scholarly journals A Comparison of Oceanic Precipitation Estimates in the Tropics and Subtropics

2009 ◽  
Vol 48 (7) ◽  
pp. 1335-1344 ◽  
Author(s):  
Kenneth P. Bowman ◽  
Cameron R. Homeyer ◽  
Dalon G. Stone
Keyword(s):  
Remote Sensing ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Geographical Patterns ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Environmental Laboratory ◽  
Remote Sensing Systems ◽  
Microwave Imager

Abstract A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

2014 ◽  
Vol 31 (9) ◽  
pp. 1902-1921 ◽  
Author(s):  
Ji-Hye Kim ◽  
Mi-Lim Ou ◽  
Jun-Dong Park ◽  
Kenneth R. Morris ◽  
Mathew R. Schwaller ◽  
...  
Keyword(s):  
Korean Peninsula ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
International Partnerships ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR − PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between −2.0 and +1.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.

Comparison of TRMM Precipitation Retrievals with Rain Gauge Data from Ocean Buoys

2005 ◽  
Vol 18 (1) ◽  
pp. 178-190 ◽  
Author(s):  
Kenneth P. Bowman
Keyword(s):  
Satellite Data ◽  
Sampling Error ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Averaging Time ◽  
Trmm Microwave Imager

Abstract Four years of precipitation retrievals from the Tropical Rainfall Measuring Mission (TRMM) satellite are compared with data from 25 surface rain gauges on the National Oceanic and Atmospheric Administration/Pacific Marine Environment Laboratory (NOAA/PMEL) Tropical Atmosphere–Ocean Array/Triangle Trans-Ocean Buoy Network TAO/TRITON buoy array in the tropical Pacific. The buoy gauges have a significant advantage over island-based gauges for this purpose because they represent open-ocean conditions and are not affected by island orography or surface heating. Because precipitation is correlated with itself in both space and time, comparisons between the two data sources can be improved by properly averaging in space and/or time. When comparing gauges with individual satellite overpasses, the optimal averaging time for the gauge (centered on the satellite overpass time) depends on the area over which the satellite data are averaged. For 1° × 1° areas there is a broad maximum in the correlation for gauge-averaging periods of ∼2 to 10 h. Maximum correlations r are in the range 0.6 to 0.7. For larger satellite averaging areas, correlations with the gauges are smaller (because a single gauge becomes less representative of the precipitation in the box) and the optimum gauge-averaging time is longer. For individual satellite overpasses averaged over a 1° × 1° box, the relative rms difference with respect to a rain gauge centered in the box is ∼200% to 300%. For 32-day time means over 1° × 1° boxes, the relative rms difference between the satellite data and a gauge is in the range of 40% to 70%. The bias between the gauges and the satellite retrievals is estimated by correlating the long-term time-mean precipitation estimates across the set of gauges. The TRMM Microwave Imager (TMI) gives an r2 of 0.97 and a slope of 0.970, indicating very little bias with respect to the gauges. For the Precipitation Radar (PR) the comparable numbers are 0.92 and 0.699. The results of this study are consistent with the sampling error estimates from the statistical model of Bell and Kundu.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

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.

Changes in TRMM Rainfall due to the Orbit Boost Estimated from Buoy Rain Gauge Data

2007 ◽  
Vol 24 (9) ◽  
pp. 1598-1607 ◽  
Author(s):  
Jeremy D. DeMoss ◽  
Kenneth P. Bowman
Keyword(s):  
Sampling Error ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Rate Dependent ◽  
Wide Range ◽  
The Mean ◽  
Microwave Imager ◽  
Trmm Microwave Imager

Abstract During the first three-and-a-half years of the Tropical Rainfall Measuring Mission (TRMM), the TRMM satellite operated at a nominal altitude of 350 km. To reduce drag, save maneuvering fuel, and prolong the mission lifetime, the orbit was boosted to 403 km in August 2001. The change in orbit altitude produced small changes in a wide range of observing parameters, including field-of-view size and viewing angles. Due to natural variability in rainfall and sampling error, it is not possible to evaluate possible changes in rainfall estimates from the satellite data alone. Changes in TRMM Microwave Imager (TMI) and the precipitation radar (PR) precipitation observations due to the orbit boost are estimated by comparing them with surface rain gauges on ocean buoys operated by the NOAA/Pacific Marine Environment Laboratory (PMEL). For each rain gauge, the bias between the satellite and the gauge for pre- and postboost time periods is computed. For the TMI, the satellite is biased ∼12% low relative to the gauges during the preboost period and ∼1% low during the postboost period. The mean change in bias relative to the gauges is approximately 0.4 mm day−1. The change in TMI bias is rain-rate-dependent, with larger changes in areas with higher mean precipitation rates. The PR is biased significantly low relative to the gauges during both boost periods, but the change in bias from the pre- to postboost period is not statistically significant.

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

2005 ◽  
Vol 22 (4) ◽  
pp. 365-380 ◽  
Author(s):  
David B. Wolff ◽  
D. A. Marks ◽  
E. Amitai ◽  
D. S. Silberstein ◽  
B. L. Fisher ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Marshall Islands ◽  
Error Statistics ◽  
Tropical Rainfall ◽  
Nasa Goddard Space ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Rates ◽  
Good Agreement

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent validation techniques show good agreement between gauge-measured and radar-estimated rainfall. A comparison of the NASA GV products and those developed independently by the University of Washington for a subset of data from the Kwajalein Atoll site also shows good agreement. A comparison of NASA GV rain intensities to satellite retrievals from the TRMM Microwave Imager (TMI), precipitation radar (PR), and Combined (COM) algorithms is presented, and it is shown that the GV and satellite estimates agree quite well over the open ocean.

scholarly journals First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

2016 ◽  
Vol 17 (11) ◽  
pp. 2799-2814 ◽  
Author(s):  
M. F. Rios Gaona ◽  
A. Overeem ◽  
H. Leijnse ◽  
R. Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Land Surface ◽  
Tropical Rainfall Measuring Mission ◽  
Ground Truth ◽  
Rain Gauge ◽  
First Year ◽  
Radar Rainfall ◽  
High Resolution Data ◽  
Precipitation Estimates ◽  
Global Precipitation

Abstract The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Sign in / Sign up

Export Citation Format

Share Document