Download COMPARISON OF DOWNWELLING SURFACE LONGWAVE

COMPARISON OF DOWNWELLING SURFACE LONGWAVE
RADIATION FLUXES DERIVED FROM METEOSAT-8 WITH IN SITU
MEASUREMENTS
Cristina Madeira and Carlos C. DaCamara
Instituto de Meteorologia, Rua C ao Aeroporto, 1749-077 Lisbon, Portugal
ABSTRACT
Downwelling Surface Longwave Radiation Flux (DSLF) over land is one of the operational
products delivered by the Satellite Application Facility for Land Surface Analysis (LSA SAF),
which has started its initial operational phase on February 2005. The DSLF product is
currently being produced every 30 minutes over the European Region of the Meteosat-8
disk.
DSLF is the result of atmospheric absorption, emission and scattering within the entire
atmospheric column and may be defined as the thermal irradiance reaching the surface in
the thermal infrared spectrum (4-10µm). It is a particularly difficult parameter to retrieve
since satellites cannot directly measure it. However hybrid methods that make a combined
use of satellite and NWP data are one of the most suitable techniques for operational
purposes.
The adopted algorithm to compute DSLF consists of an hybrid method based on two
different bulk parameterisation for clear and cloudy sky conditions using as input ECMWF
forecasts of 2m-temperature, 2m-dew point temperature and total column water vapour
(TCWV) as well as the cloud products from Satellite Application Facility on support to
Nowcasting and Very Short Range Forecasting (NWC SAF) (Cloud Mask and Effective
Cloudiness).
The validation of DSLF product includes the comparison with in situ measurements and we
present the first results aiming to assess the quality of the product against the user
requirements (errors of the order of 5%). The study is presented for two ground truth sites
over Europe (Carpentras and Roissy).
1 INTRODUCTION
The Downwelling Surface Longwave Radiation Flux (DSLF) over land is one of the
operational products currently being delivered by the Satellite Application Facility for
Land Surface Analysis (LSA SAF), which has started its initial operational phase on
February 2005. The DSLF product is produced every 30 minutes over the European
Region of the Meteosat-8 disk as well as over Northern Africa, Southern Africa and
South America. Figure 1 shows an example of the DSLF product, which is available
on a near-real time basis from the LSA SAF website (http://landsaf.meteo.pt/).
Figure 1 - DSLF product for the European region for April, 17 2005 at 1200UTC. DSLF field (W/m 2;
upper panel), and respective quality flags (lower panel).
The quality of the DLSF product strongly depends on the accuracy of both cloudy
pixel detection and atmospheric column characterisation (temperature and humidity).
The adopted strategy for validation of DSLF consists of three main steps: 1) intercomparison with similar products namely the Downward Longwave Irradiance (DLI)
product from the Ocean & Sea Ice Satellite Application Facility (O&SI SAF); 2)
comparison with in situ measurements namely from the Baseline Surface Radiation
Network (BSRN); 3) evaluation of input errors namely from ECMWF forecasts fields.
In this paper a comparison with two ground truth sites over Europe (Carpentras and
Roissy) is presented aiming to assess the quality of the product against the user
requirements (relative errors less than 5%).
2 DESCRIPTION OF ALGORITHMS
The operational algorithm to compute DSLF consists of an hybrid method based on
two different bulk parameterisation schemes, namely those proposed by Dilley and
O’Brien (1998) and the Josey et al. (2003) respectively for clear and cloudy sky
conditions. However for clear sky conditions Prata’s (1996) scheme may be also
used as an alternative. ECMWF forecasts of 2m-temperature, 2m-dew point
temperature and total column water vapour (TCWV) are used as inputs together with
two cloud products (Cloud Mask and Effective Cloudiness) from the NWC SAF. The
choice of the algorithms was based on sensitivity studies carried out during the
development phase of the LSA SAF project (see the Product User Manual for DSLFSAF_LAND_IM_PUM_DSLF_1.2.pdf - available at the LSA SAF website).
