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
While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial ac...While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, Climate AP, to generate scale-free(i.e., specific to point locations) climate data for historical(1901–2015) and future(2011–2100)years and periods. Climate AP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways(RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, Climate AP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the Climate AP output was evaluated through comparisons of Climate AP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. Climate AP reduced prediction error by up to27% and 60% for monthly temperature and precipitation,respectively, relative to the original baselines data. The improvements for baseline portions of historical and futurewere more substantial. Applications and limitations of the software are discussed.展开更多
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.
基金funded by a research grant"Adaptation of Asia-Pacific Forests to Climate Change"(APFNet/2010/PPF/001)funded by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation
文摘While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, Climate AP, to generate scale-free(i.e., specific to point locations) climate data for historical(1901–2015) and future(2011–2100)years and periods. Climate AP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways(RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, Climate AP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the Climate AP output was evaluated through comparisons of Climate AP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. Climate AP reduced prediction error by up to27% and 60% for monthly temperature and precipitation,respectively, relative to the original baselines data. The improvements for baseline portions of historical and futurewere more substantial. Applications and limitations of the software are discussed.
