649 A Real Time Satellite Algorithm for Estimating Heat Index, Based on GOES13 and MODIS Data

Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Nazario D. Ramirez-Beltran, Univ. of Puerto Rico, Mayaguez, Puerto Rico; and C. M. Salazar, J. M. Castro, J. E. Gonzalez, M. Angeles, and E. W. Harmsen

A real time algorithm is proposed to estimate the heat index and is based on using GOES-13 and MODIS data. The algorithm has three main steps a) estimation of relative humidity; b) estimation of air temperature and c) estimation of heat index.

The main strategy to estimate relative humidity (RH) consists on using the physical parameters that are close related to the dynamics of relative humidity at the upper and surface levels. Precipitable water (PW) is a physical parameter that is inherent to the relative humidity since it is measuring the depth of water in a column of the atmosphere, assuming that all water molecules in that column will precipitated. However, since this parameter is a measure of the total column surface parameters should be added to estimate RH at the surface level. One of the surface parameters associated to the dynamics of relative humidity is the land surface temperature (LST) since the atmospheric absorption and the surface emissivity are inherently accounted for producing variations on RH. Another physical and important parameter that is associate to the dynamics of the relative humidity at the surface level is the NDVI; because, it has been shown that the surface emissivity is a function of NDVI (Cihlar et al. 1997) and it modulates the variations of RH. MODIS and GOES-13 data are used to hourly retrieve PW, NDVI and LST at 4 km spatial resolution. The retrieve algorithm is based on using self-organizing artificial neural networks to identify homogenous climatic regions and stochastic transfer function models are used to estimate the relative humidity. Hourly dew point and air temperatures from weather stations located in Mesoamerica and the Caribbean countries are used to calibrate and validate the proposed algorithm.

A time series model is proposed to estimate air temperature, which exhibits three major components: a trend, a seasonal, and a stochastic component. The trend is represented by a deterministic model and exhibits the intrinsic surface properties such orography, soil, and vegetation. The seasonal behavior is imposed by the relative position of the Earth with respect to Sun and has two periodic components associated to daily and annual variations. The stochastic behavior is represented by a transfer function model, which includes the impulse response function and an autoregressive moving average model. The impulse response function modulates the infrared radiation effects at the top-clouds and at the surface level. During the daytime the major source of variation is the solar irradiance given by the visible channel, and during the nighttime only the brightness temperature from the water vapor and thermal infrared channels were used to correlate with air temperature. The intervention of clouds is modeled by the impulse response function using brightness temperature from the thermal channel.

Finally, the air temperature and relative humidity will be combined to estimate the heat index in hourly basis. Usually the extreme values of heat index are used for advising heat warning events. The heat index extreme events (HIEE) in the MAC region is an extraordinary hot event where the maximum-daytime heat index and the minimum-nighttime heat index both exceed the corresponding 97th percentiles and this hot event must persist for at least two consecutive days.

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