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Impact of Cloud Model Microphysics on Passive Microwave Retrievals of Cloud Properties. Part I: Model Comparison Using EOF Analyses

Michael I. Biggerstaff, Eun-Kyoung Seo, Svetla M. Hristova-Veleva, Kwang-Yul Kim
2006 Journal of Applied Meteorology and Climatology  
Hristova-Veleva (2000) noted that the parameterization of cloud ice was more dependent on NCIO in scheme B.  ...  A more detailed description of the microphysical schemes and model initialization procedures can be found in Hristova-Veleva (2000) .  ... 
doi:10.1175/jam2372.1 fatcat:2gxdfb7lcralbpl6y72lxewdzu

Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements

F. Joseph Turk, Ramon Padullés, Chi O. Ao, Manuel de la Torre Juárez, Kuo-Nung Wang, Garth W. Franklin, Stephen T. Lowe, Svetla M. Hristova-Veleva, Eric J. Fetzer, Estel Cardellach, Yi-Hung Kuo, J. David Neelin
2019 Remote Sensing  
The theoretical vertical resolution of the retrieval is limited by diffraction within the atmosphere and estimated to be ~60 m [24] .  ...  The theoretical vertical resolution of the retrieval is limited by diffraction within the atmosphere and estimated to bẽ 60 m [24] .  ... 
doi:10.3390/rs11202399 fatcat:raio4hpvl5a7xerzos4ign3pjq

Evaluating and Extending the Ocean Wind Climate Data Record

Frank J. Wentz, Lucrezia Ricciardulli, Ernesto Rodriguez, Bryan W. Stiles, Mark A. Bourassa, David G. Long, Ross N. Hoffman, Ad Stoffelen, Anton Verhoef, Larry W. O'Neill, J. Tomas Farrar, Douglas Vandemark (+5 others)
2017 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
0.3 m/s.  ...  Color scale is in units of m/s.  ... 
doi:10.1109/jstars.2016.2643641 pmid:28824741 pmcid:PMC5562405 fatcat:tjs5ggbcmfcwjpwbyl5crgl2ei

Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Mark A. Bourassa, Thomas Meissner, Ivana Cerovecki, Paul S. Chang, Xiaolong Dong, Giovanna De Chiara, Craig Donlon, Dmitry S. Dukhovskoy, Jocelyn Elya, Alexander Fore, Melanie R. Fewings, Ralph C. Foster (+22 others)
2019 Frontiers in Marine Science  
Recent observational (e.g., Hristova-Veleva et al., 2016) and modeling (e.g., Hristova-Veleva et al., 2018; Saiprasanth et al., 2018) studies provide strong indications for the predictive capability  ...  The footprint averaged winds reached 60 m/s in very intense TC.  ...  Copyright © 2019 Bourassa, Meissner, Cerovecki, Chang, Dong, De Chiara, Donlon, Dukhovskoy, Elya, Fore, Fewings, Foster, Gille, Haus, Hristova-Veleva, Holbach, Jelenak, Knaff, Kranz, Manaster, Mazloff  ... 
doi:10.3389/fmars.2019.00443 fatcat:mbket5dcjvaibhd2j5ngkyvvri

An Eye on the Storm: Integrating a Wealth of Data for Quickly Advancing the Physical Understanding and Forecasting of Tropical Cyclones

Svetla M. Hristova-Veleva, P. Peggy Li, Brian Knosp, Quoc Vu, F. Joseph Turk, William L. Poulsen, Ziad Haddad, Bjorn Lambrigtsen, Bryan W. Stiles1, Tsae-Pyng Shen, Noppasin Niamsuwan, Simone Tanelli (+21 others)
2020 Bulletin of The American Meteorological Society - (BAMS)  
Hristova-Veleva et al. 2018a). (Fig. 9) .  ...  Hristova-Veleva et al. 2012b ) that goes beyond the comparison of "best track" metrics.  ... 
doi:10.1175/bams-d-19-0020.1 fatcat:lywutoq6cvgh3lxzayzmvydz74

