COMPARISON BETWEEN DAILY METEOROLOGICAL DATA COLLECTED BY AUTOMATIC AND CONVENTIONAL STATIONS.

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1 COMPARISON BETWEEN DAILY METEOROLOGICAL DATA COLLECTED BY AUTOMATIC AND CONVENTIONAL STATIONS. Hilton S. Pinto (1,3), Giampaolo Q. Pellegrino (1, 4), Daniela B. Fonsechi (1, 5), Gustavo Coral (1,6), Paulo H. Caramori (2,7), Ana M. H. De Ávila (1,8) (1) Centro de Ensino e Pesquisas em Agricultura e I.B., Universidade Estadual de Campinas. Cidade Universitária Zeferino Vaz, Campinas, SP, Brazil Pq1A-CNPq. (2) Instituto Agronômico do Paraná. Rod. Celso Garcia Cid, Km375, Londrina, PR, Brazil (3) hilton@cpa.unicamp.br; (4) giam@cpa.unicamp.br; (5) danifonsechi@cpa.unicamp.br; (6) guscoral@cpa.unicamp.br; (7) caramori@iapar.br; (8) avila@cpa.unicamp.br Abstract Daily meteorological data collected by automatic (AWS) and conventional (CWS) weather stations were statistically analyzed in order to verify the difference of measurements due to sensor types. The hypothesis is that mechanical sensors have slower response time than the electronic ones, which leads to different values of the observed meteorological elements, mainly for wind, rainfall and temperature. Data from 2000 to 2005 for three places in Paraná State - Brazil and from 1997 to 2006 for one place in the State of S. Paulo were analyzed. An alternative method for calculating the AWS instant wind speed and maximum/minimum temperature was simulated collecting data every second and averaging them for one minute. Significant differences were observed for wind speed, precipitation and temperature, showing the necessity of data correction when substituting CWS by AWS. Key words: meteorological data, meteorological sensors, automated weather stations. Introduction The objective of this paper was to compare daily meteorological data observed by automatic and conventional weather stations, in order to verify the difference of measurements due to sensor types. The general hypothesis is that mechanical sensors have slower response time than the electronic ones and this could lead to different values of the observed meteorological elements, in particular wind gusts, total daily rainfall and extreme temperatures. Powell (1992) and Lockhart (1995) shows that one minute averaged data for wind gusts represents better the real velocity than the peak observed during the same period, and shows better correspondence with data collected with mechanical anemographs of standard stations. Three specific hypotheses were tested in the present work: 1. There is no statistical difference between the meteorological data measured on conventional and automatic weather stations (CWS and AWS, respectively); 2. The greater the precipitation intensity the greater errors in AWS total precipitation measurements and 3. The inclusion of smoothing techniques in the basic stations software can correct differences between CWS and AWS observations.

2 Material and Methods Four pairs of CWS and AWS data were used, from four different surface meteorological stations: one of the State University of Campinas in São Paulo State and three of the SIMEPAR Technologic Institute (AWS) and Agronomic Institute of Parana (CWS) in Londrina, Paranavaí and Pato Branco in Paraná State. Their location and meteorological data analyzed are presented on Table 1. All of the statistical analyses were run under the R free software ( May, 2006). In Paraná State, all AWS were furnished by Sutron Corporation with wind sensors at 10 meters above surface and in S. Paulo State they were furnished by Engespaço with wind sensors at 5 meters above surface. Each CWS has its wind sensor at the same high than its respective AWS. Daily Data Synoptic Data Location/State Campinas/SP Londrina/PR Paranavaí/PR Pato Branco/PR Data period Lat/Long/Alt 22.8 S/47.05 W/ 597m 23.2 S/51.1W/ 488m 23.1 S/52.3 W/ 441m 26.1 S/52.4 W/86 3m Tmax Tmin Tm -- Tc -- Txn -- P RHm -- WSmax T9 -- T15 -- T21 -- RH9 -- RH15 -- RH21 -- WS WS WS Table 1. Geographic coordinates, data period and pair of CWS/AWS meteorological data available for four Brazilian municipalities. (T, P, RH and WS are temperature, rainfall, relative humidity and wind speed, respectively. Index: max, min, m, c and xn are daily maximum, minimum, 24h mean, compensated mean and maximum/minimum average; and 9, 15 and 21 are synoptic local time 9h, 15h and 21h data, respectively). 1. Comparison CWS - AWS Some basic questions related to the first hypothesis are: How similar are datasets generated by AWS and CWS? or Are we artificially changing the climate when we just substitute a CWS by an AWS?. To answer these questions, the first step was an exploratory comparison analysis using linear regression statistics. If data are well correlated and there are no super or sub estimation, the determination coefficient (R 2 ) and the independent variable coefficient (a) should be close to 1, and the intercept (b) should close to zero. The results of this analysis were similar to other tests and were not presented here.

