INITIAL ANALYSIS OF AWS-OBSERVED TEMPERATURE Wang Yng, Lu Xaonng, Ren Zhhua, Natonal Meteorologcal Informaton Center, Bejng, Chna Tel.:+86 684755, E-mal:cdcsjk@cma.gov.cn Abstract From, n Chna meteorologcal observed records by the Automatc Weather Statons (AWS) have been used n scentfc research. Usng AWS-observed monthly and annual mean temperature n -4 and the dfference between AWS- and man-observed temperature are studed. Frst, there are the homogenety test of annual mean temperature and annual mean mnmum temperature n by Cramer s test, of monthly mean temperature and monthly mean mnmum temperature n 996-5 by maxmum lkelhood rato test. Secondly, n order to test drft of AWS nstrument, the trend of monthly mean temperature n -4 s analyzed. The ntal results show there s a slght nfluence on annual mean temperature and annual mean mnmum temperature by the change of nstrument; there are certan nfluences on monthly mean temperature and monthly mean mnmum temperature by the change of nstrument;.3 percent of AWS-observed temperature data have trend to change due to nstrument drft possbly, whch s very lttle. key words: AWS temperature homogenety test Introducton The study of clmatc change s based on the homogeneous long-perod tme seres reflectng the change of clmate. Any nhomogeneous tme seres may lead to an naccurate result of research. It has been revealed that n the meteorologcal tme seres from long-perod observaton, such elements as the relocaton of statons, the change of nstruments, the change of observaton tmes and the gradual change of staton envronment wll make the clmatc tme seres nhomogeneous [-4]. From the study of meteorologsts abroad, the causes brngng nhomogenety vary n the nfluence on dfferent clmate element data. The relocaton of statons, the man reason leadng to nhomogenety, has a sgnfcant nfluence on the nhomogenety of all weather elements except pressure. The change of observng nstrument s also one of the mportant causes. Snce 98s ASOS has been set throughout USA, and then consderable researches have been made on the nfluence of observaton system change on the data. Those researches show that there exsts a sgnfcant dfference between ASOS data and manual observaton data. For example, the daly mean maxmum temperature observed by MMTS n Amercan Meteorologcal Bureau s.6 [5] lower than that from tradtonal lqud-glass thermograph. The qualty control and qualty evaluaton of the AWS (automatc weather staton)-observed data are the great concern for the meteorologcal feld. There are many documents and papers from WMO and scentsts all over the world 6-. And n recent years studes on AWS-observed data have appeared n Chna too 3-4. Snce, AWS-observed data have become the formal record n Chna. Automatc observaton system s replacng manual observaton used n the past 5 years and great changes take place n observatonal nstrument. For example, platnum resstance thermometers are used n automatc observaton statons n Chna, replacng the mercury thermometer used n manual observaton. The dfference between auto and manual observaton s nevtable. In hstory any change of observaton nstruments wll brng the dfference of data, especally the change from manual observaton to automatc observaton wth greatly dfferent observatonal prncples for observaton nstrument. Then clmatc researchers are concernng such questons as the AWS-observed data qualty, the contnuty between AWS-observed and manual observed data, especally the homogenety of
temperature data. In ths paper we try to analyze the AWS-observed data. Snce the tme seres of automatc observaton are short n Chna and the replacement of automatc observaton s just startng, so wth longer observatonal tme seres and more automatc statons further analyss s stll needed. There are two parts n ths paper. Frst there s an analyss on the dfference between AWS-observed and manual-observed data, ncludng the homogenety test of the mean temperature around the year of and that of the monthly mean temperature n 996-5. Second, there s an analyss on the tendency of monthly mean temperature data n -4 to test whether there exsts the observatonal drft of nstruments n automatc statons.. Methods and Data. Method The offcal data of auto observaton n natonal base statons have been ncreasng by year. In, there are 46 automatc weather statons, 6 statons n 3, 45 statons n 4, and 69 statons n 5. It can be seen that the tme seres of AWS-observed data startng from are stll very short (namely there are only three-year data from to 4). It s not very satsfyng to have homogenety test of the tme seres from 