Future Observing System Simulation Experiments

AbstractAs operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.

[1]  R. J. Purser,et al.  A bending angle forward operator for global positioning system radio occultation measurements , 2013 .

[2]  Steven E. Koch,et al.  An observing system simulation experiment for the unmanned aircraft system data impact on tropical cyclone track forecasts , 2014 .

[3]  Dick Dee,et al.  Adaptive bias correction for satellite data in a numerical weather prediction system , 2007 .

[4]  René Laprise,et al.  The resolution of global spectral models , 1992 .

[5]  John Derber,et al.  Enhanced radiance bias correction in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation data assimilation system , 2014 .

[6]  Jing Guo,et al.  Development and validation of observing‐system simulation experiments at NASA's Global Modeling and Assimilation Office , 2013 .

[7]  R. Anthes,et al.  Exploring earth's atmosphere with radio occultation: contributions to weather, climate and space weather , 2011 .

[8]  Peter Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part I: Implementation , 2010 .

[9]  Robert Atlas,et al.  Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction , 2015 .

[10]  L. Cucurull Improvement in the Use of an Operational Constellation of GPS Radio Occultation Receivers in Weather Forecasting , 2010 .

[11]  Robert Atlas,et al.  Rigorous Evaluation of a Fraternal Twin Ocean OSSE System for the Open Gulf of Mexico , 2014 .

[12]  Deborah K. Smith,et al.  A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications , 2011 .

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

[14]  Z. Pu,et al.  LIDAR-MEASURED WIND PROFILES The Missing Link in the Global Observing System , 2014 .

[15]  William L. Smith,et al.  AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases. , 2006 .

[16]  Robert Atlas,et al.  Atmospheric Observations and Experiments to Assess Their Usefulness in Data Assimilation , 1997 .

[17]  Robert Atlas,et al.  The Effects of Marine Winds from Scatterometer Data on Weather Analysis and Forecasting , 2001 .

[18]  Richard A. Anthes,et al.  Radio occultation observations as anchor observations in numerical weather prediction models and associated reduction of bias corrections in microwave and infrared satellite observations , 2014 .

[19]  Z. Pu,et al.  An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone , 2010 .

[20]  Michiko Masutani,et al.  Observation system simulation experiments for a global wind observing sounder , 2012 .

[21]  Christopher Grassotti,et al.  Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System , 1990 .

[22]  J. Woollen,et al.  Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments , 2015 .

[23]  T. N. Krishnamurti,et al.  An Observing System Simulation Experiment for the Laser Atmospheric Wind Sounder (LAWS). , 1993 .

[24]  Peter Lynch,et al.  Diabatic initialization using recursive filters , 1994 .

[25]  Robert Atlas,et al.  OSSE impact analysis of airborne ocean surveys for improving upper-ocean dynamical and thermodynamical forecasts in the Gulf of Mexico , 2015 .

[26]  Robert Atlas,et al.  Impact Of Satellite Temperature Sounding And Wind Data On Numerical Weather Prediction , 1985 .

[27]  Clifford H. Dey,et al.  Observing-Systems Simulation Experiments: Past, Present, and Future , 1986 .

[28]  William Bell,et al.  Progress towards the assimilation of all‐sky infrared radiances: an evaluation of cloud effects , 2014 .

[29]  J. Derber,et al.  Introduction of the GSI into the NCEP Global Data Assimilation System , 2009 .

[30]  Niels Bormann,et al.  Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind , 2014 .

[31]  T. Vukicevic,et al.  A data assimilation technique to account for the nonlinear dependence of scattering microwave observations of precipitation , 2015 .

[32]  J. Derber,et al.  Variational Correction of Aircraft Temperature Bias in the NCEP’s GSI Analysis System , 2015 .

[33]  Luca Delle Monache,et al.  A Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty Information , 2011 .

[34]  Niels Bormann,et al.  Atmospheric Motion Vectors from Model Simulations. Part II: Interpretation as Spatial and Vertical Averages of Wind and Role of Clouds , 2014 .