# Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- (1)
- Matricardi and McNally [29] generated 20 PC scores from 165 out of 8461 IASI channels in their research, which meant a genuine portion of observational information was unavoidably discarded. Moreover, the PC-based fast-forward radiative-transfer model (RTM) in their study can only assimilate IASI observations, meaning further validation from other experiments using different instruments.
- (2)
- Collard et al. [30] focused on the noise cancellation capability of PCA and its impact on the DA system by assimilating reconstructed radiance observations via a channel-selection method. Their results indicated that signal-to-noise ratio improvement (noise cancellation) can enhance the impact of IR radiance observations in the DA system.
- (3)
- Lu and Zhang [31] highlighted the PC scores’ information content preservation capability, but the PC scores are still not generated from full-spectrum radiance observation.

## 2. Materials and Methods

## 3. Results

#### 3.1. PC-Score vs. Selected-Channel Radiance Assimilation: Case Studies

#### 3.2. PC-Score vs. Selected-Channel Radiance Assimilation: Four-Month-Long Evaluation

#### 3.3. Convection-Resolving Resolution Case Studies

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Correlation coefficient matrix in principle component (

**a**,

**c**) and radiance (

**b**,

**d**) space for CrIS (

**a**,

**b**) and IASI (

**c**,

**d**).

**Figure A2.**Weighting function for CrIS and IASI in PC and radiance space. Profile dataset comes from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) 60-level sample profile dataset from the Monitoring Atmospheric Composition and Climate (MACC) project (available at https://nwp-saf.eumetsat.int/site/download/profile_datasets/60l_macc.dat.tar.bz2 (accessed on 3 February 2023)).

