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IMS Model Suite Post-Processing
Complex
Monitoring and
Forecasting
Suite
IMS4
Model Suite Post-Processing
IMS CLDB
FEATURES:
• High Availability
FEATURES:
•
• High resolution, short term weather forecasting and nowcasting
• Radiation and pollutant crisis modelling
• Road condition modelling
• Visibility modelling
• Sand storm modelling
The IMS Model Suite provides a composition of modern
models and technologies for meteorology, hydrology,
radiation and air quality.
Models run both operatively and inresearch mode. IMS4
Model Suite is an integrated, easy-to-use powerful system for modeling, processing, visualization and model
validation.
support of servers centers for demanding
meteorological simulations
• Basic and Advanced Training, Support, System
monitoring
the statistical achievement.
Visualization of model outputs can be set up to easy inter-
IMS Model Suite is a complex system for supporting forecasters as well as crisis managers. It is designed from regular daily forecasting purposes, to emergency crisis regime.
The decision system consists of several models and tools:
• Nowcasting integrating radar, satellite and field data
• Nowcasting integrating radar, satellite and flash data:
Nowcasting utilizes time sequences of radar, satellite
or lightning data to identify thunderstorms (intensive
precipitation) and extrapolate their positions a few
steps into the future
• Hydrological Model
• Model Validation Tools for easy monitoring of
simulation quality
• High Performance Computing - installation and
MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice.
Model Output Statistics IMS Model Suite MOS
It-Processing
MOS (Model Output Statistics) is a computational
post-processing scheme applied to the output of a
numerical weather prediction model. It is an objective
weather forecasting technique.
way, products form Severe Weather Nowcasting Network
will be converted to predictors. Climatic data can be used
as additional predictors in the IMS Model Suite MOS.
IMS Model Suite MOS applies 2 kinds of methods:
• Statistical (linear)
• Data mining (machine learning)
• Corrects for systematic model biases
• Probability forecast calculation
• Calculation of elements not directly predicted by model
(e.g. agrometeorological variables, evaporation, indices,
departure from normals)
The statistical method develops a set of equations by
statistical regression that correlate model outputs and real
observations. It utilizes the forecast and measurements
database. Once a global model has completed the forecast
(ECMWF, NCEP etc...), specific data is collected from model
GRIB fields and the MOS equations are applied to it. Before
publishing the product, the IMS Model Suite MOS output is
post-processed to check for meteorological and statistical
consistency; then categorical forecasts are generated.
The statistical approach is successfully applied to predict
locally adjusted continuous weather parameters:
• Air pressure (station surface elevation and mean sea
level)
• Air and ground temperature, including extremes (high
and low temperatures)
• Relative humidity
• Dew point
• Wind speed and direction and gusts
• Cloud cover
• Precipitation occurrence and amount (accumulation
over 1hr, 3hr, 6hr, 12hr, 24hr and other intervals)
• Global solar radiation (surface insolation)
• Others
Functions
Forecast spatial resolution, lead times, time
resolution and update times
The MOS methods are applied to exact location of
forecasting site. For spatial calculations, a grid of 10x10 km
resolution is used. Optionally, it can be reconfigured into
4x4 km network or other resolutions.
• Lead time - Short and medium range (1-10 days)
• 6 hourly forecast output for the 0 - 10 day forecast
period, and hourly forecast output for at least the 0 – 5
day forecast period.
• Update times: 1 hour
The parameters are configurable.
The data mining approach is based on neural networks
and decision trees. These methods of artificial intelligence
has been successfully applied by many centres worldwide
to improve meteorological forecasts. In MicroStep-MIS,
data mining was successfully applied to Fog Probability,
Visibility and Low Clouds detection and prediction (Bartok
et all, 2012). In addition, the IMS Model Suite MOS neural
networks are trained on past observation to predict
Probability of Precipitation, Thunderstorm probability,
Precipitation Type Probability (Hail, Rain, Ice, Snow
probability).
The key terms of the process are predictors (the variables
at the input of MOS module) and predictand (the
weather element that is being predicted).The longer is
the observational database, the better equations can be
developed and the better neural network can be trained.
One of IMS Model Suite MOS advantages is inclusion of
real-time measurements from meteorological networks
collected and distributed via IMS UDCS/CLDB. In a similar
MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice.
