Search Results

You are looking at 1 - 10 of 12 items for

  • Author or Editor: Lucrezia Ricciardulli x
  • Refine by Access: All Content x
Clear All Modify Search
Andrew Manaster
,
Lucrezia Ricciardulli
, and
Thomas Meissner

Abstract

Reliable sources for validating wind observations made by spaceborne microwave radiometer and scatterometer sensors above 15 m s−1 are scarce. Anemometers mounted on oil platforms provide usable wind speed measurements that can help fill this gap. In our study we compare wind speed observations from six microwave satellites (WindSat, AMSR-E, AMSR2, SMAP, QuikSCAT, and ASCAT) with wind speed records from 10 oil platform anemometers in the North and Norwegian Seas that were provided by the Norwegian Meteorological Institute. We study various forms of the vertical wind profile, which is required to convert anemometer winds to a reference height of 10 m above sea level. We create and analyze matchups between satellite and anemometer winds and find good agreement up to wind speeds of 30 m s−1 within the margin of errors. We also evaluate wind speeds from several analyses [ECMWF, NCEP, and Cross-Calibrated Multi-Platform (CCMP)]. We find them to be significantly lower than the anemometer winds with their biases increasing systematically with increasing wind speed. Important components of our analysis include a detailed discussion on the quality control of the anemometer winds and a quantitative analysis of the uncertainties in creating the matchups.

Full access
Lucrezia Ricciardulli
and
Rolando R. Garcia

Abstract

The forcing of equatorial waves by convective heating in the National Center for Atmospheric Research Community Climate Model (CCM3) is investigated and compared with the forcing deduced from observations of convective clouds. The analysis is performed on two different simulations, wherein convection is represented by the Zhang–McFarlane and the Hack parameterization schemes, respectively. Spectra of equatorial waves excited by convective heating (Rossby, Kelvin, and gravity waves) are obtained by projecting the heating field onto Hough modes; the dynamical response to the heating is then calculated in terms of the vertical component of the Eliassen–Palm flux, F z , focusing on waves that are able to propagate into the middle atmosphere. The same analysis is repeated using observations of outgoing longwave radiation as a proxy for tropical convection. Comparison of CCM3 results with those derived from observations indicates that high-frequency heating variability is underestimated in both CCM3 simulations, despite the fact that time-mean values of convective heating are well represented. Moreover, the two convective parameterization schemes differ substantially from each other: Compared to observations, F z is severely underestimated at most frequencies when CCM3 is run with the Zhang–McFarlane scheme. When the Hack scheme is used, F z at frequencies |ω| < 0.5 cycles per day is comparable to the observations, but it is underestimated at higher frequencies. Misrepresentation of the variability of convective heating is likely to have important consequences for the dynamical simulation of the middle atmosphere and even the troposphere.

Full access
Lucrezia Ricciardulli
and
Frank J. Wentz

Abstract

Space-based observations of ocean surface winds have been available for more than 25 years. To combine the observations from multiple sensors into one record with the accuracy required for climate studies requires a consistent methodology and calibration standard for the various instruments. This study describes a new geophysical model function (GMF) specifically developed for preparing the QuikSCAT winds to serve as a backbone of an ocean vector wind climate data record. This paper describes the methodology used and presents the quality of the reprocessed winds. The new Ku-2011 model function was developed using WindSat winds as a calibration truth. An extensive validation of the Ku-2011 winds was performed that focused on 1) proving the consistency of satellite winds from different sensors at all wind speed regimes; 2) exploring and understanding possible sources of bias in the QuikSCAT retrievals; 3) validating QuikSCAT wind speeds versus in situ observations, and comparing observed wind directions versus those from numerical models; 4) comparing satellite observations of high wind speeds with measurements obtained from aircraft flying into storms; 5) analyzing case studies of satellite-based observations of winds in tropical storms; and 6) illustrating how rain impacts QuikSCAT wind speed retrievals. The results show that the reprocessed QuikSCAT data are greatly improved in both speed and direction at high winds. Finally, there is a discussion on how these QuikSCAT results fit into a long-term effort toward creating a climate data record of ocean vector winds.

