A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

[1]  T. N. Krishnamurti,et al.  Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part I: 1–5-Day Forecasts , 2009 .

[2]  M. Collins,et al.  El Niño in a changing climate: a multi-model study , 2005 .

[3]  N. Bindoff,et al.  Performance of downscaled regional climate simulations using a variable‐resolution regional climate model: Tasmania as a test case , 2013 .

[4]  A. Arakawa,et al.  Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I , 1974 .

[5]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[6]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[7]  Manabu Kanda,et al.  Length-Scale Similarity of Turbulent Organized Structures over Surfaces with Different Roughness Types , 2013, Boundary-Layer Meteorology.

[8]  Xiefei Zhi,et al.  A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data , 2012, Acta Meteorologica Sinica.

[9]  W. Rossow,et al.  Advances in understanding clouds from ISCCP , 1999 .

[10]  M. Kanamitsu,et al.  Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations , 2014, Asia-Pacific Journal of Atmospheric Sciences.

[11]  David S. Richardson,et al.  Benefits of increased resolution in the ECMWF ensemble system and comparison with poor‐man's ensembles , 2003 .

[12]  Michel Rixen,et al.  Enhanced ocean temperature forecast skills through 3-D super-ensemble multi-model fusion , 2010 .

[13]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[14]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.

[15]  Joong‐Bae Ahn,et al.  Statistical downscaling methods based on APCC multi‐model ensemble for seasonal prediction over South Korea , 2014 .

[16]  Michael Fiorino,et al.  Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific , 2003 .

[17]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[18]  T. N. Krishnamurti,et al.  Interpretation of Seasonal Climate Forecast Using Brier Skill Score, The Florida State University Superensemble, and the AMIP-I Dataset , 2002 .

[19]  R. Clark,et al.  Multi‐model multi‐analysis ensembles in quasi‐operational medium‐range forecasting , 2002 .

[20]  Alessandro Berni,et al.  Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles , 2009 .

[21]  S. Klein,et al.  GFDL's CM2 Global Coupled Climate Models. Part I: Formulation and Simulation Characteristics , 2006 .

[22]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[23]  S. Saha,et al.  The NCEP Climate Forecast System , 2006 .

[24]  John A. Knaff,et al.  Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS) , 2005 .

[25]  Bin Wang,et al.  Mechanism of Concentric Eyewall Replacement Cycles and Associated Intensity Change , 2011 .

[26]  Philip Jales,et al.  Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission , 2015 .

[27]  T. Krishnamurti,et al.  Lead time for medium range prediction of the dry spell of monsoon using multi-models , 2013, Journal of Earth System Science.

[28]  Yuqing Wang,et al.  Downscaling of Climate Change in the Hawaii Region Using CMIP5 Results: On the Choice of the Forcing Fields* , 2013 .

[29]  M. Milelli,et al.  Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region , 2010 .

[30]  T. Krishnamurti,et al.  Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America , 2014, Climate Dynamics.

[31]  Masao Kanamitsu,et al.  Description of the NMC Global Data Assimilation and Forecast System , 1989 .

[32]  T. N. Krishnamurti,et al.  Real-Time Multimodel Superensemble Forecasts of Atlantic Tropical Systems of 1999 , 2003 .

[33]  R. Cook.  Influential Observations in Linear Regression , 1979 .

[34]  T. N. Krishnamurti,et al.  Improvement of the Multimodel Superensemble Technique for Seasonal Forecasts , 2003 .

[35]  T. Krishnamurti,et al.  Use of APHRODITE Rain Gauge-Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method , 2014 .

[36]  H. V. Dijk,et al.  Combined forecasts from linear and nonlinear time series models , 1999 .

[37]  M. Kanamitsu,et al.  Dynamical Global Downscaling of Global Reanalysis , 2008 .

[38]  Martyn P. Clark,et al.  Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States , 2003 .

[39]  Gert-Jan Steeneveld,et al.  Modeling the evolution of the atmospheric boundary layer coupled to the land surface for three contrasting nights in CASES-99 , 2006 .

[40]  T. N. Krishnamurti,et al.  Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate , 2009 .

[41]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[42]  Steven Cocke,et al.  Seasonal Predictions Using a Regional Spectral Model Embedded within a Coupled Ocean–Atmosphere Model , 2000 .

[43]  Zong‐Liang Yang,et al.  An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations , 2012 .

[44]  C. Ronchi,et al.  Regional climate models downscaling in the Alpine area with multimodel superensemble , 2012 .

[45]  Mats Hamrud,et al.  Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System , 1998 .

[46]  I. Kang,et al.  Superensemble prediction of regional precipitation over Korea , 2004 .

