Surface heat ¯ux estimation with wind-pro®ler/RASS and radiosonde
observations
Jennifer M. Jacobs
a,*, Richard L. Coulter
b, Wilfried Brutsaert
c aDepartment of Civil Engineering, The University of Florida, Gainesville, FL 32611, USA
b
Environmental Research Division, Argonne National Laboratory, Argonne IL 60439, USA
c
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
Received 20 August 1998; received in revised form 28 June 1999; accepted 7 July 1999
Abstract
A bulk ABL similarity approach was used to make regional estimates of the sensible heat ¯ux by combining surface temperature measurements with mixed layer temperature and wind speed pro®les. The mixed layer pro®les were measured by a 915 MHz Pro®ler/ Radio Acoustic Sounding System and by radiosondes in north-central Oklahoma at the ARM Southern Great Plains CART Central Facility. A comparison of calculated sensible heat ¯ux values with regional mean values measured at two ground stations showed good agreement withr0:88 andr0:76 for the 915 MHz pro®ler and the radiosonde data sets, respectively. Estimates of friction velocityu by means of radiosonde wind pro®les gave good results when compared to values measured by an surface eddy cor-relation system with a corcor-relation coecientr0:77. However,uvalues obtained from wind pro®les measured by the 915 MHz pro®ler were underestimated, because these velocity measurements were systematically too small. The results also show that the 915 MHz Pro®ler/Radio Acoustic Sounding System is capable of providing the needed temperature measurements to make rea-sonable regional estimates of sensible heat ¯ux. Ó 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
Surface ¯uxes can be estimated by using atmospheric boundary layer (ABL) similarity. Monin-Obukhov similarity (MOS) relates surface ¯uxes to surface vari-ables and varivari-ables in the atmospheric surface layer (ASL). Bulk ABL similarity (BAS) relates surface ¯uxes to surface variables and mixed layer atmospheric vari-ables. An advantage of the BAS approach is that the horizontal scale of the surface ¯ux estimates can be up to two orders of magnitude larger than that for ASL similarity. A disadvantage of BAS is that the mixed layer structure is depends on more variables than that of the ASL. These additional variables, such as possibly the Coriolis eect and entrainment, in¯uence transport processes in the outer region of the ABL. The various forms of the bulk similarity functions for dierent at-mospheric conditions were reviewed elsewhere (e.g., [3,5]). Previous research has mostly investigated the dependency of the BAS functions on the dimensionless variablelhi=Lwherehiis the inversion height andL
the Obukhov length ([9,5]). The research of Sugita and
Brutsaert [18] over hilly prairie at FIFE and Brutsaert and Parlange [4] over ¯at forest at HAPEX-Mobilhy showed that dierent regions with similar roughness characteristics have nearly identical optimal bulk simi-larity functions.
The bulk similarity approach for surface momentum ¯ux calculations requires knowledge of the mixed layer average wind speed and the atmospheric stability. Sen-sible heat ¯ux calculations normally require knowledge of the average potential temperature in the mixed layer, the surface potential temperature, the surface ¯ux of momentum, and the atmospheric stability. Temperature dierences between the surface temperature and the mixed layer temperature control the rate at which heat is exchanged between the surface and the atmosphere. As these temperature dierences are on the order of 5±10C, relatively small temperature measurement dierences may result in signi®cantly dierent estimates of sensible heat ¯ux.
In past applications of BAS, radiosondes have been the primary source for necessary measurements of wind speed, temperature, and humidity in the ABL. Such measurements can be assumed to re¯ect the surface conditions upwind from the radiosonde release point. However, the brief measurement period as it traverses *
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the ABL, may limit the radiosonde's ability to capture representative averages. The recently developed active radar pro®lers with radio acoustic sounding systems (RASS) do not suer from this temporal sampling problem. Indeed, these ground-based remote sensing instruments are able to monitor the atmospheric boun-dary layer continuously, and they can provide wind speed and temperature measurements for most reason-able time periods of interest.
