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Jackson Tan, Walter A. Petersen, Pierre-Emmanuel Kirstetter, and Yudong Tian

Abstract

The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

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Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

Abstract

A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

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Emad Habib, Alemseged Tamiru Haile, Yudong Tian, and Robert J. Joyce

Abstract

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.

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Yudong Tian, Christa D. Peters-Lidard, Bhaskar J. Choudhury, and Matthew Garcia

Abstract

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.

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, and Yudong Tian

Abstract

The downwelling shortwave radiation on the earth’s land surface is affected by the terrain characteristics of slope and aspect. These adjustments, in turn, impact the evolution of snow over such terrain. This article presents a multiscale evaluation of the impact of terrain-based adjustments to incident shortwave radiation on snow simulations over two midlatitude regions using two versions of the Noah land surface model (LSM). The evaluation is performed by comparing the snow cover simulations against the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The model simulations are evaluated using categorical measures, such as the probability of detection of “yes” events (PODy), which measure the fraction of snow cover presence that was correctly simulated, and false alarm ratio (FAR), which measures the fraction of no-snow events that was incorrectly simulated. The results indicate that the terrain-based correction of radiation leads to systematic improvements in the snow cover estimates in both domains and in both LSM versions (with roughly 12% overall improvement in PODy and 5% improvement in FAR), with larger improvements observed during snow accumulation and melt periods. Increased contribution to PODy and FAR improvements is observed over the north- and south-facing slopes, when the overall improvements are stratified to the four cardinal aspect categories. A two-dimensional discrete Haar wavelet analysis for the two study areas indicates that the PODy improvements in snow cover estimation drop to below 10% at scales coarser than 16 km, whereas the FAR improvements are below 10% at scales coarser than 4 km.

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Viviana Maggioni, Mathew R. P. Sapiano, Robert F. Adler, Yudong Tian, and George J. Huffman

Abstract

This study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gauges detect precipitation), false alarm (satellite detects precipitation, but the reference is zero), and hit (both satellite and gauges detect precipitation). Each case is explored and explicitly modeled to create a unified approach that combines all four scenarios. Results show that the estimated probability distributions are able to reproduce the probability density functions of the benchmark precipitation, in terms of both expected values and quantiles of the distribution. The spatial pattern of the error is also adequately reproduced by PUSH, and good agreement between observed and estimated errors is observed. The model is also able to capture missed precipitation and false detection uncertainties, whose contribution to the total error can be significant. The resulting error estimates could be attached to the corresponding high-resolution satellite precipitation products.

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Bin Yong, Die Liu, Jonathan J. Gourley, Yudong Tian, George J. Huffman, Liliang Ren, and Yang Hong

Abstract

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.

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Huan Wu, Robert F. Adler, Yang Hong, Yudong Tian, and Fritz Policelli

Abstract

A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.

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Huan Wu, Robert F. Adler, Yudong Tian, Guojun Gu, and George J. Huffman

Abstract

A multiple-product-driven hydrologic modeling framework (MMF) is utilized for evaluation of quantitative precipitation estimation (QPE) products, motivated by improving the utility of satellite QPE in global flood modeling. This work addresses the challenge of objectively determining the relative value of various QPEs at river basin/subbasin scales. A reference precipitation dataset is created using a long-term water-balance approach with independent data sources. The intercomparison of nine QPEs and corresponding hydrologic simulations indicates that all products with long-term (2002–13) records have similar merits as over the short-term (April–June 2013) Iowa Flood Studies period. The model performance in calculated streamflow varies approximately linearly with precipitation bias, demonstrating that the model successfully translated the level of precipitation quality to streamflow quality with better streamflow simulations from QPEs with less bias. Phase 2 of the North American Land Data Assimilation System (NLDAS-2) has the best streamflow results for the Iowa–Cedar River basin, with daily and monthly Nash–Sutcliffe coefficients and mean annual bias of 0.81, 0.88, and −2.1%, respectively, for the long-term period. The evaluation also indicates that a further adjustment of NLDAS-2 to form the best precipitation estimation should consider spatial–temporal distribution of bias. The satellite-only products have lower performance (peak and timing) than other products, while simple bias adjustment can intermediately improve the quality of simulated streamflow. The TMPA research product (TMPA-RP; research-quality data) can generate results approaching those of the ground-based products with only monthly gauge-based adjustment to the TMPA real-time product (TMPA-RT; near-real-time data). It is further noted that the streamflow bias is strongly correlated to precipitation bias at various time scales, though other factors may play a role as well, especially on the daily time scale.

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Yudong Tian, Christa D. Peters-Lidard, Robert F. Adler, Takuji Kubota, and Tomoo Ushio

Abstract

Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction Center (CPC) unified gauge analysis is used as the reference data. The evaluation shows that GSMaP does well in capturing the spatial patterns of precipitation, especially for summer, and that it has better estimation of precipitation amount over the eastern than over the western CONUS. Meanwhile, GSMaP shares many of the challenges common to other satellite-based products, including that it underestimates in winter and overestimates in summer. In winter, GSMaP has on average one-half less precipitation over the western region and one-third less over the eastern region, whereas in summer it has about three-quarters and one-quarter more estimated precipitation over the two respective regions, respectively. Most of the summer overestimates (winter underestimates) are from an excessive (insufficient) number of strong events (>20 mm day−1). Overall, GSMaP’s performance is comparable to other satellite-based products, with slightly better probability of detection during summer, and the different satellite-based estimates as a group have better agreement among themselves during summer than during winter.

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