A global map of measurement uncertainties in satellite‐based precipitation estimates has been produced by computing the variance from an ensemble of six different TRMM‐era data sets at daily, 0.25° ...resolution. This analysis yields a lower‐bound estimate of the uncertainties, and a consistent global view of the error characteristics and their regional and seasonal variations, and reveals many undocumented error features over areas with no validation data available. The uncertainties are relatively small (40–60%) over the oceans, especially in the tropics, and over southern South America. There are large uncertainties (100–140%) over high latitudes (poleward of 40° latitude), especially during the cold season. High relative uncertainties are also evident through the seasons over complex terrain areas, including the Tibetan Plateau, the Rockies and the Andes. Coastlines and water bodies also indicate high measurement uncertainty. The estimated global uncertainties also exhibit systematic seasonal, regional as well as rain‐rate dependencies, with lowest uncertainties over tropical oceanic regions with strong, convective precipitation, and highest ones over wintery, complex land surfaces with light precipitation.
A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing‐based runoff‐routing model to form the Dominant ...river tracing‐Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real‐time Global Flood Monitoring System (GFMS). The GFMS uses real‐time satellite‐based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (∼12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real‐time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research‐quality and real‐time satellite precipitation products. Evaluation results are slightly better for the research‐quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ∼0.9 and the false alarm ratio is ∼0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi‐global domain. Validation using real‐time precipitation across the tropics (30°S–30°N) gives positive daily Nash‐Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.
Key Points
Coupled VIC with a physically based routing model for real‐time flood estimation
GFMS gives promising flood estimation with satellite‐based precipitation
Evaluation indicates improvements needed in precipitation and hydrologic model
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.
The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate ...regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.
Current orbital land precipitation products have serious shortcomings in detecting light rain and snowfall, the most frequent types of global precipitation. The missed precipitation is then ...propagated into the merged precipitation products that are widely used. Precipitation characteristics such as frequency and intensity and their regional distribution are expected to change in a warming climate. It is important to accurately capture those characteristics to understand and model the current state of the Earth's climate and predict future changes. In this work, the precipitation detection performance of a suite of precipitation sensors, commonly used in generating the merged precipitation products, are investigated. The high sensitivity of CloudSat Cloud Profiling Radar (CPR) to liquid and frozen hydrometeors enables superior estimates of light rainfall and snowfall within 80°S–80°N. Three years (2007–2009) of CloudSat precipitation data were collected to construct a climatology reference for guiding our analysis. In addition, auxiliary data such as infrared brightness temperature, surface air temperature, and cloud types were used for a more detailed assessment. The analysis shows that no more than 50% of the tropical (40°S–40°N) precipitation occurrence is captured by the current suite of precipitation measuring sensors. Poleward of 50° latitude, a combination of various factors such as an abundance of light rainfall, snowfall, shallow precipitation‐bearing clouds, and frozen surfaces reduces the space‐based precipitation detection rate to less than 20%. This shows that for a better understanding of precipitation from space, especially at higher latitudes, there is a critical need to improve current precipitation retrieval techniques and sensors.
Key Points
CloudSat is useful to identify missing precipitation from space
More than 50% of precipitation is not detected by current sensors
Precipitation is missed mainly from nonconvective clouds and over frozen land
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.
The spatial error structure of surface precipitation derived from successive versions of the TRMM Multisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison ...with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the Continental United States (CONUS) for 3 years from June 2008 to May 2011. The TMPA products include the version‐6(V6) and version‐7(V7) real‐time products 3B42RT (3B42RTV6 and 3B42RTV7) and research products 3B42 (3B42V6 and 3B42V7). The evaluation shows that 3B42V7 improves upon 3B42V6 over the CONUS regarding 3 year mean daily precipitation: the correlation coefficient (CC) increases from 0.85 in 3B42V6 to 0.92 in 3B42V7; the relative bias (RB) decreases from −22.95% in 3B42V6 to −2.37% in 3B42V7; and the root mean square error (RMSE) decreases from 0.80 in 3B42V6 to 0.48 mm in 3B42V7. Distinct improvement is notable in the mountainous West especially along the coastal northwest mountainous areas, whereas 3B42V6 (also 3B42RTV6 and 3B42RTV7) largely underestimates: the CC increases from 0.86 in 3B42V6 to 0.89 in 3B42V7, and the RB decreases from −44.17% in 3B42V6 to −25.88% in 3B42V7. Over the CONUS, 3B42RTV7 gained a little improvement over 3B42RTV6 as RB varies from −4.06% in 3B42RTV6 to 0.22% in 3B42RTV7. But there is more overestimation with the RB increasing from 8.18% to 14.92% (0.16–3.22%) over the central US (eastern).
