Methods, current status, and prospect of targeted observation

Targeted observation is an observation strategy by which the concerned phenomenon is observed. In geoscience, targeted observation is mainly related to the forecasts of weather events or predictions of climate events. This paper will first review the history of targeted observation, and then introduce the main methods used in targeted observation. The discussion on the theoretical basis of targeted observation includes its advantages and limitations. After presenting the current situation of domestic and international targeted observations in atmospheric and oceanic sciences, the methods used for targeted observation, and their effect evaluation and testing are mainly discussed here. Finally, the author presents his suggestion about the prospect of further development in the field, and how to extend the method of targeted observation to deal with numerical model errors.

[1]  Florence Rabier,et al.  An update on THORPEX-related research in data assimilation and observing strategies , 2008 .

[2]  J. Thepaut,et al.  The value of targeted observations Part II : The value of observations taken in singular vectors-based target areas , 2007 .

[3]  Wolfgang Knorr,et al.  Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties , 2000 .

[4]  Renhe Zhang,et al.  South China Heavy Rainfall Experiments (SCHeREX) , 2011 .

[5]  Mu Mu,et al.  Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations , 2013 .

[6]  W. Duan,et al.  Contribution of the location and spatial pattern of initial error to uncertainties in El Niño predictions , 2012 .

[7]  Qiang Wang,et al.  An extension of conditional nonlinear optimal perturbation approach and its applications , 2010 .

[8]  Istvan Szunyogh,et al.  Propagation of the effect of targeted observations: The 2000 Winter Storm Reconnaissance program , 2002 .

[9]  G. Collatz,et al.  The relationship between the Rubisco reaction mechanism and models of photosynthesis , 1990 .

[10]  Xiangde Xu,et al.  A China-Japan Cooperative JICA Atmospheric Observing Network over the Tibetan Plateau (JICA/Tibet Project) : An Overviews (Special Issue on Japan-China Meteorological Disaster Reduction Corporation Research Center Project (JICA/Tibet Project)) , 2012 .

[11]  Observation System Experiments for Typhoon Nida (2004) Using the CNOP Method and DOTSTAR Data , 2011 .

[12]  Jean-Noël Thépaut,et al.  The value of observations. II: The value of observations located in singular‐vector‐based target areas , 2007 .

[13]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[14]  M. Mu,et al.  The roles of spatial locations and patterns of initial errors in the uncertainties of tropical cyclone forecasts , 2012, Advances in Atmospheric Sciences.

[15]  K. Emanuel,et al.  Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model , 1998 .

[16]  R. Morss,et al.  Evaluating Observing Requirements for ENSO Prediction: Experiments with an Intermediate Coupled Model , 2004 .

[17]  Stephen Sitch,et al.  Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics , 2005 .

[18]  H. Riehl,et al.  ON THE PREDICTION OF 24-HOUR HURRICANE MOTION , 1956 .

[19]  P. Leeuwen,et al.  Measuring the Impact of Observations on the Predictability of the Kuroshio Extension in a Shallow-Water Model , 2012 .

[20]  Xiangyu Huang,et al.  Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using the Weather Research Forecasting (WRF) model , 2011 .

[21]  S. Aberson,et al.  An Observing System Experiment for Tropical Cyclone Targeting Techniques Using the Global Forecast System , 2011 .

[22]  Xiangyu Huang,et al.  Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using theWeather Research Forecasting (WRF) model , 2011 .

[23]  Craig H. Bishop,et al.  A Comparison of Adaptive Observing Guidance for Atlantic Tropical Cyclones , 2006 .

[24]  T. Hamill,et al.  Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations , 2002 .

[25]  Jing-Shan Hong,et al.  Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR): An Overview , 2005 .

[26]  M. Mu,et al.  A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation , 2009 .

[27]  Istvan Szunyogh,et al.  Sensitivity of Forecast Errors to Initial Conditions with a Quasi-Inverse Linear Method , 1997 .

[28]  Istvan Szunyogh,et al.  The Effect of Targeted Dropsonde Observations during the 1999 Winter Storm Reconnaissance Program , 2000 .

[29]  Andrew D. Friend,et al.  CO2 stabilization, climate change and the terrestrial carbon sink , 2000 .

[30]  Real-time adaptive observation guidance using singular vectors for Typhoon Jangmi (200815) in T-PARC 2008 , 2011 .

[31]  T. Palmer,et al.  Singular Vectors, Metrics, and Adaptive Observations. , 1998 .

[32]  Chris Snyder,et al.  Summary of an Informal Workshop on Adaptive Observations and FASTEX , 1996 .

[33]  Wang Hong.  A Preliminary Application of Conditional Nonlinear Optimal Perturbation to Adaptive Observation , 2007 .

[34]  Rolf H. Langland,et al.  Adjoint-Based Targeting of Observations for Fastex Cyclones, , 1996 .

[35]  Mu Mu,et al.  Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts , 2012 .

[36]  D. Stammer,et al.  Optimal Observations for Variational Data Assimilation , 2004 .

[37]  H. Dijkstra,et al.  The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation , 2013 .

[38]  Koji Yamashita,et al.  The Influence of Assimilating Dropsonde Data on Typhoon Track and Midlatitude Forecasts , 2011 .

[39]  James C. McWilliams,et al.  Sensitivity to Surface Forcing and Boundary Layer Mixing in a Global Ocean Model: Annual-Mean Climatology , 1997 .

[40]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[41]  Bin Wang,et al.  Conditional Nonlinear Optimal Perturbations: Adjoint-Free Calculation Method and Preliminary Test , 2010 .

[42]  Johanna Baehr,et al.  Optimization of an Observing System Design for the North Atlantic Meridional Overturning Circulation , 2008 .

[43]  Sim D. Aberson,et al.  The Impact of Dropwindsonde Data from the THORPEX Pacific Area Regional Campaign and the NOAA Hurricane Field Program on Tropical Cyclone Forecasts in the Global Forecast System , 2011 .

[44]  Istvan Szunyogh,et al.  The North Pacific Experiment (NORPEX-98): Targeted Observations for Improved North American Weather Forecasts , 1999 .

[45]  Hui Xu,et al.  Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions , 2014, Theoretical and Applied Climatology.

[46]  Bin Wang,et al.  Conditional nonlinear optimal perturbation and its applications , 2003 .

[47]  E. Sarachik,et al.  Reducing Errors in Temperature and Salinity in an Ocean Model Forced by Restoring Boundary Conditions , 2004 .

[48]  Craig H. Bishop,et al.  Ensemble Transformation and Adaptive Observations , 1999 .

[49]  A. Köhl.  Anomalies of Meridional Overturning: Mechanisms in the North Atlantic , 2005 .

[50]  Sim D. Aberson,et al.  The Impact of Dropwindsonde Observations on Typhoon Track Forecasts in DOTSTAR and T-PARC , 2011 .

[51]  Neill E. Bowler,et al.  The MOGREPS short‐range ensemble prediction system , 2008 .