Nowcasting Surface Meteorological Parameters Using 

Successive Correction Method 

by Teizi Henmi 

ARL-TR-2898 January-2002 

Approved for public release; distribution is unlimited. 
NOTICES 

Disclaimers 

The findings in this report are not to be construed as an 

official Department of the Army position, unless so designated 

by other authorized documents. 

Citation of manufacturers’ or trade names does not constitute 

an official endorsement or approval of the use thereof. 

DESTRUCTION NOTICE—When this document is no longer 

needed, do not return it to the originator. 
Army Research Laboratory 

White Sands Missile Range, NM 88002 

ARL-TR-2898 January 2002 

Nowcasting Surface Meteorological Parameters Using 

Successive Correction Method 

Teizi Henmi 

Computational & Information Sciences Directorate 

Battlefield Environment Division 

Approved for public release; distribution is unlimited 

iii 

Contents 

Executive Summary 1

1. Introduction 2

2. Description of the BFM and Meteorological Data 3

2.1 BFM....................................................................................................................................3 

2.2 Meteorological Data 4

3. Nowcasting and Evaluation Methods 5

3.1 Nowcasting Method............................................................................................................5 

3.2 Evaluation Method .............................................................................................................8 

3.2.1 Data Comparison Method ......................................................................................8 

3.2.2 Statistical Parameters .............................................................................................9 

4. Results 11

4.1 Nowecasted and Observed Data Comparison ..................................................................11 

4.2 Time Series of the Statistical Parameters .........................................................................15 

5. Conclusions 20

References 21

Acronyms 22

Report Documentation Page 23 
iv 

Figures 

Figure 1. Terrain contours of the BFM model domain....................................................................3 

Figure 2. Surface wind vectors observed at 00:00 UTC, March 5 2002 .........................................7 

Figure 3. Surface wind vector field forecasted by the BFM for 00:00 UTC, March 5 

2002............................................................................................................................................7 

Figure 4. Wind vector field nowcasted by the successive correction method.................................8 

Figure 5. Temperature comparison between the nowcasted data and the observed data ..............11 

Figure 6. Dew point temperature comparison between the nowcasted data and the 

observed data ...........................................................................................................................12 

Figure 7. Wind vector-x component u comparison between the nowcasted data and 

the observed data......................................................................................................................12 

Figure 8. Wind vector-y component v comparison between the nowcasted data and 

the observed data......................................................................................................................13 

Figure 9. Wind speed comparison between the nowcasted data and the observed data................13 

Figure 10(A). Time series of statistical parameters for temperature between the 

nowcasted data and the observed data from both “S” and “*” sites ........................................16 

Figure 10(B). Time series of the statistical parameters for temperature between 

forecasted data by BFM and the observed data from both “S” and “*” sites ..........................16 

Figure 11(A). Time series of statistical parameters for dew point temperature between 

the nowcasted data and the observed data from both “S” and “*” sites ..................................17 

Figure 11(B). Time series of the statistical parameters for dew point temperature 

between forecasted data by BFM and the observed data from both “S” and “*” 

sites ..........................................................................................................................................17 

Figure 12(A). Time series of statistical parameters for wind speed between the 

nowcasted data and the observed data from both “S” and “*” sites ........................................18 

Figure 12(B). Time series of the statistical parameters for wind speed between 

forecasted data by BFM and the observed data from both “S” and “*” sites ..........................18 

Figure 13(A). Time series of RMSVE and MAWDD between the nowcasted data and 

the observed data from both “S” and “*” sites ........................................................................19 

Figure 13(B). Time series of RMSVE and MAWDD between the forecast data by 

BFM and the observed data from both “S” and “*” sites ........................................................19 
v 

Tables 

Table 1. Portion of daily data...........................................................................................................4 

Table 2. Dates of archived data used ...............................................................................................5 

Table 3. Statistical parameters calculated for the nowcasted data by the successive 

correction method. RMSVE = 2.8 m/sec, and MAWDD = 43o...............................................14 

Table 4. Statistical parameters calculated for the nowcasted data by the successive 

correction method using data from both “S” and “*” sites. RMSVE = 2.8 m/sec, 

and MAWDD = 26.2o ..............................................................................................................14 

Table 5. Statistical parameters between the BFM forecast data and the observed data 

at both “S” and “*” sites. RMSVE = 3.9 m/sec, and MAWDD = 55.2o..................................15 

1 

Executive Summary 

The successive correction method, a nowcasting method for surface meteorological 

parameters, such as temperature, dew point temperature, and horizontal wind vector 

components, was evaluated statistically by using data from the Mesowest mesonet. 

