1. VI Seminar Homogenization, Budapest 2008 M.Mendes, J.Neto, A.Silva, L.Nunes, P.Viterbo Instituto de Meteorologia, Portugal “Characterization of data sets for the assessment of inhomogeneities of climate data series, resulting from the automation of the observing network in Mainland Portugal

2. VI Seminar Homogenization, Budapest 2008 Current IM network with overlapping observations 30 sites with Automatic Weather Stations (AWS) and Conventional Stations (CS) 2 sites also with Present Weather Sensors (WW)

3. VI Seminar Homogenization, Budapest 2008 The problem: continuation of conventional data series with data from Automatic Weather Station?

4. VI Seminar Homogenization, Budapest 2008 OVERLAPPING PERIODS OF AWS AND CS DATA

5. VI Seminar Homogenization, Budapest 2008 Station Features Automatic Weather Stations Type I - AWS1 (15)Type II – AWS2 (15) Temperature (air,ground) Humidity Wind Global radiation Pressure Precipitation Interactive terminal (TIC) 10 minute records Temperature (air, ground) Humidity Wind Global radiation Precipitation 10 minute records Conventional Stations Principal – CS1 (15) Simple – CS2 (15) Temperat.(air, ground) Humidity Wind Sunshine duration Pressure Precipitation Visual parameters (cloud cover, cloud type, present & past weather, horiz. visibility) Daily records 1,2 hourly records/day Profissional Observers Temperature (air & ground) Wind Sunshine duration Precipitation Visual parameters (cloud cover, present & past weather, horizontal visibility,...) Daily records 1,2 hourly records/day Volunteers Present Weather Systems (2) Precipitation sensor Horizontal visibility sensor

6. VI Seminar Homogenization, Budapest 2008 Conventional Observations and Instruments Piranómetro Radiation screen Thermometers Thermo-hygrograph Rain gauge Sunshine- recorder Evaporation pan Mercury Barometer Visual observations

7. VI Seminar Homogenization, Budapest 2008 Automatic weather station sensors and equipments Wind vane and anemometer Pyranometer Rain detector Radiation shield with temp. & hum. sensors Rain gauge AWS with solar panel GSM Antena Data acquisition system

8. VI Seminar Homogenization, Budapest 2008 Data records/failures AWS vs CS (10 years data) AIR TEMPERATURE CodeParameter T009Air temperature at 09 UTC T015Air temperature at 15 UTC T018Air temperature at 18 UTC Tmax Maximum temperature (09-09 UTC) AWS – 144 consecutive 10 minute records and/or 24 consecutive hour records * CS – 1 record Tmin Minimum temperature (09-09 UTC) AWS – 144 consecutive 10 minute records and/or 24 consecutive hour records * CS – 1 record Tmn1 Maximum temperature (09-18 UTC) AWS – 60 consecutive 10 minute records and/or 10 consecutive hour records * CS – 1 record Tmx1 Minimum temperature (00-10 UTC) AWS – 66 consecutive 10 minute records and/or 11 consecutive hour records * CS – 1 record AWS - % FAILURE DAYS Nr.NDaysT009T015T018TmaxTminTmn1Tmx1 53134714.64.4 12.012.19.38.7 53527710.8 0.64.84.92.73.6 54134711.51.81.67.2 4.65.3 54328301.00.9 1.92.01.51.7 54831361.71.31.15.2 3.94.1 55130612.711.811.419.9 18.017.3 55718266.6 7.3 7.17.2 55835012.3 2.15.7 3.44.2 56035014.75.15.010.9 5.29.0 56224961.61.81.74.9 3.33.9 56735011.41.21.33.0 2.3 56832873.13.03.19.8 7.27.9 57031671.71.51.66.9 4.34.8 57135012.8 6.2 4.74.9 57531361.91.31.45.75.84.55.1 57935013.0 3.212.8 8.28.7 605313616.110.213.734.5 30.427.5 61125577.46.87.319.2 13.914.7 619310612.614.912.324.9 20.622.0 632255714.69.611.429.829.924.923.5 6357317.06.76.915.115.211.612.1 68515538.17.68.016.3 13.312.9 70231063.83.33.515.4 10.1 74435019.18.48.217.5 14.914.4 770350111.28.610.124.925.018.920.6 78335017.66.46.718.8 15.713.5 812255711.97.18.924.624.723.018.7 83535018.67.89.317.7 15.314.0 850350120.410.612.946.947.144.032.6 86427715.45.75.512.1 9.19.4 CS - % FAILURE DAYS NrNdaysT009T015T018TmaxTmin 53034710.07 0.610.42 53527710.010.330.00 54134710.390.410.39 54328300.00 54831360.00 5513060.050.100.05 55718260.040.060.04 55835010.280.29 0.28 56035010.000.020.00 56224960.250.730.310.25 56735010.010.030.01 56832870.080.590.12 57031670.00 57135010.010.020.010.00 57531360.020.060.02 57935010.000.020.00 531360.050.120.05 1125570.04 1931060.00 3225570.150.440.160.25 357310.210.240.22 8515530.170.200.17 10231060.030.020.08 14435010.000.340.00 17035010.00 18335010.34 0.35 21225570.00 0.160.00 23535010.020.300.02 25035010.16 26427710.290.370.29

