Periodic Reporting for period 4 - IASI-FT (IASI - Flux and temperature)
Período documentado: 2022-04-01 hasta 2024-03-31
Although the role of satellites in observing Earth's variability and climate change has grown significantly in recent decades, remotely-sensed data remain underutilized for accurately assessing climate change indicators. The IASI - Flux and Temperature (IASI-FT) project addressed this by providing new benchmarks for top-of-atmosphere radiative flux and temperature observations, using calibrated radiances measured twice daily at any location by the IASI instrument on the Metop satellite series. We have developed innovative algorithms and statistical tools to generate global climate data records for spectrally resolved outgoing radiances (OLR), land and sea surface temperatures, and temperatures at selected altitudes. It is crucial for society to have a stand-alone mission that matches the precision of current ground-based measurements, offering independent data to effectively monitor climate changes.
Task 1 – Atmosphere Outgoing Radiation from IASI Data
1.1 Spectrally Resolved OLR
Accurate measurements of Earth’s Outgoing Longwave Radiation (OLR) are critical for climate modeling. Traditional broadband instruments limit the ability to assess individual climate drivers. Spectrally resolved OLR (W m⁻² cm⁻¹) offers a more precise view, derived from hyperspectral infrared sounders, capturing the spectral signatures of climate feedbacks.
1.2 Method
A dedicated algorithm estimates spectrally resolved OLR from IASI radiances on Metop satellites. Derived at high spectral resolution, the OLR spans 645-2300 cm⁻¹. The method uses pre-calculated empirical angular distribution models (ADMs) developed from synthetic spectra, processed over ten years globally on a 2° x 2° grid.
1.3 Short-Term Trends in Spectral OLR
Analysis of IASI data (2008-2017) shows positive trends in the window region band, likely due to surface warming, and negative trends in the CO2 band due to rising CO2 levels and stratospheric cooling. IASI channel layer contributions reveal influences of climate drivers like CO2, CH4, and H2O.
1.4 Interannual Variations in Spectral OLR
Empirical Orthogonal Function (EOF) analysis identifies climate drivers influencing interannual OLR variability. The first mode correlates strongly with ENSO, PDO, and EMI.
1.5 A New IASI Cloud Mask for Climate Applications
A new cloud detection algorithm, using a supervised neural network and IASI radiance data, improves cloud-free scene identification for climate studies. The algorithm, now used in IASI retrieval frameworks, ensures better consistency across the IASI time series.
Task 2 – Surface Temperature from IASI Data
2.1 IASI-Derived Surface Temperature Dataset
Sea surface temperature (SST) is essential for climate monitoring. The dataset uses channels in atmospheric windows where the atmosphere is transparent in cloud-free scenes, allowing accurate global skin temperature measurements.
2.2 Method to Derive SST
A new SST dataset, derived from IASI data, captures climate trends such as the 2016 El Niño event. This SST dataset, created using a single algorithm from a well-calibrated instrument, ensures consistent data over time.
2.3 Land and Sea Temperature Retrieval
IASI radiances were used to retrieve skin temperature through an artificial neural network (ANN). Validated against ERA5 and SEVIRI datasets, the IASI Tskin product demonstrates high accuracy for climate trend analysis.
2.4 Applications
Tskin over the Arabian Peninsula was analyzed, revealing cooler temperatures over agricultural areas and urban heat island effects.
A proof of concept using IASI data for cyclone detection with a machine learning model (YOLOv3) showed promising results for tropical cyclone identification.
Task 3 – Atmospheric Temperatures from IASI Data
3.1 Radiance Record Stability
EUMETSAT reprocessed IASI radiances in 2019, ensuring homogeneity in brightness temperature data. Differences between reprocessed and operational radiances are up to 0.1 K, influenced by processing updates.
3.2 Temperature Record Stability
Comparing IASI data with ERA5 reanalysis reveals differences of 5-10 K at the surface, which decrease after the IASI L2 processor update in 2014.
3.3 New Temperature Record
An ANN was developed to retrieve temperatures from IASI radiances, validated against ERA5. Differences of up to 1 K were observed at altitudes where IASI is most sensitive.
3.4 Applications
Troposphere warming and stratosphere cooling trends were observed over 2008-2020, particularly at mid-latitudes and the poles.
Sudden Stratospheric Warming (SSW) events were analyzed, revealing temperature anomalies and their effects on weather patterns and ozone concentrations.
1.1 Spectrally Resolved OLR
Accurate measurements of Earth’s Outgoing Longwave Radiation (OLR) are critical for climate modeling. Traditional broadband instruments limit the ability to assess individual climate drivers. Spectrally resolved OLR (W m⁻² cm⁻¹) offers a more precise view, derived from hyperspectral infrared sounders, capturing the spectral signatures of climate feedbacks.
1.2 Method
A dedicated algorithm estimates spectrally resolved OLR from IASI radiances on Metop satellites. Derived at high spectral resolution, the OLR spans 645-2300 cm⁻¹. The method uses pre-calculated empirical angular distribution models (ADMs) developed from synthetic spectra, processed over ten years globally on a 2° x 2° grid.
1.3 Short-Term Trends in Spectral OLR
Analysis of IASI data (2008-2017) shows positive trends in the window region band, likely due to surface warming, and negative trends in the CO2 band due to rising CO2 levels and stratospheric cooling. IASI channel layer contributions reveal influences of climate drivers like CO2, CH4, and H2O.