Previously, several studies have compared horizontal wind and temperature estimates between pro®ler/RASS and radiosonde data. For example, May et al. [12] found the mean dierence between RASS virtual temperature measurements and radiosonde virtual temperature measurements was only a few tenths of a degree. Weber and Wuertz [21] compared 17,799 measurements of theu (east) and the v (north) horizontal wind components. The u-component had an average dierence (radio-sonde minus pro®ler) of 0.49 m sÿ1 and a regression
relationship of the formurs 0:810:97uprofiler; for the
v- component these results were 0.82 m sÿ1 and
vrs 0:770:97vprofiler. They attributed the bias to
in-strument error and sampling dierences. Martner et al. [11] concluded that for 3361 pairs of wind velocity measurements, the average dierence (radiosonde minus pro®ler) was 0.99 and 0.21 m sÿ1for theu- andv
-com-ponents, respectively. For the 745 temperature mea-surements, the average dierence wasÿ0.07C. Coulter and Lesht [7] found that the average wind speed dier-ence (radiosonde minus pro®ler) was 0.69 m sÿ1and the
temperature dierence was 0.04 K at the SGP CART Central Facility. Angevine et al. [2] compared temper-ature and wind speed measurements from a 915 MHz pro®ler/RASS system to those measured by cup and sonic anemometers and a thermometer/hygrometer mounted on a tower at 396 m above ground level. They also found the pro®ler on average underestimated the wind speeds. However, after removing data points which they interpretted to be aected by ground clutter, it was found that the average wind speed dierence (pro®ler minus anemometer) was 0.40 m sÿ1and that the RASS
temperature was greater on average than the thermom-eter and the sonic anemomthermom-eter by 1.12 and 0.98 K, re-spectively.
In summary, from these previous comparisons it ap-pears that wind speeds measured by radar pro®lers tend to be systematically smaller than those obtained by ra-diosondes by about 0.5±1.0 m sÿ1. While it could be
argued in the case of wind speed that a systematic bias of 0.5±1.0 m sÿ1 is relatively small, nothing is known
about its importance in the context of similarity for-mulations. In the case of RASS and radiosonde tem-peratures the dierences are very small in most studies and there is no clear bias one way or the other. For temperature, even such small dierences may be im-portant as sensible heat ¯ux estimates can be sensitive to
relatively small variations in temperature measurements. Clearly, a direct comparison between surface ¯uxes is important to determine what, if any, impact the mea-surement dierences for the two instruments have on surface ¯ux estimates. The purpose of the present paper is to examine the suitability of the pro®ler/RASS in-strument for estimating surface ¯uxes routinely by means of the same methods previously applied with episodic radiosonde data. The data used were obtained in an experiment that was conducted at an uncalibrated site with the identical site and instrumentation already analyzed in Coulter and Lesht [7]. The study region consisted of harvested wheat ®elds (stubble) over level terrain surrounding the Atmospheric Radiation Mea-surement (ARM) Cloud and Radiation Testbed (CART) site in north-central Oklahoma. The 0.15 m surface roughness at this site [10] was almost an order of magnitude smaller than the 1.05 m roughness at the FIFE site [17] or 1.2 m roughness at HAPEX-Mobilhy [13], two sites where BAS was calibrated.
2. Experimental data
The data for this research were acquired in June and July of 1995 at the Central Facility (CF) of the US Southern Great Plains CART ®eld research site which is operated by the US Department of Energy within its ARM Program. A detailed description of the ARM Program and the CART sites is provided by Stokes and Schwartz [16]. The CF is a 160-acre complex located in north-central Oklahoma between Lamont and Billings, Oklahoma (7300W, 36370N). A map of the CF is shown in Fig. 1. The topography of the area is ¯at with only small changes in relief; small tree stands, at dis-tances on the order of 1.5 km, dot the landscape. During the experiment, stubble ®elds covered 80% of the region and pasture and range land the remainder.
2.1. Atmospheric boundary layer pro®les
A 915 MHz Doppler radar wind pro®ler with RASS (LAPTM-3000, Radian Corporation, Inc.1
) was operat-ed at the CF's southern end. The radar wind pro®ler provided measurements of wind velocity as a function of height up to 6 km AGL by detecting scattered radio waves from temperature and moisture ¯uctuations moving with the mean wind [8,6]. The total wind vector was estimated by determining the Doppler shift in the transmitted frequency in the vertical direction and in at least two tilted planes. The system had beams along ®ve directions: 1 vertical and 4 each tilted 14 from vertical
1
in the North, East, South, and West vertical planes. The pro®ler operated in a single direction for approximately 30±45 s (dwell time) and then rotated to the next di-rection. The pro®ler cycled through the ®ve beams at low power (low-level observations) and then again through the ®ve beams at a higher power/longer pulse length (high-level observations) setting. A 50 min aver-age wind speed for each power was produced every hour by averaging values from 11 or 12 cycles. The center of the lowest range gate was 138 and 320 m for the low and high power settings, respectively. The present study used the low power 915 pro®ler wind speed observations.
