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1. Introduction The absence of small-scale moisture measurements near the surface is a major limitation in forecasting convective precipitation ( Emanuel et al. 1995 ; Dabberdt and Schlatter 1996 ; National Research Council 1998 ). Recent breakthroughs in retrieving near-surface refractivity from weather radar provide new opportunities for high-resolution, near-surface moisture measurements ( Fabry et al. 1997 ; Fabry 2004 ; Cheong et al. 2008 ). Refractivity retrievals obtained from the
1. Introduction The absence of small-scale moisture measurements near the surface is a major limitation in forecasting convective precipitation ( Emanuel et al. 1995 ; Dabberdt and Schlatter 1996 ; National Research Council 1998 ). Recent breakthroughs in retrieving near-surface refractivity from weather radar provide new opportunities for high-resolution, near-surface moisture measurements ( Fabry et al. 1997 ; Fabry 2004 ; Cheong et al. 2008 ). Refractivity retrievals obtained from the
1. Introduction The Canadian Forest Fire Weather Index (FWI) System ( Van Wagner 1987 ) has been in use across Canada for the past 30 years in the daily operations of fire management agencies ( http://cwfis.cfs.nrcan.gc.ca/ ). The FWI System uses daily weather observations (temperature, rainfall, relative humidity, and wind velocity) to estimate the moisture content of three different fuel classes and uses these to generate a set of relative indicators of potential rate of fire spread, fire
1. Introduction The Canadian Forest Fire Weather Index (FWI) System ( Van Wagner 1987 ) has been in use across Canada for the past 30 years in the daily operations of fire management agencies ( http://cwfis.cfs.nrcan.gc.ca/ ). The FWI System uses daily weather observations (temperature, rainfall, relative humidity, and wind velocity) to estimate the moisture content of three different fuel classes and uses these to generate a set of relative indicators of potential rate of fire spread, fire
1. Introduction Soil moisture is an important component of the hydrological cycle that describes the availability of water for vegetation and the capacity of the soil to retain/absorb incoming precipitation. As a result, observed (in situ and remotely sensed) or modeled soil moisture data can be readily applied to a variety of applications ( Ochsner et al. 2013 ): monitoring and/or forecasting droughts ( Otkin et al. 2016 ; Mo and Lettenmaier 2015 ; Hayes et al. 2012 ; Bell et al. 2015 ) and
1. Introduction Soil moisture is an important component of the hydrological cycle that describes the availability of water for vegetation and the capacity of the soil to retain/absorb incoming precipitation. As a result, observed (in situ and remotely sensed) or modeled soil moisture data can be readily applied to a variety of applications ( Ochsner et al. 2013 ): monitoring and/or forecasting droughts ( Otkin et al. 2016 ; Mo and Lettenmaier 2015 ; Hayes et al. 2012 ; Bell et al. 2015 ) and
1. Introduction In situ soil moisture is valuable for validating soil moisture products such as offline land surface models ( Robock et al. 2003 ; Fan et al. 2006 ; Liu et al. 2011 ; Meng et al. 2012 ; Xia et al. 2014 ) as well as coupled numerical weather and climate prediction models ( de Goncalves 2006 ; De Rosnay et al. 2009 ; Fan et al. 2011 ; Su et al. 2013 ). It also has been widely used for validating remote sensing–based soil moisture products ( Zribi et al. 2008 ; Gruhier et
1. Introduction In situ soil moisture is valuable for validating soil moisture products such as offline land surface models ( Robock et al. 2003 ; Fan et al. 2006 ; Liu et al. 2011 ; Meng et al. 2012 ; Xia et al. 2014 ) as well as coupled numerical weather and climate prediction models ( de Goncalves 2006 ; De Rosnay et al. 2009 ; Fan et al. 2011 ; Su et al. 2013 ). It also has been widely used for validating remote sensing–based soil moisture products ( Zribi et al. 2008 ; Gruhier et
1. Introduction Interactions between soil moisture and surface temperature are important on a range of spatial and temporal scales, from cities to continents and from daily to decadal and longer time scales ( Seneviratne et al. 2010 ). A negative soil moisture anomaly results in reduced evapotranspiration from the surface and a consequent increase in the sensible heat flux that leads to an increase in air temperature. Therefore, soil moisture can significantly affect the near-surface climate
1. Introduction Interactions between soil moisture and surface temperature are important on a range of spatial and temporal scales, from cities to continents and from daily to decadal and longer time scales ( Seneviratne et al. 2010 ). A negative soil moisture anomaly results in reduced evapotranspiration from the surface and a consequent increase in the sensible heat flux that leads to an increase in air temperature. Therefore, soil moisture can significantly affect the near-surface climate
