scholarly journals The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales

2007 ◽  
Vol 8 (1) ◽  
pp. 38-55 ◽  
Author(s):  
George J. Huffman ◽  
David T. Bolvin ◽  
Eric J. Nelkin ◽  
David B. Wolff ◽  
Robert F. Adler ◽  
...  
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Rate Dependent ◽  
Early Validation ◽  
Precipitation Estimates ◽  
Precipitation Analysis ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Short Time ◽  
Combined Sensor

Abstract The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.

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.

Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent

2017 ◽  
Vol 56 (7) ◽  
pp. 1867-1881 ◽  
Author(s):  
Andung Bayu Sekaranom ◽  
Hirohiko Masunaga
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Maritime Continent ◽  
Brightness Temperatures ◽  
Extreme Rain ◽  
Trmm Precipitation ◽  
Precipitation Analysis ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates

AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.

scholarly journals Evaluation of TRMM Precipitation Products over Indian Subcontinent

2014 ◽  
Vol XL-8 ◽  
pp. 355-358 ◽  
Author(s):  
J. Indu ◽  
D. Nagesh Kumar
Keyword(s):  
Indian Subcontinent ◽  
Low Frequency ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Frequency Channel ◽  
Data Product ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Data Period

Quantitatively accurate estimates of precipitation at high spatial resolutions often rely on a combination of individual satellite retrieved estimates merged using suitable techniques. Precipitation estimates from these merged data products are extensively being used by scientists and researchers for various hydro meteorological and climatological applications. The intent of this study is to evaluate the performance of gridded precipitation estimates from Tropical Rainfall Measuring Mission (TRMM) namely the 3A12 (from passive radar TRMM Microwave Imager i.e. TMI), 3A25 (from active radar Precipitation Radar i.e., PR) and 3B43 (combined data product). For this purpose, the present study utilizes 6 years of daily rainfall of spatial resolution 1° × 1°. Data period chosen is the Indian summer monsoonal months of JJAS from June 2002 to September 2007. Results indicate that over India, the TMI derived 3A12 product tends to overestimate rainfall compared to the PR derived 3A25 product for the seasonal data period. The 3A12 data product represents gridded monthly accumulations of the level 2 TMI derived 2A12 data product. The land rainfall algorithm of 2A12 is well known to be dependent on the 85 GHz high frequency channel. The present study conducts sensitivity analysis of 20 combinations of TMI frequency channels with respect to rainfall rate. The results conclude that a combination of TMI low frequency channels are better suited to model rainfall thereby questioning the usage of 85 GHz frequency channel for the generation of 2A12 rainfall estimates.

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 Real-Time Bias Reduction for Satellite-Based Precipitation Estimates

2010 ◽  
Vol 11 (6) ◽  
pp. 1275-1285 ◽  
Author(s):  
Yudong Tian ◽  
Christa D. Peters-Lidard ◽  
John B. Eylander
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Bias Reduction ◽  
Training Period ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Precipitation Analysis ◽  
Rain Rates ◽  
Climate Prediction Center

Abstract A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

scholarly journals Understanding Overland Multisensor Satellite Precipitation Error in TMPA-RT Products

2017 ◽  
Vol 18 (2) ◽  
pp. 285-306 ◽  
Author(s):  
Abebe Sine Gebregiorgis ◽  
Pierre-Emmanuel Kirstetter ◽  
Yang E. Hong ◽  
Nicholas J. Carr ◽  
Jonathan J. Gourley ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Spatiotemporal Resolution ◽  
Error Sources ◽  
Multiple Sensors ◽  
Precipitation Estimates ◽  
Level 3 ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation ◽  
Specific Error

Abstract The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively.

scholarly journals Cross Validation of Rainfall Characteristics Estimated from the TRMM PR, a Combined PR–TMI Algorithm, and a C-POL Ground Radar during the Passage of Tropical Cyclone and Nontropical Cyclone Events over Darwin, Australia

2018 ◽  
Vol 35 (12) ◽  
pp. 2339-2358 ◽  
Author(s):  
Anil Deo ◽  
S. Joseph Munchak ◽  
Kevin J. E. Walsh
Keyword(s):  
Tropical Cyclone ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Rate ◽  
Rainfall Characteristics ◽  
Significant Difference ◽  
Median Volume ◽  
Precipitation Type ◽  
Correct Direction ◽  
Microwave Imager ◽  
Trmm Microwave Imager

AbstractThis study cross validates the radar reflectivity Z; the rainfall drop size distribution parameter (median volume diameter Do); and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM), and a C-band dual-polarized ground radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and 11 non-TC overpass events are used in this study, and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate, whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR, but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do toward the “correct” direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference), but it differs considerably from that observed for the majority of the highly convective non-TC events.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

scholarly journals The Influence of Surface and Precipitation Characteristics on TRMM Microwave Imager Rainfall Retrieval Uncertainty

2015 ◽  
Vol 16 (4) ◽  
pp. 1596-1614 ◽  
Author(s):  
N. Carr ◽  
P.-E. Kirstetter ◽  
Y. Hong ◽  
J. J. Gourley ◽  
M. Schwaller ◽  
...  
Keyword(s):  
Warm Season ◽  
Surface Characteristics ◽  
Passive Microwave ◽  
Surface Radiation ◽  
Convective Precipitation ◽  
Algorithm Performance ◽  
Spatial Gradients ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Trmm Microwave Imager

Abstract Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

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