Guoqiang Tang

Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7?

AbstractThe goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42...

Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study de- scribes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest de- velopments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of- the-art cloud computing and crowd-sourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

Similarity and Error Intercomparison of the GPM and Its Predecessor-TRMM Multisatellite Precipitation Analysis Using the Best Available Hourly Gauge Network over the Tibetan Plateau

The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products.

Systematic Anomalies Over Inland Water Bodies of High Mountain Asia in TRMM Precipitation Estimates: No Longer a Problem for the GPM Era?

Two satellite precipitation products (3B42RT and 3B42V7) of the Tropical Rainfall Measuring Mission (TRMM) show systematic overestimation anomalies over inland water bodies in high mountain Asia (HMA). The relative difference (RD) was calculated between precipitation estimates over pixels containing water bodies and over pixels neighboring water bodies. A t-test was employed to check the statistical significance. Results show that ~53% of the water bodies passed the significance test at the daily scale for the TRMM products on average. Gauge adjustment alleviates the overestimation of 3B42V7 (mean RD = 20%) compared with 3B42RT (mean RD = 34%). Liquid water surfaces tend to affect the quality of TRMM Multisatellite Precipitation Analysis more significantly than ice water surfaces due to different passive microwave (MW) emission characteristics. In contrast, Global Precipitation Measurement (GPM) mission products provide more consistent precipitation estimates, with only ~4% of the water bodies passing the significance test. TRMM-based precipitation estimates are much higher than GPM products over inland water bodies, which is, however, not the case over the land. The improvement is mainly attributed to the unified and updated MW algorithm used in GPM products.

Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?

AbstractPrediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of

Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation

1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. T...

Can Near-Real-Time Satellite Precipitation Products Capture Rainstorms and Guide Flood Warning for the 2016 Summer in South China?

Precipitation is one of the most important components in the water and energy cycles. Radars are considered the best available technology for observing the spatial distribution of precipitation either from the ground since the 1980s or from space since 1998. This study, for the first time ever, compares and evaluates the only three existing spaceborne precipitation radars, i.e., the Ku-band precipitation radar (PR), the W-band Cloud Profiling Radar (CPR), and the Ku/Ka-band Dual-frequency Precipitation Radar (DPR). The three radars are matched up globally and intercompared in the only period which they coexist: 2014–2015. In addition, for the first time ever, TRMM PR and GPM DPR are evaluated against hourly rain gauge data in Mainland China. Results show that DPR and PR agree with each other and correlate very well with gauges in Mainland China. However, both show limited performance in the Tibetan Plateau (TP) known as the Earths third pole. DPR improves light precipitation detectability, when compared with PR, whereas CPR performs best for light precipitation and snowfall. DPR snowfall has the advantage of higher sampling rates than CPR; however, its accuracy needs to be improved further. The future development of spaceborne radars is also discussed in two complementary categories: (1) multifrequency radar instruments on a single platform and (2) constellations of many small cube radar satellites, for improving global precipitation estimation. This comprehensive intercomparison of PR, CPR, and DPR sheds light on spaceborne radar precipitation retrieval and future radar design.

Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

In the summer of 2016, severe storms caused serious casualties and destruction of facilities and properties over South China. Near-real-time (NRT) satellite precipitation products are attractive to rainstorm monitoring and flood warning guidance owing to its combination of timeliness, high spatiotemporal resolution, and broad coverage. We evaluate the performance of four NRT satellite products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, 3B42RT, Global Satellite Mapping of Precipitation (GSMaP) NRT, and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Late run using a high-quality merged product in the rainy June over South China. In addition, a method based on an empirical flash flood guidance and the Flash Flood Potential Index is proposed to examine the applicability of satellite products in guiding flood warning. The IMERG Late run and GSMaP NRT perform the closest-to-ground observations. 3B42RT detects the most flood warning events due to its notable overestimation of actual precipitation. We recommend that the IMERG Late run is the best NRT satellite product in capturing flood hazard events according to the Pareto Efficiency of jointly optimizing higher hit ratio and lower false alarms.

Can Satellite Precipitation Products Estimate Probable Maximum Precipitation: A Comparative Investigation with Gauge Data in the Dadu River Basin

Spaceborne precipitation radars are powerful tools used to acquire adequate and high-quality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka- and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR’s improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are ~1.7 and ~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1–12 beams and 38–49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type “other” from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research.