1. Introduction The role of soil moisture in the climate system has been thoroughly investigated over the last two decades ( Legates et al. 2011 ). Soil moisture modifies energy and moisture flux into the boundary layer ( Guo and Dirmeyer 2013 ) thereby influencing near-surface air temperature ( Hirschi et al. 2011 ; Miralles et al. 2012 ), humidity ( Ek and Holtslag 2004 ; Ford et al. 2015b ), and boundary layer instability ( Myoung and Nielsen-Gammon 2010 ; Gentine et al. 2013 ) and in
1. Introduction The role of soil moisture in the climate system has been thoroughly investigated over the last two decades ( Legates et al. 2011 ). Soil moisture modifies energy and moisture flux into the boundary layer ( Guo and Dirmeyer 2013 ) thereby influencing near-surface air temperature ( Hirschi et al. 2011 ; Miralles et al. 2012 ), humidity ( Ek and Holtslag 2004 ; Ford et al. 2015b ), and boundary layer instability ( Myoung and Nielsen-Gammon 2010 ; Gentine et al. 2013 ) and in
1. Introduction A modern numerical weather prediction (NWP) model often includes a land surface model to provide an estimation of sensible and latent heat fluxes directly to its bottom atmospheric layer. For a land surface model, the state of soil moisture is particularly important. Research has shown that weather and precipitation predictions are sensitive to the soil moisture conditions ( Sutton et al. 2006 ; Aligo et al. 2007 ; Trier et al. 2008 ; Hohenegger et al. 2009 ; Van Weverberg
1. Introduction A modern numerical weather prediction (NWP) model often includes a land surface model to provide an estimation of sensible and latent heat fluxes directly to its bottom atmospheric layer. For a land surface model, the state of soil moisture is particularly important. Research has shown that weather and precipitation predictions are sensitive to the soil moisture conditions ( Sutton et al. 2006 ; Aligo et al. 2007 ; Trier et al. 2008 ; Hohenegger et al. 2009 ; Van Weverberg
, or if lightning occurs outside of the rain shaft of a thunderstorm (commonly known as a bolt from the blue). Rorig and Ferguson (1999 , hereinafter RF99 ) showed that the occurrence of dry lightning, defined as lightning that occurs with rainfall less than 0.1 in. (2.54 mm), in the U.S. Pacific Northwest was related to high values of lower-tropospheric instability (represented by a large temperature difference between 850 and 500 hPa) combined with low atmospheric moisture levels (represented by
, or if lightning occurs outside of the rain shaft of a thunderstorm (commonly known as a bolt from the blue). Rorig and Ferguson (1999 , hereinafter RF99 ) showed that the occurrence of dry lightning, defined as lightning that occurs with rainfall less than 0.1 in. (2.54 mm), in the U.S. Pacific Northwest was related to high values of lower-tropospheric instability (represented by a large temperature difference between 850 and 500 hPa) combined with low atmospheric moisture levels (represented by
1. Introduction The effects of heterogeneities in soil moisture on the heat and moisture fluxes have been investigated using coupled atmospheric and land surface models. The models generally indicate that the heterogeneities lead to the development of mesoscale circulation patterns ( Ookouchi et al. 1984 ; Pielke and Segal 1986 ; Avissar and Pielke 1989 ; Fast and McCorcle 1991 ; Chang and Wetzel 1991 ; Emori 1998 ; Liu et al. 1999 ; Pielke 2001 ; Carleton et al. 2001 ; Berbery et al
1. Introduction The effects of heterogeneities in soil moisture on the heat and moisture fluxes have been investigated using coupled atmospheric and land surface models. The models generally indicate that the heterogeneities lead to the development of mesoscale circulation patterns ( Ookouchi et al. 1984 ; Pielke and Segal 1986 ; Avissar and Pielke 1989 ; Fast and McCorcle 1991 ; Chang and Wetzel 1991 ; Emori 1998 ; Liu et al. 1999 ; Pielke 2001 ; Carleton et al. 2001 ; Berbery et al
. Sophisticated parameterization schemes have been devised by meteorological researchers to suitably represent the evolution of boundary layer wind, temperature, moisture, and turbulence in atmospheric models. Such schemes usually allow multiple surface energy budgets for the different surface components, for example, soil, vegetation, and urban canopy ( Bélair et al. 2003 ; Masson 2000 ), and often permit surface–atmosphere interactions at multiple vertical atmospheric model levels that intersect vegetation
. Sophisticated parameterization schemes have been devised by meteorological researchers to suitably represent the evolution of boundary layer wind, temperature, moisture, and turbulence in atmospheric models. Such schemes usually allow multiple surface energy budgets for the different surface components, for example, soil, vegetation, and urban canopy ( Bélair et al. 2003 ; Masson 2000 ), and often permit surface–atmosphere interactions at multiple vertical atmospheric model levels that intersect vegetation
