**The Des Moines Sky Inits**

**Chuck Myers**

**Senior Forecaster**

**WFO DMX**

**May 2003**

**Updated August 2004**

The **Des Moines Sky Inits** developed for the GFE Sky smart
initialization are based on the work of Walcek, C. J., 1994: Cloud Cover
and Its Relationship to Relative Humidity during a Springtime
Midlatitude Cyclone. *Monthly Weather Review*, **122**, 1021-1035.

**Historical Note...**This work has been incorporated into all the
standard Sky smart initializations in GFE by the Forecast Systems
Laboratory. However, the ability to easily tweak the computations is not
available. The **Des Moines Sky Inits** provide that versatility.

From the abstract:

A Vertical distributions of fractional cloud coverage derived from the
U.S. Air Force 3DNEPH satellite, aircraft, and surface-based analysis
are compared with related standard meteorological observations over the
eastern United States.@

ADuring the period analyzed, cloud cover maximizes near 900 mb at 35%
cloud cover and decreases to near-zero cover at the surface. Above 900
mb, fractional cloudiness gradually decreases to 10%-20% cover at 200mb.
Cloud cover is positively correlated with relative humidity and
large-scale vertical velocity, and negatively correlated with wind shear
and temperature lapse rate, except in the lowest 100 mb, where cloud
cover is weakly correlated with relative humidity, vertical velocity,
wind shear, and temperature lapse rate. Mean fractional cloud coverage
observed at various humidities and pressures is derived from these
observations, and resolution-dependent algorithms for estimating cloud
coverage from relative humidity are suggested.@

AUsing relative humidity alone as an indicator of cloud coverage, cloud
amount can be assessed only to within a root-mean-square difference of
15%-30% from the 3DNEPH cloud cover, depending on the resolution at
which calculations are performed.@

Selected excerpts:

AAt a particular relative humidity, cloud amounts are greatest in the
800-500 mb layer of the troposphere\...@

AThe highest cloud amounts occur under high humidities at 800-700
mb\...@

A\...10%-20% cloud coverage occurs at humidities as low as 20%\...@

A\...fractional area of cloud coverage decreases exponentially as the
relative humidity falls below 100%, and that there is no clear Acritical
relative humidity@ where cloud coverage is always zero. Also, these
trends suggest that at 100% humidity, cloud cover can be significantly
lower than 100%.@

Equation (1) is an approximation for cloud amount *f* as a function of
relative humidity RH

(RH \< 1):

> *f* (%) = min \[ *f*~100~ exp (RH-1 / 1-RH ~e~) , 100\] , (1)

Awhere *f*~100~ is the cloud cover extrapolated to or at 100% relative
humidity, and RH ~e~ (*e*-folding relative humidity) is qualitatively
similar to the Acritical humidity@ used in previous cloud cover
formulations, although here it represents the relative humidity
depression below 100% where cloud amount decreases to 37% (*e*^-1^) of
its value at 100% humidity.@

AFigure 9 shows the best values for *f*~100~ (Fig. 9a) and (1-RH ~e~)
(Fig. 9b), along with the corresponding root-mean-square difference
between calculated and observed cloudiness.@

![](media/image1.png){width="6.010416666666667in"
height="4.018055555555556in"}

**Pressure level parameters and equations**

From the optimized linear solutions in Figure 9a, and Figure 9b, linear
pressure level equations to calculate fractional cloud cover from model
RH were deduced by plotting 1) cloud cover at RH=100% from Fig. 9a, and
2) , the calculated RH and cloud cover from the relative humidity
depression (RHdep) where cloud cover = *f(*~100)~/*e* from Fig. 9b.
Parameters and equations for various levels are listed below, along with
an extrapolated zero fractional cloud cover. The zero fractional cloud
cover is necessary to prevent a Anegative@ fractional cloud cover during
the smart initialization run.

Pres Fractional Cloud RHdep @ 1/e Calculated cloud cover @ RH

Level \@100% RH cloud cover from RHdep @ *f(*~100)~/*e* cloud cover

(mb) (%) (%) (%)

950 42 23 16 @ 77

900 58 27 22 @ 73

850 70 31 26 @ 69

800 90 35 33 @ 65

700 118 42 43 @ 58

600 96 50 36 @ 50

500 79 58 29 @ 42

400 58 65 22 @ 35

300 38 73 14 @ 27

Pres Zero Fractional Derived Linear

Level Cloud Cover Equation

(mb) (%RH)

950 64 1.13(RH) - 71

900 51 1.33(RH) - 66

850 59 1.65(RH) - 95

800 46 1.63(RH) - 73

700 35 1.79(RH) - 61

600 20 1.20(RH) - 24

500 10 0.86(RH) - 7

400 None 0.55(RH) + 3

300 None 0.33(RH) + 5

**Initial Testing**

Initial testing at WFO Des Moines discovered that the 300 mb and 400 mb
pressure levels produced too much fractional cloud cover. During the
initial test, the smart initialization accumulated fractional cloud
cover through the entire column. A significant improvement was noted in
subjective comparison to satellite imagery after removal of the
calculations for 300 mb and 400 mb since these layers had a Apositive
influence@ to total fractional cloud cover.

