Alqudah A., V. Chand rasekar, and M. Le, 2013: Investigating rainfall estimation from radar measurements using neural networks.Natural Hazards and Earth System Science,13, 535-544, .https://doi.org/10.5194/nhess-13-535-2013
Anagnostou E. N., C. A. Morales, and T. Dinkua, 2001: The use of TRMM precipitation radar observations in determining ground radar calibration biases. J. Atmos. Oceanic Technol.,18, 616-628, .https://doi.org/10.1175/1520-0426(2001)018<0616:TUOTPR>2.0.CO;2
Bolen S. M., V. Chandrasekar, 2000: Quantitative cross validation of space-based and ground-based radar observations. J. Appl. Meteor.,39, 2071-2079, .https://doi.org/10.1175/1520-0450(2001)040<2071:QCVOSB>2.0.CO2
Calheiros R. V., C. A. Morales, and E. N. Anagnostou, 2000: Precipitation structure from ground and space-based radar observations. [Available online at ]http://www.ipmet.unesp.br/publications/calheiros2000.htm
Cao Q., Y. Hong, Y. C. Qi, Y. X. Wen, J. Zhang, J. J. Gourley, and L. Liao, 2013: Empirical conversion of the vertical profile of reflectivity from Ku-band to S-band frequency. J. Geophys. Res., 118(2) 1814-1825, .https://doi.org/10.1002/jgrd.50138
Cao Q., Y. X. Wen, Y. Hong, J. J. Gourley, and P.-E. Kirstetter, 2014: Enhancing quantitative precipitation estimation over the continental United States using a ground-space multi-sensor integration approach. IEEE Geosci. Remote Sens. Lett.,11, 1305-1309, .https://doi.org/10.1109/LGRS.2013.2295768
Chand rasekar, V., R. Cifelli, 2012: Concepts and principles of rainfall estimation from radar: Multi sensor environment and data fusion. Indian Journal of Radio & Space Physics., 41, 389- 402.82a9231f05120656fbf95362105dc111http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F275154386_Concepts_and_principles_of_rainfall_estimation_from_radar_Multi-sensor_environment_and_data_fusionhttp://www.researchgate.net/publication/275154386_Concepts_and_principles_of_rainfall_estimation_from_radar_Multi-sensor_environment_and_data_fusionRainfall estimation has been pursued nearly since the dawn of civilization. It is also one of the most commonly used applications of modern, meteorological radars in most operational systems. Multi sensor approaches have made great strides in addressing the rainfall estimation problem through better sensor calibration and improved integration of observations at scales spanning many orders of magnitude such as radar, satellite and rain gauges. Data fusion techniques have demonstrated advantages in precipitation retrievals, especially for radar observations at attenuated frequencies. Data fusion has also shown benefits in merging data from multiple radars, as well as radars and satellites. This paper describes essential concepts of multi sensor rainfall estimation with a radar focus. Validation concepts for remote estimation of rainfall are also presented. Examples of data fusion and validation are illustrated through rainfall estimate comparisons between gauge and radar networks.