9. VI Seminar Homogenization, Budapest 2008 Bias results for air temperature (Differences between observations AWS-CS) BIAS531535541543548551557558560562567568570571575579605611619632635685702744770783812835850864 T0090.190.420.170.560.23-0.051.560.100.040.39-0.03 0.540.350.510.08-0.39-0.29-0.20-0.270.610.400.21-0.930.42-0.671.03-0.680.840.37 T015 0.30 0.34 -0.01 -0.27 -2.100.380.30 -0.17 2.180.450.23 T018-0.13 -0.27-0.08-0.620.01 -0.19-0.23-0.27-0.36-0.46-0.30-0.63-0.15-0.28-0.02 -1.72-0.61 0.06 Tmin0.29-0.12-0.23 -0.38-0.180.12-0.21-0.22-0.25-0.36-0.11-0.01-0.05 -0.19-0.170.011.050.44-0.091.450.02-0.49-0.590.120.590.11-0.230.02 Tmn10.33-0.10-0.21 -0.37-0.140.12-0.20 -0.24-0.35-0.030.00-0.02-0.04-0.18-0.160.031.080.44-0.091.420.03-0.48-0.540.150.600.12-0.230.04 Tmax-0.140.520.190.450.490.061.950.34-0.210.01-0.07-0.050.150.220.140.21-0.21-0.70-0.94-0.51-0.12-1.38-0.01-0.020.310.070.24-0.460.00-0.04 Tmx1-0.150.520.190.440.480.031.950.34-0.230.01-0.08 0.140.210.130.20-0.22-0.71-0.98-0.49-0.13-1.39-0.02-0.030.310.060.24-0.460.01-0.04 <-0.25 -0.25 to +0.25 >+0.25 -> 11 cases for T09, 21 cases for Tmin, 19 cases for Tmax

