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).

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

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

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

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.

Initializing and Verification of Weather Model Data 5

ATTACHMENT 1

Columbus Georgia

Initializing and Verification of Weather Model Data 6

ATTACHMENT 2

Columbus Georgia

Initializing and Verification of Weather Model Data 7

ATTACHMENT 3

Columbus Georgia

Initializing and Verification of Weather Model Data 8

ATTACHMENT 4

Columbus Georgia

ATTACHMENT 5

Columbus Georgia

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.