A
Meteorological
Distribu1on

 System
for
High‐Resolu1on

    Terrestrial
Modeling


           (MicroMet)




 





 
 
...
Introduc1on

•  Quasi‐physically
based
meteorological
model
for
high
resolu1on

   (e.g.,
30m‐1‐km)

•  Five
variable
to
u...
Preprocessor

•  Meteorological
variables
converted
to
a
common
height

•  Fills
variables
of
missing
dates
with
undefine
v...
MicroMet
Model

  A)
Spa1al
Interpola1on
(Barnes,1964)

  •  Barnes
applied
Gaussian
distance‐dependent
weighing
func1on

...
MicroMet
Model

    B)
Meteorological
Variables


     1)
Air
Temperature

    •  To
distribute
air
temperature
assuming
n...
MicroMet
Model

    B)
Meteorological
Variables


     2)
Rela1ve
Humidity

    •  RH
is
non
linear
of
eleva1on,
rela1vely...
2)
Rela1ve
Humidity
cont’

•  Td

for
all
sta1ons
adjusted
to
common
level
using
Eq.
2;

     temperature
is
Td

temperatu...
Wind
Speed
and
Direc1on

        •  Wind
speed
(W)
converted
to
zonal
,
u,
and
meridional
,
v
:

        
















...
Wind
Speed
and
Direc1on

    •  Curvature
computed
for
each
grid
defining
curvature
radius
η
(m):

    



















...
Wind
Speed
and
Direc1on


        •  Terrain
modified
wind
speed
in
(m/s
):

                        W t = W wW
        


...
4)
Solar
radia1on

    •  RH700
calculated
using
Eqs.1,2,
4
and
5.
To
calculate
cloud
cover:

                            ...
4)
Solar
radia1on

    
























































 
 
          
     
     
      
      ...
4)
Solar
radia1on
cont

    

















































 
 
 
 
 
 
 
 
 



            ψ         =...
5)
Longwave
radia1on

        •  Incoming
longwave
radia1on
calculated
considering
cloud
and

           eleva1on
related
...
6)
Surface
pressure

    •  In
absence
of
observa1ons,
pressure
is
given
by:

                                            ...
7)Precipita1on

•  Observed
precipita1on
distributed
in
the
domain
using
Barnes

   scheme

•  To
generate
topographic
ref...
Shortcomings

No
feedbacks
between
the
land
and
atmosphere
for
calcula1on
of

   near
surface
atmospheric
condi1ons

•  No...
‫ﺷﻜﺮا
‬
 ‫ً‬
Annex

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MicroMet

  1. 1. A
Meteorological
Distribu1on
 System
for
High‐Resolu1on
 Terrestrial
Modeling
 (MicroMet)
 





 
 
 Ageel
Ibrahim
Bushara

  2. 2. Introduc1on
 •  Quasi‐physically
based
meteorological
model
for
high
resolu1on
 (e.g.,
30m‐1‐km)
 •  Five
variable
to
use
MicroMet:
Precipita1on,
RH,
air
Temperature,
 Wind
Speed
and
wind
direc1on
at
each
1me
step,
in
or
near
the
 basin
 •  For

incoming
solar
and
longwave
radia1ons,
and
surface
pressure:
 MicroMet
uses
its
submodels
to
generate
distribu1ons,
or
create
 distribu1ons
from
observa1ons
as
part
of
data
assimila1on
 •  MicroMet
preprocessor:
iden1fies
and
corrects
poten1al
 deficiencies
and

fills
missing
data;
autorregressive
moving
average
 •  Spa1al
interpola1on
using
Barnes
scheme
and
subsequent
 correc1ons
for
interpolated
fields
using
Temperature‐eleva1on,
 wind‐topography,
humidity‐cloudiness
and
radia1on‐cloud‐ topography
rela1onships.





  3. 3. Preprocessor
 •  Meteorological
variables
converted
to
a
common
height
 •  Fills
variables
of
missing
dates
with
undefine
values
(‐999)
 •  Series
of
Quality
Assurance
(QA):
  



Check
for
values
outside
acceptable
range
  



Seeks
consecu1ve

values
that
exceeds
acceptable
increment
  



Find
constant
consecu1ve
values
with
no
observe
change
within
 1me
limit
 •  
Fills
missing
1me
series
data
with
calculated
values





  4. 4. MicroMet
Model
 A)
Spa1al
Interpola1on
(Barnes,1964)
 •  Barnes
applied
Gaussian
distance‐dependent
weighing
func1on
 r2 w = exp[− ] 






































































