Nowcasting is a method of diagnosing the current weather situation, and in this study is 

defined as an objective analysis of meteorological parameters that are currently observed. 

The present nowcasting method uses the Battlescale Forecast Model (BFM) forecast 

fields of surface meteorological parameters as background data in combination with 

observation data. 

Major findings of the present study included in this report are: 

1. Temperature and dew point temperature can be accurately nowcasted by using the 

successive correction method. The BFM forecast data provided good background 

gridded data for these parameters. The combination of the observed data and good 

background data resulted in surface temperature fields and dew point temperature 

fields that accurately represent the current fields. 

2. Surface horizontal wind vector fields were greatly improved by the nowcasting 

method over the BFM forecast fields. The BFM forecasting calculation alone could 

not produce reliable surface wind vector fields, but by combining the forecast with 

observation, much more improved wind fields can be obtained. 

Finally, in the Conclusions section, this report suggests that a similar study should be 

conducted in the near future using the Penn State/National Center for Atmospheric 

Research (NCAR) Mesoscale Model version 5 (MM5) data as the background data
2

1. Introduction

Mesoscale forecasting models, such as the Battlescale Forecast Model (BFM), and the 

Penn-State/National Center for Atmospheric Research (NCAR) Mesoscale Model version 

5 (MM5), produce short-range forecasting fields of surface meteorological parameters 

that are sometimes far from reality, particularly when applied to areas of complex terrain, 

such as the terrain found in Utah (Henmi, 2002). 

This report describes the method of nowcasting surface meteorological parameters by 

combining model forecast data with observed data, and then discusses the results of 

applying the nowcasting method. Nowcasting is a method of diagnosing the current 

weather situation, and in this study is defined as an objective analysis of meteorological 

parameters that are currently observed. If the current weather situations are accurately 

diagnosed, it may be possible to accurately forecast near-future weather situations. 

The successive correction method (Sashegyi and Madala, 1994), described in this report, 

has been successfully applied to nowcasting surface meteorological parameters over 

Oklahoma in a previous study (Sauter, Henmi, and Dumais, 2001). In the previous study, 

the BFM forecasting calculations were made over a model domain area of 600×600 km, 

with a 61×61 grid mesh and 10 km grid increments. Surface fields, including horizontal 

wind vector components, temperature, and dew point temperature, which were forecasted 

by the BFM, were combined with the observed data from several sites in the Oklahoma 

mesonet by the successive correction method. Nowcasted fields of the surface 

meteorological parameters were then compared with the rest of the mesonet data. The 

results of the study showed that combining surface observation data with the BFM 

forecast fields could produce more accurate fields of the surface meteorological 

parameters than model forecasting calculations, or the objective analysis of observed 

data, could determine individually. 

The purpose of this study is to examine and statistically evaluate the successive 

correction method as a tool for nowcasting the surface meteorological parameters over a 

complex terrain area. The BFM forecast data is used as background data for the method. 

Details of the method are described in the following section. The model domain over 

Utah was chosen for this study because of the complexity of the terrain and the 

abundance of public meteorological data, which is available at no cost on the Internet. 
3

2. Description of the BFM and Meteorological Data 

2.1 BFM 

The BFM is an operational forecast model developed by the U.S. Army Research 

Laboratory (ARL) (Henmi and Dumais, 1998). It has been extensively used to make 

short-range forecasts of atmospheric conditions as a component in the Integrated 

Meteorology System (IMETS). The BFM uses, for prognostic calculation, the Higher 

Order Turbulence Model for Atmospheric Calculation (HOTMAC), which was 

developed by Yamada (Yamada and Bunker, 1989). In operational mode in the IMETS, 

the BFM has been used over the model domain of 500x500 km2

 with the grid increment 

of 10 km. 

