ITCZ trend analysis via Geodesic P-spline smoothing of the AIRWAVE TCWV and cloud frequency datasets

Castelli, Elisa; Papandrea, E.; Valeri, Massimo; Greco, Fedele Pasquale; Ventrucci, Massimo; Casadio, Stefano; Dinelli, B. M.

doi:10.1016/j.atmosres.2018.07.019

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…The analysis highlighted the overall good quality of the dataset but also showed recurrent higher variability in specific areas in the Mediterranean region. We noticed that all these occurrences were associated with lee wave patterns, generated by airflows crossing one or more obstacles (Castelli et al, 2018b). These phenomena indeed induce water vapour perturbations very close to the surface, producing strong variations in TCWV.…”

Section: Lee Wave Detection Using the Airwave Datasetmentioning

confidence: 80%

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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: 80%

“…The overall good quality of the dataset suggested that it could be used for systematic lee wave detection. Indeed, oscillations of the TCWV in regions where lee waves can form have been found during quality checks of the AIRWAVE dataset (Castelli et al, 2018b).…”

Section: Introductionmentioning

confidence: 94%

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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“…The mean and the absolute standard deviation show similar features, with higher values in the ITCZ region. As can be noticed, the seasonal movements of the ITCZ from the north (in Northern Hemisphere summer) to south (in Northern Hemisphere winter) can be clearly detected (Castelli et al, 2018). In general, the standard deviations in the region where the TCWV maximum is located are of the order of 15 % and up to 20 % in polar regions.…”

Section: Selection Of the Retrieval Parametersmentioning

confidence: 85%

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

et al. 2019

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Abstract. Total column water vapour (TCWV) is a key atmospheric variable which is generally evaluated on global scales through the use of satellite data. Recently a new algorithm, called AIRWAVE (Advanced Infra-Red WAter Vapour Estimator), has been developed for the retrieval of the TCWV from the Along-Track Scanning Radiometer (ATSR) instrument series. The AIRWAVE algorithm retrieves TCWV by exploiting the dual view of the ATSR instruments using the infrared channels at 10.8 and 12 µm and nadir and forward observation geometries. The algorithm was used to produce a TCWV database over sea from the whole ATSR mission. When compared to independent TCWV products, the AIRWAVE version 1 (AIRWAVEv1) database shows very good agreement with an overall bias of 3 % all over the ATSR missions. A large contribution to this bias comes from the polar and the coastal regions, where AIRWAVE underestimates the TCWV amount. In this paper we describe an updated version of the algorithm, specifically developed to reduce the bias in these regions. The AIRWAVE version 2 (AIRWAVEv2) accounts for the atmospheric variability at different latitudes and the associated seasonality. In addition, the dependency of the retrieval parameters on satellite across-track viewing angles is now explicitly handled. With the new algorithm we produced a second version of the AIRWAVE dataset. As for AIRWAVEv1, the quality of the AIRWAVEv2 dataset is assessed through the comparison with the Special Sensor Microwave/Imager (SSM/I) and with the Analyzed RadioSounding Archive (ARSA) TCWV data. Results show significant improvements in both biases (from 0.72 to 0.02 kg m−2) and standard deviations (from 5.75 to 4.69 kg m−2), especially in polar and coastal regions. A qualitative and quantitative estimate of the main error sources affecting the AIRWAVEv2 TCWV dataset is also given. The new dataset has also been used to estimate the water vapour climatology from the 1991–2012 time series.

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“…The quality of the V2 dataset is evaluated through the same method adopted for V1 and reported in (Papandrea et al, 2018).…”

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confidence: 99%

“…As for V1, these differences are quite stable over time, with the exception of the 255 beginning of the ATSR-1 mission (1991)(1992)(1993)(1994). As explained in Papandrea et al (2018), this can be due to the failure of 3.7 µm channel that impacted the ATSR cloud screening. In general, the differences with respect to the radiosonde exhibit a higher seasonality due to pronounced variability of atmospheric and surface conditions in costal areas.…”

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confidence: 99%

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

Castelli¹,

Papandrea²,

Roma³

et al. 2018

Preprint

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Abstract. The Total Column Water Vapour (TCWV) is a key atmospheric variable and its evaluation is generally performed, at global scale, through the use of satellite data. Recently a new algorithm, called AIRWAVE (Advanced Infra-Red Water Vapour Estimator), has been developed for the retrieval of the TCWV from the Along-Track Scanning Radiometer (ATSR) instrument series. The AIRWAVE algorithm performs the TCWV retrieval exploiting the dual view of the ATSR instruments using the infra-red channels at 10.8 and 12 µm and combining nadir and forward observation geometries. The algorithm 5 was used to produce a TCWV database from the whole ATSR mission. When compared to independent TCWV products, AIRWAVE Version 1 (V1) database shows very good agreement with almost no bias all over the ATSR missions, with the exception of the polar and the costal region where AIRWAVE underestimate the TCWV amount. In this paper we describe an updated version of the algorithm, specifically developed to overcome these problems. The AIRWAVE Version 2 (V2) accounts for the atmospheric variability at different latitudes and the associated seasonality. In addition, the dependency of the 10 retrieval parameters on satellite across-track viewing angles is now explicitly handled. With the new algorithm we produced a second version of the AIRWAVE dataset. As for V1, the quality of V2 dataset is assessed through the comparison with the Special Sensor Microwave/Imager (SSM/I) and with the Analyzed Radiosounding Archive (ARSA) TCWV data. Results show significant improvements in both biases and RMSE, especially in polar and costal regions. A qualitative and quantitative estimate of the main error sources affecting the V2 TCWV dataset is also given. The new dataset has also been used to estimate 15 the water vapour climatology from the 1991-2012 time series.

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ITCZ trend analysis via Geodesic P-spline smoothing of the AIRWAVE TCWV and cloud frequency datasets

Cited by 8 publications

References 37 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

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

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

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