Center Morphing Technique Research Articles

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and sub-daily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6–12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons. Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in the NEM.

Evolution of the Diurnal Precipitation Cycle with the Passage of a Madden–Julian Oscillation Event through the Maritime Continent

Changes in the diurnal precipitation cycle as the Madden–Julian oscillation (MJO) propagates through the Maritime Continent are investigated to explore the processes behind seaward-propagating precipitation northeast of New Guinea. Satellite rainfall estimates from TRMM 3B42 and the Climate Prediction Center morphing technique (CMORPH) are combined with simulations from the Weather Research and Forecasting (WRF) Model with a horizontal resolution of 4 km. Comparison with 24-h rain gauge measurements indicates that both satellite estimates and the WRF Model exhibit systematic biases. Despite these biases, the changing patterns of offshore precipitation with the passage of the MJO show good consistency between satellite estimates and the WRF Model. In the few days prior to the main MJO envelope, light background wind, relatively clear skies, and an increasingly moist environment promote favorable conditions for the diurnal precipitation cycle. Two distinct processes are identified: 100–200 km from the coast, precipitation moves offshore as a squall line with a propagation speed of 3–5 m s−1. Farther offshore, precipitation propagates with a speed close to 18 m s−1and is associated with an inertia–gravity wave generated by diurnally oscillating heating from radiative and moist convective processes over the land. A gravity wave signature is evident even after the MJO active period when there is little precipitation. By correcting for the background flow perpendicular to the coast, potential temperature anomalies for the lead-up, active, and follow-on MJO periods are shown to collapse to a remarkably invariant shape for a given time of day.

Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins

AbstractThis study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km2) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall–runoff transformation.

Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians

AbstractSatellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such events induced by hurricane landfalls in the southern Appalachian mountainous region. A distributed hydrological model (Coupled Routing and Excess Storage model) is applied to evaluate the impact of the proposed satellite precipitation error correction on flood simulations for 20 basins of various sizes in this mountainous region. The results demonstrate significant improvements due to the NWP-based adjustment technique in terms of both the precipitation error characteristics and corresponding runoff simulations. These improvements are shown to be comparable to those from the postprocessed gauge-adjusted CMORPH precipitation product, which is promising for advancing hydrologic uses of satellite rainfall in mountainous areas lacking ground observations.

Application of satellite products and hydrological modelling for flood early warning

Floods have caused devastating impacts to the environment and society in Awash River Basin, Ethiopia. Since flooding events are frequent, this marks the need to develop tools for flood early warning. In this study, we propose a satellite based flood index to identify the runoff source areas that largely contribute to extreme runoff production and floods in the basin. Satellite based products used for development of the flood index are CMORPH (Climate Prediction Center MORPHing technique: 0.25° by 0.25°, daily) product for calculation of the Standard Precipitation Index (SPI) and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for calculation of the Topographic Wetness Index (TWI). Other satellite products used in this study are for rainfall-runoff modelling to represent rainfall, potential evapotranspiration, vegetation cover and topography. Results of the study show that assessment of spatial and temporal rainfall variability by satellite products may well serve in flood early warning. Preliminary findings on effectiveness of the flood index developed in this study indicate that the index is well suited for flood early warning. The index combines SPI and TWI, and preliminary results illustrate the spatial distribution of likely runoff source areas that cause floods in flood prone areas.

