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
In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in th...In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV,focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022,an enhanced western Pacific subtropical high(WPSH)and anomalous double blockings over the Ural Mountains and Sea of Okhotsk,respectively,were associated with the persistently anomalous high pressure over the YRV,leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the double-blocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale,the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore,the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.展开更多
Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interp...Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.展开更多
Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational...Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.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.
基金the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004)the National Natural Science Foundation of China(Grant No.42175056)+3 种基金the Natural Science Foundation of Shanghai(Grant No.21ZR1457600)Review and Summary Project of China Meteorological Administration(Grant No.FPZJ2023-044)the China Meteorological Administration Innovation and Development Project(Grant No.CXFZ2022J009)the Key Innovation Team of Climate Prediction of the China Meteorological Administration(Grant No.CMA2023ZD03).
文摘In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV,focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022,an enhanced western Pacific subtropical high(WPSH)and anomalous double blockings over the Ural Mountains and Sea of Okhotsk,respectively,were associated with the persistently anomalous high pressure over the YRV,leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the double-blocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale,the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore,the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.
基金supported by NOAA JTTI award via Grant #NA21OAR4590165, NOAA GOESR Program funding via Grant #NA16OAR4320115provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative Agreement #NA11OAR4320072, U.S. Department of Commercesupported by the National Oceanic and Atmospheric Administration (NOAA) of the U.S. Department of Commerce via Grant #NA18NWS4680063。
文摘Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.
基金This work was jointly supported by the National Natural Science Foundation of China(Grant Nos.41975137,42175012,and 41475097)the National Key Research and Development Program(Grant No.2018YFF0300103).
文摘Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.
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。
