[1] Two standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products, 3B42RT and 3B42V6, were quantitatively evaluated in the Laohahe basin, China, located within the TMPA product latitude band (50°NS) but beyond the inclined TRMM satellite latitude band (36°NS). In general, direct comparison of TMPA rainfall estimates to collocated rain gauges from 2000 to 2005 show that the spatial and temporal rainfall characteristics over the region are well captured by the 3B42V6 estimates. Except for a few months with underestimation, the 3B42RT estimates show unrealistic overestimation nearly year round, which needs to be resolved in future upgrades to the real-time estimation algorithm. Both model-parameter error analysis and hydrologic application suggest that the three-layer Variable Infiltration Capacity (VIC-3L) model cannot tolerate the nonphysical overestimation behavior of 3B42RT through the hydrologic integration processes, and as such the 3B42RT data have almost no hydrologic utility, even at the monthly scale. In contrast, the 3B42V6 data can produce much better hydrologic predictions with reduced error propagation from input to streamflow at both the daily and monthly scales. This study also found the error structures of both RT and V6 have a significant geo-topography-dependent distribution pattern, closely associated with latitude and elevation bands, suggesting current limitations with TRMM-era algorithms at high latitudes and high elevations in general. Looking into the future Global Precipitation Measurement (GPM) era, the Geostationary Infrared (GEO-IR) estimates still have a long-term role in filling the inevitable gaps in microwave coverage, as well as in enabling sub-hourly estimates at typical 4-km grid scales. Thus, this study affirms the call for a real-time systematic bias removal in future upgrades to the IR-based RT algorithm using a simple scaling factor. This correction is based on MW-based monthly rainfall climatologies applied to the combined monthly satellite-gauge research products.
[1] Similarities and differences of spatial error structures of surface precipitation estimated with successive version 6 (V6) and version 7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) algorithms are systematically analyzed through comparison with the China Meteorological Administration's national daily precipitation analysis from June 2008 to May 2011. The TMPA products include V6 and V7 real-time products 3B42RTV6 and 3B42RTV7 and research products 3B42V6 and 3B42V7. Both versions of research products outperform their respective real-time counterparts. 3B42V7 clearly improves upon 3B42V6 over China in terms of daily mean precipitation; the correlation coefficient (CC) increases from 0.89 to 0.93, the relative bias (RB) improves from À4.91% to À0.05%, and the root-mean-square error (RMSE) improves from 0.69 mm to 0.54 mm. When considering 3 year mean precipitation, 3B42V7 shows similar spatial patterns and statistical performance to 3B42V6. Both 3B42RTV7 and 3B42RTV6 demonstrate similar bias patterns in most regions of China with overestimation by 20% in arid regions (i.e., the north and west of China) and slight underestimation in humid regions (e.g., À5.82% in southern China). However, 3B42RTV7 overestimates precipitation more than 3B42RTV6 in the cold Qinghai-Tibetan plateau, resulting in a much higher RB of 139.95% (128.69%, 136.09%, and 121.11%) in terms of 3 year annual (spring, summer, and autumn) daily mean precipitation and an even worse performance during winter. In this region, 3B42RTV7 shows an overall slightly degraded performance than 3B42RTV6 with CC decreasing from 0.81 to 0.73 and RB (RMSE) increasing from 21.22% (0.95 mm) to 35.84% (1.27 mm) in terms of daily precipitation. Citation: Chen, S., et al. (2013), Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China,
The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.
Accurate estimation of high-resolution precipitation on the global scale is extremely challenging. The operational Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has created over 16 years of high-resolution quantitative precipitation estimation (QPE), and has built the foundation for improved measurements in the upcoming Global Precipitation Measurement (GPM) mission. TMPA is intended to produce the “best effort” estimates of quasi-global precipitation from almost all available satelliteborne precipitation-related sensors by consistently calibrating them with the high-quality measurements from the core instrument platform aboard TRMM. Recently, the TMPA system has been upgraded to version 7 to take advantage of newer and better sources of satellite inputs than version 6, and has attracted a large user base. A key product from TMPA is the near-real-time product (TMPA-RT), as its timeliness is particularly appealing for time-sensitive applications such as flood and landslide monitoring. TMPA-RT’s error characteristics on a global scale have yet to be extensively quantified and understood. In this study, efforts are focused on a systematic evaluation of four sets of mainstream TMPA-RT estimates on the global scale. The analysis herein indicates that the latest version 7 TMPA-RT with the monthly climatological calibration had the lowest daily systematic biases of approximately 9% over land and –11% over ocean (relative to the gauge-adjusted research product). However, there still exist some unresolved issues in mountainous areas (especially the Tibetan Plateau) and high-latitude belts, and for estimating extreme rainfall rates with high variability at small scales. These global error characteristics and their regional and seasonal variations revealed in this paper are expected to serve as the benchmark for the upcoming GPM mission.
The spatial error structure of surface precipitation derived from successive versions of the TRMM Multisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the Continental United States (CONUS) for 3 years from June 2008 to May 2011. The TMPA products include the version‐6(V6) and version‐7(V7) real‐time products 3B42RT (3B42RTV6 and 3B42RTV7) and research products 3B42 (3B42V6 and 3B42V7). The evaluation shows that 3B42V7 improves upon 3B42V6 over the CONUS regarding 3 year mean daily precipitation: the correlation coefficient (CC) increases from 0.85 in 3B42V6 to 0.92 in 3B42V7; the relative bias (RB) decreases from −22.95% in 3B42V6 to −2.37% in 3B42V7; and the root mean square error (RMSE) decreases from 0.80 in 3B42V6 to 0.48 mm in 3B42V7. Distinct improvement is notable in the mountainous West especially along the coastal northwest mountainous areas, whereas 3B42V6 (also 3B42RTV6 and 3B42RTV7) largely underestimates: the CC increases from 0.86 in 3B42V6 to 0.89 in 3B42V7, and the RB decreases from −44.17% in 3B42V6 to −25.88% in 3B42V7. Over the CONUS, 3B42RTV7 gained a little improvement over 3B42RTV6 as RB varies from −4.06% in 3B42RTV6 to 0.22% in 3B42RTV7. But there is more overestimation with the RB increasing from 8.18% to 14.92% (0.16–3.22%) over the central US (eastern).
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.
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