Evaluation of Tropical Intraseasonal Variability and Moist Processes in the NOGAPS Analysis and Short-Term Forecasts

Evaluation of the latest satellite–gauge precipitation products and their hydrologic applications over the Huaihe River basin

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products (i.e., Version-7 real-time TMPA (T-rt) and gauge-adjusted TMPA (T-adj), and Version-1.0 real-time CMORPH (C-rt) and Version-1.0 gauge-adjusted CMORPH (C-adj)) are evaluated and intercompared by using independent rain gauge observations for a 12-year (2000–2011) period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite precipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.

In this study, the recent work of Gottschalck et al. and Ebert et al. is extended by assessing the suitability of two Tropical Rainfall Measuring Mission (TRMM)-based precipitation products for hydrological land data assimilation applications. The two products are NASA’s gauge-corrected TRMM 3B42 Version 6 (3B42), and the satellite-only NOAA Climate Prediction Center (CPC) morphing technique (CMORPH). The two products were evaluated against ground-based rain gauge–only and gauge-corrected Doppler radar measurements. The analyses were performed at multiple time scales, ranging from annual to diurnal, for the period March 2003 through February 2006. The analyses show that at annual or seasonal time scales, TRMM 3B42 has much lower biases and RMS errors than CMORPH. CMORPH shows season-dependent biases, with overestimation in summer and underestimation in winter. This leads to 50% higher RMS errors in CMORPH’s area-averaged daily precipitation than TRMM 3B42. At shorter time scales (5 days or less), CMORPH has slightly less uncertainty, and about 10%–20% higher probability of detection of rain events than TRMM 3B42. In addition, the satellite estimates detect more high-intensity events, causing a remarkable shift in precipitation spectrum. Summertime diurnal cycles in the United States are well captured by both products, although the 8-km CMORPH seems to capture more diurnal features than the 0.25° CMORPH or 3B42 products. CMORPH tends to overestimate the amplitude of the diurnal cycles, particularly in the central United States. Possible causes for the discrepancies between these products are discussed.

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

This study evaluates four satellite-based precipitation datasets with gauged rainfall observations at a daily and wet season time scales. Satellite Precipitation Estimators from Climate Hazards Group Infrared Precipitation Stations (CHIRPSv2), the Climate prediction center (CPC) morphing technique (CMORPH), the Integrated Multi-satellite Retrieval for GPM (IMERG-06) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSSIANN-CCS) were used. Categorical and continuous statistical techniques were applied to evaluate the detection and estimation ability of SPEs. Among the SPEs, CMORPH was superior, 0.90 and followed by IMERG-06, 0.82 in the Probability of detection (POD), Critical success index (CSI), 0.74 and 0.70 and Accuracy, 0.75 and 0.72 at wet season, respectively. The result of bias ratio showed underestimation for all SPEs at wet season and daily time scale. The comparison of SPEs in terms of RMSE indicated that IMERG-06 was better, 75.9 followed by CMORPH, 86.0. In terms of Relative bias (RBias), IMERG-06 was the second next to CHIRPSv2. On the other hand, IMERG-06 had better RMSE at JJAS and daily time scale. Overall, the findings of this evaluation study showed the capacity of IMERG-06 product could be reliable rainfall data sources for climate and hydrological analysis of central highlands of Abbay Basin.

Using multiple satellite-gauge merged precipitation products ensemble for hydrologic uncertainty analysis over the Huaihe River basin

With many operational satellite rainfall products being available for long periods, it is now possible to examine whether these products can reproduce climatologically known rainfall characteristics over large river basins that suffer from poor surface monitoring resources. Such assessment is a prerequisite for any further hydrologic applications that rely on these products. The current study evaluates two satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis research-grade product (TMPA-3B42) and the Climate Prediction Center (CPC) morphing technique (CMORPH), over the domain of the Nile basin in eastern Africa. The large latitudinal extent of the basin, its complex topography, and its diverse land use result in widely contrasting regimes and distributions of annual and seasonal rainfall. The results suggest that the two products are fairly successful in reproducing some of the region-specific rainfall patterns across climatologically different parts of the basin. However, significant overestimation and underestimation by CMORPH and TMPA-3B42, respectively, are clearly evident over the majority of the basin and can exceed 100% of the mean annual rainfall. The biases are also evident in the seasonal rainfall cycle. The bias shows a complex dependency, in terms of magnitude and sign, on topography and latitude, especially in the central parts of the basin and over the Ethiopian Highlands region. The performance of both products is better over the equatorial region of the basin. The significant underestimation by the gauge-adjusted TMPA-3B42 product relative to CMORPH is attributed to the sparsity in operational gauges, which can adversely affect bias adjustment procedures in the TMPA algorithm. Current and future algorithmic developments are expected to bring much-needed improvements for satellite rainfall products to see full operational utility in these regions of the world.

