You are here:
Is Precipitation Partitioning Biased in Climate and Weather Models?
Citation:
Alapaty, Kiran AND J. He. Is Precipitation Partitioning Biased in Climate and Weather Models? 2018 AGU Fall meeting, Washington, DC, December 10 - 14, 2018.
Impact/Purpose:
In environmental pollution modeling, proper estimation of surface precipitation is very important. This study improves such estimations helping to improve the accuracy of end point environmental research modeling. This study calls for a development of methods to routinely produce measurements for precipitation fractions to help evaluate global and regional climate and weather models and environmental pollution models.
Description:
Proper simulation of high-resolution surface precipitation distribution and variability are very important to local aspects of environmental pollution and climate. Global and regional climate and weather models routinely evaluate total precipitation using available measurements, but quantitative evaluation of contributions by the individual components (convective and non-convective) to the total precipitation is not routinely performed. Wet bias in one component can alleviate dry bias in the other component, making the total precipitation look comparable to measurements, leading to an invisible bias. To study this aspect, TRMM measurements for convective fraction were used to quantitatively evaluate convective fractions simulated by a cumulus parameterization scheme in a regional climate modeling simulation using 12 km grid spacing. Results indicated a wet bias in convective precipitation as compared to TRMM measurements helped to counter a dry bias in grid-scale precipitation leading to total precipitation comparable to PRISM and TRMM data. A new formulation was developed for the convective cloud adjustment timescale that alleviated wet bias in convective precipitation when compared to the old formulation and TRMM measurements. Results for different grid spacing (36, 12, and 4 km) also indicated that the new method produced lower subgrid-scale precipitation with overall better precipitation estimates. Our results also suggest that evaluating both the components of the total surface precipitation rather than just the total itself can inform a need to improve cloud formulations, as demonstrated in this study which in turn can increase the accuracy of end point environmental pollution research.
