Abstract
Both convection and land surface parameterization influence seasonal precipitation forecasts. In this study, the sensitivity of dynamical downscaling seasonal precipitation forecasts to convection and land surface parameterization was investigated by nesting the Weather Research and Forecasting (WRF) model into the NCEP’s Climate Forecast System version 2 (CFSv2) retrospective forecasts with four convective schemes: Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ), Grell–Freitas (GF), and new simplified Arakawa–Schubert (NSAS) schemes, and two land surface schemes: Noah and simplified Simple Biosphere (SSiB) schemes over the Han River basin. The CFSv2 model biases are reduced when the KF convective scheme is used in the wet summer season. However, negative biases still exist especially when the combination of BMJ and SSiB schemes is used. Compared with CFSv2 reforecasts and other combinations of schemes, the forecast skills of spatial patterns of precipitation anomalies are highest when the combination of KF and Noah schemes is used in summer. In contrast, the combination of BMJ and SSiB schemes shows lowest forecast skills in summer. To understand the causes of the differences in precipitation forecasts using different parameterization schemes, the simulated moisture flux convergence, thermodynamic parameters at different pressure levels, convective available potential energy (CAPE), convective inhibition (CIN), and heat fluxes are compared with the data in the ERA-5 reanalysis dataset. The WRF model-simulated moisture flux convergence is closer to that of the ERA-5 reanalysis compared with that of the CFSv2 reforecasts in summer. The vertical thermodynamic profiles also suggest that the combination of the KF and Noah schemes has caused a more unstable atmosphere, which is crucial for precipitation. In contrast, the combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. The spatial patterns of CAPE are also improved when using the WRF model, which further enhances the precipitation forecast skills over the Han River basin.
Highlights
Seasonal precipitation forecasts are essential for water management, disaster prevention, and many other aspects [1,2,3]
A high-resolution precipitation dataset, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS v2.0), was used to evaluate the added value from the Weather Research and Forecasting (WRF) model for the period of 1999–2010 [37]. e CHIRPS was operated by calibrating TRMM satellite data and blending rain gauge observations at a spatial resolution of 0.05° from 1981 to near present. is dataset has shown good agreement with gauge observations in different regions over the world [38, 39]
In order to evaluate the seasonal forecasts from different model grids, both the CFS reforecasts and WRF model forecasts were regridded to the CHIRPS 0.05° grid using a bilinear interpolation method over the Han River basin. e Taylor diagram was used to evaluate the performance of models coupled with different parameterization schemes in terms of correlation, centered root-mean-square (RMS) difference, and standard deviation [40]. e cosine of polar angle is the correlation between forecasts and observations, while the distance from the origin is the forecasted standard deviation. e distance from the reference reflects the RMS difference between forecasts and observations
Summary
Received 30 April 2019; Revised 17 September 2019; Accepted 1 October 2019; Published 20 November 2019 Both convection and land surface parameterization influence seasonal precipitation forecasts. Compared with CFSv2 reforecasts and other combinations of schemes, the forecast skills of spatial patterns of precipitation anomalies are highest when the combination of KF and Noah schemes is used in summer. The combination of BMJ and SSiB schemes shows lowest forecast skills in summer. E WRF model-simulated moisture flux convergence is closer to that of the ERA-5 reanalysis compared with that of the CFSv2 reforecasts in summer. The combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. The combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. e spatial patterns of CAPE are improved when using the WRF model, which further enhances the precipitation forecast skills over the Han River basin
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