5.4 Measurement Sampling Rates for Daily Maximum and Minimum Temperatures

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1 5.4 Measurement Sampling Rates fr Daily Maximum and Minimum Temperatures X. Lin, K. G. Hubbard, and C. B. Baker University f Nebraska, Lincln, Nebraska 2 Natinal Climatic Data Center 1 1. INTRODUCTION The maximum and minimum temperatures are tw f the mst imprtant climate variables. With the establishment f new surface climate netwrks r the upgrade f existing netwrks, the selectin f sampling rates relevant t the determinatin f maximum and minimum temperature is critical. Althugh the time cnstant f the sensr r the sensr and shield tgether will determine the shape f the cntinuus temperature curve, it is hw this curve is sampled that determines differences in tw series f discrete measurements. Thus, the maximum and minimum f the ne-minute samples may differ frm the maximum and minimum f ne-secnd samples. This paper will present the effects f sampling rate n the bservatins f maximum and minimum air temperatures in several surface netwrks including the U. S. Climate Reference Netwrks (USCRN), the Cperative Observing Prgram (COOP), and the autmated weather statin netwrks (AW S). The issue f sampling rate fr air temperature measurements usually is related t the time cnstant f temperature sensr and temperature radiatin shield. We ften fund this issue was discussed in the atmspheric turbulence study (Kaimal and Finnigan, 1994) but hardly fund in the surface climate bservatins. The reasn fr this might be because that the past air temperature measurements in surface climate netwrks were mnitred by an analg liquid-in-glass (LIG) thermmeter and its bservatins were digitized by bservers in a virtually instantaneus way at specific bservatin times. Starting frm1980s, numbers f electrical temperature sensrs were started t use in current climate netwrks, fr examples, an MMTS thermistr used in the COOP netwrks, a platinum resistance Crrespnding authr address: Kenneth G. Hubbard, High Plains Reginal Climate Center (HPRCC), University f Nebraska-Lincln, Lincln, NE ; khubbard1@unl.edu thermmeter (PRT) used in the ASOS netwrk, and a PRT used in the USCRN netwrk, but the sampling rates f air temperature measurements t btain the daily maximum and daily minimum temperature in abve netwrks are quite different. In the COOP netwrk, the MMTS readut takes readings apprximately each 2 secnds fr daily maximum and minimum temperatures. The ASOS 1088 hygrthermmeter takes five-minute running average f ne-minute average based n apprximately ten-secnd sampling rate; the USCRN takes bservatins each five-secnd and takes five-minute discrete average t btain daily maximum and minimum temperatures. Therefre, Our intend in this study is t investigate the temperature differences r biases f daily maximum and minimum air temperatures caused by different sampling rates and different averaging algrithms. Assuming that a digital thermmeter fr btaining temperature readings is t digitize a given cntinuus air temperature curve, thus, this digitizatin implies its replacement by discrete data pints, equally spaced alng the abscissa. Based n the Taylr series expansin methd fr any cntinuus curve, the errrs in fitting that given curve by digital data can be expressed in terms f time interval and the secnd derivative f air temperature as fllw (Bath, 1974), where T, t, T air, and t are the digitizing errr, time interval f sampling, air temperature, and time. Therefre, the higher sampling rates and smaller change rates f air temperature changes, the smaller the digitizing errrs and vice versa. Cnsidering the requirement f sampling climate signal withut lss f infrmatin f air temperature signals, the Nyquist frequency is usually referred t be a reference fr determining a sampling rate in mst meterlgical and climatlgical applicatins. Based n the recmmendatins prvided by the Wrld Meterlgical Organizatin (WMO) (WMO, 1996), fr sampling extremes f meterlgical variables, the samples shuld be taken at rates at least fur times as fast as the time cnstant f

temperature system is an aspirated system. In this study, we examined the daily maximum and minimum temperature differences due t different averaging algrithms f daily maximum and minimum temperature.

