Relevant bibliographies by topics / Rain Retrieval

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Rain Retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Rain Retrieval"

1

Matrosov, Sergey Y. "Attenuation-Based Estimates of Rainfall Rates Aloft with Vertically Pointing Ka-Band Radars." Journal of Atmospheric and Oceanic Technology 22, no. 1 (January 1, 2005): 43–54. http://dx.doi.org/10.1175/jtech-1677.1.

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Abstract An approach is suggested to retrieve low-resolution rainfall rate profiles and layer-averaged rainfall rates, Ra, from radar reflectivity measurements made by vertically pointing Ka-band radars. This approach is based on the effects of attenuation of radar signals in rain and takes advantage of the nearly linear relation between specific attenuation and rainfall rate at Ka-band frequencies. The variability of this relation due to temperature, details of raindrop size distributions, and the nature of rain (convective versus stratiform) is rather small (∼10%) and contributes little to errors in rainfall rate retrievals. The main contribution to the retrieval errors comes from the uncertainty of the difference in the nonattenuated radar reflectivities in the beginning and the end of the range resolution interval. For 2- and 1-dB uncertainties in this difference, the retrieval errors due to this main contribution are less than 34% and 17%, correspondingly, for rains with Ra ≈ 10 mm h−1 at a 1-km resolution interval. The heavier rain rates are retrieved with a better accuracy since this retrieval error contribution is proportional to 1/Ra. The retrieval accuracy can also be improved but at the expense of more coarse vertical resolutions of retrievals since the main retrieval error contribution is also proportional to the reciprocal of the resolution interval. The Mie scattering effects at Ka band results in less variability in nonattenuated reflectivities (cf. lower radar frequencies), which aids the suggested approach. Given that radar receivers are not saturated, the rainfall rates can be retrieved using cloud radars that were originally designed for measuring only nonprecipitating and weakly precipitating clouds. An important advantage of the attenuation-based retrievals of rainfall is that absolute radar calibration is not required. The inclusion of rainfall information will improve the characterization of the atmospheric column obtained with such radars used for climate research. The applications of the suggested approach are illustrated using the vertically pointing Ka-band radar measurements made during a field experiment in southern Florida. The retrieval results are in good agreement with surface estimates of rainfall rates.
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Hristova-Veleva, S. M., P. S. Callahan, R. S. Dunbar, B. W. Stiles, S. H. Yueh, J. N. Huddleston, S. V. Hsiao, et al. "Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description." Journal of Applied Meteorology and Climatology 52, no. 12 (December 2013): 2828–48. http://dx.doi.org/10.1175/jamc-d-12-0237.1.

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AbstractScatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a “rain indicator” to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.
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Hilburn, K. A., and F. J. Wentz. "Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)." Journal of Applied Meteorology and Climatology 47, no. 3 (March 1, 2008): 778–94. http://dx.doi.org/10.1175/2007jamc1635.1.

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Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.
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Linkova, A. "Сonsideration of the signal attenuation in double-frequency sensing for rain intensity retrieval." RADIOFIZIKA I ELEKTRONIKA 26, no. 3 (2021): 3–10. http://dx.doi.org/10.15407/rej2021.03.003.

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Subject and Purpose. Precipitation is the main source of agricultural land moisture. The knowledge of its amount, especially during the growing season, is important information to justify necessary agronomic and land reclamation measures. The purpose of this work is to solve by regularization the inverse problem of double-frequency sensing of precipitation in the microwave range with the signal attenuation considered and analyze the influence of radar cross-section (RCS) calculation errors and the total signal attenuation measuring precision on the rain intensity retrieval results. Methods and Methodology. Numerical simulation is used in double frequency retrievals to solve the integral scattering equation by regularization methods. Results. Numerical simulation has been performed for the rain intensity retrieval with a uniform spatial profile of rain intensity in the range 1…20 mm/h. Direct and inverse iterative procedures were used for having the signal attenuation at 0.82 and 3.2 cm operating wavelengths. It has been shown that the direct iterative procedure is less effective than the inverse one. Thus, when the rain intensity exceeds 20 mm/h or when it is within 10…20 mm/h and a rain spatial extent goes over 500 m, the direct iteration scheme causes significant errors in the rain intensity retrieval. Conclusion. The analysis of the results has shown that the use of the inverse iterative procedure makes it possible to retrieve a uniform-profile rain intensity with a 25 % error for rains with a 20 mm/h intensity and a 4 km spatial extent and ± 20 % errors in the total signal attenuation and specific RCS calculated.
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Duncan, David Ian, Christian D. Kummerow, Brenda Dolan, and Veljko Petković. "Towards variational retrieval of warm rain from passive microwave observations." Atmospheric Measurement Techniques 11, no. 7 (July 25, 2018): 4389–411. http://dx.doi.org/10.5194/amt-11-4389-2018.

