Development of a Weather Running Estimate-Nowcast 

Capability for the U.S. Army IMETS 

by Teizi Henmi, Robert Dumais, and Richard Okrasinski 

ARL-TR-3647 September 2005 

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Army Research Laboratory 

White Sands Missile Range, NM 88002-5501 

ARL-TR-3647 September 2005 

Development of a Weather Running Estimate-Nowcast 

Capability for the U.S. Army IMETS 

Teizi Henmi and Robert Dumais 

Computational & Information Sciences Directorate 

Battlefield Environment Division 

and 

Richard Okrasinski 

Physical Science Laboratory 

New Mexico State University

Approved for public release; distribution is unlimited.
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Development of a Weather Running Estimate-Nowcast Capability for the U.S. Army IMETS 

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Teizi Henmi, Robert Dumais, and Richard Okrasinski 

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U.S. Army Research Laboratory 

Computational and Information Sciences Directorate 

Battlefield Environment Division (ATTN: AMSRD-ARL-CI-EM) 

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14. ABSTRACT 

A nowcast/short-range forecast method, over the domains of approximately 100 km by 100 km with grid resolution of 1.5 to 2.0 km, has been 

developed for the Integrated Meteorological System (IMETS). Meteorological data available in the IMETS, including Mesoscale Model 

Version 5 (MM5) forecast, surface, and upper-air sounding data, are used for the computation. Three-dimensional distributions of temperature, 

dew-point temperature, and horizontal wind vector components can be forecasted. The method is evaluated over two different areas: Dallas 

(TX) and Denver (CO). It was shown that the present method was valid up to 3 h over the Dallas domain, and up to 2 h over the Denver 

domain. Although further studies are needed over different areas and seasons, it is tentatively concluded that the present forecasting method can 

be used at least for 2 h. 

15. SUBJECT TERMS

Nowcast, objective analysis 

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Teizi Henmi 

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Prescribed by ANSI Std. Z39.18 
Contents 

List of Figures iv

Executive Summary vii

1. Introduction 1

2. Data Used 2

2.1 Elevation Data 3 

2.2 MM5 Data 3 

2.3 Surface Data 4 

2.4 Upper-air Radiosonde Data 5 

3. Analysis Methods to Produce WRE 6

3.1 3D Analysis of MM5 and Upper-air Radiosonde Data 6 

3.2 Surface Data Analysis 9 

3.3 MARIAH Similarity Formulae 9 

4. Nowcast Method to Complete WRE-N 10

5. Computer Programs and Input Files 11

5.1 Examples of the WRE-N 12 

5.2 Preliminary Evaluation of the WRE-N Method 18 

8. Conclusion 24

References 25

Acronyms 26

Distribution List 27

iii
List of Figures 

Figure 1. A portion of an ASCII MM5 data file used as WRE-N input, extracted from 

the MM5 GRIB datasets transmitted from AFWA........................................................ 3 

Figure 2. A portion of an ASCII surface meteorological data file used as WRE-N 

input, containing pressure, temperature, dew-point temperature, wind speed, and 

wind direction for different observation locations......................................................... 4 

Figure 3. A portion of an ASCII upper-air radiosonde sounding data file used as 

WRE-N input, containing pressure, height above sea level, temperature, dewpoint temperature, wind speed and wind direction. ....................................................... 5 

Figure 4. Elevation contour distribution for the domain of 75-by-75 grid points with 

1.5-km grid spacing, centered at 32.83° N. and 97.30° W. (Dallas domain). ............... 6 

Figure 5. MM5 grid horizontal surface wind vector distribution at 10 magl for 0000 

UTC, 10 May 2005. ..................................................................................................... 12 

Figure 6. The 10-magl horizontal surface wind vector distribution interpolated from 

the MM5 grid to the WRE-N grid for 0000 UTC, 10 May 2005................................. 13 

Figure 7. Observed 10-magl surface wind vectors valid at 0000 UTC, 10 May 2005. ..... 13 

Figure 8. The 10-magl surface wind field produced by the WRE-N analysis module 

(0000 UTC, 10 May 2005), by combining the fields shown in figures 6 and 7. ......... 14 

Figure 9. The 10 magl surface wind field valid for 0200 UTC, 10 May 2005, the 2-h 

gridded nowcast produced by the WRE-N extrapolation module. .............................. 15 

Figure 10. The 2-magl surface temperature distribution from the MM5 interpolated to 

the WRE-N grid, valid at 0000 UTC, 10 May 2005.................................................... 16 

Figure 11. The 2-magl surface temperature field produced by the WRE-N analysis 

module by combining the temperature field shown in figure 10 with observations 

valid for 00 UTC, 10 May 2005................................................................................... 16 

Figure 12. Vertical profiles of temperature and dew-point temperature valid for the 

KFWD (Fort Worth, TX) radiosonde site at 0000 UTC, 10 May 2005. Profiles 

produced by the AFWA MM5, the KFWD radiosonde, and the WRE_N analysis 

module are shown. ....................................................................................................... 17 

Figure 13. Same as figure 12, except for wind speed and wind direction. ........................ 17 

Figure 14. Terrain contour distribution for the 125-by-125 domain of 2-km grid 

spacing, centered at 39.5° N. and 104.1° W (Denver domain).................................... 18 

Figure 15. AD and CC of surface temperatures as a function of the WRE-N nowcast 

period for different β values and for the Dallas domain. ............................................. 20 

Figure 16. Same as figure 15, except for the Denver domain............................................ 20 

Figure 17. Same as figure 15, except for the dew-point temperature................................ 21 

Figure 18. Same as figure 17, except for the Denver domain............................................ 21 

iv
Figure 19. Same as figure 15, except for the wind speed. ................................................. 22 

Figure 20. Same as figure 19, except for the Denver domain............................................ 22 

Figure 21. The RMSVE and MWDDF as a function of the WRE-N nowcast period 

for the Dallas domain................................................................................................... 23 

Figure 22. Same as figure 21, except for the Denver domain............................................ 23 

v
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vi
Executive Summary 

A computationally fast, mesoscale meteorological analysis and very short range 

prediction (nowcasting) capability has been developed for the U.S. Army, specifically for 

the Integrated Meteorological System (IMETS), which is the Army’s tactical weather 

system. This tactical analysis and nowcast capability is currently referred to as the 

Weather Running Estimate-Nowcast (WRE-N) by the U.S. Army Research Laboratory, 

White Sands Missile Range, NM, which is serving as the Army lead in its development. 

