scholarly journals Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Yongdi Wang ◽  
Xinyu Sun
Keyword(s):  
Probability Distribution ◽  
Statistical Downscaling ◽  
Large Scale ◽  
North China ◽  
Daily Precipitation ◽  
Precipitation Intensity ◽  
Frequency Difference ◽  
Self Organizing Maps ◽  
Statistical Downscaling Method ◽  
Consecutive Dry Days

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

scholarly journals Development of SOM Daily Precipitation Statistical Downscaling Model And Its Applicability Assessment in North China

2021 ◽  
Author(s):  
Yongdi Wang ◽  
Xinyu Sun
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
North China ◽  
Daily Precipitation ◽  
Extreme Event ◽  
Precipitation Amount ◽  
Reanalysis Data ◽  
Frequency Difference ◽  
Downscaling Method ◽  
High Skill

Abstract A statistical downscaling method based on SOM which named SOM-SD is used over North China. It’s applicatibility by downscaling daily precipitation is evaluated. Indices are selected which represent the statistics of daily precipitation with regard to both precipitation amount (Prtot, SDII) and frequency (nr001), as well as extreme event (P95T, CWD, CDD). The large-scale predictors were extracted from the daily NCEP reanalysis data, while the predictand was high resolution gridded daily observed precipitation. A downscaling method based on SOM named SOM-SD was presented and evaluated. In evaluating, the frequency difference of wet-dry nodes is defined. And it is confirmed that there was a significant positive correlation between frequency difference and precipitation. The SOM-SD method displayed a high skill in reproducting the climatologic statistical properties of the observed precipitation. The value of BS is between 0 and 1.5×10-4. Sscore is between 0.8 and 1. The bias ranges are -7.4% and -11.6% for Prtot and SDII, -3.1days for nr001, +3.4% for P95T, -1.1 days for CWD and +3.5 days for CDD. Therefore, SOM-SD method works reasonably well.

Gridded Statistical Downscaling Based on Interpolation of Parameters and Predictor Locations for Summer Daily Precipitation in North China

2019 ◽  
Vol 58 (10) ◽  
pp. 2295-2311
Author(s):  
Yonghe Liu ◽  
Jinming Feng ◽  
Zongliang Yang ◽  
Yonghong Hu ◽  
Jianlin Li
Keyword(s):  
Distance Function ◽  
Statistical Downscaling ◽  
Spatial Dependence ◽  
Large Scale ◽  
North China ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Summer Precipitation ◽  
Single Station ◽  
Dense Grid

AbstractFew statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

2017 ◽  
Vol 18 (9) ◽  
pp. 2385-2406 ◽  
Author(s):  
Yu-Kun Hou ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Jie Chen ◽  
Sheng-Lian Guo
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Support Vector ◽  
Statistical Downscaling Method ◽  
Calibration And Validation ◽  
Downscaling Method ◽  
Calibration Methods

Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

scholarly journals Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

2007 ◽  
Vol 135 (6) ◽  
pp. 2365-2378 ◽  
Author(s):  
P. Friederichs ◽  
A. Hense
Keyword(s):  
Quantile Regression ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Daily Precipitation ◽  
Skill Score ◽  
Reanalysis Data ◽  
Conditional Quantile ◽  
Conditional Quantiles ◽  
Censored Quantile Regression ◽  
Extreme Precipitation Events

Abstract A statistical downscaling approach for extremes using censored quantile regression is presented. Conditional quantiles of station data (e.g., daily precipitation sums) in Germany are estimated by means of the large-scale circulation as represented by the NCEP reanalysis data. It is shown that a mixed discrete–continuous response variable, such as a daily precipitation sum, can be statistically modeled by a censored variable. Furthermore, a conditional quantile skill score is formulated to assess the relative gain of a quantile forecast compared with a reference forecast. Just like multiple regression for expectation values, quantile regression provides a tool to formulate a model output statistics system for extremal quantiles.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2008 ◽  
Vol 12 (2) ◽  
pp. 551-563 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to local and regional impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km2 per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce generally comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit limited skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Downscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting

2011 ◽  
Vol 12 (6) ◽  
pp. 1564-1580 ◽  
Author(s):  
Xiaoli Liu ◽  
Paulin Coulibaly
Keyword(s):  
Neural Network ◽  
Statistical Downscaling ◽  
Large Scale ◽  
River Flow ◽  
Daily Precipitation ◽  
Hydrologic Model ◽  
Temperature Series ◽  
Main Concern ◽  
Medium Range ◽  
Reservoir Inflow

Abstract This study investigates the use of large-scale ensemble weather predictions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System [GFS; formerly known as Medium-Range Forecast (MRF)] for improving week-2 hydrologic forecasting. The ensemble weather predictor variables are used to downscale daily precipitation and temperature series at two meteorological stations in the Saguenay watershed in northeastern Canada. Three data-driven methods—namely, the statistical downscaling model (SDSM), the time-lagged feed-forward neural network (TLFN), and evolutionary polynomial regression (EPR)—are used as comparative downscaling models. The downscaled results of the best models are used as additional inputs in two hydrological models, namely Hydrologiska Byråns Vattenbalansavdelning (HBV2005) and a Bayesian neural network (BNN)-based hydrologic model, for up to 14-day-ahead reservoir inflow and river flow forecasting. The performance of the two hydrologic models is compared, the ultimate objective being to improve week-2 (7–14-day ahead) forecasts. To identify a suitable approach for using the ensemble weather data in the downscaling experiments, six scenarios are evaluated. It is found that the best approach to downscaling the ensemble weather predictions is to use the means of the predictor members derived from the two grid points closest to the local meteorological station of interest. The downscaling results show that all three models have a relatively good performance in downscaling daily temperature series, but the results are in general less accurate for daily precipitation. The TLFN and EPR models have quite close performance in most cases, and they both perform better than SDSM. The hydrologic forecasting results show that for both reservoir inflow and river flow, the HBV model has better performance when downscaled meteorological predictions are included, while there is no significant improvement for the BNN model. For the week-2 forecast, an improvement of about 18% on average is obtained for both streamflow and reservoir inflow forecasts. However, for the spring season where accurate peak flow forecast is of main concern, an improvement of about 26% on average is achieved. It is also shown that using only downscaled temperature in spring reservoir inflow forecasting, the improvements for week 2 range from 16% to 24%. Overall, the forecast results show that large-scale ensemble weather predictions can be effectively exploited through statistical downscaling tools for improved week-2 hydrologic forecasts. The forecast results also indicate that even imperfect medium-range (week 2) weather predictions can be very useful for producing significantly improved week-2 hydrologic forecasts.

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