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Abstract. An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rainwater during iteration. The state-dependent errors and nuanced treatment of drop distributions in precipitating regions are novel and may be applicable for future retrievals and all-sky data assimilation methods. This retrieval method can effectively increase passive microwave sensors' sensitivity to light rainfall that might otherwise be missed. Comparisons with space-borne and ground radar estimates are provided as a proof of concept, demonstrating that a passive-only variational retrieval can be sufficiently constrained from non-raining through warm rain conditions. Significant deviations from forward model assumptions cause non-convergence, usually a result of scattering hydrometeors above the freezing level. However, for cases with liquid-only precipitation, this retrieval displays greater sensitivity than a benchmark operational retrieval. Analysis against passive and active products from the Global Precipitation Measurement (GPM) satellite shows substantial discrepancies in precipitation frequency, with the experimental retrieval observing more frequent light rain. This approach may be complementary to other precipitation retrievals, and its potential synergy with the operational passive GPM retrieval is briefly explored. There are also implications for data assimilation, as all 13 channels on the GPM Microwave Imager (GMI) are simulated over ocean with fidelity in warm raining conditions.
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Kwon, Eun-Han, Byung-Ju Sohn, Dong-Eon Chang, Myoung-Hwan Ahn, and Song Yang. "Use of Numerical Forecasts for Improving TMI Rain Retrievals over the Mountainous Area in Korea." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1995–2007. http://dx.doi.org/10.1175/2007jamc1857.1.

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Abstract Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm.
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Wang, Wenyue, Klemens Hocke, and Christian Mätzler. "Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry." Remote Sensing 13, no. 11 (June 5, 2021): 2217. http://dx.doi.org/10.3390/rs13112217.

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Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.
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Wu, P., X. Dong, and B. Xi. "Marine boundary layer drizzle properties and their impact on cloud property retrieval." Atmospheric Measurement Techniques 8, no. 9 (September 3, 2015): 3555–62. http://dx.doi.org/10.5194/amt-8-3555-2015.

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Abstract. In this study, we retrieve and document drizzle properties, and investigate the impact of drizzle on cloud property retrieval in Dong et al. (2014a) from ground-based measurements at the ARM Azores facility from June 2009 to December 2010. For the selected cloud and drizzle samples, the drizzle occurrence is 42.6 %, with a maximum of 55.8 % in winter and a minimum of 35.6 % in summer. The annual means of drizzle liquid water path LWPd, effective radius rd, and number concentration Nd for the rain (virga) samples are 4.73 (1.25) g m−2, 61.5 (36.4) μm, and 0.38 (0.79) cm−3. The seasonal mean LWPd values are less than 3 % of the LWP values retrieved by the microwave radiometer (MWR). The annual mean differences in cloud-droplet effective radius with and without drizzle are 0.75 and 2.35 %, respectively, for the virga and rain samples. Therefore, we conclude that the impact of drizzle below the cloud base on cloud property retrieval is insignificant for a solar-transmission-based method, but significant for any retrievals using radar reflectivity.
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Brown, Paula J., Christian D. Kummerow, and David L. Randel. "Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones." Journal of Atmospheric and Oceanic Technology 33, no. 7 (July 2016): 1539–56. http://dx.doi.org/10.1175/jtech-d-15-0234.1.

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AbstractThe Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
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Carlin, Jacob T., Alexander V. Ryzhkov, Jeffrey C. Snyder, and Alexander Khain. "Hydrometeor Mixing Ratio Retrievals for Storm-Scale Radar Data Assimilation: Utility of Current Relations and Potential Benefits of Polarimetry." Monthly Weather Review 144, no. 8 (August 1, 2016): 2981–3001. http://dx.doi.org/10.1175/mwr-d-15-0423.1.

