جستجوی مقالات مرتبط با کلیدواژه "پیش بینی فصلی" در نشریات گروه "جغرافیا"

General circulation models (GCMs) provide valuable forecasts of world precipitation and temperature (Schepen et al., 2020). Through improved Seasonal forecasting in recent years several climates centers around the world provide operational climate Such as; the Climate Forecast System version 2 (CFSv2) by National Centers for Environmental Prediction (NCEP) (Saha et al., 2010), the European Centre for Medium-Range Weather Forecasts (ECMWF (Johnson et al., 2019), and the Geophysical Fluid Dynamics Laboratory (GFDL) (Delworth et al., 2020). These GCM outputs generally need to downscale to use in regional-scale relevant applications and more actionable end-user-oriented climate services. One way to transfer world predictions from GCMs to regional or local scales is dynamical downscaling with RCMs such as Weather Research and Forecasting model (WRF). The Initial and lateral boundary conditions from General Circulation Models (GCMs) drive these models. The mesoscale circulations, topography, and land use-land cover are displayed better by RCMs, and these models improve the extremes and regional climate variable compared to the coarse resolution GCMs. The WRF has been coupled with numerous parameterizations to resolve processes occurring within a grid box. Some research has indicated convective and planetary boundary layer (PBL) schemes have a significant influence on precipitation simulation (Li et al 2017; Njuki, S.M., et al 2021). The WRF Model version 4 provides more than 11 convective schemes and 13 planetary boundary layer (PBL) schemes. This study has attempted to assess a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over the northeast of Iran. Using the CFSV2 as Initial and lateral boundary conditions data, simulation experiments from winter to spring in seven months (from November to May) have been performed for 2019-2020). Three nested domains have applied with the outer domain at 54 km resolution and two interdomains at 18 and 6 km resolution.

The study area is located in the northeast of Iran, and climatologically, most precipitation occurs from winter to spring (November to May). On average, the western part of this region receives approximately 60% of the annual precipitation, while the rest of the areas in the east receive lower precipitation. The real-time forecast data used in this study is the 6-hourly time series from the 9-month runs operational model for seasonal prediction at the NCEP operational CFSv2. The observed precipitation data is extracted from IRIMO. The new Weather Research and Forecasting model (WRF) is applied to determine how varying physical parameterization of PBL scheme configuration processes simulate seasonal (winter and spring) precipitation. For this purpose, four group configurations have been designed.Group1: convective schemes (KFT, BMJ, GF, KF), Yonsei University PBL (YSU) for the plenary boundary layer, surface layer scheme (Revised MM5), the shortwave radiation scheme (Dudhia), the longwave radiation scheme (RRTM) and land surface models (Noah).Group 2 all four convective schemes, PBL Mellor–Yamada–Janjic (MYJ), RRTMG for long –short radiation, 5-layer thermal diffusion, and Eta for land surface and surface layer. GROUP 3; include second-order Mellor-Yamada-Nakanishi-Niino (MYNN3) as PBL scheme, same shortwave and longwave radiation (New Goddard), the surface layer (MYNN), and land surface (RUC). Finally, group4 set by ACM2 for the plenary boundary layer, The surface layer (Pleim-Xiu), the shortwave and longwave radiation schemes (GFDL), the land surface (PX), four convective schemes have been fixed in all groups. For all WRF simulations, we used the WRF single-moment 6-class microphysics scheme. In this way, a total of 20 simulation sets in 4 groups have run, and one configuration set without any cumulus scheme in domain 3 in each group.The following statistics, the correlation (R), the root mean square error (RMSE), the mean absolute error (MAE), and bias and four verification skills are calculated from the total daily precipitation over the six months out of the seven-month integration time with the first month used as spin-up.

The WRF-CFSv2 model performance was evaluated against precipitation observations from Iran's Meteorological organization. The correlation scores between the observed and predicted 6- month and winter precipitation were moderately acceptable (0.3-0.5) however decreased to 0.36 in spring. In terms of bias, group 1 (PBL1,..) configuration have considerably structures than the group4 (PBL7,..), group2 (PBL2,…), and especially group3 (PBL6,..). All configurations showed a wet bias over the study area (-0.8 mm/d, -3.55mm/d) in the 6-month prediction. It is consistent with previous studies using GCMs in this region. The significant MAE of the 6-month precipitations simulated by group 1 and PBL1-CU2، PBL1-CU0, and PBL1-CU1 scenarios were the lowest among the configuration. Meanwhile, this group of configurations showed a similar RMSE score pattern by MAE, and the lowest RMSE showed in group 1 and group 2. In all configurations, the wet bias has been persistent in the study area.The WRF prediction captured the observed precipitation by groups 2 and 3 with MYJ and MYNN3 planetary boundary layer schemes. However, the false alarm (b) in group 1 and the number of missed events (c) in group 2 of configurations were finer low.

