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

Kreitzberg, C. W.

English

NASA Technical Reports Server

Paper No. 9
NUMERICAL PREDICTION EXPERIMENTS SIMULATING THE IMPACT OF
MESOSCALE SATELLITE DATA*
Carl W. Kreitzberg, Drexel University, Philadelphia, Pennsylvania
ABSTRACT
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 satelllte-derived wind
fields in mesoscale severe storm models are mentioned
briefly.
INTRODUCTION
A revolution in mesometeorology is taking place because of
recent advances in meteorological satellites, large and small com-
puters, and data communication and display systems (Kreitzberg,
1976). The greatest impact has been made by the GOES visible and
infrared image data because of the high resolution in both space
and time, and because the data are available in real-time to many
meteorologists. Forecast applications are numerous and considerable
research is underway to extend the applicability of the data. For
example, detailed precipitation rates and wind fields can be deduced
by those with adequate image analysis systems.
The launch of TIROS-N marks the beginning of a new era in
masoscale meteorology because of the multitude of remote sounding
data available from the TOVS system. At the same time, digitized
weather radar data are becoming available with the installation of
digital video integration processor (DVIP) equipment on National
Weather Service network radars. The urgency of the need for such
information on an operational basis is demonstrated by actions of
the Federal Aviation Administration (FAA). DVIP data from several
radars are displayed in color in real-time at Air Route Traffic
Control Centers (A_rCC) and Enroute Flight Advisory Service (EFAS)
locations. They will put 150 high resolution color radar displays
into service at 20 ARTCC and 44 EFAS locations during the next 18
months to complement the laser facsimile GOES image data.
W Semiannual progress report, NASA Grant NSG-5162, and contribution
to the Fourth NASA Weather and Climate Program Science Review,
January 24 & 25, 1979.
49
https://ntrs.nasa.gov/search.jsp?R=19790012471 2020-03-20T18:27:37+00:00Z
Themesoscaledata explosion from ground-based sensors is
just beginning. The National Weather Service is moving rapidly
toward automation of surface observations (Klein and Haggard, 1978)
and experimental networks of sensors around airports are being
developed by both National Weather Service and Air Weather Service.
Networks of environmental data stations and automatic data acquisi-
tion systems are being installed by the power industry (Tennessee
Valley Authority, for example) and the Forest Service. Digital
weather stations are available to individuals for several hundred
dollars. Computer dial-up systems can collect data from these
digital networks and stations, and such data can be communicated
on the State and National Data Circuits associated with AFOS.
The hardware required to manipulate the mesoscale data fields
has been developed in NASA's AOIPS system, University of Wisconsin's
MclDAS system, and the NESS VISSR Image Registration and Gridding
System (VlRGS). The communication of ground-based point observa-
tions can be handled by AFOS and the digital radar and satellite
data can be communicated by land lines or communication satellites
(Bristor, 1978).
Numerical weather prediction research has moved toward higher
resolution numerical models (Perkey, 1976; Kreitzberg, 1978; Anthes
and Warner, 1978). It has been demonstrated that these models
develop more detailed patterns during the forecast than exist at
the initial time. Fig. 7, p. 359, in Chang and Kreitzberg (1977)
shows the detail with which squall lines can be depicted. This
forecast had been initialized using only the 1200 GM_ rawinsonde
data and a remarkably detailed and accurate squall line is shown
from 7 h to 12 h into the forecast. The papers of Perkey and Chang
demonstrate further that short-range forecasts of convective pre-
cipitation amounts are often very accurate, but they are also
rather sensitive to the initial relative humidity fields.
It is clear that the limiting factor on reliable detailed
short-range forecasts is the initial analysis. Equally clear is
the fact that much more data are being collected than are being
used in weather prediction. Currently, the weak link is the com-
posite data analysis system that uses a dynamic prediction model
to provide for four-dimensional data assimilation. A very large
effort has gone into data assimilation research on the large scale
for the Global Weather Experiment (FGGE Advisory Panel, 1978).
None of this effort has been concerned with moisture analyses,
including relative humidity and cloud water concentration. The
mass and motion adjustment is known to have important differences
on the scale of detailed prediction models compared with hemispheric
and global models.
The goal of the NASA supported research by Drexel University's
Atmospheric Sensing and Prediction Project is to expand the regional
scale numerical prediction model towards a prediction system that
includes the composite data analysis function. This has been a
small effort over several years, having begun at MSFC in 1975 and
transferred to GSFC in 1977.
The principal effort during this past year has been to up-
grade the set of data processing, analysis, prediction, and
50
diagnostic computer codes into a system of codes called Limited
Area and Mesoscale Prediction System (LAMPS). This system includes
a preprocessor and a library of data manipulation codes, together
called FLOTRAN, which has been developed in collaboration with
