Behrangi, A., et al. (2014). “An Update on the Oceanic Precipitation Rate and Its Zonal Distribution in Light of Advanced Observations from Space.” Journal of Climate 27(11): 3957-3965.
Author analyzed oceanic precipitation at zonal scales for products: GPCP (global precipitation climatology project), merged CloudSat CPR and TRMM-PR, CMAP (Climate predicting center Merged Analysis of Precipitation) at 80N to 80S. The results shown that the new merged TRMM/CloudSat estimates rain rate is 2.94 mm/day 9% higher than CMAP, 4% higher than GPCP,
Dore, M. H. (2005). “Climate change and changes in global precipitation patterns: what do we know?” Environ Int 31(8): 1167-1181.
Author summarized following general partterns: (a) increased precipitation in high latitudes (NH); (b) reductions in precipitation in China, Australia and the Small Island Staates in the Pacific; (c) equatorial regions becone more variable, i.e. increased variance.
Annual zonally averaged precipitation increased by between 7% and 12% for the zones 30N to 85N and by about 2%between 0S and 55S.
A. Becker, P. F., A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese (2013). “A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present.” Earth System Science Data 5: 71-99.
Full descrption of GPCC data set, including climatological calibration, interpolation, quality control and different version variations.
George, J. H. (1997). “Estimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation.” Journal of Applied Meteorology 36(9): 1191-1201.
The random errors contained in a finite set E of precipitation estimates result from both finite sampling and measurement–algorithm effects. The expected root-mean-square random error associated with the estimated average precipitation in E is shown to be $\sigma_r = r̄{(H − p)/pNI}^(1/2)$, where r̄ is the space–time-average precipitation estimate over E, H is a function of the shape of the probability distribution of precipitation (the nondimensional second moment), p is the frequency of nonzero precipitation in E, and NI is the number of independent samples in E. All of these quantities are variables of the space–time-average dataset. In practice H is nearly constant and close to the value 1.5 over most of the globe. An approximate form of $σ_r$ is derived that accommodates the limitations of typical monthly datasets, then it is applied to the microwave, infrared, and gauge precipitation monthly datasets from the Global Precipitation Climatology Project. As an aid to visualizing differences in $σ_r$ for various datasets, a “quality index” is introduced. Calibration in a few locations with dense gauge networks reveals that the approximate form is a reasonable first step in estimating $σ_r$.
Gebregiorgis, A. S., et al. (2018). “To What Extent is the Day 1 GPM IMERG Satellite Precipitation Estimate Improved as Compared to TRMM TMPA-RT?” Journal of Geophysical Research: Atmospheres 123(3 %@ 2169-897X): 1694-1707.
Author compared IMERG late product and TRPA product with one year dataset for CONUS, with MRMS radar data as reference. He decomposed error sources into successful detection, false detection, miss rain and explored seasonality of the differences. As the results suggests, IMERG has improved performance mainy due to: improved IR detection (PERSIANN-CCS), smoother interpolation (CMORPH-KF). Most of the error accumulates in the region where convective precipitation dominates.
Tan, J., et al. (2019). “Diurnal Cycle of IMERG V06 Precipitation.” Geophysical Research Letters.
The authors specifically analyzed diurnal cycle of IMERG precipitation data, and validated it with MRMS: over US, it is slightly lagged no more than 1 hour (median half hour).
They extended their research on global diurnal cycles, and found:
- inland near coast, precipitation peaks in the afternoon, while in the late afternoon or evening further inland.
- Over ocean, maximum precipitation occurs around midnight close to shore and in the morning futher offshore.
Some regional cases e.g. Singapore, Bangladesh and Lake Victoria illustrated the interplay between durnal and seasonal cycles, exemplifying the unprecedented ability of IMERG in capturing diurnal variability of precipitation globally.
Tan, J., et al. (2015). “Increases in tropical rainfall driven by changes in frequency of organized deep convection.” Nature 519(7544): 451-454.
Observations and models have found that changes in rainfall show patterns characterized as ‘wet-gets-wetter’ and ‘warmer-gets-wetter’. These changes in precipitation are largely located in the tropics and hence are probably associated with convection.
the spatial patterns of change in the frequency of organized deep convection are strongly correlated with observed change in rainfall, both positive and negative (correlation of 0.69), and can explain most of the patterns of increase in rainfall. In contrast, changes in less organized forms of deep convection or changes in precipitation within organized deep convection contribute less to changes in precipitation.
Wang, Z., et al. (2017). “Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility.” Atmospheric Research 196: 151-163.
