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Abnormal monsoon years and their control on erosion and sediment flux in the high, arid northwest Himalaya, Earth Planet
- Sci. Lett
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2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the Tropics
"... The bulk radar reflectivity structures, 85- and 37-GHz brightness temperatures, and lightning characteristics of precipitating systems in tropical Africa, South America, the east Pacific, and west Pacific are documented using data from the Tropical Rainfall Measuring Mission (TRMM) satellite during ..."
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The bulk radar reflectivity structures, 85- and 37-GHz brightness temperatures, and lightning characteristics of precipitating systems in tropical Africa, South America, the east Pacific, and west Pacific are documented using data from the Tropical Rainfall Measuring Mission (TRMM) satellite during August, September, and October of 1998. The particular focus is on precipitation features [defined as a contiguous area $75 km2 with either a near-surface reflectivity $20 dBZ or an 85-GHz polarization-corrected temperature (PCT) # 250 K] with appreciable rainfall, which account for the bulk of the total rainfall and lightning flash density in their respective regions. Systems over the tropical continents typically have greater magnitudes of reflectivity extending to higher altitudes than tropical oceanic systems. This is consistent with the observation of stronger ice scattering signatures (lower 85- and 37-GHz PCT) in the systems over land. However, when normalized by reflectivity heights, tropical continental features consistently have higher 85-GHz PCT than tropical oceanic features. It is inferred that greater supercooled water contents aloft in the tropical continental systems contribute to this brightness temperature difference. Lightning (as detected by the Lightning Imaging Sensor) is much more likely in tropical continental features than tropical oceanic features with similar brightness temperatures or similar reflectivity heights. Vertical profiles
WindSat radio-frequency interference signature and its identification over land and ocean
- IEEE Trans. Geosci. Remote Sens
, 2006
"... Abstract—Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Mi-crowave Scanning Radiometer–EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radio ..."
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Cited by 12 (0 self)
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Abstract—Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Mi-crowave Scanning Radiometer–EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radia-tions. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, in-formation content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal com-ponent analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model differ-ence technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk re-ductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean. Index Terms—Microwave remote sensing, radio-frequency interference (RFI), WindSat.
Global Snow Cover Monitoring with Spaceborne K_u-band Scatterometer
, 2001
"... This paper presents a study to demonstrate the potential of a spaceborne u -band scatterometer to monitor global snow cover. Global u -band data were acquired by the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) operated on the Advanced Earth Observing Satellite (ADEOS) ..."
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Cited by 7 (1 self)
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This paper presents a study to demonstrate the potential of a spaceborne u -band scatterometer to monitor global snow cover. Global u -band data were acquired by the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) operated on the Advanced Earth Observing Satellite (ADEOS) from September 1996 to June 1997. NSCAT backscatter patterns over the northern hemisphere reveals boundaries between different snow classes, defined by the Cold Regions Research and Engineering Laboratory (CRREL), Hanover, NH, snow classification system at different times of the snow season. We show the evolution of the backscatter signature throughout the entire seasonal snow cycle. Within the snow extent determined by the National Oceanic and Atmospheric Administration (NOAA), Washington, DC, and Climate Prediction Center (CPC), operational snow product, u -band backscatter data expose detailed features and rapid changes as observed in in-situ snow depth data from surface weather stations in U.S., Canada, and Russia. Sensitivity of u -band backscatter to snow conditions is illustrated with the dramatic change over the U.S. northern plains and the Canadian prairie region corresponding to the snow event leading to the 1997 Flood of the Century. We discuss snow field experiments and data analysis plan to understand snow scattering mechanisms, to interpret snow backscatter, and to derive its relationship with snow physical parameters. In view of current and future satellite u -band scatterometers, the development of algorithms for quantitative snow cover monitoring is pertinent.
Advanced rain/ no-rain classification methods for microwave radiometer observations over land,”
- J. Appl. Meteorol. Climatol.,
, 2008
"... ABSTRACT One of the goals of the Global Precipitation Measurement project, the successor to the Tropical Rainfall Measuring Mission (TRMM), is to produce a 3-hourly global rainfall map using several spaceborne microwave radiometers. It is important, although often difficult, to classify radiometer ..."
