Journal articles and book
chapters
Thomas M. Hamill
Researcher ID: C-4630-2015
Address:
NOAA Physical Sciences Laboratory
R/PSD 1, 325 Broadway
Boulder CO 80305-3328 USA
(303) 497-3060 fax -6949
e-mail: <tom.hamill@noaa.gov>
Refereed publications and book chapters, submitted,
accepted, and published:
(109) Ghazvinian, M., Zhang, Y., D.J. Seo, and N. Fernando, 2022: Improved probabilistic quantitative
precipitation forecats using short training data through deep
learning. Mon. Wea. Rev., submitted.
(108) Kravtsov, S., P. Roebber, T. M. Hamill, and J. Brown,
2021: Objective
methods for thinning the frequency of reforecasts while meeting
postprocessing and model validation needs. Wea.
Forecasting, accepted.
(107) Switanek, M., and T. M. Hamill, 2021: A
new methodology to produce more skillful United States cool season
precipitation forecasts. Water Resources Reearch,
accepted/minor.
(106) Guan, H., others, and T. M. Hamill, 2021: GEFSv12 reforecast
dataset for supporting subseasonal and hydrometeorological
applications. Mon. Wea. Rev., 150(3),
647-665.
(105) Worsnop, R. P., M. Scheuerer, F. Di Giuseppe, C. Barnard, T.
M. Hamill, and C. Vitolo, 2021: Probabilistic
fire-danger forecasting: A framework for week-two forecasts using
statistical post-processing techniques and the Global ECMWF Fire
Forecast System (GEFF). Wea. Forecasting, 36,
2113-2125.
(104) World Meteorological Organization, T. M. Hamill, and others,
2021: Guidelines
on ensemble prediction system postprocessing. WMO No.
1254.
(103) Hamill, T. M., 2021: Comparing and
Combining Deterministic Surface Temperature Postprocessing Methods
over the US. Mon Wea. Rev.,
149(10), 3289-3298.
(102) Hamill, T. M., and others, 2021: The
reanalysis for the Global Ensemble Forecast System, version 12.
Mon. Wea. Rev., 150, 59-79.
(101)
Scheuerer, M., M. B. Switanek, R. P. Worsnop, and T. M. Hamill,
2020: Using
Artificial Neural Networks for Generating Probabilistic
Subseasonal Precipitation Forecasts over California. Mon.
Wea. Rev., 148, 3489-3506,
(100)
Bellier, J., M. Scheuerer, and T. M. Hamill, 2020: Precipitation
downscaling with Gibbs sampling: An improved method for producing
realistic, weather-dependent and anisotropic fields. J.
Hydrometeor.,
(99)
Switanek, M. B., J. J. Barsugli, M. Scheuerer, and T. M. Hamill,
2020: Present and
Past Sea Surface Temperatures: A Recipe for Better Seasonal
Climate Forecasts. Wea. Forecasting, 35,
1221-1234.
(98) Hamill, T. M. , and M. Scheuerer, 2020: Improving ensemble weather
prediction system initialization: disentangling the contributions
from model systematic errors and initial perturbation size.
Mon. Wea. Rev.
149(1), 77-90.
(97)
Ben Bouallegue, Z., T. Haiden, N. J. Weber, T. M. Hamill, and D. S.
Richardson, 2020: Accounting
for Representativeness in the Verification of Ensemble
Precipitation Forecasts. Mon. Wea. Rev., 148,
2049-2062,
(96)
Worsnop, R.P., M. Scheuerer, and T.M.
Hamill, 2020: Extended-Range
Probabilistic Fire-Weather Forecasting Based on Ensemble Model
Output Statistics and Ensemble Copula Coupling. Mon. Wea. Rev., 148, 499-521.
(95) Zhang, T., Hoell, A., Perlwitz, J., J. Eischeid, D. Murray, M.
Hoerling, and T. M. Hamill, 2019: Towards
probabilistic multivariate ENSO monitoring. Geophys.
Research Letters ,
46, 10532–
10540.