10. VI Seminar Homogenization, Budapest 2008 Spatial distribution of Bias results for air temperature (AWS-CS)

11. VI Seminar Homogenization, Budapest 2008 Bias monthly results for air temperature TmaxJanFebMarAprMayJunJulAugSepOctNovDec 5-0.46-0.34-0.22-0.21-0.06-0.11-0.02-0.12-0.31-0.4-0.25-0.28 11-1.16-0.98-0.79-1.01-0.56-0.36-0.47-0.19-0.35-0.78-1.17-0.97 19-1.21-0.98-0.81-0.83-0.52-0.22-0.66-0.52-0.84-1.03-1.51-1.64 32-1.19-0.340.01-0.45-0.05-0.62-0.55-0.54-0.27-0.83-0.43-1.31 35-0.28-0.45-0.31-0.14-0.03-0.040.090.06-0.09-0.02-0.12-0.06 85-1.67-1.66-1.74-1.4-0.9-1.33-0.71-1.12-1.27-0.61-1.83-2.34 1020.060.020.04-0.12-0.030.010.02-0.050.050.01-0.14-0.02 144-0.120-0.040.01-0.190.020.150.290.17-0.22-0.19-0.13 1700.190.250.230.410.390.44 0.40.120.130.17 183-0.06-0.070.180.07-0.120.220.40.290.2-0.13-0.27-0.21 2120.20.330.290.20.280.30.330.210.260.110.19 235-0.68-0.59-0.49-0.39-0.31-0.22-0.34-0.47-0.45-0.59-0.39-0.68 250-0.19-0.1-0.19-0.060.1 0.240.140.08-0.09-0.1-0.18 26400.140.12-0.180.13-0.11-0.18-0.13-0.11-0.130.04-0.05 5571.91.862.212.382.152.081.671.981.991.571.931.84 530-0.19-0.110.02-0.1-0.14-0.05-0.32-0.05 -0.29-0.27-0.11 5350.280.40.510.670.640.740.710.650.670.440.330.24 541-0.060.020.10.150.180.460.320.480.470.220.06-0.07 5430.070.270.490.590.710.730.640.70.690.40.130.07 5480.270.310.350.410.450.690.820.850.690.410.340.21 551 -0.10.150.290.140.170.060.160.04-0.18-0.28 5580.160.20.240.340.47 0.560.530.450.30.20.16 560-0.35-0.45-0.28-0.16-0.11-0.13-0.03 -0.23-0.26-0.3 562-0.08-0.050.040.030.240.09-0.04-0.03-0.040.03-0.04 567-0.12-0.09-0.08-0.05-0.06-0.03-0.01-0.1-0.09-0.11-0.07-0.11 568-0.06-0.13-0.08-0.04-0.01-0.050.01-0.060.01-0.04-0.09 570-0.040.060.130.180.230.270.290.270.230.150.05-0.02 571-0.0600.150.250.49 0.480.380.310.160.03-0.07 575-0.05-0.080.070.150.360.370.330.260.150.17-0.04-0.06 579-0.030.020.080.170.270.370.520.50.330.130.040.01 Tmin JanFebMarAprMayJunJulAugSepOctNovDec 5-0.15-0.08-0.16-0.14-0.17-0.19-0.18-0.2-0.18-0.19-0.18-0.15 11-0.050.060.010.030.070.08-0-0.08-0.06-0.030.030.08 191.081.220.561.130.570.850.971.081.051.531.281.12 320.180.290.40.710.590.45-0.210.540.350.731.330.5 35-0.11-0.080.03-0.09-0.11-0.07-0.31-0.07-0.140.12-0.12-0.06 851.641.581.911.230.271.0810.272.182.741.181.52 1020.070.05 0.160.03-0.1-0.050.04-0.050.060.02-0.04 144-0.42-0.6-0.33-0.26-0.3-0.43-0.55-0.65 -0.57-0.56-0.53 170-0.64-0.58-0.57-0.49-0.57-0.68-0.53-0.61-0.66-0.56-0.59-0.66 183-0.56-0.43-0.520.07-0.280.380.051.170.60.83-0.890.03 2120.660.710.770.780.530.550.49 0.640.410.480.74 2350.070.1 0.180.20.020.21-0.080.260.24-0.10.08 250-0.13-0.11-0.12-0.26-0.37-0.42-0.44-0.33-0.24-0.05-0.01-0.18 2640.130.010.13-0.13-0.33-0.09-0.010.19-0.030.130.31-0.09 5570.20.150.09 0.10.050.080.040.10.040.30.14 5300.290.20.230.690.30.290.150.010.480.240.440.17 535-0.03 -0.09-0.18-0.1-0.17-0.19-0.23-0.18-0.12-0.04-0.06 541-0.21-0.19-0.26-0.18-0.21-0.24-0.23-0.21-0.3-0.21-0.17-0.3 543-0.04-0.17-0.22-0.15-0.22-0.31-0.35-0.39 -0.24-0.15 548-0.22-0.29-0.32-0.43-0.51-0.52-0.53-0.5-0.51-0.39-0.2-0.18 551 -0.05-0.23-0.27-0.18-0.29-0.22-0.03-0.07-0.14-0.1 558-0.4-0.3-0.2-0.13-0.12-0.14-0.12-0.16-0.1-0.2-0.25-0.43 560-0.17-0.18-0.15-0.19-0.22-0.28-0.27-0.29-0.24-0.21-0.19-0.18 562-0.2-0.21-0.26-0.25-0.41-0.32-0.27-0.22-0.26-0.25-0.18-0.14 567-0.27 -0.32-0.29-0.38-0.45-0.44-0.46-0.42-0.36-0.3-0.28 568-0.14-0.09-0.11 -0.07-0.05-0.15-0.13-0.04-0.18 -0.12 5700.070.030.010-0.06 -0.05-0.04-0.03-0.020.020.01 571-0.07-0.02-0.04-0.05-0.07-0.04-0.05-0.04-0.02-0.04-0.06-0.05 575-0.070.02-0.03-0.02-0.14-0.06-0.03-0.06-0.030.04-0.07-0.12 579-0.1-0.12-0.14-0.23-0.22-0.28 -0.26-0.18 -0.17-0.11

