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T. E. LaRow
,
S. D. Cocke
, and
D. W. Shin

Abstract

A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.

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D. W. Shin
,
J. G. Bellow
,
T. E. LaRow
,
S. Cocke
, and
James J. O'Brien

Abstract

An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected.

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T. N. Krishnamurti
,
C. M. Kishtawal
,
D. W. Shin
, and
C. Eric Williford

Abstract

This paper utilizes forecasts from a multianalysis system to construct a superensemble of precipitation forecasts. This method partitions the computations into two time lines. The first of those is a control (or a training) period and the second is a forecast period. The multianalysis is derived from a physical initialization–based data assimilation of “observed rainfall rates.” The different members of the reanalysis are produced by using different rain-rate algorithms for physical initialization. The basic rain-rate datasets are derived from satellites’ microwave radiometers, including those from the Tropical Rainfall Measuring Mission (TRMM) satellites and the Special Sensor Microwave Imager (SSM/I) data from three current U.S. Air Force Defense Meteorological Satellite Program (DMSP) satellites. During the training period, 155 experiments were conducted to find the relationship between forecasts from the multianalysis dataset and the best “observed” estimates of daily rainfall totals. This relationship is based on multiple regression and defined by statistical weights (which vary in space.) The forecast phase utilizes the multianalysis forecasts and the statistics from the training period to produce superensemble forecasts of daily rainfall totals. The results for day 1, day 2, and day 3 forecasts are compared to various conventional forecasts with a global model. The superensemble day 3 forecasts of precipitation clearly have the highest skill in such comparisons.

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T. E. LaRow
,
Y-K. Lim
,
D. W. Shin
,
E. P. Chassignet
, and
S. Cocke

Abstract

An ensemble of seasonal Atlantic hurricane simulations is conducted using The Florida State University/Center for Ocean–Atmospheric Prediction Studies (FSU–COAPS) global spectral model (Cocke and LaRow) at a resolution of T126L27 (a Gaussian grid spacing of 0.94°). Four integrations comprising the ensembles were generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) time-lagged initial atmospheric conditions centered on 1 June for the 20 yr from 1986 to 2005. The sea surface temperatures (SSTs) were updated weekly using the Reynolds et al. observed data. An objective-tracking algorithm obtained from the ECMWF and modified for this model’s resolution was used to detect and track the storms. It was found that the model’s composite storm structure and track lengths are realistic. In addition, the 20-yr interannual variability was well simulated by the ensembles with a 0.78 ensemble mean rank correlation. The ensembles tend to overestimate (underestimate) the numbers of storms during July (September) and produced only one CAT4–level storm on the Saffir–Simpson scale. Similar problems are noted in other global model simulations. All ensembles did well in simulating the large number of storms forming in the Atlantic basin during 1995 and showed an increase in the number of storms during La Niña and a decrease during El Niño events. The results are found to be sensitive to the choices of convection schemes and diffusion coefficients. The overall conclusion is that models such as the one used here are needed to better hindcast the interannual variability; however, going to an even higher resolution does not guarantee better interannual variability, tracks, or intensity. Improved physical parameterizations, such as using an explicit convection scheme and better representation of surface roughness at high wind speeds, are likely to more accurately represent hurricane intensity.

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D. W. Shin
,
S. Cocke
,
T. E. LaRow
, and
James J. O’Brien

Abstract

The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

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D. W. Shin
,
G. A. Baigorria
,
Y-K. Lim
,
S. Cocke
,
T. E. LaRow
,
James J. O’Brien
, and
James W. Jones

Abstract

A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

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Christian Kummerow
,
Y. Hong
,
W. S. Olson
,
S. Yang
,
R. F. Adler
,
J. McCollum
,
R. Ferraro
,
G. Petty
,
D-B. Shin
, and
T. T. Wilheit

Abstract

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5° grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5° grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI- and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low from both TMI and PR. The consistent bias between these two sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.

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T. N. Krishnamurti
,
Sajani Surendran
,
D. W. Shin
,
Ricardo J. Correa-Torres
,
T. S. V. Vijaya Kumar
,
Eric Williford
,
Chris Kummerow
,
Robert F. Adler
,
Joanne Simpson
,
Ramesh Kakar
,
William S. Olson
, and
F. Joseph Turk

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

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Chidong Zhang
,
Aaron F. Levine
,
Muyin Wang
,
Chelle Gentemann
,
Calvin W. Mordy
,
Edward D. Cokelet
,
Philip A. Browne
,
Qiong Yang
,
Noah Lawrence-Slavas
,
Christian Meinig
,
Gregory Smith
,
Andy Chiodi
,
Dongxiao Zhang
,
Phyllis Stabeno
,
Wanqiu Wang
,
Hong-Li Ren
,
K. Andrew Peterson
,
Silvio N. Figueroa
,
Michael Steele
,
Neil P. Barton
,
Andrew Huang
, and
Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