(94) Subramanian, A., others, and T. Hamill, 2019: Ocean
observations to improve our understanding, modeling, and
forecasting of subseasonal-to-seasonal variability.
Frontiers in Marine Science, 6,
DOI=10.3389/fmars.2019.00427
(93) Gascon, E., D. Lavers, D., T. M. Hamill, D. S.
Richardson, Z. Ben Bouallegue, M. Leutbecher, and F. Pappenberger,
2019:
Statistical post-processing of dual-resolution ensemble
precipitation forecasts across Europe. Quart. J.
Royal Meteor. Soc.,
145, 3218–
3235.
(92) Hamill, T. M., and M. Scheuerer, 2020: Benchmarking
the background forecast in rapidly updated surface temperature
analyses. Part 2: gridded benchmark. Mon. Wea. Rev.,
148, 701-717,
https://doi.org/10.1175/MWR-D-19-0028.1
(91) Hamill, T. M., 2020: Benchmarking
the background forecast in rapidly updated surface temperature
analyses. Part 1: stations. Mon Wea. Rev,
148, 689-700,
https://doi.org/10.1175/MWR-D-19-0027.1
(90) Scheuerer, M., and T. M. Hamill, 2018: Probabilistic
forecasting of snowfall amounts using a hybrid between a
parametric and an analog approach. Mon. Wea. Rev.,
147, 1047-1064.
(89)
Worsnop, R., M. Scheuerer, T. M. Hamill, and J. K. Lundquist,
2018: Generating wind power scenarios for probabilistic ramp
event prediction using multivariate statistical
postprocessing. Wind Energy Science,
available at https://www.wind-energ-sci.net/3/371/2018/.
(88) Hamill, T. M., and Scheuerer, M., 2018:
Probabilistic precipitation forecast postprocessing using quantile
mapping and rank-weighted best-member dressing. Mon.
Wea. Rev., 146, 4079-4098. Also: Online appendix 1.
(87)
Scheuerer, M., and Hamill, T.M., 2018: Generating calibrated
ensembles of physically realistic, high-resolution precipitation
forecast fields based on GEFS model output. J.
Hydrometeorology, 19 (10), 1651-1670. https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-18-0067.1
(86)
Gehne, M., T.M. Hamill, G.T. Bates, P.
Pegion, and W. Kolczynski, 2019:
Land
Surface Parameter and State Perturbations in the Global Ensemble
Forecast System. Mon.
Wea. Rev., 147, 1319-1340, https://doi.org/10.1175/MWR-D-18-0057.1
(85) Hamill, T. M., 2018: Practical
Aspects of Statistical Postprocessing. Chapter 7 in the
book
Statistical
Postprocessing of Ensemble Forecasts (Elsevier Press).
(84) Penny, S. G., and T. M. Hamill, 2017: Coupled data
assimilation for integrated earth system analysis and prediction.
Bull. Amer. Meteor. Soc., 98, ES169-ES172,
(83) Hamill, T.M., E. Engle, D. Myrick,
M. Peroutka, C. Finan, and M. Scheuerer, 2017: The
U.S. National Blend of Models for Statistical Postprocessing of
Probability of Precipitation and Deterministic Precipitation
Amount. Mon. Wea. Rev.,
145, 3441-3463,
https://doi.org/10.1175/MWR-D-16-0331.1
(82) Hamill, T. M., 2017:
Changes in the systematic errors of global reforecasts due to an
evolving data assimilation system. Mon. Wea. Rev., 145,
2479-2485.
(81) Parsons, D.B., M.M. Beland,
D.D. Burridge, P.P. Bougeault, G.G. Brunet, J.J. Caughey, S.M.
Cavallo, M.M. Charron, H.C. Davies, A. Niang, V.V. Ducrocq, P.P.
Gauthier, T.M. Hamill, P.A. Harr, S.C. Jones, R.H. Langland, S.J.
Majumdar, B.N. Mills, M.M. Moncrieff, T.T. Nakazawa, T.T.