The WRE-N discussed in this report is an initial implementation of such a capability, 

configured for a domain of approximately 100 km by 100 km, with a horizontal grid 

resolution of 1.5 km. Additionally, this WRE-N has been tailored to accept 

meteorological data sources routinely available to the IMETS, including Air Force 

Weather Agency (AFWA) Mesoscale Model Version 5 (MM5) numerical forecasts, 

surface sensor observations, and upper-air radiosonde observations. Finally, this 

implementation allows three-dimensional gridded distributions of temperature, dew-point 

temperature, and horizontal wind vector components to be analyzed and nowcasted. 

The method is evaluated over two different geographic areas: one centered near Dallas, 

TX, and the other near Denver, CO. This report shows that the method appeared to add 

value (when compared to AFWA 15-km MM5 background forecasts) for up to 3 hr over 

the Dallas domain (fairly uniform terrain) and up to 2 hr over the Denver domain 

(complex terrain). Although further studies are needed over different areas and seasons, 

we tentatively conclude that the present WRE-N method, which is based on 

extrapolation, can be used for at least two hours for many meteorological scenarios, 

especially when the background forecast model (the operational AFWA MM5) is of good 

quality on the mesoscale. 

vii
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viii
1. Introduction 

For U.S. Army operations, being able to accurately update high spatial and temporal 

resolution meteorological information, generated by fusing or assimilating current local 

meteorological observational data, is more desirable than relying on results obtained by 

previously executed model forecasts alone. Although current state-of-the-art numerical 

weather prediction (NWP) models offer an unprecedented capability to simulate smallscale mean atmospheric structures, individual deterministic model solutions tend to suffer 

from day-to-day spatial and temporal errors in amplitude, phase, and variability. These 

errors are due to many reasons, whose discussion is beyond the scope of this report. 

To correct for these kinds of errors and model biases, it is necessary to assimilate real 

meteorological observational data through, for example, NWP model forecast cycling. 

The advanced assimilation methods that have been developed over the past few decades 

now offer dynamically, physically consistent and statistically optimal methods of 

“correcting” NWP model tendencies on the fly. If the “current” weather situation is 

accurately analyzed, it may be possible to reasonably predict very short-term weather 

situations (nowcasting). Our ability to accomplish this will increase as techniques 

continue to improve and better methods of assimilating observed cloud-scale fields and 

dynamically coupling them to NWP model fields are produced. 

Rapid adjustments of intelligence and warfighter decision making tools, to account for 

changes in the local battlefield’s three-dimensional (3D) environmental state (which often 

deviate from the existing operational NWP model forecast guidance), are known as 

Weather Running Estimates (WREs). For the Army, near-surface meteorological changes 

on timescales ranging from minutes to a few hours are critical to ground operations. Such 

changes are classified as meso-γ (1- to 10-km grid spacing) scale phenomena (Orlanski, 

1975). This range is also commonly referred to as the “cloud” or “storm” scale. 

Nowcasting is a method of diagnosing the current weather situation by combining 

existing forecast guidance with observational data, using various methods of 

analysis/fusion and assimilation, and then extending this meteorological “picture” 

forward in time to produce a very short-range forecast or prediction (Browning, 1982). 

The predictive component can be produced in many ways, such as through cycling 

(coupling analyses to prognostic NWP models), extrapolation, statistical methods, or 

expert system approaches. The Weather Running Estimate-Nowcast (WRE-N) 

incorporates a 3D gridded analysis (the WRE) and a 3D short-range gridded prediction 

counterpart (the Nowcast). 

Using conventional observational data along with operational Mesoscale Model Version 

5 (MM5) NWP model data produced at the U.S. Air Force Weather Agency (AFWA), a 

relatively simple and fast (in terms of execution) WRE-N system has been developed, 

building upon earlier analysis techniques constructed at the U.S. Army Research 

Laboratory (ARL), White Sands Missile Range, NM (Henmi, 2003). In the current WREN system, separate surface and upper-air meteorological analyses are first produced 

univariately. To produce a surface analysis, local surface observations and concurrent 

1
NWP model forecast data are composed using a successive correction method (Sashegyi 

and Madala, 1994). Similarly, the upper-air analyses (produced for many predetermined 

levels above ground) are a composition of upper-air radiosonde soundings and NWP 

model forecast data, using the same scheme. After both the surface and upper-air 

analyses have executed, similarity theory formulae named MARIAH (Rachele et al., 

1995) are used to couple the surface and upper-air analyses into a single set of 3D fields. 

This 3D “snapshot” is the WRE component of the WRE-N system. The analysis method 

used to produce the WRE has been previously statistically evaluated (Henmi and Dumais, 

2004) over Utah using the University of Utah’s MesoWest data (Horel et al., 2002). That 

study showed that this method produced much better statistical results for temperature, 

dew-point temperature, and wind vectors as compared to previously generated 

background forecast fields from the Pennsylvania State University/National Center for 

Atmospheric Research MM5. 

The U.S. Army’s Integrated Meteorological System (IMETS) regularly receives (from 

AFWA) surface observation data (such as METAR), World Meteorological Organization 

(WMO) upper-air radiosonde sounding data, and WMO gridded binary (GRIB) data 

produced from operational forecasts of the MM5. The MM5 forecast data are typically 

produced twice daily at a horizontal grid spacing of 15 km. Using the AFWA operational 

MM5 fields for both current and “future” background field data, the WRE-N method has 

been developed and adapted for the IMETS. 

To produce the Nowcast component of the WRE-N, the successive corrections analysis 

method has been extended to make a short-range “prediction” for the period out to 3 h 

forward. The WRE-N has been nominally configured for a domain of approximately 

100 km by 100 km and for a horizontal grid spacing of about 1.5 km. For clarification, 

the successive corrections approach is used to produce the WRE “current” analysis, while 

a linear-interpolation method is used to extrapolate the WRE fields forward to produce 

the short-range nowcast. In extrapolating the WRE analysis forward, the AFWA MM5 

fields (valid at the desired forward times) are used as background data. The method has 

recently been applied to two different geographical domains and evaluated statistically, 

with the preliminary results presented later in this report. 