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

Summary Satellite–gauge quantitative precipitation estimate (QPE) products may reduce the errors in near real-time satellite precipitation estimates by combining rain gauge data, which provides great potential to hydrometeorological applications. This study aims to comprehensively evaluate four of the latest satellite–gauge QPEs, including NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product, NOAA’s Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias-corrected product (CMORPH CRT), CMORPH satellite–gauge merged product (CMORPH BLD) and CMORPH satellite–gauge merged product developed at the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) (CMORPH CMA). These four satellite–gauge QPEs are statistically evaluated over the Huaihe River basin during 2003–2012 and applied into the distributed Variable Infiltration Capacity (VIC) model to assess hydrologic utilities. Compared to the China Gauge-based Daily Precipitation Analysis (CGDPA) newly developed at CMA/NMIC, the four satellite–gauge QPEs generally depict the spatial distribution well, with the underestimation in the southern mountains and overestimation in the northern plain of the Huaihe River basin. Specifically, both TRMM and CMORPH CRT adopt simple gauge adjustment algorithms and exhibit relatively poor performance, with evidently deteriorated quality in winter. In contrast, the probability density function-optimal interpolation (PDF-OI) gauge adjustment procedure has been applied in CMORPH BLD and CMORPH CMA, resulting in higher quality and more stable performance. CMORPH CMA further benefits from a merged dense gauge observation network and outperforms the other QPEs with significant improvements in rainfall amount and spatial/temporal distributions. Due to the insufficient gauge observations in the merging process, CMORPH BLD features the similar error characteristics of CMORPH CRT with a positive bias of light precipitation and a negative bias of heavy precipitation, in contrast to the overall large overestimation by TRMM. The quality of QPEs directly impacts streamflow simulations, as the precipitation biases are propagated into simulated streamflow through interaction with hydrologic processes. The general streamflow pattern is well captured at multiple time scales by the simulations using the four satellite–gauge QPEs as the input forcing. CMORPH CRT shows the worst simulations in both long-term streamflow and extreme flood events, while CMORPH CMA forced streamflow simulations even outperform that forced by CGDPA. CMORPH CMA is able to reproduce the July 2003 flood event, while the other three QPEs fail to generate such extreme flood. Overall, CMORPH CMA shows great potential to improve the precipitation distribution and hydrometeorological simulations, and can serve as an alternative high quality QPE in China.

An agricultural drought severity index using quasi-climatological anomalies of remotely sensed data

ABSTRACTObtaining an agricultural drought index using solely remotely sensed products has numerous benefits over their in situ counterparts such as if a country does not have the resources to implement an in situ ground network. One such index, created by Rhee et al. (2010), uses a combination of precipitation data from the Tropical Rainfall Measuring Mission (TRMM), with land-surface temperature (LST) data and vegetation indices (VIs) using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess drought conditions. With TRMM data becoming no longer available (as of mid-2015), this study sought to test precipitation data from the Climate Prediction Center (CPC) Morphing (CMORPH) Technique over the study period of January 2003–September 2014, in order to take the place of the TRMM data set in a drought severity index (DSI). This study also attempted to refine the methodology using the quasi-climatological anomalies (short-term climatological anomalies) of each parameter within the DSI. We validated the results of the DSI against in situ percentage available water (PAW) data from a soil water balance (SWB) model over the country of Uruguay. The results of the DSI correlated well with the PAW over the warmer months (October–March) of the year with average r-values ranging from 0.74 to 0.81, but underperformed during the colder months (April–September) with average r-values ranging from 0.38 to 0.50. This underperformance is due to the fact that precipitation during this season continues to have high variability, whereas PAW stays relatively constant. Spatially the DSI correlates well over the majority of the country with the possible exception of underperformance near the coastal area in the southeastern portion of the country. Ultimately, this research has the ability to aid Uruguay in better drought monitoring and mitigation practices as well as emergency aid resource allocation.

Uncertainty and Bias in Satellite-Based Precipitation Estimates over Indian Subcontinental Basins: Implications for Real-Time Streamflow Simulation and Flood Prediction*

Abstract Real-time streamflow monitoring is essential over the Indian subcontinental river basins, as a large population is affected by floods. Moreover, streamflow monitoring helps in managing water resources in the agriculture-dominated region. In this study, the authors systematically investigated the bias and uncertainty in satellite-based precipitation products [Climate Prediction Center morphing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); and Tropical Rainfall Measuring Mission (TRMM), version 7, real-time (3B42RTV7) and gauge-adjusted (3B42V7) products] over the Indian subcontinental river basins for the period of 2000–13. Moreover, the authors evaluated the influence of bias in the satellite precipitation on real-time streamflow monitoring and flood assessment over the Mahanadi river basin. Results showed that CMORPH and PERSIANN underestimated daily mean precipitation over the majority of the subcontinental river basins. On the other hand, TRMM-3B42RTV7 overestimated daily mean precipitation over most of the river basins in the subcontinent. While gauge-adjusted products of PERSIANN (PERSIANN-CDR) and TRMM (TRMM-3B42V7) performed better than their real-time products, large biases remain in their performance to capture extreme precipitation (both frequency and magnitudes) over the subcontinental basins. Among the real-time precipitation products, TRMM-3B42RTV7 performed better than CMORPH and PERSIANN over the majority of the Indian subcontinental basins. Daily streamflow simulations using the Variable Infiltration Capacity model (VIC) for the Mahanadi river basin showed a better performance by the TRMM-3B42RTV7 product than the other real-time datasets. Moreover, daily streamflow simulations over the Mahanadi river basin showed that bias in real-time precipitation products affects the initial condition and precipitation forcing, which in turn affects flood peak timing and magnitudes.