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Abstract The assimilation of radar data into storm-scale numerical weather prediction models has been shown to be beneficial for successfully modeling convective storms. Because of the difficulty of directly assimilating reflectivity (Z), hydrometeor mixing ratios, and sometimes rainfall rate, are often retrieved from Z observations using retrieval relations, and are assimilated as state variables. The most limiting (although widely employed) cases of these relations are derived, and their assumptions and limitations are discussed. To investigate the utility of these retrieval relations for liquid water content (LWC) and ice water content (IWC) in rain and hail as well as the potential for improvement using polarimetric variables, two models with spectral bin microphysics coupled with a polarimetric radar operator are used: a one-dimensional melting hail model and the two-dimensional Hebrew University Cloud Model. The relationship between LWC and Z in pure rain varies spatially and temporally, with biases clearly seen using the normalized number concentration. Retrievals using Z perform the poorest while specific attenuation and specific differential phase shift (KDP) perform much better. Within rain–hail mixtures, separate estimation of LWC and IWC is necessary. Prohibitively large errors in the retrieved LWC may result when using Z. The quantity KDP can be used to effectively retrieve the LWC and to isolate the contribution of IWC to Z. It is found that the relationship between Z and IWC is a function of radar wavelength, maximum hail diameter, and principally the height below the melting layer, which must be accounted for in order to achieve accurate retrievals.
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Dissertations / Theses on the topic "Rain Retrieval"

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Congling, Nie. "Wind/rain backscatter modeling and wind/rain retrieval for scatterometer and synthetic aperture radar /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2288.pdf.

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Nie, Congling. "Wind/Rain Backscatter Modeling and Wind/Rain Retrieval for Scatterometer and Synthetic Aperture Radar." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1632.

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Using co-located space-borne satellite (TRMM PR, ESCAT on ERS 1/2) measurements, and numerical predicted wind fields (ECMWF), the sensitivity of C-band backscatter measurement to rain is evaluated. It is demonstrated that C-band radar backscatter can be significantly altered by rain surface perturbation, an effect that has been previously neglected. A low-order wind/rain backscatter model is developed that has inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. The wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. Rain has a more significant impact on measurements at high incidence angles than at low incidence angles. Using three distinct regimes, the conditions for which wind, rain, and both wind and rain can be retrieved from scatterometer backscatter measurements are determined. The effects of rain on ESCAT wind-only retrieval are evaluated. The additional scattering from rain causes estimated wind speeds to be biased high and estimated wind directions to be biased toward the along-track direction in heavy rains. To compensate for rain-induced backscatter, we develop a simultaneous wind/rain retrieval method (SWRR), which simultaneously estimates wind and rain from ESCAT backscatter measurements with an incidence angle of over 40 degrees. The performance of SWRR under typical wind/rain conditions is evaluated through simulation and validation with collocated TRMM PR and ECMWF data sets. SWRR is shown to significantly improve wind velocity estimates and the SWRR-estimated rain rate has relatively high accuracy in moderate to heavy rain cases. RADARSAT-1 ScanSAR SWA images of Hurricane Katrina are used to retrieve surface wind vectors over the ocean. Collocated H*wind wind directions are used as the wind direction estimate and the wind speed is derived from SAR backscatter measurements by inversion of a C-band HH-polarization Geophysical Model Function (GMF) that is derived from the VV-polarization GMF, CMOD5, using a polarization ratio model. Because existing polarization models do not fit the ScanSAR SWA data well, a recalibration model is proposed to recalibrate the ScanSAR SWA images. Validated with collocated H*wind wind speed estimates, the mean difference between SAR-retrieved and H*wind speed is small and the root mean square (RMS) error is below 4 m/s. Rain effects on the ScanSAR measurements are analyzed for three different incidence angle ranges using collocated ground-based Doppler weather radar (NEXRAD) rain measurements. Compared with the scatterometer-derived model, the rain-induced backscatter observed by the ScanSAR at incidence angles 44 to 45.7 degrees is consistent with the scatterometer-derived model when the polarization difference between HH and VV polarizations is considered.
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Menzerotolo, Rosa Ana. "Rain rate retrieval algorithm for Aquarius/SAC-D microwave radiometer." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4982.

