SHEBA Version 2 Notes (January, 2004)
Retrievals are Retrievals, Not Measurements!
The most important thing to remember when using the cloud microphysical values on this CD is that they are observation-based-retrievals, not direct measurements. All of the retrievals use numerous assumptions. These may include such approximations as spherical ice particles, gamma, exponential, or lognormal droplet/ice particle size distributions, ice particle densities parameterized on particle size, and parameterizations relating radar reflectivities to cloud properties based on aircraft data. It should also be considered that the radar-radiometer liquid cloud retrieval technique that utilizes the liquid water path (LWP) from the microwave radiometer is a retrieval-based-on-a-retrieval, since LWP is itself retrieved from radiometric brightness temperatures.
Several studies have addressed the issue of errors associated with radar-based cloud retrievals. While not definitive, the ice retrieval uncertainties are expected to be 30-50% for particle size and 50-100% for ice water content and the liquid retrieval uncertainties are expected to be 20-40% for droplet size and 20-60% for liquid water content.
In the following sections, for the retrieval methods where iteration and/or polynomial fits were not utilized, reduced equations have been provided to show how the cloud microphysics were calculated. However, it should be noted that these seemingly simple equations were derived through a complex line of reasoning, and the full derivations should be carefully examined in the indicated references.
The 35-GHz, Ka-band Millimeter Cloud Radar (MMCR, Moran et al. 1998) was operated by the NOAA Environmental Technology Laboratory at SHEBA. All retrievals are based on continuous, vertically-pointing, radar measurements that were originally collected using 4 different operating modes with variable sensitivies optimized for different cloud and precipitation situations. Typically these modes cycle every 9 s and data are collected at 45 m or 90 m range resolutions. The modes were optimally combined into a single radar product using an algorithm based on the DOE/ARM ARSCL (Active Remotely-Sensed Cloud Locations) program, and interpolated to a 1 min-45 meter time-height grid. The combination process does not necessarily guarantee reliable radar measurements, as an ARSCL-type radar file was generated even if the radar was not operating optimally (such as on May 11, 1998). Days with obviously erroneous radar data have been excluded from the cloud microphysics processing. A missing hyperlink in the dates menu indicates that radar data does not exist for that day.
Both radiometers utilized here were operated at SHEBA by the Department of Energy's Atmospheric Radiation Measurement Program (Stokes and Schwartz 1994, Ackerman and Stokes 2003).
The dual-channel, microwave radiometer measures brightness temperatures at 23.8 GHz and 31.4 GHz. This CD utilizes microwave radiometer LWP and precipitable water vapor retrievals based on oxygen and water vapor absorption models that are appropriate for the supercooled liquid clouds found in the Arctic (Westwater et al. 2001). These retrievals are an improvement over previous retrievals that were based on absorption models for warm liquid clouds only. The revised microwave radiometer retrievals were performed by ETL and are therefore not identical to the microwave radiometer retrievals in the ARM archive, which are the result of a similar but different processing procedure. The microwave radiometer retrievals are based on a statistical approach which utilizes a climatological set of rawinsonde measurements from Barrow, Alaska. A more accurate, physical retrieval approach would be to use radar data to derive the liquid water-weighted cloud temperature as described by Liljegren et al. (2001). The error in the absolute LWP values is about 25 g/m2 (Westwater et al. 2001). Note that the microwave radiometer measurements prior to December 5, 1997 suffered from irrecoverable calibration problems, and are therefore not used in a quantitative manner here.
Infrared cloud brightness temperatures were derived from the spectral measurements made by the Atmospheric Emitted Radiance Interferometer (AERI). AERI radiances were integrated over a 25 cm-1 band centered on 900 cm-1 (10.96-11.27 microns).
Depolarization Lidar Data
The Depolarization And Backscatter Unattended Lidar (DABUL, Grund and Sandberg 1996, Alvarez et al. 1998) was operated by the NOAA Environmental Technology Laboratory at SHEBA. Lidar measurements were not used directly to retrieve cloud microphysical properties; however, both the total lidar backscatter and depolarization ratio measurements were useful tools for classifying cloud scenes. Depolarization ratios less than 0.11 typically indicate the presence of spherical, or liquid, hydrometeors, while higher depolarization ratios indicate the presence of non-spherical ice particles.