In its RASS mode, the microwave radar pro®ler vertical beam was concurrently operated with con-tinously, randomly varying in frequency, sound waves and provided virtual temperature measurements up to 1.5 km [8]. Virtual temperature is de®ned by
Tv 10:61qT; 1
whereqqv=qis the speci®c humidity,qthe density of
air, and qv the density of water vapor. The RASS
es-sentially measures the Doppler shift of the acoustic en-ergy as it propagates vertically in the atmosphere, providing a scattering source for microwave energy. The virtual temperature is determined from the measured speed of soundCaand the measured vertical wind speed
wby
Tv
Caÿw 2
401:92 ÿ237:16: 2
A 10 min average virtual temperature was obtained hourly for range gates separated by 105 m which
re-sulted in a vertical resolution of 105 m. Maximum measurement heights ranged from 500 to 800 m de-pending on the atmospheric turbulence conditions and wind speed during this study. It should be noted that during the experiment, the 915 pro®ler/RASS was peri-odically only operated in the vertical mode and the correction for the vertical velocity was not included. In the vertical mode, temperature measurements can be derived and are available for analysis, but horizontal wind speeds cannot be derived.
2.2. Surface ¯ux measurements
Eddy correlation ¯ux measurements were made using a sonic anemometer/thermometer and krypton hygrometer (Applied Technologies, Inc.) mounted on a 3 m tower located immediately adjacent to a harvested wheat ®eld; it was surrounded by wheat stubble to the south and west and pasture to the north and east. The system was located approximately 300 m north of the 915 MHz pro®ler. The system produced half-hour averages of the turbulent ¯uxes of sensible heatH, latent heat LE, and momentumu.
In addition, measurements were made with an energy balance Bowen ratio (EBBR) system (Radiation and Energy Balance Systems, Inc.) which was located in a nearby (200 m) green pasture. The EBBR was located approximately 300 m NNE of the 915 MHz pro®ler. The EBBR system measured air temperature T and vapor pressure eat 0.96 and 1.96 m above the vegetation, in addition to net radiationRn and soil heat ¯uxG. Using
these measurements, the system calculated half-hour average values of sensible heat and latent heat.
For the present analyses, the locally measured sensi-ble and latent heat ¯uxes were ``regionalized'' by taking the averages of the measurements from the EBBR sta-tion and the eddy correlasta-tion stasta-tion weighted according to the regional vegetation distribution of harvested wheat and pasture, namely Hs0:8Hec0:2Hebbr.
2.3. Surface temperature measurements
Two infrared thermometers (IRTs) were used to make surface temperature measurements over a 750 m transect at the CF. The IRTs (Model 4000.2L, Everest Interscience, Inc.) had a 4 ®eld of view and a 5 Hz sampling frequency and they were mounted on a por-table yoke and carried by a porter in the manner de-scribed by Slater et al. [15]. Both IRTs viewed about the same 10 cm diameter sampling area, one from a nadir viewing angle and the other from 50nadir. The o-nadir temperatures produced better surface ¯ux esti-mates than the nadir temperatures; thus only the results from the o-nadir view angle measurements are pre-sented.
Fig. 1. A map of the CART Central Facility in the Southern Great Plains (adapted from [16]). The stars mark the eddy correlation (EC) and the energy balance Bowen ratio (EBBR) surface ¯ux stations. The solid squares mark the radiosonde release location and the 915 MHz RASS/pro®ler. The dotted line marks the surface temperature (Ts)
The surface temperature was measured between 0800 CDT and 1600 CDT during hourly walks along the transect. Additional measurements were made on the half-hour as necessary to match the atmospheric pro®ler. The transect consisted of a 600 m harvested wheat portion followed by a 150 m pasture portion and the traverse lasted approximately 12 min as shown in Fig. 1. The regional surface temperature was estimated from the average of measurements during a single transect.