2. Data Used 

The WRE-N is designed to incorporate the data most readily available in the current 

IMETS system, which includes digital terrain elevation data and both observational and 

NWP model forecast data supplied from AFWA (surface, upper-air radiosonde, and 

GRIB-formatted MM5 output). 

2
2.1 Elevation Data 

The elevation data file, which encompasses the WRE-N area of interest (AOI), is 

produced by reading the Digital Terrain Elevation Data (DTED) Level 0 from the 

Defense Mapping Agency (DMA). An existing IMETS computer program produces a 

terrain elevation data file for a 161-by-161 gridded domain centered at the desired 

WRE-N AOI, with the data interpolated from the native 30 arc second resolution to the 

desired WRE-N 1.5-km grid spacing. This is done on a pseudo-Universal Transversal 

Mercator (UTM) grid, which is the same grid used by the WRE-N. Since the generated 

elevation data covers a domain somewhat larger than that required for the WRE-N 

(100 km by 100 km), the actual terrain data used by WRE-N is clipped from the original 

file of 161-by-161 grid points. 

2.2 MM5 Data 

From the GRIB-formatted MM5 forecast data files sent from the AFWA, vertical 

distributions, or “model soundings,” of forecast fields at 3-h output intervals are extracted 

for the AOI region and placed into ASCII input files. These files include MM5 data that 

are interpolated to both the desired WRE-N “current” hour and 3 hr forward from that 

time. The base time of the MM5 must be between 9 and 24 hr earlier than the WRE 

0 hour. A portion of the data contained within the WRE-N input MM5 ASCII files is 

shown in figure 1. 

22Apr 12Z 

15.00 

121 

43 

 32.326 

 -97.798 

262.00 

 74.35 18209.64 202.70 15.81 4.70 -6.50 

 91.68 16967.91 201.90 15.45 21.10 -8.90 

 110.44 15868.92 201.90 10.87 30.50 -6.10 

 130.88 14853.94 206.30 11.21 33.80 -12.10 

 152.37 13925.03 211.40 11.55 38.10 -21.50 

 

 536.17 5244.52 263.10 22.35 14.50 .90 

 565.84 4827.54 265.90 21.80 13.10 -.70 

 595.47 4428.24 268.60 20.40 12.40 -3.30 

 625.05 4045.06 271.30 19.30 12.40 -5.90 

 

 963.80 431.50 294.10 5.91 -.30 -4.60 

 971.50 362.55 294.10 5.14 .00 -4.30 

 976.94 314.16 294.20 4.58 .30 -3.70 

 980.57 282.03 294.30 4.18 .40 -2.90 

 981.72 272.00 294.38 4.18 .35 -2.55 

 982.64 264.00 294.38 4.18 .21 -1.54 

Figure 1. A portion of an ASCII MM5 data file used as WRE-N input, extracted from the MM5 GRIB 

datasets transmitted from AFWA. 

3
The top lines of each data file contain the valid date and forecast time of the MM5 

forecast data, the MM5 model grid resolution, the number of MM5 grid point 

“soundings” in the file, and the number of MM5 vertical layers. Subsequent lines include 

the latitude, longitude, and model terrain elevation for each MM5 grid point sounding, 

followed by the forecast data for the meteorological fields at each model sigma level. The 

data columns contain pressures (mb), heights above sea level (m), temperatures (°K), 

dew-point depressions (°C), and wind vector components, u and v (m/s). 

2.3 Surface Data 

Surface meteorological observations are input into the WRE-N as ASCII formatted files 

and contain pressure (mb), temperature (°C), dew-point temperature (°C), wind speed 

(m/s), and wind direction (deg) data. Figure 2 shows a portion of the WRE-N input 

surface meteorological data file. 

Pressure (mb) 

7 

 32.680000 -96.860001 22Apr 11:53 1011.90 

 32.810001 -97.360001 22Apr 11:53 1012.10 

 

 33.180000 -96.580002 22Apr 11:53 1013.50 

 33.200001 -97.180000 22Apr 11:53 1013.10 

Temperature (C) 

12 

 32.560001 -97.300003 22Apr 12:05 17.00 

 32.660000 -97.099998 22Apr 12:09 17.00 

 

 33.200001 -97.180000 22Apr 11:53 15.00 

 33.650002 -97.199997 22Apr 12:05 12.00 

Dewpoint Temperature (C) 

12 

 32.560001 -97.300003 22Apr 12:05 16.00 

 32.660000 -97.099998 22Apr 12:09 16.00 

 

 33.200001 -97.180000 22Apr 11:53 11.00 

 33.650002 -97.199997 22Apr 12:05 9.00 

Wind Speed (m/s) 

12 

 32.560001 -97.300003 22Apr 12:05 1.54 

 32.660000 -97.099998 22Apr 12:09 1.54 

 

 33.200001 -97.180000 22Apr 11:53 3.60 

 33.650002 -97.199997 22Apr 12:05 2.06 

Wind Direction (deg) 

12 

 32.560001 -97.300003 22Apr 12:05 250.00 

 32.660000 -97.099998 22Apr 12:09 240.00 

 

 33.200001 -97.180000 22Apr 11:53 .00 

 33.650002 -97.199997 22Apr 12:05 340.00 

Figure 2. A portion of an ASCII surface meteorological data file used as WRE-N input, containing pressure, 

temperature, dew-point temperature, wind speed, and wind direction for different observation locations. 

4
2.4 Upper-Air Radiosonde Data 

The upper-air radiosonde observation data are ingested into the WRE-N as ASCII 

formatted files, similar to the MM5 and surface observations. An abbreviated portion of a 

WRE-N input upper-air radiosonde data file is shown in figure 3. Information about 

latitude; longitude; height above ground; date and hour of observation; and number of 

levels are given in the first three lines. Pressure (mb), height above sea level (m), 

temperature (°C), dew-point temperature (°C), wind speed (kn), and wind direction (deg) 

data are also given. All missing data are expressed as “-999.0.” 

