Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

Abstract

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

“…Guo et al, 2015;Blacutt et al, 2015;Ward et al, 2011;Scheel et al, 2011). Here, we further subdivide daily rainfall in five types of events, which are used to classify precipitation based on its daily intensity, ranging from no rain (dry day; < 1 mm d −1 ) to violent rain (> 40 mm d −1 ), as shown in Table 2.…”

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

“…The PDF is the proportion of the number of times that rainfall events from each bin occurs divided by the total number of rainfall events. To considering the rainfall intensity (R), this study has divided rainfall into eight bins [20]: (1) no rain (R = 0), (2) 0 < R ≤ 0.5 mm/day, (3) 0.5 mm/day < R ≤ 1 mm/day, (4) 1 mm/day < R ≤ 2 mm/day, (5) 2 mm/day < R ≤5 mm/day, (6) 5 mm/day < R ≤ 10 mm/day, (7) 10 mm/day< R ≤ 20 mm/day and (8) R > 20 mm/day. The pixels nearest the gauge stations were selected to calculate the rainfall intensities.…”

“…For example, Hirpa et al evaluated the accuracies of three products (CMORPH, PERSIANN and TRMM3BV42) and found that both CMORPH and TRMM underestimated precipitation over higher elevations [19]. Guo Hao et al evaluated four products (TRMM, CMORPH, PERSIANN, GSMaP) using in situ measurements over Central Asia from 2004 to 2006 and most of the products overestimated the precipitation [20]. Awange et al used a "three-cornered-hat" method to assess six precipitation products and indicated that the RG-merged products had higher accuracies than the satellite-only products [21].…”

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

“…In particular, pixels located closer than 27 km or farther than 185 km from the radar or behind the hilly region are excluded due to insufficient reliability of the radar data, and pixels located in proximity of major lakes are excluded due to false rainfall signals in the CMORPH estimates (e.g. Guo et al, 2015). The number of pixels analysed for each climatic region is reported in Table 1.…”