Hail is a meteorological phenomenon that directly concerns to agricultural sector in Iran. Hailstorms affect crop yield that depends on the crop species and the phonological time. In this investigation, climatological...Hail is a meteorological phenomenon that directly concerns to agricultural sector in Iran. Hailstorms affect crop yield that depends on the crop species and the phonological time. In this investigation, climatological study of hail fall has been performed through the available dataset in 118 synoptic stations across the country during a period of 20 years (1985-2004) and hail event map was drawn. After analyzing the data and considering the produced maps, regarding the number of hail occurrence in the country, the following provinces respectively illustrate the highest annual mean of hail fall;Chaharmahal-e-bakhtiari, Ilam, Tehran and Kurdistan (about 4 times/yearly). In the next step, using the statistics of the agricultural insurance affairs during 1995-2005, cultivation areas damaged by hail fall in the farms of agricultural strategic products including;irrigated and rain fed wheat, grain, rice, cotton, sugar beet and potato have been studied, and then the classified maps of hail damage have been plotted for each province and crop. The produced maps indicate that most of the damaged area by hail fall has been related to irrigated wheat crop, with an annual average of 12690.8 hectares in the whole country, then the damaged crops were ordered respectively as following: rain fed wheat, sugar beet, potato, grain, cotton and rice.展开更多
Urmia Lake in northwest of Iran, through the recent years has been extremely faced with the water crisis. Climate variations and anthropogenic impacts could be two main affiliated factors in this regard. We considered...Urmia Lake in northwest of Iran, through the recent years has been extremely faced with the water crisis. Climate variations and anthropogenic impacts could be two main affiliated factors in this regard. We considered the long term data series of precipitation, temperature and evaporation in monthly and yearly scales in order to compare to water-level values of Urmia Lake. The statistics approaches such as: standard deviation, trend analysis, T test, Pearson and Spearman correlations, liner regression are used to analyze all variables. The results released that the water-level of Urmia Lake along with the precipitation and temperature of the lake’s basin have experienced the periodic changes through 1961 to 2010, as there are some gradual dryness trends on the study area according to precipitation and temperature variations. Urmia Lake periodic water-level fluctuations show more significant correlation to temperature than the precipitation. Whiles, the water-level’s decreasing behavior especially through 1998 to 2010 is more harsh and different than the rate that is considered for precipitation’s decrease and temperature’s increase. Thus, there could be some anthropogenic factors in the basin which produced some supplementary causes to shrink Urmia Lake. Extracting the double precipitation over the basin through introducing and categorizing of atmospheric synoptic systems in order to cloud seeding operation could be one of urgent and innovative solutions to mitigate water crisis in the basin.展开更多
In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd...In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.展开更多
All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on ...All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate（IDSW-CLR）, Kriging with constant lapse rate（Kriging-CLR）, gradient inverse distance squared with linear lapse rate（GIDS-LR）, and gradient inverse distance squared with lapse rate determined by classification and regression tree（GIDS-CART）, are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly,but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error（RMSE）and mean absolute error（MAE） were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.展开更多
文摘Hail is a meteorological phenomenon that directly concerns to agricultural sector in Iran. Hailstorms affect crop yield that depends on the crop species and the phonological time. In this investigation, climatological study of hail fall has been performed through the available dataset in 118 synoptic stations across the country during a period of 20 years (1985-2004) and hail event map was drawn. After analyzing the data and considering the produced maps, regarding the number of hail occurrence in the country, the following provinces respectively illustrate the highest annual mean of hail fall;Chaharmahal-e-bakhtiari, Ilam, Tehran and Kurdistan (about 4 times/yearly). In the next step, using the statistics of the agricultural insurance affairs during 1995-2005, cultivation areas damaged by hail fall in the farms of agricultural strategic products including;irrigated and rain fed wheat, grain, rice, cotton, sugar beet and potato have been studied, and then the classified maps of hail damage have been plotted for each province and crop. The produced maps indicate that most of the damaged area by hail fall has been related to irrigated wheat crop, with an annual average of 12690.8 hectares in the whole country, then the damaged crops were ordered respectively as following: rain fed wheat, sugar beet, potato, grain, cotton and rice.
文摘Urmia Lake in northwest of Iran, through the recent years has been extremely faced with the water crisis. Climate variations and anthropogenic impacts could be two main affiliated factors in this regard. We considered the long term data series of precipitation, temperature and evaporation in monthly and yearly scales in order to compare to water-level values of Urmia Lake. The statistics approaches such as: standard deviation, trend analysis, T test, Pearson and Spearman correlations, liner regression are used to analyze all variables. The results released that the water-level of Urmia Lake along with the precipitation and temperature of the lake’s basin have experienced the periodic changes through 1961 to 2010, as there are some gradual dryness trends on the study area according to precipitation and temperature variations. Urmia Lake periodic water-level fluctuations show more significant correlation to temperature than the precipitation. Whiles, the water-level’s decreasing behavior especially through 1998 to 2010 is more harsh and different than the rate that is considered for precipitation’s decrease and temperature’s increase. Thus, there could be some anthropogenic factors in the basin which produced some supplementary causes to shrink Urmia Lake. Extracting the double precipitation over the basin through introducing and categorizing of atmospheric synoptic systems in order to cloud seeding operation could be one of urgent and innovative solutions to mitigate water crisis in the basin.
文摘In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.
文摘All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate（IDSW-CLR）, Kriging with constant lapse rate（Kriging-CLR）, gradient inverse distance squared with linear lapse rate（GIDS-LR）, and gradient inverse distance squared with lapse rate determined by classification and regression tree（GIDS-CART）, are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly,but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error（RMSE）and mean absolute error（MAE） were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.
