Significant Findings

AIRS data contributed to these significant findings in weather and weather forecasting science. They are organized by focus area and time period and only periodically updated.

Forecast Improvement
Weather
Various Studies

Forecast Improvement

Findings 2017–2019

AIRS radiances are shown to add skill to short-range weather forecasts over a relatively data-rich area. Principal Component Analysis can be used to compress the observational information content contained in the AIRS channels to a few leading principal components. Adaptive thinning of AIRS radiances is shown to substantially improve tropical cyclone representation in a global data assimilation and forecast system.

Wang, G., Zhang, Z. Q., Deng, S. M., & Liu, H. L. (2019), Assimilation of hyper-spectral AIRS brightness temperatures based on generalized variational assimilation and observation error re-estimation, Journal of Infrared and Millimeter Waves, 38(4), 464-472. https://doi.org/10.11972/j.issn.1001-9014.2019.04.012

Lu, Y. H., & Zhang, F. Q. (2019), Toward Ensemble Assimilation of Hyperspectral Satellite Observations with Data Compression and Dimension Reduction Using Principal Component Analysis, Monthly Weather Review, 147(10), 3505-3518. https://doi.org/10.1175/mwr-d-18-0454.1

Kim, S. M., & Kim, H. M. (2018), Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters, Tellus Series a-Dynamic Meteorology and Oceanography, 70, 1-16. https://doi.org/10.1080/16000870.2018.1492839.

Reale, Oreste, McGrath-Spangler, Erica L., McCarty, Will, Holdaway, Daniel, Gelaro, Ronald (2018) Impact of adaptively thinned AIRS cloud-cleared radiances on tropical cyclone representation in a global data assimilation and forecast system. Weather and Forecasting, AMS Journals Published Online: 25 April 2018 https://journals.ametsoc.org/doi/10.1175/WAF-D-17-0175.1

Singh, K.S., Bhaskaran, Prasad K., Impact of lateral boundary and initial conditions in the prediction of Bay of Bengal cyclones using WRF model and its 3D-VAR data assimilation system. Journal of Atmospheric and Solar-Terrestrial Physics, Volume 175, October 2018, Pages 64-75 https://www.sciencedirect.com/science/article/pii/S1364682617306843

Lin, H., S. S. Weygandt, A. H. N. Lim, M. Hu, J. M. Brown, and S. G. Benjamin (2017), Radiance Preprocessing for Assimilation in the Hourly Updating Rapid Refresh Mesoscale Model: A Study Using AIRS Data, Weather and Forecasting, 32(5), 1781-1800.https://dx.doi.org/10.1175/WAF-D-17-0028.1.

Findings 2006–2016

4-D assimilation of observations from the major humidity observing systems show improvements in simulated wind and temperature fields. AIRS is shown to have an especially significant impact in simulations of the upper troposphere.

Andersson, E., E. Holm, P. Bauer, A. Bejaars, G. A. Kelly, A. P. McNally, A. J. Simmons, J.-N. Thepaut, and A. M. Tompkins (2007), Analysis and forecast impact of the main humidity observing systems. Quart. J. Royal. Met. Soc., 133, 1473-1485.

Weather

Findings 2017–2019

A novel technique is introduced for detection of fog/low cloud using AIRS data. The use of AIRS profiles above the surface level provides surface-based CAPE values that are very similar to those computed from Vaisala radiosondes. Use of AIRS temperature and moisture retrievals for assessing environmental drivers of severe convective storms is enhanced by a method combining soundings with trajectory calculation. AIRS data is used to calculate stability indices that inform about the triggering of extreme rainfall events in the Indian region.

Kalmus, P., Kahn, B. H., Freeman, S. W., & van den Heever, S. C. (2019), Trajectory-Enhanced AIRS Observations of Environmental Factors Driving Severe Convective Storms, Monthly Weather Review, 147(5), 1633-1653. https://doi.org/10.1175/mwr-d-18-0055.1

Arun, S. H., Chaurasia, S., Misra, A., & Kumar, R. (2018), Fog Stability Index: A novel technique for fog/low clouds detection using multi-satellites data over the Indo-Gangetic plains during winter season, International Journal of Remote Sensing, 39(22), 8200-8218. https://doi.org/10.1080/01431161.2018.1483085.

Kumar, K. N., Phanikumar, D. V., Sharma, S., Basha, G., Naja, M., Ouarda, T., et al. (2019), Influence of tropical-extratropical interactions on the dynamics of extreme rainfall event: A case study from Indian region, Dynamics of Atmospheres and Oceans, 85, 28-40. https://doi.org/10.1016/j.dynatmoce.2018.12.002

Gartzke, J., R. Knuteson, G. Przybyl, S. Ackerman, and H. Revercomb (2017), Comparison of satellite-, model-, and radiosonde-derived convective available potential energy in the Southern Great Plains region,Journal of Applied Meteorology and Climatology, 56(5), 1499-1513. http://dx.doi.org/10.1175/JAMC-D-16-0267.1.

Various Studies

Findings 2005–2008

The assimilation of AIRS derived temperature profiles in partially cloud contaminated areas can significantly increase weather forecast skill in a global model and forecasting system.

Reale, O., J. Susskind, R. Rosenberg, E. Brin, E. Liu, L. P. Riishojgaard, J. Terry, and J. C. Jusem (2008), Improving forecast skill by assimilation of quality-controlled AIRS temperature retrievals under partially cloudy conditions, Geophys. Res. Lett., 35, L08809, doi:10.1029/2007GL033002.

Assimilating even small amounts of AIRS data improves forecast significantly: Less than 1% of AIRS spectra extends the NCEP global 6-day forecast by 6 hours in both hemispheres. AIRS data are now used routinely by major weather forecast centers around the world, including NCEP (US) and ECMWF (Europe).

Le Marshall, J., J. Jung, J. Derber, M. Chahine, R. Treadon, S. J. Lord, M. Goldberg, W. Wolfc, H. C. Liu, J. Joiner, J. Woollen, R. Todling, P. van Delst, and Y. Tahara (2006), "Improving Global Analysis and Forecasting with AIRS", Bulletin of the American Meteorological Society, 87, 891-894, doi: 10.1175/BAMS-87-7-891

McNally, A.P., Watts, P.D., Smith, J.A., Engelen, R., Kelly, G.A., Thepaut, J.N., and Matricardi, M., 2006, The assimilation of AIRS radiance data at ECMWF, QJR Meteorol. Soc., 132, 935-957. doi: 10.1256/qj.04.171

Chahine et al. (2006), 'The Atmospheric Infrared Sounder (AIRS): improving weather forecasting and providing new insights into climate', Bulletin of the American Meteorological Society, 87, 911-926, DOI: 10.1175/BAMS-87-7-911

Assimilation of the AIRS Level 2 product into the MM5 model shows that the Saharan Air Layer (SAL) may have delayed the formation of Hurricane Isabel and inhibited the development of another tropical disturbance to the East.

Wu L., S. A. Braun, J. J. Qu, X. Hao (2006), Simulating the formation of Hurricane Isabel (2003) with AIRS data, Geophys. Res. Lett., 33, L04804, doi:10.1029/2005GL024665.

Assimilation of AIRS retrieved temperature profiles into the FVDAS improves the prediction of the intensity and location of cyclones in the Southern Hemisphere.

Atlas, R. (2005), The impact of AIRS data on weather prediction, Proc. SPIE Int. Soc. Opt. Eng., 5806, 599? 606,doi:10.1117/12.602540.