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Remote Sens. 2018, 10(9), 1398; https://doi.org/10.3390/rs10091398

MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique

1
Institute of Methodologies for Environmental Analysis, National Research Council (IMAA-CNR), 85050 Tito Scalo (PZ), Italy
2
Center of Excellence Telesensing of Environment and Model Prediction of Severe events (CETEMPS), University of L’Aquila, 67100 L’Aquila, Italy
3
Euro-Mediterranean Center on Climate Change, Impacts on Agriculture, Forests and Ecosystem Service Division, 01100 Viterbo, Italy
4
Institute for Archaeological and Monumental Heritage, National Research Council (IBAM-CNR), 85050 Tito Scalo (PZ), Italy
5
Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Received: 1 August 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 2 September 2018
(This article belongs to the Section Atmosphere Remote Sensing)
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Abstract

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations. View Full-Text
Keywords: microwave; sounding; vertical profile; temperature; water vapor; random forest; OOB; ERA-Interim; IASI; IGRA microwave; sounding; vertical profile; temperature; water vapor; random forest; OOB; ERA-Interim; IASI; IGRA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style

Di Paola, F.; Ricciardelli, E.; Cimini, D.; Cersosimo, A.; Di Paola, A.; Gallucci, D.; Gentile, S.; Geraldi, E.; Larosa, S.; Nilo, S.T.; Ripepi, E.; Romano, F.; Sanò, P.; Viggiano, M. MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique. Remote Sens. 2018, 10, 1398.

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