Dilley and O’Brien’s (1998) scheme was tuned empirically by generating synthetic
data using an accurate radiative transfer model. This scheme depends on screenlevel air temperature (To) and precipitable water content (w) as follows:
6
DSLFClear
To 
w

= 59.38 + 113.7 
 + 96.96
25
 273.16 
Prata’s (1996) scheme is based on the radiation transfer theory and the coefficients
were empirically fitted to observations. This scheme basically consists of an
emissivity model with a continuum absorption correction (Niemelä et al., 2001) and
the effective emissivity of the atmosphere depends on precipitable water content (w).
The proposed formula is as follows:
[
]
DSLFClear = 1 − (1 + w) exp( − (1.2 + 3.0w)1 / 2 ) σT04
In case of cloudy conditions, we rely on the parameterisation scheme proposed by
Josey et al. (2003), which is expressed by the following relationship:
DSLFcloudy = σ (T0 + 10.77 N 2 + 2.34 N − 18.44 + 0.84( D + 4.01)) 4
where σ is the Stefan-Boltzmann constant, T0 is the screen-level air temperature
[K], N is the total cloudiness fraction and D is the dew point depression [K].
Currently the operational version [OP] of the LSA SAF DSLF product is based on
Dilley and O’Brien’s and Josey´s parameterisation schemes, respectively for clear
and cloudy sky situations. A testing version [TE] is currently running for validation
purposes and relies on Prata’s parameterisation scheme for clear sky conditions and
on Josey´s scheme for cloudy sky conditions. However it is worth noting that the
total cloudiness fraction N is differently estimated in OP and TE; in OP N is directly
obtained from the effective cloudiness product whereas in TE N is computed from
the cloud mask product (also supplied by SAF NWC) by averaging cloudy and clear
sky pixels over a 3×3 matrix centred at each pixel.
3 VALIDATION
The Validation of the DSLF product is based on the comparison of satellite
estimations with in situ measurements taken at Carpentras and Roissy. Located in
southwest of France, Carpentras is characterised by low cloud occurrence and clear
sky conditions whereas Roissy is located near Paris and is typically associated to
high cloud occurrence and heavier atmospheric conditions. Versions OP and TE
were validated on an hourly basis during the period from June to August 2005.
Figure 2 shows that the clear-sky algorithm used in OP (Dilley’s scheme) tends to
underestimate DSLF (green dots) at both sites, a similar behaviour being also
observed for partially cloudy pixels (yellow dots) but exhibiting a higher dispersion
(RMSE in left panel of Table I). The cloudy algorithm has the worst performance in
situations of partially cloudy-sky, a result that agrees with those obtained in
sensitivity studies (see SAF_LAND_IM_PUM_DSLF_1.2.pdf available at LSA SAF
website). Besides partially cloudy-sky situations are those where the errors due to
mismatch of clear/cloudy classification do occur more frequently. Figure 3 shows
that Prata’s scheme (used in TE for clear-sky conditions) also tends to
underestimate the DSLF but the decrease is much less pronounced. This may be
confirmed by looking at Table I that shows for clear-sky conditions a bias in OP that
is more than twice the one in TE. The observed underestimation may be explained in
part by errors in the atmospheric column characterization mainly in surface
temperature. For cloudy conditions the same algorithm (Josey’s scheme) is used
and differences between OP and TE relate to the way the total cloudiness fraction is
estimated. A significant improvement was obtained in the case of TE, especially for
partially cloudy pixels, an indication that a correct estimation of cloudiness is
essential for an adequate performance of the method.
Figure 2 – Scatterplots of LSA SAF DSLF and of differences between DSLF as obtained using the OP
version and in situ measurements versus DSLF values estimated at Roissy (upper panels) and
Carpentras (lower panels), for the June-August 2005 period. The dots are coloured as follows: green –
clear-sky pixels; yellow – partially cloudy; and blue –fully cloudy pixels.
Tables I – Bias and root mean square error (RMSE) of Meteosat-8-derived DSLF for Carpentras and
Roissy, for the period between June and August 2005. Results are presented for clear sky, cloudy,
fully-cloudy, partially cloudy, and all (last row) situations. Left and right panels refer to the OP and the
TE versions respectively.