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Microwave radiometers are used to measure blackbody microwave emissions emitted by natural targets. Radiative transfer theory provides a well founded physical relationship between the atmosphere and surface geophysical parameters and the brightness temperature measured by these radiometers. The atmospheric brightness temperature is proportional to the integral of the microwave absorption of water vapor, oxygen, and liquid water between the top of the atmosphere and the surface. Inverse radiative transfer models use to retrieve the water vapor, cloud liquid and oxygen content in the atmosphere are very well known; however, the retrieval of rain rate in the atmosphere is still a challenge. This project presents a theoretical basis for the rain rate retrieval algorithm, which will be implemented in the Aquarius/SAC-D Microwave Radiometer (MWR). This algorithm was developed based on the radiative transfer model theory for a single layer atmosphere using four WindSat channels. Transmissivity due to liquid water (rain and cloud liquid water) is retrieved from the four channel brightness temperatures, and a statistical regression is performed to relate the rain rate, rain physical temperature and rain height to the liquid water transmissivities at 24 GHz and 37 GHz. Empirical validation results are presented using the WindSat radiometer observations.
ID: 029809040; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.E.E.)--University of Central Florida, 2011.; Includes bibliographical references (p. 270).
M.S.E.E.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
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Allen, Jeffrey R. "An Analysis of SeaWinds Simultaneous Wind/Rain Retrieval in Severe Weather Events." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd704.pdf.

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Chen, Ruiyue. "Beamfilling correction study for retrieval of oceanic rain from passive microwave observations." Texas A&M University, 2003. http://hdl.handle.net/1969/39.

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Amarin, Ruba. "HURRICANE WIND SPEED AND RAIN RATE RETRIEVAL ALGORITHM FOR THE STEPPED FREQUENCY MICROWAVE RADIOMETER." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3218.

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This thesis presents the development and validation of the Hurricane Imaging Retrieval Algorithm (HIRA) for the measurement of oceanic surface wind speed and rain rate in hurricanes. The HIRA is designed to process airborne microwave brightness temperatures from the NOAA, Stepped Frequency Microwave Radiometer (SFMR), which routinely collects data during NOAA hurricane hunter aircraft flights. SFMR measures wind speeds and rain rates at nadir only, but HIRA will soon be integrated with an improved surface wind speed model for expanded utilization with next generation microwave hurricane imagers, such as the Hurricane Imaging Radiometer (HIRad). HIRad will expand the nadir only measurements of SFMR to allow the measurement of hurricane surface winds and rain over a wide swath Results for the validation of HIRA retrievals are presented using SFMR brightness temperature data for 22 aircraft flights in 5 hurricanes during 2003-2005. Direct comparisons with the standard NOAA SFMR empirical algorithm provided excellent results for wind speeds up to 70 m/s. and rain rates up to 50 mm/hr.
M.S.E.E.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
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Draper, David W. "Wind Scatterometry with Improved Ambiguity Selection and Rain Modeling." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/117.

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Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributers to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributer to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous wind/rain (SWR) estimation procedure can improve wind estimates during rain, while providing a scatterometer-based rain rate estimate. SWR also affords improved rain flagging for low to moderate rain rates. QuikSCAT-retrieved rain rates correlate well with TRMM PR instantaneous measurements and TMI monthly rain averages. SeaWinds rain measurements can be used to supplement data from other rain-measuring instruments, filling spatial and temporal gaps in coverage.
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Alsweiss, Suleiman Odeh. "An improved ocean vector winds retrieval approach using C- and Ku-band scatterometer and multi-frequency microwave radiometer measurements." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4832.