Classification of cloud scenes into 7 categories ("ice", "simple-ice", "liquid", "simple-liquid", "mixed", "drizzle", "rain", and "snow") was based on visual inspection of radar measurements (reflectivity, mean Doppler velocity, and Doppler spectrum width), microwave radiometer-derived products (liquid water path and precipitable water vapor), the IR brightness temperature derived from AERI measurements, lidar measurements (backscatter and depolarization ratio), rawinsonde profiles (temperature and moisture), and surface observer reports. This subjective classification was independently checked and rechecked. "Ice" and "liquid" classifications indicate that combined radar-radiometer retrievals were possible (radar + IR radiometer for ice, radar + microwave radiometer for liquid). "Simple-ice" and "simple-liquid" classifications indicate that retrievals were based on radar data only, either because radiometers were obscured by multiple cloud layers, radiometric data was unavailable, or in the case of ice, the clouds were optically thicker than ~6.
In many cases, clouds are classifed as mixed-phase, indicating that both ice and liquid appeared to exist simultaneously in a cloud layer. In general, the liquid tends to exist in thin discrete layers near the cloud top; however, for the purposes of classification, the entire layer is considered to be mixed-phase and no effort is made to place the liquid. Because the radar reflectivity is dominated by the larger ice crystals, only the non-radiometric ice microphysics retrievals are implemented in the mixed-phase clouds. Depending on the application, these retrievals should be used cautiously since the neglected liquid component may contribute to uncertainty in the retrieved ice properties. Furthermore, the liquid component may in fact be the most important phase in determining mixed-phase cloud radiative properties.
In addition to in-cloud temperatures and the microwave radiometer-derived LWP, lidar depolarization ratios and radar Doppler spectrum widths have been used to identify mixed-phase cloud situations. Lidar depolarization ratios below 0.11 indicate the presence of cloud liquid. However, lidar signals also attenuate in optically thick clouds, preventing lidar measurements in many deep and/or multilayered cloud scenes. The radar Doppler spectrum width is also used as a qualitative indicator that clouds may be mixed-phase. Coincident depolarization lidar measurements that indicate mixed-phase cloud conditions have shown that a widened Doppler spectrum width is often associated with mixed-phase clouds. Wide spectrum widths indicate a wide range of hydrometeor fall velocities which may suggest a wide range of hydrometeor sizes associated with both water droplets and ice crystals in the same volume. Doppler spectrum widths, and their use in cloud classification, are a topic of ongoing research.
Precipitation classifications were most often made according to the radar mean Doppler velocities and the temperature. Drizzle was characterized by fall speeds that were typically larger than ~0.2 m/s and reflectivities higher than -15 dBZ. Rain was most often indicated by a clear melting layer signature (brightband) in the radar reflectivities, and velocities greater than ~2 m/s. Liquid phase precipitation was typically classified at temperatures above freezing. Snow was classified at below freezing temperatures with velocities typically greater than 0.5 m/s and high reflectivities.
The netcdf files contain a mask field that indicates which retrievals were run at any given location in a cloud time-height scene. The classification codes are described below. Note that some of the equations below utilize "dBZ" (radar reflectivity factor) and some utilize "Z" (radar reflectivity in units off mm6/m3). Z is related to dBZ by the equation: Z=10dBZ/10.
CLASSIFICATION CODE 1 - RAIN
The RAIN retrievals assume the Marshall-Palmer drop size distribution and Rayleigh scattering conditions (which may at times be violated at K-band).
- RainRate = 10(dBZ-23)/16 [mm/hr]
- RainDropSize = 244 - RainRate0.21 [microns]
- RainWaterContent = 0.072 * RainRate0.88 [g/m3]
- RainDropConcentration = 0.00195 * RainRate0.21 [1/cm3]
CLASSIFICATION CODE 2 - SNOW
The SNOW retrievals assume the Gunn and Marshal snow size distribution.
- SnowFallRate = 10(dBZ-14.5)/9.5 [mm/hr]
- SnowFlakeSize = 392 * SnowFallRate0.48 [microns]
- SnowWaterContent = 0.25 * SnowFallRate0.9 [g/m3]
- SnowFlakeCondentration = 0.00149 * SnowFallRate-0.39 [1/cm3]
CLASSIFICATION CODE 3 - LIQUID CLOUD, RADAR-ONLY METHOD
The radar-only LIQUID retrievals assume a lognormal droplet size distribution with a lognormal width of 0.31 (Frisch et al. 2002).
- LiquidWaterContent = c * Z0.5 [g/m3]
- DropletEffectiveRadius = d * Z0.166 [microns]
- c = (pi/6) * e-0.432 * N0.5
- d = 50 * e-0.048 * N-0.166
- N = 75 cm-3
CLASSIFICATION CODE 4 - LIQUID CLOUD, RADAR-ONLY AS WELL AS RADAR + MICROWAVE RADIOMETER METHODS
All Code 3 retrievals are implemented in addition to a radar-radiometer technique that utilizes the microwave radiometer-derived LWP scaled by radar reflectivity profiles to distribute liquid water contents vertically in the cloud (Frisch et al. 1995).