Paccagnella, F.F. Rabier, J.L. Redelsperger, C.C. Riedel, R.W.
Saunders, M.A. Shapiro, R.R. Swinbank, I.I. Szunyogh, C.C.
Thorncroft, A.J. Thorpe, X.X. Wang, D.D. Waliser, H.H. Wernli, and
Z.Z. Toth, 2017: THORPEX
research and the science of prediction. Bull. Amer. Meteor. Soc., 98, 807-830,
doi: 10.1175/BAMS-D-14-00025.1.
(80) Scheuerer, M. T. M. Hamill, B. Whitin, M. He, and A. Henkel,
2016: A
method for preferential selection of dates in the Schaake shuffle
approach to constructing spatio-temporal forecast fields of
temperature and precipitation. Water
Resources Research. Appendix.
(79) Scheuerer, M., S. Gregory, T. M. Hamill, and P. E. Shafer,
2016: Probabilistic
precipitation type forecasting based on GEFS ensemble forecasts of
vertical temperature profiles. Mon. Wea. Rev.,
145, 1401-1412.
(78) Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E.
Trenberth, 2016: Comparison
of global precipitation estimates across a range of temporal and
spatial scales.
J. Climate, 29, 7773-7795,
doi: 10.1175/JCLI-D-15-0618.1.
(77) Hodyss, D., E. Satterfield, J. McClay, T. M. Hamill, and M.
Scheuerer, 2016: Inaccuracies
with multimodel postprocessing methods involving weighted,
regression-corrected forecasts. Mon. Wea. Rev., 144, 1649-1668,
doi: 10.1175/MWR-D-15-0204.1.
(76) Swinbank, R., others, and T. M. Hamill, 2016: The
TIGGE project and its achievements. Bull. Amer.
Meteor. Soc., 97, 49-67, doi:
10.1175/BAMS-D-13-00191.1.
(75) Moore, B. J., T. M. Hamill, E. M. Sukovich, T. Workoff,
and F. E. Barthold, 2015: The utility of the NOAA reforecast dataset
for quantitative precipitation forecasting over the coastal western
United States. J. Operational Meteor., 3
(12), 133-144. DOI: http://dx.doi.org/10.15191/nwajom.2015.0312
(74) Rabier, F., A. J. Thorpe, A. R. Brown, M. Charron, J. D. Doyle,
T. M. Hamill, J. Ishida, B. Lapenta, C. A. Reynolds, and M. Satoh,
2015: Global
Environmental Prediction. Book chapter from WMO World
Weather Research Program book Seamless
Prediction of the Earth System: from Minutes to Months.
(73)
McGovern, A., D. Gagne, J. Basara, T.M.
Hamill, and D. Margolin, 2015: Solar
energy prediction: an international contest to initiate
interdisciplinary research on compelling meteorological
problems. Bull. Amer.
Meteor. Soc., 96,
1388-1395, doi:
10.1175/BAMS-D-14-00006.1.
(72)
Galarneau, T.J. and T.M. Hamill, 2015: Diagnosis
of track forecast errors for tropical cyclone Rita (2005) using
GEFS reforecasts. Wea.
Forecasting, 30,
1334-1354, doi:
10.1175/WAF-D-15-0036.1.
(71) Scheuerer, M., and T. M. Hamill, 2015: Statistical
post-processing of ensemble precipitation forecasts by fitting
censored, shifted Gamma distributions. Mon. Wea. Rev.,
143,
4578-4596. Also appendix
A and appendix
B and appendix
C.
(70) Hamill, T. M., and R. Swinbank, 2015: Stochastic
forcing, ensemble prediction systems, and TIGGE.
Book chapter from WMO World Weather Research Program book
Seamless Prediction of the Earth System: from
Minutes to Months.
(69) Hamill, T. M., M. Scheuerer, and G. T. Bates, 2015: Analog
probabilistic precipitation forecasts using GEFS Reforecasts and
Climatology-Calibrated Precipitation Analyses. Mon.
Wea. Rev.,
143, 3300-3309. Also: online appendix A
and appendix
B.