3. Methodology
In the BAS approach, the surface ¯uxes can be esti-mated by the following equations for momentum ¯uxu and sensible heat ¯uxH:
ujVa ln
average mixed layer scalar windV2
a u2av2a,ua andva
the average wind speeds in the x- and y-directions, re-spectively, hi the inversion height, zo the momentum
roughness length, hs the potential temperature at the
surface, ha the average mixed layer potential
tempera-ture, cp the speci®c heat at constant pressure, zoh the
scalar roughness for sensible heat, and Bw and C the
bulk similarity functions for momentum and sensible heat, respectively. Based on an analysis of surface layer pro®les by Monin±Obukhov similarity, it was estimated that the regional (i.e., mesogamma scale) surface roughness for the ARM CART Central Facility can be taken aszo0:15 m, the displacement heightd is
neg-ligible, and the scalar roughness is zoh0:0038 m for
measurements from the o-nadir viewing IRT [10]. Previous studies showed that zoh may be a function of
solar elevation or u [14,19,20]. However, Jacobs and Brutsaert [10] found no relationship between zoh and
either solar angle oru. Therefore, a constantzohvalue is
used in this study. Asd is negligible, it is omitted from the formulations in what follows. The surface rough-nesses were derived at the local scale using wind speed measurements at 10 m. Independent regional ¯ux esti-mates made radiosonde surface layer pro®les and these roughness values were compared to eddy correlation measurements. The sensible heat ¯uxes were in excellent agreement (r0:92). Moderate agreement was found for momentum ¯uxes (r0:66).
A variety of forms of the bulk similarity functions have been proposed for stable, neutral, and unstable atmospheric conditions. Previous research on these has mostly dealt with their dependency on the dimensionless
variablelhi=L(e.g., [9,5]).L, the Obukhov length, is
wheregis the acceleration of gravity,Ethe surface ¯ux of water vapor, and Ta the air temperature near the
ground.
Sugita and Brutsaert [18], Brutsaert and Parlange [4] tested several Bw functions. Among those, the Bw
for-mulations that were used to calculateuin this analysis, are:
Bwa; 6
Bwalnÿ hi=L b; 7
where a and b are empirical constants. The FIFE ex-periment, which provided data for Sugita and Brutsaert [18], was conducted over hilly prairie terrain in Kansas where the roughness characteristics for the region are zo1:05 m and d26:9 m. The measurements at
HAPEX-Mobilhy (H-M), used by Brutsaert and Par-lange [4], were made above the Landes forest in France and zo 1:2 m and d6:0 m. Both of these
experi-ments used mixed layer radiosonde wind measureexperi-ments. With the FIFE data, the constants were found to be a0:500 andb1:72 for Eq. (7). With the H-M wind pro®le data, the constants were found to be a3:362 for (6), anda0:374 andb2:408 for (7). Because the FIFE and the H-M functions for (7) give very similarBw
values, it was decided to use their averages, namely a0:437 and b2:064.
The formulations of Sugita and Brutsaert [18] forC were used to calculate H in the present analysis; they are:
Ca; 8
Calnÿ hi=L b; 9
where a and b are constants. With the FIFE data, the constants were found to be a0:739 and b2:95 for (9).
4. Analysis and discussion
4.1. Comparison of mean values in the mixed layer
the saturated vapor pressure) and the equations of state for dry air and water vapor.
For each pro®le, the measurements made between the bottom and top of the mixed layer were averaged. The top of the mixed layer was taken as the boundary layer inversion height hi; this was initially determined for
each radiosonde pro®le as the height at which dh=dp>4C/100 mb where h is the potential tempera-ture andp the pressure. The results were con®rmed by inspection. The inversion heights for the 915 MHz pro-®ler/RASS measurements were determined by interpo-lating between the inversion heights of the nearest two radiosonde pro®les; the bottom of the mixed layer was taken as 0:10hi. Most wind speed pro®les had 10±20
measurements in the mixed layer with both instruments. The RASS temperature pro®les also had 10±20 mea-surements in the mixed layer, where as the radiosonde temperature pro®les usually had 50±100. Examples of typical virtual temperature and wind pro®les obtained by the pro®ler and the radiosonde are shown in Fig. 2; the solid lines show the pro®le obtained by the radiosonde launched within a few hundred meters of the pro®ler.
A pair of measurements was compared when the ra-diosonde launch occurred within the averaging interval of the pro®ler. Fig. 3 shows 12 comparisons of wind speed measurements from the radiosonde and from the 915 MHz pro®ler. These wind speeds have an excellent correlation (r0:95). For the 12 wind speeds, the av-erage dierence (VrsÿV915) is 0.45 m sÿ1, the standard
deviation of the dierences is 0.85 m sÿ1, and the
re-gression relationship V915 ÿ0:631:03 Vrs. A T-test
witha0:05 indicated that the 915 MHz pro®ler gives signi®cantly lower mixed layer wind speed measure-ments than the radiosonde (p0.047). The negative bias of the pro®ler wind speed is consistent with the previous comparisons discussed in Section 1. This bias could also be due the dierence in sampling methodology between the pro®ler and the radiosonde. The pro®ler position is stationary (Eulerian) while the balloon is transported with the mean horizontal wind (Langrangian).