CLASSIFICATION CODE 5 - DRIZZLE
At present no drizzle retrievals are implemented.
Note that there is not currently a standard ice crystal size definition across the research community. All retrievals utilized here derive the mean diameter which characterizes the assumed exponential distribution of physical particle sizes. The mean diameter is related to the median volume diameter by: MeanDiameter = MedianDiameter/3.54 for the exponential particle size distribution. For the ice particle sizes plotted in the "Particle Size" browser panel, ice particle diameters are converted to effective radii to be consistent with the units for the retrieved cloud droplet sizes. The equations used for the conversion are:
- EffectiveRadius = 13.74 * MeanDiameter0.3 for MeanDiameters >= 23.7 microns
- EffectiveRadius = 1.5 * MeanDiameter for MeanDiameters < 23.7 microns
CLASSIFICATION CODE 6 - ICE CLOUD, RADAR-ONLY METHODS
The radar-only, empirical ICE retrieval method uses:
- IceWaterContent = a * Zb [g/m3]
- MeanDiameter = 40.5 * a-0.53 * Z0.53(1-b) [microns]
- a = monthly values of "a" were determined from periods during which the Radar-Radiometer method (See CODE 7) was implemented on single layer, optically thin ice clouds
- b = 0.63
METHOD 2 (Matrosov et al. 2002)
The radar-only, Doppler velocity-reflectivity ICE retrieval method is appropriate for MeanDiameters greater than 15 microns.
MeanDiameter is determined using a particle characteristic size - fall velocity relationship over 20-min averages of Doppler velocity measurements [microns]
- IceWaterContent = 1100 * Z / MeanDiameter1.9 [g/m3]
CLASSIFICATION CODE 7 - ICE CLOUD, RADAR-ONLY METHODS AS WELL AS RADAR + IR RADIOMETER METHOD
All Code 6 retrievals are implemented in addition to a radar-IR radiometer ICE retrieval method that uses AERI-derived brightness temperatures to tune the coefficient "a" (Matrosov 1999). The tuned method is appropriate for MeanDiameters greater than about 15 microns.
- IceWaterContent = a * Zb [g/m3] MeanDiameter = 40.5 * (Z/IceWaterContent)0.53 [microns]
- b is scaled linearly through the cloud depth with a value of 0.7 at cloud base and 0.55 at cloud top.
- a is determined iteratively from AERI brightness temperature measurements.
CLASSIFICATION CODE 8 - MIXED-PHASE CLOUD (ICE AND LIQUID PRESENT IN SAME CLOUD LAYER)
Code 6 ICE retrievals are implemented to derive the microphysical properties of the ice component only. In future versions, an attempt may be made to retrieve the mixed-phase cloud liquid microphysical properties.
CLASSIFICATION CODE 9 - UNCERTAIN
Code 6 ICE retrievals are implemented.
Calculations of Optical Depth
Calculations of optical depth are not straight forward due to the frequent occurrence of multiple cloud layers (often ice, liquid, and mixed-phase in the same vertical column), and a prevalence of radiometrically thick cloud layers. To approximate a combined optical depth for the total cloud column, the following procedure is used.
For Liquid Layers:
The microwave radiometer value of LWP is used if available, otherwise the LWP from the Code 3 radar retrieval is calculated by vertically integrating the retrieved LWC. The Code 3 output is used in both cases to determine a layer-mean, LWC-weighted droplet effective radius. For mixed-phase clouds and drizzle regions, the layer-mean DropletEffectiveRadius is assumed to be 10 microns. OpticalDepth is then calculated using:
- OpticalDepth (Liquid) = LWP * (0.029 + 1.3 / DropletEffectiveRadius)
For Ice layers:
The IWP is calculated by vertically integrating the IWC. The layer-mean, IWC-weighted ParticleMeanDiameter is derived from Code 6, Method 1 results. OpticalDepth is then calculated using:
- OpticalDepth (Ice)= IWP * (0.021 + 1.27 / ParticleMeanDiameter)
- TotalOpticalDepth = OpticalDepth (Liquid) + OpticalDepth (Ice)
Future versions of this data set will include:
- Improved cloud classification masks that use lidar depolarization ratio measurements to identify the location of liquid water layers in mixed-phase clouds.
- Implementation of drizzle retrievals.
- Implementation of more current precipitation retrievals for 35-GHz radar.
The development of this data set has been a long complex process. Comments, suggestions, and/or identification of errors are much appreciated and should be directed to Taneil Uttal.