(68) Scheuerer, M., and T. M. Hamill, 2014: Variogram-based
proper scoring rules for probabilistic forecasts of two
multivariate quantities. Mon. Wea. Rev., 143,
1321-1334. doi: http://dx.doi.org/10.1175/MWR-D-14-00269.1
(67) Bauer, P., L. Magnusson, J.-N. Thepaut, and T. M. Hamill,
2014: Aspects
of ECMWF model performance in polar areas. Quart. J.
Royal Meteor. Soc., DOI: 10.1002/qj.2449
(66) Baxter, M. A., G. M. Lackmann, K. M. Mahoney, T. E. Workoff,
and T. M. Hamill, 2014: Verification
of precipitation reforecasts over the Southeast United States.
Wea. Forecasting, 29, 1199-1207.
(65) Torn, R., J. S. Whitaker, T. M. Hamill, and G. J. Hakim,
2014: Diagnosis
of the source of GFS medium-range track errors in Hurricane Sandy
(2012). Mon. Wea. Rev., 143, 132-152.
(64) Moore, B. J., E. M. Sukovich, R. Cifelli, and T. M. Hamill,
2014:
Climatology and environmental characteristics of extreme
precipitation events in the southeastern United States.
Mon. Wea. Rev.,
143, 718-741.
(63) Hamill, T. M., 2014: Performance
of operational model precipitation forecast guidance during the
2013 Colorado Front Range floods. Mon. Wea. Rev.,
142, 2609-2618. Also, appendices A, B, and C.
(62) Wick, G. A., P. J. Neiman, F. M. Ralph, and T. M. Hamill, 2014:
Evaluation
of the forecasts of water vapor signature of atmospheric rivers in
operational weather prediction models. Wea.
Forecasting, 28, 1337-1352.
(61) Hamill, T. M., and G. N. Kiladis, 2013: Skill
of the MJO and Northern Hemispheric blocking in GEFS medium-range
reforecasts. Mon. Wea. Rev., 142,
686-885.
(60) Hamill, T. M., F. Yang, C. Cardinali, and S. J. Majumdar,
2012: Impact
of targeted Winter Storms Reconnaissance dropwindsonde data on
mid-latitude numerical weather forecasts. Mon. Wea.
Rev., 141, 2058-2065.
(59) Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M.
Fiorino, T. J. Galarneau, Jr., Y. Zhu, and W. Lapenta, 2013:
NOAA's second-generation global medium-range ensemble reforecast
data set. Bull Amer. Meteor. Soc., 94,
1553-1565.
(58) Whitaker, J. S., and T. M. Hamill, 2012: Evaluating
methods to account for system errors in ensemble data
assimilation. Mon.
Wea. Rev., 140, 3078-3089.
(57) Hamill, T. M., 2012: Verification
of TIGGE Multi-model and ECMWF Reforecast-Calibrated Probabilistic
Precipitation Forecasts over the Conterminous US. Mon. Wea. Rev., 140, 2232-2252.
(57a) Hamill, T. M., 2012: Online
appendix to Verification of TIGGE multi-model and ECMWF
reforecast-calibrated probabilistic precipitation forecasts over
the conterminous US. Mon.
Wea. Rev.
(56) Hirschberg, P.A., E. Abrams. A. Bleistein, W. Bua, L. Delle
Monache, T. W. Dulong, J. E. Gaynor, B. Glahn, T. M. Hamill, J. A.
Hansen, D. C. Hilderbrand, R. N. Hoffman, B. H. Morrow, B. Philips,
J. Sokich, N. Stuart, 2011: A
weather and climate enterprise strategic implementation plan for
generating and communicating forecast uncertainty
information. Bull.
Amer. Meteor. Soc., 92,
1651-1666.
(55) Hagedorn, R., Buizza, R., Hamill, T. M., Leutbecher, M., and T.