The temperature pro®les were compared when they were made within a half-hour of each other. The 56 pairs of mean radiosonde and RASS virtual temperature mixed layer averages are shown in Fig. 4. For these temperature measurement pairs, the maximum temper-ature dierence is approximately 2C, the average dif-ference (Tv;915ÿTv;rs) 0.46C and the standard deviation
of the dierences 1.16C. The temperatures are well correlated (r0:96), but their dierence is statistically signi®cant (p0.0044). This dierence is somewhat larger than the results of [12,11,7]. This study only an-alyzes daytime measurements while the earlier studies used both measurements made during the day and the night. During the daytime, temperature ¯uctuations are usually large, thus the dierence between the radiosonde and RASS measurements would be expected to vary
more in this study than in the previous studies. Never-theless, the present result is consistent with recent studies by Angevine and Ecklund [1] and Angevine et al. [2], who also found a positive bias (RASS reads high) in comparisons between the RASS and other instruments and who attributed the bias to range error, wind and turbulence error, and approximations made to convert the measured acoustic velocity to virtual temperature.
Fig. 2. Pro®les of (a) wind speed and (b) virtual temperature obser-vations from the 915 MHz pro®ler (hollow symbols) and radiosondes (solid line) on day 182 at 0930 (hollow diamonds), 1230 (hollow tri-angles), and 1530 (hollow squares). Individual radiosonde wind speed observations are marked by the solid symbols. The boundary layer inversion height is indicated by an arrow. Wind speed measurements are oset by 10 m sÿ1and 20 m sÿ1for the 1230 and the 1530 pro®les,
4.2. Sensible heat ¯ux analyses
Coincident wind speed and temperature pro®les were used to calculate u andH. The analyzed pro®les con-sisted of those measured (i) under unstable conditions that were identi®ed with the criteria that the Obukhov length was negative (L<0) and (ii) under clear sky or
fully overcast conditions (to avoid sampling uncertainty of surface temperature under partially sunlit condi-tions). The selection resulted in 13 915 MHz pro®les and 21 radiosonde wind pro®les listed in Table 1 that could be used in the ¯ux analysis. The radiosonde and the 915 MHz pro®les were analyzed separately.
Eq. (1) was ®rst inverted to determine the RASS mixed layer temperature from the RASS virtual tem-perature measurements and the radiosonde measure-ments of q. The surface ¯uxes H and u were then calculated by iteratively solving (3)±(5) for each pro®le in Table 1. Because the eect of evaporation on stability is usually small, the values ofEnecessary to determineL were simply taken from the surface measurements. The logarithmic Bw function (7) was used in (3), with
a0:437 andb2:064. The FIFE Cfunction (9) was used in (4), witha0:739 andb2:95. Mean values of H and Hs and the ratio of the means hHsi=hHi were
calculated for the data set. The relationship H b0b1Hs and the correlation coecient r were
determined with an ordinary least squares regression. The results are shown in Table 2. The 915 MHz dataset yielded somewhat better results than the radiosonde dataset. However, both regression relationships are skewed and both datasets overestimateHs on average.
New best ®t constantsaandbforCwere determined for these datasets in an attempt to improve the H esti-mates. For each pair of constants, the surface ¯uxes H and u were recalculated for each pro®le by iteratively solving (3)±(5). The following criteria after Sugita and Brutsaert [18] were used by trial and error to identify these constants. The ratio of the average value of the calculated surface ¯uxes to the average value of the measured surface ¯uxes is as close as to 1 as possible; the regression relationship (e.g., H b0b1Hs) shall
have b1 close to 1, b0 close to 0, and the correlation
coecient rclose to 1. This approach seeks to identify the constants that on average produce unbiased surface ¯ux estimates of the measured surface ¯uxes. The overall results of this procedure are given in Table 2 and the individual ¯ux results for each pro®le are given in Table 3. Fig. 5 shows theHvalues calculated by (4), (3), and (5) with the optimized Cfunctions as compared to the measured Hs values. For the 915 MHz dataset, the
resulting best ®t was a constant C5:9. For the ra-diosonde dataset,Cwas given by (9) wherea1:53 and b ÿ1:90. Each dataset had a regression intercept close to 0, a regression slope close to 1, and good correlation. The FIFECfunction and the optimizedCfunctions are shown in Fig. 6 along with the hi=L and Cvalues
determined for the 13 915 MHz and the 21 radiosonde wind pro®les when calculated by inverting (4) and sub-stituting the measured regional value Hs forH and the
measured u values. This ®gure shows some scatter, particularly for values ofhi=Lthat are close to zero. The
C values appear to be slightly dependent on hi=L.
