Arm Mobile Facility Surface Meteorology (Met) Handbook.

The Atmospheric Radiation Measurement (ARM) Mobile Facility Surface Meteorology station (MET) uses mainly conventional in situ sensors to obtain 1-min statistics of surface wind speed, wind direction, air temperature, relative humidity (RH), barometric pressure, and rainrate. Additional sensors may be added to or removed from the base set of sensors depending upon the deployment location, climate regime, or programmatic needs. In addition, sensor types may change depending upon the climate regime of the deployment. These changes/additions are noted in Section 3

Comparison of Meteorological Measurements from Sparse and Dense Surface Observation Networks in the U.S. Southern Great Plains

The primary objective of this study was to analyze the spatial variability of temperature and relative humidity across Kansas (KS) and Oklahoma (OK) for sparse and dense networks by comparing data from (1) the Surface Meteorological Observing System (SMOS) installations at the Atmospheric Radiation Measurement (ARM; Peppler et al. 2008) Program’s Southern Great Plains site and (2) the Oklahoma Mesonet (OKM; McPherson et al. 2007). Given the wealth of observations available from these networks, this study provided the unique opportunity to determine, within a quantifiable statistical limit, an optimal distance between stations deployed for observation of the climatological values of temperature and relative humidity. Average distances between a given station and its closest neighboring station for the ARM SMOS (~ 70 km) and the OKM (~ 30 km; Brotzge and Richardson 2003) networks provided an excellent framework for comparisons of sparse and dense observations (Figure 1). This study further lays groundwork for a future investigation to determine the necessary spacing between observations for initialization of gridded numerical models

Universality of rain event size distributions

We compare rain event size distributions derived from measurements in climatically different regions, which we find to be well approximated by power laws of similar exponents over broad ranges. Differences can be seen in the large-scale cutoffs of the distributions. Event duration distributions suggest that the scale-free aspects are related to the absence of characteristic scales in the meteorological mesoscale.Comment: 16 pages, 10 figure

Comparison of meteorological measurements from sparse and dense surface observational networks in the U.S. southern Great Plains.

The primary objective of this study was to analyze the spatial variability of temperature and relative humidity across Kansas (KS) and Oklahoma (OK) for sparse and dense networks by comparing data from (1) the Surface Meteorological Observing System (SMOS) installations at the Atmospheric Radiation Measurement (ARM; Peppler et al. 2007) Program's Southern Great Plains site and (2) the Oklahoma Mesonet (OKM; McPherson et al. 2007). Given the wealth of observations available from these networks, this study provided the unique opportunity to determine, within a quantifiable statistical limit, an optimal distance between stations deployed for observation of the climatological values of temperature and relative humidity. Average distances between a given station and its closest neighboring station for the ARM SMOS ({approx} 70 km) and the OKM ({approx} 30 km; Brotzge and Richardson 2003) networks provided an excellent framework for comparisons of sparse and dense observations (Figure 1). This study further lays groundwork for a future investigation to determine the necessary spacing between observations for initialization of gridded numerical models. The spatial variability of temperature and relative humidity was examined over KS and OK by comparing observations between station pairs located in three primary domains: (1) a sparse domain in KS, consisting only of ARM SMOS stations; (2) a dense domain centered in northern OK, consisting of both ARM SMOS and OKM stations; and (3) a dense domain centered in central OK, also consisting of both ARM SMOS and OKM stations (Figure 2). In addition, the ARM SMOS stations in OK were utilized to create two secondary sparse domains. Before the observations were compared, quality control (QC) beyond the standard ARM range test was added through implementation of tighter range tests specified by data quality objectives (DQOs). Furthermore, instances of poor-quality data were removed from the data set on the basis of ARM data quality reports (DQRs). Finally, to account for spatial differences in terrain, temperature observations were corrected to mean sea level by using a standard lapse rate of 6.5 C km{sup -1} and the elevation of each observing station. For the comparison, a central station was chosen in each domain. Observations during the time period 2004-2006 from each of the other stations within a respective domain were compared to those from this central station. The Pearson correlation coefficient ({rho}) and root-mean-square difference (RMSD) were the statistics used to quantify the relationship between station pairs. For each domain, the {rho} and RMSD values were plotted against the distance separating each station pair, and a least-squares (LS) regression line was fitted to the values. The regression slopes and intercepts were compared between the various domains. The results of this analysis demonstrated positive correlations between all individual station pairs for both temperature and relative humidity. In addition, the {rho} and RMSD values for both temperature and relative humidity exhibited, in general, a linear relationship with distance from a central station. The calculated slope and intercept values were comparable across most domains, and spatial differences in temperature were smaller than those for relative humidity. The findings suggest that although the sparse networks studied might provide an accurate spatial representation for climatological values of temperature and relative humidity over the specific distances between stations, the relative importance of the temperature and relative humidity observations is a critical consideration in network design