Evaluating Satellite-Based Diurnal Cycles of Precipitation in the African Tropics

AbstractPrecipitation plays a major role in the energy and water cycles of the earth. Because of its variable nature, consistent observations of global precipitation are challenging. Satellite-based precipitation datasets present an alternative to in situ–based datasets in areas sparsely covered by ground stations. These datasets are a unique tool for model evaluations, but the value of satellite-based precipitation datasets depends on their application and scale. Numerous validation studies considered monthly or daily time scales, while less attention is given to subdaily scales. In this study subdaily satellite-based rainfall data are analyzed in West Africa, a region with strong diurnal variability. Several satellite-based precipitation datasets are validated, including Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), TRMM 3G68 products, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) morphing technique (CMORPH) data. As a reference, highly resolved in situ data from the African Monsoon Multidisciplinary Analysis–Couplage de l’Atmosphere Tropical et du Cycle Hydrologique (AMMA-CATCH) are used. As a result, overall the satellite products capture the diurnal cycles of precipitation and its variability as observed on the ground reasonably well. CMORPH and TMPA data show overall good results. For locally induced convective rainfall in the evening most satellite data show slight delays in peak precipitation of up to 2 h.

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.

Evaluation of Remotely Sensed Precipitation and Its Performance for Streamflow Simulations in Basins of the Southeast Tibetan Plateau

Abstract Four satellite-based precipitation products [TMPA real time (T-rt), its gauge-adjusted version (T-adj), Climate Prediction Center (CPC) morphing technique (CMORPH) real time (C-rt), and its gauge-adjusted version (C-adj)] were evaluated by a gauge-based synthesis dataset. Further, these products along with the CMORPH gauge–satellite blended version (C-ga), which is virtually C-adj in precipitation ungauged regions and is controlled by gauge analysis over regions of a dense station network, were intercompared with daily streamflow predicted by the distributed vegetation interface processes (VIP) model in the Lhasa and Gongbo basins of the southeast Tibetan Plateau. Results show these satellite-based products perform better in summer than in other seasons. Relative to the gauge-based synthesis dataset, for areal precipitation of the Lhasa basin from 2007 to 2010, biases of C-rt and T-rt are −10.49% and 157.88%, respectively. Biases of C-adj and T-adj are 3.42% and 24.12%, respectively. The C-rt bias is underestimation of the volume of observed rainfall correctly detected and overestimation of the volume of falsely alarmed rainfall, while T-rt bias comes from overestimation of the volume of observed rainfall correctly detected. Simulation efficiencies of stream discharges driven by T-adj and C-adj are better than those by T-rt and C-rt, which are consistent with the accuracies of these products. With benchmarked model parameters using the gauge-based dataset, C-adj presents well for simulation, while T-adj needs parameter recalibration to achieve good skills. Compared to T-adj and C-adj, better simulation could be obtained by C-ga in precipitation-gauged regions.

Comparing global precipitation data sets in eastern Africa: a case study of Kilombero Valley, Tanzania

