The topics discussed include the following: verification of cloud prediction from the PSU/NCAR mesoscale model; results form MAPS/NGM verification comparisons and MAPS observation sensitivity tests to ACARS and profiler data; systematic errors and mesoscale verification for a mesoscale model; and the COMPARE Project and the CME

Benjamin, Stan

Koch, Steve

Kuo, Bill

Staniforth, Andrew

Warner, Tom

English

NASA Technical Reports Server

• Aerosol-heterogeneous chemistry
Over the eastern and central U. S. sulfates are a major, if not dominant, aerosol particle
species. Water content and optical characteristics of clouds are crucially dependent upon the
NH4+/SO4 -- molar ratio. Cumulus clouds are major chemical reactors where SO 2 gas is
transformed into sulfate aerosol. Anecdotal data have suggested that the sulfate is not
completely neutralized; thus, field studies are necessary to evaluate the level of
neutralization of the aerosol and the amount of sulfate production in nonprecipitating
cumulus clouds. These processes must be further elucidated in support of the development
and validation of a Regional Particulate Model that will be adapted from RADM.
3.10 Validation of mesoscale models
Bill Kuo: Verification of mesoscale models
N )4- 24891
Tom Warner:
Stan Benjamin:
Steve Koch:
Andrew Staniforth:
Verification of chmd prediction from the PSU/NCAR mesoscale model
Results from MAPS/NGM ver(fication comparisons and MAPS
observation sensitivity tests to ACARS and profiler data
Systematic errors and mesoscale verification for a mesoscale model
The COMPARE Project aml the CME
Bill Kuo opened the session by giving a review on the general methodologies used in
the verification of mesoscale models. He then described the recent verification of an
experimental mesoscale numerical weather prediction model during STORM-FEST, in
which a 20-km version of the PSU/NCAR mesoscale model was used to provide
experimental numerical guidance. His results showed that this model (which was not
tuned prior to its use in support of STORM-FEST) gave a superior performance over the
NMC Nested Grid Model (NGM). This indicates that a mesoscale model which employs
advanced physical parameterizations and more realistic topography has a strong potential to
improve short-range local forecasting. By verifying the model forecasts against the three-
hour special rawinsondes and hourly profiler observations, Kuo was able to examine the
model's systematic biases. He showed that the PSU/NCAR model has a wet bias in the
humidity fields above 500 mb. By 36-h, the accumulated positive bias can be as high as 30%.
He also noted that the model has a weaker diurnal variation in the surface-air temperature
than that shown by the surface station observations. These results showed that further
improvement in model precipitation and planetary-boundary-layer parameterization is
needed.
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https://ntrs.nasa.gov/search.jsp?R=19940019918 2020-06-16T16:52:44+00:00Z
Tom Warner presented a study on the verification of cloud prediction from the
PSU/NCAR mesoscale model, wherein model-predicted cloud cover (based on relative
humidity and cloud water) were compared against the Air Force real-time Nephanalysis of
cloud cover over 20 days. Predictions of the vertical distribution of clouds were found to
contain significant biases. Although the total cloud-cover bias scoreswere reasonably good
in general, the model has less skill for "scattered" and "broken" coverage, and higher skill
for mostly clear or mostly cloudy conditions. The use of model-predicted cloud water as a
predictor of cloud fraction shows promise.
Stan Benjamin discussed the results from verification cornparisons between the MAPS
(Mesoscale Atmospheric Prediction System) and NGM models, with emphasis on the
impact of ACARS and profiler data on short-range forecasts. Based on verification against
rawinsonde observations, he showed that MAPS 3-h and 6-h forecasts are superior to 12-h
forecasts from NGM at most levels. This difference is most pronounced for wind forecasts
near jet levels. Observational sensitivity tests presented also showed that ACARS-reported
observations from commercial aircraft are the most important asynoptic observation for
improving short-range forecasts over the United States at the current time. However, the
observations from wind profilers also provide a smaller but consistent improvement to
wind and height forecasts. Based on the statistical verification of a large number of cases
against both point observations and grid data, Benjamin concluded that verification against
point observations were less ambiguous because of independence from the objective
analysis scheme used and less scale dependence. He emphasized that the key element in
verifying mesoscale forecasts is the availability of mesoscale observations.
Steve Koch gave a presentation on systematic errors and mesoscale verification of the
Mesoscale Atmospheric Simulation System (MASS). Although he conducted this
assessment nearly a decade ago, it still serves as an exemplary approach to model validation,
this being the first known attempt to systematically evaluate the ability of a modeling
system to predict MCS activity without resorting to the usual case study "tuning" approach.
Nearly 30 simulations were evaluated, first for their systematic errors at the synoptic scale,
and then for their accuracy in "predicting" MCS likelihood in terms of convective predictor
fields (the model at that time did not explicitly predict convective precipitation), verified
against Manually Digitized Radar (MDR) data. Clearly, were one to perform a similar
evaluation today, this method should be modernized to use digitized NEXRAD data and
explicit model prediction of convective precipitation. Nonetheless, an important result of
the model evaluation was that systematic errors in predicted synoptic-scale fields adversely
affected the model's ability to "'predict" MCSs. In particular, the evaluation revealed errors
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in the model map transformation and boundary condition codes, inadequacies in the
initialization of moisture over and near the Gulf of Mexico, and the need to include a
convective parameterization scheme in the model to avoid systematic thickness errors. An
interesting use of the temporal behavior in model forecast error statistics was shown to be
uniquely capable of revealing certain systematic errors.
Andrew Staniforth described an international community effort in the verification of
mesoscale models--the COMPARE (Comparison of Model Prediction and Research
Experiments) Project under the auspices of the CAS/JSC Working Group on Numerical
Experimentation. Recherche en Prevision Numerique of Environment Canada has taken
the lead in this effort. The idea is to cornpare in a controlled way the results of many
mesoscale models (and eventually mesoscaledata assimilation systems) from both research
and operational communities, on a representative testbed of casesselected primarily from
Intensive Observation Periods of well-instrumented observational campaigns. Through
model and data assimilation intercomparisons, they hope to improve communication
among modelers, increase understanding of mesoscalephenomena and predictability, and
improve the performance of various components of mesoscale models (particularly
parameterizations) and mesoscale data assimilation systems, leading ultimately to
improved models for both operational forecasting and research applications. The first case
chosen for this project is the March 6-8, 1986 IOP of the Canadian/US CASP/GALE
(Canadian Atlantic Storms Project/Genesis of Atlantic Lows Experiment) field study. The
common data set (stemming from a regional reanalysis of the observed data, and using a
"standardized" data-distribution format to facilitate the distribution of future cases) has
recently been distributed to participants to initialize their models and make forecasts. A
workshop is planned for the Spring of 1994to discuss the ensuing results. The preparation
of a second case,an IOP drawn from the Franco- Spanish PYREX field study, is underway,
and the selection of further cases is under discussion. The chairman of COMPARE ended
his presentation by inviting the organizers of the CME to consider collaborating with the
COMPARE project by providing a set of scientific hypotheses to be verified by coordinated
numerical experimentation using data and analyses drawn from a CME IOP.
 94-24392
3.11 Techniques and resources for storm-scale numerical weather prediction
Kelvin Droegemeier: Non-hydrostatic modeling and prediction in the mid-I990's
and beyond: strategies for dealing with data, parallel
processors, and communication
Georg Grelh Multi-scale application of the 5th-generation PSU/NCAR mesoscale model
5O


Validation of mesoscale models

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