Long-Term Comparison of Collocated Instantaneous Rain Retrievals from the TRMM Microwave Imager and Precipitation Radar over the Ocean

Seo, Eun‐Kyoung; Hristova-Veleva, S. M.; Liu, Guosheng; Ou, Mi-Lim; Ryu, Geun-Hyeok

doi:10.1175/jamc-d-14-0235.1

Cited by 7 publications

(6 citation statements)

References 36 publications

(27 reference statements)

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“…The PR rain-rate comparisons help support the idea that biases are able to be distinguished through the precipitation regimes; however, TMI still exhibits positive and negative regions of bias associated with the organized convective systems. Recent work by Seo et al (2015) discovered that positive differences between TMI and PR might be attributed to regions of stratiform rainfall contained within the TMI footprint/field of view (FOV).…”

Section: Comparison Of Rainfall Retrieved Betweenmentioning

confidence: 99%

“…Negative biases can exist where the Bayesian retrieval is unable to completely distinguish observed brightness temperatures from those within the database, allowing the inclusion of lighter-rain profiles during higher-rain-intensity cases. Similarly, the inclusion of all convective and stratiform FOVs in the weighting process may also lead to biases (Seo et al 2015) if brightness temperatures within the raining FOV are not distinguishable between convective and stratiform rainfall.…”

Section: Examining Retrieval Biasesmentioning

confidence: 99%

“…Time-dependent discrepancies between the TMI and PR rain-rate anomalies are visible between the varying phases of ENSO, where TMI rain rates exhibit variability correlated with the ENSO-3.4 index but PR does not. As an example, differences between TMI and PR are observed during isolated El Niño events in 1998, 2007; these events contributed to a notable increase in oceanic precipitation anomalies from passive microwave rain estimates, whereas the active PR rainfall remains nearly constant. The opposite occurs during La Niña periods, when TMI rain rates are generally underestimated compared to PR.…”

Section: Introductionmentioning

confidence: 99%

“…Three isolated El Niño events (1998, 2007, and 2010) are highlighted with arrows. (b) TMI-PR differences for a 3-month mean for December 2008, January 2009, and February 2009 (c) TMI-PR differences for a 3-month mean for December 2009, January 2010, and February 2010. retrievals as related to larger-scale environmental features or precipitation microphysics (e.g., Berg et al 2002Berg et al , 2006Nesbitt et al 2004;Shige et al 2006Shige et al , 2008Adler et al 2012;Seo et al 2007Seo et al , 2015. To grasp the magnitude of the difference between the satellite products while also understanding which products produce an accurate retrieval, an independent source of rainfall estimates such as described in the TRMM-GPM ground validation (GV) program (Wolff et al 2005) becomes necessary.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure representation of the diverse nature of convection within the tropics, recent work has begun to segregate precipitation events by large-scale features (e.g., Rasmussen et al 2013;Liu and Zipser 2014;Seo et al 2015;Petkovic and Kummerow 2016;Henderson et al 2017). Further, Wang et al (2008) discussed how increased information on the large-scale environment might be necessary to reconcile TMI and PR discrepancies related to ENSO.…”

Section: Introductionmentioning

confidence: 99%

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A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases

Henderson

Kummerow

Marks

et al. 2017

Journal of Atmospheric and Oceanic Technology

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Over the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño–Southern Oscillation (ENSO) events. This manuscript describes the application of defined precipitation regimes to aid the validation of instantaneous rain rates from TRMM using the S-band radar located on the Kwajalein Atoll. Through the evaluation of multiple case studies, biases in rain-rate estimates from the TRMM radar (PR) and radiometer (TMI) are best explained when derived as a function of precipitation organization (e.g., isolated vs organized) and precipitation type (convective vs stratiform). When examining biases at a 1° × 1° scale, large underestimates in both TMI and PR rain rates are associated with predominately convective events in deep isolated regimes, where TMI and PR retrievals are underestimated by 37.8% and 23.4%, respectively. Further, a positive bias of 33.4% is observed in TMI rain rates within organized convective systems containing large stratiform regions. These findings were found to be consistent using additional analysis from the DYNAMO field campaign. When validating at the TMI footprint scale, TMI–PR differences are driven by stratiform rainfall variability in organized regimes; TMI overestimates this stratiform precipitation by 92.3%. Discrepancies between TMI and PR during El Niño events are related to a shift toward more organized convective systems and derived TRMM rain-rate bias estimates are able to explain 70% of TMI–PR differences during El Niño periods. An extension of the results to passive microwave retrievals reveals issues in discriminating convective and stratiform rainfall within the TMI field of view (FOV), and significant reductions in bias are found when convective fraction is constrained within the Bayesian retrieval.

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Section: Comparison Of Rainfall Retrieved Betweenmentioning

confidence: 99%

Section: Examining Retrieval Biasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases

Henderson

Kummerow

Marks

et al. 2017

Journal of Atmospheric and Oceanic Technology

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Impact of Long-Term Observation on the Sampling Characteristics of TRMM PR Precipitation

Hirose

Takayabu

Hamada

et al. 2017

Journal of Applied Meteorology and Climatology

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Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (≥100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day−1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling.

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A 0.01° Resolving TRMM PR Precipitation Climatology

Hirose

Okada

2018

Journal of Applied Meteorology and Climatology

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In this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.

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Long-Term Comparison of Collocated Instantaneous Rain Retrievals from the TRMM Microwave Imager and Precipitation Radar over the Ocean

Cited by 7 publications

References 36 publications

A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases

A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases

Impact of Long-Term Observation on the Sampling Characteristics of TRMM PR Precipitation

A 0.01° Resolving TRMM PR Precipitation Climatology

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