After several weeks of more subjective comparisons to imagery, it became
apparent that too much fractional cloud cover was still being
consistently produced. This was especially true at the mid levels since
100 percent relative humidity at 700mb can produce 118 percent
fractional cloud cover. A simple averaging correction was introduced,
based on the number of fractional cloud layers calculated. This
averaging lowered fractional cloud cover too much, so much so, that it
was very difficult to produce an overcast sky. To maintain the
underlying scientific basis, the number of fractional cloud layers used
to produce the average fractional cloud cover was reduced by one. This
had an immediate effect of increasing the cloud cover where relatively
humidity was high, with minor effects where the relative humidity was
low. The GFSlr(old MRF) and DGEX use this correction since 850, 700, and
500 mb relative humidity data are all that is available for both models.
The GFSlr does indeed have 300 mb relative humidity, but to keep things
a bit simpler, I used the same computation for both models.

More subjective comparisons showed that the shorter range models
(various Eta(s), RUC, and NGM and GFS (the shorter range GFS uses the
same init as the Etas)) were still Alight@ on fractional cloud cover,
even after reintroduction of the 400mb and 300mb levels. The layer
divisor in the shorter range models has been reduced to 5, (from a total
of 9 layers) which produces acceptable results.

As an aside, during testing, it was discovered that the GFSlr does
indeed have 400mb relative humidity data in its netCDF files. But,
strangely enough, geopotential height data is missing, which is needed
for the computations.

**Installation**

These instructions assume you are logged into px1f as \'ifps\'.

1\. Copy the DMX_Sky_Inits.tar file to a directory of your choice.

2\. Untar the file, **tar xvf DMX_Sky_Inits.tar**. A file listing should
include MyAVN.py, MyMRF.py, MyNGM.py, MyEta.py, MyMesoEta.py, and
MyRUC.py. **Note:** You will have to rename these files to the new model
naming standard in IFPS16. See step 3 for DGEX.

3\. In your /awips/GFESuite/primary/etc/SITE directory, determine if you
have local SmartInit files. If you do, then cut and paste sections of
the DMX_Sky_Inits as necessary into your local files. This applies to
the DGEX smartinit, if you desire to change it; since the DGEX Sky
responds to high levels of relative humidity very rapidly, i.e., it uses
a very steep curve. Be sure to comment out the current "Sky" portion in
the DGEX smartinit. If you do not have local SmartInit files, simply
copy the files into the directory.

4\. In the same directory, open your localConfig.py and add the smart
initialization files for each model as exampled below for the Eta model.
The MesoEta model is a special case.

serverConfig.INITMODULES\[AMyEta@\] = \[AETA@\]

del serverConfig.INITMODULES\[AEta@\]

serverConfig.INITMODULES\[AMyMesoEta@\] = \[AMESOETAU@, AMESOETAS@\]

del serverConfig.INITMODULES\[AMesoEta@\]

5\. Change directory to /awips/GFESuite/primary/bin and test the syntax
of your updated localConfig file by ./runIFPServer ‑n. If there are no
errors, move on. Otherwise, fix your localConfig.py file until there are
no Python errors.

6\. Stop and restart your ifpServer.

7\. Test your **Des Moines Sky Inits** as suggested below.

**Suggestions**

To increase efficiency in the use of GFE in forecasting, WFO Des Moines
displays forecast elements as GFE weather groups under the
WeatherElement drop-down menu. Current groups are MaxT, MinT, T, Td,
Wind, Sky, SnowAmt, QPF, Short_PoP_Wx, Long_PoP_Wx, and Public. Once a
weather element group is loaded into Grid Manager, it is toggled to the
vertical display. All model, MOS, and HPC initializations of that
specific group are loaded into the Grid Manager for inspection. Each
group is a single element except for the PoP_Wx groups which display
both the PoP and Wx weather elements. The PoP_Wx groups were sub-divided
into short range and long range to aid in preventing display overflow of
the Grid Manager in the vertical.

At the very least, a Sky group will have to be created to test the smart
initializations for your area. It is recommended that you start with the
default smart initialization, then make adjusts to the number of layers
to fine tune fractional cloud cover to subjective cloud cover from
satellite imagery. Keep in mind that, Ausing relative humidity alone as
an indicator of cloud coverage, cloud amount can be assessed only to
within a root-mean-square difference of 15%-30%.@

Testing of the **Des Moines Sky Inits** can be accomplished following
this procedure after installation:

1\. Load your Sky group with the Grid Manager in the vertical in GFE.

2\. Open a Linux xterm window as user IFPS.

3\. In /awips/GFESuite/primary/bin do ./ifpInit -a MyEta (the .py file
extension is not required).

4\. Monitor how your Sky weather element changes in the GFE Spatial
Editor, making sure to stay ahead of the smart initialization processing
by stepping ahead in the Grid Manager.

5\. It would be advantageous to have another xterm open to tweak the
appropriate SmartInit python file by changing the layer divisor. You may
try less than whole numbers, such as 2.4 for a divisor. Make your
changes, and run the script again. It will be obvious when the python
script has errors. Continue to monitor the Sky inits over a period of
time and make adjustments.

6\. If you desire, you can decide NOT to install a specific model Sky
Init, such as the NGM. By doing that, you can get a feel for the
differences between the default Sky init and your newly installed inits.

7\. Coordinate your changes with the surrounding WFOs that share your
ISC grids.

**WARNING**

A topographic check is part of each model Sky SmartInit. This prevents
cloud development below ground level. At the same time, NO testing of
the impact of this feature has been completed for high terrain. It is
unknown what the impact of adjusting the layer divisor will be on layers
that disappear and reappear around high terrain.

**ACKNOWLEDGEMENT**

Thanks go out to Mr. Mike Romberg of the Forecast Systems Laboratory in
Boulder, Colorado for python assistance and the forecasters at WFO Des
Moines for valuable input.