97 to 4. So Cramer s test s appled here to test whether there s any nterrupton n the annual data tme seres around the year, and then to analyze the dfference between AWS-observed and manual observed data n annual tme seres. For the monthly tme seres, the maxmum lkelhood rato test s used because there are samples of monthly mean temperature durng the ten years of 996-5... Cramer s Test [5] for Annual Mean Temperature To test the hypothess of equal mean value, we should assume the equalty of ensemble varance. Frst make the test of equal varance, elmnate the statons falng n the test and then make Cramer s test [6]. Cramer s test s smlar to t-test, but t s not the comparson between two samples, but the comparson of mean value between sub tme seres and general tme seres to see whether there s a sgnfcant dfference. Make the orgnal hypothess Assume there s no sgnfcant dfference between the mean value of general and sub tme seres. Construct the t value to test the mean value of the general tme seres n ( n ) ~ t = * l n n ( + ~ () l ) n s the sample length of tme seres, n s the sub tme seres sample, and ~ l s gven from formula (). Formula () follows the t-dstrbuton wth the degree of freedom of n-. ~ l x x s = () x and x are the mean value of sub tme seres x and general tme seres x respectvely. s s the mean varance of the general tme seres. After the sgnfcance level s determned, from the degree of freedom γ= n-, refer to the t-dstrbuton. If t t, then the orgnal hypothess s dened, whch ndcates there s a sgnfcant dfference n the mean value of general tme seres. If there s no nterrupton n, then dvde the year of nto two parts to test. Before, there are manual observed data and after there are AWS-observed data. To test the dfference between auto and manual observed data s to test whether there s a sgnfcant dfference n the mean
value of sub and general tme seres n -4. Startng from 99, the mean value of 99-4 represents the mean value of the general tme seres, and that of -4 s the sub tme seres. Test whether there s a sgnfcant dfference n the mean value of sub and general tme seres n -4... Maxmum Lkelhood Rato Test [7] for the Homogenety Test of Monthly Mean Temperature To test whether the temperature of one staton s nhomogeneous, one method s to compare the temperature tme seres of ths staton wth the reference tme seres. The reference tme seres s taken by usng the mean value of monthly temperature from the neghbor statons whch have the smlar clmatc stuaton to the tested staton. Select 5 statons closest to the tested staton wthn two longtudes and lattudes and 3-meter alttude as the reference statons. If there are fewer than 3 qualfed statons, then no test s gven. Calculate the anomaly tme seres of mean temperature of all months to elmnate the annual perod of monthly temperature. The anomaly tme seres of mean temperature of all months s used as the orgnal tme seres of the test. The comparson between the tme seres of the tested staton and ts neghborng statons can be llustrated n the followng formula. k j= ρ ( x q = ( y y), =, n k ρ j j= j x ) j j (3) Among t y and x are the monthly temperature of the tested staton and the k neghborng statons respectvely, j whle ρ j s the correlaton coeffcent of the tested staton and the k neghborng statons. In formula (3) y and x are obtaned by averagng monthly temperature (ncludng all the months n record). So there are values of y and *k values of x, whch means there are values for each of the k neghborng statons. By weghng the neghborng statons of the tested staton wth correlaton coeffcent, we can make these neghborng statons wth relatve large correlaton wth the tested staton occupy more weghng whle makng reference tme seres. 3 To make lkelhood rato test, t s necessary to normalze the q tme seres. z 4 Test proceeds. ( q q) =, = n (4) s, q Suppose (4) follows normal dstrbuton, we can use zero hypothess H and hypothess H to check the abnormalty n the data (that s, y tme seres). H : z N(,),, n = H : z z N( µ,), =, a N( µ,), = a +, n Here N (g, h) s the normal dstrbuton of the mean value g and the standard devaton. If H s dened and H s approved, then t means there s a sgnfcant change n the y tme seres. The lkelhood rato test can be shown n the followng. L( H ) T = ln (5) L( H)