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The analysis of these satellite data provided confidence in the capability of the simulation to generate realistic active/passive measurements at the top of the atmosphere. Results are very encouraging, and they show that the new algorithm can retrieve accurate ocean surface wind speeds in realistic hurricane conditions using the rain corrected Ku-band scatterometer measurements. They demonstrate the potential to improve wind measurements in extreme wind events for future wind scatterometry missions such as the proposed GCOM-W2.; This dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi-frequency passive microwave observations provided by a companion sensor and also from simultaneous C-band scatterometer measurements. The synergy of these active and passive measurements enables improved correction for rain effects, which enhances the utility of Ku-band scatterometer measurements in extreme wind events. The OVW retrieval algorithm is based on the next generation instrument conceptual design for future US scatterometers, i.e. the Dual Frequency Scatterometer (DFS) developed by NASA's Jet Propulsion Laboratory. Under this dissertation research, an end-to-end computer simulation was developed to evaluate the performance of this active/passive technique for retrieving hurricane force winds in the presence of intense rain. High-resolution hurricane wind and precipitation fields were simulated for several scenes of Hurricane Isabel in 2003 using the Weather Research and Forecasting (WRF) Model. Using these numerical weather model environmental fields, active/passive measurements were simulated for instruments proposed for the Global Change Observation Mission- Water Cycle (GCOM-W2) satellite series planned by the Japanese Aerospace Exploration Agency. Further, the quality of the simulation was evaluated using actual hurricane measurements from the Advanced Microwave Scanning Radiometer and SeaWinds scatterometer onboard the Advanced Earth Observing Satellite-II (ADEOS-II).
ID: 029810202; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 108-111).
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
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9

DeMoss, Jeremy. "Changes in Tropical Rainfall Measuring Mission (TRMM) retrievals due to the orbit boost estimated from rain gauge data." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1732.

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Mülmenstädt, Johannes, Odran Sourdeval, Julien Delanoë, and Johannes Quaas. "Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals." Geophysical research letters (2015), 42, S. 6502-6509, 2015. https://ul.qucosa.de/id/qucosa%3A14690.

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A climatology of thermodynamic phase of precipitating cloud is presented derived from global—land and ocean—, retrievals from Cloudsat, CALIPSO, and Moderate Resolution Imaging Spectroradiometer. Like precipitation rate, precipitation frequency is dominated by warm rain, defined as rain produced via the liquid phase only, over the tropical oceans outside the Intertropical Convergence Zone and by cold rain, produced via the ice phase, over the midlatitude oceans and continents. Warm rain is very infrequent over the continents, with significant warm rain found only in onshore flow in the tropics, and over India, China, and Indochina. Comparison of the properties of precipitating and nonprecipitating warm clouds shows that the scarcity of warm rain over land can be explained by smaller effective radii in continental clouds that delay the onset of precipitation. The results highlight the importance of ice-phase processes for the global hydrological cycle and may lead to an improved parameterization of precipitation in general circulation models.
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Books on the topic "Rain Retrieval"

1

Gossard, Earl E. Procedural guide for the retrieval of dropsize distributions in water clouds from ground-based clear-air-sensing doppler radar observations. [Boulder, Colo.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1988.

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Baldini, Michela, and Teresa Spignoli, eds. L'Approdo. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-8453-617-4.

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In December 1945 the "L'Approdo" transmissions were launched at the RAI headquarters in Florence. The radio programme, one of the most important in Italy at the time, went on the air up to 1977, being accompanied from 1952 by a magazine and from 1963 to 1972 by a television programme. The three parallel cultural "enterprises" boasted an impressive number of important collaborators, gravitating around the decisive figure of Carlo Betocchi as leader and organiser. Nevertheless, despite its significance, even the adventure of "L'Approdo" was destined to die. When the transmissions and the publication of the magazine ceased, an entire cultural élite had to come to terms not only with the objective difficulties, but with a crisis of trust and of commitment in the face of what were now irreversible changes in the country. Yet – precisely because "L'Approdo" had battled for an approach that was destined to become minority with the triumph of the new media society – the retrieval of its history and the reconstruction through voices, pages and images of one of the first examples of encounter and mediation between culture and communication appears particularly significant. The methods and the emphatic planning of the entire experience emerge clearly from the first issue of the magazine, produced here in anastatic reprint, and above all from the enclosed CD-Rom which proposes, along with the tables of contents of "L'Approdo", the files and records of the entire correspondence (over 20,000 unpublished pieces) and details of the surviving scripts of the transmissions… In short, we finally have at our disposal material that enables us to reconstruct – through the traces of a programme and a magazine and of the intellectuals who collaborated on them – thirty years of culture and utopia, of compromise and enthusiasm, clustered around the birth, growth and death of an articulated project of "cultural policy".
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Satellite rainfall retrieval by logistic regression. Landover, MD: Applied Research Corporation, 1986.

