Deutscher Wetterdienst

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1 Long-term solar UV radiation reconstructed by Artificial Neural Networks (ANN) Uwe Feister 1, Jürgen Junk 2, and Michael Woldt 3 1 Deutscher Wetterdienst, Lindenberg, Germany 2 Département Environnement et Agro-Biotechnologies - Gabriel Lippmann, Luxembourg 3 Brandenburg Technical University Cottbus, Germany with support from the SCOUT-O3 project and the action COST 726

2 Re-construction method Preparation and features of input data at - Potsdam and - Lindenberg results of re-construction - at Potsdam - at Lindenberg - at other sites sensitivity tests conclusions METEOROLOGICAL OBSERVATORY POTSDAM N, 13 5 E, 17 m asl

3 ANN model applied to reconstruct longterm UV radiation at sites selected for the modeling exercise within SCOUT-O3 Here: focus on results for Potsdam and Lindenberg to consider small-scale spatial effects in input and output data

4 Potsdam

5 Lindenberg 73 km

6 Artificial Neural Network (ANN) Model (earlier approach: non-linear regression, Feister et al. 22, Photochemistry and Photobiology 76, ) Input layer Hidden layer Output layer month global irradiation diffuse irradiation sunshine duration ERYTHEMAL UV column ozone cloud cover horizontal visibility snow cover surface albedo solar zenith angle UVB1 (< 315 nm) UVB2 (< 32 nm) UVA (315 4 nm) optimum relations between input and output parameters (need not be prescribed)

7 Input data for ANN models UV measurements and predictors Data set 2 24 split in 3 independent sets with period based on COST SCOUT-O3 agreements production set 7% training set 15% test set 15% Additional independent test data set set up and training of the model 1 Independent data sets for uncertainty estimates (further reduction of uncertainty possible by use of more data for training, production and test with whole data set )

8 UV radiation input data for training and testing the ANN model Site Instru Ment Data period Scan range (nm) WL Step (nm) Spectral extension Spectra per day Cosine correction Calibrati on Calibration checks Potsdam Brewer MKII #3 Brewer MKIII #118 1/1995 4/ Slaper et al Geophys. Res. Lett. 22, to 18 9 to 18 Feister et al. 1997, Solar Energy 6, No. 6, ) DXW 1 W based on FEL 1 W from PTB 5 W Brewer lamps Linden berg Brewer MKIV #78 SPECTRO 32D 1/ /24 1/25 7/ Slaper et al Not needed 9 to to 1 Bais et al. 25, EGU, Vienna, 25 Not needed Intern. Ozone Services 1 W FEL from PTB 5 W Brewer lamps 2 W KS 3 (Metzdorf et al. 23, Metrologia 4, 66 69)

9 Corrections: - to Brewer 78: only between 1/1995 and 8/1996 based on internal report of calibration by Naebert et al to SPECTRO32 D: according to calibration checks with KS3 (25 27) Estimates of hourly and daily broad-band UV irradiation: -low sampling frequency of Brewer scans results in additional uncertainty of hourly and daily totals (3 to 6 min time steps 3..6 % uncertainty with sunny conditions, and % with all weather conditions) POTSDAM , N = 19,551 H ERY (t) / H ERY (t = 1 min) SAMPLING TIME STEP t (min)

10 Objective: hourly and daily UV irradiation from Brewer spectra Problem: fast UV irradiance changes due to cloud variability, but only ~ 1 Brewer spectrum per hour Example for period BENTHAM DM 15: Δt= 6 min SPECTRO 32 D: Δt= 1 min UV INDEX LINDENBERG JULY 16, 26 SPECTRO32D, 1 min BENTHAM DM15, 6 min UTC

11 Method to derive daily totals: ratio UV / GLOBAL averaged over Brewer scan time (5 to 8 minutes) assume ratio remains constant between two spectral scans (.5 h to more than 1 h for Brewer) ratio dependence on SZA and cloud attenuation is taken into account might be refined by separating the two effects ERYTHEMAL IRRADIANCE (W m -2 ) ER LINDENBERG JULY 16, UTC ERY / GLOBAL (%)

12 Comparison of daily irradiation Potsdam, 22: Brewer (~ 9 to 18 spectra per day) vs. Bentham (~ 8 to 16 spectra per day) Lindenberg, 25: Brewer (~ 9 to 18 spectra per day) vs. SPECTRO (45 to 1 spectra per day) Estimated uncertainty of daily erythemal irradiation derived from Brewer spectra by comparison (Brewer, Bentham, 2 24): % ERY BEN OR SPECTRO (J/cm²) ERY BREWER (J/cm²) BREWER VS. BENTHAM 25 BREWER VS SPECTRO

