Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)
-
Published:2020-05-26
Issue:5
Volume:13
Page:2659-2680
-
ISSN:1867-8548
-
Container-title:Atmospheric Measurement Techniques
-
language:en
-
Short-container-title:Atmos. Meas. Tech.
Author:
Coopmann OlivierORCID, Guidard VincentORCID, Fourrié NadiaORCID, Josse Béatrice, Marécal Virginie
Abstract
Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out. The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3 %, humidity by 1.8 % and ozone by 0.9 % compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference47 articles.
1. Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a
limited-area model, Mon. Weather Rev., 128, 644–667, 2000. a 2. Borbas, E. E., Hulley, G., Feltz, M., Knuteson, R., and Hook, S.: The Combined
ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High
Spectral Resolution Application, Remote Sensing, 10, 643, https://doi.org/10.3390/rs10040643, 2018. a 3. Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M., and
McNally, A.: Enhancing the impact of IASI observations through an updated
observation-error covariance matrix, Q. J. Roy.
Meteor. Soc., 142, 1767–1780, 2016. a, b, c, d 4. Boukachaba, N.: Apport des observations satellitaires hyperspectrales
infrarouges IASI au-dessus des continents dans le modèle
météorologique à échelle convective AROME, PhD thesis, INP
Toulouse, available at: http://www.theses.fr/2017INPT0065 (last access: 18 May 2020), 2017. a 5. Chevallier, F., Di Michele, S., and McNally, A. P.: Diverse profile datasets
from the ECMWF 91-level short-range forecasts, European Centre for
Medium-Range Weather Forecasts, 2006. a
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|