Search Results (15)

Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH₄ product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m⁻² (0.12% relative) and the mean RMSE is 39.24 mol m⁻² (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing. Full article

The Arctic is warming rapidly, with atmospheric rivers (ARs) amplifying ice melt, extreme precipitation, and abrupt temperature shifts. Detecting ARs in the Arctic remains challenging, because AR detection algorithms designed for mid-latitudes perform poorly in polar regions. This study introduces a regional deep learning (DL) image segmentation model for Arctic AR detection, leveraging large-ensemble (LE) climate simulations. We analyse historical simulations from the Climate Change in the Arctic and North Atlantic Region and Impacts on the UK (CANARI) project, which provides a large, internally consistent sample of AR events at 6-hourly resolution and enables a close comparison of AR climatology across model and reanalysis data. A polar-specific, rule-based AR detection algorithm was adapted to label ARs in simulated data using multiple thresholds, providing training data for the segmentation model and supporting sensitivity analyses. U-Net-based models are trained on integrated water vapour transport, total column water vapour, and 850 hPa wind speed fields. We quantify how AR identification depends on threshold choices in the rule-based algorithm and show how these propagate to the U-Net-based models. This study represents the first use of the CANARI-LE for Arctic AR detection and introduces a unified framework combining rule-based and DL methods to evaluate model sensitivity and detection robustness. Our results demonstrate that DL segmentation achieves robust skill and eliminates the need for threshold tuning, providing a consistent and transferable framework for detecting Arctic ARs. This unified approach advances high-latitude moisture transport assessment and supports improved evaluation of Arctic extremes under climate change. Full article

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models. Full article

In the past, the possibility to retrieve the total column of water vapour (TCWV) from the thermal infrared (TIR) day and night measurements above water surfaces of the dual-view Along Track Scanning Radiometers (ATSR) has been demonstrated, and an algorithm, named Advanced InfrarRed Water Vapour Estimator (AIRWAVE), was developed and successfully applied to the measurements of the (A)ATSR instrument series. A similar instrument, the Sea and Land Surface Temperature Radiometer (SLSTR), is currently operating on board the Sentinel 3 satellite series. In this paper, we demonstrate that the AIRWAVE algorithm can be successfully applied to the SLSTR instrument to obtain reliable TCWV measurements. The steps performed for upgrading the algorithm are thoroughly described. The new AIRWAVE algorithm makes use of parameters computed offline with a state-of-the-art radiative transfer model using the most recent spectroscopic data and continuum model. For the parameters calculation, a new climatology capable of representing the average atmospheric and sea surface status during SLSTR measurements has been developed. The new algorithm, named AIRWAVE-SLSTR, has been implemented in both IDL and Python languages. In the frame of an EUMETSAT contract, AIRWAVE-SLSTR has been applied to a full year of SLSTR measurements (2021) and the retrieved TCWV have been validated with the help of both satellite- and ground-based measurements. The correlation of the retrieved TCWV with satellite MW measurements is 0.94 and the average bias is of the order of 0.66 kg/m${}^2$. When compared to ground-based measurements, the average correlation is 0.93 and the bias −0.48 kg/m${}^2$. The obtained accuracy is well within the requirements set for both numerical weather predictions (1–5 kg/m${}^2$) and for coastal altimetry applications (1.8–3 kg/m${}^2$). Therefore, the AIRWAVE-SLSTR algorithm can be safely applied to obtain a long time series of reliable TCWV above water surfaces. Full article

A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement. Full article

A novel measurement concept specifically tuned to monitoring tropospheric water vapour’s vertical distribution has been demonstrated on a theoretical basis and is currently under development for space deployment. The NDSA (Normalised Differential Spectral Attenuation) technique derives the integrated water vapour (IWV) along the radio link between a transmitter and a receiver carried by two LEO satellites, using the linear correlation between the IWV and a parameter called spectral sensitivity. This is the normalised incremental ratio of the spectral attenuation at two frequencies in the Ku and K bands, with the slope of the water vapour absorption line at 22.235 GHz. Vertical profiles of WV can be retrieved by inverting a set of IWV measurements acquired in limb geometry at different tangent altitudes. This paper provides a comprehensive insight into the NDSA approach for sounding lower tropospheric WV, from the theoretical investigations in previous ESA studies, to the first experimental developments and testing, and to the latest advancements achieved with the SATCROSS project of the Italian Space Agency. The focus is on the new results from SATCROSS activities; primarily, on the upgrading of the instrument prototype, with improved performance in terms of its power stability and the time resolution of the measurements. Special emphasis is also placed on discussing tomographic inversion methods capable of retrieving tropospheric WV content from IWV measurements, i.e., the least squares and the external reconstruction approaches, showing results with different spatial features when applied to a given atmospheric scenario. The ultimate goal of deploying the NDSA measurement technique from space is thoroughly examined and conclusions are drawn after presenting the results of an Observing System Simulation Experiment conducted to assess the impact of NDSA data assimilation on environmental model simulations. Full article