N. Palmer, 2012: Comparing
TIGGE multi-model forecasts with reforecast-calibrated ECMWF
ensemble forecasts. Quart
J. Royal Meteor Soc., 138, 1814-1827.
(54) Galarneau, T. J., Hamill, T. M., Dole, R. M., and J. Perlwitz,
2012: A
Multi-scale analysis of the extreme weather events over western
Russia and northern Pakistan during July 2010. Mon. Wea. Rev., 140, 1639-1664. DOI
10.1175/MWR-D-11-00191.1
(53) Hamill, T. M., M. J. Brennan, B. Brown, M. DeMaria, E. N.
Rappaport, and Z. Toth, 2012: NOAA's
future ensemble based hurricane products. Bull Amer. Meteor. Soc., 93,
209-220. Also: online Appendix
A and Appendix
B.
(52) Hamill, T. M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S.
J. Benjamin, 2011: Predictions
of 2010's tropical cyclones using the GFS and ensemble-based data
assimilation methods. Mon.
Wea. Rev., 139,
3243-3247.
(51) Hamill, T. M., and J. S. Whitaker, 2011: What
constrains spread growth in forecasts initialized from ensemble
Kalman filters? Mon. Wea.
Rev., 139,
117-131.
(50) Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. J. Benjamin,
2011: Global
ensemble predictions of 2009's tropical cyclones initialized with
an ensemble Kalman filter. Mon. Wea. Rev., 139,
668-688.
(49) Schaake, J., Pailleux, J., Thielen, J., Arritt, R., Hamill, T.,
Luo, L. F., Martin, E., McCollor, D., Pappenberger, F., 2010
(April): Summary of recommendations of the first workshop on
Postprocessing and Downscaling Atmospheric Forecasts for Hydrologic
Applications held at Meteo-France, Toulouse, France, 15-18 June
2009. Atmospheric Science Letters.
11(2):p. 59-63. DOI:
10.1002/asl.267
(48) Hamill, T. M., J. S. Whitaker, J. L. Anderson, and C. Snyder,
2009: Comment
on "Sigma-point Kalman filter data assimilation methods for
strongly nonlinear systems. J. Atmos. Sci., 66,
3498-3500.
(47) Bougeault, P., Z. Toth, many others, T. M. Hamill, and many
others, 2009: TheTHORPEX
Interactive Grand Global Ensemble (TIGGE). Bull Amer. Meteor. Soc., 91, 1059-1072.
(46) Wang, X., T. M. Hamill, J. S. Whitaker, C. H. Bishop, 2009: A
comparison of the hybrid and EnSRF analysis schemes in the
presence of model error due to unresolved scales. Mon. Wea. Rev., 137, 3219-3232.
(45) Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth,
2008: Ensemble
data
assimilation with the NCEP Global Forecast System. Mon.
Wea. Rev., 136, 463-482.
(44) Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill,
2008: A hybrid ETKF-3DVAR data
assimilation scheme for the WRF model. Part II: real observation
experiments. Mon.
Wea. Rev., 136,
5132-5147.
(43) Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill,
2008: A hybrid ETKF-3DVAR data
assimilation scheme for the WRF model. Part I: observing system
simulation experiments. Mon. Wea. Rev., 136,
5116-5131.
(42) Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic
forecast
calibration using ECMWF and GFS ensemble reforecasts. Part
II: precipitation. Mon.
Wea. Rev., 136,
2620-2632.
(41) Hagedorn, R, T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic
forecast
calibration using ECMWF and GFS ensemble reforecasts. Part I:
2-meter temperature. Mon.
Wea. Rev., 136,
2608-2619.
(40) Hamill, T. M., 2007: Making
the AMS carbon neutral: offsetting the impacts of flying to
conferences. Bull. Amer. Meteor. Soc., 88, 6-9.
(39) Hamill, T. M., and J. S. Whitaker, 2007: Ensemble calibration of 500
hPa geopotential height and 850 hPa and 2-meter temperatures using
reforecasts. Mon. Wea. Rev., 135, 3273-3280.
