Improving Weather Forecast Skill through Reduced-Precision Data Assimilation

AbstractA new approach for improving the accuracy of data assimilation, by trading numerical precision for ensemble size, is introduced. Data assimilation is inherently uncertain because of the use of noisy observations and imperfect models. Thus, the larger rounding errors incurred from reducing precision may be within the tolerance of the system. Lower-precision arithmetic is cheaper, and so by reducing precision in ensemble data assimilation, computational resources can be redistributed toward, for example, a larger ensemble size. Because larger ensembles provide a better estimate of the underlying distribution and are less reliant on covariance inflation and localization, lowering precision could actually permit an improvement in the accuracy of weather forecasts. Here, this idea is tested on an ensemble data assimilation system comprising the Lorenz ’96 toy atmospheric model and the ensemble square root filter. The system is run at double-, single-, and half-precision (the latter using an emulation t...

[1]  Herb Sutter,et al.  A Fundamental Turn Toward Concurrency in Software , 2008 .

[2]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[3]  Jeffrey L. Anderson.  A Local Least Squares Framework for Ensemble Filtering , 2003 .

[4]  Tomoo Ushio,et al.  “Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress , 2016, Proceedings of the IEEE.

[5]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[6]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[7]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[8]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[9]  Istvan Szunyogh,et al.  A local ensemble Kalman filter for atmospheric data assimilation , 2004 .

[10]  J. Whitaker,et al.  Evaluating Methods to Account for System Errors in Ensemble Data Assimilation , 2012 .

[11]  E. Lorenz.  Predictability of Weather and Climate: Predictability – a problem partly solved , 2006 .

[12]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[13]  Takemasa Miyoshi,et al.  Impact of Removing Covariance Localization in an Ensemble Kalman Filter: Experiments with 10 240 Members Using an Intermediate AGCM , 2016 .

[14]  Peter D. Düben,et al.  Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware , 2014 .

[15]  T. Palmer,et al.  More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators? , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Jeffrey L. Anderson,et al.  A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts , 1999 .

[17]  I. Moroz,et al.  Stochastic parametrizations and model uncertainty in the Lorenz ’96 system , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[18]  E. Kalnay,et al.  Balance and Ensemble Kalman Filter Localization Techniques , 2011 .

[19]  Stephen E. Cohn,et al.  Treatment of Observation Error due to Unresolved Scales in Atmospheric Data Assimilation , 2006 .

[20]  Peter D. Düben,et al.  Reliable low precision simulations in land surface models , 2018, Climate Dynamics.

[21]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[22]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

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

[24]  Peter D. Düben,et al.  A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model , 2017 .

[25]  Seung-Jong Baek,et al.  Higher Resolution in an Operational Ensemble Kalman Filter , 2014 .

[26]  Peter D. Düben,et al.  On the use of scale‐dependent precision in Earth System modelling , 2017 .

[27]  Peter D. Düben,et al.  Single Precision in Weather Forecasting Models: An Evaluation with the IFS , 2017 .

[28]  Peter D. Düben,et al.  rpe v5: an emulator for reduced floating-point precision in large numerical simulations , 2016 .

[29]  Lars Nerger,et al.  On Serial Observation Processing in Localized Ensemble Kalman Filters , 2015 .

[30]  Fuqing Zhang,et al.  Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation , 2016 .

[31]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[32]  T. Miyoshi.  The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter , 2011 .

[33]  Toshiyuki Imamura,et al.  The 10,240‐member ensemble Kalman filtering with an intermediate AGCM , 2014 .

[34]  Peter D. Düben,et al.  The use of imprecise processing to improve accuracy in weather & climate prediction , 2014, J. Comput. Phys..

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