Transcript Near-Surface Data Assimilation in the NCEP Regional GSI system:

Near-Surface Data Assimilation
in the NCEP Regional GSI system:
Use of Mesonet Data &
a New Forward Operator
Seung-Jae Lee
Introduction
• The NCEP analysis system
- many changes since 1978
- 3DVAR-Gridpoint Statistical-Interpolation (GSI)
System
- can assimilate diverse kinds of obs.
• Importance of and demand for Real-Time
Mesoscale Analysis (RTMA)
- near-surface data assimilation is one of the
challenges to conquer
• U.S. meso-network systems measure and
provide information on the environment at the
size and duration of mesoscale wx events.
Purposes
• assimilation of the sfc mesonet data in the
NCEP regional GSI system
- test of regional background error are also
considered
• understanding the characteristics of
innovations of the sfc mesonet data
Mesonet data: QC
• We at NCEP currently do not perform any automated
platform-specific QC on any surface data and simply
honor quality markers provided by the FSL- MADIS.
• NCEP could (but not currently doesn’t) place manual
quality markers on the mesonet data. This would be
done by either putting reports on the rejectlist which
might be updated monthly or on a report-by-report basis
by the NCEP Senior Duty Meteorologists.
• We perform some gross checks and flag data that are
outside reasonable limits, data with mising lat/lon etc.
Mesonet data: PrepBUFR file
• all mesonet data are included but the observation error
for all mass and wind observations is set to missing
 In this study, we modify the observation error file to
assign the mesonet observations the same observation
error as for METAR mass and wind observations
• The time window over which data collection is performed
is +/- 1.5 hours, and the 3DVAR analysis experiment is
valid for t=0h.
• Near-surface data used:
Mesonet data, METAR data, Synoptic land data,
Synoptic sea data + minor sfc data
Modification
to background error statistics
• Until now, the background error for regional
assimilation has been a downscaled version
from the background error derived from the
global model ( Default)
• In the existing approach, horizontal scales are
estimated from derivatives, but in the new
approach, these are estimated using autocovariances (W.-S. Wu 2005, personal
communication)
First Guess
eastern
• WRF-NMM 8 km
western
central
• There are three domains available for the WRFNMM 8 km model:
Initial fields for the western, central, eastern
domains are at 06, 12 and 18 UTC, respectively.
• Experiments are carried out for each model
domain: Single time analyses
experimental design
analysis
use of
mesonet
data
background
error
Note
AN1
No
global
operation (control run)
AN2
Yes
global
impact of mesonet data
AN3
Yes
regional
(WRF-NMM)
impact of new regional
background error statistics
Case 1: western USA,
0600 UTC 14 Feb 2005
Nighttime (around midnight)
Clear sky; large-scale nighttime sfc inversion
analysis increments (case 1)
A -G < 0
over much of the domain
Mean values of (Oi – Gi)
at each category of dP
The obs are consistently colder
than the model, leading to the
large negative anal inc. at the sfc.
Part of the reason this is undesirable
is that T inc are coupled with W inc
in approximate geos. balance
and large wind inc are created at middle
levels in the troposphere.
Case 2: central USA,
1200 UTC 10 Mar 2005
Early morning (around 6-7 am)
- sfc inversion by clear weather
- A low pressure system in the northern-central region
Analysis Increments (case 2)
6th-7th : 920-900hPa
- Smaller and detailed str
- Positive in the N-eastern
region where the L is located
Case 3: eastern USA,
1800 UTC 23 Mar 2005
Daytime (2 pm)
A large low-pressure system over the eastern coast
Analysis increments (case 3)
Unstable sfc
Small bias
Even in the east coast case,
the A-G is large.
It just has smaller scales.
Accumulated Statistics of (O-G)
• Understanding the characteristics of
innovations (observed–guess) of the nearsurface data
• Long-term statistics: 1 month
• Scatter diagram of (O-G) during May 2005
for nighttime (0600 UTC),
early morning western (1200 UTC),
daytime eastern (1800 UTC) domains.
• Linear regression, bias, rmse etc.
Western domain (0600 UTC)
Sfc mesonet T data have a
considerable amount of outliers
compared with other land sfc T data.
 Bad obs or local effects
Some stations can be seen to
produce the same values regardless
of model fcsts.
Central domain (1200 UTC)
Very few outliers in METAR
 possibility of local effect
Many outliers
This can mean quality markers