(38) Schaake, J. C., T. M. Hamill, R. Buizza, and M. Clark, 2007: HEPEX, the Hydrological Ensemble
Prediction Experiment. Bull. Amer. Meteor. Soc., 88, 1541-1547.
(37) Hamill, T. M., 2007:
Comments on "Calibrated Surface Temperature forecasts from the
Canadian ensemble prediction system using Bayesian Model Averaging.
Mon. Wea. Rev., 135,
4226-4230.
(36) Wilks, D. S., and T. M. Hamill, 2007: Comparison of ensemble-MOS methods
using GFS reforecasts. Mon. Wea. Rev., 135,
2379-2390.
(35) Wang, X., T. M. Hamill, and C. Snyder, 2007: On
the
theoretical equivalence of differently proposed ensemble/3D-Var
hybrid analysis schemes Mon. Wea. Rev.,
135, 222-227.
(34) Wang, X., T. M. Hamill, C. Snyder, and C. H. Bishop, 2006: A
comparison of hybrid ensemble transform Kalman filter-OI and
ensemble square-root filter analysis schemes. Mon. Wea.
Rev., 135, 1055-1076.
(33) Hamill, T. M., 2006:
Ensemble-based atmospheric data assimilation Chapter 6 of Predictability
of Weather and Climate, Cambridge Press, 124-156.
(32) Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: is
it real skill or is it the varying climatology? Quart.
J. Royal Meteor. Soc., 132,
2905-2923.
(31) Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative
precipitation forecasts based on reforecast analogs: theory and
application Mon. Wea. Rev., 134, 3209-3229.
(30) Sutton, C. J., T. M. Hamill, and T. T. Warner, 2006: Will Perturbing Soil Moisture Improve
Warm-Season Ensemble Forecasts? A Proof of Concept Mon.
Wea. Rev., 134, 3174-3189.
(30a) Sutton, C. J., T. M. Hamill, and T. T. Warner, 2006: Appendix to "Will Perturbing Soil
Moisture Improve Warm-Season Ensemble Forecasts? A Proof of
Concept" Mon. Wea. Rev..
(29) Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2006: Reforecasts, an important dataset for
improving weather predictions. Bull. Amer. Meteor. Soc.,
87,33-46.
(28) Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved
scales in ensemble data assimilation: a comparison of different
approaches Mon. Wea. Rev., 133, 3132-3147.
(27) Hamill, T. M., R. S. Schneider, H. E. Brooks, G. S. Forbes, H.
B. Bluestein, M. Steinberg, D. Melendez, and R. M. Dole, 2005: The May 2003 Extended Tornado Outbreak
Bull. Amer. Meteor. Soc., 86, 531-542.
(27a) Hamill, T. M., R. S. Schneider, H. E. Brooks, G. S. Forbes, H.
B. Bluestein, M. Steinberg, D. Melendez, and R. M. Dole, 2005: Supplement 1 to The May 2003 Extended
Tornado Outbreak Bull. Amer. Meteor. Soc..
(27b) Hamill, T. M., R. S. Schneider, H. E. Brooks, G. S. Forbes, H.
B. Bluestein, M. Steinberg, D. Melendez, and R. M. Dole, 2005: Supplement 2 to The May 2003 Extended
Tornado Outbreak Bull. Amer. Meteor. Soc..
(26) Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble re-forecasting: improving
medium-range forecast skill using retrospective forecasts Mon.
Wea. Rev., 132, 1434-1447.
(25) Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2003: Reanalysis without radiosondes using
ensemble data assimilation. Mon. Wea. Rev. , 132,
1190-1200.
(24) Tippett, M., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J.
S. Whitaker, 2003: Ensemble
square-root filters. Mon. Wea. Rev., 131,1485-1490.
(23) Hamill, T. M., 2003: Evaluating
forecasters' rules of thumb: a study of D(Prog)/Dt. Wea.
Forecasting, 18, 933-937.
(22) Snyder, C., T. M. Hamill, and S. J. Trier, 2003: Linear evolution of error covariances in a
quasigeostrophic model. Mon. Wea. Rev., 131,
189-205.