13 Example: daily irradiation from measurements (global and erythemal) at Lindenberg ERYTHEMAL IRRADIATION (J m -2 ) LINDENBERG YEAR GLOBAL IRRADIATION (J cm -2 )

14 Ratios of daily erythemal (Brewer and Spectro 32D) to global irradiation, Lindenberg 1/1995 to 7/27 DAILY ERY / GLOBAL (%).2.1 LINDENBERG Potsdam AUG 13, 21 8 Okta Sc LIGLO 452 J/cm² LIERY.156 J/cm² POGLO 363 J/cm² POERY.814 J/cm²

15 Distance between Potsdam and Lindenberg of 73 km allows additional checks of daily irradiation extreme differences of daily UV irradiation at the sites need not be errors, but can be related to different cloud conditions ERY LINDENBERG (J/m²) ALL WEATHER DIFFUSE / GLOBAL < ERY POTSDAM (J/m²)

16 Ratios between daily erythemal and global irradiation at Potsdam compared to ratios at Lindenberg extreme differences in erythemal UV between the sites do not appear in the ratios UV/GLOBAL extreme differences need not be due to instrument malfunction, but can be cloudrelated no further corrections other than those from calibrations and calibration checks have been applied to the Brewer and SPECTRO UV data ERY / GLOBAL LINDENBERG (%) AUG. 13, ERY / GLOBAL POTSDAM (%)

17 INPUT DATA BASE POTSDAM POTSDAM UV IRRADIANCE AOD COLUMN OZONE (DAILY) GLOBAL AND DIFFUSE IRRADIATION (HOURLY) HEIGHT OF FRESH SNOW (DAILY) SNOW HEIGHT AND TYPE (DAILY) SUNSHINE DURATION (HOURS/DAY) VISIBILITY (3 OBS PER DAY) CLOUD COVER (3 OBS PER DAY) VIS (HOURLY) CLOUD LAYERS (HOURLY) LOW CLOUD HEIGHTS (HOURLY) YEAR

18 1 Input parameters: different short-term variabilities and spatial correlations Examples: DAILY SUNSHINE DURATION and DAILY COLUMN OZONE DAILY SUNSHINE DURATION DAILY COLUMN OZONE DAILY SD LINDENBERG (min) O 3 BREWER LINDENBERG (DU) DAILY SD POTSDAM (min) O 3 BREWER POTSDAM (DU)

19 Monthly variation and long-term effects MONTHLY MEANS AND RUNNING MONTHLY MEANS GLOBAL IRRADIATION COLUMN OZONE MONTHLY GLOBAL IRRADIATION (J / m²) POTSDAM YEAR COLUMN OZONE (DU) POTSDAM El Chichon YEAR MONTHLY DOBSON 12 MONTH R.A. DOBSON 12 MONTH R.A. BREWER Pinatubo

20 MONTHLY SUNSHINE POTSDAM LINDENBERG ANNUAL SUNSHINE DURATION and GLOBAL IRRADIATION MONTHLY SUNSHINE DURATION LINDENBERG (h) JULY MONTHLY SUNSHINE DURATION POTSDAM (h) ANNUAL SUNSHUINE DUARTION (h) SD POTSDAM SD LINDENBERG GLOBAL POTSDAM YEAR ANNUAL GLOABL IRRADIATION (kj cm -2 )

21 ANN model uncertainties of daily UV-A, UV-B, ERY (POTSDAM, N ~ 258) (relative STD of measured UVA, UVB, ERY ~ 75% 95%) PREDICTORS 1 MONTH 2 SUNSHINE DURATION 3 GLOBAL IRRADIATION 4 DIFFUSÈ IRRADIATION 5 CLOUD COVER 6 SNOW HEIGHT 7 COLUMN OZONE 8 HORIZONTAL VISIBILITY 9 ALBEDO 1 SOLAR ZENITH ANGLE A: bias B: relative RMS between model and measurements ( ) ANN NO PREDICTORS TIME PERIOD UV-A (%) UV-B (%) ERY (%) A B A B A B # # # # # #

22 RESULTS: 6 different ANN versions (Potsdam, , daily erythemal irradiation) X: measured Y: modeled ANN 6 ANN 1 = decreasing number of predictors increasing uncertainty with decreasing number and importance of predictors

23 RESULTS: Daily erythemal irradiation (measured vs. modeled) in dependence of cloudiness expressed by ratios between daily diffuse and global irradiation D/G no systematic effect of clouds in the differences modeled to measured erythemal irradiation

