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Pao-Shin Chu
and
Jianxin Wang

Abstract

Tropical cyclones in the vicinity of Hawaii have resulted in great property damage. An estimate of the return periods of tropical cyclone intensities is of particular interest to governments, public interest groups, and private sectors.

A dimensionless quantity called relative intensity (RI) is used to combine all available information about the tropical cyclone characteristics at different places and times. To make a satisfactory estimate of the return periods of tropical cyclone intensities, a large number of RIs are simulated by the Monte Carlo method based on the extreme value distribution. The return periods of RIs and the corresponding maximum wind speeds associated with tropical cyclones are then estimated by combining the information about the intensities and occurrences. Results show that the return periods of maximum wind speeds equal to or greater than 125, 110, 100, 80, 64, 50, and 34 kt are estimated to be 137, 59, 33, 12, 6.6, 4, and 3. 2 years, respectively.

The Monte Carlo method is also used to estimate the confidence intervals of the return periods of tropical cyclone intensities. The sensitivity test is conducted by removing the portion of the data prior to satellite observations. For maximum wind speeds less than 80 kt, estimates of return periods from the shorter dataset (1970–95) are almost identical to those when the complete duration time series are used (1949–95).

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Jianxin Wang
and
David B. Wolff

Abstract

Ground-validation (GV) radar-rain products are often utilized for validation of the Tropical Rainfall Measuring Mission (TRMM) space-based rain estimates, and, hence, quantitative evaluation of the GV radar-rain product error characteristics is vital. This study uses quality-controlled gauge data to compare with TRMM GV radar rain rates in an effort to provide such error characteristics. The results show that significant differences of concurrent radar–gauge rain rates exist at various time scales ranging from 5 min to 1 day, despite lower overall long-term bias. However, the differences between the radar area-averaged rain rates and gauge point rain rates cannot be explained as due to radar error only. The error variance separation method is adapted to partition the variance of radar–gauge differences into the gauge area–point error variance and radar-rain estimation error variance. The results provide relatively reliable quantitative uncertainty evaluation of TRMM GV radar-rain estimates at various time scales and are helpful to understand better the differences between measured radar and gauge rain rates. It is envisaged that this study will contribute to better utilization of GV radar-rain products to validate versatile space-based rain estimates from TRMM, as well as the proposed Global Precipitation Measurement satellite and other satellites.

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Pao-Shin Chu
and
Jianxin Wang

Abstract

Tropical cyclones in the vicinity of Hawaii are rare. However, when they occurred, they caused enormous property damage. The authors have examined historical records (1949–95) of cyclones and classified them into El Niño and non–El Niño batches. A bootstrap resampling method is used to simulate sampling distributions of the annual mean number of tropical cyclones for the above two batches individually. The statistical characteristics for the non–El Niño batch are very different from the El Niño batch.

A two-sample permutation procedure is then applied to conduct statistical tests. Results from the hypothesis testing indicate that the difference in the annual mean number of cyclones between El Niño and non–El Niño batches is statistically significant at the 5% level. Therefore, one may say with statistical confidence that the mean number of cyclones in the vicinity of Hawaii during an El Niño year is higher than that during a non–El Niño year. Likewise, the difference in variances between El Niño and non–El Niño batches is also significant. Cyclone tracks passing Hawaii during the El Niño batch appear to be different from those of the non–El Niño composite. A change in large-scale dynamic and thermodynamic environments is believed to be conducive to the increased cyclone incidence in the vicinity of Hawaii during an El Niño year.

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Jianxin Wang
and
David B. Wolff

Abstract

This study evaluates space-based rain estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using ground-based measurements from the radar (GR) and tipping-bucket rain gauges (TG) over the TRMM Ground Validation (GV) site at Melbourne, Florida. The satellite rain products are derived from the TRMM Microwave Imager (TMI), precipitation radar (PR), and combined (COM) rain algorithms using observations from both instruments. The TRMM satellite and GV rain products are spatiotemporally matched and are intercompared at multiple time scales over the 12-yr period from 1998 to 2009. On monthly and yearly scales, the TG agree excellently with the GR because the GR rain rates are generated using the TG data as a constraint on a monthly basis. However, large disagreements exist between the GR and TG at shorter time scales because of their significantly different spatial and temporal sampling modes. The yearly biases relative to the GR for the PR and TMI are generally negative, with a few exceptions. The COM bias fluctuates from year to year over the 12-yr period. The PR, TMI, and COM are in good overall agreement with the GR in the lower range of rain rates, but the agreement is notably worse at higher rain rates. The diurnal cycle of rainfall is captured well by all products, but the peak satellite-derived rainfall (PR, TMI, and COM) lags the peak from the ground measurements (GR and TG) by ~1 h.

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Jianxin Wang
and
David B. Wolff

Abstract

Given the decade-long and highly successful Tropical Rainfall Measuring Mission (TRMM), it is now possible to provide quantitative comparisons between ground-based radars (GRs) and the spaceborne TRMM precipitation radar (PR) with greater certainty over longer time scales in various tropical climatological regions. This study develops an automated methodology to match and compare simultaneous TRMM PR and GR reflectivities at four primary TRMM Ground Validation (GV) sites: Houston, Texas (HSTN); Melbourne, Florida (MELB); Kwajalein, Republic of the Marshall Islands (KWAJ); and Darwin, Australia (DARW). Data from each instrument are resampled into a three-dimensional Cartesian coordinate system. The horizontal displacement during the PR data resampling is corrected. Comparisons suggest that the PR suffers significant attenuation at lower levels, especially in convective rain. The attenuation correction performs quite well for convective rain but appears to slightly overcorrect in stratiform rain. The PR and GR observations at HSTN, MELB, and KWAJ agree to about ±1 dB on average with a few exceptions, whereas the GR at DARW requires +1 to −5 dB calibration corrections. One of the important findings of this study is that the GR calibration offset is dependent on the reflectivity magnitude. Hence, it is proposed that the calibration should be carried out by using a regression correction rather than by simply adding an offset value to all GR reflectivities.

This methodology is developed to assist TRMM GV efforts to improve the accuracy of tropical rain estimates, but can also be applied to the proposed Global Precipitation Measurement and other related activities over the globe.

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Jianxin Wang
,
Brad L. Fisher
, and
David B. Wolff

Abstract

This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

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Yagmur Derin
,
Pierre-Emmanuel Kirstetter
,
Noah Brauer
,
Jonathan J. Gourley
, and
Jianxin Wang

Abstract

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are examined over three coastal regions of the United States: the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes.

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David A. Marks
,
David B. Wolff
,
David S. Silberstein
,
Ali Tokay
,
Jason L. Pippitt
, and
Jianxin Wang

Abstract

Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.

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Daniel C. Watters
,
Patrick N. Gatlin
,
David T. Bolvin
,
George J. Huffman
,
Robert Joyce
,
Pierre Kirstetter
,
Eric J. Nelkin
,
Sarah Ringerud
,
Jackson Tan
,
Jianxin Wang
, and
David Wolff

Abstract

NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.

Significance Statement

Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Open access