32.830002 -97.300003 196.000000 

20Apr 00:00 

83 

 1000.00 97.00 -999.00 -999.00 -999.00 -999.00 

 988.00 196.00 24.40 17.40 6.00 160.00 

 925.00 773.00 18.40 15.70 13.00 170.00 

 885.00 -999.00 14.80 14.50 -999.00 -999.00 

 850.00 1493.00 13.40 11.80 13.00 195.00 

 800.00 -999.00 10.00 9.20 -999.00 -999.00 

 792.00 -999.00 10.60 8.80 -999.00 -999.00 

 784.00 -999.00 13.60 8.70 -999.00 -999.00 

 

 60.00 -999.00 -64.70 -81.70 -999.00 -999.00 

 50.00 20620.00 -62.90 -80.90 5.00 270.00 

 43.00 -999.00 -59.70 -79.70 -999.00 -999.00 

 34.00 -999.00 -60.10 -79.10 -999.00 -999.00 

 30.00 23810.00 -56.10 -76.10 3.00 285.00 

 27.00 -999.00 -52.70 -74.70 -999.00 -999.00 

 23.00 -999.00 -54.10 -75.10 -999.00 -999.00 

 20.00 26410.00 -50.70 -73.70 7.00 240.00 

 17.00 -999.00 -46.10 -70.10 -999.00 -999.00 

 12.00 -999.00 -43.70 -66.70 -999.00 -999.00 

 10.00 31050.00 -40.50 -66.50 18.00 250.00 

 9.00 -999.00 -38.50 -64.50 -999.00 -999.00 

Figure 3. A portion of an ASCII upper-air radiosonde sounding data file used as WRE-N input, containing 

pressure, height above sea level, temperature, dew-point temperature, wind speed, and wind direction. 

Figure 4 shows an example of one of the WRE-N domains evaluated in this report, 

depicting the terrain and meteorological data sources used as inputs. The elevation 

contours shown are for a 75-by-75 grid point domain with a 1.5-km horizontal resolution, 

centered at 32.83° N. and 97.30° W. The locations of the MM5 grid points (15-km 

resolution) are marked with an asterisk (*), and surface and upper-air data locations are 

represented with an “S” and a “U,” respectively. The city of Dallas, TX, is located near 

the center area of this domain, referred to as the Dallas domain in this report. 

5
Figure 4. Elevation contour distribution for the domain of 75-by-75 grid points with 1.5-km grid 

spacing, centered at 32.83° N. and 97.30° W. (Dallas domain). 

NOTE: The “*” symbol represents MM5 grid points, while “S” and “U” represent locations of 

surface and upper-air radiosonde meteorological observations, respectively. 

3. Analysis Methods to Produce WRE 

3.1 3D Analysis of MM5 and Upper-Air Radiosonde Data 

The 15-km resolution MM5 forecast data, as shown in figure 1, are first interpolated 

spatially 1) onto predetermined height levels above mean sea level and 2) to the WRE-N 

grid point locations at the finer horizontal grid resolution (1.5 km). 

There are differences between the terrain heights of grid points interpolated from MM5 

forecast data and those obtained from DTED Level 0. Three-dimensional temperature and 

pressure field errors, due to the terrain height difference, are corrected as follows: 

6
Surface temperature and pressure are recalculated as 

Z

dz

dT TT ss )( Δ∗+= 0 (1) 

Z

dz

dP PP ss )( Δ∗+= 0 (2) 

where 

Ts: corrected surface temperature, 

Ps: corrected surface pressure, 

Ts0: surface temperature interpolated from MM5 data, 

Ps0: surface pressure interpolated from MM5 data, 

dT/dz: temperature gradient (6.5 °C per 1,000 m), 

dP/dz: pressure gradient (9.0 mb per 100 m), 

ΔZ: terrain height difference, Zs0 – Zs, 

Zs0: terrain height interpolated from MM5 data, and 

Zs: terrain height from DTED Level 0. 

Vertical temperature profile is recalculated as 

 )() )( ()( Z Z Z

TZ T s

sc

sc

s Z

T

ZT −∗ −

− += . (3) 

Here Zc is the height determined empirically to correct vertical temperature profile. 

Vertical pressure profile is recalculated using the hydrostatic equation: 

 ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

−∗= − + )

)(

1

)(

1 )()( 2

(exp)()(

1 2

2 1 12

Z Z ZZ ZZ R TT

g PP . (4) 

After these steps are accomplished, the data are then vertically interpolated once again to 

the vertical coordinate levels of the WRE-N. This sigma-z vertical coordinate of the 

WRE-N is defined as 

z

z

z

g

g

H

z

H −

− =

* (5) 

where 

z = the Cartesian vertical coordinate, 

zg = the ground elevation, 

H = the material surface top of the model in the z

*

 coordinate, and 

H = the corresponding height in the z coordinate defined by z g HH max += , 

7
where 

zg max = the maximum value of the terrain elevation in the domain of the WRE-N. 

Currently, the vertical extent of the model ( H ) is 7,000 m above the highest grid point 

elevation of the domain. 

The following procedures are taken to interpolate the 15-km resolution MM5 forecast 

data to the 3D spatial locations of the WRE-N AOI: 

1. MM5 parameters (temperature, dew-point temperature, and horizontal u and v wind 

components) are vertically interpolated to 30 predetermined vertical levels 

(zi, i = 1, 30). 

2. Horizontal interpolation of MM5 parameters (on zi levels) to WRE-N grid point 

locations are performed for each parameter, using a 1/r2 weighting factor, where “r” 

is the distance between the MM5 grid point and the WRE-N grid point. 

3. An arbitrary parameter φ on a zi level is linearly interpolated to a z* level of the 

WRE-N grid: 

( )

( )( ) ζ ζζ

φ φ φφ k st

k k

k k

z k z − −

− +=

+

+

1

1 *

)( (6) 

 where z is the Cartesian height above sea level of the z st

* level, calculated from 

equation 1 as 

( )

H

zH z

zzz

g g

gst

−

+= * + max (7) 

 and ζ k

is the height of the k

th

zi level. 