2 temperature sensrs. Althugh the time cnstant f any air temperature sensr has t be specified under a given wind speed cnditin, the reference sampling rate in ur study used was taken in each tw secnds because the USCRN temperature system is an aspirated system. In this study, we examined the daily maximum and minimum temperature differences due t different averaging algrithms f daily maximum and minimum temperature. 3. PRELIMINARY RESULTS AND DISCUSSION 2. EXPERIMENTAL MEASUREMENTS The experimental measurements in ur test bed were cnducted frm July 2004 t Octber 2004 at the University f Nebraska s Hrticulture Experiment Site (40 83' N, 96 67' W, elevatin 383m). The grund surface height was maintained at abut 8 cm by mwing. In this study, tw USCRN PRT temperature sensrs were installed inside the USCRN radiatin shield and the Cttn Regin Shelter (CRS), respectively. An HMP45C temperature and relative humidity sensr was hused in the Gill radiatin shield which cnfiguratin is cmmnly used in the autmated weather statin netwrk. Therefre, three temperature systems included in this study are, USCRN sensr plus USCRN shield, USCRN sensr plus CRS, and HMP45C plus Gill shield. All temperature measurements were taken by using a CR23X data lgger (Campbell Scientific. Inc. ) and were sampled each tw secnds. There were six types f daily maximum air temperature (Tmax) and daily minimum air temperature (Tmin) bserved in this study in terms f different averaging algrithms (Table 1). The descriptins f different averaging daily Tmax and Tmin were listed in Table 1. All temperature sensrs were newly calibrated immediately befre the measurement perid began. In ur study, the Tmax and Tmin difference r bias is defined as the Tmax r Tmin difference relative t the daily Tmax r Tmin btained frm bservatins in a tw-secnd sampling rate. Currently data were available fr 90 days during ur bservatins. Since nly minute-data were cntinuusly cllected in the CR23X data lgger the calculatin f the secnd derivatives f ambient temperature was derived frm six minute bservatins which was centered by the time f daily Tmax r Tmin ccurrence in terms f tw-secnd bservatins. Fig. 1. Daily Tmax differences/biases in the USCRN temperature system due t five different averaging algrithms (CRN1mR2s, CRN5mR2s, CRN1mAVE, CRN5mR1m, and CRN5mAVE). Fig. 2. Nrmalized frequencies f Tmax differences in the USCRN. Figure 1 shws a time series f daily Tmax differences in the USCRN temperature system fr all bservatins. All Tmax differences were negative and it indicates that a cling bias existed in all f Tmax averaging algrithms. The CRN1mR2s and CRN1mAVE were relatively clse t the reference Tmax (CRN2s) but all five-minute averaging methds had a larger cling bias especially fr the

CRN5mAVE, which algrithm is currently used in the fficial USCRN peratins. The nrmalized frequency distributins f each Tmax difference were shwn in Fig. 2.

On the 90-day averages, the average cling biases were -0.21, - 0.28, -0.47, -0.48, and -0.62 C, respectively fr the CRN1mR2s, CRN1mAVE, CRN5mR1m, CRN5mR2s, and CRN5mAVE.

3 CRN5mAVE, which algrithm is currently used in the fficial USCRN peratins. The nrmalized frequency distributins f each Tmax difference were shwn in Fig. 2. The results indicates that the five-minute discrete average had the largest cling bias and the ne-minute running average f each tw-secnd sample was the smallest cling bias. On the 90-day averages, the average cling biases were -0.21, , -0.47, -0.48, and C, respectively fr the CRN1mR2s, CRN1mAVE, CRN5mR1m, CRN5mR2s, and CRN5mAVE. Apparently, any five-minute averaging algrithm might nt be acceptable fr a high quality surface climate netwrk because the result frm ur 90-day experiments is equivalent t a seasnal average f Tmax bservatins and the cling bias ver ne degree in centigrade was nt uncmmn fr the Tmax (Fig. 1). Nte that in the fficial ASOS peratin system, a five-minute running average algrithm (equivalent t the CRN5R1m in this study) is used fr btaining the Tmax. Hwever, this Tmax algrithm still culd intrduce abut half-degree C cling bias n three-mnth average. shuld be nted that the result in this paper is preliminary and mre explicit analysis will be cnducted when the number f bservatin day increases. Fr the daily Tmin differences, the warming bias/difference was much mre than the cling bias in a time series f daily Tmin difference (Fig. 4). Hwever, the magnitudes f daily Tmin differences were relatively smaller than the Tmax differences. Fig. 4. As fr Fig.1, but fr daily Tmin differences/biases in the USCRN temperature system. Fig. 3. Variatins f daily Tmax differences (between the CRN5mAVE and CRN2s) with changes f the secnd derivative f ambient temperature in the USCRN temperature system. With limited bservatin days, Figure 3 shws the variatins f daily Tmax differences. In general, the Tmax cling bias increased with increases f the secnd derivative f ambient temperature. Nte that the X axis in Fig. 3 refers t a summatin f abslute secnd-derivative value f six cnsecutive minutes. Our intend is t find a way t reveal the relatin between the Tmax cling bias and the change rates f ambient temperature change. It Fig. 5. Nrmalized frequencies f Tmin differences in the USCRN.

