On the theoretical aspects of improved fog detection and prediction in India

Abstract

The polluted Indo-Gangetic Basin (IGB) in northern India experiences fog (a condition when visibility degrades below 1km) every winter (Dec-Jan) causing a massive loss of economy and even loss of life due to accidents. This can be minimized by improved fog detection (especially at night) and forecasting so that activities can be reorganized accordingly. Satellites detect fog at night by a positive brightness temperature difference (BTD). However, fixing the right BTD threshold holds the key to accuracy. Here I demonstrate the sensitivity of BTD in response to changes in fog and surface emissivity and their temperatures and justify a new BTD threshold. Further I quantify the dependence of critical fog droplet number concentration, NF (i.e. minimum fog concentration required to degrade visibility below 1km) on liquid water content (LWC). NF decreases exponentially with an increase in LWC from 0.01 to 1g/m3, beyond which it stabilizes. A 10 times low bias in simulated LWC below 1g/m3 would require 107 times higher aerosol concentration to form the required number of fog droplets. These results provide the theoretical aspects that will help improving the existing fog detection algorithm and fog forecasting by numerical models in India.