In this study, the WRF model performance was evaluated for various physical parameterizations in predicting precipitation for varying planetary boundary layer (PBL) schemes and Cumulus schemes over northeast Iran.Based on the sensitivity analysis, is concluded that the set that performs best for the region is YSU PBL, MM5 SL, Dudhia shortwave radiation, RRTM longwave radiation, and Noah LSM schemes.And using a cumulus scheme for grid resolutions between 3km and 10km is gray, as respects is not clear whether a cumulus scheme should be used or not. So, recommended testing a configuration set of no cumulus scheme mode to determine if using a cumulus scheme is ideal for your particular run.

Seasonal forecasting has always been one of the challenges in forecasting Iran's diverse climate. In the last one or two decades, many efforts have been made to develop and improve the climate models of the restricted area and to minimize these challenges, but the problems and challenges still remain. Convective parameterization schemes are always one of the sources of error in regional climate models that have a significant impact on model outputs. Therefore, one of the most important issues in implementing the model is choosing the appropriate convective scheme from the existing schemes. One of the methods of forecasting precipitation in our country is the use of dynamical downscaling by RegCM model. Most of the studies that have been done for this purpose in the country so far have considered single convection schemes for the whole country, the results of which have not shown a significant improvement in rainfall forecasting.

In this study, a relatively new approach was adopted, so that convection schemes were selected appropriate to the climate of the region, and then the final forecast of the entire country by regional integration of each climate zone was presented. In this paper a relatively new perspective of the climatic zones of the regions, was used for optimum configuration of the RegCM4.5 model; The study area in this study is Iran, which includes 25 to 41 degrees north latitude and 47 to 63 degrees east longitude, but the model area ranges from 30 to 70 degrees east longitude and 10 to 55 degrees north latitude. It covers important geographical features, including mountains and seas. In this study, the output of the CFSv2 global climate model originating from November 1 in each year as the boundary condition data has been used and the CRU precipitation data has been used as reanalysis data to test the output of the RegCM model. Because CRU data are averaged monthly, they are suitable for studies that examine monthly averages. CRU data have already been used by various researchers in the country to validate the output of the RegCM model. After selecting the schemes of the planetary boundary layer and surface layer, the selection of the appropriate Cumulus Parametrization Schemas(CPS) was done based on Iran's climatic classification using the Demarten index. This method is the simplest and most common method for climate classification that precipitation and temperature variables are effective in calculating climate index, and precipitation and temperature data have also been used from the CRU database. For this purpose, Iran was first divided into seven very humid, humid, semi-humid, Mediterranean, semi-arid and arid climates based on the Demarten index, and each grid points of the study area were assigned the relevant climate index. The share of each climatic class in zoning was obtained as follows; Arid 32.4%, Semi-Arid 30.1%, Mediterranean 7.6%, Semi-humid 7.6%, Humid 10.5% and Highly humid 11.8%. The study period was 5 rainy seasons 2019-2014 (November to May) that the beginning of each simulation with the initial condition data on the first of November and its end at the end of May (as the end of the rainy season in the country) in each year. The horizontal resolution considered to be 30 km regional model, the planetary boundary layer schemes and the surface layer Holslag and BATS were considered, respectively. Kuo, Grell, Emanuel, Tiedtke and Kain convection schemes were tested during this period to achieve optimal configuration.

In the first stage, mean precipitation and its RMSE from individual and integrated schemas were calculated, but due to the fact that the Emanuel and Kain schemes did not rank higher in any of the model experiments in terms of climatic classes and have more errors than others, theywere removed from the configuration selection process. The results showed that in very humid, humid, semi-humid and humid climates the Tiedtke convection scheme, in the semi-arid regions the Grell scheme and in the arid areas of the Kuo scheme had the least bias compared to other convection schemes. Therefore, the seasonal forecast of the country was presented by combining regional schemas, the average bias of which was calculated at 0.45, 0.79, 1.01 and 0.69 mm in the integrated schemes of Tiedtke, Grell and Kuo, respectively. On the other hand, in addition to calculating the ability of different schemas to predict precipitation using the RMSE index, the area under the ROC curve was also calculated in three classes less than normal (BN), normal (NN) and more than normal (AN) for different climates. For this purpose, in each precipitation layer, the number of schemas that predicted precipitation in different climates and had the largest area under the curve compared to other schemes was extracted. ROC diagrams of different schemas showed that Tiedtke and Grell schemas have the highest ability to predict less than normal, normal and more than normal rainfall classes. The results showed that the regional integrated scheme (TGK: Tiedtke, Grell and Kuo) showed an improvement of 54 to 126% compared to the individual schemas. In general, it can be said that choosing the optimal configuration based on the idea of climate-based convection scheme can increase the performance of the RegCM4.5 regional model in seasonal precipitation forecast in Iran.

Although a study with a regional climatic zones perspective was not found on Iran, but some studies have found the Tiedtke scheme suitable for our country (Alizadeh Choubari et al., 1398), which with the findings of this study in which the Tiedke scheme for four of the six climates used in this study are considered appropriate. On the other hand, Zarrin and Dadashi (1399) used the Grell scheme to study the events of the partial rainfall in Iran by RegCM4 model, which in this study was found to be suitable for semi-arid climate. In addition, it was observed that in the study period of seven months, the most RMSE error occurred in April, which is the month of transition from cold to warm season.