(1)
 f (dn) 
r,
distance
between
observa1on
and
grid
point,
f(dn),filter
 parameter
smoothing
interpolated
field
 €•  Bernes
applies
two
successive
correc1ons:
 •  1)
using
Eq.
1,assign
values
for
all
grids
 •  2)
Decreasing
influence
radius,
residuals,
difference
correc1on
 added
to
first‐pass
field
 •  MicroMet
can
extrapolate
data
to
valleys
or
mountainous
regions


  5. 5. MicroMet
Model
 B)
Meteorological
Variables

 1)
Air
Temperature
 •  To
distribute
air
temperature
assuming
neutral
atmospheric
 stability
and
defining
varying
air
temperature
lapse
rate
(or
user
 defined)

 •  Sta1on
temperature
adjusted
to
a
common
level:
 T0 = Tstn − Γ(z 0 − z stn ) 
















































































































(2)
 •  Reference
sta1ons
used
to
interpolate
grids
using
Barnes
scheme
 •  Gridded
topography
data
used
to
adjust
reference
level
gridded
 € temperature
to
topographic
eleva1on
data
using:
 



















































































































(3)
 T = T0 − Γ(z − z 0 )
  6. 6. MicroMet
Model
 B)
Meteorological
Variables

 2)
Rela1ve
Humidity
 •  RH
is
non
linear
of
eleva1on,
rela1vely
linear
dewpoint
(Td)
 temperature
used
for
eleva1on
adjustment
 •  Convert
sta1on
RH
to
Td
(0C)
using
air
temperature
T
(0C):
 bT es = a exp( ) 





















































































































(4)
















 c+T e RH = 





















































































































(5)
 es € •  From
equa1on
5,
we
get
(e),
then
Td
(0C)
can
be
calculated
:
 € c ln(e / a) 





















































































































(6)

 Td = b − ln(e / a)
  7. 7. 2)
Rela1ve
Humidity
cont’
 •  Td

for
all
sta1ons
adjusted
to
common
level
using
Eq.
2;
 temperature
is
Td

temperature
and
dewpoint
temperture
lapse
 rate,
lambda
(m‐1)
is
vapor
pressure
coefficient
(Table
1)
 





















































































































(7)
 c Γ =λ d b •  Using
Barnes
scheme,
reference
level
dewpoint
temperatures
 interpolated
to
model
grid
 •  Eq.3
is
used
to
obtain
Td

for
each
eleva1on
 •  Gridded
Td

converted
to
RH
using
Eqs.
(4)
and
(5)





  8. 8. Wind
Speed
and
Direc1on
 •  Wind
speed
(W)
converted
to
zonal
,
u,
and
meridional
,
v
:
 




































































































 
 


(8)
 u = −W sinθ 




































































































 
 


(9)
 u = −W cosθ •  u
and
v

interpolated
for
model
grid
using
Barnes
scheme
 € •  Resul1ng
values
converted
back
to
wind

speed
and
direc1on
 € 































































































 





 
 



(10)

 2 2 W= u +v 3π v 




































































































 




 




(11)
 − tan−1 ( ) θ= 2 u •  Gridded
wind
speed
&
direc1on
modified
to

account
topography
 € 2  ∂z  2  ∂z  € −1 β = tan  +   



































































 
 
 
 
 
 



 



(12)

  ∂x   ∂y   ∂z  3π ∂y  − tan−1 























































 
 
 
 
 
 
 
 








(13)




 ξ=  ∂z  2 € ∂x  
  9. 9. Wind
Speed
and
Direc1on
 •  Curvature
computed
for
each
grid
defining
curvature
radius
η
(m):
 




































































































 
 
  z − 1 (z + z )  z − 1 (z + z ) 2 2 W E S N  + + 




































































































 
 


(14)
 2η 2η 1  Ω=   c 4 z − 1 (z SW + z ZE ) z − 12 (z NW + z SE )   2 +   2 2η 2 2η   •  Slope
in
direc1on
of
wind
Ωs:
 































































































 





 
 



(15)















 Ω = β cos(θ − ξ ) s € •  Ωc
and
Ωs
are
scaled
to
(‐0.5

and
0.5
)
 •  Wind
weighing
factor
Ww
used
to
modify
wind
speed
 € W =1+ γ Ω + γ Ω 



































































 
 
 
 
 
 



 



(16)
 w ss cc •  
Ωc+Ωs
=1,
Ww
to
be
between
0.5
and
1.5
 € 























































 
 
 
 
 
 
 
 








  10. 10. Wind
Speed
and
Direc1on
 •  Terrain
modified
wind
speed
in
(m/s
):
 W t = W wW 































































































 





 
 



(17)















 •  Wind
direc1ons
modified
by
diver1ng
factor
θd
:
 € θ d = −0.5Ωs sin[2(ξ − θ )] 


















































































































(18)
 •  Terrain
modified
wind
direc1on:
 



































































 
 
 
 
 
 
 



(16)
 θ =θ +θ € t d 























































 
 