In the face of limited or no precipitation data, global precipitation data sets (GPDs) may provide a viable alternative to gauge or ground radar data. This study aims to provide guidance to the choice of GPDs targeting scales relevant to water resources management in data poor regions. Specifically, the 34 000 km2 Kilombero Valley in central Tanzania, where water resource management is seen as integral to poverty reduction and food security, is used as a case study for performance evaluation of seven GPDs and their ensemble mean against the Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis research-grade product v7 (TRMMv7). The GPDs include one satellite rainfall product [Climate Prediction Center morphing technique v1.0 CRT (CMORPH)], three reanalysis products [Climate Forecasting System Reanalysis (CFSR), European reanalysis interim (ERA-i) and Modern Era Retrospective-Analysis for Research and Applications (MERRA)] and three interpolated data sets [Climate Research Unit Time Series 3.21 (CRU), Global Precipitation and Climatology Center v6 data set (GPCC) and University of Delaware Air Temperature and Precipitation v3.01 data set (UDEL)]. Standard statistical performance measures and spatial patterns were evaluated for the common overlap time period 1998–2010. For this region, the principal seasonality of the climatology was well represented in all GPDs; however, the intraseasonal variability and the spatial precipitation patterns were less well represented. The ensemble mean and GPCC had the best performance with regard to the analysis of the time series while CMORPH and GPCC had the best performance with regard to the spatial pattern analysis. These results indicate that the spatial scale intended for application is a major factor impacting the suitability of a given GPD for hydrometrological studies that form a basis for development of water management strategies.

A merging scheme for constructing daily precipitation analyses based on objective bias‐correction and error estimation techniques

AbstractA new merging scheme (referred to as HL‐OI) was developed to combine daily precipitation data from high‐resolution gauge (HRG) observations, The Climate Prediction Center morphing technique (CMORPH) satellite estimates, and National Centers for Environmental Prediction (NCEP) numerical simulations over China to perform reliable high‐resolution daily precipitation analyses. The scheme is designed using a three‐step strategy of removing systemic biases, reducing random errors, quantitatively estimating error variances, and combining useful information from each data source. First, a cumulative distribution function matching procedure is adopted to reduce biases and provide unbiased background fields for the following merging processes. Second, the developed error estimation algorithm is implemented to quantify both the background and observation errors from the background departures. Third, the bias‐corrected NCEP and CMORPH data are combined with the HRG data using the optimal interpolation (OI) objective analysis technique. The magnitudes and spatial structures of both observation errors and background errors can be estimated successfully. Results of cross‐validation experiments show that the HL‐OI scheme effectively removes most of systemic biases and random errors in the background fields compared to the independent gauge observations and is robust even with imperfect background fields. The HL‐OI merging scheme significantly improves the temporal variations, agreements between the spatial patterns, frequency, and locations of daily precipitation occurrences. When information from gauge observations, satellite estimates, and model simulations are combined simultaneously, the merged multisource analyses perform better than dual‐source analyses. These results indicate that each independent information source of daily precipitation contributes to improving the quality of the final merged analyses under the framework of HL‐OI scheme.

Assessment of the Spatial and Seasonal Variation of the Error–Intensity Relationship in Satellite-Based Precipitation Measurements Using an Adaptive Parametric Model

AbstractStudies have been reported about the efficacy of satellites for measuring precipitation and about quantifying their errors. Based on these studies, the errors are associated with a number of factors, among them, intensity, location, climate, and season of the year. Several error models have been proposed to assess the relationship between the error and the rainfall intensity. However, it is unknown whether these models are adaptive to different seasons, different regions, or different types of satellite-based estimates. Therefore, how the error–intensity relationship varies with the season or region is unclear. To investigate these issues, a parametric joint pdf model is proposed to analyze and study the 9-yr satellite-derived precipitation datasets of Climate Prediction Center (CPC) morphing technique (CMORPH); PERSIANN; and the real-time TRMM product 3B42, version 7 (TRMM-3B42-RTV7). The NEXRAD Stage IV product is the ground reference. The adaptability of the proposed model is verified by applying it to three locations (Oklahoma, Montana, and Florida) and by applying it to cold season, warm season, and the entire year. Then, the heteroscedasticities in the errors of satellite-based precipitation measurements are investigated using the proposed model under those scenarios. The results show that the joint pdfs have the same formulation under these scenarios, whereas their parameter sets were adaptively adjusted. This parametric model reveals detailed information about the spatial and seasonal variations of the satellite-based precipitation measurements. It is found that the shape of the conditional pdf shifts across the intensity ranges. At the ~10–20 mm day−1 range, the conditional pdf is L shaped, while at the ~40–60 mm day−1 range, it becomes more bell shaped. It is also concluded that no single satellite-based precipitation product outperforms others with respect to the different scenarios (i.e., seasons, regions, and climates).

Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins–Araguaia basin in Brazil

Summary This study investigates the applicability of error corrections to satellite-based precipitation products in streamflow simulations. A three-year time series (2008–2011) is considered across 19 sub-basins of the Tocantins–Araguaia basin (764,000 km 2 ), located in the center-north region of Brazil. A raingauge network (24 h accumulation) of approximately 300 collection points (∼1 gauge every 2500 km 2 ) is used as reference for evaluating the following four satellite rainfall products: the Tropical Rainfall Measuring Mission real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), the Global Satellite Mapping of Precipitation (GSMaP), and the NOAA Hydroestimator (HYDRO-E). Ensemble streamflow simulations, for both dry and rainy seasons, are obtained by forcing the Distributed Hydrological Model developed by the Brazilian National Institute for Space Research (MHD–INPE) with the satellite rainfall products, corrected using a two-dimensional stochastic satellite rainfall error model (SREM2D). The ensemble simulations are evaluated using streamflow output derived by forcing the model with reference rainfall gauge data. SREM2D is able to correct for errors in the satellite precipitation data by pushing the modeled streamflow ensemble closer to the reference river discharge, when compared to the simulations forced with uncorrected rainfall input. Ensemble streamflow error statistics (MAE and RMSE) show a decreasing trend as a function of the catchment area for all satellite products, but the rainfall-to-streamflow error propagation does not show any dependence on the basin size.

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

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 ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. 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%).

An assessment of three satellite-based precipitation data sets as applied to the Thailand region

The study examines three satellite-based data sets to estimate long-term precipitation for the Thailand region: the Tropical Rainfall Mapping Mission (TRMM) 3B43, the Climate Prediction Centre morphing technique (CMORPH), and a locally developed regression model using the geostationary meteorological satellite (GMS) covering the Thailand region. Data for the first two sets were available at a spatial resolution of 0.25° × 0.25°, while the local regression model used data from the GMS at a resolution of 5 km × 5 km. The statistical regression model was developed by relating long-term monthly average precipitation from 27 rain gauge stations with concurrent satellite data in the visible and thermal infrared bands. The model was then tested against independent data from 27 rain gauge stations. Satellite/rain gauge ratios were estimated, and a smooth spline surface was used to correct the error from the model. Data from the three approaches were compared with a rain gauge network. The TRMM relation performed better than CMORPH, and the performance for GMS was comparable to TRMM with root mean square different and mean bias difference of 33.6 and 4.2%, respectively. The locally developed regression model was used to produce monthly and yearly total rainfall maps from the GMS data for the entire country.

Reply to “Comments on ‘Error Analysis of Satellite Precipitation Products in Mountainous Basins’”

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

Multiscale Hydrologic Applications of the Latest Satellite Precipitation Products in the Yangtze River Basin using a Distributed Hydrologic Model

Abstract The present study aims to evaluate three global satellite precipitation products [TMPA 3B42, version 7 (3B42 V7); TMPA 3B42 real time (3B42 RT); and Climate Prediction Center morphing technique (CMORPH)] during 2003–12 for multiscale hydrologic applications—including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling—via a distributed hydrological model in the Yangtze River basin. The comparison shows that the 3B42 V7 data generally have a better performance in annual water budgeting and monthly streamflow simulation, but this superiority is not guaranteed for daily simulation, especially for flood monitoring. It is also found that, for annual water budgeting, the positive (negative) bias of the 3B42 RT (CMORPH) estimate is mainly propagated into the simulated runoff, and simulated evapotranspiration tends to be more sensitive to negative bias. Regarding streamflow simulation, both near-real-time products show a region-dependent bias: 3B42 RT tends to overestimate streamflow in the upper Yangtze River, and, in contrast, CMORPH shows serious underestimation in those downstream subbasins while it is able to effectively monitor streamflow into the Three Gorges Reservoir. Using 394 selected flood events, the results indicate that 3B42 RT and CMORPH have competitive performances for near-real-time flood monitoring in the upper Yangtze, but for those downstream subbasins, 3B42 RT seems to perform better than CMORPH. Furthermore, the inability of all satellite products to capture some key features of the July 2012 extreme floods reveals the deficiencies associated with them, which will limit their hydrologic utility in local flood monitoring.

Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.