In this study, the BFM is used over a model domain of 125x125 km2

, with the 2.5 km 

grid increments and 51x51 grid points, centered at 40.5 °N and 112 °W, as displayed in 

figure 1. The sites designated by “S” and “*” in figure 1 represent the locations of 

Mesowest observation sites. Observed data at the “S” sites were used for nowcasting 

calculations, and the observation data at the “*” sites were used for evaluating the results 

of nowcasting calculations. The selection of the “S” sites was made arbitrarily, and the 

number of sites used (18), was determined from pre-trials of the successive correction 

method. 

Figure 1. Terrain contours of the BFM model domain. 
4

2.2 Meteorological Data 

As seen in figure 1, there are numerous surface observation data available for the model 

domains. The data is publicly available from the University of Utah Mesowest 

cooperative anonymous File Transfer Protocol (FTP) site. 

Mesowest data, contained in a file called total.dat, includes data for the past 24-h period, 

in 15 min intervals, for the western United States. A detailed description of Mesowest 

data can be found in Horel et al. (2002). 

For this study, daily data files that contained data only at even hours, starting at 00:00 

UTC (universal time coordinate) for a 24-h period, were produced for the study area. 

Table 1 illustrates a portion of the file containing daily data . The table shows the 

following (from left to right): station name, day, hour, minute, latitude, longitude, wind 

direction, wind speed (knots), temperature (°F), dew point temperature (°F), relative 

humidity (%), and 24-h accumulated precipitation. 

 Table 1. Portion of daily data. 