Contingency Table | |||
---|---|---|---|

Observation | |||

Happen | Not Happen | ||

Happen | a | b | |

Forecast | Not Happen | c | d |

## References

- Collard, A.D.; McNally, A.P. The assimilation of Infrared Atmospheric Sounding Interferometer radiances at ECMWF. Q. J. R. Meteorol. Soc.
**2009**, 135, 1044–1058. [Google Scholar] [CrossRef] - Hilton, F.; Armante, R.; August, T.; Barnet, C.; Bouchard, A.; Camy-Peyret, C.; Capelle, V.; Clarisse, L.; Clerbaux, C.; Coheur, P.-F.; et al. Hyperspectral Earth Observation from IASI: Five Years of Accomplishments. Bull. Am. Meteorol. Soc.
**2012**, 93, 347–370. [Google Scholar] [CrossRef] - McNally, A.P.; Watts, P.D.; Smith, J.A.; Engelen, R.; Kelly, G.A.; Thépaut, J.N.; Matricardi, M. The assimilation of AIRS radiance data at ECMWF. Q. J. R. Meteorol. Soc.
**2006**, 132, 935–957. [Google Scholar] [CrossRef] [Green Version] - Collard, A.D. Selection of IASI channels for use in numerical weather prediction. Q. J. R. Meteorol. Soc.
**2007**, 133, 1977–1991. [Google Scholar] [CrossRef] - Eresmaa, R.; Letertre-Danczak, J.; Lupu, C.; Bormann, N.; McNally, A.P. The assimilation of Cross-track Infrared Sounder radiances at ECMWF. Q. J. R. Meteorol. Soc.
**2017**, 143, 3177–3188. [Google Scholar] [CrossRef] - Carminati, F.; Xiao, X.; Lu, Q.; Atkinson, N.; Hocking, J. Assessment of the Hyperspectral Infrared Atmospheric Sounder (HIRAS). Remote Sens.
**2019**, 11, 2950. [Google Scholar] [CrossRef] [Green Version] - Eresmaa, R. Infrared Fourier Spectrometer 2 (IKFS-2) radiance assimilation at ECMWF. Q. J. R. Meteorol. Soc.
**2020**, 147, 106–120. [Google Scholar] [CrossRef] - Gambacorta, A.; Barnet, C.D. Methodology and Information Content of the NOAA NESDIS Operational Channel Selection for the Cross-Track Infrared Sounder (CrIS). IEEE Trans. Geosci. Remote Sens.
**2012**, 51, 3207–3216. [Google Scholar] [CrossRef] - Ventress, L.; Dudhia, A. Improving the selection of IASI channels for use in numerical weather prediction. Q. J. R. Meteorol. Soc.
**2013**, 140, 2111–2118. [Google Scholar] [CrossRef] - Noh, Y.-C.; Huang, H.-L.; Goldberg, M.D. Refinement of CrIS channel selection for global data assimilation and its impact on the global weather forecast. Weather Forecast.
**2021**, 36, 1405–1429. [Google Scholar] [CrossRef] - Smith, W.L.; Revercomb, H.; Weisz, E.; Tobin, D.; Knuteson, R.; Taylor, J.; Menzel, W.P. Hyperspectral Satellite Radiance Atmospheric Profile Information Content and Its Dependence on Spectrometer Technology. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2021**, 14, 4720–4736. [Google Scholar] [CrossRef] - Vittorioso, F.; Guidard, V.; Fourrié, N. An Infrared Atmospheric Sounding Interferometer—New Generation (IASI-NG) channel selection for numerical weather prediction. Q. J. R. Meteorol. Soc.
**2021**, 147, 3297–3317. [Google Scholar] [CrossRef] - Garand, L.; Heilliette, S.; Buehner, M. Interchannel Error Correlation Associated with AIRS Radiance Observations: Inference and Impact in Data Assimilation. J. Appl. Meteorol. Clim.
**2007**, 46, 714–725. [Google Scholar] [CrossRef] - Bormann, N.; Collard, A.; Bauer, P. Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS and IASI data. Q. J. R. Meteorol. Soc.
**2010**, 136, 1051–1063. [Google Scholar] [CrossRef] - Miyoshi, T.; Kalnay, E.; Li, H. Estimating and including observation-error correlations in data assimilation. Inverse Probl. Sci. Eng.
**2012**, 21, 387–398. [Google Scholar] [CrossRef] - Weston, P.P.; Bell, W.; Eyre, J.R. Accounting for correlated error in the assimilation of high-resolution sounder data. Q. J. R. Meteorol. Soc.
**2014**, 140, 2420–2429. [Google Scholar] [CrossRef] - Coopmann, O.; Guidard, V.; Fourrié, N.; Josse, B.; Marécal, V. Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP). Atmos. Meas. Tech.
**2020**, 13, 2659–2680. [Google Scholar] [CrossRef] - Bernard, F.; Pasternak, F.; Davancens, R.; Baldit, E.; Luitot, C.; Penquer, A.; Calvel, B.; Buil, C. Overview of IASI-NG the new generation of infrared atmospheric sounder. In Proceedings of the International Conference on Space Optics—ICSO 2014, Kobe, Japan, 7–9 May 2014. [Google Scholar] [CrossRef] [Green Version]
- Jolliffe, I.T.; Cadima, J. Principal component analysis: A review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.
