TIGR-like atmospheric-profile databases for accurate radiative-flux computation

F. Chevallier; A. Chédin; F. Cheruy; J. J. Morcrette

doi:10.1002/qj.49712656319

TIGR-like atmospheric-profile databases for accurate radiative-flux computation

F. Chevallier
, A. Chédin
, F. Cheruy
, J. J. Morcrette

Research output: Contribution to journal › Article › peer-review

Abstract

This note summarizes the characteristics of different topological methods for sampling vertical profiles of heterogeneous variables, like atmospheric temperature and water-vapour concentration. The methods presented follow the approach developed for the successive Thermodynamic Initial Guess Retrieval (TIGR) databases at Laboratoire de Meteorologie Dynamique. The most recent one is applied to select limited numbers of profiles (some thousands) by the sampling of a much larger dataset (more than one million profiles) from the European Centre for Medium-Range Weather Forecasts short-range forecasts. The sampled datasets are then used for training a neural-network-based radiative-flux profile computation model (NeuroFlux).

Original language	English
Pages (from-to)	777-785
Number of pages	9
Journal	Quarterly Journal of the Royal Meteorological Society
Volume	126
Issue number	563
DOIs	https://doi.org/10.1002/qj.49712656319
Publication status	Published - 1 Jan 2000

Keywords

Artificial neural networks
Long-wave radiative transfer
Sampling methods

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title = "TIGR-like atmospheric-profile databases for accurate radiative-flux computation",

abstract = "This note summarizes the characteristics of different topological methods for sampling vertical profiles of heterogeneous variables, like atmospheric temperature and water-vapour concentration. The methods presented follow the approach developed for the successive Thermodynamic Initial Guess Retrieval (TIGR) databases at Laboratoire de Meteorologie Dynamique. The most recent one is applied to select limited numbers of profiles (some thousands) by the sampling of a much larger dataset (more than one million profiles) from the European Centre for Medium-Range Weather Forecasts short-range forecasts. The sampled datasets are then used for training a neural-network-based radiative-flux profile computation model (NeuroFlux).",

keywords = "Artificial neural networks, Long-wave radiative transfer, Sampling methods",

author = "F. Chevallier and A. Ch{\'e}din and F. Cheruy and Morcrette, \{J. J.\}",

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AU - Chevallier, F.

AU - Chédin, A.

AU - Cheruy, F.

AU - Morcrette, J. J.

PY - 2000/1/1

Y1 - 2000/1/1

N2 - This note summarizes the characteristics of different topological methods for sampling vertical profiles of heterogeneous variables, like atmospheric temperature and water-vapour concentration. The methods presented follow the approach developed for the successive Thermodynamic Initial Guess Retrieval (TIGR) databases at Laboratoire de Meteorologie Dynamique. The most recent one is applied to select limited numbers of profiles (some thousands) by the sampling of a much larger dataset (more than one million profiles) from the European Centre for Medium-Range Weather Forecasts short-range forecasts. The sampled datasets are then used for training a neural-network-based radiative-flux profile computation model (NeuroFlux).

AB - This note summarizes the characteristics of different topological methods for sampling vertical profiles of heterogeneous variables, like atmospheric temperature and water-vapour concentration. The methods presented follow the approach developed for the successive Thermodynamic Initial Guess Retrieval (TIGR) databases at Laboratoire de Meteorologie Dynamique. The most recent one is applied to select limited numbers of profiles (some thousands) by the sampling of a much larger dataset (more than one million profiles) from the European Centre for Medium-Range Weather Forecasts short-range forecasts. The sampled datasets are then used for training a neural-network-based radiative-flux profile computation model (NeuroFlux).

KW - Artificial neural networks

KW - Long-wave radiative transfer

KW - Sampling methods

U2 - 10.1002/qj.49712656319

DO - 10.1002/qj.49712656319

M3 - Article

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SP - 777

EP - 785

JO - Quarterly Journal of the Royal Meteorological Society

JF - Quarterly Journal of the Royal Meteorological Society

IS - 563

ER -

TIGR-like atmospheric-profile databases for accurate radiative-flux computation

Abstract

Keywords

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