Fig. 4. Comparison of the mean virtual temperature over the mixed layer measured by the radiosondes and by the RASS. The pro®les were made within a half-hour of each other. The solid line is 1:1. The cor-relation coecient isr0:96.
However, for the 915 MHz dataset ignoring this de-pedence produced the best estimates of H. There is no systematic dierence between the 13 915 MHz values and the 21 radiosonde values.
4.3. Estimation ofu
The u estimates obtained in Section 4.2 were com-pared to those measured by the eddy correlation system. For the 13 915 MHz pro®les and 21 radiosonde wind
pro®les, the u results are summarized in Table 4. Overall, the agreement is quite good. This result shows that the average of the FIFE and the HAPEX- Mobilhy Bw functions gives reasonable u results at an uncali-brated site. However, the u;s values were
underesti-mated by the 915 MHz pro®ler wind speed measurements and overestimated by the radiosonde measurements; modi®cations of theCfunction did not signi®cantly change this discrepancy. The analysis described in Section 4.2 was also carried out using a
Table 1
Data for each pro®le
Day of Time Va hs ha hi L Ta q P
year (CDT) (m sÿ1) (K) (K) (m) (m) (C) (g kgÿ1) (hPa)
Radiosonde data
178 093000 2.6 304.4 300.7 152 ÿ27 21.6 11.9 975.7
178 101000 1.8 309.8 302.1 474 ÿ5 24.2 12.0 975.5
178 122800 3.1 317.7 304.3 1509 ÿ6 29.1 10.8 976.4
178 152900 5.8 317.4 305.7 2027 ÿ23 31.2 11.5 974.9
179 123000 3.2 304.1 302.3 698 ÿ45 26.4 15.9 975.7
179 125100 2.7 308.2 302.4 607 ÿ12 27.7 16.1 975.4
179 152900 4.8 313.3 306.0 1747 ÿ26 30.9 15.1 974.2
180 123300 4.6 301.4 297.3 589 ÿ52 21.1 16.2 978.3
181 092900 11.3 300.7 295.4 286 ÿ295 19.9 0.0 982.7
181 123000 7.8 300.6 298.0 1555 ÿ164 23.4 0.0 983.6
182 093200 4.4 296.6 294.5 380 ÿ82 17.1 9.4 983.6
182 123000 4.7 308.3 297.5 1383 ÿ18 23.1 7.9 983.1
182 153000 4.4 308.7 299.1 1537 ÿ16 25.0 8.2 981.3
184 092800 12.2 301.6 298.9 200 ÿ696 21.4 13.9 968.3
184 122900 14.7 311.5 304.9 1309 ÿ281 27.6 14.7 965.8
184 152900 16.2 314.1 308.8 1823 ÿ383 31.9 15.6 963.1
185 092900 9.4 306.0 303.5 1100 ÿ300 26.7 16.4 962.1
185 123700 11.7 312.7 306.3 1170 ÿ184 30.0 11.7 962.7
185 153800 5.8 312.7 306.8 1498 ÿ45 30.8 12.1 964.7
186 092900 10.3 302.6 298.5 227 ÿ331 20.7 10.8 975.1
186 122900 10.5 312.8 305.4 2848 ÿ108 28.3 9.3 975.2
915MHz pro®ler/RASS data
178 083629 4.0 300.1 298.8 152 ÿ77 18.7 11.5 975.8
178 093627 1.8 307.0 300.8 205 ÿ6 22.1 11.9 975.7
179 143628 5.5 317.4 304.7 1368 ÿ18 29.8 16.2 974.7
179 152614 4.9 313.7 306.5 1727 ÿ24 30.9 15.2 974.3
182 093637 2.8 299.6 295.1 406 ÿ13 17.3 9.4 983.6
182 103635 2.8 302.5 296.2 744 ÿ10 20.6 8.6 983.6
182 113634 2.0 306.6 296.8 1081 ÿ4 22.3 8.2 983.2
182 123632 3.3 308.1 298.2 1388 ÿ9 23.2 7.9 983.1
182 133631 3.5 308.6 299.2 1439 ÿ10 24.0 8.3 982.6
182 143630 3.5 309.1 299.7 1491 ÿ10 24.5 8.3 981.8
182 153630 3.0 308.6 299.9 1537 ÿ8 25.1 8.3 981.3
182 163630 2.8 308.3 301.0 1537 ÿ8 25.2 8.2 980.8
184 093620 8.0 302.0 300.4 239 ÿ210 21.6 13.9 968.3
Table 2
Comparisons between the average measuredHsvalues and the averageHvalues calculated using Eq. (3) for the radiosonde and the RASS pro®lesa
Pro®ler Eqs. a b hHi hHi hHsi=hHi r Intercept (b0) Slope (b1)
(W mÿ2) (W mÿ2) (W mÿ2)
RS (9) 0.739 2.95 182.83 132.86 0.727 0.763 ÿ28.78 1.16
915 MHz (9) 0.739 2.95 154.28 144.86 0.939 0.876 ÿ33.95 1.30
RS (9) 1.53 ÿ1.90 132.76 132.86 1.001 0.851 ÿ3.83 1.03
915 MHz (8) 5.90 ÿ 145.10 144.86 0.998 0.819 ÿ6.56 1.05
a