Here what among the parentheses s the rato of lkelhood functon, obtaned from the followng formula. a n exp ( z µ ) + ( z µ ) L( H ) = = a+ = (6) n L( H) exp z = Here the numerator and the denomnator have the drect rato wth standard probablty densty functon of z tme seres. If n (6) the rato s over the crtcal value, then n can be assumed that the mean value =(,a) s dfferent from the mean value of =(a+,n) n the y tme seres. From (5) and (6), the maxmum lkelhood estmator s the sample average of z and z. The denyng standard s shown n the followng. T = az + ( n a z > C (7) ) Here C s the crtcal value of the selected sgnfcance level. Determne the crtcal value C n the sgnfcance level.5. If one or more T>C, then there s probably a sgnfcant change n the y tme seres...3 Testng Method for the Changng Trend of monthly data () Testng Procedure for the Data Changng Trend Establsh the mean value tme seres of the reference staton. Fnd 5 statons closest to the tested staton wthn two longtudes and lattudes and 3-meter alttude, and get the mean value of the 5 statons n each month as the mean value of the reference staton. Establsh the contrast value tme seres of the mean value of the tested staton and the reference staton, used as the tested tme seres. 3 Determne the trend by usng lnear tendency estmaton and the trend test of cumulatve contrast value of rank statstcs. If both tests are passed, then t can be assumed that the tested staton may have the changng trend. 4 If the reference staton of the tested staton also has the changng trend, then check the tested staton agan after elmnatng the reference staton to see whether there s a changng trend. 5 Analyze the trend. If t exceeds the order of magntudes of clmate change, then t probably has the changng trend. () Lnear Tendency Estmaton and the Trend Test of Cumulatve Rank Statstcs Lnear Tendency Estmaton: Establsh the unvarate lnear regresson of the tested tme seres and the tme of all months. The regresson coeffcent (b) ndcates the trend. x represents the anomaly of each month, t represents the months correspondng to x. Then establsh the unvarate lnear regresson equaton. x ˆ = a + (=,, or 36) (8) bt It shows the relaton between x and tme t. Here regresson coeffcent b shows the trend of the tested tme seres. b> means x has a rsng tendency wth the ncrease of t, and b< means x has a declnng tendency wth the ncrease of tme t. At the same tme the value of b wll reflect the speed of the ncreasng or decreasng. The formulae to calculate regresson coeffcent b and the constant number a are omtted. In order to analyze the lnear correlaton of x and tme t, we calculate the correlaton coeffcent of tme t and x. The sgnfcance test of correlaton coeffcent s made to see whether there s a sgnfcant change n the
trend. If γ >γ, then x has a sgnfcant changng trend wth the change of tme t, otherwse there s no sgnfcant changng trend. If the sgnfcance test wth the correlaton coeffcent α=.5 has been passed, then t s regarded to have a trend. Trend Test of Cumulatve Contrast Value of Rank Statstcs x ndcates the contrast value of each month, and when =,y =x y =y - +x (=,,3,, or 36) y s the cumulatve contrast value of each month. For y, n the tme of, when =,,,n-, then when y j > y r = ( j=+,,n) (9) others Namely rank r s the number of samples wth the value y j,j=+,,n more than the value of y after the tme of. Calculate the statstcs. Z = n = 4 r n( n ) () Set the sgnfcant level. Suppose α=.5, then 4n +.5 =.96 [ ] Z () 9n( n ) If Z > Z.5, then t can be assumed that the trend s sgnfcant n the sgnfcance level α=.5.. Data There are obvous dfference n the observatonal nstruments between manual and automatc observaton of mean mnmum temperature and mean temperature, changng from mnmum thermometer and dry-bulb thermometer to platnum resstance thermometer. The two elements are typcal, chosen to test and analyze the dfference n temperature observaton. The mean value of 4 observatons a day s taken for both manual and automatc statons nstead of 4 tmes of observaton because only the nfluence of nstrument change s the purpose of study. Snce, 46 automatc statons have functoned, among whch the staton 5746 (Sanxa staton) s not nvolved n the test due to ts short tme seres, so the data from altogether 45 statons are used n the test of annual tme seres. In the homogeneous test of monthly men temperature and monthly mean mnmum temperature n 996-5, the monthly data last tll the June of 5 and data from July to December of 5 are mssng. In the test of monthly mean temperature changng tread for 36 months n -4, staton 57458 (Wufeng staton) and staton 58437 (Huangshan staton) are not ncluded n the test snce there s no reference staton, so altogether 43 statons are taken n ths test. All the data are based on the annual and monthly values n 97-5 from the database of Natonal Meteorologcal Informaton Center wth qualty control.