3 To test the hypothesis that there is no statistical difference between the meteorological data measured by CWS and AWS, nonparametric tests were chosen because there are different kinds of data to be tested, which may have different distribution types. Using the powerful of a nonparametric test in detecting population differences when certain distribution assumptions are not satisfied, the Wilcoxon Mann-Whitney test can be used (Conover, 1980). This test is described by Easton & McColl (May 2006) as one of the most powerful of the nonparametric tests for comparing two populations, and it does not assume any data distribution nor that the differences between the samples are normally distributed, as required by the two sample t-test, its parametric counterpart. The alternative hypothesis on Wilcoxon Mann-Whitney test is that the distribution function differs only in the location aspect, if at all. To test if both the CWS and the AWS data can be assumed as coming from the same distribution, it was also applied the Kolmogorov-Smirnov test (Conover, 1980). These two nonparametric tests were applied over original data and over its standardized anomalies, z, given by (Wilks, 1995): z = ( x μ ) iy where x iy is the i-day value in a specific year y; μ i and s i are, respectively, the CWS historical mean and standard deviation for the i-day over the years, i.e., the mean and standard deviation value for the subset of CWS data for the n i-days of the n years in analysis. The purpose was to remove the location and spread influences from the original dataset, which could be significant because of the data seasonality. The second hypothesis is based on some observations that AWS precipitation data presents greater errors during high intensity rain what can be caused by high impact of the water flow in the bascule system of the gauge. To verify this constraint, precipitation hourly intensities were used for data analysis of Londrina and Paranavaí, where pluviograms from CWS were available. These data were used to classify rainy days in subsets grouped by intensity classes: A: (0-10 mm/h), B: (10-20 mm/h), C: (20-30 mm/h), D: (30-40 mm/h) and E: (> 40 mm/h). For each subset, CWS/AWS pairs of daily precipitation totals were compared by applying the Wilcoxon Mann-Whitney test. Additionally, Tukey Honest Significant Differences Test was applied to compare the subsets anomalies, calculated as described above, but letting μ i and s i be the subset mean and standard deviation, respectively. This anomaly transformation is essential, given that the data subset was pre-selected by intensity and the comparison of their original mean would only show this selection tendency. The test determines the confidence intervals of the differences between the classes means and is intended to check if AWS errors increase with the precipitation intensity classes, considering CWS data as a standard. s i i 2. Data Smoothing Exploratory analysis also suggests that AWS sensors are more sensitive to instant fluctuation events than CWS, mainly for wind gusts. This fluctuation can mainly affect the daily extreme values, increasing the difference between CWS and AWS measures. This can also affect average values calculated from instantaneous measurements such as compensated mean for daily temperature. To mitigate this errors, the authors, based on the