Daniel Anderson at the National Center for Atmospheric Research.
FLOTRAN has the power and flexibility to take concise, simple code
that is relatively easy to write correctly, document internally,
and read, and then generate FORTRAN code from it that can be com-
piled by most computer systems. Therefore, it is relatively easy
to change when doing complex research experiments and easy to main-
tain in spite of its size. It is portable to other computer sys-
tems. The dynamic prediction model is the heart of LAMPS but only
comprises 15% of the code (5,000 out of 35,000 statements).
Model nesting experiments have been performed to demonstrate
the sensitivity of forecasts to grid resolution and the use of
forecasts on a larger grid to initialize forecasts on a smaller
grid. Results reported in Kreltzberg and Chang (1978) demonstrate
the need for moisture profiles over the Gulf of Mexico and document
the ability of smaller grid models to generate narrower, more in-
tense squall lines using the same data as lower resolution models.
It is clear that more vertical resolution near the tropopause is
necessary for 35-km horizontal resolution models to reach their
full potential; otherwise, convective cloud tops are limited to
the model tropopause instead of the real tropopause.
DATA IMPACT STUDIES
The urgent requirement for satellite humidity data over the
offshore waters lead us to investigate the potential of the
NIMBUS-6 sounder data off the U. S. West Coast on August 18, 1975.
Our standard relative humidity analysis scheme uses the National
Meteorological Center analysis as the first guess field. The
rawlnsonde data averaged over the model layer (l km in this example)
and centered at the model level (as shown in Fig. la) is then used
to refine the analysis. Our analysis with the first guess and the
rawinsonde data is given in Fig. lb. Satellite soundings from
NIMBUS-6 provided by the University of Wisconsin are located at
points shown in Fig. ic. The analysis from the first guess plus
the satellite and rawinsonde data is shown in Fig. id.
Clearly, the density of satellite data, collected between
0800 GMT and i000 GMT, is exactly what we need off the West Coast.
The satellite analysis in Fig. id is drastically different from
the rawlnsonde analysis in Fig. ib (which is the same over the
ocean as the first guess because there are no rawinsonde data
there). The satellite analysis is qualitatively reasonable in
view of the visible and infrared image data in this storm.
The forecast with and without satellite data will be shown at
the NASA Program Review. The cloud and precipitation forecasts
will be greatly different, but the gross synoptic forecasts will
not be too different after 24 h.
Much more remains to be done on this NIMBUS-6 case in terms
of detailed validation of the humidity analyses compared with the
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cloud image data. Also important will be quantitative comparison
of the forecasts with and without the satellite humidity data.
The most significant results will be those comparing the two
forecasts with the observed evolution of cloudiness as the storm
develops and moves onshore, and those comparing the forecast with
the observed precipitation along the coast.
The geopotential height gradients from NIMBUS-6 are used in
the pressure field and wind analyses in LAMPS. The impact of
these data on the analyses and the forecasts will be examined
separately and then combined with the satellite humidity analyses.
It is expected that some tests and tuning will be required to
properly mix the satellite geopotential gradient and the first
guess geopotentlal data. Problems may arise along the coast where
observation types change.
The insertion of wind fields derived from GOES image data is
the third problem. We will be inserting real data from the Neasho
and Omaha tornado cases and the May 20, 1977, severe storm case
(AVE-IV, AVSSE-II, SESAME '77 cases). To demonstrate feasibility
and to demonstrate dynamic response as a function of grid resolu-
tion, perturbation scales, and amplitude, a series of experiments
with idealized perturbations is being conducted.
REFERENCES
Anthes, R. A. and T. T. Warner, 1978: Development of hydrodynamic
models suitable for air pollution and other mesometeorological
studies. Mon. Wea. Rev., 106, 1045-1078.
Brlstor, C. L., 1978: The case for an expanded digital WEFAX.
Bull. Amer. Meteor. Soc., 59, 1145-1149.
Chang, C. B. and C. W. Kreitzberg, 1977: Severe weather prediction
studies. Preprints, 3rd Conference on Numerical Weather Predic-
tion (Omaha), AMS, Boston, pp. 354-361.
FGGE Advisory Panel, 1978: The Global Weather Experiment--Perspec-
tives on Its Implementation and Exploitation. NAS, Washington,
D.C., pp. 104.
Klein, W. H. and W. G. Haggard, 1978: Automation in meteorology.
Preprints, Conference on Atmospheric Environment of Aerospace
Systems and Applied Meteorology (New York, NY), AMS, Boston,
pp. 174-179.
Kreitzberg, C. W. and C. B. Chang, 1978: Sensitivity of precipi-
tation forecast to grid resolution (data and density). GARP
Programme on Numerical Experimentation Report No. 18, pp. 81-84.
__, 1978: Progress and problems in regional numerical weather
prediction. Computational Fluid Dynamics, SIAM-AMS Proceedings,
Vol.XI, Amer. Math. Soc., Providence, RI, pp. 32-58.
_, 1976: Interactive applications of satellite observations
and numerical modeling. Bull. Amer. Meteor. Soc., 57, 679-685.
Perkey, D. J., 1976: A description and preliminary results from
a flne-mesh model for forecasting quantitative precipitation.
Mon. Wea. Rev., 104, 1513-1526.
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