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Testing a Novel Method for Initializing Air Parcel Back Trajectories in Precipitating Clouds Using Reanalysis Datafinal departure from the PBL, 2) reanalysis data reliably describe actual meteorological conditions, and 3) appropriate altitudes are selected to begin back trajectories. This article tests the sensitivity of a new method for addressing the third requirement. Finding the proper altitude to initiate a back trajectory is important for at least two reasons. First, wind direction and velocity change with altitude, and therefore air parcels released at different heights may indicate very different vapor
final departure from the PBL, 2) reanalysis data reliably describe actual meteorological conditions, and 3) appropriate altitudes are selected to begin back trajectories. This article tests the sensitivity of a new method for addressing the third requirement. Finding the proper altitude to initiate a back trajectory is important for at least two reasons. First, wind direction and velocity change with altitude, and therefore air parcels released at different heights may indicate very different vapor
( Wood and Bretherton 2004 ). Other authors (e.g., Karlsson et al. 2010 ) have used highly accurate estimates of cloud-top height from the Multiangle Imaging SpectroRadiometer (MISR) to investigate the subtropical PBL height evolution. A global climatology for PBL height can be derived from reanalysis data, which combines information from observations and models; it can provide important insight into the physics of the boundary layer from a global perspective and can be used to evaluate weather and
( Wood and Bretherton 2004 ). Other authors (e.g., Karlsson et al. 2010 ) have used highly accurate estimates of cloud-top height from the Multiangle Imaging SpectroRadiometer (MISR) to investigate the subtropical PBL height evolution. A global climatology for PBL height can be derived from reanalysis data, which combines information from observations and models; it can provide important insight into the physics of the boundary layer from a global perspective and can be used to evaluate weather and
global surface wind fields that are regularly spaced both temporally and spatially. The scatterometer-derived ocean vector winds are complementary to the conventional observing network, and the utility of these observations in data assimilation is applicable both in terms of forecasting ( Yu and Mcpherson 1984 ; Atlas et al. 2001 ; Bi et al. 2011 ; Liu et al. 2018 ) and reanalysis ( Goswami and Sengupta 2003 ; Dee et al. 2011a , b ). A scatterometer determines surface roughness from a measured
global surface wind fields that are regularly spaced both temporally and spatially. The scatterometer-derived ocean vector winds are complementary to the conventional observing network, and the utility of these observations in data assimilation is applicable both in terms of forecasting ( Yu and Mcpherson 1984 ; Atlas et al. 2001 ; Bi et al. 2011 ; Liu et al. 2018 ) and reanalysis ( Goswami and Sengupta 2003 ; Dee et al. 2011a , b ). A scatterometer determines surface roughness from a measured
in such simulations due to inaccurate initial and boundary conditions. With the development of various satellite retrievals, as well as improvements in the in situ observational network, the accuracy of model initial conditions and forecast performance can be improved through the application of data assimilation techniques. Various reanalysis projects are taking advantage of such improved initial conditions to generate high quality data. We adopt the same approach and are applying WRF and its
in such simulations due to inaccurate initial and boundary conditions. With the development of various satellite retrievals, as well as improvements in the in situ observational network, the accuracy of model initial conditions and forecast performance can be improved through the application of data assimilation techniques. Various reanalysis projects are taking advantage of such improved initial conditions to generate high quality data. We adopt the same approach and are applying WRF and its
may still be relevant. b. Quantifying and analyzing flash drought with reanalysis data An atmospheric reanalysis ( CCSP 2008 ) is, in essence, a mathematically optimal merging of observations and Earth system model physics that results in spatially and temporally comprehensive quantitative estimates of atmospheric and land surface variables across the globe. The input observations are generally extensive, and the modeled physical formulations impart appropriate physical behaviors to the variables
may still be relevant. b. Quantifying and analyzing flash drought with reanalysis data An atmospheric reanalysis ( CCSP 2008 ) is, in essence, a mathematically optimal merging of observations and Earth system model physics that results in spatially and temporally comprehensive quantitative estimates of atmospheric and land surface variables across the globe. The input observations are generally extensive, and the modeled physical formulations impart appropriate physical behaviors to the variables
