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IASI - Flux and temperature

Periodic Reporting for period 2 - IASI-FT (IASI - Flux and temperature)

Reporting period: 2019-04-01 to 2020-09-30

Although the role of satellites in observing the variability and change of the Earth system has increased in recent decades, remotely-sensed observations are still underexploited to accurately assess climate change fingerprints. The IASI - Flux and Temperature (IASI-FT) project aims at providing new benchmarks for top-of-atmosphere radiative flux and temperature observations using the calibrated radiances measured twice a day at any location by the IASI instrument on the suite of MetOp satellites. In this project we have develop innovative algorithms and statistical tools to generate climate data records at the global scale, of

(1) spectrally resolved outgoing radiances,

(2) land and sea skin surface temperatures, and

(3) temperatures at selected altitudes.

It is important for society to have a stand-alone mission, able to perform as well as the current local measurements (ground-based network) that can provide independent information to monitor the climate changes.
Task 1 – Atmosphere outgoing radiation derived from IASI data
Our understanding of the Earth’s climate system and our ability to model future climate changes relies on accurate measurements of the Earth’s Outgoing Longwave Radiation (OLR) (W m-2) at the top of the atmosphere , i.e. the radiation emitted by the Earth-atmosphere system and leaving to space. While OLR has been monitored by dedicated broadband instruments a better constraint can be obtained from spectrally resolved OLR derived from hyperspectral infrared sounders by giving access to the spectral signature of particular climate feedbacks and processes.

A first achievement was the development of a dedicated algorithm for the estimation of the spectrally resolved OLR from radiance measurements made by the IASI sounder on board Metop satellites. The spectral OLR is derived at the spectral sampling of the instrument (0.25 cm-1) in the range 645-2300 cm-1. It relies on the use of pre-calculated empirical angular distribution models that directly link the directional radiance measurement to the corresponding OLR.

Short-term trends in the spectral OLR
We next started to use the IASI data to analyze short-term trends in the spectral OLR. A first analysis reveals immediately: 1) a positive trend in the window region band (except at high latitude) due to the increasing surface temperature and 2) a negative trend in the CO2 band forced by the increases in CO2 concentrations (stratospheric cooling). The outcomes should be of great value for modelers to help them in better constraining their climate models.
The second study, on the interannual variations in the spectral OLR, investigates the correlation between the OLR and well-known climate factors. Some meaningful questions that will be investigated include: 1) what are the climate phenomena that influence the most the interannual variability of OLR in different spectral bands? 2) Are these phenomena independent or intercorrelated?

Task 2 – Surface temperature derived from IASI data
Sea surface temperature (SST) is an essential variable for monitoring climate. Satellite data are able to provide systematic global temperature data, at least in cloud-free areas, from pole to pole on a regular basis. Emissivity of the surface, which is a function of surface type, viewing angle and wavelength, and temperature, is a primary variable affecting the upwelling radiance. Over the sea, the emissivity is close to one. Above land, accounting for variations in surface emissivity correctly will be essential. For this task, we will carefully select radiative channels in the atmospheric windows, where the atmosphere is relatively transparent for cloud-free scenes. This dataset is used along with appropriate statistical tools to derive accurate skin temperatures locally across the whole globe.

Method to derive sea surface temperature (SST)
In this WP the goal is to create a new SST dataset derived from reprocessed (stable) IASI measurements, that could be used in climate studies, to determine trends and variability (e.g.: ENSO). The IASI SST was computed using Planck’s Law and simple atmospheric corrections, and compared to ECMWF’s ERA5, Hadley Center’s HadISST and NOAA’s OISSTv2. This comparison showed that our dataset produces similar means, variability and trends as other datasets. Features like the strong 2016 El Niño event, the warming global trend, the cooling trend in the North Atlantic and the warming trend over the Mediterranean are captured well by the IASI dataset.

Method to derive temperature over land and sea
The method is two-step. First a rigorous analysis based on information theory and entropy reduction technique has been performed on the IASI spectral channels to determine those with the highest information content relevant to skin temperature retrieval. Second, these selected channels were used as input in an artificial neural network training. The training is performed with clear-sky IASI radiances with ECMWF latest reanalysis (ERA5) skin temperature product as target. Once the network is built, it was applied to the IASI record.
The Tskin product was then validated with different widely used data sets, such as ERA5, EUMETSAT, and SEVIRI satellite product, and with ground based measurements from two different set of in-situ station data. The results show the potential of ANN in mapping radiances globally and locally to skin temperature.

Task 3 –Assessment of the stability of the radiance record
Since 2007, the processing of IASI data done by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT) has improved, but due to IASI’s huge data flow, the whole dataset has not yet been reprocessed backwards. In 2019, EUMETSAT reprocessed IASI radiances with the latest version of the processing algorithm. We compared IASI operational radiances with the reprocessed ones to assess their homogeneity. In brightness temperatures, the differences between the two datasets range from 0.02 K at 700 cm-1 to 0.1 K at 2200 cm-1.

For temperatures, we compared IASI-A and B with ERA5 reanalysis temperatures. We found differences of ~5-10 K at the surface and between 1 and 5 K in the atmosphere (Figure 3.2). These differences decrease abruptly after the release of the IASI L2 processor version 6 in 2014.

Task 4 (new) - Analysis of the impact of the Covid-19 lockdown on air pollution (new WP)
Now that the methodology is well established, we are building atlases of OLR and temperature, for the 15 years of IASI observations.
After 2.5 years, we have already address several goals : demonstration of the feasibility and we developed dedicated retrieval algorithm and tools.
Innovative methods based on neural networks were set-up.
Figure 2.4. Validation of the Tskin ANN product (TANN) from the neural net training of IASI radiance
Figure 2.3. Linear annual trends in SST for the 2008-2019 period.
Figure 3.2. Differences between IASI-A and ERA5 (in dark colors), and IASI-B and ERA5 (in lighter co
Figure 2.1 Time series of the multivariate ENSO index (top) and the global mean SST over the latitud
Figure 2.2. Spatial structure of La Niña (left) and El Niño (right), as seen in the SST anomalies fo
Figure 1.2. Top : Example of IASI spectrum (in Brightness Temperature [K]), Second: Spectral trends
Figure 1.1. Conceptual flowchart of the OLR retrieval algorithm.
Figure 3.1. Evolution of the differences Rreproc – Roper as a function of the Field of Regard (FoR)