[47]  T. N. Krishnamurti,et al.  Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part II: Anomaly , 2012 .

[48]  Louis W. Uccellini,et al.  50th Anniversary of Operational Numerical Weather Prediction , 2007 .

[49]  M. Clark,et al.  Hydrological responses to dynamically and statistically downscaled climate model output , 2000 .

[50]  Yongkang Xue,et al.  Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability , 2013, Climate Dynamics.

[51]  John Derber,et al.  The Use of TOVS Cloud-Cleared Radiances in the NCEP SSI Analysis System , 1998 .

[52]  Stephen J. Lord,et al.  Improved Skill for the Anomaly Correlation of Geopotential Heights at 500 hPa , 2003 .

[53]  Evaluation of several different planetary boundary layer schemes within a single model, a unified model and a multimodel superensemble , 2008 .

[54]  G. Faluvegi,et al.  The added value to global model projections of climate change by dynamical downscaling: A case study over the continental U.S. using the GISS‐ModelE2 and WRF models , 2012 .

[55]  S. Esbensen,et al.  Determination of Bulk Properties of Tropical Cloud Clusters from Large-Scale Heat and Moisture Budgets , 1973 .

[56]  Swadhin K. Behera,et al.  Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts , 2005 .

[57]  Upmanu Lall,et al.  Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction , 2004 .

[58]  M. Montgomery,et al.  A theory for vortex rossby‐waves and its application to spiral bands and intensity changes in hurricanes , 1997 .

[59]  Andrew P. Morse,et al.  DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) , 2004 .

[60]  M. Leutbecher.  A Reduced Rank Estimate of Forecast Error Variance Changes due to Intermittent Modifications of the Observing Network. , 2003 .

[61]  P. L. Houtekamer,et al.  A System Simulation Approach to Ensemble Prediction , 1996 .

[62]  R. Houze,et al.  The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite , 2015, Reviews of geophysics.

[63]  Sulochana Gadgil,et al.  The Indian monsoon and its variability , 2003 .

[64]  S. Aberson,et al.  The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for High-Resolution Data: The Impact of Airborne Doppler Radar Observations in an OSSE , 2012 .

[65]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[66]  Charles Troupin,et al.  Super-ensemble techniques applied to wave forecast: performance and limitations , 2010 .

[67]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[68]  C. Cardinali.  Monitoring the observation impact on the short‐range forecast , 2009 .

[69]  T. N. Krishnamurti,et al.  A new approach to the cumulus parameterization issue , 2003 .

[70]  A. Mitra,et al.  Impact of a Nonlocal Closure Scheme in a Simulation of a Monsoon System over India , 2002 .

[71]  M. Mahmud.  Skill of a superensemble forecast over equatorial Southeast Asia , 2004 .

[72]  Sandro Carniel,et al.  A note on the multimodel superensemble technique for reducing forecast errors , 2008 .

[73]  T. N. Krishnamurti,et al.  Improving Multimodel Forecasts of the Vertical Distribution of Heating Using the TRMM Profiles , 2010 .

[74]  Surajit Chattopadhyay,et al.  On the bias correction of general circulation model output for Indian summer monsoon , 2013 .

[75]  Albert A. M. Holtslag,et al.  Local Versus Nonlocal Boundary-Layer Diffusion in a Global Climate Model , 1993 .

[76]  Akiyo Yatagai,et al.  A 44-Year Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges , 2009 .

[77]  Craig H. Bishop,et al.  The THORPEX Interactive Grand Global Ensemble , 2010 .

[78]  C. Ziehmann.  Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models , 2000 .

[79]  Carol Anne Clayson,et al.  Seasonal Prediction of Sea Surface Temperature Anomalies Using a Suite of 13 Coupled Atmosphere–Ocean Models , 2006 .

[80]  P. L. Houtekamer,et al.  Toward Random Sampling of Model Error in the Canadian Ensemble Prediction System , 2010 .

[81]  J. Molinari,et al.  Convective Bursts, Downdraft Cooling, and Boundary Layer Recovery in a Sheared Tropical Storm , 2013 .

[82]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[83]  T. Krishnamurti,et al.  Hurricane forecasts using a suite of large-scale models , 2011 .

[84]  A. Simmons,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. I: Experimental results with simplified physics , 2007 .

[85]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[86]  T. N. Krishnamurti,et al.  Cumulus Parameterization and Rainfall Rates I , 1980 .

[87]  Masahiro Kazumori,et al.  Impact Study of AMSR-E Radiances in the NCEP Global Data Assimilation System , 2008 .