Taking the Beijiang River Basin as the case study, we used nine statistical evaluation indices and the Variable Infiltration Capacity (VIC) distributed hydrological model to quantitatively evaluate the performance and the hydrological utility of three Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) products: the near-real-time “Early” run and “Late” run IMERG products (IMERG-E and IMERG-L), and the post-real-time “Final” run IMERG product (IMERG-F) over south China during 2014–2015, with the last-generation Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42-V7 product as comparison. The IMERG-F presents satisfactory accuracy with high correlation coefficient (CC=0.63) and low relative bias (0.92%), while the IMERG-E and IMERG-L performs relatively poorly featuring low correlation (with CC of 0.49 and 0.52 respectively) with the ground observations. All of the three IMERG products present apparently higher probability of detection (POD, 0.64–0.67) but have higher false alarm ratio (FAR, ≧0.14) than the 3B42-V7. The hydrological simulation under scenario I (model calibrated by the gauge observations) shows that, the IMERG-F, with a high Nash–Sutcliffe coefficient of efficiency (NSCE) of 0.742, presents better hydrological performance than the 3B42-V7; the IMERG-E and IMERG-L perform poorly for the whole simulation period with NSCE lower than 0.35 and relative bias higher than 28% while perform satisfactorily during the flood season with apparently higher NSCE of 0.750 and 0.733 respectively. The hydrological simulation under scenario II (model calibrated by the 3B42-V7) shows that the performance of all the IMERG products was significantly improved. Generally, the IMERG-F has high accuracy and good hydrological utility, while the IMERG-E and IMERG-L products have satisfactory hydrological utility during the flood season and thus have great potential for the real-time application such as flood forecasting. The late-run IMERG-L presents little improvement in performance comparing with the early-run IMERG-E, therefore, the timelier IMERG-E is recommended to be firstly considered.
Wang, C., et al. (2018). “Global intercomparison and regional evaluation of GPM IMERG Version-03, Version-04 and its latest Version-05 precipitation products: Similarity, difference and improvements.” Journal of Hydrology 564: 342-356.
The overarching goal of this study is to intercompare the newly released Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 (V05) products with its former Version 04 (V04) and Version 03 (V03) products and also assess any differences and improvements, with cross-evaluation against the Global Precipitation Climatology Project (GPCP) Version 2.3, Multi-Source Weighted-Ensemble Precipitation (MSWEP) Version 2.1 and the dense gauge networks in China. Firstly, the gauge-adjusted products (Final run) of V03, V04 and V05 are compared over the globe. Then, the near-real-time products without gauge adjustments (Early and Late run) and Final run products of all versions are evaluated against ground-based observations comprised of more than 30,000 gauges over Mainland China at 0.1° × 0.1° grid and hourly and daily temporal scales. The primary conclusions are: (1) globally, both V04 and V05 Final run show significant differences and improvements from V03. Particularly, the overall mean oceanic precipitation of V04 and V05 increases by +31.36% and +28.81% respectively from that of V03 and much closer to GPCP and MSWEP; (2) over Mainland China, the Early and Late run products of the same version (V03 or V04) generally have similar performance, while V04 Early and Late run have better performance in most regions than the corresponding run of V03 except in the arid Xinjiang Province and the mountainous Tibetan Plateau; and (3) V04 and V03 Final run show comparable performance, while V05 Final run generally improves upon both V04 and V03 and has the best performance among the seven standard IMERG products. The improvement of V05 Final run is particularly evident in southeastern and western China. At a timely matter, the study provides first-hand global and regional assessment feedback to IMERG algorithm developers and also sheds insights for GPM precipitation product users across the world.
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z. and Hong, Y.: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sensing of Environment, 240, 111697, doi:10.1016/j.rse.2020.111697, 2020.
The author compared nine satellite and reanalysis products of accuracy towards precipitation observation in China. The insights are:
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Evaluate from different scales:
(1) Rainfall Analysis:
a. daily scale from 2000 to 2018
(a) The overall performance; (b) regional and seasonal performance; (c) annual trends of accuracy indices;b. Hourly scale from 2013 to 2015
(2) Snowfall Analysis:
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Error decomposition
(1) Many indices to describe the error: RMSE, ME, bias score, POD, 1-FAR(SR), KGE, CSI; (2) further decomposed RMSE into systematic and random componentsl; (3) utilized TC to measure the results;
Conclusion: IMERG has three rooms for further improvement: (1) precipitation classification algorithm for snowfall (currently using wet-bulb temperature); (2) use daily gauge to calibrate; (3) including more PMW sensors to fill the overpass gaps
A decent overview of all the state-of-the-art satellite precipitation products and their deficiencies over continents.
Approaching with hydrologic utility
The authors compared 19 global precipitation products with regard to two lumped hydrologic models to inform which global precipitation products work better.
In fact, IMERG and CMORPH apprea to be the most efficient one, although the others highly depend on basin characteristics and uncertainty via error propagation.