If there are no upper-air radiosonde sounding data available for a desired time, the 3D 

data of )(

*

z φ (as determined by equation 6) are used as the 3D background dataset for 

the creation of the WRE-N. For the nowcast periods of the WRE-N (t0+30, t0+60, t0+90, 

t0+120, t0+150, t0+180 min), this is how background MM5 fields are always generated, 

since no radiosonde data are available at these times. If there are local upper-air 

radiosonde sounding data available at the WRE/analysis time (t0), the 3D MM5 data 

calculated in equation 2 are also composed with observed upper-air radiosonde sounding 

data as follows: 

1. At each radiosonde location, sounding data (such as horizontal wind vector 

components, temperature, and mixing ratio) are vertically interpolated to the zi

levels using a linear interpolation method. 

2. At each zi level, the successive correction method is performed using the MM5 3D 

data generated in equation 6 as first-guess background data. 

3. Linear vertical interpolation from zi to zst is performed for each WRE-N parameter 

and grid point. 

8
3.2 Surface Data Analysis 

Upon completion of the upper-air analyses described in section 3.1, a separate set of 

surface analyses are performed for each WRE-N parameter. Using the AFWA operational 

15-km resolution MM5 as background data, objective analyses of surface temperature, 

dew-point temperature, and horizontal u and v wind vector components are made. The 

following variant of the successive correction method is used in these analyses. 

For a given analysis time, if φa (i, j) is the analyzed value of some meteorological 

parameter at grid point (i, j) and φb(i, j) is the corresponding MM5 background value for 

that parameter (at that grid point), we can write 

 (8) 

)(),(),(

,, 1

φφ , φφ kbko

m

k

ijk a b jiji += ∑w − =

where φ ,ko

is the observation at the k

th location, w is the weight for each observation, ,ijk

φ ,kb

is the value of the background at the observation point derived by a bilinear 

interpolation method, and m is the number of observations. 

The weighting factor is defined as 

r

w

ijk

ijk 2

,

,

1 = (9) 

where is the distance between the k r ,ijk

th observation point and the grid point (i, j). 

The method is repeated in an iterative fashion, so that the background field is updated by 

the latest analysis after each iteration: 

()()1( ))( (10) ,, 1 , n n n

kako

m

k

ijk a φφ a +=+ ∑w −φφ =

where n)( φ a is the value of the analysis at the grid point after the n

th iteration, and 

)( , n φ ka is its value interpolated for the k

th location after the nth iteration. 

In this development, the iteration was repeated three times, for practical purposes, to 

obtain the final value. 

3.3 MARIAH Similarity Formulae 

The equations and approach for determining the similarity scaling constants for wind, 

temperature, and specific humidity―referred to as MARIAH (Rachele et al., 1995)―are 

used to combine surface and upper-air analysis data. For practical purposes, it is assumed 

9
that the lowest level above ground of the upper-air analysis data is modified by the 

surface analysis. Details of this method have been reviewed by Henmi and Dumais 

(2004). 

4. Nowcast Method to Complete WRE-N 

After the analysis (WRE) component of the WRE-N (described in section 3), the 

following procedures are used to produce a nowcast out to 3 h for the same domain, the 

final component of the WRE-N system. 

For the current WRE time (time = t0), an arbitrary parameter, such as temperature, dewpoint temperature, or u and v wind vector components, is expressed by X0, where X

represents the meteorological parameter. For the t0 + 3 h nowcast time (time = t3), only 

the 3D MM5 forecast data is available (no observations exist in the future). These MM5 

forecast data are then analyzed at time t3 for the domain of interest, using the methods 

discussed in section 3. An arbitrary MM5 forecast parameter at time t3 is expressed by X3. 

The time rate of change of parameter X between time t0 and t3 is calculated as 

tt

XX

dt

dX

03

03

−

− = . (11) 

If no previous nowcast has been made within the 3-h period preceding time t0, a nowcast 

for the parameter X at time t between t0 and t3 is generated by 

)( XX 0 tt 0 dt

dX

t β −••+= . (12) 

Here, β is an empirical parameter to be determined in section 6. 

If a previously generated WRE-N dataset that has been generated within Δt prior to the 

currently desired WRE-N time (t0) exists, the most recently generated WRE-N fields will 

be utilized in producing the new nowcast. To do so, new variables XΔt are defined for X at 

Δt. The time rate of change of X from Δt to t3 is calculated as 

( )

( )t t

XX

dt

dX t

t

Δ+

− = Δ

Δ

⎟

⎠

⎞ ⎜

⎝

⎛

3

3 (13) 

 and the nowcast of X at time t due to XΔt is calculated as 

)( ' tt

dt

dX

XX t

t t •+= ⎟ Δ+•

⎠

⎞ ⎜

⎝

⎛

Δ

Δ β . (14) 

If both Xt

 and X t

'

 values are available, the new nowcast value of X at time t is 

calculated as 

10
'

' '

ww

w XX

t

t t t w

X

+

+ = , (15) 

where 

wt and w’ are weighting factors defined as 

t w t t

0

1

− = (16) 

and 

w Δ+ tt =

' 1 . (17) 

In practice, Δt is limited to less than 3 h. If previous nowcast fields are 3 h or older than 

the current desired WRE-N time t0, they are not used. An empirical parameter β is used to 

adjust the time rate of change of X in equations 2 and 4. In section 6, reasonable values 

for β will be investigated. 

5. Computer Programs and Input Files 

The present WRE-N is composed of the following FORTRAN 77 program and subprograms: 

1. nowcast_main.f: This is the main module that reads MM5, upper-air radiosonde 

sounding data, and terrain data. The subroutines 3dobj.f, 3dobj_dpt.f, 

3dobj_ncomp.f, and nowcast_imets.f are called from within this program. After 

upper-air and surface objective analyses of meteorological data are executed (and 

merged using MARIAH similarity theory) for times t0 and t3 (discussed in section 

3), nowcast calculations are then produced as discussed in section 4. An output file 

short_range_fcst_file is produced upon completion of this program, representing 

the final 4D WRE-N grid. 

2. 3dobj.f: This subroutine is used when no upper-air radiosonde sounding data are 

available. The MM5 forecast data are three-dimensionally analyzed onto grid points 

of the WRE-N for times t0 and t3.