BGRUT 2 516 0 0 40.93 -112.56 90.00 7.80 62.00 35.50 37.00-9999.00 

BR4 2 516 0 0 41.44 -112.16 148.00 0.90 65.00 27.40 24.00-9999.00 

DVB 2 516 0 0 40.62 -111.48 248.00 2.60 56.00 19.70 24.00-9999.00 

EMPUT 2 516 0 0 40.61 -111.53 245.00 4.30 45.00 20.30 37.00-9999.00 

FPK 2 516 0 0 41.03 -111.84 323.00 13.00 38.00 8.50 29.00-9999.00 

FWP 2 516 0 0 40.66 -112.20 244.00 3.50 43.00 17.20 35.00-9999.00 

GNI 2 516 0 0 41.33 -112.85 96.00 8.70 62.00 29.40 29.00-9999.00 

HAT 2 516 0 0 41.07 -112.59 42.00 7.00 62.00 23.80 23.00-9999.00 

LDS1 2 516 0 0 40.77 -111.89 359.00 3.50 63.00 17.50 17.00-9999.00 

LMR 2 516 0 0 41.06 -112.89 104.00 0.00 60.00 22.10 23.00-9999.00 

LMS 2 516 0 0 41.70 -112.85 148.00 2.60 67.00 20.80 17.00-9999.00 

MBY 2 516 0 0 40.61 -111.48 282.00 9.60 48.00 16.50 28.00-9999.00 

PCB 2 516 0 0 40.65 -111.51 347.00 1.70 60.00 24.10 25.00-9999.00 

PCS 2 516 0 0 40.65 -111.52 266.00 1.70 55.00 20.80 26.00-9999.00 

PRP 2 516 0 0 41.26 -112.44 341.00 6.10 53.00 10.60 18.00-9999.00 

SB1 2 516 0 0 41.19 -111.84 312.00 3.50 55.00 13.50 19.00-9999.00 

SNH 2 516 0 0 40.55 -111.85 340.00 4.30 65.00 26.30 23.00-9999.00 

SNI 2 516 0 0 41.20 -111.86 20.00 0.90 50.00 15.60 25.00-9999.00 

SNV 2 516 0 0 40.62 -111.51 269.00 0.90 52.00 20.70 29.00-9999.00 

SNX 2 516 0 0 41.04 -112.23 48.00 4.30 62.00 27.70 27.00-9999.00 

SNZ 2 516 0 0 40.88 -111.87 339.00 0.00 66.00 25.00 21.00-9999.00 

TPC 2 516 0 0 40.43 -111.71 192.00 4.30 52.00 23.10 32.00-9999.00 

WBU 2 516 0 0 40.71 -111.56 271.00 4.30 61.00 25.00 25.00-9999.00 

WCR 2 516 0 0 40.55 -111.32 261.00 7.80 57.00 16.30 20.00-9999.00 

KMS 2 516 0 0 40.56 -111.13 253.00 3.50 57.00 18.50 22.00-9999.00 

SKY 2 516 0 0 39.58 -111.25 249.00 8.70 52.00 16.30 24.00-9999.00 

UT1 2 516 0 0 41.20 -111.11 225.00 10.50 56.10 16.20 20.00-9999.00 

UT12 2 516 0 0 40.64 -111.90 0.00 5.80 66.00 21.90 18.00-9999.00 

UT20 2 516 0 0 40.75 -111.90 337.50 6.80 66.70 20.80 17.00-9999.00 

UT23 2 516 0 0 40.72 -111.90 0.00 7.20 67.60 20.70 16.00-9999.00 

UT5 2 516 0 0 40.71 -111.80 270.00 6.00 64.90 17.20 15.00-9999.00 

UT7 2 516 0 0 40.48 -111.90 0.00 12.60 65.10 19.90 17.00-9999.00 

UT9 2 516 0 0 40.68 -112.26 22.50 15.60 63.10 23.40 22.00-9999.00 

SND 2 516 0 0 40.37 -111.59 167.00 7.00 55.00 19.80 25.00-9999.00 

QSA 2 516 0 0 40.83 -112.01 5.00 7.00 63.00 22.50 21.00-9999.00 

KEM 2 516 0 0 41.76 -110.58 270.00 16.50 54.00 25.60 33.00-9999.00 
5

 Table 1. Portion of daily data (continued). 

LGP 2 516 0 0 41.71 -111.71 288.00 10.40 36.00 14.60 41.00-9999.00 

CASD 2 516 0 0 39.22 -111.02 257.40 8.80 74.10 28.20 18.20-9999.00 

ELMO 2 516 0 0 39.40 -110.81 286.60 5.90 77.00 20.60 12.10-9999.00 

FERR 2 516 0 0 39.09 -111.09 359.30 11.60 68.80 17.70 14.10-9999.00 

GUNN 2 516 0 0 39.16 -111.81 333.20 11.20 74.60 27.00 17.00-9999.00 

LITW 2 516 0 0 39.32 -111.33 339.80 7.20 67.50 33.90 28.80-9999.00 

MOLE 2 516 0 0 39.06 -111.14 202.50 10.50 70.90 18.20 13.30-9999.00 

CUPC 2 516 0 0 40.36 -111.90 333.40 6.30 67.20 27.40 22.30-9999.00 

CUPE 2 516 0 0 40.49 -111.47 255.40 6.30 65.80 25.90 22.00-9999.00 

CUPF 2 516 0 0 40.22 -111.11 273.60 11.70 54.60 31.00 40.40-9999.00 

CUPG 2 516 0 0 40.37 -111.03 355.30 7.30 54.40 30.30 39.50-9999.00 

CUPI 2 516 0 0 40.56 -110.70 354.40 0.00 55.80 14.60 19.40-9999.00 

MSI01 2 516 0 0 40.72 -111.86 325.00 5.60 69.70 25.60 19.00-9999.00 

BBN 2 516 0 0 40.89 -111.85 330.00 2.60 63.00 17.50 17.00-9999.00 

 ARAU1 2 516 0 0 40.59 -113.02 40.00 6.10 64.00 13.00 14.00-9999.00 

 BRAU1 2 516 0 0 40.88 -110.83 90.00 4.30 54. 9.00 26.00-9999.00 

The Navy Operational Global Atmospheric Prediction System (NOGAPS) data, which 

provides information globally for every 1° grid point, was used for initialization and 

time-dependent lateral boundary conditions of the BFM. The forecast data for the 

periods of 0-h, 12-h, and 24-h, initialized at 00:00 UTC, was obtained at 13 pressure 

levels between 1000 mb and 100 mb. The data was obtained through the Defense 

Modeling and Simulation Office (DMSO), Master Environmental Library (MEL) Web 

site. Upper-air sounding data was obtained from the University of Wyoming Web site. 

For this study, data archived for the period of January-March 2002 was used. Table 2 

shows the dates of the archived data used for model calculations of 24-h forecasting. 

 Table 2. Dates of archived data used. 

Month, 2002 Days 

January 15, 22, 23, 24, 25, 28, 29, 30 

February 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 20, 21, 22, 26, 27, 28 

March 4, 5, 6, 7, 8, 11, 12, 14 

3. Nowcasting and Evaluation Methods 

3.1 Nowcasting Method 

From the NOGAPS data, horizontal wind vector components, temperature, dew point 

temperature and geopotential height at 13 different pressure levels for the forecast periods 

of 00:00–12:00 UTC, and 12:00–24:00 UTC are interpolated at each pressure level. The 

data is then vertically interpolated from the pressure levels to the BFM’s height levels. 

