# Presentation: 396129

## Evaluating Cloud Retrieval Algorithms within the ARM BBHRP Framework

| Eli Mlawer, Jennifer DelamereAtmospheric and Environmental Research, Inc. |  | Dave Turner, Patrick Heck
University of Wisconsin-Madison | Tim Shippert, Chuck Long, Connor Flynn, Chitra Sivaraman, Sally McFarlane, Jennifer Comstock, Krista Gaustad
Batelle Pacific Northwest Laboratory |  | Mark Miller
Rutgers University | Karen Johnson, David Troyan, Mike Jensen, Maureen Dunn
Brookhaven National Laboratory |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| _x0005_Eugene Clothiaux
Penn State University | Matthew Shupe
NOAA - CIRES | Ric Cederwall, Shaocheng Xie, John Yio, Steve Klein
Lawrence Livermore National Laboratory | Joe Michalsky
NOAA ARL | Mandy Khaiyer, David Rutan, Rich Ferrare, Dave Doelling, Pat Minnis
NASA - LaRC | Minghua Zhang
SUNY - Stony Brook | Shepard Clough
Clough Associates | Roger Marchand
University of Washington |

- _Objectives of the Broadband Heating Rate Profile (BBHRP) Project_
- Compute **heating rate profiles** at all ACRFs based on in-situ measurements
- Evaluate new data sources using **radiative closure analysis**
-  emphasis on evaluation of cloud retrieval algorithms
- Generate **dataset** of measured and modeled radiation using ‘baseline’ cloud 		retrieval

- “So the first question that we need to answer is:  Given a specified three-dimensional field of cloud properties, can we compute with sufficient accuracy the solar and terrestrial radiative flux transfer and associated atmospheric heating rates through the clouds?”
- Ackerman and Stokes, *Physics Today*

- _Recent Developments_
- Release of new PI product for SGP (ver 1.5)
-  1-min ICA calculations averaged over 30 min.
-  Extensive improvements to I/O netcdfs
- Evaluation of numerous cloud retrievals at SGP
- Evaluation of Shupe-Turner retrieval at NSA
- Discovery of issues with GOES TOA flux values

- _Planned Developments_
- Release of PI product for NSA
-  Microbase or Shupe-Turner?
- Release of ‘testbed’ to allow easy input of retrieved cloud	properties into BBHRP
- Intercomparison at PYE of CLOWD retrieval methods
-  Development continues at TWP (including TWP-ICE)

- _What’s On This Poster_
- Radiative closure results are shown for four different cloud retrieval approaches relative to the results from the baseline retrieval Microbase.  These results confirm that an accurate knowledge of the liquid water path  is unsurpassed in importance with respect to the effect of clouds on radiation.  Different reasonable simple assumptions about the other cloud properties provide different, but comparable, closure results.  Further progress will likely come from more sophisticated approaches, which to date have not achieved superior closure than the baseline retrieval.

- _A Critical Step:  Accurate Liquid Water Path Amounts_

- _Evaluating Simple Reasonable Assumptions_

- SW Diffuse: Water Clouds
- SW Diffuse: Mixed-phase Clouds
- LW TOA: Water Clouds

- Microbase           	Microbase w/o MWR LWP

- The Microbase retrieval obtains relative values of layer LWP from radar, but scales these values so that the column LWP agrees with LWP retrieved from the MWR, which provides a more accurate specification. For radiation calculations, the importance of these more accurate LWP values is indicated below by the greatly improved closure statistics for the shortwave diffuse flux at the surface. The closure statistics for the LW TOA and SW TOA (not shown) flux are not affected.

- SW Diffuse: Water Clouds
- SW Diffuse: Mixed-phase Clouds
- SW TOA: Mixed-phase Clouds
- LW TOA: Mixed-phase Clouds

- Microbase                     ‘Sengupta’

- Microbase employs the assumption that the liquid number density (Nd) is 200/cm3 to determine the reff from each layer’s LWC. Alternatively, Sengupta et al. suggested using a fixed value of reff=7.5 m (i.e. a varying Nd in each layer). This study evaluates the relative radiative closure resulting from these assumptions. Retrieval ‘Sengupta’ consists of Microbase layer LWPs and reff=7.5 m.  The reff values from Microbase tend to be smaller (above left), leading to higher computed diffuse surface fluxes (left). However, the two retrievals yield effectively equivalent closure statistics (slight advantage to ‘Sengupta’).

- _Frisch Retrieval - Radar-derived Effective Radius_

- SW Diffuse: Water Clouds
- SW TOA: Water Clouds
- SW Diffuse: Mixed-phase Clouds
- SW TOA: Mixed-phase Clouds

- A well-known cloud property retrieval is by Frisch et al. This approach obtains the liquid number density from ratio of integrated radar reflectivity and LWP; a lognormal distribution is then used to compute LWC(z) and reff. This approach yields similar column LWP values as Microbase (left), but far different flux calculations due to much greater reff values (not shown). The closure statistics clearly indicate that the effective radius values in this retrieval are too high.

- A BBHRP run was performed using a cloud property retrieval approach based on Marchand et al. (2007), which combines a radar-reflectivity-only retrieval, a radar-reflectivity-and-Doppler-velocity retrieval, a radar-reflectivity-microwave-radiometer retrieval, and a lidar-only based retrieval. The results shown here are for ice clouds, but are consistent with the results from all cloud types.

- _First Results from a More Sophisticated Retrieval_

- Microbase                         Frisch

- Microbase                         Marchand

- SW Diffuse-vs-IWP:    Ice Clouds
- SW TOA: Ice Clouds
- LW Surface: Ice Clouds
- LW TOA: Ice Clouds