(21) Hamill, T. M., C. Snyder, and J. S. Whitaker, 2002: Ensemble forecasts and the properties of
flow-dependent analysis-error covariance singular vectors. Mon.
Wea.
Rev., 131, 1741-1758.
(20) Snyder, C., and T. M. Hamill, 2003:
Lyapunov stability of a turbulent baroclinic jet in a
quasigeostrophic model. J. Atmos. Sci., 60,
683-688.
(19) Hamill, T. M., C. Snyder, and R. E. Morss, 2002: Analysis-error statistics of a
quasigeostrophic model using 3-dimensional variational
assimilation. Mon. Wea. Rev., 130, 2777-2790.
(18) Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed
observations. Mon. Wea. Rev., 130, 1913-1924.
(17) Hamill, T. M., 2002: Adaptive observations. Published in Encyclopedia
of the Atmospheric Sciences, Elsevier Science, Ltd.,
2537-2542.
(16) Hamill, T. M., and C. Snyder, 2002:
Using improved background error covariances from an ensemble
Kalman filter for adaptive observations. Mon. Wea. Rev.,
130, 1552-1572.
(15) Hamill, T. M., Whitaker, J. S., and C. Snyder, 2001: Distance-dependent filtering of
background error covariance estimates in an ensemble Kalman
filter. Mon. Wea. Rev., 129, 2776-2790.
(14) Hamill, T. M., 2001: Interpretation
of rank histograms for verifying ensemble forecasts. Mon.
Wea. Rev., 129, 550-560.
(13) Hamill, T. M., C. Snyder, D. P. Baumhefner, Z. Toth, and S. L.
Mullen, 2000: Ensemble forecasting
in the short to medium range: report from a workshop. Bull.
Amer. Meteor. Soc., 81, 2653-2664.
(12) Hamill, T. M., and C. Snyder, 2000:
A hybrid ensemble Kalman filter / 3D-variational analysis
scheme. Mon. Wea. Rev., 128,
2905-2919. (nominated for NCAR's publication of the year award,
2000)
(11) Hamill, T. M., and A. Church, 2000: Conditional tornado probabilities
From RUC-2 forecasts, Wea. Forecasting, 15,
461-475.
(10) Hamill, T. M., C. Snyder, and R. E. Morss, 2000: A comparison of probabilistic
forecasts from bred, singular vector, and perturbed observation
ensembles. Mon. Wea. Rev., 128, 1835-1851.
(9) Hamill, T. M., 1999: Hypothesis
tests for evaluating numerical precipitation forecasts. Wea.
Forecasting, 14, 155-167.
(8) Hamill, T. M., 1998:
Comments on "Short-Range Ensemble Forecasting of Explosive
Australian East-Coast Cyclogenesis" Wea. Forecasting,
13,1205-1207.
(7) Hamill, T. M., and S. J. Colucci, 1998:
Evaluation of Eta/RSM Ensemble Probabilistic Precipitation
Forecasts. Mon. Wea. Rev., 126, 711-724.
(6) Hamill, T. M., 1997: Reliability
diagrams for multi-category probability forecasts. Wea.
Forecasting., 12, 736-741.
(5) Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta/RSM Short-Range
Ensemble Forecasts. Mon. Wea. Rev., 125,
1312-1327.
(4) Wilks, D. S., and Hamill, T. M., 1995: Potential economic value of ensemble-based
surface weather forecasts. Mon. Wea. Rev., 123,
3565-3575.
(3) Hamill, T. M., and D. S. Wilks, 1994: The difficulty in assessing short-range
forecast uncertainty: demonstration with a probability-based
contest. Wea. Forecasting, 10, 619-630.
(2) Hamill, T. M., and T. Nehrkorn, 1993: A short-term cloud forecast scheme
using cross-correlations. Wea. Forecasting, 8,
401-411.
(1) Hamill, T. M., R. P. d'Entremont, and J. T. Bunting, 1992: A description of the Air Force real-time
nephanalysis model. Wea. Forecasting, 7,
288-306.
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