlier removed (OOR). Error analysis between BMA and the state‐of‐the‐art IMERG in the summer of 2014 further proved that the performance of BMA was superior with respect to multisatellite precipitation data merging. This study demonstrates that BMA provides a new solution for blending multiple satellite data in regions with limited gauges.

Abstract. The mesoscale snow distribution over the Namco lake area of the Tibetan Plateau on October 2005 has been investigated in this paper. The base and revised experiments were conducted using the Weather Research Model (WRF) with three nested grids that included a 1 km finest grid centered on the Namco station. Our simulation ran from 6 October through to 10 October 2005, which was concurrent with long term meteorological observations. Evaluation against boundary layer meteorological tower measurements and flux observations showed that the model captured the observed 2 m temperature and 10 m winds reasonably well in the revised experiment. The results suggest that output snow depth maximum amounts from two simulated experiments were centered downwind of the Namco lakeshore. Modified surface state variable, for example, surface skin temperature on the lake help to increase simulated credibility.

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