4 works done by Powell (1992) and Lockhart (1995), suggest an alternative method for calculating the AWS wind gust and maximum/minimum temperatures, considering the response time of the mechanical anemometer and of the mercury thermometer as a parameter for comparison with the electronic sensors. For Campinas-SP, the software of the AWS datalogger was modified in order to simultaneously calculate regular and smoothed wind speed and temperature every 30 minutes, and to summarize daily data. Therefore, for smoothed data, instead of considering as extreme daily values the maximum peak collected by the sensors for wind and temperature, it was calculated the one-minute average of the data collected every second. Extreme values of these one-minute averages were used as daily extreme values. A two week dataset was collected to compare both datasets using the Wilcoxon Mann-Whitney test. Additionally, three different models to calculate mean daily temperature were generated. Assuming that the real mean temperature would be the average of every one-hour mean temperature, the 24h mean temperature (T m ) was calculated and compared to the average of maximum and minimum temperature (T xn ) and to the compensated mean temperature (T c ) given by the formulas: T max + T T min xn = and 2 T max + T min + T T 9 + c = 5 2T 21 Result and Discussion 1. Comparison CWS / AWS P-values for Wilcoxon Mann-Whitney test are presented in Table 2 for original and anomaly data. P-values for Kolmogorov-Smirnov test results are presented in Table 3. P-values analysis for both Wilcoxon Mann-Whitney and Kolmogorov-Smirnov tests show that that most part of CWS/AWS pairs of RH and WS data are statistically different. These differences occurred even on daily integrated data or on the instantaneous synoptic ones. This observation leads to the conclusion that the differences are due to the behavior of the sensors, once they are, at least for the instantaneous data, measuring the same phenomenon. The smoothing method is assumed to be a simulation of the CWS for wind speed and temperature and its analysis may confirm this conclusion. An interesting situation happened in this RH and WS analysis. Wilcox Mann-Whitney test does not reject the null hypothesis that both data samples don t differ in location (p-value < 0,05 or p-value < 0,01 for 5% or 1% significance level, respectively), but the Kolmogorov- Smirnov test does reject the null hypothesis that both data samples came from the same distribution. This happened to Paranavaí and Pato Branco RH15 and RH21, and for Londrina and Paranavaí, WS15 and WS21 synoptic data. This also happens for some anomaly comparisons.

5 Synoptic Anomaly Data Daily Anomaly Data Synoptic Data Daily Data Location/State Campinas/SP Londrina/PR Paranavaí/PR Pato Branco/PR Tmax Tmin < 2.2e Tm Tc Txn P RHm -- < 2.2e E E-13 WSmax E-13 < 2.2e T E E T T E-05 RH9 -- < 2.2e-16 < 2.2e-16 < 2.2e-16 RH E RH E WS E E WS WS < 2.2e Tmax Tmin < 2.2e Tm Tc Txn P E E E-08 RHm WSmax E E T E-13 T E-14 T E-14 RH E-14 RH E-11 RH E-10 WS WS WS Table 2. Wilcoxon Mann-Whitney p-value P(W<=w) for daily and synoptic data and for original and anomaly data (T, P, RH and WS are temperature, rainfall, relative humidity and wind speed, respectively. Index: max, min, m, c and xn are daily maximum, minimum, 24h mean, compensated mean and maximum/minimum average; and 9, 15 and 21 are synoptic local time 9h, 15h and 21h data, respectively. -- Means not available or not calculated).

6 Location/State Campinas/SP Londrina/PR Paranavaí/PR Pato Branco/PR Tmax Tmin < 2.2e Tm Tc Txn P RHm -- < 2.2e E E-10 WSmax -- < 2.2e-16 < 2.2e T E E T T E-04 RH9 -- < 2.2e-16 < 2.2e E-16 RH E RH21 -- < 2.2e-16 < 2.2e E-16 WS9 -- < 2.2e-16 < 2.2e WS15 -- < 2.2e-16 < 2.2e WS21 -- < 2.2e-16 < 2.2e Tmax Tmin < 2.2e Tm Tc Txn P -- < 2.2e-16 < 2.2e-16 < 2.2e-16 RHm WSmax -- < 2.2e E T E-15 T < 2.2e-16 T < 2.2e-16 RH E E-15 RH E-15 RH E-13 WS E E WS E WS E Table 3. Kolmogorov-Smirnov p-value P(D<=d) for daily and synoptic data and for original and anomaly data (T, P, RH and WS are temperature, rainfall, relative humidity and wind speed, respectively. Index: max, min, m, c and xn are daily maximum, minimum, 24h mean, compensated mean and maximum/minimum average; and 9, 15 and 21 are synoptic local time 9h, 15h and 21h data, respectively. -- Means not available or not calculated). Daily Data Synoptic Data Daily Anomaly Data Synoptic Anomaly Data For temperature and precipitation the general behavior is different. CWS/AWS pairs of original data do not differ statistically, except for Campinas minimum temperature, which suggests a malfunction or calibration of the AWS sensor. Again, the Wilcox Mann-Whitney test does not reject and the Kolmogorov-Smirnov test rejects the null hypothesis for P in Campinas. When analyzing T min and P anomaly, i.e., data without seasonality, all station pairs of probability distribution become statistically different for the Kolmogorov-Smirnov test. Statistical comparison of total precipitation data, whose segregation was based on precipitation intensity classes, is shown in Table 4. The p-values, resulted from Wilcox