The 0-h data produced for the height levels is then combined with upper air sounding 

data obtained at Salt Lake City, Utah. The BFM forecasting calculations of two 12-h 

periods per day are made by using this data as the initialization and lateral boundary 
6

condition data. Details of the BFM forecasting method are described in Henmi and 

Dumais (1998). 

The BFM forecasting data, containing hourly data, is used as the background data for the 

nowcasting method for surface temperature, dew point temperature, and horizontal wind 

vector components, u and v. The successive correction method (Sashegyi and Madala, 

1994) that follows is used in this study: 

If xa(i,j) is the nowcasted value at the grid point (i, j), and xb(i,j) is the value of the 

background (also known as the first guess) at the grid point, we can write: 

 ( , ) ( , ) ( ) , , 1

x x w , xo k xb k

m

k a b k ij i j = i j +∑ − =

 (1) 

where 

xo,k is the observation at the k

th location, wk,ij is the weight for each observation, 

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

interpolation method (described in the next section), and m is the number of 

observations. 

In this study, the weighting factor is defined as: 

 

r

w

k ij

k ij 2

,

,

1 = (2) 

where 

rk,ij is the distance between k

th observation point to grid point (i, j). 

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

latest analysis after each iteration: 

 )) ( 1) ( ) ( ( , , 1 , x n x n w x x n o k a k

m

k a a k ij

+ = +∑ − = (3) 

where 

xa(n) is the value of the analysis at the grid point after the n

th iteration, and xa,k is 

its value interpolated for k

th location after the nth iteration. 

In this study, the iteration was repeated three times to obtain the final values. 

Figures 2, 3, and 4 illustrate the nowcasting procedures. Figure 2 shows surface wind 

vectors observed at 00:00 UTC, March 5, 2002, while figure 3 shows surface wind vector 

fields forecasted by the BFM for 00:00 UTC, March 5, 2002. The data displayed in both 

figure 2 and 3 were used for the nowcasting calculation (displayed in figure 4). 
7

 Figure 2. Surface wind vectors observed at 00:00 

 UTC, March 5 2002. 

 Figure 3. Surface wind vector field forecasted by the BFM for 

 00:00 UTC, March 5 2002. 
8

 Figure 4. Wind vector field nowcasted by the successive 

 correction method. 

3.2 Evaluation Method 

3.2.1 Data Comparison Method 

After the nowcasting calculation is completed, the bilinear interpolation is conducted to 

obtain the nowcasted data at the locations of surface observation sites. 

Suppose a surface observation location (x’ and y’) is surrounded by four model grid 

points. An interpolated value, φ’, of an arbitrary variable, φ at (x’, y’), is calculated 

using a bilinear interpolation method, such as: 

 φ1=φ(i, j)+(x’ –x)[φ(i+1, j) - φ(i, j)] 

φ2= φ(i, j+1) +(x’ –x)[φ(i+1, j+1) - φ(i, j+1)] 

 φ’(x’, y’)=φ1 + (y’-y)[φ2 - φ1] (4) 

where 

(i, j) is the southwest grid point of the four grid points surrounding a surface 

observation location (x’, y’), and (x, y) is the location for the grid point (i, j): 

φ( i, j) is an arbitrary variable at (x, y). 
9

3.2.2 Statistical Parameters 

The following statistical parameters between nowcasted data and surface observation data 

are calculated using the data available in the model domain. The parameters are 

calculated hourly for the entire nowcasting period. Statistical parameters are calculated 

for temperature, relative humidity, wind speed, and horizontal wind vector components, u 

and v. 

Mean Difference (MD):

(5) 

 where 

the subscripts o and p represent observation and prediction, respectively. 

 The subscript i represents the i

th surface station, and the subscript j

represents the j

th forecast day. n is the number of surface stations, and m is 

the total number of forecast days. 

A nonzero mean difference (MD) indicates bias. For instance, if the MD value is 

positive, it indicates that the model tends to over-forecast. 