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ABSTRACT One of the goals of the Global Precipitation Measurement project, the successor to the Tropical Rainfall Measuring Mission (TRMM), is to produce a 3-hourly global rainfall map using several spaceborne microwave radiometers. It is important, although often difficult, to classify radiometer observations over land as either "rain" or "no rain" because background land surface conditions change significantly with time and location. In this study, a no-rain brightness temperature database was created to infer land surface conditions using simultaneous observations by TRMM Microwave Imager (TMI) and precipitation radar (PR) with a resolution of 1 month and 1°latitude ϫ 1°longitude. This paper proposes new rain/no-rain classification (RNC) methods that use the database to determine the background brightness temperature. The proposed RNC methods and the RNC method developed for the Goddard profiling algorithm (GPROF; the standard rain-rate retrieval algorithm for TMI) are applied to all TMI observations for the entire year of 2000, and the results are evaluated against the RNC made by PR as the "truth." The first method (M1) simply uses the average brightness temperature at 85-GHz vertical polarization [denoted as TB (85 V)] under no-rain conditions as the background brightness temperature at 85-GHz vertical polarization [denoted as TB e (85 V)]. The second method (M2) uses a regression equation between TB (85 V) and TB (22 V) under no-rain conditions from the database. Here, TB e (85 V) is calculated by substituting the observed TB (22 V) into the regression equation. The ratio of accurate rain detection by GPROF to all rain occurrences detected by PR was 59%. This ratio was 57% for M1 and 63% for M2. The ratio with the weight of the rain rate was 81% for M1 and 86% for M2; it was 80% for GPROF. These comparisons were made by setting a threshold using a constant coefficient k 0 to make the ratio of false rain detection to all no-rain occurrences detected by PR almost the same (approximately 0.85%) for all three methods. Further comparisons among the methods are made, and the reasons for the differences are investigated herein.
The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors
, 2000
"... This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The impro ..."
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This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm’s poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scat-tering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme’s Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm’s operational version have been
Evaluation of the successive V6 and V7TRMM multisatellite precipitation analysis over the Continental United States
- Water Resour. Res
, 2013
"... [1] The spatial error structure of surface precipitation derived from successive versions of the TRMMMultisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the C ..."
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[1] The spatial error structure of surface precipitation derived from successive versions of the TRMMMultisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the Continental United States (CONUS) for 3 years from June 2008 to May 2011. The TMPA products include the version-6(V6) and version-7(V7) real-time products 3B42RT (3B42RTV6 and 3B42RTV7) and research products 3B42 (3B42V6 and 3B42V7). The evaluation shows that 3B42V7 improves upon 3B42V6 over the CONUS regarding 3 year mean daily precipitation: the correlation coefficient (CC) increases from 0.85 in 3B42V6 to 0.92 in 3B42V7; the relative bias (RB) decreases from 222.95 % in 3B42V6 to 22.37 % in 3B42V7; and the root mean square error (RMSE) decreases from 0.80 in 3B42V6 to 0.48 mm in 3B42V7. Distinct improvement is notable in the mountainous West especially along the coastal northwest mountainous areas, whereas 3B42V6 (also 3B42RTV6 and 3B42RTV7) largely underestimates: the CC increases from 0.86 in 3B42V6 to 0.89 in 3B42V7, and the RB decreases from244.17 % in 3B42V6 to 225.88 % in 3B42V7. Over the CONUS, 3B42RTV7 gained a little improvement over 3B42RTV6 as RB varies from 24.06 % in 3B42RTV6 to 0.22 % in 3B42RTV7. But there is more overestimation with the RB increasing from 8.18 % to 14.92 % (0.16–3.22%) over the central US (eastern). Citation: Chen, S., et al. (2013), Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the
A cloud-removal algorithm for SSM/I data
- IEEE Transactions on Geosciences and Remote Sensing
, 1999
"... Abstract—Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brig ..."
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Abstract—Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate smallscale distortion caused by clouds or precipitation. Mean, secondhighest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is biased high. MMA provides a more accurate brightness temperature estimate in areas of atmospheric distortion, while the mean is superior in regions with little or no distortion. A hybrid algorithm is developed that is a combination of MMA and mean. It utilizes the strengths of both to create a superior algorithm for regions with varying levels of distortion. Uses of composite images produced by these algorithms include studies of vegetation change, land cover classification, and surface parameter extraction. Index Terms — Cloud removal, compositing, electromagnetic atmospheric interference, microwave radiometry.
t t The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)
"... The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 " x 2.5 " latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis ..."
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The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 " x 2.5 " latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the pre-microwave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution. satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation. The data set also contains the individual input fields, a combined 1.
An Examination of Version-5 Rainfall Estimates from the TRMM Microwave Imager, Precipitation Radar, and Rain Gauges on Global, Regional, and Storm Scales
, 2003
"... An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Clim ..."
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An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Climatology Centre (GPCC) global rain gauge analyses and 2) by comparing the rainfall estimates from the PR and TMI on a rainfall feature-by-feature basis within the narrow swath of the PR using a 1-yr database of classified precipitation features (PFs). The former is done to evaluate the overall biases of the TMI and PR relative to ‘‘ground truth’’ to examine regional differences in the estimates; the latter allows a direct comparison of the estimates with the same sampling area, also identifying relative biases as a function of storm type. This study finds that the TMI overestimates rainfall in most of the deep Tropics and midlatitude warm seasons over land with respect to both the GPCC gauge analysis and the PR (which agrees well with the GPCC gauges in the deep Tropics globally), in agreement with past results. The PR is generally higher than the TMI in midlatitude cold seasons over land areas with gauges. The analysis by feature type reveals that the TMI overestimates relative to the PR are due to overestimates in mesoscale convective systems and in most features with 85-GHz polarization-corrected temperature of less than 250 K (i.e., with a significant optical depth of precipitation ice). The PR tended to be