ORCID Profile
0000-0002-0069-0107
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Publisher: Elsevier BV
Date: 05-2003
Publisher: Elsevier
Date: 2021
Publisher: IEEE
Date: 07-2018
Publisher: Copernicus GmbH
Date: 19-11-2018
DOI: 10.5194/ISPRS-ARCHIVES-XLII-5-519-2018
Abstract: Abstract. Studies have shown that aerosol loading particularly due to the absorbing particles over Himalayan region might impact the snow properties and cover. Hence, a comprehensive study on the aerosol distribution over this large mountain ecosystem, particularly focussing on the absorbing particles requires utmost attention. The purpose of this study is to provide a statistical view of Aerosol Absorption Optical Depth (AAOD) levels derived from Ozone Monitoring Instrument (OMI) at 388 nm wavelength during the time period 2005–2017 over the Central and Western Himalayan region (25 – 40° N and 70 – 88° E). To extract the aerosol data for the specified Himalayan region the NOAA Digital Elevation Model (DEM) has been utilized, followed by geo-processing of the daily AAOD data. Outcomes of such studies could provide useful inputs for capacity building activities using the remote sensing resources. We have presented the yearly, monthly and seasonal variability of absorbing aerosols levels over the study region.
Publisher: Unpublished
Date: 2018
Publisher: American Meteorological Society
Date: 06-2022
Abstract: Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.
No related grants have been discovered for Soubhik Biswas.