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

Qi Zhang

doi:10.4236/ijg.2021.129043

Abstract

This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS and IASI, on improving the accuracy of initial condition in numerical weather prediction. To accomplish this, we develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency. This project’s primary goal is to demonstrate that assimilation of infrared observation in a full-channel mode could improve the accuracy of initial condition compared to selected-channel assimilation. Results show that full-channel assimilation performs better than selected-channel assimilation in modifying low and middle troposphere (1000 - 700 hPa, 700 - 400 hPa) temperature and water vapor field, while marginal improvements from temperature and water vapor field could be found over upper troposphere (400 - 100 hPa). This research also proves the feasibility of an alternative path to data assimilation for the full usage of hyperspectral infrared sounding observation in numerical weather prediction.

Highlights

Weather forecasting centers have been assimilating atmospheric infrared remote sensing observations into their Numerical Weather Prediction (NWP) system since the High-Resolution Infrared Sounder (HIRS) went into operation [1]
We develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency
In the Cross-track Infrared Sounder (CrIS) data coverage area, most conventional observations locate in Europe, South Africa, Central Pacific, and Alaska

Summary

Introduction

Weather forecasting centers have been assimilating atmospheric infrared remote sensing observations into their Numerical Weather Prediction (NWP) system since the High-Resolution Infrared Sounder (HIRS) went into operation [1]. The assimilation scheme of hyperspectral infrared data used by major numerical centers is still focusing on taking advantage of a subset of channel observations among the entire spectrum, known as selected-channel [19] [20] [21] which inevitably renders the information content from full-spectrum observation. Even though assimilation of a subset channel observation can maintain the efficiency of NWP system as high as possible while minimizing the loss of effective observational information, it is undeniable that most of the operational NWP systems cannot take full advantage of the complete information gathered by hyperspectral infrared sensors, which virtually hinders the accuracy advancement in weather prediction. Recent studies [22] [23] have shown that the accuracy of initial condition can be further improved by adding new channel observations to the operational channel selection scheme, which provides proof for the hypothesis that full-channel assimilation of hyperspectral infrared observation is more helpful in improving initial condition’s accuracy than selected-channel scheme

Methods

Results

Conclusion