7 Mann-Whitney test were calculated for AWS/CWS pairs of data. There are no statistical differences between the series. Class A B C D E Londrina/PR Panavaí/PR Table 4. Wilcoxon Mann-Whitney p-value P(W<=w) for total precipitation at Londirna and Paranavaí grouped by days with different precipitation intensities (Classes: A: (0-10 mm/h), B: (10-20 mm/h), C: (20-30 mm/h), D: ]30-40 mm/h], and E: > 40 mm/h), and confidence intervals of the total precipitation anomalies at Londrina (left) and Paranavaí (right). Figure 1 presents Tukey test results for the five precipitation anomaly classes for Londrina and Paranavaí. For Londrina, the class E (days grouped by intensity > 40 mm/h) statistically differs from the others and is greater because the differences in mean levels of classes are always positive. The same happens for class D (days grouped by intensity mm/h) for Paranavaí. Another common behavior of these two locations is that the confidence intervals are larger when comparison involves classes D or E than for the other ones. These results suggest that errors are actually greater if a high intensity precipitation occurs, but there isn t a progressive tendency while the intensity increases. Indeed, for Paranavaí the last class E does not differ from the others. 2. Data Smoothing Table 5 shows the p-values for regular/smoothed pairs of data. Only comparisons for WS max resulted in rejection of null hypothesis, i.e., there are statistical differences between them. This behavior agrees with results presented in Table 2 and 3, and suggests that the proposed method can be used to smooth the high frequency of the AWS data series for WS and, on the other hand, does not cause statistical differences on the other element data. The absence of CWS hourly data did not allow to state that this correction method permits decrease AWS errors, when compared to CWS. The Wilcoxon Mann-Whitney p-values for comparison of the three types of AWS mean temperature are given in Table 5. Based on both comparison tests that detected no differences for T c and T xn CWS/AWS pairs of data (Table 2 and 3) and on Table 6 results, where statistical differences were detected, one can conclude that the 24h mean temperature (T m ) regularly estimated by AWS, is not a good substitute for the T c or T xn commonly calculated on Brazilian meteorological centers. On the other hand, at least for these three PR locations, T c or T xn does not seem to represent well the actual mean temperature, which must be close to T m.

8 Londrina/PR 95% family-wise confidence level Paranavaí/PR 95% family-wise confidence level A-C A-D C-D A-B C-B D-B A-E C-E D-E B-E B-A B-C A-C B-E A-E C-E B-D A-D C-D E-D Differences in mean levels of classe Differences in mean levels of classe Figure 1. Results of Tukey mean test for anomalies of total precipitation at Londrina (left) and Paranavaí (right), classified by days with different precipitation intensities (A: 0-10 mm/h, B: mm/h, C: mm/h, D: mm/h, and E: > 40 mm/h). Data Type Test WSm WSmax Tmax Tmin Tm Daily W p-value D p-value Every W p-value < 2.2e min D p-value < 2.2e Table 5. Wilcoxon Mann-Whitney (W) and Kolmogorov-Smirnov (D) p-values for the regular/smoothed pairs of Campinas AWS data.

9 Camparison Londrina/PR Paranavaí/PR Pato Branco/PR Tm x Tc 3.96E Tm x Txn 2.81E E E-10 Table 6. Wilcoxon Mann-Whitney p-value P(W<=w) for AWS average temperatures at Londrina, Pranavaí and Pato Branco (T m = 24h mean temperature; T xn = average of maximum and minimum temperature; T c = compensated mean temperature). Acknowledgements Authors acknowledge Cepagri/Unicamp, Instituto Agronômico do Paraná and Instituto Tecnologico SIMEPAR for providing dataset and facilities to make possible the analyses. Bibliography Conover, W. J Practical nonparametric statistics. John. Wiley, New York. 584p. Easton, V. J and McColl, J. H Statistics Glossary. Lockhart, T Wind Characterization Standard. Memorandum to Attendees of the Wind Standards Workshop of 10/29-30/1992. Meteorological Standards Institute. 11 p. Powell, M. D Wind Measurement and Archival Under the Automated Surface Observing System (ASOS) User Concerns and Opportunity for Improvement. Submitted Bul. Am. Met. Soc.. OAA/AOML/Hurricane Res. Division. The R Project for Statistical Computing Wilks, D., S Statistical methods in the atmospheric Sciences. Academic Press. 467p.