Full access
Lucrezia Ricciardulli
and
Prashant D. Sardeshmukh

Abstract

The time- and space scales of tropical deep convection are estimated via analysis of 3-hourly Global Cloud Imagery (GCI) data for 3 yr at 35–70-km resolution. The emphasis is on estimating local time- and space scales rather than traditional zonal wavenumber–frequency spectra. This is accomplished through estimation of local spatial lag autocorrelations, the conditional probability of convection at neighboring points, and the expected duration of convective events. The spatial autocorrelation scale is found to be approximately 130 km, and the mean duration of convective events approximately 5.5 h, in the convectively active areas of the Tropics. There is a tendency for the spatial autocorrelation scales to be shorter over the continents than oceans (95–155 versus 110–170 km). The expected duration of convective events likewise tends to be shorter (4–6 versus 5–7 h). In the far western Pacific, these differences are sharp enough to legitimize the notion of the Indonesian archipelago as an extended maritime continent with a distinctive shape. Consistent with many other studies, the diurnal variation of the convection is also found to be strikingly different over the continents and oceans. The diurnal amplitude over land is comparable to the long-term mean, raising the possibility of significant aliasing across timescales. The simple analysis of this paper should be useful in evaluating and perhaps even improving the representation of convective processes in general circulation models.

Full access
Antonietta Capotondi
,
Prashant D. Sardeshmukh
, and
Lucrezia Ricciardulli

Abstract

El Niño–Southern Oscillation (ENSO) is commonly viewed as a low-frequency tropical mode of coupled atmosphere–ocean variability energized by stochastic wind forcing. Despite many studies, however, the nature of this broadband stochastic forcing and the relative roles of its high- and low-frequency components in ENSO development remain unclear. In one view, the high-frequency forcing associated with the subseasonal Madden–Julian oscillation (MJO) and westerly wind events (WWEs) excites oceanic Kelvin waves leading to ENSO. An alternative view emphasizes the role of the low-frequency stochastic wind components in directly forcing the low-frequency ENSO modes. These apparently distinct roles of the wind forcing are clarified here using a recently released high-resolution wind dataset for 1990–2015. A spectral analysis shows that although the high-frequency winds do excite high-frequency Kelvin waves, they are much weaker than their interannual counterparts and are a minor contributor to ENSO development. The analysis also suggests that WWEs should be viewed more as short-correlation events with a flat spectrum at low frequencies that can efficiently excite ENSO modes than as strictly high-frequency events that would be highly inefficient in this regard. Interestingly, the low-frequency power of the rapid wind forcing is found to be higher during El Niño than La Niña events, suggesting a role also for state-dependent (i.e., multiplicative) noise forcing in ENSO dynamics.

Full access
Thomas Meissner
,
Lucrezia Ricciardulli
, and
Frank J. Wentz

Abstract

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing science data since April 2015. Though designed to measure soil moisture, the SMAP radiometer has an excellent capability to measure ocean winds in storms at a resolution of 40 km with a swath width of 1,000 km. SMAP radiometer channels operate at a very low microwave frequency (L band, 1.41 GHz, 21.4 cm), which has good sensitivity to ocean surface wind speed even in very high winds and with very little impact by rain. This gives SMAP a distinct advantage over many spaceborne ocean wind sensors such as C-band [Advanced Scatterometer (ASCAT)] or Ku-band [Rapid Scatterometer (RapidScat)] scatterometers and radiometers operating at higher frequencies [Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, Advanced Microwave Scanning Radiometer (AMSR), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)], which either lose sensitivity at very high winds or degrade in rainy conditions. This article discusses the major features of a new ocean wind vector retrieval algorithm designed for SMAP. We compare SMAP wind fields in recent intense tropical cyclones with wind measurements from current scatterometer missions as well as WindSat. The most important validation source in hurricanes is the airborne stepped frequency microwave radiometer (SFMR), whose wind speeds are matched with SMAP in space and time. A comparison between SMAP and SFMR winds for eight storms in 2015, including Patricia, one of the strongest hurricanes ever recorded, shows excellent agreement up to 65 m s–1 without degradation in rain.

Full access
Hye-Yeong Chun
,
Jung-Suk Goh
,
In-Sun Song
, and
Lucrezia Ricciardulli

Abstract

Latitudinal variations of the convective source and vertical propagation condition of inertio-gravity waves (IGWs) in the tropical region (30°S–30°N) are examined using high-resolution Global Cloud Imagery (GCI) and 6-hourly NCEP–NCAR reanalysis data, respectively, for 1 yr (March 1985–February 1986). The convective source is estimated by calculating the deep convective heating (DCH) rate using the brightness temperature of the GCI data. The latitudinal variation of DCH is found to be significant throughout the year. The ratio of the maximum to minimum values of DCH in the annual mean is 3.2 and it is much larger in the JuneAugust (JJA) and December–February (DJF) means. Spectral analyses show that DCH has a dominant period of 1 day, a zonal wavelength of about 1600 km, and a Gaussian-type phase-speed spectrum with a peak at the zero phase speed.

