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
et al. 2000 ). An initial evaluation of a reanalysis record is therefore a useful undertaking. The purpose of this study is to provide a basic overview of the quality of MERRA in polar regions. To this end we focus on the atmospheric moisture budget, which has recently been the subject of other studies. A companion paper examines the representation of the atmospheric energy budget in MERRA over high latitudes (Cullather and Bosilovich 2011, manuscript submitted to J. Climate ). The surface
et al. 2000 ). An initial evaluation of a reanalysis record is therefore a useful undertaking. The purpose of this study is to provide a basic overview of the quality of MERRA in polar regions. To this end we focus on the atmospheric moisture budget, which has recently been the subject of other studies. A companion paper examines the representation of the atmospheric energy budget in MERRA over high latitudes (Cullather and Bosilovich 2011, manuscript submitted to J. Climate ). The surface
1. Introduction Most land surface models (LSMs) used with atmospheric general circulation models (AGCMs) keep track of the moisture state of the soil with a prognostic (state) variable called “soil moisture” or “soil wetness,” typically defined at a number of vertical subsurface levels. Given the general dearth of in situ large-scale observations of soil moisture, this model-generated quantity is often made available to the scientific community as a data product. The National Centers for
1. Introduction Most land surface models (LSMs) used with atmospheric general circulation models (AGCMs) keep track of the moisture state of the soil with a prognostic (state) variable called “soil moisture” or “soil wetness,” typically defined at a number of vertical subsurface levels. Given the general dearth of in situ large-scale observations of soil moisture, this model-generated quantity is often made available to the scientific community as a data product. The National Centers for
idealized conditions of low-level dry air inflow into a land region from an adjacent ocean. The LN07 prototype demonstrates how the characteristics of such inflow convective margins—for example, the location of the transition between nonconvecting and convecting conditions—depend on dynamic and thermodynamic variables, including low-level circulation, inflow moisture, and tropospheric temperature. For simplicity, the LN07 analysis neglected effects of land surface conditions such as soil moisture on
idealized conditions of low-level dry air inflow into a land region from an adjacent ocean. The LN07 prototype demonstrates how the characteristics of such inflow convective margins—for example, the location of the transition between nonconvecting and convecting conditions—depend on dynamic and thermodynamic variables, including low-level circulation, inflow moisture, and tropospheric temperature. For simplicity, the LN07 analysis neglected effects of land surface conditions such as soil moisture on
the amount of water available for aquifer recharge. Thus, soil moisture observations are being utilized by water resource managers in their long- and short-term management of water storage facilities (i.e., dams and reservoirs). Our ability to make accurate long-term observations of soil moisture on regional scales can also have a large impact on our ability to understand the impact of global climate change on our water supply. Providing timely weather, hydrological, and climatological forecasts
the amount of water available for aquifer recharge. Thus, soil moisture observations are being utilized by water resource managers in their long- and short-term management of water storage facilities (i.e., dams and reservoirs). Our ability to make accurate long-term observations of soil moisture on regional scales can also have a large impact on our ability to understand the impact of global climate change on our water supply. Providing timely weather, hydrological, and climatological forecasts
the climatology of drylines, have not been researched as comprehensively. There have only been a few studies that focused on the evapotranspiration or soil moisture effect on the dryline ( Ziegler et al. 1995 ; Shaw 1995 ; Grasso 2000 ), and all of them incorporated atmospheric models. Curiously, no studies used observed soil moisture data and its effect on the dryline. This study examines observed soil moisture values as a surrogate for evapotranspiration and relates them to the daily position
the climatology of drylines, have not been researched as comprehensively. There have only been a few studies that focused on the evapotranspiration or soil moisture effect on the dryline ( Ziegler et al. 1995 ; Shaw 1995 ; Grasso 2000 ), and all of them incorporated atmospheric models. Curiously, no studies used observed soil moisture data and its effect on the dryline. This study examines observed soil moisture values as a surrogate for evapotranspiration and relates them to the daily position
1. Introduction Soil moisture is an important component of the hydrological cycle as it plays an integral role in mass and energy exchange between the land surface and the atmosphere. As a result, accurate estimation of soil moisture can improve weather and streamflow forecasting in climate and hydrological models ( Berg and Mulroy 2006 ; Reichle et al. 2007 , 2008 ). Remotely sensed soil moisture data have become readily available from a variety of satellite platforms such as the Advanced
1. Introduction Soil moisture is an important component of the hydrological cycle as it plays an integral role in mass and energy exchange between the land surface and the atmosphere. As a result, accurate estimation of soil moisture can improve weather and streamflow forecasting in climate and hydrological models ( Berg and Mulroy 2006 ; Reichle et al. 2007 , 2008 ). Remotely sensed soil moisture data have become readily available from a variety of satellite platforms such as the Advanced
particular challenge of defining metrics for satellite retrievals of surface (top 5 cm) soil moisture and for data products (including rootzone soil moisture) that are derived from the assimilation of the surface retrievals into a land model. Remote sensing of terrestrial microwave emission and radar backscatter in the L-band spectral range is sensitive to the water content of soils in a 0–5-cm surface layer. Such retrievals will soon be available from the Soil Moisture Ocean Salinity (SMOS) mission
particular challenge of defining metrics for satellite retrievals of surface (top 5 cm) soil moisture and for data products (including rootzone soil moisture) that are derived from the assimilation of the surface retrievals into a land model. Remote sensing of terrestrial microwave emission and radar backscatter in the L-band spectral range is sensitive to the water content of soils in a 0–5-cm surface layer. Such retrievals will soon be available from the Soil Moisture Ocean Salinity (SMOS) mission
( Hayashi 1970 ; Lindzen 1974 ). The simplest form of the wave-CISK feedback is that large-scale atmospheric waves produce regions of low-level moisture convergence where vigorous convections develop; latent heat released by the convections induces upward motions and further strengthens low-level convergence. Numerical models with the convective parameterization (CP) based on the wave-CISK concept did reproduce the CCEWs with some success (e.g., Hayashi and Sumi 1986 ; Lau and Peng 1987 ), but they
( Hayashi 1970 ; Lindzen 1974 ). The simplest form of the wave-CISK feedback is that large-scale atmospheric waves produce regions of low-level moisture convergence where vigorous convections develop; latent heat released by the convections induces upward motions and further strengthens low-level convergence. Numerical models with the convective parameterization (CP) based on the wave-CISK concept did reproduce the CCEWs with some success (e.g., Hayashi and Sumi 1986 ; Lau and Peng 1987 ), but they
1. Introduction Soil moisture is a crucial variable for numerical weather and climate prediction as it controls the partitioning of energy into latent and sensible heat fluxes at the soil–atmosphere interface. In addition it is a key variable in hydrological processes (i.e., runoff, evaporation from bare soil, and transpiration from the vegetation cover) and has an impact on plant growth and carbon fluxes ( Dirmeyer et al. 1999 ; Entekhabi et al. 1999 ). Soil moisture is also important for
1. Introduction Soil moisture is a crucial variable for numerical weather and climate prediction as it controls the partitioning of energy into latent and sensible heat fluxes at the soil–atmosphere interface. In addition it is a key variable in hydrological processes (i.e., runoff, evaporation from bare soil, and transpiration from the vegetation cover) and has an impact on plant growth and carbon fluxes ( Dirmeyer et al. 1999 ; Entekhabi et al. 1999 ). Soil moisture is also important for