**2016**, 374, 20150202. [Google Scholar] [CrossRef] [Green Version] - Huang, H.-L.; Antonelli, P. Application of Principal Component Analysis to High-Resolution Infrared Measurement Compression and Retrieval. J. Appl. Meteorol.
**2001**, 40, 365–388. [Google Scholar] [CrossRef] - Antonelli, P.; Revercomb, H.E.; Sromovsky, L.A.; Smith, W.L.; Knuteson, R.O.; Tobin, D.C.; Garcia, R.K.; Howell, H.B.; Huang, H.-L.; Best, F.A. A principal component noise filter for high spectral resolution infrared measurements. J. Geophys. Res. Atmos.
**2004**, 109, D23102. [Google Scholar] [CrossRef] - Goldberg, M.D.; Zhou, L.; Wolf, W.W.; Barnet, C.; Divakarla, M.G. Applications of principal component analysis (PCA) on AIRS data. In Proceedings of the Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, Honolulu, HI, USA, 9–11 November 2004. [Google Scholar] [CrossRef]
- Tobin, D.C.; Antonelli, P.B.; Revercomb, H.E.; Dutcher, S.T.; Turner, D.D.; Taylor, J.K.; Knuteson, R.O.; Vinson, K.H. Hyperspectral data noise characterization using principle component analysis: Application to the atmospheric infrared sounder. J. Appl. Remote Sens.
**2007**, 1, 013515. [Google Scholar] [CrossRef] - Liu, X.; Smith, W.L.; Zhou, D.K.; Larar, A. Principal component-based radiative transfer model for hyperspectral sensors: Theoretical concept. Appl. Opt.
**2006**, 45, 201–209. [Google Scholar] [CrossRef] [PubMed] - Havemann, S.; Thelen, J.; Taylor, J.P.; Keil, A. The Havemann-Taylor Fast Radiative Transfer Code: Exact fast radiative transfer for scattering atmospheres using Principal Components (PCs). In Proceedings of the AIP Conference Proceedings, Noida, India, 15–17 March 2009. [Google Scholar] [CrossRef]
- Matricardi, M. A principal component based version of the RTTOV fast radiative transfer model. Q. J. R. Meteorol. Soc.
**2010**, 136, 1823–1835. [Google Scholar] [CrossRef] - Liu, Q.; Van Delst, P.; Chen, Y.; Groff, D.; Han, Y.; Collard, A.; Weng, F.; Boukabara, S.-A.; Derber, J. Community radiative transfer model for radiance assimilation and applications. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 3700–3703. [Google Scholar] [CrossRef]
- Saunders, R.; Hocking, J.; Turner, E.; Rayer, P.; Rundle, D.; Brunel, P.; Vidot, J.; Roquet, P.; Matricardi, M.; Geer, A.; et al. An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev.
**2018**, 11, 2717–2737. [Google Scholar] [CrossRef] [Green Version] - Matricardi, M.; McNally, A.P. The direct assimilation of principal components of IASI spectra in the ECMWF 4D-Var. Q. J. R. Meteorol. Soc.
**2013**, 140, 573–582. [Google Scholar] [CrossRef] - Collard, A.D.; McNally, A.P.; Hilton, F.I.; Healy, S.B.; Atkinson, N.C. The use of principal component analysis for the assimilation of high-resolution infrared sounder observations for numerical weather prediction. Q. J. R. Meteorol. Soc.
**2010**, 136, 2038–2050. [Google Scholar] [CrossRef] - Lu, Y.; Zhang, F. Toward Ensemble Assimilation of Hyperspectral Satellite Observations with Data Compression and Dimension Reduction Using Principal Component Analysis. Mon. Weather Rev.
**2019**, 147, 3505–3518. [Google Scholar] [CrossRef] - Zhang, Q. Impacts on Initial Condition Modification from Hyperspectral Infrared Sounding Data Assimilation: Comparisons between Full-Spectrum and Channel-Selection Scheme Based on Two-Month Experiments Using CrIS and IASI Observation. Int. J. Geosci.