12. VI Seminar Homogenization, Budapest 2008 Example of statistic analysis for individual series (1/4) StatisticsJanFebMarAprMayJunJulAugSepOctNovDecTot Ndays2321861791621611491471321581991901892084 MeanCS3.363.505.996.029.3413.3913.6314.9012.958.965.213.887.92 MeanAWS4.444.726.557.169.9114.2314.6015.9813.9910.486.505.018.97 MinCS0.1 1.02.05.07.010.07.03.00.1 MinAWS-2.4-1.9-4.70.31.55.58.110.48.13.70.0-0.3-4.7 MaxCS10.09.013.015.018.523.021.023.020.019.012.011.023.0 MaxAWS11.410.215.015.720.624.623.525.721.618.714.211.925.7 StdDevCS2.1921.9002.7292.7603.3063.5242.8622.9242.6192.4802.3441.9874.838 StdDevAWS2.7312.6503.2673.0133.4603.4993.0613.4802.5912.7132.7022.4174.988 AssimetryCS0.6360.3660.0080.9150.5440.2630.3550.4250.2530.4520.2900.1520.519 AssimetryAWS0.245-0.196-0.4170.3240.6300.2860.5170.9290.3720.0140.3460.0340.401 KurtosisCS-0.279-0.206-0.2160.9240.107-0.053-0.363-0.230-0.1001.3970.0620.220-0.515 KurtosisAWS-0.155-0.4340.742-0.0520.8780.1750.0810.1770.2860.1010.158-0.102-0.268 Bias1.0751.2230.5621.1310.5710.8460.9691.081.0481.5271.2821.1221.05 Tmin.: Cabril

13. VI Seminar Homogenization, Budapest 2008 Example of statistic analysis for individual series (2/4) Tmax.: Cabril StatisticsJanFebMarAprMayJunJulAugSepOctNovDecTot Ndays2321861791621611491471321581991901892084 MeanCS12.4814.7116.3017.8621.1426.3827.7829.6426.3219.4814.9413.1919.26 MeanAWS11.2813.7315.4917.0320.6326.1627.1229.1225.4818.4513.4411.5518.32 MinCS5.07.08.0 11.015.017.018.016.512.06.07.05.0 MinAWS3.55.17.08.09.213.015.917.315.18.96.45.83.5 MaxCS24.022.028.030.035.036.038.039.035.033.526.019.039.0 MaxAWS23.622.327.429.934.236.337.639.935.031.425.419.639.9 StdDevCS2.7233.3294.4644.8575.0535.2544.7254.5714.3633.7503.3322.5777.010 StdDevAWS3.0593.8374.6195.2435.4975.6695.0385.1244.3914.3233.1532.7367.439 AssimetryCS0.336 - 0.0390.3700.4220.284 - 0.352 - 0.2730.000 - 0.0770.5660.2090.1550.554 AssimetryAWS0.414 - 0.0300.4310.4520.339 - 0.378 - 0.0940.003 - 0.0620.4550.2780.4010.554 KurtosisCS1.329 - 0.501 - 0.352 - 0.296 - 0.400 - 0.637 - 0.361 - 0.397 - 0.7630.1850.731 - 0.534 - 0.585 KurtosisAWS1.354 - 0.563 - 0.385 - 0.598 - 0.440 - 0.507 - 0.632 - 0.415 - 0.647 - 0.2190.451 - 0.082 - 0.583 Bias - 1.205 - 0.983 - 0.808 - 0.834 - 0.517 - 0.216 - 0.663 - 0.518 - 0.838 - 1.030 - 1.505 - 1.639 - 0.939

14. VI Seminar Homogenization, Budapest 2008 Example of statistic analysis for individual series (3/4) Tmin: Lisboa StatisticsJanFebMarAprMayJunJulAugSepOctNovDecTot Ndays230211229239253239285 2812662462673031 MeanCS7.358.4110.5611.5414.0916.9418.1018.8817.6415.0611.008.3513.42 MeanAWS7.258.2910.4211.3113.8716.6617.8318.6217.4614.8710.838.2413.23 MinCS0.52.1-0.66.87.612.314.214.113.59.24.51.2-0.6 MinAWS0.51.9-0.46.77.511.713.814.813.99.24.21.4-0.4 MaxCS15.014.015.517.124.324.226.628.222.721.418.815.428.2 MaxAWS15.013.715.416.623.924.326.028.222.720.918.415.328.2 StdDevCS2.9362.3432.6281.9262.3432.0872.1482.0621.6702.0652.8172.9744.635 StdDevAWS2.8642.2702.5931.9292.3282.1262.1232.0231.6362.0352.7702.9464.575 AssimetryCS0.218-0.317-0.8740.0810.8970.5491.3481.1850.353-0.0580.0820.101-0.203 AssimetryAWS0.250-0.313-0.8340.1040.9050.5881.3051.2800.454-0.0660.0790.141-0.190 KurtosisCS-0.083-0.4011.574-0.1112.2750.4952.3102.3470.373-0.253-0.262-0.320-0.560 KurtosisAWS-0.022-0.3481.457-0.1192.1920.5902.2272.5540.299-0.268-0.244-0.250-0.578 Bias-0.099-0.121-0.138-0.225-0.224-0.283-0.277-0.26-0.184-0.181-0.171-0.114-0.192