24 difference in % 8% 6% 4% 2% % -2% -4% -6% -5% -1% -2% 5% 1% 2% Jan Feb Mrz Apr Mai Jun Jul Aug Sep Oct Nov Dec time [month] UV-B ANN model dependence on column ozone for UV-B and UV-A Potsdam, ΔO3= -2%, -1%, - 5%, +2%, +1%, +5% difference in % 8% 6% 4% 2% % -5% -1% -2% 5% 1% 2% Jan Feb Mrz Apr Mai Jun Jul Aug Sep Oct Nov Dec UV-A UV-B and ERY depend on ozone, UV-A is almost independent of ozone -2% -4% -6% time [month]

25 Climatology as a predictor: uncertainty is not reduced, if seasonal ozone variation is included, compared to no ozone ERY modeled with no zone ERY modeled with measured column ozone ERY modeled with long-term daily average ozone

26 ANN MODEL RESULTS: EXAMPLE POTSDAM ERYTHEMAL IRRADIATION ERYTHEMALIRRADIATION (J / cm²) long-term variability of input data is reflected 4.5 in re-constructed erythemal UV ( dimming, 4. brightening, ozone decline ) 3.5 POTSDAM ( ), RUNNING MONTHLY AVERAGES ERYTHEMAL (MODEL) GLOBAL (MEASURED) OZONE (MEASURED) YEAR GLOBAL IRRADIATION (J / m²) O 3 (DU)

27 ANN model results: variability in all spectral regions: UVERY, UV-A, UV-B, Potsdam UV-A (J cm -2 ) UV ER (J cm -2 ) ER POTSDAM, ANNUAL TOTALS UVER UV-B UV-A YEAR UV-B (J cm -2 )

28 ANN model results: Anomalies of UV-B and UV-A (referred to longterm average) show different long-term changes due to ozone (UV-B) and cloud+aerosol effects (UV-A and UV-B) UV (PERCENTAGE OF MEAN ) POTSDAM, RUNNING MONTHLY MEANS REFERRED TO OVERALL MEANS EL CHICHON MT PINATUBO UV-B UV-A YEAR

29 ANN MODEL RESULTS: changes in measured DIFFUSE / GLOBAL may have slightly affected ratios of modeled UV / GLOBAL ( UV-A) ozone effect to UV-B/GLOBAL and UVER/GLOBAL UV / GLOBAL ANOMALY (%) POTSDAM, ANNUAL UV/GLOBAL AND DIFFUSE /GLOBAL ANOMALIES UVER/G UV-B/G UV-A/G D/G YEAR DIFFUSE / GLOBAL ANOMALY (%)

30 Re-constructed annual totals of erythemal irradiation at Potsdam and Lindenberg derived from site input data show similar long-term patterns ANNUAL ERYTHEMAL IRRADIATION (J cm -2 ) POTSDAM LINDENBERG YEAR

31 6 5 Deutscher Wetterdienst ANN application to other sites (predictors only GLOB, O3, SZA) ER modeled [J m -2 ]ER Y = 1.187x R 2 = ER measured [J m -2 ]ER site independent model Std RMSE (i) RMSR (ii) MPR (iii) Bilthoven Jokioinen Potsdam Thessaloniki training period training period training period training period test period test period test period test period ER modeled [J m -2 ]ER ER modeled [J m -2 ]ER ER modeled [J m -2 ]ER Y =.9593x R 2 =.9823 Y =.9356x R 2 =.9812 Y =.9547x R 2 = ER measured [J m -2 ]ER ER measured [J m -2 ]ER ER measured [J m -2 ]ER

32 CONCLUSIONS ANN model use for re-construction of UV irradiation Future task: data of more sites will be processed, and uncertainties of re-construction for site-independent application be re-determined Advantages fast method statistical distributions of input and output parameters, and relations between them need not be known cross-correlations between input parameters are accounted for (important predictors used, less important are shown), example: unknown aerosol variation affects radiation components (global, diffuse) and visibility assumptions on missing meteorological input data ( climatology ) not needed no parameterizations needed (e.g. aerosol optical depths from visibility no assumptions or climatology needed to describe parameters that are not easily available (microphysical features of clouds, height profiles of aerosol characteristics, ) update (re-training) possible, if new data become available Disadvantages errors and peculiarities of input data used for training are transferred to the ANN (cosine error, bias in dose estimates, horizon obstruction, ); training is time consuming (CPU) sufficient amount of input data needed that describes whole atmospheric variability variations that are not in the training data set are not re-constructed (example: long-term variation in extraterrestrial UV irradiance differing from variation in VIS/NIR covered by G)

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