**2021**, 12, 763–783. [Google Scholar] [CrossRef] - Wang, X. Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework. Mon. Weather Rev.
**2010**, 138, 2990–2995. [Google Scholar] [CrossRef] [Green Version] - Whitaker, J.S.; Hamill, T.M.; Wei, X.; Song, Y.; Toth, Z. Ensemble Data Assimilation with the NCEP Global Forecast System. Mon. Weather Rev.
**2008**, 136, 463–482. [Google Scholar] [CrossRef] [Green Version] - Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D.M.; Barré, J. Generalized background error covariance matrix model (GEN_BE v2.0). Geosci. Model Dev.
**2015**, 8, 669–696. [Google Scholar] [CrossRef] [Green Version] - Benjamin, S.G.; Weygandt, S.S.; Brown, J.M.; Hu, M.; Alexander, C.R.; Smirnova, T.G.; Olson, J.B.; James, E.; Dowell, D.C.; Grell, G.A.; et al. A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh. Mon. Weather Rev.
**2016**, 144, 1669–1694. [Google Scholar] [CrossRef] - Hollingsworth, A.; Lönnberg, P. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus A Dyn. Meteorol. Oceanogr.
**1986**, 38A, 111–136. [Google Scholar] [CrossRef] - Lönnberg, P.; Hollingsworth, A. The statistical structure of short-range forecast errors as determined from radiosonde data Part II: The covariance of height and wind errors. Tellus A Dyn. Meteorol. Oceanogr.
**1986**, 38A, 137–161. [Google Scholar] [CrossRef] - Skamarock, W.C.; Klemp, J.B. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys.
**2008**, 227, 3465–3485. [Google Scholar] [CrossRef] - Lin, Y.; Mitchell, K.E. The NCEP stage II/IV hourly precipitation analyses: Development and applications. In Proceedings of the 19th Conference on Hydrology, San Diego, CA, USA, 9 January 2005. [Google Scholar]
- Thompson, R.L.; Smith, B.T.; Grams, J.S.; Dean, A.R.; Broyles, C. Convective Modes for Significant Severe Thunderstorms in the Contiguous United States. Part II: Supercell and QLCS Tornado Environments. Weather Forecast.
**2012**, 27, 1136–1154. [Google Scholar] [CrossRef] [Green Version] - Woodcock, F. The Evaluation of Yes/No Forecasts for Scientific and Administrative Purposes. Mon. Weather Rev.
**1976**, 104, 1209–1214. [Google Scholar] [CrossRef] - Gupta, H.V.; Kling, H.; Yilmaz, K.K.; Martinez, G.F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol.
**2009**, 377, 80–91. [Google Scholar] [CrossRef] [Green Version] - UFS Development Team Unified Forecast System (UFS) Short-Range Weather (SRW) Application, version 1.0.0; Zenodo: Genève, Switzerland, 2021. [CrossRef]
- Feng, J.; Wang, X. Impact of Increasing Horizontal and Vertical Resolution during the HWRF Hybrid EnVar Data Assimilation on the Analysis and Prediction of Hurricane Patricia (2015). Mon. Weather Rev.
**2021**, 149, 419–441. [Google Scholar] [CrossRef] - Lu, X.; Wang, X. Improving Hurricane Analyses and Predictions with TCI, IFEX Field Campaign Observations, and CIMSS AMVs Using the Advanced Hybrid Data Assimilation System for HWRF. Part II: Observation Impacts on the Analysis and Prediction of Patricia (2015). Mon. Weather Rev.
**2020**, 148, 1407–1430. [Google Scholar] [CrossRef] - Yin, R.; Han, W.; Gao, Z.; Li, J. Impact of High Temporal Resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case With GRAPES Global 4D-Var Assimilation System. Geophys. Res. Lett.
**2021**, 48, e2021GL093672. [Google Scholar] [CrossRef] - Feng, J.; Qin, X.; Wu, C.; Zhang, P.; Yang, L.; Shen, X.; Han, W.; Liu, Y. Improving typhoon predictions by assimilating the retrieval of atmospheric temperature profiles from the FengYun-4A’s Geostationary Interferometric Infrared Sounder (GIIRS). Atmospheric Res.
**2022**, 280, 106391. [Google Scholar] [CrossRef]

