Relating rainfall retrieval parameters to network and environmental features to improve rainfall estimates from commercial microwave links in the tropics

Potentially the greatest benefit of Commercial Microwave Links (CMLs) as opportunistic rainfall sensors lies in regions that lack dedicated rainfall sensors, most notably low- and middle income countries. However, current CML rainfall retrieval algorithms are predominantly tuned and applied to (European) CML networks in temperate or Mediterranean climates. This study investigates whether local quantitative precipitation estimates from CMLs in a tropical region, specifically Sri Lanka, can be improved by optimizing two dominant parameters in the rainfall retrieval algorithm RAINLINK, namely the wet-antenna correction factor Aa and the relative contribution of minimum and maximum received signal levels α. Using a grid search, based on ten months of CML data from 22 link-gauge clusters consisting of 105 sub-links that lie within 1 km of a daily rain gauge, optimal values of Aa and α are first derived for the entire country and compared to the default RAINLINK values. Subsequently, the CMLs are grouped by link length, frequency, climate zone, and daily rainfall depth classes, and Aa and α are derived for each of these classes. Calibrating parameters on all clusters across the country only leads to minor improvements. The actual optimal Aa and α values depend on the performance metric favored. Calibrating on network properties, particularly short link length and high frequency classes, does significantly improve rainfall estimates. By relating the optimal Aa and α values to known network meta data, the results from this study are potentially applicable to other tropical CML networks that lack nearby reference rainfall data.