15. VI Seminar Homogenization, Budapest 2008 Example of statistic analysis for individual series (4/4) StatisticsJanFebMarAprMayJunJulAugSepOctNovDecTot Ndays230211229239253239285 2812662462673031 MeanCS14.2015.7318.1418.9922.2326.3527.6428.7126.5322.0117.3714.4421.34 MeanAWS14.1815.7518.2219.1522.5026.7228.1529.2126.8522.1417.4114.4521.56 MinCS7.010.010.111.413.417.220.621.419.516.111.78.57.0 MinAWS6.910.010.211.513.217.220.821.519.415.911.78.86.9 MaxCS22.020.827.529.435.339.037.741.538.733.225.519.241.5 MaxAWS22.120.628.029.435.439.338.542.039.033.325.719.442.0 StdDevCS2.3262.2263.4293.4314.1354.1953.9113.9053.6783.0002.3132.1156.106 StdDevAWS2.3472.2183.4323.4944.1854.2573.9763.9603.8063.0372.3462.1136.284 AssimetryCS-0.199-0.0890.4370.7650.8000.4410.5960.7490.5490.8820.259-0.2460.398 AssimetryAWS-0.145-0.1130.4250.7310.7470.4290.6020.6770.4990.8480.272-0.2030.397 KurtosisCS0.676-0.3240.1480.2710.418-0.266-0.3180.019-0.2400.8840.231-0.415-0.529 KurtosisAWS0.732-0.3150.1530.1830.366-0.300-0.329-0.039-0.3480.8720.216-0.461-0.565 Bias-0.0250.0180.0800.1690.2710.3740.5150.4990.3250.1300.0410.0100.212 Tmax: Lisboa