The vertical propagation condition of IGWs is determined, in the zonal wavenumber and frequency domain, by two factors: (i) latitude, which determines the Coriolis parameter, and (ii) the basic-state wind structure in the target height range of wave propagation. It was found that the basic-state wind significantly influences the wave propagation condition in the lower stratosphere between 150 and 30 hPa, and accordingly a large portion of the source spectrum is filtered out. This is prominent not only in the latitudes higher than 15° where strong negative shear exists, but also near the equator where strong positive shear associated with the westerly phase of the quasi-biennial oscillation (QBO) filters out large portions of the low-frequency components of the convective source. There is no simple relationship between the ground-based frequency and latitude; lower latitudes are not always favorable for low-frequency IGWs to be observed in the stratosphere. The basic-state wind in the Tropics, which has seasonal, annual, and interannual variations, plays a major role not only in determining the wave propagation condition in the stratosphere but also in producing convective sources in the troposphere.

Full access
Philip J. Rasch
,
Mark J. Stevens
,
Lucrezia Ricciardulli
,
Aiguo Dai
,
Andrew Negri
,
Robert Wood
,
Byron A. Boville
,
Brian Eaton
, and
James J. Hack

Abstract

The Community Atmosphere Model version 3 (CAM3) is the latest generation of a long lineage of general circulation models produced by a collaboration between the National Center for Atmospheric Research (NCAR) and the scientific research community. Many aspects of the hydrological cycle have been changed relative to earlier versions of the model. It is the goal of this paper to document some aspects of the tropical variability of clouds and the hydrologic cycle in CAM3 on time scales shorter than 30 days and to discuss the differences compared to the observed atmosphere and earlier model versions, with a focus on cloud-top brightness temperature, precipitation, and cloud liquid water path. The transient behavior of the model in response to changes in resolution to various numerical methods used to solve the equations for atmospheric dynamics and transport and to the underlying lower boundary condition of sea surface temperature and surface fluxes has been explored.

The ratio of stratiform to convective rainfall is much too low in CAM3, compared to observational estimates. It is much higher in CAM3 (10%) than the Community Climate Model version 3 (CCM3; order 1%–2%) but is still a factor of 4–5 too low compared to observational estimates. Some aspects of the model transients are sensitive to resolution. Higher-resolution versions of CAM3 show too much variability (both in amplitude and spatial extent) in brightness temperature on time scales of 2–10 days compared to observational estimates. Precipitation variance is underestimated on time scales from a few hours to 10 days, compared to observations over ocean, although again the biases are reduced compared to previous generations of the model. The diurnal cycle over tropical landmasses is somewhat too large, and there is not enough precipitation during evening hours. The model tends to produce maxima in precipitation and liquid water path that are a few hours earlier than that seen in the observations over both oceans and land.

Full access
Franklin R. Robertson
,
Jason B. Roberts
,
Michael G. Bosilovich
,
Abderrahim Bentamy
,
Carol Anne Clayson
,
Karsten Fennig
,
Marc Schröder
,
Hiroyuki Tomita
,
Gilbert P. Compo
,
Marloes Gutenstein
,
Hans Hersbach
,
Chiaki Kobayashi
,
Lucrezia Ricciardulli
,
Prashant Sardeshmukh
, and
Laura C. Slivinski