. Results of Dfferences Between Automatc and Manual Observed Temperature. Test of Annual Mean Mnmum Temperature Cramer s test s appled to test the annual mean mnmum temperature n 68 statons n 99-4. On the sgnfcance level of.5, altogether 73 statons have changes (.e. relocaton or the start of automatc staton) n 99s or around, among whch 7 statons (excludng the nfluence of staton relocaton) are ncluded n the 45 automatc statons snce. Further test shows that there s no nterrupton of neghborng statons around the 7 statons, whch means the sgnfcant dfference of these statons s not caused by the clmate change of mnmum temperature rsng. Then the sgnfcant dfference of annual mean mnmum temperature and long-perod tme seres n -4 n the 7 statons s caused by the change of nstruments n automatc statons, and t accounts for 5.6% of all the 45 automatc statons from. The result of the sgnfcance level.5 (whch s there are 5.6% of automatc statons startng from havng nterruptons n ) shows that the change of nstruments n automatc statons has a certan nfluence on the annual mean mnmum temperature. Table: automatc weather statons from wth sgnfcant dfference wth long tme seres n annual mean mnmum temperature Provnce Staton Code contrast value wth long tme seres Laonng 54339.76 Laonng 54337.5 Laonng 5434.89 Anhu 5836.73 Hube 57545.5 Hube 57583.5 Hube 5847.55. Test of Annual Mean Temperature The annual mean temperature s tested by the same method and procedure as testng the annual mean mnmum temperature. The result shows that on the sgnfcance level of.5, altogether 6 statons have changes (.e. relocaton or the start of automatc staton) n 99s or around. Excludng the nfluence from the staton relocaton and clmate change, there are 5 statons (wthout the nfluence of staton relocaton) n the 45 automatc statons startng from. Then the sgnfcant dfference of annual mean temperature and long-perod tme seres n -4 n the 5 statons s caused by the change of nstruments n automatc statons, and t accounts for.% of all the 45 automatc statons from. Table: automatc weather statons from wth sgnfcant dfference wth long tme seres n annual mean temperature Provnce Staton Code contrast value wth long tme seres Laonng 54339.63 Laonng 54337.48 Laonng 5434.64 Hube 57545.44 Jangsu 5859.6 As shown n Fgure, from the annual mean temperature and annual mean mnmum temperature data n 99-4 n the staton 54339, there s a sgnfcant dfference n annual mean temperature, annual mean mnmum temperature and general tme seres from.