Mean Absolute Difference (AD): 

(6) 

 

Root Mean Square Error (RMSE): 

(7) 

Good agreements between observation and forecast are, in general, related to 

smaller values of AD and RMSE. 

mn

AD

∑∑ x p i j xo i j −

=

, , , ,

mn

RMSE

x p i j xo i j

m

j

n

i

2

, , , , 1 1

( − )

=

∑∑= =

mn

MD

m

j

n

i

∑∑ x p i j xo i j = =

−

= 1 1 , , , , ( )
10

Root Mean Square Vector Error (RMSVE): 

(8) 

This parameter measures the differences of both wind speed and wind direction. 

Good agreements of wind vectors are related to small values of the RMSVE. 

Correlation Coefficient (CC): 

(9) 

 Here: 

and xo and xp are the means of observed and forecast values, respectively. 

mn

RMSVE

∑∑ uo i j up i j vo i j v p i j − + −

=

[( ) ( ) ]

2

, , , ,

2

, , , ,

∑∑ ∑∑

∑∑

== ==

= = = m

i

n

j

m

i

n

j o i j p i j

m

i

n

j o i j p i j

y y

y y

CC

11 11

2

, ,

2

, ,

1 1 , , , ,

y xo i j xo o i j

= − , , , ,

p i j p p i j y = x − x , , , ,
11

Mean Wind Direction Difference (MWDDF):

 mn

MWDDF

m

j

n

i

∑∑ p i j o i j = =

−

= 1 1

θ , , θ , ,

 (10) 

 where 

θ is the wind direction. 

4. Results 

4.1 Nowecasted and Observed Data Comparison 

Figures 5 through 9 show scatter diagrams that compare nowcasted data and observed 

data. These figures represent twelve hour data between 00:00 and 12:00 UTC, during the 

period of January-March (31 days), 2002. As mentioned previously, nowcasted fields 

were calculated by the successive correction method from the BFM forecast data and the 

observation data at the “S” sites that are shown in figure 1. Observation data used for 

these scatter diagrams is obtained only from the “*” sites that are shown in figure 1. 

 Figure 5. Temperature comparison between the nowcasted 

 data and the observed data. 
12

 Figure 6. Dew point temperature comparison between the 

 nowcasted data and the observed data. 

 Figure 7. Wind vector-x component u comparison between the 

 nowcasted data and the observed data. 

 
13

 Figure 8. Wind vector-y component v comparison between the 

 nowcasted data and the observed data. 

 Figure 9. Wind speed comparison between the nowcasted data and 

 the observed data. 
14

Statistical parameters for figures 5–9 are calculated in table 3. These parameters are 

calculated for the data obtained from “*” sites in the nowcasting hours of 00:00–12:00 

UTC, during the period of January-March (31 days), 2002. The parameters are calculated 

for the nowcasted data by the successive correction method using the BFM forecast data, 

and the 20-station data, which is marked by “S” in figure 1. The observed data was 

obtained at the stations marked by “*” in figure 1, and are used for these statistics. 

Table 3. Statistical parameters calculated for the nowcasted data by the successive correction method. 

RMSVE = 2.8 m/sec, and MAWDD = 43o

. 

Parameters Temperature 

 (°C) 

Dew Pt. 

Temp.(°F) 

 U (m/sec) V(m/sec) Speed(m/sec) 

CC .89 .88 .36 .58 .46 

MD .6 -.4 .1 .3 -.1 

AD 2.0 2.1 1.6 1.7 1.7 

RMSE 3.0 3.1 2.3 2.4 2.3 

Number of 

Data 

 16,015 16,120 12,546 12,546 12,546 

The statistics comparison between the nowcasted data and the observed surface data, 

including all the sites (“S” and “*”) for the same data set, are given in table 4, which 

shows that the values are superior to table 3. By using the successive correction method, 

the agreements at “S” sites between calculated and observed values are almost perfect. 

Table 4. Statistical parameters calculated for the nowcasted data by the successive correction method using 

data from both “S” and “*” sites. RMSVE = 2.8 m/sec, and MAWDD = 26.2o

. 

Parameters Temperature 

 (°C) 

Dew Pt. 

