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  • Author or Editor: R. Boers x
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R. Baedi
,
R. Boers
, and
H. Russchenberg

Abstract

A model for the radar reflectivity of boundary layer water clouds is constructed using cloud droplet spectra fitted to a truncated gamma distribution. The spectra were derived from several recent field experiments. Realistic space-based radar returns are simulated that take into account the pulse shape, digitization interval, averaging volume, and variations in droplet concentration, cloud depth, and cloud-top height. The results show that the long pulse length of the proposed radar is responsible for smearing out the real reflectivity spatially so that the space-based detected clouds occupy a volume far exceeding that of the “observed” cloud. However, the effect of smearing is reduced by the limited receiver sensitivity. Cloud volume of boundary layer clouds is overestimated by between 30% and 100% using proposed radar parameters. Even if clouds are detected, the radar reflectivity convoluted by the pulse shape is sufficiently different from the originally observed reflectivity to seriously impede the retrieval of accurate cloud liquid water content.

Full access
R. Boers
,
A. van Lammeren
, and
A. Feijt

Abstract

Errors in cloud optical depth retrieved from pyranometer irradiances are estimated using a fractal model of cloud inhomogeneity. The cloud field is constructed from a two-dimensional array of pixels. For each of the pixels, which are 200 × 200 m2 in size, the radiative transfer is calculated using the independent pixel approximation. If cloud cover is 100%, the retrieval bias can be positive or negative for individual 10-min averaged transmittances, depending on the position of cloud inhomogeneities with respect to the pyranometer. The mean bias is always negative. Increasing the averaging time to 40 min reduces the scatter in the bias, although the mean bias remains −1.0, a value that depends on the choice of fractal model. If cloud cover is less than 100%, but there is no independent means to omit partly cloudy periods from the irradiance records, the negative retrieval bias will increase with reduced cloud cover and optical depth. Below optical depths of 5, the retrieval errors are so large that no meaningful results are obtained despite the fact that retrievals may appear to be reasonable. The simulations herein cannot take account of three-dimensional photon transport. The results of this study demonstrate that it is essential to measure cloud fraction and the variability of the cloud structure if optical depth is to be retrieved from pyranometer observations. Extra instruments recommended for ground-based remote sensing of cloud optical depth are a cloud lidar, powerful enough to probe the entire troposphere, and a microwave radiometer to measure the total column liquid water.

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R. Boers
,
H. Klein Baltink
,
H. J. Hemink
,
F. C. Bosveld
, and
M. Moerman

Abstract

The development of a radiation fog layer at the Cabauw Experimental Site for Atmospheric Research (51.97°N, 4.93°E) on 23 March 2011 was observed with ground-based in situ and remote sensing observations to investigate the relationship between visibility and radar reflectivity. The fog layer thickness was less than 200 m. Radar reflectivity values did not exceed −25 dBZ even with visibilities less than 100 m. The onset and evaporation of fog produce different radar reflectivity–visibility relationships. The evolution of the fog layer was modeled with a droplet activation model that used the aerosol size distribution observed at the 60-m altitude tower level as input. Radar reflectivity and visibility were calculated from model drop size spectra using Mie scattering theory. Since radiative cooling rates are small in comparison with cooling rates due to adiabatic lift of aerosol-laden air, the modeled supersaturation remains low so that few aerosol particles are activated to cloud droplets. The modeling results suggest that the different radar reflectivity–visibility relationships are the result of differences in the interplay between water vapor and cloud droplets during formation and evaporation of the fog. During droplet activation, only a few large cloud droplets remain after successfully competing for water vapor with the smaller activated droplets. These small droplets eventually evaporate (deactivate) again. In the fog dissolution/evaporation stage, only these large droplet need to be evaporated. Therefore, to convert radar reflectivity to visibility for traffic safety products, knowledge of the state of local fog evolution is necessary.

Full access
Gijs de Boer
,
Brian J. Butterworth
,
Jack S. Elston
,
Adam Houston
,
Elizabeth Pillar-Little
,
Brian Argrow
,
Tyler M. Bell
,
Phillip Chilson
,
Christopher Choate
,
Brian R. Greene
,
Ashraful Islam
,
Ryan Martz
,
Michael Rhodes
,
Daniel Rico
,
Maciej Stachura
,
Francesca M. Lappin
,
Antonio R. Segales
,
Seabrooke Whyte
, and
Matthew Wilson

Abstract

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Open access