Annual mean temperature Annual mean mnmum temperature Temperature(. ) 35 5 5 5 95 99 99 99 993 994 995 996 997 998 999 3 4 Year Fgure : annual mean temperature and annual mean mnmum temperature n staton 54339 n 99-4.3 Homogeneous Test of Monthly Mean Temperature The monthly mean temperature n 996-5 n 658 statons of Chna has been tested. Both the calculatons n reference [6] and n ths paper show that n the test of maxmum lkelhood rato the maxmum of T value tme seres often appears n the two ends of one record. Because the mean value s estmated to appear at the begnnng or the end of the tme seres based on relatvely fewer observatons, we calculate the stuaton when the maxmum n T-value tme seres exceeds the crtcal value and the two ends of tme seres are not taken. On the sgnfcance level of.5, there are 4 automatc statons whose nterruptng tme bascally matches the tme when automatc statons started functonng (.e. the tme occurs n the same year), and there are 3 statons, occupyng 45.5% of all 3 statons wth nterruptons, whose nterruptons are caused by staton relocaton. Namely 45.5% statons have nterruptons n tme seres due to the staton relocaton or the start of automatc statons. Further test confrms that 7 statons among the 4 automatc statons were relocated around the year of, so t can be assumed that the nterrupton s caused by relocaton of statons. The nterrupton of the rest 7 statons s caused by the use of automatc statons, and the 7 statons take up 4.% of all the 45 automatc statons tll 4. Table 3: nterruptons n automatc statons n the homogeneous test of monthly mean temperature Year Staton 585 ( relocaton) 5759() 57355( relocaton) 587( relocaton)585() 3 53959(4 relocaton) 5633(4) 5786(4 relocaton) 4 533 53663 54436 5466 5466 5475 5487(relocaton) 565 5667 56768(relocaton) 577 576 5745 57554 5766 57669 Note: the number n the brackets s the startng year of automatc statons; statons wth nterruptons n 4 are all the automatc statons startng from 4 The dfference between the nterruptng tme and the tme when automatc statons started s calculated (wth the unt of month). Table 4: the dfference between the nterruptng tme and the tme when automatc statons startng (months) Tme Dfferece -~-9-8~-6-5~-3 -~ 3~5 6~8 9~ Num. of Statons 3 4 5 4 Percentage % 5.9% % 7.6% 3.5% 9.4% 3.5%
From Table 4, t can be seen that 4.% statons have the dfference between the nterruptng tme and the tme when automatc statons started wthn 5 months, whch means nearly half statons have the nterruptng tme dfferent from the tme of automatc statons functonng n 5 months, and most nterruptons occur after the use of automatc statons. The above calculaton shows that the nterrupton of monthly temperature tme seres s assocated wth the observaton of automatc statons. The AWS-observed record has a certan nfluence on the homogenety of monthly mean temperature tme seres. In Fgure and 3, (a), (b), (c), (d) are respectvely the monthly mean temperature anomaly tme seres, q seres, z seres, and t seres of the latest 5-year data (from Jan. of to June of 5) n the tested seres of staton 5633 and staton 577. From the fgures, t can be seen that the tested mean value changes bascally conform to the tme of automatc statons startng functonng. For example, the staton 5633 has used the formal record from automatc observaton snce 4, and n the May of 3, there occurs a sgnfcant change n data (exceedng the crtcal value of 9.3 on the sgnfcance level of.5, and takng the maxmum). The staton 577 started automatc staton observaton n 4, and the nterrupton of tme seres appeared n July of 4..5.5.5.5.5 -.5 -.5 - -.5 - - -.5 3 4 5 3 4 5 (a) (b).5.5 8 -.5 6-4 -.5 - -.5 3 4 5 (c ) (d) Fgure : the monthly mean temperature anomaly tme seres, q seres, z seres, and t seres of the latest 5-year data of the staton 5633