data sources to compile TC size data for ROCI and several 10-m wind radii (e.g., Demuth et al. 2006 ; Landsea and Franklin 2013 ). These studies, however, may also have been negatively influenced by the heterogeneous sampling of the TC wind field. More recently, atmospheric reanalysis data has been used to compute TC size using the radius of the azimuthal-mean environmental pressure ( Knaff and Zehr 2007 ). Finally, Knaff et al. (2014) constructed estimates of the radius in which the azimuthal
data sources to compile TC size data for ROCI and several 10-m wind radii (e.g., Demuth et al. 2006 ; Landsea and Franklin 2013 ). These studies, however, may also have been negatively influenced by the heterogeneous sampling of the TC wind field. More recently, atmospheric reanalysis data has been used to compute TC size using the radius of the azimuthal-mean environmental pressure ( Knaff and Zehr 2007 ). Finally, Knaff et al. (2014) constructed estimates of the radius in which the azimuthal
reanalysis (ERA-Interim). ERA-Interim is the latest global atmospheric reanalysis produced by the ECMWF, and it covers the period from 1 January 1989 to the present. The gridded data product includes a large variety of 3-hourly surface parameters and 6-hourly upper-air parameters ( Dee et al. 2011 ). This reanalysis has a spatial resolution of 1.5° and 37 pressure levels [increasing by 14 levels from the preceding version 40-yr ECMWF Re-Analysis (ERA-40)]. On the other hand, CFSR spans the 31-yr period
reanalysis (ERA-Interim). ERA-Interim is the latest global atmospheric reanalysis produced by the ECMWF, and it covers the period from 1 January 1989 to the present. The gridded data product includes a large variety of 3-hourly surface parameters and 6-hourly upper-air parameters ( Dee et al. 2011 ). This reanalysis has a spatial resolution of 1.5° and 37 pressure levels [increasing by 14 levels from the preceding version 40-yr ECMWF Re-Analysis (ERA-40)]. On the other hand, CFSR spans the 31-yr period
, 2000 ; Xu et al. 2018 ; Yan and Liu 2014 ; Yang et al. 2014 ). Attempts were made with remote sensing techniques to calculate trends ( Qin et al. 2009 ; Salama et al. 2012 ; Zhong et al. 2011 ). Some recent studies used the general circulation model data downscaled with fine-resolution regional climate models to calculate the long-term trends ( Amato et al. 2019 ; Zhang et al. 2017 ). Moreover, some studies combined in situ and reanalysis data to understand the spatial pattern of historical
, 2000 ; Xu et al. 2018 ; Yan and Liu 2014 ; Yang et al. 2014 ). Attempts were made with remote sensing techniques to calculate trends ( Qin et al. 2009 ; Salama et al. 2012 ; Zhong et al. 2011 ). Some recent studies used the general circulation model data downscaled with fine-resolution regional climate models to calculate the long-term trends ( Amato et al. 2019 ; Zhang et al. 2017 ). Moreover, some studies combined in situ and reanalysis data to understand the spatial pattern of historical
–NCAR and observation. They found that the bias in islands was more significant than inland, illuminating that the different underlying surfaces lead to a different performance of atmosphere profile from reanalysis datasets. Xiang-Shu and Guo (2008) used radiosonde data to study the characteristic of water vapor transport during the SCSMEX and found the water vapor transport would be affected by the change of atmosphere dynamic and thermal field. Two studies did not evaluate water vapor flux from
–NCAR and observation. They found that the bias in islands was more significant than inland, illuminating that the different underlying surfaces lead to a different performance of atmosphere profile from reanalysis datasets. Xiang-Shu and Guo (2008) used radiosonde data to study the characteristic of water vapor transport during the SCSMEX and found the water vapor transport would be affected by the change of atmosphere dynamic and thermal field. Two studies did not evaluate water vapor flux from
1. Introduction Fire weather indices can be used to portray the compounded influence of various weather conditions (temperature, wind speed, and humidity) and fuel information (such as moisture content) of relevance to wildfires (commonly referred to as bushfires in Australia). The purpose of this study is to investigate various approaches of bias correction of data from long-term atmospheric models (in this case, reanalysis data). This could be used to produce a calibrated long-term fire
1. Introduction Fire weather indices can be used to portray the compounded influence of various weather conditions (temperature, wind speed, and humidity) and fuel information (such as moisture content) of relevance to wildfires (commonly referred to as bushfires in Australia). The purpose of this study is to investigate various approaches of bias correction of data from long-term atmospheric models (in this case, reanalysis data). This could be used to produce a calibrated long-term fire