Key Points
Quantified error structures of the latest two‐version TRMM products over CONUS
The 3B42V7 effectively improves upon Version‐6 products over the CONUS
The 3B42V6 has systematic gauge‐adjustment issues in central CONUS
Satellite‐based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) ...high‐resolution, satellite‐based precipitation data sets are evaluated over the contiguous United States against a gauge‐based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations. (2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (>40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (<10 mm/d), up to 40%. (4) Hit bias and missed precipitation are the two leading error sources. In summer, positive hit bias, up to 50%, dominates the total errors for most products. (5) In winter, missed precipitation over mountainous regions and the northeast, presumably snowfall, poses a common challenge to all the data sets. On the basis of the findings, we recommend that future efforts focus on reducing hit bias, adding snowfall retrievals, and improving methods for combining gauge and satellite data. Strategies for future studies to establish better links between the errors in the end products and the upstream data sources are also proposed.
► Satellite snow depth (SD) and snow cover fraction (SCF) products are assimilated. ► SD assimilation improves the overall snow prediction. ► SCF assimilation improves both snow and streamflow ...predictions. ► Improved snow prediction does not guarantee improved streamflow prediction. ► Removing cloud cover improves SCF assimilation results.
Several satellite-based snow products are assimilated, both separately and jointly, into the Noah land surface model for improving snow prediction in Alaska. These include the standard and interpreted versions of snow cover fraction (SCF) data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the snow depth (SD) estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The satellite-based SD estimates are adjusted against in situ observations via statistical interpolation to reduce the potentially large biases, prior to being assimilated using an ensemble Kalman filter. A customized, rule-based direct insertion approach is developed to assimilate the two SCF datasets. Our results indicate that considerable overall improvement on snow prediction can be achieved via assimilating the bias-adjusted satellite SD estimates; however, the improvement does not always translate into improvements in streamflow prediction. Assimilating the standard MODIS SCF is found to have little impact on snow and streamflow predictions, while assimilating the interpreted SCF estimates, which have reduced cloud coverage and improved snow mapping accuracy, has resulted in the most consistent improvements on snow and streamflow predictions across the study domain.
Underground in situ pyrolysis for oil shale extraction is currently significant; the evolutions in microstructure, porosity, and permeability parameters are essential factors in evaluating the ...productivity of oil shale after pyrolysis. With the underground oil shale reservoir core, obtained from Jimsar Sag in the Junggar Basin in China, as the research object, the samples were subjected to the treatment at different high temperatures (400°C, 500°C, 600°C, and 700°C). The NMR and FE-SEM experiments on oil shale samples were conducted; the T2 relaxation spectra, pore size distribution, and porosity and permeability variation were analyzed; and the relationships between movable fluid saturation and porosity and permeability were established, respectively. The results showed that when the thermal treatment temperature increased, the porosity and permeability of oil shale rose continuously but showed different laws. With the temperature being lower than 400°C, the porosity increased slowly, and the growth rate of porosity increased rapidly when the thermal treatment temperature was higher than 500°C. In the pyrolysis temperature range of 25°C~400°C, the growth rate of permeability was relatively slow. With the continuously enhancing temperature (500°C~600°C), the growth rate of permeability accelerated rapidly. When the temperature continued to rise (700°C), the increase of permeability began to slow down. There is a nonlinear correlation between porosity and movable fluid saturation and an approximately linear correlation between permeability and movable fluid saturation. The findings showed that 600°C was the suitable temperature for the pyrolysis of oil shale.
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