3.5.5.5 -.5 - -.5 -.5.5 -.5 - -.5 3 4 5 3 4 5 (a) (b) 5 8 4 6 3 - - 4 8 6 4-3 (c) (d) Fgure 3: the monthly mean temperature anomaly tme seres, q seres, z seres, and t seres of the latest 5-year data of the staton 577.4 Homogenous Test of Monthly Mean Mnmum Temperature The test of monthly mean mnmum temperature s made by the same method mentoned above. It turns out that statons have nterruptng pont. Fnd reasons for each nterruptng pont. 64 statons, occupyng 3.5% of all statons wth nterruptons, have found causes, among whch 4 statons, takng up 9.5% of all statons, have the record of staton relocaton around the nterruptng tme. 3 statons started the functon of automatc statons wthn one year around the nterruptng month. And 3 statons among the 3 statons have the record of staton relocaton, so the rest statons account for 4.9% of all the 45 automatc statons tll 4. Namely t has been tested out that 4.9% of automatc statons have nterruptng pont n monthly mean mnmum temperature and nhomogenety appears n the tme seres. Table 5: nterruptons of automatc statons n the homogeneous test of monthly mean mnmum temperature Year Staton 585( relocaton) 5836() 3 53959(4 relocaton) 54579(4) 5633(4)5657(4)5736(4) 577(4) 576(4) 5836 () 4 533 53663 53738 54436 5475 565 56374 56444 56768(relocaton) 56954 5766 5768 57669 (note: the number n the brackets s the year when automatc statons started, and relocaton means the relocaton
around the year of ) 3. Testng Result of Monthly Mean Temperature Changng Trend 43 statons whch have been automatc weather statons snce have been tested. The result s shown n Table 6. Table 6: testng result of monthly mean temperature changng trend n -4( ) Staton Trend s contrast value s contrast value Reason 5859. -.3 -. 57476(Jngzhou) -.3.5. relocaton n 4 58436(Nngguo) -.3 -.7 -.9 5836. -. -.4 5434 -..3. 5746... 5759 -. -.7.9 583(Hefe) -.3 -.3 -.7 relocaton n 4 583... 5838 -...7 As shown n Table 4, whle testng the values of 36 months n successve 3 years of -4, there are statons have the changng trend. Among the statons, the greatest trend (absolute value) s.3 /month, equvalent to about.3 /year. There are altogether 3 statons, whch are staton 57476 (Jngzhou, Hube provnce), staton 58436 (Nngguo, Anhu provnce) and staton 583 (Hefe, Anhu provnce). However, from record, t s found that the statons of Jngzhou and Hefe were relocated n 4 (respectvely 4 meters and meters away from the orgnal staton), and the rest statons have no great change n staton locaton and envronment. From Fgure 3, t also can be seen that there s a sgnfcant ncontnuty n the annual tme seres of staton Jngzhou and staton Hefe n the year of 4. The changng trend of.3 /year exceeds the order of magntude of clmate changng trend, so only staton Nngguo of Anhu provnce n the 3 statons wth greatest changng trend has the changng trend of tme seres whch s caused not by the change of clmate or the staton envronment, but probably by the drft of nstrument. In the test, only staton s possbly nfluenced by nstrument drft, occupyng.3% of 43 tested statons, whch s very low. Snce the tme seres are short, further test s stll requred. From Fgure 4, t can be seen that the 3 statons have a sgnfcant dfference wth the mean temperature of each month of the reference staton n the trend of contrast values. Hefe.6.4..8.6.4. -. -.4 y = -.7x +.968 -.6 3 4 -.4 -. - -.8 -.6 -.4 -...4.6.8 Jngzhou y = -.333x +.346
.5 Nngguo -.5 - -.5 y = -.47x -.354-3 4 Fgure 4: the contrast value of the three statons and the reference staton n monthly mean temperature n -4 4. Concluson ) From the above analyss, after statstcal test, t s necessary to further analyze the reasons for nterruptons, to verfy the real stuaton of statons to determne the nterruptons caused by staton relocaton or the change of automatc staton. It s very essental for the analyss on the real reason of data change and on the nfluence of automatc staton observaton on temperature data. ) The change of automatc staton nstruments has a certan nfluence on the homogenety of annual mean temperature and annual mean mnmum temperature. On the sgnfcance level of.5, for annual mean temperature, 5 statons have nterruptons wth obvous causes, occupyng.