# Presentation: 718891

## MLS Version 2 Cloud Ice Products and Validation Plan 

- Dong L. Wu and Jonathan H. Jiang
- Jet Propulsion Laboratory
- California Institute of Technology

## Changes of cloud ice products in V2.1

- Swath name: Ice water content (IWC)
-  Useful pressure range: 68-261 hPa;
-  Dynamic range:  0-120 mg/m3;
-  Improved precision at pressures of 68-215 hPa;
-  Reduced number of false alarms for pressures < 177 hPa;
-  Improved retrieval for large IWC values, where V1.5 underestimates.
- Swath name: Ice water path (IWP) – *New!*
-  Based on low-tangent-height 240 GHz radiances;
-  A slant path above ~6 km;
-  Will be converted to the vertical column in v2.2;

**In the standard product file (IWC):**

- MLS-Aura_L2GP-IWC_v02-10-c04_2005d028.he5

## Provisional Release of V2.1 IWC

- Caveats:
-   Use the same procedure as in v1.5 to remove the bias;
-   Use 3-sigma threshold to screen the data for cloud detection.
-   No correction factor is needed for v2.1.

- V2.1

- V1.5

| Pressure (hPa) | V1.5 IWC Prec (mg/m3) | V2.1 IWC Prec (mg/m3) |
| --- | --- | --- |
| 83 | 0.04 | 0.03 |
| 100 | 0.05 | 0.04 |
| 121 | 0.09 | 0.06 |
| 147 | 0.39 | 0.21 |
| 177 | 0.86 | 1.1 |
| 215 | 3.1 | 4.6 |
| 261 | 7.5 | 12.5 |

- Estimated Precision (1 sigma)

## V2.1 vs. V1.5 IWC: Improvements on biases and false alarms

- V1.5

- V2.1

- 2005d028

- Biases;
- False alarms at high latitudes;

- Little biases;
- Reduced false alarms at high latitudes;
- Slightly noisier at p >177 hPa;

## Consistent cloud detection between v1.5 and v2.1 at pressures < 178 hPa.

More IWC > 50 mg/m3 in v2.1

Better handling of volcano cases in v2.1

Large differences at 215 and 261 hPa

- V1.5 IWC (mg/m3)

- V2.1 IWC (mg/m3)

- V2.1 IWC (mg/m3)

- 2005d028

- Consistent cloud detection between v1.5 and v2.1 at pressures < 178 hPa.
- More IWC > 50 mg/m3 in v2.1
- Better handling of volcano cases in v2.1
- Large differences at 215 and 261 hPa

- Volcano clouds

## V2.1 vs. V1.5 IWC:  Time series

- MLS V1.5
- IWC
- (g/m3)

- MLS V2.1
- IWC
- (g/m3)

- AIRS
- Cld Top
- Pressure
- (hPa)

- AIRS
- Cld Frac

**V2.1 *****vs.***** V1.5 IWC: **** Time series**

- 2005d028

## V1.5

- V2.1

-  lacks of IWC > 50 mg/m3
-  some large negative outliers
-  latitude-dependent biases
-  false alarms at high latitudes

-  more IWC > 50 mg/m3
-  no large negative outliers
-  little latitude-dependent biases
-  reduced false alarms at high latitudes

**V2.1 *****vs.***** V1.5 IWC statistics: **** Probability Density Function (PDF)**

**316 hPa**

**261 hPa**

**177 hPa**

**215 hPa**

**121 hPa**

**147 hPa**

**100 hPa**

**83 hPa**

**316 hPa**

**261 hPa**

**177 hPa**

**215 hPa**

**121 hPa**

**147 hPa**

**100 hPa**

**83 hPa**

## V1.5 IWC

- Maps for 2004 d261, d267-269, d273

- Lacks of mid-latitude clouds
- Inconsistency among clouds at different levels near Patagonia

## V2.1 IWC

- Maps for 2004 d261, d267-269, d273

- Similar morphology at 147-100 hPa
- More mid-latitude clouds at 177-261 hPa

## V2.1 IWP>6km

- V2.1 IWP>6km

- Map for 2004 d261, d267-269, d273

## Validation Plans

- IWC comparisons
-  CloudSat and TRMM (D. Wu, A. Heymsfield, + ..)
-  AIRS (J. Jiang)
-  OMI (J. Jiang)
-  MODIS (H. Pumphrey + students)
-  Ground-based radars (D. Wu + ...)
- IWP comparisons
-  CloudSat (D. Wu, A. Heymsfield, + ...)
-  AMSU-B (D. Wu)

## Sampling and Noise Issues

**MLS:  ~200 x 13 km****2****  **

**AIRS**
**15 x 15 km****2**

**CloudSat**
**1.5 x 3.5 km****2**

**Notes:**
How to tell modelers about this cloudy picture with instruments of different sampling geometry and sensitivities 
It’s difficult because satellite instruments have different sizes of footprints, varying from MLS 200x13 km2 to cloudsat 1.5x3.5 km2
One can get around this problem somewhat by averaging high-resolution observations for the equivalent area of low-resolution observations, and comparing the statistics. If they are the same clouds, the statistics should be similar.  