Improved hurricane active/passive simulated wind vector retrievals

Suleiman Alsweiss, Peth Laupattarakasem, Salem El-Nimri, W. Linwood Jones, Svetla Hristova-Veleva
2010 2010 IEEE International Geoscience and Remote Sensing Symposium  
Fig. 1 a 1 ) WRF simulation for Hurricane Katrina surface wind vectors (m/s)Fig.1 b) WRF simulation for HurricaneKatrina rain rates (mm/hr)The unique aspect of this technique is that it exploits the coincident  ... 
doi:10.1109/igarss.2010.5652385 dblp:conf/igarss/AlsweissLEJH10 fatcat:ulilus4i2fbp5h7d2qspvzqiw4

Changes in the functional characteristics of tumor and normal cells after treatment with extracts of white dead-nettle

Ralitsa Veleva, Bela Petkova, Veselina Moskova-Doumanova, Jordan Doumanov, Milena Dimitrova, Petya Koleva, Kirilka Mladenova, Svetla Petrova, Zhenya Yordanova, Veneta Kapchina-Toteva, Tanya Topouzova-Hristova
2014 Biotechnology & Biotechnological Equipment  
The in vitro growth occurred under aseptic controlled environmental conditions (16/8 h light/dark, 60 mmol m ¡2 s ¡1 photosynthetic photon flux density, Philips TLD-33, at 25 C, and 60%À70% relative air  ... 
doi:10.1080/13102818.2014.989094 pmid:26019631 pmcid:PMC4433899 fatcat:3iva3c7hpfdgtcbmpsizm724wm

Joint analysis of convective structure from the APR-2 precipitation radar and the DAWN Doppler wind lidar during the 2017 Convective Processes Experiment (CPEX)

F. Joseph Turk, Svetla Hristova-Veleva, Stephen L. Durden, Simone Tanelli, Ousmane Sy, G. David Emmitt, Steve Greco, Sara Q. Zhang
2020 Atmospheric Measurement Techniques  
During the May–June 2017 Convective Processes Experiment (CPEX), NASA DC-8-based airborne observations were collected from the JPL Ku- and Ka-band Airborne Precipitation Radar (APR-2) and the 2 µm Doppler  ...  Figure 2 shows the DC-8 flight tracks on this date taken from the JPL CPEX data portal (http://cpex.jpl.nasa.gov, 19 August 2020; Hristova-Veleva et al., 2020) and superimposed upon GOES-16 geostationary  ...  On the north side of the AOI, the winds were mainly southwesterly near 10 m s −1 , with 2 km level winds more southerly with weaker 5 m s −1 speeds.  ... 
doi:10.5194/amt-13-4521-2020 fatcat:h2fm6cj2svdzjo5yieiowcsgiy

Long-Term Comparison of Collocated Instantaneous Rain Retrievals from the TRMM Microwave Imager and Precipitation Radar over the Ocean

Eun-Kyoung Seo, Svetla Hristova-Veleva, Guosheng Liu, Mi-Lim Ou, Geun-Hyeok Ryu
2015 Journal of Applied Meteorology and Climatology  
., Hristova-Veleva et al. 2013) .  ...  In the legend, C, S, M, and A represent convective, stratiform, mixed, and all rain types, respectively. put into the collocation procedures, there will always be mismatches between the two measurements  ... 
doi:10.1175/jamc-d-14-0235.1 fatcat:uu2klconcjg4vitqc27zj7444y

Regional Intensification of the Tropical Hydrological Cycle During ENSO

Graeme L. Stephens, Maria Z. Hakuba, Mark J. Webb, Matthew Lebsock, Qing Yue, Brian H. Kahn, Svetla Hristova-Veleva, Anita D. Rapp, Claudia J. Stubenrauch, Gregory S. Elsaesser, Julia Slingo
2018 Geophysical Research Letters  
L., Hakuba, M. Z., Webb, M. J., Lebsock, M., Yue, Q., Kahn, B. H., et al. (2018). Regional intensification of the tropical hydrological cycle during ENSO.  ...  pressure velocity O 500 of about 0.02 Pa/s that translates into À3 m/day.  ... 
doi:10.1029/2018gl077598 fatcat:kzx7yhj2xngirjedzu6x2x6yze

Impact of Microphysical Parameterizations on Simulated Hurricanes—Using Multi-Parameter Satellite Data to Determine the Particle Size Distributions that Produce Most Realistic Storms

