Search Results
Abstract
A two-week prediction experiment was performed with the GISS atmospheric model on a global data set beginning 20 December 1972 to test the sensitivity of the model to sea-surface temperature (SST) variations. Use of observed SST's in place of climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a marked local effect on predicted precipitation over the ocean, with enhanced convection over warm SST anomalies. However, use of observed SST's did not lead to any detectable general improvement in forecast skill. The influence of the SST anomalies on daily predicted fields of pressure and geopotential was small up to about one week compared with the growth of prediction error, and no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea-level pressure and 500 mb height predicted by the model were similarly insensitive to the SST anomalies.
Abstract
A two-week prediction experiment was performed with the GISS atmospheric model on a global data set beginning 20 December 1972 to test the sensitivity of the model to sea-surface temperature (SST) variations. Use of observed SST's in place of climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a marked local effect on predicted precipitation over the ocean, with enhanced convection over warm SST anomalies. However, use of observed SST's did not lead to any detectable general improvement in forecast skill. The influence of the SST anomalies on daily predicted fields of pressure and geopotential was small up to about one week compared with the growth of prediction error, and no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea-level pressure and 500 mb height predicted by the model were similarly insensitive to the SST anomalies.
Abstract
In the absence of a current capability for global routine daily soil moisture observation, an infrared technique using existing instrumentation is sought. Numerical modeling results are reported from a pilot study, the purpose of which was to develop such a technique and to determine the quality and reliability of soil moisture information which it can produce.
In order to determine which physical parameters observable from GOES are most sensitive to soil moisture and which are less prone to interference by seasonal changes, atmospheric effects, vegetation cover, etc., a detailed one-dimensional boundary layer-surface-soil model was employed. The model is described briefly. Results of sensitivity tests are presented which show that the mid-morning differential of surface temperature with respect to absorbed solar radiation is optimally sensitive to soil moisture. A case study comparing model results with GOES infrared data confirms the sensitivity of this parameter to soil moisture and also establishes the applicability of the model to predicting area-averaged surface temperature changes.
A series of model runs were then used to develop a simulated surface temperature dataset from which a soil moisture algorithm was developed. This algorithm uses only GOES observations to separate the soil moisture signal from the interfering effects on the surface temperature. It is shown that soil moisture can be most accurately estimated by this method in dry or marginal agricultural areas where drought is a frequent threat. Sources of error, including the effects of advection and clouds, are examined and methods of minimizing errors are discussed.
Abstract
In the absence of a current capability for global routine daily soil moisture observation, an infrared technique using existing instrumentation is sought. Numerical modeling results are reported from a pilot study, the purpose of which was to develop such a technique and to determine the quality and reliability of soil moisture information which it can produce.
In order to determine which physical parameters observable from GOES are most sensitive to soil moisture and which are less prone to interference by seasonal changes, atmospheric effects, vegetation cover, etc., a detailed one-dimensional boundary layer-surface-soil model was employed. The model is described briefly. Results of sensitivity tests are presented which show that the mid-morning differential of surface temperature with respect to absorbed solar radiation is optimally sensitive to soil moisture. A case study comparing model results with GOES infrared data confirms the sensitivity of this parameter to soil moisture and also establishes the applicability of the model to predicting area-averaged surface temperature changes.
A series of model runs were then used to develop a simulated surface temperature dataset from which a soil moisture algorithm was developed. This algorithm uses only GOES observations to separate the soil moisture signal from the interfering effects on the surface temperature. It is shown that soil moisture can be most accurately estimated by this method in dry or marginal agricultural areas where drought is a frequent threat. Sources of error, including the effects of advection and clouds, are examined and methods of minimizing errors are discussed.
