Uncertainty reduction in quantitative precipitation prediction by tuning of Kain–Fritch scheme input parameters in the WRF model using the simulated annealing optimization method

Abstract

In the study, using the simulated annealing (SA) optimization method, tried to reduce the uncertainty of the prediction of the Weather Research and Forecasting (WRF) numerical model in convective rainfall forecasts. To this end, three parameters Pd, Pe and Ph in Kain–Fritch convective scheme that's related to downdraft mass flux, entrainment mass flux and starting height of downdraft above updraft source layer, respectively, are optimized using SA algorithm to achieve better values. Two nested domain were used in the study with 30 and 10 km resolution which inner domain cover southern coasts of the Caspian Sea for the study area. Runtime of the model was 36 hr with the first 12 hr spin-up time. Study period selected October 12, 2012 for training algorithm and October 8, 2015 for test run. After 100 iteration of the algorithm, 1, 1 and 50 was obtained for Pd, Pe and Ph respectively, while default values of these parameters was 0, 0, and 150. Results show that in both cases, model with default values underestimates rainfall amount and after optimization, model performance improves. Also, spatial distribution of the model precipitation forecast was less than observations and after optimization, spatial distribution improves. Statistical analysis of results indicated Mean Bias (MB) and root mean square error (RMSE) in training case were −4.6 and 17.1 in model with default values and became −3.7 and 14.3 in optimized one, respectively. Also, MB and RMSE for test case increased to −8.4 and 23.8, respectively, in model result with optimized parameters from −10.6 to 29.4 in model with default parameters.