IMS4 Model Suite Post-Processing
IMS Model Suite Post-Processing
IMS4 Model Suite Post-Processing
IMS Model Suite Post-Processing
IMS Model Suite Ensemble-MOS
Centers such as ECMWF, NCEP, UK Met Office and others
produce also ensemble forecasts and publish them (free or
charged) in form of GRIB files. FM 92 GRIB format editions 1
and 2 are defined by standard of WMO (WMO No. 306) and
are read by MicroStep-MIS decoders.
IMS Model Suite Ensemble-MOS utilizes the advantages of
ensemble forecasts and calibrates them using past forecast
error, as IMS Model Suite does for deterministic forecasts in
the MOS process.
Training datasets
• NCEP reforecast dataset 1979 to the present (Hamil et.
all 2006)
• ECMWF forecast archive
Continuous improvement
As the forecast and measurements database grows
for country of interest (Zambia), the IMS Model Suite
Ensemble-MOS continually utilizes these data to make
better calibration and consequently, better forecasts. The
quality of forecasts is continuously reviewed by Verification
Tool.
It-Processing
extreme events. Some methods are better in shorter term
forecast, some in longer term (6-10 day forecast).
Other predictors (actual data, nowcasts) can be included
into the process by employing additional Statistical (linear)
and Data mining methods known from non-ensemble MOS
(firstly, calibrated probabilities are calculated, then used as
predictors together with actual data and nowcasts).
Forecast spatial resolution, lead times, time
resolution and update times:
The MOS methods are applied to exact location of
forecasting site. For spatial calculations, a grid of 10x10 km
resolution is used.
• Lead time - Short and medium range (1-10 days)
• 6 hourly forecast output for the 0 - 10 day forecast
period, and hourly forecast output for at least the 0 – 5
day forecast period.
• Update times: 1 hour
The parameters are configurable.
Methods
The IMS Model Suite Ensemble-MOS is open and
configurable system that uses several known methods
and is prepared for incorporation of other methods if
they prove advantageous. MicroStep-MIS has successfully
operated data mining (machine learning) methods neural
networks and decision trees for fog forecasting. Currently
used methods in Ensemble-MOS are:
• Logistic regression
• Gaussian ensemble dressing
• Nonhomogeneous Gaussian regression
Each method has the task of learning a target function that
maps each attribute set to one of the predefined classes. In
meteorological terms it means, that we can fit a function
that gives us the probability of weather event, based on
current ensemble mean and spread and verification results
of previous ensemble forecasts.
Mathematically, the regression functions is fitted iteratively
by the means of maximum likelihood in case of logistic
regression. Gaussian ensemble dressing equation is
expressed analytically. In the case of Nonhomogeneous
Gaussian regression the parameters of the equation are fit
also iteratively to minimize the error for the training data.
The results from individual calibration methods are
combined in the resulting forecast, as some methods have
best overall results, while others exhibit greater accuracy in
MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice.
Integration of Nowcasting
It-Processing
Standard MOS - Learning phase
Historical Forecast
(Hindcast)
Developing Regression
Equations
and/or
Training of Neural
Network
Historical Observations
Standard MOS - Operation
Actual Forecast
Regression
Equations
Site Specific
Forecast
Neural
Network
Actual Observations
Lightning Detection
and Nowcasting
Site Specific
Warning
After enough data is collected
Advanced MOS - Learning phase
Historical Forecast
(Hindcast)
Developing Regression
Equations
and/or
Ensemble-MOS
Coeficients
Historical Observations
Training of
Neural
Network
Historical Lightning
Detection and
Historical Nowcasting
Advanced MOS - Operation
Actual Forecast
Actual Observations
Regression
Equations
and/or
Ensemble-MOS
Equations
Training of
Neural
Network
Actual Lightning Detection
and Nowcasting
Contact us for more information
Cavojskeho 1, 841 04 Bratislava, Slovak Republic
tel.: +421 2 602 00 100, fax: +421 2 602 00 180
www.microstep-mis.com, [email protected]
© 2015, All specifications subject to change without prior notice
Monitoring and Information Systems
Site Specific
Forecast
and Warning
IMS4 Model Suite Post-Processing
IMS Model Suite Post-Processing