Utilization of satellite data and regional scale numerical models in short range weather forecasting

Overwhelming evidence was developed in a number of studies of satellite data impact on numerical weather prediction that it is unrealistic to expect satellite temperature soundings to improve detailed regional numerical weather prediction. It is likely that satellite data over the United States would substantially impact mesoscale dynamical predictions if the effort were made to develop a composite moisture analysis system. The horizontal variability of moisture, most clearly depicited in images from satellite water vapor channels, would not be determined from conventional rawinsondes even if that network were increased by a doubling of both the number of sites and the time frequency

Numerical prediction experiments simulating the impact of mesoscale satellite data

Recent developments in mesometeorology are summarized to place this research in perspective. Recent advances in computer analysis and forecast system development that provide the basis for the simulation tests are discussed. The impact of NIMBUS-6 humidity data on analyses off the West Coast are shown and incorporation of geopotential gradient data is discussed. Experiments to demonstrate the feasibility of incorporating satellite-derived wind fields in mesoscale severe storm models are mentioned briefly

LAMPS software

The dynamic prediction model along with its macro-processor capability and data flow system from the Drexel Limited-Area and Mesoscale Prediction System (LAMPS) were converted and recorded for the Perkin-Elmer 3220. The previous version of this model was written for Control Data Corporation 7600 and CRAY-1a computer environment which existed until recently at the National Center for Atmospheric Research. The purpose of this conversion was to prepare LAMPS for porting to computer environments other than that encountered at NCAR. The emphasis was shifted from programming tasks to model simulation and evaluation tests

Effects of mesoscale weather disturbances on contamination concentrations. Fourth technical progress report, August 1, 1976--October 31, 1977

The objective of this research is the development and verification of a regional scale numerical weather prediction model for use in forecasting air pollution concentrations. The scope includes verification of meteorological forecasts of flow fields, boundary layer structure and precipitation using three hourly rawinsonde data in the central and eastern United States. A prototype regional numerical weather forecast system has been developed. This system includes codes to read in the first guess data and the observational data needed to initialize the prediction model. Other codes use these data to perform an isentropic analysis of the wind, temperature and pressure fields and analysis of the humidity field using optimal interpolation. Subsequent codes transform these analyses to the prediction grid and adjust the wind field to remove the vertically integrated mass convergence. The output from that code is the input to the fine mesh prediction model that uses a 140 km grid size and makes a 24 h forecast. Fine mesh model output is used to initialize the mesoscale model and provide time dependent lateral boundary tendencies to the mesoscale model. The mesoscale model is on a 35 km grid and normally makes a 12 h forecast during some portion of the 24 h fine mesh forecast period

Effects of mesoscale weather disturbances on contamination concentrations. First technical progress report, June 1, 1973--February 28, 1974

Progress is reported in the development of a mathematical model to be used to dynamically predict the motion and dispersion properties of the atmosphere on the mesoscale which will, in turn, permit calculation of contamination concentrations. (LCL