Abstract
In order to efficiently and self-consistently describe the transport ofhigh-latitude ionospheric plasma into the magnetosphere,we have developeda Dynamic coupled Fluid-Kinetic(DyFK)model for describing plasmaflow along a magnetic flux tube.The collision-dominated ionospheric plasmais treated with a low-speed fluid approach for altitudes between 120-1100km,while a generalized semikinetic approach is used for the topside andhigher altitudes,starting at 800km.This paper presents a description ofthe new DyFK model,along with illustrative results obtained from modelingthe effects of soft(100eV)auroral electron precipitation using the newmodel.The results demonstrate the accuracy and efficiency of the newDyFK model and illustrate how soft electron precipitation provides a mechanismto drive observed upflows of high-latitude ionospheric plasma alonggeomagnetic field lines.
Highlights
It is well known that if the mass and wind fields in the initial state are not dynamically balanced, spurious high-frequency large-amplitude inertial gravity-wave oscillations occur in numerical weather prediction based on primitive equations
Three 24-hr forecasts are produced starting from adiabatic digital filtering initialization (ADFI), diabatic digital filtering initialization technique (DDFI) and uninitialized analysis
The performance of ADFI and DDFI on the model forecasts are evaluated by comparing the ADFI and DDFI forecasts with those obtained from NOi input
Summary
It is well known that if the mass and wind fields in the initial state are not dynamically balanced, spurious high-frequency large-amplitude inertial gravity-wave oscillations occur in numerical weather prediction based on primitive equations. Lynch and Huang (1992) have applied a non-recursive adiabatic digital filtering initialization (ADFI) procedure to initialize the input data for a high-resolution limited area model (HIRLAM). Huang and Lynch (1993) have extended ADF1 to include diabatic processes and formulated a non-recursive diabatic digital filtering initialization technique (DDFI) Their results from applications of DFI to HIRLAM have shown that both ADFI and DDFI can yield results which are at least as good as those of NMI. Both the adiabatic (ADFI) and diabatic (DDFI) versions are used separately in the model and their impact on the forecast performance of the model for short range prediction over Indian monsoon region is examined and discussed with reference to the uninitialized forecast
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