Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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.