1.4 Interannual Variations in Spectral OLR
Empirical Orthogonal Function (EOF) analysis identifies climate drivers influencing interannual OLR variability. The first mode correlates strongly with ENSO, PDO, and EMI.
1.5 A New IASI Cloud Mask for Climate Applications
A new cloud detection algorithm, using a supervised neural network and IASI radiance data, improves cloud-free scene identification for climate studies. The algorithm, now used in IASI retrieval frameworks, ensures better consistency across the IASI time series.
Task 2 – Surface Temperature from IASI Data
2.1 IASI-Derived Surface Temperature Dataset
Sea surface temperature (SST) is essential for climate monitoring. The dataset uses channels in atmospheric windows where the atmosphere is transparent in cloud-free scenes, allowing accurate global skin temperature measurements.
2.2 Method to Derive SST
A new SST dataset, derived from IASI data, captures climate trends such as the 2016 El Niño event. This SST dataset, created using a single algorithm from a well-calibrated instrument, ensures consistent data over time.
2.3 Land and Sea Temperature Retrieval
IASI radiances were used to retrieve skin temperature through an artificial neural network (ANN). Validated against ERA5 and SEVIRI datasets, the IASI Tskin product demonstrates high accuracy for climate trend analysis.
2.4 Applications
Tskin over the Arabian Peninsula was analyzed, revealing cooler temperatures over agricultural areas and urban heat island effects.
A proof of concept using IASI data for cyclone detection with a machine learning model (YOLOv3) showed promising results for tropical cyclone identification.
Task 3 – Atmospheric Temperatures from IASI Data
3.1 Radiance Record Stability
EUMETSAT reprocessed IASI radiances in 2019, ensuring homogeneity in brightness temperature data. Differences between reprocessed and operational radiances are up to 0.1 K, influenced by processing updates.
3.2 Temperature Record Stability
Comparing IASI data with ERA5 reanalysis reveals differences of 5-10 K at the surface, which decrease after the IASI L2 processor update in 2014.
3.3 New Temperature Record
An ANN was developed to retrieve temperatures from IASI radiances, validated against ERA5. Differences of up to 1 K were observed at altitudes where IASI is most sensitive.
3.4 Applications
Troposphere warming and stratosphere cooling trends were observed over 2008-2020, particularly at mid-latitudes and the poles.
Sudden Stratospheric Warming (SSW) events were analyzed, revealing temperature anomalies and their effects on weather patterns and ozone concentrations.
The IASI-FT project provided innovative tools and data for understanding and monitoring climate change, significantly advanced climate science, and supported the development of new datasets.
Innovative Approach to Climate Monitoring
Spectrally Resolved Observations: IASI-FT pushed the boundaries of how we observe and analyze the Earth's outgoing longwave radiation (OLR). Traditional methods provided broad, spectrally integrated data, but IASI-FT used spectrally resolved OLR, which allowed for a more detailed understanding of specific climate feedbacks and processes. This innovation made it possible to isolate and study the effects of individual greenhouse gases and other climate drivers with unprecedented precision.
Development of New Climate Data Benchmarks
High-Resolution Temperature Records: The project developed high-resolution temperature records by utilizing advanced algorithms and artificial neural networks. These records are crucial for understanding temperature trends across different atmospheric layers, providing more accurate data than previously available. This new data has improved the accuracy of climate models and enhanced our ability to detect and analyze climate change fingerprints.
Impact on Climate Science
Application to Real-World Climate Issues
Detection of Climate Trends: The project was instrumental in detecting and analyzing climate trends, such as the warming of the troposphere and the cooling of the stratosphere. These findings are vital for understanding the long-term impacts of climate change and for informing policy decisions.
Support for Environmental Policy: The high-quality data generated by IASI-FT can be used to support environmental policy by providing robust evidence of climate change and its effects. This is crucial for developing effective strategies to mitigate and adapt to the changing climate.
Contribution to Future Research
The methodologies and data sets developed during the IASI-FT project serve as a foundation for future research. The project's success has paved the way for new studies that build on its findings, ensuring that its impact will be felt for years to come.
Innovative Approach to Climate Monitoring
Spectrally Resolved Observations: IASI-FT pushed the boundaries of how we observe and analyze the Earth's outgoing longwave radiation (OLR). Traditional methods provided broad, spectrally integrated data, but IASI-FT used spectrally resolved OLR, which allowed for a more detailed understanding of specific climate feedbacks and processes. This innovation made it possible to isolate and study the effects of individual greenhouse gases and other climate drivers with unprecedented precision.
Development of New Climate Data Benchmarks
High-Resolution Temperature Records: The project developed high-resolution temperature records by utilizing advanced algorithms and artificial neural networks. These records are crucial for understanding temperature trends across different atmospheric layers, providing more accurate data than previously available. This new data has improved the accuracy of climate models and enhanced our ability to detect and analyze climate change fingerprints.
Impact on Climate Science
Application to Real-World Climate Issues
Detection of Climate Trends: The project was instrumental in detecting and analyzing climate trends, such as the warming of the troposphere and the cooling of the stratosphere. These findings are vital for understanding the long-term impacts of climate change and for informing policy decisions.
Support for Environmental Policy: The high-quality data generated by IASI-FT can be used to support environmental policy by providing robust evidence of climate change and its effects. This is crucial for developing effective strategies to mitigate and adapt to the changing climate.
Contribution to Future Research
The methodologies and data sets developed during the IASI-FT project serve as a foundation for future research. The project's success has paved the way for new studies that build on its findings, ensuring that its impact will be felt for years to come.