3. 3dobj_dpt.f and 3dobj_ncomp.f: When upper-air radiosonde sounding data are 

available within the WRE-N time and space domain, the MM5 forecast data are 

composed with the observed upper-air radiosonde data (discussed in section 3). 

These programs are used only for time t0.

4. nowcast_imets.f: The observed surface meteorological data are objectively analyzed 

with a successive correction method (discussed in section 3) using the data analyzed 

by 3dobj.f or 3dobj_dpt.f/3dobj_ncomp.f as background data, and are subject to the 

MARIAH similarity formulas. 

11
To use as much observed data as possible per WRE-N cycle, all surface observation data 

observed within ± 30 min of t0 and all upper-air radiosonde sounding data taken within 2 

hr of t0, are used. Only gross quality control measures are used at this time, and currently 

no special weighting is used to differentiate between ingest data of different ages. The 

present scheme is designed to cycle at hourly intervals, although 30-min cycling is 

possible. Also, the present configuration of the WRE-N produces output fields at 30-min 

intervals out to 3 h. 

5.1 Examples of the WRE-N 

In this section, a WRE-N example valid at 0000 Universal Time Coordinated (UTC), 10 

May 2005 for the previously described Dallas domain is shown. Figure 5 shows the 

15-km resolution MM5 horizontal wind vector distribution at the 10-m level for 

0000 UTC, 10 May 2005, prior to interpolation onto the finer-resolution WRE-N grid. 

Figure 5. MM5 grid horizontal surface wind vector distribution 

at 10 magl for 0000 UTC, 10 May 2005. 

Figure 6 is the 15-km resolution MM5 horizontal wind vector distribution at the 10-magl 

level, after interpolation onto the finer-resolution 1.5-km WRE-N grid for the Dallas 

domain. In this figure, for graphical clarity, vectors are drawn only at every other grid 

point. Observed surface wind vectors at the same valid time are shown in figure 7. 

12
Figure 6. The 10-magl horizontal surface wind vector distribution 

interpolated from the MM5 grid to the WRE-N grid for 

0000 UTC, 10 May 2005. 

NOTE: For clarity, only vectors at every other grid point 

are drawn. 

Figure 7. Observed 10-magl surface wind vectors valid at 

0000 UTC, 10 May 2005. 

13
Maximum and minimum wind speeds obtained from just the interpolated 15-km 

resolution MM5 forecast data for this case are 6.9 and 1.5 m/s, respectively. Figure 8 

shows the time t0 WRE 10-magl surface wind field, valid for 0000 UTC, 10 May 2005, 

obtained after the various successive correction analyses and similarity theory 

calculations (to merge the upper-air and surface analyses) were executed. Maximum and 

minimum wind speeds of the WRE wind fields are increased from those obtained solely 

from the MM5to 8.9 and 2.3 m/s, respectively. Notice that the WRE wind directions are 

also modified. Figure 9 shows the 2 h (t0 + 2 h) nowcast 10-magl surface level wind field 

valid at 0200 UTC, 10 May 2005, based on the WRE wind field (t0) shown in figure 8 

(discussed in section 4). 

Figure 8. The 10-magl surface wind field produced by the 

WRE-N analysis module (0000 UTC, 10 May 2005), 

by combining the fields shown in figures 6 and 7. 

14
Figure 9. The 10 magl surface wind field valid for 0200 UTC, 

10 May 2005, the 2-h gridded nowcast produced by 

the WRE-N extrapolation module. 

The 2-magl level surface temperature field generated purely from the 15-km MM5 

forecast data is shown in figure 10. For this temperature field, the maximum and 

minimum temperatures were 32.8 and 30.3 °C, respectively. Figure 11 shows the 2-magl 

level surface temperature field after the execution of all the analysis steps necessary to 

produce the t0 WRE. The 2-magl level surface WRE temperature field has both maximum 

and minimum temperatures decreasing to 27.0 and 22.5 °C, respectively. This is more 

than 5 oC lower than temperatures predicted by the operational AFWA MM5 15-km 

resolution forecasts. 

15
Figure 10. The 2-magl surface temperature distribution 

from the MM5 interpolated to the WRE-N grid, 

valid at 0000 UTC, 10 May 2005. 

Figure 11. The 2-magl surface temperature field produced 

by the WRE-N analysis module by combining 

the temperature field shown in figure 10 with 

observations valid for 00 UTC, 10 May 2005. 

Figure 12 shows vertical distributions (valid at 0000 UTC, 10 May 2005) at the KFWD 

(Fort Worth, TX) radiosonde site for temperature and dew-point temperature. Three 

vertical profiles are plotted, generated from the original 15-km resolution MM5 forecast, 

16
the raw upper-air radiosonde sounding observation, and the WRE. The vertical profiles of 

wind speed and wind direction for the same location and time are shown in figure 13. 

Figure 12. Vertical profiles of temperature and dew-point temperature valid 

for the KFWD (Fort Worth, TX) radiosonde site at 0000 UTC, 10 

May 2005. Profiles produced by the AFWA MM5, the KFWD 

radiosonde, and the WRE-N analysis module are shown. 

Figure 13. Same as figure 12, except for wind speed and wind direction. 

17
5.2 Preliminary Evaluation of the WRE-N Method 

All 15-km resolution MM5 data, hourly surface observation data, and upper-air 

radiosonde sounding data were collected over the Dallas domain shown in figure 3 for the 

period from 16 April–12 May 2005. In this 111 km-by-111 km area (75-by-75 grid points 

with a 1.5-km grid resolution), there are 16 regularly reporting surface stations. 

Similar datasets were collected for the period from 16 May–25 May 2005 over the 

Denver domain shown in figure 14, covering an area of 248 km by 248 km (125-by-125 

grid points with a 2-km grid resolution) centered at 39.5° N. and 104.1° W. There are 

only 12 surface stations within this domain, all over the plains area to the east of the 

Rocky Mountain frontal range. 

Since there are only a limited number of surface and upper-air radiosonde stations 

available in both domains, all the observations are used for producing the WRE analyses 

at time t0 (0 h). In the remainder of this section, all available surface observations are also 

used to evaluate the quality of the WRE-N fields at times t0, t0+1 h, t0+2 h, and t0+3 h. 

