Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrat...

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Precipitation seasonality and variability over the Tibetan plateau as resolved by the High Asia reanalysis

Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales

[1] A gauge-based analysis of hourly precipitation is constructed on a 0.25° latitude/longitude grid over China for a 3 year period from 2005 to 2007 by interpolating gauge reports from ∼2000 stations collected and quality controlled by the National Meteorological Information Center of the China Meteorological Administration. Gauge-based precipitation analysis is applied to examine the performance of six high-resolution satellite precipitation estimates, including Joyce et al.'s (2004) Climate Prediction Center Morphing Technique (CMORPH) and the arithmetic mean of the microwave estimates used in CMORPH; Huffman et al.'s (2007) Tropical Rainfall Measuring Mission (TRMM) precipitation product 3B42 and its real-time version 3B42RT; Turk et al.'s (2004) Naval Research Laboratory blended product; and Hsu et al.'s (1997) Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Network (PERSIANN). Our results showed the following: (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.

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Performance of high‐resolution satellite precipitation products over China

Summary The post-real time product of Day-1 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is evaluated over Mainland China from April to December 2014 at the hourly timescale, against data from hourly ground-based observations. In addition, the IMERG product is compared with its predecessor-the Version-7 post-real-time 3B42 (3B42V7) product of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) at its original 3-hourly and then daily timescales for the same period. All the products are cross-evaluated at gridded, regional, and national scales. Results show that: (1) the Day-1 IMERG shows appreciably better performance than 3B42V7 at both sub-daily and daily timescales, and all the three spatial scales. The gap between the two products is more significant at the sub-daily resolution; (2) Out of the six sub-regions of China, IMERG especially performs better than 3B42V7 at the mid- and high-latitudes, as well as relatively dry climate regions; (3) IMERG can better reproduce the probability density function (PDF) in terms of precipitation intensity, particularly in the low ranges; and (4) although IMERG better captures the precipitation diurnal variability, both products have room to further improve their capability, particularly in the dry climate and high-latitude regions. This study is among the earliest evaluation and comparison of IMERG and 3B42V7 products, which could be valuable in providing reference for the development of IMERG algorithms, associated global products, and various applications as well.

First-Year Evaluation of GPM Day-1 IMERG and TMPA Version-7 Legacy Products over Mainland China at Multiple Spatiotemporal Scales

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D02115, doi:10.1029/2010JD014741, 2011 Evaluation of satellite‐retrieved extreme precipitation rates across the central United States A. AghaKouchak, 1 A. Behrangi, 2 S. Sorooshian, 1 K. Hsu, 1 and E. Amitai 3,4 Received 10 July 2010; revised 10 September 2010; accepted 27 October 2010; published 26 January 2011. [ 1 ] Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite‐based estimates are not well integrated into operational and decision‐making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite‐ derived precipitation products (CMORPH, PERSIANN, TMPA‐RT, and TMPA‐V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar‐based, gauge‐adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA‐RT and TMPA‐V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably. Citation: AghaKouchak, A., A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai (2011), Evaluation of satellite‐retrieved extreme precipitation rates across the central United States, J. Geophys. Res., 116, D02115, doi:10.1029/2010JD014741. 1. Introduction [ 2 ] Precipitation plays a significant role in weather research, monitoring, and predictions. Improving our understanding of weather and climate, along with the development of reliable and uninterrupted measurements, are essential for proper assessment of weather conditions. Currently, in situ and radar‐based precipitation data are the major input for streamflow forecasts, flash flood warnings, and weather watches across the United States. While some regions have long‐term historical in situ precipitation measurements, poor spatial sampling makes the data inadequate to support monitoring, detection, and forecast studies. On the other hand, in most parts of the globe (except in a few developed countries), radar installations for precipitation measurements Department of Civil and Environmental Engineering, University of California, Irvine, California, USA. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. School of Earth and Environmental Sciences, Chapman University, Orange, California, USA. Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2010JD014741 are not available. In the United States, with one of the most sophisticated radar measurement networks in the world, regions with extensive topographic relief (e.g., the western and southwestern United States) suffer from poor or non- existent radar coverage [Maddox et al., 2002]. In fact, Maddox et al. [2002] showed that at lower levels (e.g., 1000 m above ground level), which are closer estimates to ground‐level precipitation, the radar coverage area is sub- stantially smaller than at higher levels (e.g., 3000 m above ground level). [ 3 ] Clearly, the lack or absence of ground‐based precipi- tation networks hampers the development and use of flood and drought warning models, hydrological models, and extreme weather monitoring and decision‐making systems. Therefore, there exists the need to achieve alternative esti- mates of precipitation with sufficient sampling density, reliability, and accuracy to enable utilization of data for operational applications. Satellite‐derived precipitation esti- mates have the potential to improve precipitation observation at a global scale. In recent years, the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and many other inter- national sponsored satellite missions have led to an increase in available precipitation data. These remotely sensed data have several advantages over in situ measurements, including D02115 1 of 11

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Evaluation of satellite-retrieved extreme precipitation rates across the central United States

Hourly or 3-hourly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite products and rain gauge records are used to characterize East Asian summer monsoon rainfall, including spatial patterns in June–August (JJA) mean precipitation amount, frequency, and intensity, as well as the diurnal and semidiurnal cycles. The results show that the satellite products are comparable to rain gauge data in revealing the spatial patterns of JJA precipitation amount, frequency, and intensity, with pattern correlation coefficients for five subregions ranging from 0.66 to 0.94. The pattern correlation of rainfall amount is higher than that of frequency and intensity. Relative to PERSIANN, the TRMM product has a better resemblance with rain gauge observations in terms of both the pattern correlation and rootmean-square error. The satellite products overestimate rainfall frequency but underestimate its intensity. The diurnal (24 h) harmonic dominates subdaily variations of precipitation over most of eastern China. A late-afternoon maximum over southeastern and northeastern China and a near-midnight maximum over the eastern periphery of the Tibetan Plateau are seen in the rain gauge data. The diurnal phases of precipitation frequency and intensity are similar to those of rainfall amount in most regions, except for the middle Yangtze River valley. Both frequency and intensity contribute to the diurnal variation of rainfall amount over most of eastern China. The contribution of frequency to the diurnal cycle of rainfall amount is generally overestimated in both satellite products. Both satellite products capture well the nocturnal peak over the eastern periphery of the Tibetan Plateau and the late-afternoon peak in southern and northeastern China. Rain gauge data over the region between the Yangtze and Yellow Rivers show two peaks, with one in the early morning and the other later in the afternoon. The satellite products only capture the major late-afternoon peak.

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Yang Pan

Papers

Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China: A Comparison of Satellite Data with Rain Gauge Observations