Data Assimilation Experiments

1. Observing system experiments (OSE): 

Observing System Experiments are identical twin experiments in which a set of data assimilation runs are conducted where one particular observation type (or subtype) is switched on and off, to study the impact of that particular observation category. (George et al. 2021; Bushair et al. 2021; Rani et al. 2023; Rani et al. 2022)

2. Observation System Replacement Experiments (OSRE): 

Observing System Replacement Experiments are used to understand the possible impact of the upgradation of the observing system at some geographical location (targeted location). Sensitivity run in OSRE uses a downgraded version of observing systems, which is identical to the observation currently available at the targeted location (in resolution and coverage), to replace already existing (upgraded) observing systems at a different geographical location. The result from OSRE is interpreted in such a way as to understand the potential impact of the upgradation of the observing system at the targeted location.

3. Observing System Simulation Experiments (OSSE):

An Observing System Simulation Experiment is a model experiment that evaluates the usefulness of a proposed observing system before the availability of actual observation data. A long free model run with high spatial and temporal resolution, known as Nature Run is the most critical component of an OSSE. Nature Run is used as an alternative reality . Both the existing and the proposed observations are synthetically simulated from a Nature Run (model simulated state) and the forecast skill is computed against the same "Nature Run".  Qualities of a standard OSSE as described by Hoffman and Atlas (2016) are as follows:

• 1) A state-of-the-art numerical model should be used for the Nature Run (which is used to represent the atmosphere or the earth system); 

• 2) The model used for Nature Run should be realistically (or significantly) different from the model used for assimilation and forecasting; 

• 3) the assimilation methodology must conform to current or future practices; 

• 4) observations should be simulated with realistic coverage and accurately calibrated errors; and 

• 5) the entire OSSE system must be validated to ensure that the accuracy of analyses and forecasts and that the impact of existing observing systems in the OSSE are comparable to the accuracies and impacts of the same observing systems in the real world.

4. Sensitivity Observing System Experiments (SOSE):

Sensitivity Observing System Experiments focus on real extreme events that were badly forecast operationally, which cannot be done in an OSSE. SOSE uses sensitivity structures to correct the (incorrect) forecast initial state with a constraint that these structures do not conflict with existing observations. The computed analysis corrections do not affect the total analysis error but do improve the forecast. The synthetic data in SOSE requires the true atmospheric state (which is unknown). Hence corrected analyses are used to simulate future observations. A major component of SOSE as described by Marseille et al (2006, 2008 a and b) is the determination of a so-called adapted analysis, also denoted as a ‘pseudo true atmospheric state’ which qualifies the following conditions:

• 1) improves the 2-day forecast, 

• 2) is compatible with existing (real) observations

• 3) has realistic spatial structures, that is, analysis adaptations should resemble real analysis errors.

References:

1. George, G.; Halloran, G,; Kumar, S.; Rani, S. I.; Bushair, M. T.; Jangid, B. P.; George, J. P.; Maycock, A.; (2021) Impact of Aeolus horizontal line of sight wind observations in a global NWP system. Atmospheric Research; volume 261, 15 October 2021, 105742. https://doi.org/10.1016/j.atmosres.2021.105742

2. Bushair, M.T.; Rani, S.I.; George, G.; Kumar, S.; Kumar, S.; George J.P.; (2021) Role of Space-Borne Sea Surface Winds on the Simulation of Tropical Cyclones Over the Indian Seas. Pure and Applied Geophysics; volume 178; issue 11; pages 4665-4686. https://doi.org/10.1007/s00024-021-02890-0

3. Rani, S.I.; Jangid, B.P.; Francis, T.; Sharma, P.; George, G.; Kumar, S.; Thota, M.S.; George, J.P.; Nath, S.; Gupta,M.D.; Mitra A.K.; (2023) Assimilation of aircraft observations over the Indian monsoon region: Investigation of the effects of COVID-19 on a reanalysis. Quarterly Journal of the Royal Meteorological Society; volume 149, issue 752, April 2023, Part A; pages 894-910. https://doi.org/10.1002/qj.4439

4. Rani S.I.; Jangid, B.P.; Kumar, S.; Bushair, M.T.; Sharma, P.; George, J.P.; George, G.; Gupta, M.D.; (2022) Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF. Quarterly Journal of the Royal Meteorological Society. volume148, issue744, pages 1344-1367. https://doi.org/10.1002/qj.4264

5. Hoffman, R.N.; Atlas, R.; (2016) Future observing system simulation experiments. Bulletin of American Meteorological Society; volume 97; issue 9; pages 1601-1616; https://doi.org/10.1175/BAMS-D-15-00200.1

6.  Marseille,G.J.; Stoffelen,A; Barkmeijer,J; (2006) PIEW-Prediction Improvement of Extreme Weather. Final report for ESA contract No. 17112/03/NL/MM

7. Marseille, G.J.; Stoffelen, A.; Barkmeijer, J.; (2008) Sensitivity Observing System Experiment (SOSE)—a new effective NWP-based tool in designing the global observing system. Tellus A; volume 60; issue 2; pages 216–233; https://doi.org/10.1111/j.1600-0870.2007.00288.x

8.  Marseille, G.J.; Stoffelen, A; Barkmeijer,J; (2008) Impact assessment of prospective space-borne Doppler wind lidar observation scenarios. Tellus A; volume 60; issue 2; pages 234-248; https://doi.org/10.1111/j.1600-0870.2007.00289.x