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m${}^2$, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m${}^2$ and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations. Full article

A new retrieval of total column water vapour (TCWV) from daytime measurements over land of the Ocean and Land Colour Instrument (OLCI) on-board the Copernicus Sentinel-3 missions is presented. The Copernicus Sentinel-3 OLCI Water Vapour product (COWa) retrieval algorithm is based on the differential absorption technique, relating TCWV to the radiance ratio of non-absorbing band and nearby water vapour absorbing band and was previously also successfully applied to other passive imagers Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS). One of the main advantages of the OLCI instrument regarding improved TCWV retrievals lies in the use of more than one absorbing band. Furthermore, the COWa retrieval algorithm is based on the full Optimal Estimation (OE) method, providing pixel-based uncertainty estimates, and transferable to other Near-Infrared (NIR) based TCWV observations. Three independent global TCWV data sets, i.e., Aerosol Robotic Network (AERONET), Atmospheric Radiation Measurement (ARM) and U.S. SuomiNet, and a German Global Navigation Satellite System (GNSS) TCWV data set, all obtained from ground-based observations, serve as reference data sets for the validation. Comparisons show an overall good agreement, with absolute biases between 0.07 and 1.31 kg/m² and root mean square errors (RMSE) between 1.35 and 3.26 kg/m². This is a clear improvement in comparison to the operational OLCI TCWV Level 2 product, for which the bias and RMSEs range between 1.10 and 2.55 kg/m² and 2.08 and 3.70 kg/m², respectively. A first evaluation of pixel-based uncertainties indicates good estimated uncertainties for lower retrieval errors, while the uncertainties seem to be overestimated for higher retrieval errors. Full article

Integrated water vapour (IWV) is the total amount of precipitable water in an atmospheric column between the Earth’s surface and space. The implication of its variability and trend on the Earth’s radiation budget and precipitation makes its monitoring on a regular basis important. ERA-Interim reanalysis (ERA) and radiosonde (RS) data from 1988 to 2018 were used to investigate variability and trend in IWV over Peninsular Malaysia. ERA performed excellently when gauged with RS. Trend analysis was performed using the non-parametric Mann–Kendall and Theil–Sen slope estimator tests. ERA and RS IWV revealed double fluctuations at the seasonal time scale, with maxima in May and November, which are the respective beginnings of the southwest monsoon (SWM) and northeast monsoon (NEM) seasons, as well as coincidental peaks of precipitation in the region. IWV decreased in a southeast–northwest orientation, with regional maximum domiciled over the southeastern tip of the region. Steep orography tended to shape intense horizontal gradients along the edges of the peninsular, with richer gradients manifesting along the western boundary during SWM, which harbours more water vapour in the peninsular. IWV trends, both at the annual and seasonal time series, were positive and statistically significant at the 95% level across the stations, except at Kota Bharu, where a nonsignificant downward trend manifested. Trends were mostly higher in the NEM, with the greatest rate being 0.20 ± 0.42 kgm⁻² found at Penang. Overall, the IWV trend in Peninsular Malaysia was positive and consistent with the upward global changes in IWV reported elsewhere. Full article

A multi-parametric approach was applied to climatological data before the Ms 8.0 2008 Wenchuan and Ms 7.0 2013 Lushan earthquakes (EQs) in order to detect anomalous changes associated to the preparing phase of those large seismic events. A climatological analysis for seismic Precursor Identification (CAPRI) algorithm was used for the detection of anomalies in the time series of four parameters (aerosol optical depth, AOD; skin temperature, SKT; surface latent heat flux, SLHF and total column water vapour, TCWV). Our results show a chain of processes occurred within two months before the EQs: AOD anomalous response is the earliest, followed by SKT, TCWV and SLHF in the EQs. A close spatial relation between the seismogenic Longmenshan fault (LMSF) zone and the extent of the detected anomalies indicates that some changes occurred within the faults before the EQs. The similarity of time sequence of the anomalies between the four parameters may be related to the same process: we interpret the observed anomalies as the consequence of the upraising of gases from a fluid-rich middle/upper crust along pre-existing seismogenic faults, and of their release into the atmosphere. Our multi-parametric analytical approach is able to capture phenomena related to the preparation phase of strong EQs. Full article

To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m²/decade to 1.22 ± 0.16 kg/m²/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates. Full article