Temperature and dew-point are evaluated at the 2-magl level, and winds are evaluated at 

the 10-magl level. Statistical parameters, such as mean absolute difference (AD) and 

correlation coefficient (CC), are calculated at each WRE-N time period for the 

parameters of temperature, dew-point temperature, and wind speed. Root mean square 

vector error (RMSVE) and mean wind direction difference (MWDDF) are also calculated 

for the horizontal wind vector. These statistical parameters are described in Henmi 

(2002). 

Figure 14. Terrain contour distribution for the 125-by-125 

domain of 2-km grid spacing, centered at 

39.5° N. and 104.1° W. (Denver domain). 

NOTE: The “*” symbol represents MM5 grid points, and the 

“S” and “U” symbols represent the locations of the surface and 

upper-air radiosonde meteorological observations, respectively. 

18
WRE-N calculations with different values of β (0.0, 0.2, 0.4, 0.6, 0.8, and 1.0), along 

with WRE-N produced with no observation data (MM5 data interpolation), are compared 

with observations for the surface fields. A β-value of 0.0 represents persistence. On the 

other hand, a β-value of 1.0 represents total reliance on the pure AFWA MM5 forecast at 

the end period (t0 + 3 h). 

Figures 15 and 16 show the time series of AD and CC for surface temperature for both 

the Dallas and Denver domains, respectively. For the Dallas domain, the current WRE-N 

method produced better statistics than relying solely on the existing AFWA MM5 

forecasts for up to 3 h forward from the WRE time t0. However, for the complex terrain 

of the Denver domain, at the WRE-N time of t0+3 h, the statistics of the AFWA MM5 

exceeded those obtained by the current WRE-N method (for all β-values tested). For the 

Denver domain, the current WRE-N method produced better statistics than those from the 

AFWA MM5 up until t0+2 h. It should also be noted that the statistics for surface 

temperature produced from the AFWA MM5 over the Denver domain are better than 

those produced over the Dallas domain. 

Figures 17 and 18 are similar to figures 15 and 16, except they show surface dew-point 

temperature. For the Dallas domain, a persistence forecast (β = 0.0) produced the best 

statistics and a β = 1.0 produced the worst statistics, indicating that over this domain dewpoint temperature changes slowly with time (and may not be forecasted well by the 

AFWA MM5). On the contrary, for the Denver domain, the best surface dew-point 

temperature statistics were obtained with a β = 1.0. Over the Dallas domain, the present 

WRE-N method could be statistically better than the pure AFWA MM5 forecast up to 

t0+ 3 h, but over the Denver domain, the same WRE-N method was statistically better 

than the AFWA MM5 only out to t0+ 2 h. 

Figures 19 and 20 display similar statistics for wind speed. It is noted that the surface 

wind speed statistics of the AFWA MM5 forecasts over the Denver domain are better 

than those produced over the Dallas domain. Over the Dallas domain, the present 

WRE-N method, with various values of β, yielded better statistics than the AFWA MM5 

out to t0+ 3 h, with β = 0.2 producing the best statistics. This may indicate that wind 

speed changes slowly with time over this domain and is more uniform in distribution. 

Over the Denver domain, the method using β = 1.0 produced the best statistics, with 

β = 0.0 generating the worst statistics for wind speed. Again, this may be a consequence 

of the surface wind field varying significantly across the Denver domain due to the 

complex topography, and the AFWA MM5 model may have some skill at 

predicting/resolving the local diurnal mesoscale wind field. 

The RMSVE and MWDDF surface wind field statistics are plotted in figures 21 and 22 

for the Dallas and Denver domains, respectively. For wind vectors, the AFWA MM5 

forecast is considerably better for the Dallas domain than for the Denver domain. For 

both domains, the RMSVE and MWDDF statistics generated by the present WRE-N 

method (with various β values) are smaller than those from just the AFWA MM5 

forecasts out to t0+ 3 h. It can be seen that both the surface wind RMSVE and MWDDF 

statistics for the Dallas domain are smaller than those for the Denver domain. 

From these results, it can be tentatively concluded that, for practical purposes, an 

appropriate value for β is 0.5. With further study and added complexity, this parameter 

19
could be allowed to vary based on a particular meteorological and geographical scenario, 

though to do so in an automated fashion, without user input, would be challenging. 

Figure 15. AD and CC of surface temperatures as a function 

of the WRE-N nowcast period for different β

values and for the Dallas domain. 

NOTE: The β values are given at the right ends of lines. 

The line with “MM5” refers to AFWA MM5 forecast data 

(i.e., the background fields for the WRE-N). 

Figure 16. Same as figure 15, except for the Denver domain. 

20
Figure 17. Same as figure 15, except for the dew-point temperature. 

Figure 18. Same as figure 17, except for the Denver domain. 

21
Figure 19. Same as figure 15, except for the wind speed. 

Figure 20. Same as figure 19, except for the Denver domain. 

22
Figure 21. The RMSVE and MWDDF as a function of the 

WRE-N nowcast period for the Dallas domain. 

Figure 22. Same as figure 21, except for the Denver domain. 

23
8. Conclusion 

An initial WRE-N method for the IMETS has been developed, discussed, and evaluated. 

In the present method, meteorological parameters including temperature, dew-point 

temperature, and u and v horizontal wind vector components (and surface pressure, 

although it was not discussed) are analyzed and nowcasted. At first, the 3D fields are 

computed across the entire WRE-N 3D grid for each meteorological parameter 

(arbitrarily represented by X0 and X3) at times t0 and t0 + 3 h, respectively. The nowcast of 

these parameters, at times between t0 and t0 + 3 h, are done under the assumption that 

these parameters change linearly with the time rate of change, multiplied by an empirical 

parameter β. Additionally, in cases where a WRE-N parameter field (XΔt) calculated from 

a previous time Δt (less than 3 h prior to t0) exists, the WRE-N is based on X0, X3, and XΔt. 

Only the most recent WRE-N field prior to the t0 is used in the calculations. 

