# Presentation: 424360

## The Gridded Cloud Object Data and Evaluation of ECMWF Operational Analysis and Re-analysis Data

- Science Directorate
- NASA Langley Research Center
- Hampton, VA
- *SSAI, Hampton, VA

**The Gridded Cloud Object Data and Evaluation of ECMWF Operational Analysis and Re-analysis Data**

## Objectives

- How physical and radiative properties of tropical deep convective cloud systems are changed with matched atmospheric dynamics and sea surface temperature (SST)?
- How well does the ECMWF model reproduce the observed cloud physical and radiative properties with its operational analysis and re-analysis products?
-        The January-August 1998 TRMM CERES data are used in this study (Xu et al. 2005, 2007 for details)

## What is a cloud object?

- A contiguous patch of cloudy regions with a single dominant cloud-system type; no mixture of different types
- The shape and size of a cloud object is determined by
- the satellite footprint data
- the footprint selection criteria
- Selection criteria for deep convective (DC) cloud objects:
- Cloud optical depth () > 10
- Cloud top height (Ht) > 10 km
- Footprint cloud fraction = 100%
- Located between 25 S and 25 N
- Data available from the NASA/LaRC cloud object webpage (http://cloud-object.larc.nasa.gov)
- footprint data from CERES SSF (Level 2)
- statistical information on cloud physical properties
- matched meteorological data (incl. advective forcing from ECMWF)

## Why “gridded” cloud objects?

- There are optically thin (< 10) and shallow-cloud (Ht < 10 km) footprints adjacent to a deep convective (DC) cloud object within a tropical convective cloud system
- Physical properties of tropical convective cloud systems are contributed by both the DC cloud-object footprints and the adjacent footprints (non-DC); the proportion of their areas is a critical factor
- Since model grid meshes are regularly shaped and sized, the irregular shape and size of a cloud object are difficult to handle when evaluating model performance with the cloud object data
- By allowing mixture of different cloud types associated with a predominant cloud-system type, one can gain a better understanding of physical processes of an “nearly entire” cloud system

## The “gridded” cloud object

-   Cloud object: a contiguous region with similar cloud physical properties ( > 10, Ht > 10 km for DC cloud object)
-   “Gridded” cloud object: also includes neighboring areas (blue areas) surrounding a cloud object and small areas of footprints that satisfy the cloud object criteria (isolated red areas)
-  Statistics of red and blue areas are examined separately or combined

**swath**

**swath**

## Total numbers of DC and non-DC footprints for size categories

- The ratio of DC (red) over non-DC (blue) footprints increases
- (0.54 to 1.13) as the cloud object size increases

- 500

- 899

- 858

- # cloud object

## PDFs of TOA albedo for size categories

- Albedo for non-DC footprints are independent of cloud-object size (due to sampling over the entire tropics)
- Albedo for DC footprints are strongly dependent upon size (i.e., stronger large-scale ascent for larger objects)
- The overall pdfs reflect primarily the change of the ratio of DC and non-DC footprints with size, and secondarily the change of the DC pdfs with size

- 100-150 km

- > 300 km

- 150-300 km

## PDFs of cloud optical depth for size categories

- NB: pdf values extend to 128....
- As in albedo, the DC pdfs change with size (i.e., large-scale dynamics)
- The proportions of DC and non-DC footprints primarily determine the pdfs of all footprints
- The pdfs of TOA albedo are consistent with those of  

- Frequency at any bin interval:
- Aall pdfall = Adc pdfdc + Andc pdfndc
- A: the total number of footprints

## Total number of DC and non-DC footprints for SST ranges of the large size category

- The ratio of DC over non-DC footprints does not increase
- as cloud-object-mean SST increases

- 46

- 127

-  263

- 64

- # cloud object

## PDFs of TOA albedo for SST ranges

- Albedo for DC footprints are *not *strongly dependent upon SST
- Albedo for non-DC footprints are (i.e., weaker large-scale ascent in higher SST regions with more optically thin clouds)
- The overall pdfs reflect the change of non-DC albedo with SST, due to the constant proportion of DC and non-DC footprints

## How to convert the vertical profiles of grid-averaged cloud properties from large-scale models to pdfs of subgrid-cell cloud physical properties measured at satellite footprints?                (Xu 2008, Mon. Wea. Rev., submitted)

**Evaluation of ECMWF operational **
**analysis (EOA) and re-analysis (ERA-40) data**

**Notes:**
Slide #26:
This comparison will emphasize cloud/radiative properties. The frequencies of occurrence will also be shown. There are about 300 cloud objects in the March 2000 period, and more than 2000 cloud objects for the January-August 1998 period.