Abstract

Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

Free access
Robert J. H. Dunn
,
Freya Aldred
,
Nadine Gobron
,
John B. Miller
,
Kate M. Willett
,
Melanie Ades
,
Robert Adler
,
R. P. Allan
,
John Anderson
,
Orlane Anneville
,
Yasuyuki Aono
,
Anthony Argüez
,
Carlo Arosio
,
John A. Augustine
,
Cesar Azorin-Molina
,
Jonathan Barichivich
,
Aman Basu
,
Hylke E. Beck
,
Nicolas Bellouin
,
Angela Benedetti
,
Kevin Blagrave
,
Stephen Blenkinsop
,
Olivier Bock
,
Xavier Bodin
,
Michael G. Bosilovich
,
Olivier Boucher
,
Gerald Bove
,
Dennis Buechler
,
Stefan A. Buehler
,
Laura Carrea
,
Kai-Lan Chang
,
Hanne H. Christiansen
,
John R. Christy
,
Eui-Seok Chung
,
Laura M. Ciasto
,
Melanie Coldewey-Egbers
,
Owen R. Cooper
,
Richard C. Cornes
,
Curt Covey
,
Thomas Cropper
,
Molly Crotwell
,
Diego Cusicanqui
,
Sean M. Davis
,
Richard A. M. de Jeu
,
Doug Degenstein
,
Reynald Delaloye
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
Gregory Duveiller
,
James W. Elkins
,
Thomas W. Estilow
,
Nava Fedaeff
,
David Fereday
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Stacey M. Frith
,
Lucien Froidevaux
,
Martin Füllekrug
,
Judith Garforth
,
Jay Garg
,
Matthew Gentry
,
Steven Goodman
,
Qiqi Gou
,
Nikolay Granin
,
Mauro Guglielmin
,
Sebastian Hahn
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
Debbie L. Hemming
,
Martin Hirschi
,
Shu-pen (Ben) Ho
,
Robert Holzworth
,
Filip Hrbáček
,
Daan Hubert
,
Petra Hulsman
,
Dale F. Hurst
,
Antje Inness
,
Ketil Isaksen
,
Viju O. John
,
Philip D. Jones
,
Robert Junod
,
Andreas Kääb
,
Johannes W. Kaiser
,
Viktor Kaufmann
,
Andreas Kellerer-Pirklbauer
,
Elizabeth C. Kent
,
Richard Kidd
,
Hyungiun Kim
,
Zak Kipling
,
Akash Koppa
,
Jan Henning L’Abée-Lund
,
Xin Lan
,
Kathleen O. Lantz
,
David Lavers
,
Norman G. Loeb
,
Diego Loyola
,
Remi Madelon
,
Hilmar J. Malmquist
,
Wlodzimierz Marszelewski
,
Michael Mayer
,
Matthew F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
Annette Menzel
,
Christopher J. Merchant
,
Diego G. Miralles
,
Stephen A. Montzka
,
Colin Morice
,
Leander Mösinger
,
Jens Mühle
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Tiina Nõges
,
Ben Noll
,
John O’Keefe
,
Tim J. Osborn
,
Taejin Park
,
Cecile Pellet
,
Maury S. Pelto
,
Sarah E. Perkins-Kirkpatrick
,
Coda Phillips
,
Stephen Po-Chedley
,
Lorenzo Polvani
,
Wolfgang Preimesberger
,
Colin Price
,
Merja Pulkkanen
,
Dominik G. Rains
,
William J. Randel
,
Samuel Rémy
,
Lucrezia Ricciardulli
,
Andrew D. Richardson
,
David A. Robinson
,
Matthew Rodell
,
Nemesio J. Rodríguez-Fernández
,
Karen H. Rosenlof
,
Chris Roth
,
Alexei Rozanov
,
This Rutishäuser
,
Ahira Sánchez-Lugo
,
Parnchai Sawaengphokhai
,
Verena Schenzinger
,
Robert W. Schlegel
,
Udo Schneider
,
Sapna Sharma
,
Lei Shi
,
Adrian J. Simmons
,
Carolina Siso
,
Sharon L. Smith
,
Brian J. Soden
,
Viktoria Sofieva
,
Tim H. Sparks
,
Paul W. Stackhouse Jr.
,
Ryan Stauffer
,
Wolfgang Steinbrecht
,
Andrea K. Steiner
,
Kenton Stewart
,
Pietro Stradiotti
,
Dimitri A. Streletskiy
,
Hagen Telg
,
Stephen J. Thackeray
,
Emmanuel Thibert
,
Michael Todt
,
Daisuke Tokuda
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald van der A
,
Robin van der Schalie
,
Gerard van der Schrier
,
Mendy van der Vliet
,
Guido R. van der Werf
,
Arnold. van Vliet
,
Jean-Paul Vernier
,
Isaac J. Vimont
,
Katrina Virts
,
Sebastiàn Vivero
,
Holger Vömel
,
Russell S. Vose
,
Ray H. J. Wang
,
Markus Weber
,
David Wiese
,
Jeanette D. Wild
,
Earle Williams
,
Takmeng Wong
,
R. I. Woolway
,
Xungang Yin
,
Ye Yuan
,
Lin Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
,
Markus Ziese
, and
Ruxandra M. Zotta
Free access