Quantitative Precipitation Forecast of a Tropical Cyclone through Optimal Parameter Estimation in a Convective Parameterization

et al. 2014

Geosci. Model Dev.

Abstract. Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2-4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

Section: Micro-gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Hong

et al. 2014

Geosci. Model Dev.

“…Since heavy rainfalls including typhoon cause serious damages to both human life and property, improving the their forecast accuracy is of utmost importance. Many studies have been done to improve the quantitative precipitation forecast using numerical weather prediction models and satellite or radar data (e.g., Fritsch et al, 1998;Lee et al, 2006;Kim and Oh, 2010;Feng and Kitzmiller, 2006;Yu et al, 2013). In order to improve the forecast accuracy, it is essential to understand the spatial and temporal characteristics as well as the occurrence and development mechanisms of the precipitation systems.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical assessment of warm-season precipitation observations in Korea based on the composite precipitation and satellite water vapor data

Lee

Hydrol. Earth Syst. Sci.

2018

Self Cite

Among the meteorological disasters, heavy rainfalls cause the second largest damage in Korea, following typhoons. To manage the potential disasters due to heavy rainfalls, understanding the observational characteristics of precipitation is of utmost importance. In this study, we investigate the spatial and temporal characteristics of warmseason precipitation in Korea, according to the precipitation types, by conducting the geostatistical analyses such as an autocorrelogram, Moran's I and general G on the composite (radar + station) precipitation data. The e-folding distance of precipitation ranges from 15 to 35 km, depending on the spatial distribution, rather than intensity, of precipitation, whereas the e-folding time ranges from 1 to 2 h. The directional analyses revealed that the warm-season precipitation systems in Korea, especially those with a high precipitation amount, have high spatial autocorrelations in the southwest-northeast and west-east directions, in association with the frontal rainfalls, convection bands, etc. Furthermore, the cluster versus dispersion patterns and the hot versus cold spots are analyzed through Moran's I and general G, respectively. Water vapor, represented by the brightness temperature, from three Himawari-8 water vapor bands also shows similar characteristics with precipitation but with strong spatial correlation over a much longer distance (∼ 100 km), possibly due to the continuity of water vapor. We found that, under the e-folding-based standard, the current observation network of Korea is sufficient to capture the characteristics of most precipitation systems; however, under a strict stan-dard (e.g., autocorrelation of 0.6), a higher-resolution observation network is essentially required -especially in local areas with frequent heavy rainfalls -depending on the directional features of precipitation systems. Establishing such an observation network based on the characteristics of precipitation enables us to improve monitoring, tracking, and prediction skills of high-impact weather phenomena as well as to enhance the utilization of numerical weather prediction.

“…and 4) applying the optimal parameter estimation technique, e.g., using the genetic algorithm (Lee et al, 2006;Yu et al, 2013;20 Hong et al, 201420 Hong et al, , 2015, can bring about further improvement of prediction.…”

mentioning

confidence: 99%

Uncertainty in predicting the Eurasian snow: Intercomparison of land surface models coupled to a regional climate model

Kim

2019

Preprint

Self Cite

Abstract. Variability of large and synoptic scale circulations in Asia is strongly affected by the winter and spring Eurasian snow. Therefore, an accurate prediction of the Eurasian snow is of the utmost importance in predicting the climate and weather phenomena in Asia. Most global/regional models are coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. In this study, using the Weather Research and Forecasting (WRF) model, we make intercomparision of LSMs in terms of simulating the Eurasian snow. Simulations are carried out from 1 June 2009 to 31 August 2010, including a spin-up time of 6 months, by employing four different LSMs – the Unified Noah LSM, the Noah LSM with multiparameterization options (Noah-MP), the Rapid Update Cycle (RUC) LSM, and the Community Land Model version 4 (CLM4). The NCEP Final (FNL) Operational Global Analysis data are used as initial and boundary conditions. The LSM results are evaluated using the Canadian Meteorological Centre Daily Snow Depth Analysis Data, the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Monthly L3 Global 0.05Deg Climte Modeling Grid (CMG) Version 6, and the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo Product. Although all the LSMs represent reasonable results, the Noah-MP represents the most accurate predictions in all three variables (snow depth, fractional snow cover, and albedo), in terms of not only quantitative aspects but also spatial correlation patterns. Our results indicate that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to the global/regional climate models, and hence the future climate projections.