## Matching a cloud object with ECMWF grids

- Spatially, draw a rectangular area covering the most easterly, westerly, southerly and northerly footprints of each cloud object
- Temporally, match within 3 h because ECMWF data are available every 6 h
- Grid sizes: 0.5625° x 0.5625° for EOA, 1.125° x 1.125° for ERA-40

**Cloud object**

**GCM lat/lon grid lines**

**Surrounding area**

- ECMWF grid-mesh cloud fraction

**Notes:**
Slide #27:
       The first step is to match cloud objects with ECMWF grid cells. We draw a rectangular area covering the four outermost footprints of a cloud object. These are determined by latitude/longitude grid lines. The temporal time is within 3 h because ECMWF data are available every 6 hour.

## Converting ECMWF-forecasted cloud fields to pdfs of subgrid-cell cloud physical properties

**Converting ECMWF-forecasted cloud fields to pdfs of subgrid-cell cloud physical properties**

-  Divide each EOA/ERA-40 grid into 30/120 subcolumns (~100 km2, footprint size)
-  Use cloud overlap assumption to construct cloud distribution in subcolumns
-  from an ECMWF/ERA-40 predicted cloud fraction profile
-  Use the Fu-Liou radiation code to obtain cloud optical properties and radiative
-        fluxes for each subcolumn; determine cloud height and temperature
- 4.    Select “cloud object” subcolumns 10 & Ht >10 km) and construct pdfs

-  1

- Height (km)

- 0

- 5

- 10

- 15

- Subcolumns                    30

- Height (km)

- 0

- 0.7

- Cloud fraction

- 0

- 5

- 10

- 15

**Notes:**
Slide #28:
The goal of the detailed methodology is to convert ECMWF predicted cloud fields, cloud fraction, liquid water content and ice water content in a grid cell into satellite observations. First, we divide each model grid into 30 or 120 subcolumns, which have an equivalent size of 100 km2, the averaged size of satellite footprints. Second, we use cloud overlap assumption, maximum-random overlap to construct cloud distributions in subcolumns, as shown in the right panel. Third, we use the Fu-Liou radiation code to obtain cloud optical properties and radiaitve fluxes for each subcolumns; determine the cloud height and temperature. Last, we select cloud object subcolumns with cloud object selection criteria and construct pdfs.

## The ratios of DC and no-DC subcolumns

- Cloud physical properties will be examined for the large size category
- Note the large underestimate of the DC population for this category

## PDFs of cloud-top temperature and height

- For DC pdfs, EOA has clouds too
- close to the tropopause; ERA-40
- eliminates those clouds, but shifts
- the power of pdf to slightly lower
- heights
- Modified cloud parameterization
- produces more shallow clouds
- at 0.2-3 km range (shallow clouds)
- at the expense of high clouds
- Mid-level clouds (5-11 km) are
- underestimated by both models
- The overestimate of upper-level
- clouds are also contributed by
- non-DC population

## PDFs of TOA radiative fluxes

- Radiative fluxes agree with observations reasonably well despite of large disagreement in cloud physical properties, esp. for
- ERA-40
- Optically thin ( < 1) also contribute to radiative budget and water vapor distribution is probably more accurate in ERA-40

## Summary and future work, 1

- The ratio of DC over non-DC footprints changes greatly (0.54 to 1.13) as the large-scale dynamics (cloud object size) change, but not much as SST changes
- The changes of the overall pdfs of cloud properties reflect primarily (1) those of the ratio of DC and non-DC footprints with large-scale dynamics (size), and (2) secondarily the changes of the DC pdfs with dynamics (size)
- On the other hand, the changes of the overall pdfs of cloud properties with SSTs are solely related to those of non-DC pdfs

## Summary and future work, 2

- The pdfs of cloud physical properties from ECMWF operational analysis and ERA-40 are generally similar to those observed
- The discrepancies are larger for ERA-40 than EOA for DC and overall pdfs of most parameters except for radiative fluxes, due to changes in cloud parameterization and downgrade of data assimilation technique
- The cloud parameterization at ECMWF has recently been improved (Bechtold et al. 2004, 2008); it is worthwhile to confirm these conclusions using the ERA Interim data
- Aqua CERES data will be analyzed to confirm the findings

## PDFs of  and IWP for size categories

- EOA agrees with observations
- much better for both DC (cloud
- objects only) and overall (gridded
- cloud objects) populations
- Changed cloud parameterization
- in Sept. 1999; ERA-40 used the
- modified parameterization
- Narrower ranges of  and IWP
- of DC pdfs in ERA-40
- Underestimate of the DC portion
- by ERA-40 also contributes to the
- large power at the lowest bin of
- the overall pdfs
- Downgrade of data assimilation
- technique (4D var -> 3D var),
- changes in parameterization are
- the likely causes, not the change
- in the model resolution
** **