placed on the data by the
FSL-MADIS QC are of little value
not only for wind but also T data.
 Proper QC is required.
Eastern domain (1800 UTC)
Slope and correlation coeff. of land
sfc T data are good and similar
in all three domains.
However,
in the case of synoptic sea sfc data,
they show a peculiar pattern in the
western domain: a steep slope and
very low correlation coeff. (<0.5)
 Ocean wave effect etc.
The nighttime western and central domains
indicate a model warm bias. The western
domain, in particular, shows a model
warm bias of about +2.2 C at nighttime
when compared with the eastern domain
which did not show any obvious bias.
The o-g statistics as a function of surface
pressure difference in the eastern domain
seems to indicate that the mesonet
observations have small temperature bias
(about +0.5 C).
Horizontal distribution of stations with large (O-G)
Unlike the other
two types of sfc
stations, surface
mesonet stations
are very dense,
particularly
around large cities.
Stations with large
innovations are
distributed uniformly
in the nighttime
western and
central domains,
while are mainly
located in the large
cities in the daytime
eastern domain.
In the case of the eastern domain, it is 14 LST and the synoptic situation is characterized
by many local and unstable situations with small-scale variation in daytime. In the case of
the western (central) domain, it is midnight (dawn) and frequent large-scale inversion
situation prevail.
The number of stations as a function of surface pressure difference
Western (0600 UTC)
Central (1200 UTC)
Eastern (1800 UTC)
The western domain revealed an asymmetric distribution
that implies that there are many stations where the model surface is
higher than the observation surface. This could partly account for the
model warm bias in the western domain at nighttime.
In the other two domains, the distributions are comparatively axisymmetric.
A complex forward operator
Motivation
Cost function
1 T 1
J  [ x B x  ( Hx  y )T (O  F ) 1 ( Hx  y )]
2
x : an N-component vector of analysis increment
B : the N by N previous forecast-error covariance matrix
y  yobs  Hx guess
O : the M by M observational (instrumental)-error covariance matrix
F : the M by M representativeness (forward operator)-error covariance matrix
H : a linear or nonlinear transformation operator
y : an M-component vector of observational residuals; that is,
M : the number of degrees of freedom in the analysis; and
N : the number of observations.
H, the forward model
• Converts the analysis variables to the
observation type and location (May include
variable transformations in case of, for example,
radar or satellite data assimilations).
• The quality of simulated observation, the model
state converted to “observation units”, depends
on the accuracy of the forward operator:
Inaccurate pseudo-observations give rise to
errors in observation increments leading to bad
analyses.
• The accuracy of anl is dependent on the
effectiveness of algorithm used to match
obs with the bg values.
• Currently, there does NOT exist any
forward model for near-surface variables
in the NCEP GSI system: Just involves
simple interpolation of the background
value to the location of the observation.
the new fwd operator
• Uses a similarity theory based on Dyer and
Hicks formula which has been adopted in model
PBL parameterization such as surface layer
process in MRF PBL scheme (Hong and Pan
1996).
• realized in the MM5-3DVAR system (Barker et al.
2004). It has been used for surface data
assimilation in operational analysis systems and
has proven to be encouraging in many contexts
(e.g., Shin et al. 2002; Hwang 2005).
assumptions
• All the surface observation sites are assumed to be
located at the model surface, regardless of the actual
difference in elevation between the surface
measurement and the model surface.
• The different types of surface observations are directly
assimilated without any modification. The observed
surface pressure is still reduced to the model’s lowest
level.
• Near-surface wind and mass variables are obtained at
different heights using the surface layer similarity theory:
Near-surface wind is obtained at 10 m, while
temperature and specific humidity are obtained at 2 m.
Forward model description
• Monin-Obukhov similarity theory
Input & output
2nd
Lowest
Lowest
10 m
2m
surface
ps2, ts2, qs2
EL
ps, ts, qs, us,vs, hs
SL
Atmosphere = PBL(SL+EL) + FA
u10, v10
t2, q2
psfc, tg
roughness, xland
near-surface variables
wind
Wz
Uz  Us 
W
Wz
Vz  Vs 
W
temperature