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Robert J. H. Dunn
,
John B. Miller
,
Kate M. Willett
,
Nadine Gobron
,
Melanie Ades
,
Robert Adler
,
Mihai Alexe
,
Richard P. Allan
,
John Anderson
,
Orlane Anneville
,
Yasuyuki Aono
,
Anthony Arguez
,
Carlo Arosio
,
John A. Augustine
,
Cesar Azorin-Molina
,
Jonathan Barichivich
,
John E. Barnes
,
Hylke E. Beck
,
Nicolas Bellouin
,
Angela Benedetti
,
Kevin Blagrave
,
Stephen Blenkinsop
,
Olivier Bock
,
Xavier Bodin
,
Michael Bosilovich
,
Olivier Boucher
,
Dennis Buechler
,
Stefan A. Buehler
,
Diego Campos
,
Laura Carrea
,
Kai-Lan Chang
,
Hanne H. Christiansen
,
John R. Christy
,
Eui-Seok Chung
,
Laura M. Ciasto
,
Scott Clingan
,
Melanie Coldewey-Egbers
,
Owen R. Cooper
,
Richard C. Cornes
,
Curt Covey
,
Jean-François Créatux
,
Theresa Crimmins
,
Thomas Cropper
,
Molly Crotwell
,
Joshua Culpepper
,
Diego Cusicanqui
,
Sean M. Davis
,
Richard A. M. de Jeu
,
Doug Degenstein
,
Reynald Delaloye
,
Martin T. Dokulil
,
Markus G. Donat
,
Wouter A. Dorigo
,
Hilary A. Dugan
,
Imke Durre
,
Geoff Dutton
,
Gregory Duveiller
,
Thomas W. Estilow
,
Nicole Estrella
,
David Fereday
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Bryan Franz
,
Stacey M. Frith
,
Lucien Froidevaux
,
Martin Füllekrug
,
Judith Garforth
,
Jay Garg
,
Badin Gibbes
,
Steven Goodman
,
Atsushi Goto
,
Alexander Gruber
,
Guojun Gu
,
Sebastian Hahn
,
Leopold Haimberger
,
Bradley D. Hall
,
Ian Harris
,
Deborah L. Hemming
,
Martin Hirschi
,
Shu-peng Ho
,
Robert Holzworth
,
Filip Hrbáček
,
Guojie Hu
,
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
,
Zak Kipling
,
Akash Koppa
,
Benjamin M. Kraemer
,
Natalya Kramarova
,
Andries Kruger
,
Sofia La Fuente
,
Alo Laas
,
Xin Lan
,
Timothy Lang
,
Kathleen O. Lantz
,
David A. Lavers
,
Thierry Leblanc
,
Eric M. Leibensperger
,
Chris Lennard
,
Yakun Liu
,
Norman G. Loeb
,
Diego Loyola
,
Stephen C. Maberly
,
Remi Madelon
,
Florence Magnin
,
Shin-Ichiro Matsuzaki
,
Linda May
,
Michael Mayer
,
Matthew F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
Annette Menzel
,
Christopher J. Merchant
,
Michael F. Meyer
,
Diego G. Miralles
,
Leander Moesinger
,
Ghislaine Monet
,
Stephan A. Montzka
,
Colin Morice
,
Ivan Mrekaj
,
Jens Mühle
,
David Nance
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Ben Noll
,
John O’Keefe
,
Timothy J. Osborn
,
Taejin Park
,
Mark Parrington
,
Cécile Pellet
,
Mauri S. Pelto
,
Kyle Petersen
,
Coda Phillips
,
Don Pierson
,
Izidine Pinto
,
Stephen Po-Chedley
,
Paolo Pogliotti
,
Lorenzo Polvani
,
Wolfgang Preimesberger
,
Colin Price
,
Merja Pulkkanen
,
William J. Randel
,
Samuel Rémy
,
Lucrezia Ricciardulli
,
Andrew D. Richardson
,
David A. Robinson
,
Willy Rocha
,
Matthew Rodell
,
Nemesio Rodriguez-Fernandez
,
Karen H. Rosenlof
,
Alexei Rozanov
,
Jozef Rozkošný
,
Olga O. Rusanovskaya
,
This Rutishauser
,
C. T. Sabeerali
,
Ahira Sánchez-Lugo
,
Parnchai Sawaengphokhai
,
Verena Schenzinger
,
Robert W. Schlegel
,
Martin Schmid
,
Udo Schneider
,
Fumi Sezaki
,
Sapna Sharma
,
Lei Shi
,
Svetlana V. Shimaraeva
,
Eugene A. Silow
,
Adrian J. Simmons
,
Sharon L. Smith
,
Brian J. Soden
,
Viktoria Sofieva
,
Tim H. Sparks
,
O.P. Sreejith
,
Paul W. Stackhouse Jr.
,
Ryan Stauffer
,
Wolfgang Steinbrecht
,
Andrea K. Steiner
,
Pietro Stradiotti
,
Dmitry A. Streletskiy
,
Divya E. Surendran
,
Stephen J. Thackeray
,
Emmanuel Thibert
,
Maxim A. Timofeyev
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald van der A
,
Robin van der Schalie
,
Gerard van der Schrier
,
Arnold J.H. van Vliet
,
Piet Verburg
,
Jean-Paul Vernier
,
Isaac J. Vimont
,
Katrina Virts
,
Sebastián Vivero
,
Holger Vömel
,
Russell S. Vose
,
Ray H. J. Wang
,
Xinyue Wang
,
Taran Warnock
,
Mark Weber
,
David N. Wiese
,
Jeannette D. Wild
,
Earle Williams
,
Takmeng Wong
,
Richard Iestyn Woolway
,
Xungang Yin
,
Zhenzhong Zeng
,
Lin Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
,
Markus Ziese
,
Ruxandra M. Zotta
,
Cheng-Zhi Zou
,
Jessicca Allen
,
Amy V. Camper
,
Bridgette O. Haley
,
Gregory Hammer
,
S. Elizabeth Love-Brotak
,
Laura Ohlmann
,
Lukas Noguchi
,
Deborah B. Riddle
, and
Sara W. Veasey
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