**Figure 1.**Observational error covariance in PC space (

**a**,

**c**) and channel space (

**b**,

**d**) for CrIS (

**a**,

**b**) and IASI (

**c**,

**d**).

**Figure 4.**Temperature (

**a**) and specific humidity (

**b**) RMSE departure of PC-score assimilation (red line) and selected-channel radiance assimilation (green line).

**Figure 5.**CSI departure for precipitation within 2.5 and 7.5 mm/h (

**a**–

**c**) and above 7.5 mm/h (

**d**–

**f**) from PC-score assimilation (red line) and selected-channel radiance assimilation (green line).

**Figure 6.**PC-score assimilation (red line) and selected-channel radiance assimilation (green line) STP departure for the 2019Mar03 (

**a**), 2020Mar03 (

**b**), and 2020Apr12 (

**c**) cases.

**Figure 7.**The temperature (

**a**,

**e**), specific humidity (

**b**,

**f**), u-component wind (

**c**,

**g**), and v-component (

**d**,

**h**) wind RMSE profiles from the PC-score assimilation experiment and CTL analysis (

**a**–

**d**) and 12 h lead-time forecast (

**e**–

**h**).

**Figure 8.**The ACC profiles of temperature (

**a**), specific humidity (

**b**), u-component wind (

**c**), and v-component wind (

**d**) from the PC-score assimilation system (red) and CTL (black).

**Figure 9.**Hanssen and Kuipers discriminant (

**a**), multi-category Hanssen and Kuipers discriminant (

**b**), and Kling–Gupta efficiency (

**c**) time series from the PC-score assimilation system (red) and CTL (black).

**Figure 10.**Time series of the fixed-layer significant tornado parameter (

**a**) and its POD (

**b**) derived from the PC-score assimilation system forecast result (red) and CTL (black).

**Figure 11.**The contribution of each variable in the calculation of the significant tornado parameter, with the red (black) line representing the PC-score experiment forecast result (CTL).

**Figure 12.**Time series of the EHI (

**a**) and its POD (

**b**) derived from the PC-score assimilation experiment forecast result (red) and CTL (black).

**Figure 13.**Comparison between the HRRR and pseudo-operational forecasts initialized at 00:00 UTC 7 September (

**a**,

**b**) and 00:00 UTC 7 September (

**c**,

**d**). The black and gray marks (with different shapes representing the outbreak time) are the hail outbreak locations.

**Figure 14.**Comparison between the HRRR and pseudo-operational forecasts initialized at 12:00 UTC 15 December (

**a**,

**b**) and 00:00 UTC 16 December 00:00 UTC (

**c**,

**d**). The black and gray marks (with the different shapes representing the outbreak time) are the hail outbreak locations.

Model Settings | |
---|---|

Version | ARW 4.3, non-hydrostatic |

Map projection | Lambert |

Grid points | 400 × 257 |

Vertical Layers | 51 |

Model top | 50 |

Lateral boundary conditions | RAP |

Horizontal/Vertical Advection | Fifth-order upwind |

Time step | Adjusted time step, maximum 45 s. |

Damping option | Rayleigh, dampcoef = 0.2${\mathrm{s}}^{-1}$, zdamp = 5000m |

Horizontal diffusion | Sixth-order (0.12) |

Forecast lead time | 18 h |

Radiation scheme | RRTMG |

Land surface scheme | RUC |

Land use category | MODIS 24 category |

Planetary-boudary and surface layer scheme | MYNN |

Shallow convection scheme | Grell-Freitas |

Deep convection scheme | Grell-Freitas |

Cloud Microphysics scheme | Thompson aerosol aware |

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## Share and Cite

**MDPI and ACS Style**

Zhang, Q.; Shao, M.
Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction. *Atmosphere* **2023**, *14*, 580.
https://doi.org/10.3390/atmos14030580

**AMA Style**

Zhang Q, Shao M.
Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction. *Atmosphere*. 2023; 14(3):580.
https://doi.org/10.3390/atmos14030580

**Chicago/Turabian Style**

Zhang, Qi, and Min Shao.
2023. "Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction" *Atmosphere* 14, no. 3: 580.
https://doi.org/10.3390/atmos14030580