NIM/ECOR/M1 - Missing data

Data are missing and unrecoverable.

• condensation pressure(cp)
• covariance vc(cvar_vc)
• number of u samples removed due to spikes(n_spk_u)
• number of v samples removed due to spikes(n_spk_v)
• number of w samples removed due to spikes(n_spk_w)
• latent heat of vaporization(lv)
• Vertical angle of wind(phi)
• standard deviation of wind direction(std_wind_dir)
• covariance uq(cvar_uq)
• covariance ut(cvar_ut)
• covariance uv(cvar_uv)
• uw covariance(cvar_uw)
• covariance uc(cvar_uc)
• number of c samples removed due to spikes(n_spk_c)
• lat(lat)
• mixing ratio(mr)
• mean value of out of range values and spikes of c -9999 if no spikes(mean_spk_c)
• number of t samples removed due to spikes(n_spk_t)
• covariance of tq(cvar_tq)
• number of q samples removed due to spikes(n_spk_q)
• average atmospheric pressure (IGRA internal sensor)(atm_pres)
• mean value of out of range values and spikes of q -9999 if no spikes(mean_spk_q)
• lon(lon)
• mean co2 concentration (c)(mean_c)
• mean value of \spike\ u samples(mean_spk_u)
• mean value of out of range values and spikes of t -9999 if no spikes(mean_spk_t)
• mean value of \spike\ w samples(mean_spk_w)
• mean value of \spike\ v samples(mean_spk_v)
• number of bad or out of range v samples(n_bad_v)
• number of bad or out of range w samples(n_bad_w)
• 0=real or 1=dummy value of mr(real_mr)
• latent heat flux(lv_e)
• Friction velocity(ustar)
• number of bad or out of range t samples(n_bad_t)
• number of bad or out of range u samples(n_bad_u)
• vector averaged wind direction(wind_dir)
• number of bad or out of range q samples(n_bad_q)
• wT covariance(cvar_wt)
• Dummy altitude for Zeb(alt)
• CO2 flux(fc)
• covariance wq(cvar_wq)
• number of bad or out of range c samples(n_bad_c)
• rotated covariance uc(cvar_rot_uc)
• Retrieved water vapor density profile(rho)
• covariance wc(cvar_wc)
• h(h)
• number of samples with bad sonic status flag(n_bad_son_ic)
• momentum flux (dynamic)(k)
• 0=real or 1=dummy value of lv(real_lv)
• base time(base_time)
• covariance vt(cvar_vt)
• covariance vq(cvar_vq)
• number of samples with \IRGA hardware problem\ flag(n_bad_irga)
• vw covariance(cvar_vw)
• rotated covariance vq(cvar_rot_vq)
• number of valid w samples(n_good_w)
• number of valid v samples(n_good_v)
• rotated covariance vt(cvar_rot_vt)
• 0=real or 1=dummy value of rho(real_rho)
• vertical (elevation) wind angle(elev)
• number of valid q samples(n_good_q)
• standard deviation of wind elevation angle(std_elev)
• number of valid u samples(n_good_u)
• number of valid t samples(n_good_t)
• rotated covariance wc(cvar_rot_wc)
• rotated covariance vw(cvar_rot_vw)
• variance of v(var_rot_v)
• variance of u(var_rot_u)
• variance of w(var_rot_w)
• number of valid c samples(n_good_c)
• number of samples with IRGA optical path blocked flag(n_bad_irga_light)
• rotated covariance ut(cvar_rot_ut)
• 0=real or 1=dummy value of cp(real_cp)
• average voltage of IRGA cooler(mean_cooler)
• time(time)
• rotated covariance uq(cvar_rot_uq)
• covariance of qc(cvar_qc)
• rotated covariance vc(cvar_rot_vc)
• rotated covariance uw(cvar_rot_uw)
• rotated covariance uv(cvar_rot_uv)
• Water vapor density (q)(mean_q)
• Virtual temperature (T)(mean_t)
• Cross-boom wind velocity (v)(mean_v)
• Down-boom wind velocity (u)(mean_u)
• skewness of variable v(skew_v)
• skewness of variable w(skew_w)
• average temperature (IGRA internal sensor)(temp_irga)
• skewness of variable t(skew_t)
• skewness of variable u(skew_u)
• skewness of variable q(skew_q)
• mean horizontal wind speed(mean_rot_u)
• rotated mean v(mean_rot_v)
• covariance of tc(cvar_tc)
• Vertical wind velocity (w)(mean_w)
• vector averaged wind speed(wind_spd)
• rotated mean w(mean_rot_w)
• skewness of variable c(skew_c)
• variance of c(var_c)
• rotated covariance wt(cvar_rot_wt)
• variance of q(var_q)
• rotated covariance wq(cvar_rot_wq)
• kurtosis of variable c(kurt_c)
• variance of variable w(var_w)
• Time offset of tweaks from base_time(time_offset)
• variance of t(var_t)
• variance of variable u(var_u)
• variance of variable v(var_v)
• kurtosis of variable w(kurt_w)
• kurtosis of variable v(kurt_v)
• kurtosis of variable u(kurt_u)
• kurtosis of variable t(kurt_t)
• kurtosis of variable q(kurt_q)

NIM/ECOR/M1 - Flux Data Suspect for Some Wind Directions

NIM: For some wind directions the horizontal fetch was not representative of the field in 
which the AMF was located.  Therefore, for the wind direction ranges 90-170 (buildings) 
and 220-280 (trees) degrees, the fluxes are affected by insufficient fetch and  surfaces, 
buildings, or vegetation that are not similar to the local field conditions.

• h(h)
• latent heat flux(lv_e)
• CO2 flux(fc)
• Friction velocity(ustar)

NIM/ECOR/M1 - Effects on ECOR CO2 Flux and Concentration By Aircraft

Aircraft landings, departures, and running aircraft on the airport pad
were found to produce large spikes in the half hourly CO2 flux and small spikes in CO2 
concentration on many days during the entire deployment of the AMF at NIM.  This influence 
was found on 40% of the days in March and April 2006 and sometimes for multiple periods in 
a day; this was typical of  the year of data.  The spikes range from only several 
micromoles s-1 m-2 to one hundred or more for flux (a typical spike was in the twenties) and 
near zero to 1.0 mmoles m-3 for CO2 concentration (typically around 0.15).

The aircraft influence was caused by persistent easterly winds; the airport
terminal pad and the nearest part of the runway were almost directly to the
east of the ECOR location.

Occasionally an influence on water vapor density was detected, but this was fairly rare 
and usually of very small magnitude.

• covariance wc(cvar_wc)
• rotated covariance wc(cvar_rot_wc)
• covariance uc(cvar_uc)
• covariance vc(cvar_vc)
• CO2 flux(fc)
• rotated covariance vc(cvar_rot_vc)
• covariance of tc(cvar_tc)
• rotated covariance uc(cvar_rot_uc)
• mean co2 concentration (c)(mean_c)
• variance of c(var_c)