The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) version 2: algorithm evolution, dataset description and performance improvements

Castelli, Elisa; Papandrea, E.; Roma, Alessio Di; Dinelli, B. M.; Casadio, Stefano; Bojkov, Bojan

doi:10.5194/amt-12-371-2019

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…The quality of the AIRWAVE V2 TCWV dataset (Castelli et al, 2019) was verified by not only using external correlative measurements but also by adopting internal quality checks (e.g. range of validity of the TCWV values and their spatial variability).…”

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

“…Each cell is included in our analysis only if the fraction of cloud-free TCWV products is larger than 20 % (in order to ensure robust statistics). At latitudes typical of the investigated areas, these choices mean that the random uncertainty (noise) in the mean TCWV is reduced, by averaging within each grid cell, to less than 1 % (in a single pixel, the TCWV error due to noise was estimated to be 4.8 % and 5.2 % for AATSR and ATSR-2, respectively, in the midlatitudes; for more details, see Castelli et al, 2019).…”

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

“…The atmospheric conditions of the reported cases, all characterised by intense and constant wind direction, allowed the AGW propagation over long horizontal distances, trapping the wave vertically. Wind jets in the lee of mountain ranges may indeed gener- ate horizontal waves which may propagate in a wave duct or wave guide horizontally over distances of even several hundreds of kilometres (Cheng and Alpers, 2010).…”

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

“…Lee waves can be identified through the use of satellite instruments, e.g. the synthetic aperture radar (SAR), that can detect, in the backscattered radar power, the small-scale sea surface roughness, enabling the estimation of the varying wind speeds at the sea surface (Cheng and Alpers, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The quality of the AIRWAVE products has been verified using both satellite and radiosonde correlative measurements (Papandrea et al, 2018;Schröder et al, 2018). To date, the dataset is available in two versions; the latest version (V2) is obtained with an improved version of the AIRWAVE algorithm which accounts for the atmospheric variability at different latitudes and associated seasonality, reducing both biases and root-mean-square error, especially in polar and coastal regions (Castelli et al, 2019). The overall good quality of the dataset suggested that it could be used for systematic lee wave detection.…”

Section: Introductionmentioning

confidence: 99%

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Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

et al. 2019

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Abstract. Atmospheric gravity waves generated downstream by orography in a stratified airflow are known as lee waves. In the present study, such mesoscale patterns have been detected, over water and in clear-sky conditions, using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset, which contains about 20 years of day and night products, obtained from the thermal infrared measurements of the Along Track Scanning Radiometer (ATSR) instrument series. The high accuracy of such data, along with the native 1 km×1 km spatial resolution, allows the investigation of small-scale features such as lee waves. In this work, we focused on the Mediterranean Sea, the largest semi-enclosed basin on the Earth. The peculiarities of this area, which is characterised by complex orography and rough coastlines, lead to the development of these structures over both land and sea. We developed an automatic tool for the rapid detection of areas with high probability of lee wave occurrence, exploiting the TCWV variability in spatial regions with a 0.15∘×0.15∘ area. Through this analysis, several occurrences of structures connected with lee waves have been observed. The waves are detected in spring, autumn and summer seasons, with TCWV values usually falling in the range of 15 to 35 kg m−2. In this article, we describe some cases over the central (Italy) and the Eastern Mediterranean Basin (Greece, Turkey and Cyprus). We compared a case of perturbed AIRWAVE TCWV fields due to lee waves occurring over the Tyrrhenian Sea on 18 July 1997 with the sea surface winds from the synthetic aperture radar (SAR), which sounded the same geographical area, finding a good agreement. Another case has been investigated in detail: on 2 August 2002 the Aegean Sea region was almost simultaneously sounded by both the second sensor of the ATSR series (ATSR-2) and the Advanced ATSR (AATSR) instruments. The AIRWAVE TCWV fields derived from the two sensors were successfully compared with the vertically integrated water vapour content simulated with the Weather Research and Forecasting (WRF) numerical model for the same time period, confirming our findings. Wave parameters such as amplitude, wavelength and phase are described through the use of the Morlet continuous wavelet transformation (CWT). The performed analysis derived typical wavelengths from 6 to 8 km and amplitudes of up to 20 kg m−2.

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Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

et al. 2019

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Water Vapor Retrieval From MODIS NIR Channels Using Ground-Based GPS Data

Liu

2020

IEEE Trans. Geosci. Remote Sensing

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A novel algorithm for water vapor retrieval from MODerate resolution Imaging Spectroradiometer (MODIS) Near Infrared (NIR) Channels is proposed in this research. In contrast to conventional retrieval algorithms based on radiative transfer methods, this algorithm uses the empirical regression functions to calculate precipitable water vapor (PWV). In this study, water vapor data observed from January 1 st , 2003 to December 31 st , 2017 from 464 GPS stations situated in western North America serve as reference data to determine the relationship between the transmittance of the water vapor absorption channels and atmospheric water vapor content. The model is trained on different subsets of the training data through the bootstrap resampling method. Validation results against PWV observations during the period 2010-2017 from 5 globally distributed GPS stations illustrate that the algorithm can significantly improve the accuracy of MODIS NIR water vapor data, with root-mean-squares error (RMSE) reduction of 22.48% from 7.670 mm to 5.946 mm for 2-channel ratio method and 21.69% from 7.670 mm to 6.006 mm for 3-channel ratio method for MODIS/Terra satellite data, and RMSE reduction of 16.42% from 7.191 mm to 6.010 mm and 15.26% from 7.191 mm to 6.094 mm for PWV derived from 2-channel and 3-channel ratio method from Aqua, respectively, for MODIS/Aqua satellite data.

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Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

et al. 2021

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In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

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The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) version 2: algorithm evolution, dataset description and performance improvements

Cited by 4 publications

References 19 publications

Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

Water Vapor Retrieval From MODIS NIR Channels Using Ground-Based GPS Data

Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

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