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Comparing TRMM Rainfall Retrieval With NOAA Buoy Rain Gauge Data. Storming Media, 2002.

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A new inversion-based algorithm for retrieval of over-water rain rate from SSM/I multichannel imagery. [Washington, DC: National Aeronautics and Space Administration, 1994.

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R, Stettner David, and United States. National Aeronautics and Space Administration., eds. A new inversion-based algorithm for retrieval of over-water rain rate from SSM/I multichannel imagery. [Washington, DC: National Aeronautics and Space Administration, 1994.

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R, Stettner David, and United States. National Aeronautics and Space Administration., eds. A new inversion-based algorithm for retrieval of over-water rain rate from SSM/I multichannel imagery. [Washington, DC: National Aeronautics and Space Administration, 1994.

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G, Strauch R., and Environmental Research Laboratories (U.S.), eds. Further guide for the retrieval of dropsize distributions in water clouds with a ground-based clear-air-sensing doppler radar. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1989.

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W, Spencer Roy, Remote Sensing Systems (Firm), and United States. National Aeronautics and Space Administration., eds. SSM/I rain retrievals within a unified all-weather ocean algorithm. Santa Rosa, CA: The Systems, 1996.

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Book chapters on the topic "Rain Retrieval"

1

Iguchi, Toshio, and Ziad S. Haddad. "Introduction to Radar Rain Retrieval Methods." In Advances in Global Change Research, 169–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24568-9_10.

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Jiang, Jyun-Yu, Yi-Shiang Tzeng, Pei-Ying Huang, and Pu-Jen Cheng. "Analyzing the Spatiotemporal Effects on Detection of Rain Event Duration." In Information Retrieval Technology, 506–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_46.

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Shige, Shoichi, Munehisa K. Yamamoto, and Aina Taniguchi. "Improvement of TMI Rain Retrieval Over the Indian Subcontinent." In Remote Sensing of the Terrestrial Water Cycle, 27–42. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch2.

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Anagnostou, Emmanouil N. "Assessment of Satellite Rain Retrieval Error Propagation in the Prediction of Land Surface Hydrologi." In Measuring Precipitation From Space, 357–68. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_28.

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Elhassnaoui, Ismail, Zineb Moumen, Hicham Ezzine, Marwane Bel-lahcen, Ahmed Bouziane, Driss Ouazar, and Moulay Driss Hasnaoui. "Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm." In Impacts of Climate Change on Agriculture and Aquaculture, 1–34. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3343-7.ch001.

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In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.
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Yurchak, Boris. "The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity." In Current Topics in Tropical Cyclone Research. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88683.

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Spiral cloud-rain bands (SCRBs) are some of the most distinguishing features inherent in satellite and radar images of tropical cyclones (TC). The subject of the proposed research is the finding of a physically substantiated method for estimation of the TC’s intensity using SCRBs’ configuration parameters. To connect a rainband pattern to a physical process that conditions the spiraling feature of a rainband, it is assumed that the rainband’s configuration near the core of a TC is governed primarily by a streamline. In turn, based on the distribution of primarily forces in a TC, an analytical expression as a combination of hyperbolic and logarithmic spirals (HLS) for the description of TC spiral streamline (rainband) is retrieved. Parameters of the HLS are determined by the physical parameters of a TC, particularly, by the maximal wind speed (MWS). To apply this theoretical finding to practical estimation of the TC’s intensity, several approximation techniques are developed to “convert” rainband configuration to the estimation of the MWS. The developed techniques have been tested by exploring satellite infrared imageries and airborne and coastal radar data, and the outcomes were compared with in situ measurements of wind speeds and the best track data of tropical cyclones.
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Conference papers on the topic "Rain Retrieval"

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Laviola, S., and V. Levizzani. "Rain Retrieval Using the 183 GHz Absorption Lines." In 2008 Microwave Radiometry and Remote Sensing of the Environment (MICRORAD 2008). IEEE, 2008. http://dx.doi.org/10.1109/micrad.2008.4579505.