constant Bw function (Bw3:8 and Bw3:1 for the
pro®ler and radiosonde data sets, respectively) with both the FIFE C function and the optimized C functions. However, in both cases, the results were either not substantially dierent or somewhat worse.
The eddy correlation system, mounted at a height of 2 m measures u values at the local scale, with wind speeds that are aected mainly by the local roughness zo0:08 m, approximately. In contrast, mixed layer
wind speeds are aected by a more regional roughness, in the present case approximately zo0:15 m or
per-haps even larger. Therefore, in principle at least, one can expect that the regional u values captured by the pro®ler and radiosonde should be at least as large and likely larger than those measured by the eddy correla-tion system. In this sense, the radiosonde u results which exceed those from the eddy correlation system, are reasonable. However, the pro®ler underpredicts the
u values. This result is likely due to the fact that the pro®ler measurements systematically underestimate the wind speed values by approximately 0.5±1.0 m sÿ1, as
discussed in Sections 1 and 4.1. To test the eect of the wind speed underestimates on u calculations, the 915 pro®ler dataset (see Table 1) was reanalyzed with the pro®ler wind speeds increased by 0.5 m sÿ1. For a 0.5 m
sÿ1 increase, the calculated u
values were more con-sistent with surface measurements (hui hu;si 0:35
m sÿ1). This increase is probably insucient, and larger
wind speed corrections would further increase the cal-culateduvalues. Further research is necessary into the pro®ler algorithm and other factors (possibly ground clutter) to improve the present understanding of this systematic dierence between the pro®ler and radio-sonde wind speed measurements. Until this phenome-non is better understood, the pro®ler wind speed measurements should probably be increased by a value
Table 3
The calculated and measured surface ¯ux values for each pro®lea
Day of year Time (CDT) Calculated Measured
u H u;s Hs LEs
(m sÿ1) (W mÿ2) (m sÿ1) (W mÿ2) (W mÿ2)
Radiosonde data
178 093000 0.25 45.0 0.28 53.4 92.5
178 101000 0.18 96.6 0.16 109.8 112.5
178 122800 0.26 252.8 0.35 292.2 204.6
178 152900 0.42 269.2 0.40 240.2 171.5
179 123000 0.25 21.1 0.17 72.0 127.4
179 125100 0.23 78.1 0.20 105.9 168.0
179 152900 0.35 136.3 0.26 141.7 156.5
180 123300 0.36 69.0 0.34 53.7 128.6
181 092900 0.82 154.2 0.45 54.8 157.5
181 123000 0.50 53.4 0.37 64.6 197.7
182 093200 0.34 30.0 0.34 99.0 172.5
182 123000 0.37 229.6 0.25 167.4 268.5
182 153000 0.34 194.8 0.33 165.4 296.0
184 092800 0.86 72.4 0.62 115.6 90.4
184 122900 0.92 224.7 0.72 221.1 246.1
184 152900 0.97 183.7 0.82 188.3 297.5
185 092900 0.60 52.8 0.80 98.6 132.8
185 123700 0.77 189.5 0.60 179.0 301.1
185 153800 0.41 115.1 0.60 121.5 282.8
186 092900 0.76 108.0 0.34 98.9 112.6
186 122900 0.66 212.4 0.44 147.1 279.8
915MHz pro®ler/RASS data
178 083629 0.35 45.2 0.34 32.1 55.8
178 093627 0.20 114.4 0.27 57.0 101.2
179 143628 0.42 351.7 0.35 254.5 173.7
179 152614 0.37 164.6 0.25 144.7 156.8
182 093637 0.25 95.6 0.33 108.5 175.2
182 103635 0.25 115.7 0.34 123.0 190.6
182 113634 0.18 127.4 0.28 182.0 262.4
182 123632 0.27 180.6 0.28 176.6 274.6
182 133631 0.28 179.2 0.40 171.4 306.5
182 143630 0.28 177.9 0.36 182.6 312.8
182 153630 0.25 140.9 0.32 162.9 295.3
182 163630 0.23 108.1 0.35 168.3 258.3
184 093620 0.61 86.4 0.62 119.5 95.0
on the order of 0.5 m sÿ1 prior to estimating surface
¯uxes.