16. VI Seminar Homogenization, Budapest 2008 Statistical Testing: Total data mean values (AWS-CS) TmaxJanFebMarAprMayJunJulAugSepOctNovDecTotal 51.2280.9850.4190.4230.1240.2400.0410.3010.6851.0250.6250.6320.913 113.5262.2701.6531.8801.0460.7601.0260.3950.7731.9093.4652.8962.952 194.4802.6401.6831.4850.8780.3411.1630.8671.7022.5384.5215.9954.193 322.3870.901-0.0140.7920.0791.0411.0420.9530.5131.4240.5052.3951.459 350.4830.5840.3870.2040.0300.021-0.073-0.0770.0840.0170.1350.1210.222 854.3443.4282.8462.2400.9622.1390.8881.7231.9661.3354.2727.4954.402 102-0.290-0.058-0.1080.4120.081-0.027-0.0560.164-0.210-0.0340.5450.0780.103 1440.552-0.0120.117-0.0250.434-0.044-0.415-0.826-0.4410.6620.8020.6820.084 170-0.744-0.926-0.709-1.141-0.921-1.063-1.194-1.290-1.236-0.376-0.502-0.897-1.719 1830.2280.220-0.367-0.1220.188-0.491-0.987-0.687-0.5170.3270.7330.723-0.310 212-0.501-0.925-0.679-0.455-0.536-0.626-0.772-0.467-0.525-0.286-0.513-0.656-0.992 2352.7762.0021.2431.0520.6750.5390.9971.3641.2111.7691.3753.5362.032 2500.6240.2320.4180.116-0.136-0.201-0.519-0.321-0.1920.1510.2390.5320.009 264-0.002-0.428-0.3240.374-0.2290.2480.3740.3010.2900.326-0.1490.2130.182 557-4.546-4.575-3.602-3.131-3.771-3.518-2.431-3.120-3.241-3.293-5.322-5.938-6.592 5300.9750.459-0.0840.5390.6810.1651.8350.2520.2631.3421.1230.5441.370 535-1.273-1.719-1.628-2.074-1.654-2.059-2.167-2.098-2.106-1.695-1.403-1.184-3.276 5410.307-0.066-0.322-0.545-0.564-1.368-1.117-1.738-1.760-0.905-0.2030.297-1.323 543-0.316-1.034-1.352-1.566-1.705-1.607-1.604-1.859-1.953-1.369-0.516-0.402-2.973 548-1.223-1.169-1.012-1.119-1.089-1.672-2.267-2.353-1.887-1.421-1.404-1.141-2.990 5510.277-0.258-0.235-0.174-0.146-0.051-0.146-0.0630.2400.545-0.126 558-0.608-0.744 -0.891-0.959-1.098-1.375-1.418-1.189-0.898-0.840-0.792-1.602 5601.3141.2100.7080.3600.2270.2890.0780.0850.5310.7481.1141.4501.108 5620.3600.163-0.120-0.082-0.484-0.2020.1020.0910.096-0.0800.1260.169-0.043 5670.3260.2260.1850.1080.1130.0740.0180.2430.2180.2880.2100.3560.330 5680.1880.3480.1820.0770.0260.105-0.0260.159-0.0280.0940.2470.2620.249 5700.140-0.184-0.361-0.453-0.487-0.684-0.794-0.752-0.557-0.441-0.1670.084-0.704 5710.2240.000-0.402-0.621-1.048-1.171-1.259-1.006-0.766-0.459-0.1180.332-1.064 5750.1590.211-0.164-0.354-0.759-0.888-0.833-0.643-0.359-0.4850.1180.250-0.618 5790.114-0.083-0.251-0.535-0.732-0.967-1.559-1.514-1.030-0.498-0.197-0.057-1.333 TminJanFebMarAprMayJunJulAugSepOctNovDecTotal 50.3290.2150.3290.4440.5370.6910.6920.8420.7470.7020.3850.2801.018 110.146-0.146-0.013-0.094-0.192-0.2470.0050.2660.2270.110-0.098-0.197-0.041 19-4.678-5.115-1.767-3.524-1.515-2.079-2.805-2.728-3.576-5.858-4.941-4.930-6.901 32-0.304-0.637-0.734-1.800-1.593-1.3590.544-1.600-1.108-1.686-2.045-0.883-1.879 350.1650.172-0.0400.1670.1500.0590.5440.1430.212-0.1540.1210.0910.251 85-3.693-4.880-4.609-3.153-0.443-2.821-2.239-0.537-5.636-7.900-2.879-3.903-6.909 102-0.203-0.187-0.163-0.759-0.1190.5010.275-0.2390.278-0.266-0.0570.121-0.146 1441.0251.5020.8560.8171.0352.0352.6833.2182.2781.6311.2561.2423.239 1701.4911.6381.7251.5771.9612.8002.1502.9632.8112.0051.4251.7584.050 1831.2230.8171.230-0.1840.825-1.478-0.234-3.971-2.155-1.8671.356-0.042-0.675 212-1.397-1.636-1.641-1.974-1.438-1.730-1.694-1.516-1.882-1.015-0.739-1.216-2.991 235-0.207-0.296-0.294-0.814-0.711-0.066-0.8410.322-1.250-0.9430.301-0.269-0.734 2500.2950.2530.3570.6640.9151.1761.3631.0170.9820.1350.0140.3371.082 264-0.353-0.035-0.4010.4531.0780.3900.029-0.7330.139-0.396-0.7200.218-0.155 557-0.525-0.449-0.235-0.221-0.294-0.146-0.168-0.100-0.314-0.126-0.936-0.446-0.614 530-0.860-0.712-1.154-2.921-1.434-1.899-1.319-0.089-2.392-0.707-1.143-0.419-2.240 5350.129 0.3540.9370.4010.8100.9971.2491.0930.6540.1530.2460.978 5410.7730.6811.0280.8500.9971.2601.4541.3941.6490.9250.4200.9271.842 5430.1170.4940.6770.5650.9081.2311.8392.0331.7300.8930.4140.3751.730 5480.7351.1341.1182.0122.0502.3523.0092.4452.6941.7650.6730.6453.249 5510.0460.4600.2670.3230.3470.3190.0490.1160.2130.0770.439 5581.1290.9170.6670.5110.4340.5810.4710.7470.5110.7490.8201.4181.527 5600.6240.6070.5010.6400.6170.9060.8950.9300.8920.8710.7360.7791.623 5620.6830.8140.9800.9611.4191.1001.0170.8161.2290.9690.5590.4631.866 5670.7710.9031.0191.0631.1731.5861.6331.6301.7871.4210.8720.7552.397 5680.4780.2760.3270.2650.1830.1190.3930.3650.1300.5630.5470.3970.670 570-0.261-0.112-0.038-0.0070.1800.2020.1850.1320.1210.085-0.090-0.0540.071 5710.2870.0810.1260.1450.1720.0920.1260.1120.0710.1490.2350.2620.320 5750.190-0.0490.0830.0790.4120.2330.1180.2250.132-0.1480.1850.3180.291 5790.3650.5380.5661.2741.0781.4681.5501.5221.3221.0170.6780.4461.627 Z- values: two tailed test (significance levels: 10%, 5% and 1%) For each month, results are significant (90%) for most of the stations; For each station results may change between Tmax and Tmin

