Details of Low Latitude Medium Range Numerical Weather Prediction Using a Global Spectral Model

Abstract

This is a continuation of a recent study on medium range numerical weather prediction utilizing a global spectral model and FGGE/MONEX observations.In the present study the impact of diabatic initialization and steep orography over the monsoon region are examined in a number of medium range numerical prediction experiments.The steep orography is the so-called 'Envelope Orography' which is almost a kilometer higher than the conventional orography over most major global mountain chains. The physical initialization, proposed here, contains a reconstruction of the humidity field such that a close balance between the advective and the radiative forcing is achieved over most of the rain-free areas. Over the tropical rain areas the humidity analysis is structured to a cumulus parameterization scheme of the global spectral model and the observed rain. The initial observations of the rain come from a mix of satellite and surface based observations.The proposed physical initialization recovers a substantial part of the observed rain.Two interesting prediction experiments on tropical cyclogenesis-one on the formation of the onset vortex and the other on the formation of a monsoon depression are described in the context of the physical initialization and steep orography.In both cases realistic forecasts on the medium range time frame are obtained.A realistic track (or motion) of the Arabian Sea onset vortex was obtained with the steeper orography.The storm was noted to move too far inland when a smoothed orography was used. The experiments on the Bay of Bengal depression show a major improvement with the physical initialization. The formation and track up to 6 days is in close agreement with observations.The major result of this study is on the prediction of the time averaged motion field or the stationary components.Results of four medium range prediction experiments from the winter and summer FGGE periods are illustrated.These 7 to 10 day averaged motion fields contain details on many spatial scales, most of these details are reasonably simulated by the prediction.This success in the prediction of the stationary component of the flow field is attributable to the improved physical parameterization in the model.

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

“…A number of sensitivity tests have been performed for rainfall assimilation at various forecast/research centers like in European Centre for Medium-Range Weather Forecasts [Heckley et al, 1990;Puri and Miller, 1990], Florida State University [Krishnamurti et al, 1984[Krishnamurti et al, , 1993Tsuyuki, 1997], and National Centers for Environmental Prediction (NCEP) [Treadon, 1996[Treadon, , 1997. For many fundamental reasons, rainfall assimilation is a more complex problem compared to assimilation of conventional or clear-sky satellite radiance [Errico et al, 2007].…”

A Physical Initialization Method for the Economical Prognostic Arakawa-Schubert Scheme

“…This method makes use of precipitation parameterization schemes to adjust the model's initial variables in order to retrieve the observed precipitation. The results of Krishnamurti et al (1984) and other studies (Donner 1988;Aonashi 1993) revealed the importance of the (C) 1997, Meteorological Society of Japan physical initialization as a means of incorporation of observed precipitation data. One difficulty with physical initialization is that different techniques are required for different precipitation parameterization schemes.…”

“…Most NWP models, however, can not use precipitation data directly because precipitation itself is not a prognostic variable but is obtained from other model variables by utilizing precipitation parameterization schemes. Krishnamurti et al (1984) proposed "the physical initialization" method to incorporate precipitation data into an NWP model indirectly. This method makes use of precipitation parameterization schemes to adjust the model's initial variables in order to retrieve the observed precipitation.…”

“…One difficulty with physical initialization is that different techniques are required for different precipitation parameterization schemes. So far, physical initialization methods for Kuo-type schemes (Krishnamurti et al 1984;Donner 1988) and a moist convective adjustment scheme (Aonashi 1993) have been established. The development of the physical initialization method for Arakawa-Schubert-type schemes, however, are at the early stage, while some studies (for example, Kasahara et al 1996) have worked on this problem.…”

An Initialization Method to Incorporate Precipitation Data into a Mesoscale Numerical Weather Prediction Model

“…2) Krishnamurti et al (1984) proposed a "physical initialization" method which used the precipitation scheme for their NWP model to estimate initial fields from observed precipitation. They retrieved initial humidity and divergence in such a way that the precipitation scheme would produce the observed rainfall rate at the initial time.…”