scholarly journals Improvement of TMI Rain Retrievals in Mountainous Areas

2013 ◽  
Vol 52 (1) ◽  
pp. 242-254 ◽  
Author(s):  
Shoichi Shige ◽  
Satoshi Kida ◽  
Hiroki Ashiwake ◽  
Takuji Kubota ◽  
Kazumasa Aonashi
Keyword(s):  
Heavy Rainfall ◽  
Microwave Radiometer ◽  
Vertical Motion ◽  
Tropical Rainfall Measuring Mission ◽  
Moisture Flux ◽  
Passive Microwave ◽  
Ancillary Data ◽  
Orographic Rainfall ◽  
Microwave Imager ◽  
Trmm Microwave Imager

AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.

Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land

2008 ◽  
Vol 47 (11) ◽  
pp. 3016-3029 ◽  
Author(s):  
Shinta Seto ◽  
Takuji Kubota ◽  
Nobuhiro Takahashi ◽  
Toshio Iguchi ◽  
Taikan Oki
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Retrieval Algorithm ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

scholarly journals Improvements of Rain/No-Rain Classification Methods for Microwave Radiometer over Coasts by Dynamic Surface-Type Classification

2016 ◽  
Vol 33 (6) ◽  
pp. 1257-1270 ◽  
Author(s):  
Tomoaki Mega ◽  
Shoichi Shige
Keyword(s):  
Microwave Radiometer ◽  
Coastal Region ◽  
Tropical Rainfall Measuring Mission ◽  
Effective Field ◽  
Surface Type ◽  
Dynamic Surface ◽  
Surface Classification ◽  
Microwave Imager ◽  
Footprint Area ◽  
Trmm Microwave Imager

AbstractThe rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region.

scholarly journals Optimization of Cloud-Radiation Databases for Passive Microwave Precipitation Retrievals over Ocean

2016 ◽  
Vol 33 (8) ◽  
pp. 1649-1671 ◽  
Author(s):  
Eun-Kyoung Seo ◽  
Sung-Dae Yang ◽  
Mircea Grecu ◽  
Geun-Hyeok Ryu ◽  
Guosheng Liu ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Optimization Method ◽  
Passive Microwave ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Trmm Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractUsing Tropical Rainfall Measuring Mission (TRMM) observations from storms collected over the oceans surrounding East Asia, during summer, a method of creating physically consistent cloud-radiation databases to support satellite radiometer retrievals is introduced. In this method, vertical profiles of numerical model-simulated cloud and precipitation fields are optimized against TRMM radar and radiometer observations using a hybrid empirical orthogonal function (EOF)–one-dimensional variational (1DVAR) approach.The optimization is based on comparing simulated to observed radar reflectivity profiles and the corresponding passive microwave observations at the frequencies of the TRMM Microwave Imager (TMI) instrument. To minimize the discrepancies between the actual and the synthetic observations, the simulated cloud and precipitation profiles are optimized by adjusting the contents of the hydrometeors. To reduce the dimension of the hydrometeor content profiles in the optimization, multivariate relations among hydrometeor species are used.After applying the optimization method to modify the simulated clouds, the optimized cloud-radiation database has a joint distribution of reflectivity and associated brightness temperatures that is considerably closer to that observed by TRMM PR and TMI, especially at 85 GHz. This implies that the EOF–1DVAR approach can generate profiles with realistic distributions of frozen hydrometeors, such as snow and graupel. This approach may be similarly adapted to operate with the variety and capabilities of the passive microwave radiometers that compose the Global Precipitation Measurement (GPM) constellation. Furthermore, it can be extended to other oceanic regions and seasons.

scholarly journals Understanding the Sources of Satellite Passive Microwave Rainfall Retrieval Systematic Errors Over Land

2017 ◽  
Vol 56 (3) ◽  
pp. 597-614 ◽  
Author(s):  
Veljko Petković ◽  
Christian D. Kummerow
Keyword(s):  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
Systematic Difference ◽  
Passive Microwave ◽  
System Structure ◽  
Test Bed ◽  
Organization Level ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rainfall Estimates

AbstractAnalyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.

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.

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.

Land Contamination Correction for Passive Microwave Radiometer Data: Demonstration of Wind Retrieval in the Great Lakes Using SSM/I

2014 ◽  
Vol 31 (10) ◽  
pp. 2094-2113 ◽  
Author(s):  
John Xun Yang ◽  
Darren S. Mckague ◽  
Christopher S. Ruf
Keyword(s):  
Great Lakes ◽  
Microwave Radiometer ◽  
Simulated Data ◽  
Passive Microwave ◽  
Current Standard ◽  
Wind Retrieval ◽  
Microwave Imager ◽  
Wind Retrievals ◽  
Land Water ◽  
Buoy Measurements

Abstract Passive microwave radiometer data over the ocean have been widely used, but data near coastlines or over lakes often cannot be used because of the large footprint with mixed signals from both land and water. For example, current standard Special Sensor Microwave Imager (SSM/I) products, including wind, water vapor, and precipitation, are typically unavailable within about 100 km of any coastline. This paper presents methods of correcting land-contaminated radiometer data in order to extract the coastal information. The land contamination signals are estimated, and then removed, using a representative antenna pattern convolved with a high-resolution land–water mask. This method is demonstrated using SSM/I data over the Great Lakes and validated with simulated data and buoy measurements. The land contamination is significantly reduced, and the wind speed retrieval is improved. This method is not restricted to SSM/I and wind retrievals alone; it can be applied more generally to microwave radiometer measurements in coastal regions for other retrieval purposes.

scholarly journals Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References

2008 ◽  
Vol 47 (12) ◽  
pp. 3170-3187 ◽  
Author(s):  
Xin Lin ◽  
Arthur Y. Hou
Keyword(s):  
United States ◽  
Tropical Rainfall Measuring Mission ◽  
Passive Microwave ◽  
Earth Observing System ◽  
Temporal Sampling ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Cross Track ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.

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