## Spatial variability: sampling differences and averaging consideration

- AIRS FOV
- 15km × 15km

- MLS FOV
- 250km × 13km

- AIRS FOV
- #46

## Lesson from MLS-AIRS Radiance comparisons

**Lesson from MLS-AIRS Radiance comparisons**

- AIRS radiance (K)

- MLS cloud-induced radiance (K)

- 230 km long,
- min AIRS Rad

**147 hPa or ~14 km**

- 230 km long,
- avg. AIRS Rad

- 610 km long,
- min AIRS Rad

**AIRS 734 cm****-1**** **
**Rad =240 K**

- The quantities that are not addable:
- IR/Vis/UV cloud induced radiances
- 		AIRS, MODIS, OMI
- Cloud top pressure/height
- 		maybe, limited/small area?

- The quantities that are addable:
- Cloud IWC:  MLS, CloudSat, TRMM
- Cloud IWP:  MLS, CloudSat, AMSU-B
- Cloud fraction:   MLS + many

## Effect of Instrument Noise

- TRMM Radar Reflectivity at 15 km

- Raw data

- Threshold
- 30.2 dBZ

- Threshold
- 30.6 dBZ

**Effect of Instrument Noise**

- Cloud detection threshold is the common problem for all techniques.
- Comparisons between model and observation should not depend on what thresholds are used!

**Notes:**
Associated with the sensitivity issues is instrument noise, which determines the lower limit of sensitivity.
TRMM radar as an example. The TRMM radar signal is too noisy to detect most ice clouds but can sometimes observe strong deep convective clouds.
In this case, at 15km, if we use a threshold of 30.2 dBZ, we will detect two cloud towers with many false alarms. 
However, if we raise the threshold slightly to 30.6 dBZ, we will find much fewer false alarms but clearer cloud towers.
Cloud detection threshold is the common problem for all techniques. We need to be careful on how cloud fraction and frequency are reported since the threshold problem might overwhelm weak climate change signals. 

## IWC

**IWC**

**Occurrence **
**Frequency **

- 0

**Probability Density Function **
**(PDF) of Actual Clouds**

- False alarms

- Missing
- clouds

- Clouds
- Impenetrable

**Cloud Measurements Limited by Noise and Sensitivity**

**Notes:**
What would be a more reliable approach.
Let’s say this is a statistical distribution of cloud IWC, or probability density function, we want to measure; and here is the instrument noise.
We set a detection threshold and we have the saturation problem at the high end, and end up with this red curve we call our cloud measurements. Depending on what threshold is used, the average IWC can be quite different and misleading when we compare our results to others.
Because we have missing clouds at both ends and create false alarm.
So, let’s not set any threshold or limit, just compare this curve with the curves from other sensors. 

## Connecting IWC Observations Through PDF

**CloudSat**

**MLS**

**CALIPSO**

**TRMM**

**Notes:**
Here is the reality of cloud statistics observed by MLS (in diamond) and TRMM (in cross)/
One can see that TRMM is much noisier than MLS in measuring cloud IWC but there are still some clouds with very large IWC values. One can draw the instrument noise from this diagram, which prevents the instruments from detecting clouds of low IWC. MLS also exhibits saturation problem at ~50 mg/m3, which means it cannot penetrate these thick-and-dense clouds. Therefore, here are the ranges of MLS and TRMM sensitivities.
We are looking for the new instruments, cloudsat and calipso, to fill up the gaps so that we will have a more complete picture about the statistics of cloud ice distribution.

## Summary for MLS Cloud Ice Measurements

- Significant improvements of v2.1 over v1.5 IWC;
- New IWP>6km product in v2.1
- Caution required for comparing/validating cloud products (e.g., quantities that are addable or not);
- PDFs as very useful diagnostics to compare precision, accuracy and sensitivity of IWCs measured by different sensors;
- Importance of spatial averaging because IWC statistics are sensitive the size of the volume averaged.

- New SO2 retrieval in v2.1 (Contact Bill Read @ MLS);
- Ability of separating between volcano clouds and SO2 in v2.1.

**Additional Notes:**