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

A Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH) algorithm is developed to integrate the passive microwave (PMW) precipitation estimates from low-Earth-orbit (LEO) satellites and infrared (IR) observations from geostationary (GEO) platforms. With the new algorithm, the precipitation analysis at a grid box of 8 × 8 km2 is defined in three steps. First, PMW estimates of instantaneous rain rates closest to the target analysis time in both the forward and backward directions are propagated from their observation times to the analysis time using the cloud system advection vectors (CSAVs) computed from the GEO–IR images. The “prediction” of the precipitation analysis is then defined by averaging the forward- and backward-propagated PMW estimates with weights inversely proportional to their error variance. The IR-based precipitation estimates are incorporated if the gap between the two PMW observations is longer than 90 min. Validation tests showed substantial improvements of the KF-based CMORPH against the original version in both the pattern correlation and fidelity of probability density function (PDF) of the precipitation intensity. In general, performance of the original CMORPH degrades sharply with poor pattern correlation and substantially elevated (damped) frequency for light (heavy) precipitation events when PMW precipitation estimates are available from fewer LEO satellites. The KF-based CMORPH is capable of producing high-resolution precipitation analysis with much more stable performance with various levels of availability for the PMW observations.

Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

Evaluation on the accuracy of global satellite precipitation products is of great interest to the hydrologic community. Recently, Mei et al. (2014) evaluated the performance of four widely used satellite precipitation products over an Alpine basin in northeastern Italy. Yong (2015) commented on the representativeness of these results by comparing their findings to other studies, giving particular emphasis on a similar evaluation study over mainland China. The four quasi-global satellite products involved in Mei et al. (2014) are the TMPA 3B42 in real time [3B42-RT; calibrated according to the climatology of TMPA 3B42, version 6 (3B42V6); hereafter named 3B42-RT-CCA]; TMPA 3B42, version 7 (3B42-V7); Climate Prediction Center (CPC) morphing technique (CMORPH); and PERSIANN [see section 2b in Mei et al. (2014) for descriptions]. Yong (2015) states that selection of real-time products [e.g., QMORPH (a variation on CMORPH), Global Satellite Mapping of Precipitation in near–real time (GSMaP_ NRT), and the uncalibrated 3B42-RT (hereafter named 3B42-RT-UC)] would have been more appropriate for evaluating the potential of satellite precipitation estimation in real-time hydrological applications. We agree that the near-real-time satellite datasets can be of great interest to the hydrologic community focusing on flood hazard warnings. However, we believe that evaluation of post-real-time satellite precipitation products provides evidence on their potential use for a number of water resource applications (e.g., water budget calculations, derivation of precipitation intensity–frequency–duration curves, and derivation of rainfall thresholds for hydrologic hazard warning systems), which is of interest to the hydrologic community as well. Moreover, Mei et al. (2014) presented a comparison of a near-real-time (i.e., the 3B42-RT-CCA) product with the corresponding gauge-adjusted (3B42-V7) product, which provides an assessment on the effectiveness of current climatological and post-real-time adjustment techniques in satellite precipitation estimation. The comments in Yong (2015) focused particularly on the results reported in Table 4 of Mei et al. (2014) and specifically regarding the effect that climatological gauge adjustment may have on the random error of satellite estimates for moderate to high rainfall rates. Yong (2015) states that ‘‘[b]ecause of the dynamic balance between systematic and random errors caused by the CCA, we speculate that the RMSE values of uncalibrated 3B42-RT might also be lower than 3B42-RT in this Italian basin.’’ To address this point, we have expanded the analysis presented in Table 4 of Mei et al. (2014) to include the 3B42-RT-UC product and contrasted its error characteristics to the corresponding error properties of the climatological-mean-adjusted (3B42-RT-CCA) and postreal-time (3B42-V7) products. Results shown in Table 1 confirm the quoted statement by Yong (2015); namely, 3B42-RT-UC is characterized with a lower degree of random error than that of the 3B42-RT-CCA at event scale. Moreover, the cold season error statistics [RMSE and correlation coefficient (CC)] of 3B42-RT-UC exhibit improvements over both 3B42-V7 and 3B42-RT-CCA. Yong (2015) further commented on the results and justification we gave regarding the higher cold season correlation coefficient values in 3B42-RT-CCA relative to 3B42-V7.Mei et al. (2014) stated that this is due to the Corresponding author address: Emmanouil N. Anagnostou, CEE, University of Connecticut, 261 Glenbrook Rd., Unit 3037, Storrs, CT 06269. E-mail: manos@engr.uconn.edu JUNE 2015 CORRES PONDENCE 1445

Limited availability of surface‐based rainfall observations constrains the evaluation of satellite rainfall products over many regions. Observations are also often not available at time scales to allow evaluation of satellite products at their finest resolutions. In the present study, we utilized a 3‐month rainfall data set from an experimental network of eight automatic gauges in Gilgel Abbay watershed in Ethiopia to evaluate the 1‐hourly, 8 × 8‐km Climate Prediction Center morphing technique (CMORPH) rainfall product. The watershed is situated in the Lake Tana basin which is the source of the Blue Nile River. We applied a suite of statistical metrics that included mean difference, bias, standard deviation of differences and measures of association. Our results indicate that the accuracy of the CMORPH product shows a significant variation across the basin area. Its estimates are mostly within ±10 mm h−1 of the gauge rainfall observations; however, the product does not satisfactorily capture the rainfall temporal variability and is poorly correlated (<0.27) to gauge observations. Its poor rain detection capability led to significant underestimation of the seasonal rainfall depth (total bias reaches up to −52%) with large amounts of hit rain bias as well as missed rain and false rain biases. In the future refinement of CMORPH algorithm, more attention should be given to reducing missed rain bias over the mountains of Gilgel Abbay, whereas equal attention should be given to hit, missed rain and false rain biases over other parts of the watershed. Copyright © 2012 John Wiley & Sons, Ltd.