5. Summary
An analysis of the bulk ABL similarity approach was conducted with the mixed layer pro®les measured by a 915 MHz Pro®ler/RASS and by radiosondes at the ARM CART Central Facility in Oklahoma. Simulta-neous pro®les showed that the wind speed and the temperature measurements by the pro®ler and the ra-diosondes were signi®cantly dierent. Both instruments were able to provide good estimates of H, and the 915 MHz pro®ler/RASS gave as good or better results than the radiosondes. This result is encouraging as the 915 MHz pro®ler/RASS may be operated continuously with little manual intervention. However, the 915 MHz pro®ler underpredicted the u values because the
mea-sured V values were underestimated on average. Until the matter is better understood, it may be advisable to correct the 915 MHz pro®ler wind speeds by 0.5 m sÿ1or
perhaps larger prior to estimating surface ¯uxes. Clearly, additional ®eld tests to study the bulk similarity function using simultaneous 915 MHz Pro®ler/RASS and radiosondes pro®les are desirable.
Acknowledgements
Part of this work was supported through National Aeronautics and Space Administration (NGT-51211 of the Graduate Student Researcher Program, NAS5-31723 and NAG8-1518), Argonne National Laboratory (No. 970632401) and the National Science Foundation
Fig. 6. The bulk similarity functionCvalues versus stability (hi=L).
The dotted line is the FIFEC0:739lnhi=L 2:95 curve, the solid
line isC5:9 as optimized for the 915 MHz dataset, and the dashed line isC1:53lnhi=L ÿ1:90 as optimized for the radiosonde dataset.
The individualCvalues were calculated usingHsfor each pro®le. The
Cvalues for the 915 MHz pro®ler dataset and the radiosonde dataset are crosses and diamonds, respectively.
Table 4
Comparisons between the average measuredu;svalues and the averageuvalues calculated using Eq. (2) for the radiosonde and the RASS pro®lesa
Pro®ler Eqs. hui hu;si hu;si=hui r Intercept (b0) Slope (b1)
(m sÿ1) (m sÿ1) (m sÿ1)
FIFE C Functions
RS (9) 0.52 0.42 0.812 0.780 0.09 1.03
915 MHz (9) 0.30 0.35 1.146 0.758 ÿ0.01 0.90
Optimized C Function
RS (9) 0.51 0.42 0.830 0.784 0.08 1.01
915 MHz (8) 0.30 0.35 1.142 0.766 ÿ0.03 0.95
aThe ®rst two comparisons were conducted using the FIFE parameters
a0:739 andb2:95 forC. The second two comparisons use optimizedC functions wherea1:53 andb ÿ1:90 for the radiosonde pro®les anda5:90 andb0 for the 915 pro®ler.
Fig. 5. Comparison betweenHsandHcalculated from (3), (4), and (5)
(ATM-9708622). The authors also appreciate the op-portunity to take necessary measurements at the US Department of Energy ARM CART Central Facility. Data were obtained from the Atmospheric Radiation Measurement (ARM) Program sponsored by the US Department of Energy, Oce of Energy Research, Of-®ce of Health and Environmental Research, Environ-mental Sciences Division.
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