Diurnal variations of summer precipitation over contiguous China are studied using hourly rain‐gauge data from 588 stations during 1991–2004. It is found that summer precipitation over contiguous China has large diurnal variations with considerable regional features. Over southern inland China and northeastern China summer precipitation peaks in the late afternoon, while over most of the Tibetan Plateau and its east periphery it peaks around midnight. The diurnal phase changes eastward along the Yangtze River Valley, with a midnight maximum in the upper valley, an early morning peak in the middle valley, and a late afternoon maximum in the lower valley. Summer precipitation over the region between the Yangtze and Yellow Rivers has two diurnal peaks: one in the early morning and another in the late afternoon.

(1) all six satellite products are capable of capturing the overall spatial distribution and temporal variations of precipitation reasonably well; (2) performance of the satellite products varies for different regions and different precipitation regimes, with better comparison statistics observed over wet regions and for warm seasons; (3) products based solely on satellite observations present regionally and seasonally varying biases, while the gauge-adjustment procedures applied in TRMM 3B42 remove the large-scale bias almost completely; (4) CMORPH exhibits the best performance in depicting the spatial pattern and temporal variations of precipitation; and (5) both the relative magnitude and the phase of the warm season precipitation over China are estimated quite well, but the early morning peak associated with the Mei-Yu rainfall over central eastern China is substantially under-estimated by all satellite products.

Through optimizing the daily precipitation climatology, a new high‐resolution (0.25° × 0.25° lat./lon.) gridded daily precipitation analysis over Mainland China was developed based on the optimal interpolation (OI) method. This product, based on about 2400 gauge stations over Mainland China from 1955 to the present, is called the China Gauge‐based Daily Precipitation Analysis (CGDPA). In this study, using independent precipitation observations as the benchmark, CGDPA and the Climate Prediction Center Unified gauge dataset (CPC_UNI) from the National Oceanic and Atmospheric Administration (NOAA) are validated from May to September of 2008–2010 on a 0.5° × 0.5° lat./lon. grid. The CGDPA has smaller bias and root mean square error, and higher spatial correlation with the validation data than CPC_UNI. Further investigation indicates that this improvement is mainly owing to the larger number of gauges used in the CGDPA. The East Asia gauge analysis (EA_Gauge) is also introduced to comparatively evaluate the capabilities of monitoring precipitation events with different rainfall rates over Mainland China. CGDPA can capture more strong rainfall events while CPC_UNI and EA_Gauge tend to smooth the precipitation structure and miss more local strong rainfall events with precipitation larger than 25 mm day−1 over Mainland China. The long‐term precipitation time series described by the CGDPA and EA_Gauge agree very well while CPC_UNI substantially underestimates precipitation especially over the sparse‐gauged regions and after 1982. CGDPA is thus suggested as a readily available input to applications over Mainland China, whenever possible.

Manabendra Saharia (2014) Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau, This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005-2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods -arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic meanshow insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.

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