Split window (SW) methods, which have been successfully used to retrieve measurements of land surface temperature (LST) and sea surface temperature (SST) from MODIS images, were exploited to evaluate the SST data of three sections of Italian coastal waters. For this purpose, sea surface emissivity (SSE) values were estimated by adding the effects of salinity and total suspended particulate matter (SPM) concentrations, sea surface wind speed, and zenith observation angle. The total column atmospheric water vapor contents were retrieved from MODIS data. SST data retrieved from MODIS images using these algorithms were compared with SST_skin measurements evaluated from in situ data. The comparison showed that the algorithms for retrieving LST measurements minimized the error in SST data in near-land coastal waters with respect to the algorithms for retrieving SST measurements: a method for retrieving LST measurements highlighted the smallest root-mean-square deviation (RMSD) value (0.48 K) and values of maximum bias and standard deviation (σ) equal to −3.45 K and 0.41 K; the current operation algorithm for retrieving LST data highlighted the smallest values of maximum bias and σ (−1.37 K and 0.35 K) and an RMSD value of 0.66 K; and the current operation algorithm for retrieving global measurements of SST showed values of RMSD, maximum bias, and σ equal to 0.68 K, −1.90 K, and 0.40 K, respectively. Full article

In order to assess the strength of the water vapour feedback within Arctic climate change, 15 years of the total column-integrated density of water vapour (TCWV) from the moderate resolution imaging spectrometer (MODIS) are analysed. Arctic TCWV distribution, trends, and anomalies for the 2001–2015 period, broken down into seasons and months, are analysed. Enhanced local spring TCWV trends above the terrestrial Arctic regions are discussed in relation to land snow cover and vegetation changes. Upward TCWV trends above the oceanic areas are discussed in lien with sea ice extent and sea surface temperature changes. Increased winter TCWV (up to 40%) south of the Svalbard archipelago are observed; these trends are probably driven by a local warming and sea ice extent decline. Similarly, the Barents/Kara regions underwent wet trends (up to 40%), also associated with winter/fall local sea ice loss. Positive late summer TCWV trends above the western Greenland and Beaufort seas (about 20%) result from enhanced upper ocean warming and thereby a local coastal decline in ice extent. The Mackenzie and Siberia enhanced TCWV trends (about 25%) during spring are found to be associated with coincident decreased snow cover and increased vegetation, as a result of the earlier melt onset. Results show drier summers in the Eurasia and western Alaska regions, thought to be affected by changes in albedo from changing vegetation. Other TCWV anomalies are also presented and discussed in relation to the dramatic decline in sea ice extent and the exceptional rise in sea surface temperature. Full article

Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University of Porto to retrieve improved WTC for radar altimeter missions. The GPD+ are WTC estimated by space-time objective analysis, by combining all available observations in the vicinity of the point: valid measurements from the on-board microwave radiometer (MWR), from GNSS coastal and island stations and from scanning imaging MWR on board various remote sensing missions. The GPD+ corrections are available both for missions which do not possess an on-board microwave radiometer such as CryoSat-2 (CS-2) and for all missions which carry this sensor, by addressing the various error sources inherent to the MWR-derived WTC. To ensure long-term stability of the corrections, the large set of radiometers used in this study have been calibrated with respect to the Special Sensor Microwave Imager (SSM/I) and the SSM/I Sounder (SSM/IS). The application of the algorithm to CS-2 and Geosat Follow-on (GFO), as representative altimetric missions without and with a MWR aboard the respective spacecraft, is described. Results show that, for both missions, the new WTC significantly reduces the sea level anomaly (SLA) variance with respect to the model-based corrections. For GFO, the new WTC also leads to a large reduction in SLA variance with respect to the MWR-derived WTC, recovering a large number of observations in the coastal and polar regions and full sets of tracks and several cycles when MWR measurements are missing or invalid. Overall, the algorithm allows the recovery of a significant number of measurements, ensuring the continuity and consistency of the correction in the open-ocean/coastal transition zone and at high latitudes. Full article

Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the ambit of ESA funded project CP4O, an improved WTC for CryoSat-2 data over ocean is under development, based on a data combination algorithm (DComb) through objective analysis of WTC values derived from all existing global-scale data types. The scope of this study is the analysis and inter-calibration of the large dataset of total column water vapor (TCWV) products from scanning MWR aboard Remote Sensing (RS) missions for use in the WTC computation for CryoSat-2. The main issues regarding the computation of the WTC from all TCWV products are discussed. The analysis of the orbital parameters of CryoSat-2 and all other considered RS missions, their sensor characteristics and inter-calibration is presented, providing an insight into the expected impact of these datasets on the WTC estimation. The most suitable approach for calculating the WTC from TCWV is investigated. For this type of application, after calibration with respect to an appropriate reference, two approaches were found to give very similar results, with root mean square differences of 2 mm. Full article