Publications

Ramrez- Beltrn, ND; Salazar, CM; Snchez, JMC; Gonzlez, JE (2019). A satellite algorithm for estimating relative humidity, based on GOES and MODIS satellite data. INTERNATIONAL JOURNAL OF REMOTE SENSING, 40(24), 9237-9259.

Abstract
A real-time algorithm is proposed to estimate hourly relative humidity at the surface level. The algorithm was developed with Geostationary Operational Environmental Satellite (GOES)-13 and the Moderate-Resolution Imaging Spectroradiometer (MODIS) data. The methodology is also designed to operate with the newer GOES-16 satellite data. The algorithm uses the brightness temperatures from the water vapour (6.7 mu m) channel and albedo from the near-infrared (3.9 mu m) channel to model short-term variations. The algorithm also includes three physical parameters extracted from satellites to complement the short-term variations of relative humidity at surface level. The parameters are: precipitable water (PW), land surface temperature (LST), and normalized difference vegetation index (NDVI). Two years (2010-2011) of hourly data from 458 ground stations were used to build the model. Validation was conducted with 2012 (GOES-13) and 2018 (GOES-16) data. Stations are located in the Caribbean islands, including the Peninsula of Florida, and in Mesoamerica countries, including part of Mexico, Central America, Colombia, and Venezuela. A regression model was used to estimate relative humidity, and validation was performed over different rainfall seasons. Results show that the average mean absolute error, root mean squared error, and the coefficient of multiple determination were 7.4, 9.9, and 0.58, respectively.

DOI:
10.1080/01431161.2019.1629715

ISSN:
0143-1161