Combined lidar‐radar remote sensing: Initial results from CRYSTAL‐FACE

McGill, Matthew J.; Li, L.; Hart, William D.; Heymsfield, Gerald M.; Hlavka, Dennis L.; Racette, P.; Tian, Lin; Vaughan, Mark; Winker, David M.

doi:10.1029/2003jd004030

Cited by 71 publications

(60 citation statements)

References 28 publications

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“…Commonly observed differences between lidar-and radar-derived cloudiness that have been previously reported are seen in Fig. 2 (Comstock et al, 2002;McGill et al, 2004). When CloudSat (the radar) and CALIOP (the lidar) both detect clouds (6-15 • S), the lidar observes higher cloud tops than the radar.…”

Section: An Illustrative Cloudy Snapshotsupporting

confidence: 56%

“…Kahn et al, 2005), (2) reduce the effects of certain types of sampling biases, including those introduced by the attenuation of surface-based lidar and cloud radar in thick and precipitating clouds (Comstock et al, 2002;McGill et al, 2004), (3) provide a larger and statistically robust set of observations for comparison, and (4) facilitate near-global sampling for most types of clouds.…”

Section: Introductionmentioning

confidence: 99%

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Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

et al. 2008

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Abstract. The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2-3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6-3.0±1.2-3.6 km (−5.8 to −0.2±0.5-2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Correspondence to: B. H. Kahn (brian.h.kahn@jpl.nasa.gov) Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed.

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Section: An Illustrative Cloudy Snapshotsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

et al. 2008

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“…The 94 GHz (3.2 mm wavelength) cloud radar system (CRS; Li et al, 2004) and 9.6 GHz (3.1 cm wavelength) ER-2 Doppler radar (EDOP; Heymsfield et al, 1996) measure the radar reflectivity factor and mean Doppler velocity with a vertical gate spacing of 37.5 m. The 94 GHz radar reflectivity factor is calibrated against the 9.6 GHz radar near the cloud top (McGill, 2004), and the mean Doppler velocity measurements are calibrated using the surface signal (Li et al, 2004). The path-integrated attenuation (PIA) of the 94 GHz radar is estimated over the ocean using the surface reference technique (L'Ecuyer and .…”

Section: Measurements Used In the Retrievalmentioning

confidence: 99%

Improved rain rate and drop size retrievals from airborne Doppler radar

2017

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Abstract. Satellite remote sensing of rain is important for quantifying the hydrological cycle, atmospheric energy budget, and cloud and precipitation processes; however, radar retrievals of rain rate are sensitive to assumptions about the raindrop size distribution. The upcoming EarthCARE satellite will feature a 94 GHz Doppler radar alongside lidar and radiometer instruments, presenting opportunities for enhanced retrievals of the raindrop size distribution.We demonstrate the capability to retrieve rain rate as a function of drop size and drop number concentration from airborne 94 GHz Doppler radar measurements using CAP-TIVATE, the variational retrieval algorithm developed for EarthCARE. For a range of rain regimes observed during the Tropical Composition, Cloud and Climate Coupling field campaign, we explore the contributions of mean Doppler velocity and path-integrated attenuation (PIA) measurements to the retrieval of rain rate, and the retrievals are evaluated against independent measurements from an independent 9.6 GHz Doppler radar. The retrieved drop number concentrations vary over 5 orders of magnitude between very light rain from melting ice and warm rain from liquid clouds. In light rain conditions mean Doppler velocity facilitates estimates of rain rate without PIA, suggesting the possibility of EarthCARE rain rate estimates over land; in moderate warm rain, drop number concentration can be retrieved without mean Doppler velocity, with possible applications to CloudSat.

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“…Lidar measurements are used to observe the clouds missed by the CRS since lidar is more sensitive to small ice particles and optically thin cirrus than radar. McGill et al [35] found that the τ missed by the CRS, but detected by the CPL, is in the range of 0.15-0.45. The CPL is a backscatter lidar with three wavelengths of 0.355, 0.532, and 1.064 µm [36].…”

Section: During Tcmentioning

confidence: 99%

“…The CPL measurements are used to compensate for the optically thin cirrus part missed by the CRS measurements [35]. Figure 7 shows the vertical structure of the cloud system as seen from the CRS and CPL measurements and the 15-m resolution τ profiles constructed from the CRS and CPL data for the pure ice layer.…”

Section: Optical Thickness Profiles From Combined Crs and Cpl Measurementioning

confidence: 99%

Simulations of Infrared Radiances over a Deep Convective Cloud System Observed during TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

et al. 2012

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Retrievals of ice cloud properties using infrared measurements at 3. 7, 6.7, 7.3, 8.5, 10.8, and 12.0 μm can provide consistent results regardless of solar illumination, but OPEN ACCESSRemote Sens. 2012, 4 3023 are limited to cloud optical thicknesses τ < ~6. This paper investigates the variations in radiances at these wavelengths over a deep convective cloud system for their potential to extend retrievals of τ and ice particle size D e to optically thick clouds. Measurements from an imager, an interferometer, the Cloud Physics Lidar (CPL), and the Cloud Radar System (CRS) aboard the NASA ER-2 aircraft during the NASA TC 4 (Tropical Composition, Cloud and Climate Coupling) experiment flight during 5 August 2007, are used to examine the retrieval potential of infrared radiances over optically thick ice clouds. Simulations based on coincident in situ measurements and combined cloud τ from CRS and CPL measurements are comparable to the observations. They reveal that brightness temperatures at these bands and their differences (BTD) are sensitive to τ up to ~20 and that for ice clouds having τ > 20, the 3.7-10.8 µm and 3.7-6.7 µm BTDs are the most sensitive to D e . Satellite imagery appears to be consistent with these results suggesting that τ and D e could be retrieved for greater optical thicknesses than previously assumed. But, because of sensitivity of the BTDs to uncertainties in the atmospheric profiles of temperature, humidity, and ice water content, and sensor noise, exploiting the small BTD signals in retrieval algorithms will be very challenging.

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Combined lidar‐radar remote sensing: Initial results from CRYSTAL‐FACE

Cited by 71 publications

References 28 publications

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

Improved rain rate and drop size retrievals from airborne Doppler radar

Simulations of Infrared Radiances over a Deep Convective Cloud System Observed during TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

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