% of all the 45 automatc statons. For the mean mnmum temperature, the test shows that on the sgnfcance level of.5, 5.6% of all the automatc statons startng from have nterruptons n, whch means the change of automatc staton nstruments has a certan nfluence on the annual mean mnmum temperature. 3) The change of automatc staton nstruments has a certan nfluence on the homogenety of monthly mean temperature and monthly mean mnmum temperature. Due to the automatc staton, respectvely 4.% and 4.9% of all tested automatc statons (45) have nterruptng ponts n ther tme seres. 4).3% of AWS-observed data have the trend n tme seres possbly caused by nstrument drft, whch s very low. In the tested 36 months, there s no obvous drft n the nstrument of the 43 automatc statons startng from. 5) The analyss and the test are based on data whch are stll very short and cover not many statons, especally the frst group of automatc weather statons from have good techncal support, so the tested result n ths paper may not represent the general stuaton n Chna. Wth the longer observatonal tme seres and more automatc weather statons, further analyses wll be made. In our country, wth the change of ground temperature observaton system, the nfluence on data should be our great concern and study focus. Reference. Potter k W. Illustraton of a new test for detectng a shft n mean precptaton seres. Mon.Wea Rev., 98,9:4~45.. Easterlng D R Peterson T C.Technques for Detectlng and AdJustng for Artfcal Dscontlnultles n Clmatologcal Tme Seres: A Revew.In: Ffth Internatonal Meetng on Statstcal Clmatology, 99.8~3. 3. Easterlng D R, Peterson T C. A new method for detectng undocumented dscontnutes n clmatologcal tme sceres.int.j.clmatol,995,5:369~377.
4. Herzog J,Muller W G.Homogenlzaton of Varous Clmatologcal Parameters n the GermanWeather servce.in:proceedngs of the Frst Semnar for Homogenlzatlon of Surface Clmatologlcal Data, 996.~. 5. W. M. Wendland and W. Armstrong, Comparson of maxmum-mnmum resstance and lqud-n-glass thermometer records, J. Atmos. Ocean. Tech., 993, : 33-37 6. WMO,CBS/OPAG-IOS/4,Gudelnes on Qualty Control Procedures for Data from Automatc Weather Statons 7. WMO-TD No.833 Report and revew about data processng and qualty control procedures nvolved n the converson of manually operated statons to automatcally operated staton,997 8. WMO-TD No.85, Gudelnes on Clmate Observaton Networks and Systems,3 9. WMO-TD No.86, Gudelnes on Clmate metadata and homogenzaton,3. Noaa: Automated Surface Observng System (ASOS) User s Gude, From: http://www.nws.noaa.gov/asos/aum-toc.pdf 998. WMO-No. 8,, Gude to Meteorologcal Instruments and Methods of Observaton, 996. Ernest Rudel, Clmate Data Contnuty Usng Automatc Weather Statons? Proceedngs of ICEAWS 3, Torremolnos, Span 3. Hu Yufeng., Dfferences between Data of automatc and manual observaton. Journal of Appled Meteorologcal Scence, 4, Vol.5,No.6. 79-76 4. Wang Yng, Lu Xaonng. Comparatve analyss of AWS- and man- observed temperatures. Journal of Appled Meteorologcal Scence,,Vol.3,No.6,74-748 5. WEI Fengyng. Dagnotc and predctve technology n modern clmatologc statstcs. Bejng: Chna Meteorologcal Press, 999, 6-66 6. Tu Qpu, wang Junde, Dng Yuguo, Sh Humn. Meteorologcal appled probablty statstcs. Bejng: Chna Meteorologcal Press, 984, 5-64 7. Matthew J.Menne,Claude E.Duchon, Qualty Assurance of Monthly Temperature Observatons at the Natonal Clmatc Data Center,From http://lwf.ncdc.noaa.gov/oa/hofn/coop/ams-3th-appled-clmate.pdf 8. Huang Jayou. Analyss of Clmate change trend and wave,meteorologcal Monthly, 995,(7),54-57 9. Chna Meteorologcal Admnstraton, Regulaton for surface meteorologcal observaton. Bejng: Chna Meteorologcal Press, 3, 35-46