BAECC: a field campaign to elucidate the impact of Biogenic Aerosols on Clouds and Climate

Observations obtained during an 8-month deployment of AMF2 in a boreal environment in Hyytiälä, Finland, and the 20-year comprehensive in-situ data from SMEAR-II station enable the characterization of biogenic aerosol, clouds and precipitation, and their interactions. During “Biogenic Aerosols - Effects on Clouds and Climate (BAECC)”, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program deployed the ARM 2nd Mobile Facility (AMF2) to Hyytiälä, Finland, for an 8-month intensive measurement campaign from February to September 2014. The primary research goal is to understand the role of biogenic aerosols in cloud formation. Hyytiälä is host to SMEAR-II (Station for Measuring Forest Ecosystem-Atmosphere Relations), one of the world’s most comprehensive surface in-situ observation sites in a boreal forest environment. The station has been measuring atmospheric aerosols, biogenic emissions and an extensive suite of parameters relevant to atmosphere-biosphere interactions continuously since 1996. Combining vertical profiles from AMF2 with surface-based in-situ SMEAR-II observations allow the processes at the surface to be directly related to processes occurring throughout the entire tropospheric column. Together with the inclusion of extensive surface precipitation measurements, and intensive observation periods involving aircraft flights and novel radiosonde launches, the complementary observations provide a unique opportunity for investigating aerosol-cloud interactions, and cloud-to-precipitation processes, in a boreal environment. The BAECC dataset provides opportunities for evaluating and improving models of aerosol sources and transport, cloud microphysical processes, and boundary-layer structures. In addition, numerical models are being used to bridge the gap between surface-based and tropospheric observations

Influence of biogenic emissions from boreal forests on aerosol-cloud interactions

Boreal forest acts as a carbon sink and contributes to the formation of secondary organic aerosols via emission of aerosol precursor compounds. However, these influences on the climate system are poorly quantified. Here we show direct observational evidence that aerosol emissions from the boreal forest biosphere influence warm cloud microphysics and cloud-aerosol interactions in a scale-dependent and highly dynamic manner. Analyses of in situ and ground-based remote-sensing observations from the SMEAR II station in Finland, conducted over eight months in 2014, reveal substantial increases in aerosol load over the forest one to three days after aerosol-poor marine air enters the forest environment. We find that these changes are consistent with secondary organic aerosol formation and, together with water-vapour emissions from evapotranspiration, are associated with changes in the radiative properties of warm, low-level clouds. The feedbacks between boreal forest emissions and aerosol-cloud interactions and the highly dynamic nature of these interactions in air transported over the forest over timescales of several days suggest boreal forests have the potential to mitigate climate change on a continental scale. Our findings suggest that even small changes in aerosol precursor emissions, whether due to changing climatic or anthropogenic factors, may substantially modify the radiative properties of clouds in moderately polluted environments. Emissions from the boreal forest biosphere can substantially increase aerosol load above the forest and influence the radiative properties of clouds, according to analysis of observations from a monitoring station in Finland.Peer reviewe

Surface and Tower Meteorological Instrumentation at NSA Handbook - January 2006

The Surface and Tower Meteorological Instrumentation at Atqasuk (METTWR2H) uses mainly conventional in situ sensors to measure wind speed, wind direction, air temperature, dew point and humidity mounted on a 10-m tower. It also obtains barometric pressure, visibility, and precipitation data from sensors at or near the base of the tower. In addition, a Chilled Mirror Hygrometer is located at 1 m for comparison purposes. Temperature and relative humidity probes are mounted at 2 m and 5 m on the tower. For more information, see the Surface and Tower Meteorological Instrumentation at Atqasuk Handbook

ARM Mobile Facility Surface Meteorology Handbook - October 2008

The ARM Mobile Facility Surface Meteorology station (AMF MET) uses mainly conventional in situ sensors to obtain 1-minute statistics of surface wind speed, wind direction, air temperature, relative humidity, barometric pressure, and rain-rate. Additional sensors may be added to or removed from the base set of sensors depending upon the deployment location, climate regime or programmatic needs. Additionally, sensor types may change depending upon the climate regime of the deployment. These changes/additions are noted in the Deployment Locations and History section