Temp. (°C) 

U (m/sec) V (m/sec) Speed 

 (m/sec) 

CC .92 .92 .62 .73 .63 

MD .4 -.3 .0 .2 .0 

AD 1.4 1.5 1.1 1.2 1.1 

RMSE 2.5 2.6 1.9 2.0 1.9 

Number of 

Data 

 21,503 21,705 17,773 17,773 17,608 

Similar statistics between the BFM forecast data and the observation data, including both 

“S” and “*” sites shown in figure 1, are given in table 5 for the same data period as the 

period used for figure 3. 
15

Table 5. Statistical parameters between the BFM forecast data and the observed data at both “S” and “*” 

sites. RMSVE = 3.9 m/sec, and MAWDD = 55.2o

. 

Parameters Temperature 

(°C) 

Dew Pt. 

Temp. (°C) 

U (m/sec) V (m/sec) Speed 

 (m/sec) 

CC .84 .79 .28 .39 .28 

MD .2 -2.6 .6 .0 -.3 

AD 2.4 3.9 1.9 1.9 1.9 

RMSE 3.4 4.8 2.7 2.8 2.6 

Number of 

Data 

 21,614 21,723 17,753 17,753 17,753 

As seen when comparing tables 4 and 5, the nowcast data, as a whole, produces better 

statistical results than the BFM forecast data. In particular, the wind field statistics 

produced by nowcasting are substantially improved when compared to those produced by 

forecasting. 

4.2 Time Series of the Statistical Parameters 

As mentioned in section 3, “Nowcasting and Evaluation Methods,” the BFM forecast 

calculations are done twice a day for 12 hour (00:00–12:00 UTC), and two files 

containing forecast data for 12-h periods are produced before the nowcasting calculation. 

Time series of the statistical parameters, MD, CC, and AD are shown in figures 10(A) 

and (B), 11(A) and (B), and 12(A) and (B). The time series of these parameters, 

calculated between the nowcasted data and the observed data from both the “S” and “*” 

sites, are shown in figures 10(A), 11(A), and 12(A). For comparison, those parameters 

calculated between the BFM forecast data and the observed data are shown in figures 

10(B), 11(B), and 12(B). Figures 10, 11, and 12 represent, respectively, temperature, 

dew point temperature, and wind speed. Figures 13(A) and 13(B) represent the root 

mean square vector error (RMSVE) and mean wind direction difference (MWDDF). The 

calculations are made every hour during two 12-h periods, using the data for 31 days in 

the period of January through March 2002. 

In these figures, thin lines represent the period between 00 and 12 h, and thick lines 

represent the period between 12 and 24 h. 
16

 Figure 10(A). Time series of statistical parameters for temperature between the nowcasted data 

 and the observed data from both “S” and “*” sites. 

 Figure 10(B). Time series of the statistical parameters for temperature between forecasted data 

 by BFM and the observed data from both “S” and “*” sites. 
17

 Figure 11(A). Time series of statistical parameters for dew point temperature between the 

 nowcasted data and the observed data from both “S” and “*” sites. 

 Figure 11(B). Time series of the statistical parameters for dew point temperature between 

 forecasted data by BFM and the observed data from both “S” and “*” sites. 
18

 Figure 12(A). Time series of statistical parameters for wind speed between the nowcasted data 

 and the observed data from both “S” and “*” sites. 

 Figure 12(B). Time series of the statistical parameters for wind speed between forecasted data by 

 BFM and the observed data from both “S” and “*” sites. 
19

Figure 13(A). Time series of RMSVE and MWDDF between the nowcasted data and the 

 observed data from both “S” and “*” sites. 

 Figure 13(B). Time series of RMSVE and MWDDF between the forecast data by BFM and the 

 observed data from both “S” and “*” sites. 

Figures 10(B) and 11(B) show that temperature and dew point temperature fields at the 

surface can be forecasted fairly well by the BFM. However, figures 10(A) and 11(A) 
20

show that the nowcasting method further improves the agreements between calculation 

and observation. 

From figures 12(B) and 13(B), it is inferred that forecasting the surface wind vector fields 

by the BFM should be substantially improved. By combining the forecast fields with 

observed data, substantially improved wind vector fields were obtained. The mean AD 

of wind speed is improved from about 2 m/sec to 1 m/sec, the CC of wind speed from 

less than 0.4 to about 0.6, and the MWDDF from 60° to 30°. 

5. Conclusions 

The successive correction method was examined and evaluated statistically as a 

nowcasting tool for surface meteorological parameters, including temperature, dew point 

temperature, and horizontal wind vector components. The BFM forecast fields of the 

surface meteorological parameters were used as background data. 

