The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

Cadeddu, Maria; Liljegren, J. C.; Turner, David D.

doi:10.5194/amt-6-2359-2013

Cited by 176 publications

(123 citation statements)

References 48 publications

(36 reference statements)

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…We use the PWV observations from this MWR nearest the sonde launch time to scale the soundings launched from C1. Based on Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) RH uncertainly estimates, PWV uncertainty in RS92 sonde data is ±2 mm (Yu et al, 2015); PWV uncertainty in MWR retrievals is 0.2-0.5 mm (Turner et al, 2007;Cadeddu et al, 2013). Under some circumstances, the scaling may act to reverse the M09 corrections however, this step is justified because (1) the M09 corrections were developed under clear-sky conditions and (2) RS92 accuracy also changes for batches manufactured at different times.…”

Section: Corrections To Sounding Humidity Observationsmentioning

confidence: 99%

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

et al. 2015

View full text Add to dashboard Cite

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

show abstract

Section: Corrections To Sounding Humidity Observationsmentioning

confidence: 99%

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

et al. 2015

View full text Add to dashboard Cite

show abstract

“…4; Cimini et al, 2009). The GVRP has an uncertainty of 1.5 K for T B measurements (Cadeddu, 2010;Cadeddu et al, 2013).…”

Section: A M Dzambo Et Al: Comparing Radiosonde Humidity Correctiomentioning

confidence: 99%

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016

View full text Add to dashboard Cite

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS).The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from groundbased microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the groundbased retrievals.The two closure activities -done for clear-sky scenesuse the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG-and MILOcorrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover -a condition not accounted for in the radiance closure experiments.

show abstract

“…The MWR is a microwave receiver that detects the microwave emissions of the vapor and liquid water molecules. It measures cloud LWP in the zenith direction within a field of view of 6 • (Liljegren, 2000; Cadeddu et al, 2013). τ c can then be estimated as (Stephens, 1978) …”

Section: Other Cloud Optical Depth Measurementsmentioning

confidence: 99%

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

et al. 2016

View full text Add to dashboard Cite

Abstract. A method for retrieving cloud optical depth (τ c ) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red-blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red-blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ 0 ), τ c , solar pixel angle/scattering angle (ϑ s ), and pixel zenith angle/view angle (ϑ z ). The effects of these parameters are described and the functions for radiance, I λ (τ c , θ 0 , ϑ s , ϑ z ), and RBR(τ c , θ 0 , ϑ s , ϑ z ) are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c , where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I meas λ (ϑ s , ϑ z ), in addition to RBR meas (ϑ s , ϑ z ), to obtain a unique solution for τ c . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003)

show abstract

The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

Cited by 176 publications

References 48 publications

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Contact Info

Product

Resources

About