17. VI Seminar Homogenization, Budapest 2008 Statistical testing of monthly data differences to normal values 1961-90 Z- values: two tailed test (significance levels: 10%, 5% and 1%) Nr.YM 61-90 vs. AWS 61-90 vs. CS AWS vs. CS 19200011-4.771-8.1521.865 19200110.874-2.4652.244 192001101.726-3.0303.281 19200361.701-1.0351.989 19200383.650-0.7423.233 19200390.433-5.0853.711 19200310-3.481-8.3142.543 19200412.477-0.1991.928 1920043-2.677-3.6690.315 1920044-1.497-4.2971.354 19200451.026-0.1580.860 19200465.1232.4602.029 1920047-1.047-2.7471.224 19200412-4.088-5.2500.637 1920051-2.280-3.7070.691 1920052-8.983-9.919-0.852 1920053-0.2350.294-0.367 19200551.0001.501-0.488 19200564.2045.902-1.191 1920057-0.3620.490-0.582 19200582.1273.374-0.388 1920059-5.529-0.929-2.457 192005100.932-5.6944.655 Nr.YM 61-90 vs. AWS 61-90 vs. CS AWS vs. CS 5791997122.7013.155-0.319 579199824.9065.072-0.243 579199870.0000.694-0.494 5791999101.8161.8450.000 57920004-1.105-0.553-0.391 579200181.6083.259-1.145 57920019-0.754-0.247-0.353 5792001103.7003.988-0.218 579200441.1321.705-0.401 579200490.3751.519-0.801 5792004100.0000.902-0.642 579200530.1450.281-0.101 579200556.6667.558-0.502 579200572.5103.687-0.771 57920062-3.135-2.896-0.158 579200633.3213.493-0.193 579200647.3678.060-0.520 579200654.2864.895-0.433 579200669.47111.049-1.116 579200674.2904.944-0.456 579200691.5192.774-0.865 5792006109.76811.235-1.189 579200612-1.410-1.3390.000 Tmin Nr.YM 61-90 vs. AWS 61-90 vs. CS AWS vs. CS 5791997122.4272.826-0.218 579199825.5765.967-0.389 579199870.728-0.1460.619 579199910-2.913-2.9400.000 57920004-6.228-6.7030.223 579200180.000-1.5941.089 57920019-1.154-1.7510.410 579200110-2.834-3.5240.500 579200442.4322.1760.204 57920049-0.190-0.7710.406 579200410-0.138-0.2780.098 579200530.000-0.3190.227 579200552.6092.2030.353 579200571.1150.3780.530 57920062-1.653-1.385-0.165 57920063-2.025-2.0740.000 579200644.0083.9320.125 579200655.2834.8490.287 579200661.5301.0470.365 579200672.5152.3890.105 579200690.5920.3620.169 5792006101.6501.3230.191 579200612-0.716-0.7200.000 Nr.YM 61-90 vs. AWS 61-90 vs. CS AWS vs. CS 19200011-6.320-4.819-1.175 1920011-2.6740.283-2.138 192001100.1592.347-1.500 19200363.1243.1010.293 19200384.5355.705-0.292 19200391.1313.207-1.392 19200310-2.350-0.165-1.781 19200411.0036.164-3.970 1920043-0.6581.900-1.787 19200442.4603.344-0.436 19200452.1483.040-0.418 19200467.2307.2690.000 19200473.0774.845-0.920 19200412-1.1451.616-1.912 19200511.0173.299-1.405 1920052-0.3661.541-1.327 19200531.1841.481-0.157 19200553.3224.605-1.055 19200564.4275.230-0.358 19200572.0262.775-0.484 19200585.8085.9180.000 19200591.1693.096-1.419 192005101.6832.336-0.084 Tmax

18. VI Seminar Homogenization, Budapest 2008 Climatological analysis of extreme values Tmin Nr.M Cold Days (AWS) Tmin<-10ºC Cold Days (CS) Tmin<-10ºC Tropical Night (AWS) Tmin>20ºC Tropical Night (CS) Tmin>20ºCTN10 (ºC) T<TN10 (AWS) T<TN10 (CS)TN90 (ºC) T>TN90 (AWS) T>TN90 (CS)Ndays 1910000226187.9411232 19200001.517138.617186 19300003111310511179 19400003461226162 19500015.410513.51314161 19600489.510818816149 197001811.523152104147 198005231214619.5917132 199000410.617719.513158 191000007.5291214.924199 191100003.3211212.502190 191200002.222139.902189 579100003.48911 10230 579200004.23311.943211 579300006.57712.323 229 579400007.72213.41614239 57950075102215.42319253 579600211812.50017.92824239 579700453914.70221.287285 579800605015.41119.82220285 579900221614.8322075281 57910001111.12216.92017266 5791100007.291014.687246 5791200004.610 12.587267 At Lisboa AWS detects more tropical nights than the CS, the opposite at Cabril

