The document discusses the importance of initializing and verifying weather model data in the Mission Execution Forecast Process. It provides examples comparing two model forecasts for Columbus, Georgia to actual weather conditions, illustrating the need to initialize models with current observations. Initializing and verifying models helps forecasters identify biases and inaccuracies so they can issue more accurate warnings. Training programs should address each model's strengths and weaknesses and how initialization improves forecasts.

1. INITIALIZING AND
VERIFICATION OF WEATHER
MODEL DATA
Training
William Hennix
Initializing and verification of weather forecast models for operational support is an important
step in the Mission Execution Forecast Process (MEFP).

2. Initializing and Verification of Weather Model Data 1
Table of Contents
Initializing and Verification of Weather Model Data ...........................................................................................2
The problem..........................................................................................................................................................2
Summary...............................................................................................................................................................4
Attachment 1.........................................................................................................................................................5
Attachment 2.........................................................................................................................................................6
Attachment 3.........................................................................................................................................................7
Attachment 4.........................................................................................................................................................8
Attachment 5.........................................................................................................................................................8
Bibliography .........................................................................................................................................................9

3. Initializing and Verification of Weather Model Data 2
Initializing and Verification of Weather Model Data
Initializing and verification of weather forecast models for operational support is an important
step in the Mission Execution Forecast Process (MEFP).
The problem
Providing weather support to clients based on model data without initialization and
verification enhances the age-old saying, “the weatherman does not know what he talking
about.” To illustrate the value of initialization review attachment 1 and 2, which is two separate
simulated weather radar, model data for 12 February 2013 at 2100z. Compared both models to
what actually happened on 12 February 2013 at 2100z with attachment 3. The area of
concentration is Columbus Georgia, the situation heavy rain moving into the area with possibility
of flooding. What do you brief your customers?
Meteorologist technicians develop an understanding of weather models through the
initialization and verification process. Several studies published in the American Meteorological
Society discuss the importance of improving short and long-term operational forecast through the
verification process. The verification process incorporates synoptic-scale climatological forecast
as part of the initial model run. When training weather forecasts on how to initialize and verify
model data for future use it is important to understand how the model determines the initial hour.
Without a clear understanding of the process, technicians may assume the initial hour of the
model is current data only. Using the false assumption leads to misinterpretations of the forecast
model’s accuracy.
If a meteorological technician used the model, data from attachment 1 as the determinate
factor for issuing a 2” inch weather warning for rain in Columbus Georgia they would have not
issued a warning to their clients. Attachment 4 which is a close up from attachment 1 clearly

4. Initializing and Verification of Weather Model Data 3
indicates no precipitation for Columbus Georgia. However, if the data from attachment 1, was
initialized with the information from attachment 3 the forecaster would noticed the model had
position the precipitation incorrectly.
If a meteorological technician used the model, data from attachment 2 as the determinate
factor of issuing a 2” inch weather warning for rain in Columbus Georgia they would have issued
a warning to their clients. Attachment 5 which is a close up from attachment 2 clearly indicates
precipitation for Columbus Georgia. However, if the data from attachment 5 was initialized with
the information from attachment 3 the forecaster would noticed the model had position the
precipitation incorrectly.
Insuring meteorological technicians produce the best products possible requires
consistent training and preparation. Developing annual training programs that address each
weather models strength and weakness is the first step to improving the accuracy of weather
forecast. Currently part of the next generation, forecasting process includes using current forecast
models like the Global Forecast System (GFS) and Weather Research and Forecasting (WRF) to
create new products. The advancements of computer technology generated a new philosophy of
initializations and verification called Nowcasting. Huang, Isaac, & Sheng, (2012) conducted a
comprehnsive study of the process; the study and conclusion provides support on why and how
developing and initiating sound procedures improves the forecast product for operational use on
a microscale.
Cui, Toth, Zhu, & Hou collectively have been reporting on and presenting the changes to
the forecast models, at national weather conferences in the Unites States and Canada since 1996.
Together Cui, Toth, Zhu, Hou, (2012) collaborated on an article explaining the process of
initialization and verification of Global Ensemble Forecast model. They explain the logic behind

5. Initializing and Verification of Weather Model Data 4
the correction factor and discuss the primary strengths of the model. Their article provides
addition support for technicians to understand the changes applied to the forecast model and how
those changes have affected the model data. Their research explaining the bias and corrections
associated with the GFS model along with the specific changes associated with each model
upgrade.
Attachments 1-5 demonstrate the importance of initialization and verification of weather
data, a weather warning for 2” within 12 hours was issued to the local clients at 07:00 on 12
February 2013. Fort Benning Weather station recorded 4” of rain that evening. The early
warning allowed military personnel to secure equipment, cancel all outdoor activities and safely
transport training troops to dry locations. Initializing and verification of weather model data had
a direct impact on military operations.
Summary
Initial and continuation training should include current information on model forecast
advancements along with situational awareness scenarios related to significant weather events.
The training should focus on the positive and negative impacts of initializing model data. What
was a bias in the model yesterday might be corrected with improved algorithms tomorrow. Fort
Benning Weather Station needs to develop and maintain initialization and verification training
program as part of the over training program.

6. Initializing and Verification of Weather Model Data 5
ATTACHMENT 1
Columbus Georgia

7. Initializing and Verification of Weather Model Data 6
ATTACHMENT 2
Columbus Georgia

8. Initializing and Verification of Weather Model Data 7
ATTACHMENT 3
Columbus Georgia

9. Initializing and Verification of Weather Model Data 8
ATTACHMENT 4
Columbus Georgia
ATTACHMENT 5
Columbus Georgia

10. Initializing and Verification of Weather Model Data 9
Bibliography
Cui, B., Toth, Z., Zhu, Y., & Hou, D. (2012). Bias correction for global ensemble forecast.
Weather and Forecasting, 27 (2), 396-410.
Dale, B. Huang, X. Lui, Z.; Auligne, T. Zhang, X.; Rugg, S. Ajjajii, R. Bourgeois, A. Bray, J.
Chen, Y. Demirtas, M. Guo, Y. Henderson, T. Huang, W. Lin, H. Michalakes, J. Rizvi, S.
Zhang, X. (2012). The weather research and forecasting model's community
variational/ensemble data assimilation system WRFDA. Bulletin of American
Meteorological Society, 93 (6), 831-843.
Huang, L. X., Isaac, G. A., & Sheng, G. (2012). Intergrating NWP forecasts and observation data
to improve nowcasting accuracy. American Meteorological Society, 27 (4), 938-953.
Messager, C., & Faure, V. (2012). Validation of remote sensing and weather model forecasts in
the Agulhas ocean area 57 degrees south by ship observations. South Africa Journal of
Science, 108 (3/4), 1-10.
Roberts, R. D., Anderson, A. R., Nelson, E., Brown, B. G., Wilson, J. W., Pocernich, M., et al.
(2012). Impacts of forecaster involvement on convective storm intitiation and evolution
nowcasting. Weather and Forecasting, 27 (5), 1061-1089.