Chand rasekar, V., K. S. Ramanujam, H. Chan, M. Le, A. Alqudah, 2014: Rainfall estimation from spaceborne and ground based radars using neural networks. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symposium, Quebec City, QC, IEEE, 4966-4969, .https://doi.org/10.1109/IGARSS.2014.6947610
Chen C.-S., B. P.-T. Chen, F. N.-F. Chou, and C.-C. Yang, 2010: Development and application of a decision group back-propagation neural network for flood forecasting.J.Hydrol.,385, 173-182, .https://doi.org/10.1016/j.jhydrol.2010.02.019
de Oliveira, M. M. F., N. F. F. Ebecken, J. L. F. de Oliveira, I. de Azevedo Santos, 2009: Neural network model to predict a storm surge.Journal of Applied Meteorology and Climatology,48, 143-155, .https://doi.org/10.1175/2008JAMC1907.1
Dolan B., T. J. Lang, S. W. Nesbitt, R. Cifelli, and S. A. Rutledge, 2011: Investigation of rainfall characteristics using TRMM PR and ground based radar. 2011 AGU Fall Meeting, AGU, San Francisco, California.,USA.0e0b238cacd596652eee1440472f0d48http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011AGUFM.H43C1234Dhttp://adsabs.harvard.edu/abs/2011AGUFM.H43C1234DDespite relatively good agreement between reflectivity profiles, comparisons of rainfall statistics derived from TRMM Precipitation Radar (PR) deviate from ground-based radar (GR) observations in various field locations across the globe. TRMM PR rain rate probability distribution functions underestimate the occurrence of high rain rates (> 80 mm hr-1) compared with similar ground-based statistics, and similarly, GR distributes the total rain volume over a larger range of rain rates. Analysis of ten years of TRMM data over three field sites has shown that the greatest disagreements occur in the most intense convection, such as over land and during the east and break wind regimes over the Amazon and Australia, respectively. These differences are investigated further in this study. Ten years of TRMM PR data are analyzed in conjunction with data collected during two field experiments involving the NCAR S-Pol radar. S-Pol was deployed in Brazil in the Amazon during TRMM LBA in 1998-1999 and near Mazatlan, Mexico as part of the North American Monsoon Experiment (NAME) in 2004. Additionally, multiple years of data from the Australian Bureau of Meteorology CPOL radar located in Darwin, Australia, are examined to extend the robustness of the GR observations beyond the relatively short field campaigns. Polarimetric data collected by the two radars are used to characterize the differences between TRMM PR and GR observations as a function of bulk hydrometeor type. For example, profiles with significant graupel, as identified by GR, are analyzed to investigate the role of mixed phase in the PR retrievals. The vertical variability of D0 is examined as a function of reflectivity and related to the underlying microphysical conditions using the polarimetric data provided by the GR observations. Spatial variability of D0 is also explored by correlating D0 values derived from GR at different heights. Several significant changes were made to the TRMM processing algorithms in the latest release of the data, version 7 (V7). Among other modifications, the attenuation correction algorithm was adjusted to improve retrievals over land. V6 and V7 reflectivity profiles and rain statistics are compared with ground based radar observations from the three field sites in order to understand the impact of the algorithm changes. Differences in rain rate retrievals between V6 and V7 as a function spatial and temporal regimes, such as land and ocean (NAME and Darwin) and large scale wind regimes (LBA and Darwin), are investigated.
Ebtehaj A. M., E. Foufoula-Georgiou, 2011: Adaptive fusion of multisensor precipitation using Gaussian-scale mixtures in the wavelet domain.J. Geophys. Res.,116, 1-19, https://doi.org/10.1029/2011JD016219.
Fu Y. F., R. C. Yu, Y. P. Xu, Q. N. Xiao, and G. S. Liu, 2003: Analysis on precipitation structures of two heavy rain cases by using TRMM PR and IMI.Acta Meteorol. Sin.,61, 421-431, . (in Chinese with English abstract)https://doi.org/10.11676/qxxb2003.041
Gourley J. J., Y. Hong, W. A. Petersen, K. Howard, Z. Flamig, and Y. Wen, 2010: Creating synergy between ground and space-based precipitation measurements. 2010 AGU Fall Meeting, AGU, San Francisco, California.,USA.325363f7f281bf9f7fe320df2c7e700ahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010AGUFM.H12C..05Ghttp://adsabs.harvard.edu/abs/2010AGUFM.H12C..05GAs the successor of the Tropical Rainfall Measuring Mission (TRMM) satellite launched in 1997, the multi-national Global Precipitation Measurement (GPM) Mission, to be launched in 2013, will provide next-generation global precipitation estimates from space within a unified framework. On the ground, several countries worldwide are in the throes of expanding their weather radar networks with gap-filling radars and upgrading them to include polarimetric capabilities. While significant improvements in precipitation estimation capabilities have been realized from space- and ground-based platforms separately, little effort has been focused on aligning these communities for synergistic, joint development of algorithms. In this study, we demonstrate the integration of real-time rainfall products from the Tropical Rainfall Measurement Mission (TRMM) into the National Severe Storms Laboratory鈥檚 (NSSL) National Mosaic and QPE (NMQ/Q2; http://nmq.ou.edu) system. The NMQ system enables a CONUS-wide comparison of TRMM products to NEXRAD-based Q2 rainfall products. Moreover, NMQ鈥檚 ground validation software ingests and quality controls data from all automatic-reporting rain gauge networks throughout the US and provides robust graphical and statistical validation tools, accessible by anyone with internet access. This system will readily incorporate future products from GPM as well as those from the dual-polarization upgrade to the NEXRAD network. While initial efforts are on the intercomparison of rainfall products, we envision this system will ultimately promote the development of precipitation algorithms that capitalize on the strengths of spatiotemporal and error characteristics of space and ground remote-sensing data. An example algorithm is presented where the vertical structure of precipitating systems over complex terrain is more completely resolved using combined information from NMQ and TRMM precipitation radar (PR), leading to more accurate surface rainfall estimates.
