Open AccessEvaluation of Forecast Potential with GCM-Driven Fields for Pollution over an Urban Air Basin
Author(s) -
Prashant Goswami,
J. N. Baruah
Publication year - 2013
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jamc-d-11-0130.1
Subject(s) - environmental science , gcm transcription factors , particulates , climatology , data assimilation , forcing (mathematics) , work (physics) , pollutant , meteorology , atmospheric sciences , general circulation model , climate change , geography , geology , mechanical engineering , engineering , ecology , oceanography , chemistry , organic chemistry , biology
Species like suspended particulate matter (SPM), respirable suspended particulate matter (RSPM), sulfur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ) not only act as atmospheric pollutants but also affect long-term climate through radiative and chemical forcing. Earlier work has shown that the daily variations in these species over a location could be simulated quite well by considering daily meteorological fields from NCEP–NCAR reanalysis data in combination with models for natural and anthropogenic sources over Delhi, India. In the present work this possibility is explored by simulating the pollutant concentrations by using forecast fields from an atmospheric general circulation model (AGCM); this takes the model closer to a forecast model. Because of the coarse resolution, however, the present work considers an air basin rather than a detailed spatiotemporal distribution. Although the GCM has been tested at a resolution below 50 km, one of the objectives of the present work is to also compare and assess the impacts of a GCM-generated field with reference to NCEP–NCAR reanalysis data used in earlier work. In the present work the interaction is one way, and active chemistry for the species is not considered; thus the work should be regarded as a minimal forecast model, especially for SO 2 and NO 2 . Similarly, although the present work quantifies forecast skill, the skill estimated is again for a minimal configuration as aspects like data assimilation are not considered here. It is shown that the GCM-driven model has skill comparable to skill obtained when using NCEP–NCAR reanalysis data.