Svetla Hristova-Veleva, Ziad Haddad, Alexandra Chau, Bryan W. Stiles, F. Joseph Turk, P. Peggy Li, Brian Knosp, Quoc Vu, Tsae-Pyng Shen, Bjorn Lambrigtsen, Eun-Kyoung Seo, Hui Su
2021 Atmosphere  
For example, Hristova-Veleva, S. et al.  ...  light orange, N 0r = 80 × 10 6 m −4 and N 0g = 80 × 10 6 m −4 -in dark orange, N 0r = 400 × 10 6 m −4 and N 0g = 200 × 10 6 m −4 -in purple.  ... 
doi:10.3390/atmos12020154 fatcat:vjck6b2xuvdirhgqjbeox23yti

Annual Modulation of Diurnal Winds in the Tropical Oceans

Donata Giglio, Sarah T. Gille, Bruce D. Cornuelle, Aneesh C. Subramanian, Francis Joseph Turk, Svetla Hristova-Veleva, Devon Northcott
2022 Remote Sensing  
G = G S G D G M G L G F , (4) m =       m S m D m M m L m F       . ( 5 ) The estimate of the model coefficients (m) for the different components is described in Section 3.2.  ...  The GTMBA moorings measure winds at 4 m above the ocean surface.  ... 
doi:10.3390/rs14030459 fatcat:serpbxvsnnbz5nn75olrbtmbtm

Examination of the Daily Cycle Wind Vector Modes of Variability from the Constellation of Microwave Scatterometers and Radiometers

F. Joseph Turk, Svetla Hristova-Veleva, Donata Giglio
2021 Remote Sensing  
For speeds less than 15 m s −1 , there is no significant bias. On average, for wind speeds > 15 m s −1 , QuikSCAT is biased slightly low relative to TMI.  ...  In this case, the predominant wind blows from the east at around 5 m s −1 (middle panels), but buried within this value is the diurnal wind variability, which has a magnitude of only around 0.2 m s −1  ... 
doi:10.3390/rs13010141 fatcat:ag6f5n4dqnavbomorvveddpghy

NASA's Genesis and Rapid Intensification Processes (GRIP) Field Experiment

Scott A. Braun, Ramesh Kakar, Edward Zipser, Gerald Heymsfield, Cerese Albers, Shannon Brown, Stephen L. Durden, Stephen Guimond, Jeffery Halverson, Andrew Heymsfield, Syed Ismail, Bjorn Lambrigtsen (+4 others)
2013 Bulletin of The American Meteorological Society - (BAMS)  
We also would like to thank Bruce Anderson, Aaron Bansemer, Richard Blakeslee, Michael Goodman, Johnny Hall, Svetla Hristova-Veleva, Doug Mach, Robert Rogers, Lin Tian, and Luke Ziemba for their assistance  ...  s −1 to slightly more than 60 m s −1 . markedly.  ...  near or above 60 m s −1 after 0000 UTC 17 September.  ... 
doi:10.1175/bams-d-11-00232.1 fatcat:sqbftloya5hdfpq46j4xjo5evq

A Model for the Complete Radial Structure of the Tropical Cyclone Wind Field. Part I: Comparison with Observed Structure*

Daniel R. Chavas, Ning Lin, Kerry Emanuel
2015 Journal of the Atmospheric Sciences  
Very special thanks to Bryan Stiles, Svetla Hristova-Veleva, and their team at the NASA Jet Propulsion Laboratory for their help working with their excellent QuikSCAT tropical cyclone database.  ...  ER11 M m 22(C k /C d ) 5 2(r/r m ) 2 2 2 (C k /C d ) 1 (C k /C d )(r/r m ) 2 , (6) where M m 5 r m V m 1 1 2 fr 2 m (7) is the angular momentum at the radius of maximum wind 1 and C k /C d is the ratio  ...  Crosses are values estimated from fit of model to median profile of M/M m as a function of r/r m within each bin for cases where V m /fr m $ 10.  ... 
doi:10.1175/jas-d-15-0014.1 fatcat:iq4xpklsyvd7xcyxdaietyw5ba
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