In a study that was done over the model domain of Oklahoma, statistical results similar to 

the results achieved in this study were obtained by using observed data that was taken at 

only three or five sites (Sauter, Henmi, and Dumais, 2001). In addition, the model 

domain size of Oklahoma was 600x600 km2 with 10 km grid increments. The current 

study used data obtained at 18 stations over the model domain of 125x125 km2

 with 2.5 

km grid increments. 

By using the successive correction method, temperature and dew point temperature can 

be nowcasted well. The major reason for this is that the BFM forecasting calculation 

could provide good background fields of surface temperature and dew point temperature. 

Surface horizontal wind vector fields were really improved by the nowcasting calculation 

over the BFM forecast fields. The BFM forecast calculation alone could not produce 

reliable surface wind vector fields, but by combining the forecast with observation, much 

more improved wind fields could be obtained. 

In the mesoscale model evaluation study of the MM5 and the BFM over the model 

domains of Utah, a model similar to the model of the present study (Henmi, 2002), it was 

found that the MM5 could produce better surface meteorological forecast data than the 

BFM. Therefore, if the fields produced by the MM5 were used as the background data, 

the study might have obtained better statistical results than those obtained by using the 

BFM forecast data. A similar study using the MM5 data as the background data will be 

conducted in the near future. 
21

References 

Henmi, T. Evaluation Study of Mesoscale Models of MM5 and BFM Over the Model 

Domains of Utah State Using Surface Meteorological Data by Mesowest. ARL 

Technical Report, submitted for publication, 2002. 

Henmi, T.; Dumais, T. Jr. The Battlescale Forecast Model (BFM); ARL-TR-1032; U.S. 

Army Research Laboratory: WSMR, NM, 1998. 

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

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

United States. Bulletin of the American Meteorological Society 2002, 83, 211-225. 

Master Environmental Library. http://mel.dmso.mil, Defense Modeling and Simulation 

Office. 

Sauter, B.; Henmi, T.; Dumais, R. Comparing Nowcasting Methods Using Oklahoma 

Mesonet Data, Proceedings of BACIMO. 2001. 

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

Modeling of the Atmosphere, Meteorological Monographs 1994, 25 (47). 

University of Utah Mesowest. ftp://met.utah.edu, University of Utah. 

Upper Air Data. http://weather.uwyo.edu/upperair, University of Wyoming, College of 

Engineering, Department of Atmospheric Science. 

Yamada, T.; Bunker, S. A Numerical Model Study of Nocturnal Drainage Flows with 

Strong Wind and Temperature Gradients. Journal of Applied Meteorology 1989, 82, 

407–430. 
22

Acronyms 

AD Absolute Difference 

ARL U.S. Army Research Laboratory 

BFM Battlefield Forecast Model 

CC Correlation Coefficient 

DMSO Defense Modeling and Simulation Office 

FTP File Transfer Protocol 

HOTMAC Higher Order Turbulence Model for Atmospheric Circulation 

IMETS Integrated Meteorology System 

MWDDF Mean Absolute Wind Direction Difference 

MD Mean Difference 

MEL Master Environmental Library 

MM5 Mesoscale Model version 5 

NCAR National Center for Atmospheric Research 

NOGAPS Navy Operational Global Atmospheric Prediction System 

RMSE Root Mean Square Error 

RMSVE Root Mean Square Vector Error 

UTC Universal Time Coordinate 
23

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Nowcasting surface meteorological parameters using successive correction 

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Teizi Henmi 

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14. ABSTRACT 

The successive correction method was examined and evaluated statistically as a nowcasting method for surface meteorological 

parameters including temperature, dew point temperature, and horizontal wind vector components. The Battlescale Forecast 

Model (BFM) forecast fields of the surface meteorological parameters were used as background data. 

By this method, temperature and dew point temperature could be nowcasted well. The major reason for this is that the BFM 

forecasting calculation could provide good background fields of surface temperature and dew point temperature. Surface 

horizontal wind vector fields were really improved by the nowcasting calculation. The BFM forecast calculation alone could not 

produce reliable surface wind vector fields, but by combining the forecast data with observation, much more reliable wind field

could be obtained than what would be determined by the BFM alone.

15. SUBJECT TERMS

Nowcasting, surface meteorological parameters, successive correction method 

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Teizi Henmi 

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