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Kou L. L., Z. G. Chu, N. Li, and Z. H. Wang, 2016: Three-dimensional fusion of reflectivity factor from TRMM precipitation radar and ground-based radar.Acta Meteorologica Sinica,74, 285-297, . (in Chinese with English abstract)https://doi.org/10.11676/qxxb2016.018
Kozu, T., Coauthors, 2001: Development of precipitation radar onboard the tropical rainfall measuring mission (TRMM) satellite.IEEE Transactions on Geoscience and Remote Sensing,39, 102-117, .https://doi.org/10.1109/36.898669
Lee Y.-R., D.-B. Shin, J.-H. Kim, and H.-S. Park, 2015: Precipitation estimation over radar gap areas based on satellite and adjacent radar observations.Atmospheric Measurement Techniques,8, 719-728, .https://doi.org/10.5194/amt-8-719-2015
Li Z., D. W. Yang, and Y. Hong, 2013: Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River.J. Hydrol.,500, 157-169, .https://doi.org/10.1016/j.jhydrol.2013.07.023
Liao L., R. Meneghini, 2009: Validation of TRMM precipitation radar through comparison of its multiyear measurements with ground-based radar.Journal of Applied Meteorology and Climatology,48, 804-817, .https://doi.org/10.1175/2008JAMC1974.1
Liao L., R. Meneghini, and T. Iguchi, 2001: Comparisons of rain rate and reflectivity factor derived from the TRMM precipitation radar and the WSR-88D over Melbourne, Florida, Site. J. Atmos. Oceanic Technol.,18, 1959-1974, .https://doi.org/10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2
Liu H. P., V. Chand rasekar, and G. Xu, 2001: An adaptive neural network scheme for radar rainfall estimation from WSR-88D observations. J. Appl. Meteor.,40, 2038-2050, .https://doi.org/10.1175/1520-0450(2001)040<2038:AANNSF>2.0.CO;2
Mahani S. E., R. Khanbilvardi, 2009: Generating multi-sensor precipitation estimates over radar gap areas. WSEAS Transactions on Systems, 8, 96- 106.10412dc502d54fc318fecbb41d081526http%3A%2F%2Fdl.acm.org%2Fcitation.cfm%3Fid%3D1513532.1513542http://dl.acm.org/citation.cfm?id=1513532.1513542ABSTRACT Generating a multi-sensor precipitation product over radar gap area is the objective of the present study. A merging approach is developed to improve Satellite-based Precipitation Estimates (SPE) by merging with ground-based Radar Rainfall (RR) estimates because remote satellites are the only source that can collect information from areas where are inaccessible by ground-based radar and/or rain gauge networks. The merging algorithm is capable of extending radar information from pixels with available RR to their neighboring pixels with no radar information by merging RR with SPE, which is, usually, available for all pixels. SPE is combined with RR using the weighting-based approach of Successive Correction Method (SCM) after local bias correction of SPE with respect to RR. High resolution satellite infrared-based rainfall estimates from the NESDIS Hydro Estimator algorithm (HE), at hourly 4 km 脳 4 km basis, is selected to be merged with radar-based NEXRAD Stage IV rainfall measurements to generate rainfall product for the radar gap areas. To be able to validate the generated rainfall against NEXRAD, different size areas with available radar rainfall are selected as radar gap regions. The developed merging technique is evaluated for several study cases in summer 2003 and 2004. The results show that generated rainfall for the radar gap areas are more correlated with RR (average 0.67) than original HE with RR (average 0.36) and the RMSE between merged and radar rainfall (average 2.8 mm) is less than the RMSE between satellite and radar rainfall (average 4.48 mm). And also, the pattern and intensity of the generated rainfall for radar gap area became more similar to the pattern and value of RR. In addition, the enhancement of the generated rainfall with respect to RR is more significant for high rainfall amounts.
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