# 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 |

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**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