 
 
 
 
 
 








  11. 11. 4)
Solar
radia1on
 •  RH700
calculated
using
Eqs.1,2,
4
and
5.
To
calculate
cloud
cover:
  RH 700 − 100  σ c = 0.832 exp 

















































































































(20)
  41.6   •  Solar
radia1on
striking
earth’s
surface:
 € Q = S [ψ cos Z ] cos i + ψ 

















































































































(21)
 si dir dif cosZ = sinδ sinφ + cosδ cosφ sinτ 













































































































(22)
 € •  Φ
la1tude,
ζ
hour
angle
from
local
solar
noon,
Z
solar
zenith
angle
  h − 12  


























































 
 




 
 
 


(23)
 τ = π   12  € h
hour
of
the
day,
δ
solar
declina1on
angle
given
by:
 €
  12. 12. 4)
Solar
radia1on
 
























































 
 
 
 
 
 
 
 
 



(24)
  d −d r cos 2π   δ = φT    dy    •  ΦT
Solar
la1tude
of
tropic
Cancer,
d
day
of
the
year
,
dr
day
of
the
 summer
sols1ce,
dy
number
of
days
in
a
year
 € cos i = cos β cos Z + sin β sin Z cos(µ − ξ s ) 

































































































 
 
 



(25)
 •  Solar
azimuth
with
south
having
zero
azimuth:

  cosδ sin τ  µ = sin  













































































































(26)
  sin Z  •  To
account
for
scapering,
absorp1on

and
reflec1on
of
solar
by
 cloud,
solar
radia1on
is
scaled
by:
 € 




















 (0.6 − 0.2 cos Z )(1 − σ =
 





 )
 
 
 
 
 
 (27)
 ψ dir c
  13. 13. 4)
Solar
radia1on
cont
 

















































 
 
 
 
 
 
 
 
 


 ψ = (0.3 − 0.1cos Z )σ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 (28)
 dif c •  If
observa1ons
available,
the
difference
between
model
and
 observa1on
is
computed,
and
spa1ally
distributed
using
Barnes
 € scheme
 •  Above
difference
added
to
model

having
final
spa1ally
 distributed
solar
radia1ons
(
data
assimila1on)

 




















 
 
 
 
 
 
 
 
 
 
 
 
 
 

  14. 14. 5)
Longwave
radia1on
 •  Incoming
longwave
radia1on
calculated
considering
cloud
and
 eleva1on
related
varia1ons:
 
 
 
 
 
 Qli 
= 
 
 
4 
 
 
 
 
 (29)
 
 εσT •  Atmosphere

emissivity,
ε,
is
given
by:
 € 





 ε = 
k (1 + Z σ
 )[1 −
 X 
exp(−Y e 
 )
] 
 

 
 
 
 /T 
 (30)
 2 s c s s C s = C1 
 

 
 
 



 


 z
<
200
 € 

 
 
C 
= C
 +
 (z − z 
)( 
 
 
 



 
 200
≤
z
≤
3000
 C −C 2 1 ) s 1 1 z 2 − z1 € 













 C
 = 
C 
 
 
 


3000

<
z














 s 2 € 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 (31)
 •  
It
uses
data
assimila1on
technique
as
for
solar
radia1on
 €
  15. 15. 6)
Surface
pressure
 •  In
absence
of
observa1ons,
pressure
is
given
by:
 z p = p0 exp(− ) 





























































 
 
 
 
 














(32)
 H •  P0
sea
level
pressure
(101.3
KPa),
H
is
scale
height
of
 € atmosphere
(
about
8000m)
 •  If
observa1ons
available
they
can
be
combined
with
surface
 pressure
model
as
part
of
data
assimila1on


  16. 16. 7)Precipita1on
 •  Observed
precipita1on
distributed
in
the
domain
using
Barnes
 scheme
 •  To
generate
topographic
reference
surface,
sta1ons
eleva1ons
 also
interpolated
to
model
grid
 •  Precipita1on
adjustment
func1on
is
non
linear
func1on
of
 eleva1on
difference
 •  Modelled
liquid
water
precipita1on
rate
computed
using:
 1 + χ (z − z 0 )  p = p0   



































































 
 
 
 
 
 



(33)
 1 − χ (z − z 0 )  •  P0
interpolated
sta1on
precipita1on,z0
is
interpolated
sta1on
 eleva1on
surface,
✗
(Km‐1)
is
a
factor
is
defined
to
vary
 seasonally
(Table
1)
 

  17. 17. Shortcomings
 No
feedbacks
between
the
land
and
atmosphere
for
calcula1on
of
 near
surface
atmospheric
condi1ons
 •  No

surface
energy
balance

  18. 18. ‫ﺷﻜﺮا
‬ ‫ً‬
  19. 19. Annex


×