MicroMet in GEOtop

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

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

18. ‫ﺷﻜﺮا ‬
‫ً‬

19. Annex