 Tz  Psfc 

Tz   g  ( s   g ) 

 T  1000 

specific
humidity

 Qz 
Qz   q g  ( q s  q g ) 

 Q 

R / Cp
Z = 2 or 10 meter
stability functions
 hs 
 W  log    m
 z0 
 hs 
 T  log    h
 z0 
 z
 W z  log    mz
 z0 
 z
 Tz  log    hz
 z0 
 ku*hs hs 
  h
 Q  log 


K
z
a
q
0


 Qz
 ku* z

z
   hz
 log 

 K

z
a
q
0


Turbulence regime
determines
 m, h function type
turbulence regimes
(= Ribc)
0.2
0
(4) Free
convection
(2) Damped
mechanical
(3) Forced
convection
Rib  0 and  v 2   v1
Rib
(1) Stable
Stability-dependent functions
 z
 10  log  
 z0 
(1) Stable
 hs 
 m  10  log  
 z0 
(2) Damped
 z
 hs 
 5Rib
 5Rib
 log  
m 
 log    mz 
1.1  5Rib
1.1  5Rib
 z0 
 z0 
 mz
(3) Forced convection
 m   mz  0
where
 h   m ,  hz   mz ,  m  10
(4) Free convection
1 x 
 m ( x)  2 log 
  y  2 arctan( x)  2 arctan(1.0)
 2 
 h ( x)  2 y
 1 x2 
hs 


x  1  16  , y  log 
L

 2 
1/ 4

 hs 
if u*  1 / 100
 Rib log  ,
z0 
hs 


L  ghs  2  m 
k
u*  , if u*  1 / 100
 s
L


2( vg   vs ),
ghs   vs   vg
 2
 Vc  
Rib 
2
2 

 s  U s  Vs  Vc 
0,
(U s  Vs  Vc )1/ 2
u*  k
 hs 
log    m
 z0 
2
2
2
m
k
L
( s   g )
 hs 
log    h
 z0 
if  vg   sg
if  vg   sg
Implementation and tests
• Incorporated into the latest GSI version
with some modification
 Jun 2005
• Application
- eastern domain, 1800 UTC 23 Jun 2005
• Parallel runs (during early July 2005)
- western domain: Jul 7, 11-13, 18
- central: Jul 2-14, 17-18
- eastern: Jul 6-12, 14,15,17
Case: eastern domain,
1800 UTC 23 Jun 2005
Large innovation sites
Old
simple
interpol.
New
fwd
model
Innovations as a function of dp
Worse over sea, why?
- Because of setting z=2 m (not the height of buoy) over water also.
- Ocean wave effect -> roughness is uncertain.
- Physics etc.
 Despite this, the total bias was reduced from 2.2 to 0.9.
Parallel run results: Early July, 2005
old
Western, 0600 UTC
new
old
Central, 1200 UTC
new
old
Eastern, 1800 UTC
new
old
Western, 0600 UTC
new
old
Central, 1200 UTC
new
old
Eastern, 1800 UTC
new
Summary and Conclusions
• We have assimilated sfc mesonet data in the NCEP
regional GSI using the same observation error as that
adopted for METAR data within the WRF-NMM (8 km)
3DVAR system.
• In the single-time anl experiments, the anl field was
shown to contain mesoscale (smaller and detailed)
structures as mesonet data are added.
• When the regional bg err statistics are used, the overall
pattern is similar to the downscaled global version but
the amplitude is somewhat intensified. It is believed to be
the results of smaller (shallower) vertical structures in the
regional bg err covar.
• sfc mesonet T data were found to have a considerable
amount of outliers compared with other land sfc T data.
• The nighttime western and central domains indicated a
model warm bias. The western domain, in particular,
showed a model warm bias of about +2.2 C at nighttime
when compared with the eastern domain which did not
show any obvious bias.
• Stations with large innovations are distributed uniformly
in the nighttime western and central domains, while they
are mainly located in the large cities in the daytime
eastern domain.  These differences could also be the
result of urban heat island effects (not contemplated in
the model) or erroneous station groups.
• The current near-surface observation operator in
the NCEP GSI system was improved from a
simple linear interpolation to a more complex
similarity model.
• In the intercomparison of the old and new
forward operators using case experiments and
long term runs, the complex forward model is
shown to improve the innovation statistics
substantially.  This is due to the realistic
considerations of surface characteristics (e.g.,
roughness length) and atmospheric stability
within surface layer.
• Sources of disagreement between observations and the
model include observation errors and errors related to
the model. It is shown that the latter was reduced by
introducing a more complex forward model in the NCEP
regional GSI analysis system.
• The new operator has significant and practical potential
to assimilation of diverse surface data in the NCEP
regional GSI system. It can be applied to not only
regional but also global GSI system.
• It has diverse applications to atmospheric and oceanic
data assimilation and can be used for not only surface
data over land and but also satellite data, such as
QuikSCAT sea surface wind.
On-gong or possible future works
• Tangent-linear and adjoint versions are
under implementation.
• Application of new fwd model to GSI2DVAR
• Connection with non-linear quality control
to the mesonet data
• Link with anisotropic background error
covariances
Thank you !