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Long, D. "Ultra High Resolution Rain Retrieval from QuikSCAT Data." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.1059.

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Liu, Shuyan, Christopher Grassotti, Quanhua Liu, Yong-Keun Lee, and Ryan Honeyager. "The NOAA Microwave Integrated Retrieval System Multiple Satellite Rain Rate Retrieval and Monitoring." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900172.

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Linkova, A., and G. Khlopov. "Retrieval of rain intensity by three-frequency radar sensing." In 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW). IEEE, 2016. http://dx.doi.org/10.1109/msmw.2016.7538166.

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Congling Nie and David G. Long. "Simultaneous wind and rain retrieval for ERS scatterometer measurements." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423844.

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Srinivasa Ramanujam, K., and C. Balaji. "A Fast Polarized Microwave Radiative Transfer Model for a Raining Atmosphere." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22228.

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Retrieval of vertical rain structure and hence the estimation of surface rain rate is of central importance to various missions involving remote sensing of the earth’s atmosphere. Typically, remote sensing involves scanning the earth’s atmosphere at visible, infra red and microwave frequencies. While the visible and infra red frequencies can scan the atmosphere with higher spatial resolution, they are not suited for scanning under cloudy conditions as clouds are opaque under these frequencies. However, the longer wavelength microwave radiation can partially penetrate through the clouds without much attenuation thereby making it more suitable for meteorological purposes. The retrieval algorithms used for passive microwave remote sensing involve modeling of the radiation in the earth’s atmosphere where in the clouds and precipitating rain (also known as hydrometeors) emit / absorb / scatter. Additionally, it has been observed that the rain droplets tend to polarize the microwave signal emitted by the earth’s surface. In view of this, the first step in the development of a rainfall retrieval algorithm for any satellite mission is to simulate the radiances (also known as brightness temperatures) that would have been measured by a typical radiometer for different sensor frequencies and resolutions. Towards this, a polarized microwave radiation transfer code has been developed in house for a plane parallel raining atmosphere (henceforth called as forward model) that depicts the physics as seen by a satellite. Physics based retrieval algorithm often involves repeated execution of the forward model for various raining scenario. However, due to the complexity involved in the radiation modeling of the raining atmosphere which is participating in nature, the forward model suffers from the drawback that it requires enormous computational effort. In the present work, a much quicker alternative is proposed wherein the forward model can be replaced with an Artificial Neural Network (ANN) based Fast Forward Model (AFFM). This AFFM can be used in conjunction with an appropriate inverse technique to retrieve the rain structure. Spectral microwave brightness temperatures at frequencies corresponding to the Tropical Rainfall Measuring Mission (TRMM) of National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) are first simulated using an in-house polarized radiate on transfer code for sixteen past cyclones in the North Indian Ocean region in the period (2000–2005), using the hydrometeor profiles retrieved from the Goddard Profiling Algorithm (GPROF) of the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI). This data is split into two sets: while the first set of data is used for training the network, the remainder of the data is used for testing the ANN. The results obtained are very encouraging and shows that neural network is capable of predicting the brightness temperature accurately with the correlation coefficient of over 99%. Furthermore, the execution of the forward model on an Intel Core 2 Quad 3.0 GHz processor based, 8 GB DDR3 RAM workstation took 3 days, while the AFFM delivers the results in 10 seconds.
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Klugmann, Dirk, and Ondrej Fiser. "Application of single drop scattering algorithms to rain related retrieval." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423552.

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Klugmann, D., and O. Fiser. "Mie versus Point Matching Algorithm for Radar Rain Properties Retrieval." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779894.

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Han, Congzheng, Shu Duan, and Yongheng Bi. "Rain Rate Retrieval from Millimeter-Wave Propagation Measurements in China." In 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC). IEEE, 2018. http://dx.doi.org/10.23919/ursi-at-rasc.2018.8471362.

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Amarin, Ruba A., Linwood Jones, James Johnson, Chris Ruf, Tim Miller, and Shuyi Chen. "Hurricane imaging radiometer wind speed and rain rate retrieval: Part-2. Analysis of retrieval accuracy." In 2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010). IEEE, 2010. http://dx.doi.org/10.1109/microrad.2010.5559560.

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