Forest fires occur in Portugal every year during late spring, summer and fall. However, the beginning and end of the most severe season of forest fires are very variable, as is their intensity, the area and the number...Forest fires occur in Portugal every year during late spring, summer and fall. However, the beginning and end of the most severe season of forest fires are very variable, as is their intensity, the area and the number of occurrences. It is obvious, that vegetation stress and droughts are strongly linked to the occurrence of forest fires and burned area, showing a strong response to the drought. The vegetation health index (VHI), retrieved from the NOAA/NESDIS, shows good results in the detection of droughts, monitoring vegetation conditions in different countries. VHI is computed combining two terms: vegetation condition index (VCI), and temperature condition index (TCI) reflecting moisture and thermal vegetation conditions. The main objective of this study was to investigate the potential of VHI-method to monitor environmental conditions, favourable to forest fires in Portugal. Results of the study show that 88% of forest fires with burned area higher than 1,000 ha in a week, are well related with vegetation stress or drought conditions, detected with VHI-method. The results also show that the monitoring of the evolution of the VHI indexes is important for prevention burnt areas, especially in the spring, since it can indicate conditions for vegetation growth, which increases the fuel availability and the fire risk in the summer.展开更多
Soil water excess,as well as deficit,leads to vegetation stress,i.e.,photosynthesis decline,stomata closure,growth reduction,decrease in respiration and biomass production.Therefore,vegetation response can be used as ...Soil water excess,as well as deficit,leads to vegetation stress,i.e.,photosynthesis decline,stomata closure,growth reduction,decrease in respiration and biomass production.Therefore,vegetation response can be used as indicator of changing in soil conditions,which corresponds to such phenomena as drought or soil waterlogging and associated natural disasters.During last 20 years,National Oceanic and Atmosphere Administration,National Environmental Satellite Data and Information Services(NOAA/NESDIS)satellite-based vegetation health indices(VHI)were successfully used for monitoring environmentally-based vegetation stress,including droughts,fire risk,soil saturation and other natural hazards around the world.In this study,the VHI were applied to verify the possibility their utilization for detection landslide risk areas in Madeira Island.Vegetation condition index(VCI)and registered precipitation were analyzed together with information on landslide occurrence in recent years.展开更多
This paper is to examine the impact of satellite data on the systematic error of operational B-model in China.Em- phasis is put on the study of the impact of satellite sounding data on forecasts of the sea level press...This paper is to examine the impact of satellite data on the systematic error of operational B-model in China.Em- phasis is put on the study of the impact of satellite sounding data on forecasts of the sea level pressure field and 500 hPa height.The major findings are as follows. (1)The B-model usually underforecasts the strength of features in the sea level pressure(SLP)field,i.e.pressures are too low near high pressure systems and too high near low pressure systems. (2)The nature of the systematic errors found in the 500 hPa height forecasts is not as clear cut as that of the SLP forecasts,but most often the same type of pattern is seen,i.e.,the heights in troughs are not low enough and those in ridges are not high enough. (3)The use of satellite data in the B-model analysis/forecast system is found to have an impact upon the model's forecast of SLP and 500 hPa height.Systematic errors in the vicinity of surface lows/500 hPa troughs over the oceans are usually found to be significantly reduced.A less conclusive mix of positive and negative impacts was found for all other types of features.展开更多
为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,...为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。展开更多
总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weathe...总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和加拿大气象中心(Canadian Meteoro-logical Centre,CMC)建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法(ECMWF)、增殖向量法(NCEP)和观测扰动法(CMC)更新为现在的集合资料同化—奇异向量法(ECMWF)、重新尺度化集合转换法(NCEP)和集合卡尔曼滤波(CMC)。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划(the THORPEX interactive grand global ensemble)的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统(North American ensemble forecast system,NAEFS)为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建。展开更多
文摘Forest fires occur in Portugal every year during late spring, summer and fall. However, the beginning and end of the most severe season of forest fires are very variable, as is their intensity, the area and the number of occurrences. It is obvious, that vegetation stress and droughts are strongly linked to the occurrence of forest fires and burned area, showing a strong response to the drought. The vegetation health index (VHI), retrieved from the NOAA/NESDIS, shows good results in the detection of droughts, monitoring vegetation conditions in different countries. VHI is computed combining two terms: vegetation condition index (VCI), and temperature condition index (TCI) reflecting moisture and thermal vegetation conditions. The main objective of this study was to investigate the potential of VHI-method to monitor environmental conditions, favourable to forest fires in Portugal. Results of the study show that 88% of forest fires with burned area higher than 1,000 ha in a week, are well related with vegetation stress or drought conditions, detected with VHI-method. The results also show that the monitoring of the evolution of the VHI indexes is important for prevention burnt areas, especially in the spring, since it can indicate conditions for vegetation growth, which increases the fuel availability and the fire risk in the summer.
文摘Soil water excess,as well as deficit,leads to vegetation stress,i.e.,photosynthesis decline,stomata closure,growth reduction,decrease in respiration and biomass production.Therefore,vegetation response can be used as indicator of changing in soil conditions,which corresponds to such phenomena as drought or soil waterlogging and associated natural disasters.During last 20 years,National Oceanic and Atmosphere Administration,National Environmental Satellite Data and Information Services(NOAA/NESDIS)satellite-based vegetation health indices(VHI)were successfully used for monitoring environmentally-based vegetation stress,including droughts,fire risk,soil saturation and other natural hazards around the world.In this study,the VHI were applied to verify the possibility their utilization for detection landslide risk areas in Madeira Island.Vegetation condition index(VCI)and registered precipitation were analyzed together with information on landslide occurrence in recent years.
文摘This paper is to examine the impact of satellite data on the systematic error of operational B-model in China.Em- phasis is put on the study of the impact of satellite sounding data on forecasts of the sea level pressure field and 500 hPa height.The major findings are as follows. (1)The B-model usually underforecasts the strength of features in the sea level pressure(SLP)field,i.e.pressures are too low near high pressure systems and too high near low pressure systems. (2)The nature of the systematic errors found in the 500 hPa height forecasts is not as clear cut as that of the SLP forecasts,but most often the same type of pattern is seen,i.e.,the heights in troughs are not low enough and those in ridges are not high enough. (3)The use of satellite data in the B-model analysis/forecast system is found to have an impact upon the model's forecast of SLP and 500 hPa height.Systematic errors in the vicinity of surface lows/500 hPa troughs over the oceans are usually found to be significantly reduced.A less conclusive mix of positive and negative impacts was found for all other types of features.
文摘为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。
文摘总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和加拿大气象中心(Canadian Meteoro-logical Centre,CMC)建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法(ECMWF)、增殖向量法(NCEP)和观测扰动法(CMC)更新为现在的集合资料同化—奇异向量法(ECMWF)、重新尺度化集合转换法(NCEP)和集合卡尔曼滤波(CMC)。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划(the THORPEX interactive grand global ensemble)的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统(North American ensemble forecast system,NAEFS)为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建。