19. VI Seminar Homogenization, Budapest 2008 Climatological analysis of extreme values At Lisboa AWS detects less warm, summer and tropical days than CS, at Cabril there is seasonal dependancy Tmax Nr.M Warm Days (AWS) Tmax>20º C Summer Days (AWS) Tmax>25º C Summer Days (CS) Tmax>25º C Tropical Day T1 (AWS) Tmax>30º C Tropical Day T1 (CS) Tmax>30º C Tropical Day T1 (AWS) Tmax>35º C Tropical Day T1 (CS) Tmax>35º CTX10 (ºC) T<TX1 0 (AWS) T<TX1 0 (CS) TX90 (ºC) T>TX9 0 (AWS) T>TX9 0 (CS)Ndays 191120000007.52915.297232 192890000007.50616.13126186 193313244000010.03720.017 179 1944650121300009.51322.51716162 195837727354100011.31125.01621161 196124125878939432416.02630.02628149 1971361341039740425620.05833.51012147 1981291261091065154151821.02532.02829132 199144140928832300018.01531.5118158 191078691416110013.22925.277199 1911 42100009.741420.052190 1912000000008.01716.0106189 57 912200000011.3111016.31514230 57 924500000011.63217.81716211 57 935257109000014.6101122.01517229 57 9467731821000014.76722.91516239 57 95169178555916181116.63326.61415253 57 9623023112913852557820.54331.61215239 57 9 7285 1982157075152423.311733.9912285 57 98285 2382538698232824.49733.01618285 57 99279 16817757695523.0161433.246281 57 91019519742 340018.810927.566266 57 91134 11000013.94520.789246 57 9120000000011.68817.378267

20. VI Seminar Homogenization, Budapest 2008 Connection with the Project “SIGN”: Signatures of environmental change in the observations of the Geophysical Institutes Recovery of 19th and early 20th century Portuguese historical meteorological data M.Valente,M.Barros,L.Nunes,E.Alves,R.Trigo,E.Pinhal,F.Coelho,M.Mendes,J.Miranda This work presents the joint efforts of the 3 Portuguese Geophysical Institutes (of Lisbon, Oporto and Coimbra) and the Portuguese Meteorology Institute to convert to a digital database the historical meteorology data, recorded since 1856 until 1940 in several publications by the institutes. The different sets of historical data contain monthly, daily and sometimes hourly records of pressure, temperature, precipitation, humidity, wind speed and direction, cloud cover, evaporation & ozone. The published data cover several stations in mainland Portugal, the Azores and Madeira islands and in former Portuguese African and Asian colonies. One of the aims is to use the data to study the changes that have taken place in the historical records during the last 150 years, when the recovered data are joined with the post-1941 data stored in the Meteorology Institute digital database. The other aim is to make the data available to the meteorology community at large. Direct observations of pressure data for Lisbon and for the 1856-1940 period were prioritized and have been manually digitized, being later subjected to quality control tests. Digital historical records of Lisbon temperature, relative humidity and precipitation data have been obtained through corrected OCR techniques applied to published hourly or bi-hourly tables. Preliminary digital results are also available for several stations in mainland Portugal, Azores and Madeira. All datasets are subjected to an initial quality control test, to detect wrong values, with more comprehensive tests to be applied at later stages. At the same time, detailed metadata files are being compiled for each station. First analysis results for the digital historical database are available.

21. VI Seminar Homogenization, Budapest 2008 Final remarks/questions Availability of 10 years of daily data x 30 stations Overlapping data series have been characterized and compared, Some results regarding air temperature have been shown, but many other variables (humidity, pressure, …) have also been analyzed, For Tmax & Tmin 2/3 of stations have bias +/-0.25ºC, for T09 only 1/3 of stations There is a problem with missing data from AWS, which lowers the confidence, Climatological extremes are different if calculated with AWS or CS! For air temperature (well behaved variable, 2 types of inhomogenities were shown: seasonal dependence and offset For most of the stations, conventional observations will stop in a couple of years (only few sites will remain for more years), so, we’ll have to rely on AWS data, Then, most recent “break-point” of the series will be known (CS=>AWS), An homogenization plan is required! First for monthly data and then daily data... Continuation of the SIGN project is desirable IM-Portugal welcomes cooperation in this filed (in relation with COST HOME?)