Journal articles and book chapters

Thomas M. Hamill

Researcher ID: C-4630-2015

Address:

NOAA Earth System Research Lab, Physical Sciences Division
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:

(97) Ben Bouallegue, Z., T.  Haiden, N. J. Weber, T. M. Hamill, and D. S. Richardson, 2019:   Accounting for representativenexs in the verification of ensemble precipitation forecasts.   Mon. Wea. Rev., submitted.
(96) Worsnop, R., M. Scheuerer, and T. M. Hamill, 2019: Extended-range probabilistic fire-weather forecasting based on Ensemble Model Output Statistics and Ensemble Copula Coupling.   Mon. Wea. Rev., conditionally accepted.
(95) Zhang, T., Hoell, A., Perlwitz, J., J. Eischeid, D. Murray, M. Hoerling, and T. M. Hamill.   Towards probabilistic multivariate ENSO monitoring.  Geophys. Research Letters,
https://doi.org/10.1029/2019GL083946.
(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, section Ocean Observation
(93) Gascon, E., D. Lavers, D.,  T. M. Hamill, D. S. Richardson, Z. Ben Boallegue, M. Leutbecher, and F. Pappenberger, 2019:   Statistical post-processing of dual-resolution ensemble precipitation forecasts across EuropeQuart. J. Royal Meteor. Soc., in press.
(92) Hamill, T. M., and M. Scheuerer, 2019: Benchmarking the background forecast in rapidly updated surface temperature analyses.  Part 2: gridded benchmark.  Mon. Wea. Rev., accepted/major.
(91) Hamill, T. M., 2019: Benchmarking the background forecast in rapidly updated surface temperature analyses.  Part 1: stations.  Mon Wea. Rev, accepted/major.
(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., Hamill, T. M., Bates, G. T., Pegion, P., and W. Kolczynski, 2018: Land-surface parameter and state perturbations in the Global Ensemble Forecast SystemMon. Wea. Rev., 147, 13191340.
(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 predictionBull. Amer. Meteor. Soc., 98, ES169-ES172,
https://doi.org/10.1175/BAMS-D-17-0036.1
(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 ResearchAppendix.
(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 2010Mon. 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 temperatureMon. 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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