4 It is clear that ne-minute averaging algrithms were better than five-minute averaging algrithms (Figs. 4 and 5). On average, all Tmin differences were psitive which suggests that current Tmin algrithms fr the USCRN and ASOS might be encuntered a warming bias fr daily Tmin recrds. Therefre, the variatins f daily Tmin differences increased with the increases f the secnd derivative f ambient temperature (Fig. 6). Fig. 6. Variatins f daily Tmin differences (between the CRN5mAVE and CRN2s) with changes f the secnd derivative f ambient temperature in the USCRN temperature system. Due t the space limitatins, the preliminary results fr the USCRN PRT sensr in the CRS are nt shwn, but are similar t the USCRN system fr bth Tmax and Tmin. Hwever, tw time series f daily Tmax and Tmin fr the HMP45C sensr hused in the Gill shield were shwn in Figure 7. It is bvius that the Tmax and Tmin differences fr the HMP45C system were much smaller than the USCRN system and the system equipped with USCRN PRT sensr hused in the CRS. Therefre, the time cnstant f temperature sensr plays a mre imprtant rle t respnse the Tmax and Tmin. The larger the time cnstant f temperature sensrs, the lnger the time integratin/average is inherently embedded. In ther wrds, the high frequency temperature variatins are insensitive t the temperature sensr having a larger time cnstant. Therefre, fr surface temperature hmgeneity adjustment frm earlier CRS with LIG thermmeters t the current USCRN PRT sensrs, it is necessary t evaluate the effects f time cnstant f temperature sensrs used in histrical climate data sets. Fig. 7. Daily Tmax (tp panel) and Tmin (bttm panel) differences/biases in the HMP45 temperature system due t five different averaging algrithms (HMP1mR2s, HMP5mR2s, HMP1mAVE, HMP5mR1m, and HMP5mAVE). 4. SUMMARY AND CONCLUSIONS During the CRS era, the fficial LIG thermmeter is abut 60 secnds (refers t 3 m s -1 ventilatin rate, 63% respnse) with an instantaneus reading fr Tmax and Tmin but during the MMTS era, the time cnstant f MMTS sensr is apprximately 20 secnds with a tw-secnd sampling rate. Up t the date, the USCRN sensr has a similar time cnstant t the MMTS sensr but with a five-minute discrete average fr calculating Tmax and Tmin. Withut any dubt, these changes f time cnstant f temperature sensrs, sampling rates, daily Tmax and Tmin averaging algrithms will intrduce the uncertainties in the daily Tmax and Tmin climate data. In this study, ver 0.5 C average difference was detected fr the Tmax and abut 0.15 C average difference fr the Tmin in the USCRN temperature system. Therefre, the MMTS Tmax/Tmin might be higher/lwer than the CRS by

5 the LIG if nly cnsidering sampling issues althugh the statistical results shws ttally different (Quayle et al., 1991). The statistical results in Quayle s wrk includes all uncertainties between the CRS and the MMTS such as the slar radiatin and wind speed effects, and embedded electrical and sensr errrs (Hubbard and Lin 2002, Hubbard et al., 2004, Lin and Hubbard 2004). In the HMP45C sensr hused in the Gill shield, the crrespnding differences were less than 0.2 C because f larger time cnstant fr the HMP45C sensr. The Tmax and Tmin differences caused by different sampling rates and different averaging algrithms increased with increases f the secnd derivatives f ambient temperatures and they were strngly assciated with the time cnstant f each temperature system. REFERENCES Bath, M, 1974: Sepctral Analysis in Gephysics. Oxfrd, New Yrk. Kaimal, J. C., and J. J. Finnigan, 1994: Atmspheric Bundary Layer Flws, 289pp., Oxfrd Univ. Press, New Yrk.. Hubbard, K. G. and X. Lin, 2002: Realtime data filtering mdels fr air temperature measurements. Gephysical Research Letters. 29(10): 67-1:67-4. Hubbard, K.G., X. Lin, C.B. Baker, and B. Sun, 2004: Air temperature cmparisn between the MMTS and the USCRN temperature systems. J. Atms and Oceanic Technlgy, 21(10): Quayle R. G., D. R. Easterling, T. R. Karl, and P. Y. Hughes,1991: Effects f recent thermmeter changes in the Cperative statin netwrk. Bull. Amer. Meter. Sc., 72: Lin, X. and K. G. Hubbard, 2004: Sensr and electrnic biases/errrs in air temperature measurements in cmmn weather statin netwrks. J. Atms. Oceanic Tech., 7: W MO, 1996: Guide t meterlgical instruments and methds f bservatin. Sixth Editin. W MO-N.8, Geneva, Switzerland. Wrld Meterlgical Organizatin. Table 1. Different daily Tmax and Tmin averaging algrithms based n the tw-secnd sampling rate in the USCRN system, CRS system, and Gill system with an HMP45C sensr.

NAME TEMPERATURE AND HUMIDITY. I. Introduction

NAME TEMPERATURE AND HUMIDITY. I. Introduction NAME TEMPERATURE AND HUMIDITY I. Intrductin Temperature is the single mst imprtant factr in determining atmspheric cnditins because it greatly influences: 1. The amunt f water vapr in the air 2. The pssibility