Clear
Cloudy
(Full+Part)
Cloudy - Fully
Cloudy Partially
TOTAL
Number of
Cases
1920
OP
Bias
(Wm-2)
-22.7
Number
of Cases
1476
TE
Bias
(Wm-2)
-8.8
RMSE
(Wm-2)
28.6
RMSE
(Wm-2)
14.9
974
-23.3
40.3
1248
-3.7
21.9
525
-10.4
32.5
668
-6.5
21.7
449
-38.5
47.9
580
-0.6
22.1
2894
-22.9
33.0
2724
-6.5
18.4
Figure 3 As in Figure 2, but for DSLF as obtained from the TE version.
Results shown in Table II refer to the percentage of cases for three classes of
relative errors (Rel_Err) of DSLF as derived from Meteosat-8. Statistics were also
based on a comparison of DSLF with measurements at Carpentras and Roissy, for
the period between June and August 2005. User requirements (i.e. relative errors
less than 5%) are met in 70% of the cases in TE, showing a significant increase
when compared to OP where this value was only met in about 34% of the cases.
Tables II – Percentage of cases for three classes of relative errors (Rel_Err) of DSLF as derived from
Meteosat-8. Statistics were estimated based on a comparison of DSLF with measurements at
Carpentras and Roissy, for the period between June and August 2005. Left and right panels refer to
the OP and the TE versions respectively.
OP
TE
Clear
Cloudy
(Full+Part)
Cloudy - Fully
Cloudy Partially
Rel_Err ≤ 5
(%)
5
<Rel_Err≤
10
(%)
Rel_Err > 10
(%)
Rel_Err ≤ 5
(%)
5 <Rel_Err
≤ 10
(%)
Rel_Err >
10
(%)
35.5
49.1
15.4
78.1
19.8
2.1
30.5
29.1
40.4
60.9
29.6
9.5
41.3
34.5
24.2
60.5
30.4
9.1
17.8
22.7
59.5
61.4
28.6
10.0
TOTAL
33.8
42.3
23.9
70.2
24.3
5.5
4 CONCLUSIONS
This paper presents the first validation exercise of the DSLF product focusing on two
sites in Europe and it is anticipated to extend the validation activity to all four regions
covered.
The underestimation of DSLF by both clear-sky algorithms (i.e. Dilley and O’Brien,
1998 and Prata, 1986) is in agreement with the results obtained by Niemelä et al.
(2001). This underestimation may be caused in part by errors in the characterization
of atmospheric column namely the surface temperature (as obtained by ECMWF
forecasts of 2m temperature) and therefore part of the future work will be devoted to
the evaluation of the input errors in the algorithm. However results obtained here for
clear sky in the TE version are extremely encouraging because the user
requirements (i.e. relative errors less than 5%) are fulfilled in about 78% of the
cases.
For cloudy conditions a significant improvement was obtained in the case of TE
especially for partially cloudy pixels, which is an indication that new way to estimate
cloudiness is more adequate. In what respects to the user requirements results for
cloudy conditions in TE are just slightly below (about 60%) to those obtained for
clear sky.
5 REFERENCES
Dilley, A.C. and D.M. O´Brien (1998), Estimating downward clear sky long-wave
irradiance at the surface from screen temperature and precipitable water, Q. J. R.
Meteorol. Soc., 124, 1391-1401.
Josey, S.A., Pascal, R.W., Taylor, P.K., Yelland, M.J., (2003), A New Formula For
Determining the Atmospheric Longwave Flux at Ocean Surface at Mid-High
Latitudes. Journal of Geophysical Research - Oceans.
Niemelä, S., P Räisänen, H Savijärvi (2001), Comparison of surface radiative flux
parametrizations. Part I: Longwave radiation, Atmosp. Research., 58, 1-18.
Prata, A.J. (1986), A new long-wave formula for estimating downward clear-sky
radiation at the surface, Q. J. R. Meteorol. Soc., 122, 1121-1151.