This method has been applied to two different areas centered near Dallas, TX, and 

Denver, CO, and has been evaluated using surface observation data from the spring of 

2005. It was shown that an appropriate value for the empirical parameter β varies 

between meteorological parameters and different domains. An appropriate value for β

was tentatively determined as 0.5, although the statistical evaluation studies described in 

this report are based on the comparison between WRE-N values and observation for only 

limited seasonal periods and geographical areas. This evaluation study was done only for 

cases where a previous WRE-N field was not used to generate a new WRE-N. Further 

studies are needed to evaluate the present method for cases in which previous WRE-N 

fields are utilized. The present forecast method was shown valid up to 3 h over the Dallas 

domain, and up to 2 h over the Denver domain. Again, tentatively, we conclude the 

method can be valid for producing a 2- or 3-h WRE-N, adding some value over relying 

on the existing background AFWA 15-km MM5 forecast data alone. 

Because there is no physical and dynamic coupling of the WRE analysis to an NWP 

model when producing the current WRE-N fields, certain meteorological situations (such 

as those with fast moving meso/micro boundaries and thunderstorm gust fronts) will pose 

significant difficulty unless the AFWA MM5 forecast can capture these features 

accurately in time and space. To address this problem (as well as to provide a capability 

to ingest many other types of non-conventional meteorological observations), a more 

sophisticated WRE-N is being envisioned for the Distributed Common Ground Station–

Army (DGCS–A) for development later this decade. The approach being developed is 

based on diabatic initialization and would be a fully coupled analysis and NWP 

assimilation system. At this time, a LAPS (Shaw et al., 2004) and WRF (Skamarock et 

al., 2005) coupled system is being investigated for application as this advanced WRE-N. 

24
References 

Browning, K.A. Nowcasting; Academic Press: New York, 1982, p 256. 

Henmi, T. Nowcasting Surface Meteorological Parameters Using Successive Correction 

Method; ARL-TR-2898; U.S. Army Research Laboratory: White Sands Missile 

Range, NM, 2002. 

Henmi, T. Development of a Nowcasting Method for Three-Dimensional Meteorological 

Data: Preliminary Report; ARL-TR-3120; U.S. Army Research Laboratory: White 

Sands Missile Range, NM, 2003. 

Henmi, T.; Dumais, R. An Analysis Method to Nowcast Three-Dimensional 

Meteorological Data for the U.S. Army IMETS; ARL-TR-3354; U.S. Army Research 

Laboratory: White Sands Missile Range, NM, 2004. 

Horel, J.; Splitt, M.; Dunn, L.; Pechmann, J.; White, B.; Cilberti, C.; Lazarus, S.; 

Slemmer, J.; Zaff, D.; Burks, J. MESONET: Cooperative Mesonets in the Western 

United States. Bull. Amer. Met. Soc., 2002, 83, 211–225. 

Orlanski, I. A Rational Subdivision of Scales for Atmospheric Processes. Bull. Amer. 

Met. Soc., 1975, 56 (5), 527-530. 

Rachele, H.; Tunick, A.; Hansen, F.V. MARIAH―A Similarity-Based Method for 

Determining Wind, Temperature, and Humidity Profile Structure in the Atmospheric 

Surface Layer. J. of Applied Met., 1995, 34, 100–105. 

Sashegyi, K.D.; Madala, R.D. Initial Conditions and Boundary Conditions of Mesoscale 

Modeling of the Atmosphere. Met. Monographs, 1994, 25 (47), 1–12. 

Shaw, J.; Albers, S.; Birkenheuer, D.; Brown, J.; McGinley, J.; Schultz, P.; Smart, J.; 

Szoke, E. Application of the Local Analysis and Prediction System (LAPS) Diabatic 

Initialization of Mesoscale Numerical Weather Prediction Models for the IHOP-2002 

Field Experiment [CD-ROM]; 20th Conference on Weather Analysis and 

Forecasting: Seattle, WA, Amer. Meteor. Soc., 2004, p 3–7. 

Skamarock, W.L.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Barker, D.M.; Wang, W.; Powers, 

J.D. A Description of the Advanced Research WRF Version 2; NCAR/TN-468+STR; 

June 2005, p100. 

Warner, T.T.; Seaman, N.L.: A Real-Time Mesoscale Numerical Weather Prediction 

System Used for Research, Teaching and Public Service at Penn State University. 

Bull. Amer. Meteor. Soc., 1990, 71, 792–805. 

25
Acronyms 

3D three-dimensional 

AFWA Air Force Weather Agency 

AOI area of interest 

ARL U.S. Army Research Laboratory 

CC Correlation Coefficient 

DGCS–A Distributed Common Ground Station–Army 

DMA Defense Mapping Agency 

DTED Digital Terrain Elevation Data 

GRIB gridded binary 

IMETS Integrated Meteorological System 

MM5 Mesoscale Model Version 5 

MWDDF Mean Wind Direction Difference 

NWP Numerical Weather Prediction 

RMSVE Root Mean Square Vector Error 

UTC Universal Time Coordinated 

UTM Universal Transversal Mercator 

WMO World Meteorological Organization 

WRE Weather Running Estimate 

WRE-N Weather Running Estimate-Nowcast 

26
Distribution List 

 Copies 

US ARMY RESEARCH LAB 2 

AMSRD ARL CI BE 

ATTN R DUMAIS 

T HENMI 

WSMR NM 88002-5501 

PHYSICAL SCIENCE LABORATORY 1 

NMSU 

ATTN R OKRASINSKI 

PO BOX 30002 

LAS CRUCES NM 88003-8002 

US ARMY RESEARCH LAB 1

ATTN IMNE ALC IMS 

MAIL & RECORDS MGMT

ADELPHI MD 20783-1197

ADMNSTR 1 

DEFNS TECHL INFO CTR 

ATTN DTIC OCP ELECT CPY V MADDOX 

8725 JOHN J KINGMAN RD STE 0944 

FT BELVOIR VA 22060-6218 

US ARMY RESEARCH LAB 2 

AMSRD ARL CI OK TL TECHL LIB 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY RESEARCH LAB 1 

AMSRD CI OK TP TECHL LIB 

APG MD 21005 

TOTAL 8 

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INTENTIONALLY LEFT BLANK. 

28