Evaluation of Maximum Likelihood Estimation and regression methods for fusion of multiple satellite Aerosol Optical Depth data over Vietnam

2019 ◽  
Author(s):  
Pham Van Ha ◽  
Ngo Xuan Truong ◽  
Dominique Laffly ◽  
Astrid Jourdan ◽  
Nguyen Thi Nhat Thanh
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Likelihood Estimation ◽  
Regression Methods ◽  
Depth Data ◽  
Aerosol Optical Depth Data

Estimating long-term PM10-2.5 concentrations in six US cities using satellite-based aerosol optical depth data

2022 ◽  
pp. 118945
Author(s):  
Meredith Pedde ◽  
Itai Kloog ◽  
Adam Szpiro ◽  
Michael Dorman ◽  
Timothy V. Larson ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Depth Data ◽  
Us Cities ◽  
Aerosol Optical Depth Data

Using New High Resolution Satellite Based Aerosol Optical Depth Data for a Hybrid Model of Spatio-Temporal PM2.5 Exposures across North Eastern USA

2014 ◽  
Vol 2014 (1) ◽  
pp. 1521
Author(s):  
Itai Kloog* ◽  
Alexandra Chudnovsky ◽  
Allan Just ◽  
Francesco Nordio ◽  
Petros Koutrakis ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Hybrid Model ◽  
Depth Data ◽  
North Eastern ◽  
Eastern Usa ◽  
Spatio Temporal ◽  
Aerosol Optical Depth Data

A Long-Term Historical Aerosol Optical Depth Data Record (1982–2011) Over China From AVHRR

2019 ◽  
Vol 57 (5) ◽  
pp. 2467-2480 ◽  
Author(s):  
Ling Gao ◽  
Lin Chen ◽  
Jun Li ◽  
Andrew K. Heidinger ◽  
Xiaofeng Xu ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Depth Data ◽  
Data Record ◽  
Aerosol Optical Depth Data

scholarly journals Aerosol Optical Depth and the Global Brewer Network: A Study Using U.K.- and Malaysia-Based Brewer Spectrophotometers

2012 ◽  
Vol 29 (6) ◽  
pp. 857-866 ◽  
Author(s):  
Wilawan Kumharn ◽  
John S. Rimmer ◽  
Andrew R. D. Smedley ◽  
Toh Ying Ying ◽  
Ann R. Webb
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Climate Models ◽  
Visible Range ◽  
Depth Data ◽  
The United Kingdom ◽  
Brewer Spectrophotometer ◽  
Sky Conditions ◽  
Aerosol Optical Depth Data ◽  
Uv Range

Abstract Aerosols play an important role in attenuating solar radiation reaching the earth's surface and are thus important inputs to climate models. Aerosol optical depth is routinely measured in the visible range but little data in the ultraviolet (UV) are available. In the UV range it can be determined from Langley plots of direct-sun measurements from the Brewer spectrophotometer (where conditions allow) and can also be determined as the residual once the ozone and sulfur dioxide have been accounted for in the extinction observed during a normal Brewer direct-sun measurement. By comparing aerosol optical depth derived from Brewer direct-sun data in both the United Kingdom and Malaysia, two very different locations, it is determined that while most of the existing global Brewer network could contribute to aerosol optical depth data, further analysis, such as calculation of the Ångström parameter, would be dependent on latitude and sky conditions.

Catch-–effort maximum likelihood estimation of important population parameters

1997 ◽  
Vol 54 (4) ◽  
pp. 890-897 ◽  
Author(s):  
W R Gould ◽  
K H Pollock
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Regression Models ◽  
Model Development ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Linear Regression Models ◽  
Estimation Of Parameters ◽  
Regression Methods ◽  
Regression Techniques

The relative ease with which linear regression models are understood explains the popularity of such techniques in estimating population size with catch-effort data. However, the development and use of the regression models require assumptions and approximations that may not accurately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data using the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between the regression approaches and maximum likelihood estimation will be illustrated with an example of commercial fishery catch-effort data and through simulation. Our results indicate that maximum likelihood estimation consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility necessary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.

Sign in / Sign up

Export Citation Format

Share Document